From 0f0b0371d349cd7101f7edcae01c38d4bf6b7f65 Mon Sep 17 00:00:00 2001 From: jack Date: Mon, 27 Apr 2026 17:09:24 +0800 Subject: [PATCH] ++ --- api_llm_generate/api_llm_generate.py | 2 +- simple72/Tranformer/ace_lib.py | 5 + .../Tranformer/output/Alpha_candidates.json | 274 +++ .../Alpha_generated_expressions_error.json | 1 + .../Alpha_generated_expressions_success.json | 47 + simple72/Tranformer/test_config.json | 2 +- simple72/config.json | 4 +- simple72/config_minimax.json | 2 +- ...datafields_cache_IND_TOP500_D1_analyst.csv | 144 ++ ...atafields_cache_IND_TOP500_D1_earnings.csv | 9 + ...fields_cache_IND_TOP500_D1_fundamental.csv | 689 +++++++ ...tafields_cache_IND_TOP500_D1_imbalance.csv | 3 + ...ields_cache_IND_TOP500_D1_institutions.csv | 12 + .../datafields_cache_IND_TOP500_D1_macro.csv | 2 + .../datafields_cache_IND_TOP500_D1_model.csv | 1590 +++++++++++++++++ .../datafields_cache_IND_TOP500_D1_news.csv | 73 + .../datafields_cache_IND_TOP500_D1_other.csv | 31 + .../datafields_cache_IND_TOP500_D1_pv.csv | 525 ++++++ .../datafields_cache_IND_TOP500_D1_risk.csv | 7 + ...tafields_cache_IND_TOP500_D1_sentiment.csv | 211 +++ ...elds_cache_IND_TOP500_D1_shortinterest.csv | 4 + simple72/main.py | 36 +- simple72/running_error.txt | 2 +- simple72/templates/app.js | 117 +- simple72/templates/index.html | 18 +- 25 files changed, 3728 insertions(+), 82 deletions(-) create mode 100644 simple72/Tranformer/output/Alpha_candidates.json create mode 100644 simple72/Tranformer/output/Alpha_generated_expressions_error.json create mode 100644 simple72/Tranformer/output/Alpha_generated_expressions_success.json create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_analyst.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_earnings.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_fundamental.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_imbalance.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_institutions.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_macro.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_model.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_news.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_other.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_pv.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_risk.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_sentiment.csv create mode 100644 simple72/dataset/datafields_cache_IND_TOP500_D1_shortinterest.csv diff --git a/api_llm_generate/api_llm_generate.py b/api_llm_generate/api_llm_generate.py index 6c8cb35..b86765c 100644 --- a/api_llm_generate/api_llm_generate.py +++ b/api_llm_generate/api_llm_generate.py @@ -219,7 +219,7 @@ def call_llm_generate_with_anthropic(task_data, max_retries=3): # 调用 API（使用流式传输，因为操作可能超过 10 分钟） generated_content = "" with client.messages.stream( - model="MiniMax-M2.7", + model="MiniMax-M2.7-highspeed", system=system_prompt if system_prompt else "You are a helpful assistant.", messages=messages, max_tokens=32768 diff --git a/simple72/Tranformer/ace_lib.py b/simple72/Tranformer/ace_lib.py index 6bdb679..e5aa629 100755 --- a/simple72/Tranformer/ace_lib.py +++ b/simple72/Tranformer/ace_lib.py @@ -75,6 +75,11 @@ def expand_dict_columns(data: pd.DataFrame) -> pd.DataFrame: pandas.DataFrame: A new DataFrame with expanded columns. """ dict_columns = list(filter(lambda x: isinstance(data[x].iloc[0], dict), data.columns)) + + if not dict_columns: + # 没有字典列，直接返回原数据 + return data + new_columns = pd.concat( [data[col].apply(pd.Series).rename(columns=lambda x: f"{col}_{x}") for col in dict_columns], axis=1, diff --git a/simple72/Tranformer/output/Alpha_candidates.json b/simple72/Tranformer/output/Alpha_candidates.json new file mode 100644 index 0000000..58a319e --- /dev/null +++ b/simple72/Tranformer/output/Alpha_candidates.json @@ -0,0 +1,274 @@ +{ + "regression_neut(divide(, sqrt()), log(cap))": { + "template_explanation": "A direct variation of the seed alpha that transforms a generic investment\u2011quality metric using a square\u2011root compression and then removes any linear size effect via regression neutralization against log market cap. This isolates the residual quality signal that is orthogonal to firm size.", + "seed_alpha_settings": { + "instrumentType": "EQUITY", + "region": "IND", + "universe": "TOP500", + "delay": 1, + "decay": 6, + "neutralization": "NONE", + "truncation": 0.02, + "pasteurization": "ON", + "unitHandling": "VERIFY", + "nanHandling": "ON", + "maxTrade": "ON", + "maxPosition": "OFF", + "language": "FASTEXPR", + "visualization": false, + "startDate": "2014-01-01", + "endDate": "2023-12-31" + }, + "placeholder_candidates": { + "": { + "type": "data_field", + "candidates": [] + } + } + }, + "group_neutralize(ts_rank(, ), bucket(rank(cap), range=\"0,1,0.1\"))": { + "template_explanation": "Constructs a time\u2011series rank of a fundamental metric over a short window, then removes the systematic size bias by neutralising against market\u2011cap buckets. The result is a size\u2011adjusted relative strength signal that can be compared across the universe.", + "seed_alpha_settings": { + "instrumentType": "EQUITY", + "region": "IND", + "universe": "TOP500", + "delay": 1, + "decay": 6, + "neutralization": "NONE", + "truncation": 0.02, + "pasteurization": "ON", + "unitHandling": "VERIFY", + "nanHandling": "ON", + "maxTrade": "ON", + "maxPosition": "OFF", + "language": "FASTEXPR", + "visualization": false, + "startDate": "2014-01-01", + "endDate": "2023-12-31" + }, + "placeholder_candidates": { + "": { + "type": "data_field", + "candidates": [] + }, + "": { + "type": "integer_parameter", + "candidates": [ + { + "value": 5 + }, + { + "value": 10 + }, + { + "value": 20 + }, + { + "value": 60 + }, + { + "value": 120 + } + ] + } + } + }, + "regression_neut(signed_power(ts_zscore(, ), ), log(cap))": { + "template_explanation": "Standardises the investment metric with a rolling Z\u2011score, applies a signed power transformation to capture non\u2011linear relationships, and finally neutralises the effect of log market cap. This approach enhances sensitivity to extreme values while controlling for size.", + "seed_alpha_settings": { + "instrumentType": "EQUITY", + "region": "IND", + "universe": "TOP500", + "delay": 1, + "decay": 6, + "neutralization": "NONE", + "truncation": 0.02, + "pasteurization": "ON", + "unitHandling": "VERIFY", + "nanHandling": "ON", + "maxTrade": "ON", + "maxPosition": "OFF", + "language": "FASTEXPR", + "visualization": false, + "startDate": "2014-01-01", + "endDate": "2023-12-31" + }, + "placeholder_candidates": { + "": { + "type": "data_field", + "candidates": [] + }, + "": { + "type": "integer_parameter", + "candidates": [ + { + "value": 5 + }, + { + "value": 20 + }, + { + "value": 60 + }, + { + "value": 120 + }, + { + "value": 252 + } + ] + }, + "": { + "type": "float_parameter", + "candidates": [ + { + "value": 0.25 + }, + { + "value": 0.5 + }, + { + "value": 1.0 + }, + { + "value": 2.0 + }, + { + "value": 3.0 + } + ] + } + } + }, + "ts_zscore(ts_delta(, ), ) - regression_neut(, log(cap))": { + "template_explanation": "Combines the short\u2011term change of a metric (captured by its rolling delta and Z\u2011score) with the size\u2011neutralised level of the metric. The difference isolates momentum in the metric that is not explained by firm size.", + "seed_alpha_settings": { + "instrumentType": "EQUITY", + "region": "IND", + "universe": "TOP500", + "delay": 1, + "decay": 6, + "neutralization": "NONE", + "truncation": 0.02, + "pasteurization": "ON", + "unitHandling": "VERIFY", + "nanHandling": "ON", + "maxTrade": "ON", + "maxPosition": "OFF", + "language": "FASTEXPR", + "visualization": false, + "startDate": "2014-01-01", + "endDate": "2023-12-31" + }, + "placeholder_candidates": { + "": { + "type": "data_field", + "candidates": [ + { + "id": "anl39_atanbvps", + "description": "Book value (tangible) per share - most recent fiscal year" + }, + { + "id": "anl39_qtanbvps", + "description": "Book value (tangible) per share - most recent quarter" + }, + { + "id": "anl39_spvba", + "description": "Book value (Common Equity) per share - most recent fiscal year" + }, + { + "id": "anl39_spvbq", + "description": "Book value (Common Equity) per share - most recent quarter" + }, + { + "id": "anl4_bvps_high", + "description": "Book value - the highest estimation, per share" + }, + { + "id": "anl4_bvps_low", + "description": "Book value - the lowest estimation, per share" + }, + { + "id": "anl4_bvps_median", + "description": "Book value per share - Median value among forecasts" + }, + { + "id": "anl4_bvps_number", + "description": "Book value per share - number of estimations" + }, + { + "id": "est_bookvalue_ps", + "description": "Book value per share - average of estimations" + } + ] + }, + "": { + "type": "integer_parameter", + "candidates": [ + { + "value": 5 + }, + { + "value": 10 + }, + { + "value": 20 + }, + { + "value": 60 + }, + { + "value": 120 + } + ] + } + } + }, + "group_rank(ts_rank(, ), industry)": { + "template_explanation": "First ranks the metric temporally within each stock, then applies a cross\u2011sectional industry ranking. This double\u2011ranking approach extracts the industry\u2011relative performance trend while abstracting from absolute magnitude.", + "seed_alpha_settings": { + "instrumentType": "EQUITY", + "region": "IND", + "universe": "TOP500", + "delay": 1, + "decay": 6, + "neutralization": "NONE", + "truncation": 0.02, + "pasteurization": "ON", + "unitHandling": "VERIFY", + "nanHandling": "ON", + "maxTrade": "ON", + "maxPosition": "OFF", + "language": "FASTEXPR", + "visualization": false, + "startDate": "2014-01-01", + "endDate": "2023-12-31" + }, + "placeholder_candidates": { + "": { + "type": "data_field", + "candidates": [] + }, + "": { + "type": "integer_parameter", + "candidates": [ + { + "value": 10 + }, + { + "value": 20 + }, + { + "value": 60 + }, + { + "value": 120 + }, + { + "value": 252 + } + ] + } + } + } +} \ No newline at end of file diff --git a/simple72/Tranformer/output/Alpha_generated_expressions_error.json b/simple72/Tranformer/output/Alpha_generated_expressions_error.json new file mode 100644 index 0000000..0637a08 --- /dev/null +++ b/simple72/Tranformer/output/Alpha_generated_expressions_error.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/simple72/Tranformer/output/Alpha_generated_expressions_success.json b/simple72/Tranformer/output/Alpha_generated_expressions_success.json new file mode 100644 index 0000000..ec0a0bf --- /dev/null +++ b/simple72/Tranformer/output/Alpha_generated_expressions_success.json @@ -0,0 +1,47 @@ +[ + "ts_zscore(ts_delta(est_bookvalue_ps, 10), 10) - regression_neut(est_bookvalue_ps, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_number, 120), 120) - regression_neut(anl4_bvps_number, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_low, 60), 60) - regression_neut(anl4_bvps_low, log(cap))", + "ts_zscore(ts_delta(anl39_spvba, 20), 20) - regression_neut(anl39_spvba, log(cap))", + "ts_zscore(ts_delta(anl39_spvbq, 20), 20) - regression_neut(anl39_spvbq, log(cap))", + "ts_zscore(ts_delta(anl39_atanbvps, 120), 120) - regression_neut(anl39_atanbvps, log(cap))", + "ts_zscore(ts_delta(anl39_spvbq, 120), 120) - regression_neut(anl39_spvbq, log(cap))", + "ts_zscore(ts_delta(anl39_atanbvps, 10), 10) - regression_neut(anl39_atanbvps, log(cap))", + "ts_zscore(ts_delta(anl39_spvba, 5), 5) - regression_neut(anl39_spvba, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_high, 120), 120) - regression_neut(anl4_bvps_high, log(cap))", + "ts_zscore(ts_delta(anl39_qtanbvps, 20), 20) - regression_neut(anl39_qtanbvps, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_median, 5), 5) - regression_neut(anl4_bvps_median, log(cap))", + "ts_zscore(ts_delta(anl39_atanbvps, 20), 20) - regression_neut(anl39_atanbvps, log(cap))", + "ts_zscore(ts_delta(est_bookvalue_ps, 5), 5) - regression_neut(est_bookvalue_ps, log(cap))", + "ts_zscore(ts_delta(anl39_qtanbvps, 120), 120) - regression_neut(anl39_qtanbvps, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_high, 5), 5) - regression_neut(anl4_bvps_high, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_high, 20), 20) - regression_neut(anl4_bvps_high, log(cap))", + "ts_zscore(ts_delta(anl39_atanbvps, 5), 5) - regression_neut(anl39_atanbvps, log(cap))", + "ts_zscore(ts_delta(est_bookvalue_ps, 20), 20) - regression_neut(est_bookvalue_ps, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_median, 20), 20) - regression_neut(anl4_bvps_median, log(cap))", + "ts_zscore(ts_delta(est_bookvalue_ps, 60), 60) - regression_neut(est_bookvalue_ps, log(cap))", + "ts_zscore(ts_delta(anl39_spvbq, 60), 60) - regression_neut(anl39_spvbq, log(cap))", + "ts_zscore(ts_delta(anl39_spvba, 10), 10) - regression_neut(anl39_spvba, log(cap))", + "ts_zscore(ts_delta(anl39_spvba, 60), 60) - regression_neut(anl39_spvba, log(cap))", + "ts_zscore(ts_delta(anl39_spvbq, 10), 10) - regression_neut(anl39_spvbq, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_high, 60), 60) - regression_neut(anl4_bvps_high, log(cap))", + "ts_zscore(ts_delta(anl39_spvba, 120), 120) - regression_neut(anl39_spvba, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_low, 120), 120) - regression_neut(anl4_bvps_low, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_median, 60), 60) - regression_neut(anl4_bvps_median, log(cap))", + "ts_zscore(ts_delta(anl39_atanbvps, 60), 60) - regression_neut(anl39_atanbvps, log(cap))", + "ts_zscore(ts_delta(est_bookvalue_ps, 120), 120) - regression_neut(est_bookvalue_ps, log(cap))", + "ts_zscore(ts_delta(anl39_qtanbvps, 60), 60) - regression_neut(anl39_qtanbvps, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_number, 60), 60) - regression_neut(anl4_bvps_number, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_low, 10), 10) - regression_neut(anl4_bvps_low, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_low, 5), 5) - regression_neut(anl4_bvps_low, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_median, 120), 120) - regression_neut(anl4_bvps_median, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_number, 10), 10) - regression_neut(anl4_bvps_number, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_number, 5), 5) - regression_neut(anl4_bvps_number, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_low, 20), 20) - regression_neut(anl4_bvps_low, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_number, 20), 20) - regression_neut(anl4_bvps_number, log(cap))", + "ts_zscore(ts_delta(anl39_spvbq, 5), 5) - regression_neut(anl39_spvbq, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_median, 10), 10) - regression_neut(anl4_bvps_median, log(cap))", + "ts_zscore(ts_delta(anl4_bvps_high, 10), 10) - regression_neut(anl4_bvps_high, log(cap))", + "ts_zscore(ts_delta(anl39_qtanbvps, 5), 5) - regression_neut(anl39_qtanbvps, log(cap))", + "ts_zscore(ts_delta(anl39_qtanbvps, 10), 10) - regression_neut(anl39_qtanbvps, log(cap))" +] \ No newline at end of file diff --git a/simple72/Tranformer/test_config.json b/simple72/Tranformer/test_config.json index 6e45b94..de4763a 100644 --- a/simple72/Tranformer/test_config.json +++ b/simple72/Tranformer/test_config.json @@ -1,5 +1,5 @@ { - "LLM_model_name": "MiniMax-M2.7", + "LLM_model_name": "MiniMax-M2.7-highspeed", "LLM_API_KEY": "sk-cp-l_as8mjqPhsOIny9IFKZ8jzA92z1c0eRwchldhEf4KzQjs9cjVknV2o7VNCcvYUXsXFq7uF4aSgp2RxxmUHLXwPGKgIvzedM70_XUIXiBB3gu_UmLDQLfh4", "llm_base_url": "https://api.minimaxi.com/v1", "username": "jack0210_@hotmail.com", diff --git a/simple72/config.json b/simple72/config.json index 553c16a..f16b9a9 100644 --- a/simple72/config.json +++ b/simple72/config.json @@ -6,11 +6,11 @@ "llm": { "api_key": "sk-cp-l_as8mjqPhsOIny9IFKZ8jzA92z1c0eRwchldhEf4KzQjs9cjVknV2o7VNCcvYUXsXFq7uF4aSgp2RxxmUHLXwPGKgIvzedM70_XUIXiBB3gu_UmLDQLfh4", "base_url": "https://api.minimaxi.com/v1", - "model": "MiniMax-M2.7" + "model": "MiniMax-M2.7-highspeed" }, "transformer": { "top_n_datafield": 30, "data_type": "MATRIX", - "alpha_id": "pw13nJlo" + "alpha_id": "akWG7Lnx" } } diff --git a/simple72/config_minimax.json b/simple72/config_minimax.json index d82e37c..ec03e47 100644 --- a/simple72/config_minimax.json +++ b/simple72/config_minimax.json @@ -6,7 +6,7 @@ "llm": { "api_key": "sk-cp-l_as8mjqPhsOIny9IFKZ8jzA92z1c0eRwchldhEf4KzQjs9cjVknV2o7VNCcvYUXsXFq7uF4aSgp2RxxmUHLXwPGKgIvzedM70_XUIXiBB3gu_UmLDQLfh4", "base_url": "https://api.minimaxi.com/v1", - "model": "MiniMax-M2.7" + "model": "MiniMax-M2.7-highspeed" }, "transformer": { "top_n_datafield": 30, diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_analyst.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_analyst.csv new file mode 100644 index 0000000..d869f58 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_analyst.csv @@ -0,0 +1,144 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +anl39_2_curfperiodend,End date of the current fiscal period as of the point in time,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7415,291,849,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_2_curfyearend,Current fiscal year-end month as of the point-in-time date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7415,165,304,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_2_curperiodnum,"Number of the current fiscal period within the fiscal year (e.g., quarter 1–4)","{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7415,96,160,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_2_cursharesoutstanding,Current shares outstanding for the security as of the point-in-time date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.6696,136,247,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_2curfperiodend,End date of the current fiscal period as of the PIT date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7442,102,173,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_2curfyearend,"Fiscal year-end for the entity as of the current point in time (e.g., month of fiscal year end)","{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7442,93,140,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_2cursharesoutstanding,Shares outstanding for the security as of the current point in time,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.6723,117,186,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_aepsinclxo,EPS including extraordinary items - most recent fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9792,323,722,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_agrosmgn,Gross Margin - 1st historical fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8688,209,455,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_agrosmgn2,Gross Margin - 2nd historical fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8694,124,180,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_analyststart,Point-in-time effective start date when an analyst begins coverage of a given security or company,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,1.0,167,486,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_atanbvps,Book value (tangible) per share - most recent fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9841,222,422,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_curfperiodend,Calendar date of the current fiscal period end as of the point-in-time date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7415,61,93,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_curfyearend,Fiscal year-end month for the entity as of the point-in-time date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7415,65,98,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_cursharesoutstanding,Shares outstanding for the security as of the point-in-time date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.6696,81,117,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_epschngin,EPS Change % - prior quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9161,143,253,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_ghcspea,EPS Change % - year over year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9716,216,456,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_ghcspemtt,"Percent change in EPS, trailing 12 months versus the prior trailing 12 months","{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.852,123,192,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_grosmgn5yr,Gross Margin 5-year average,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7857,135,194,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_ptmepsincx,Earnings per share including extraordinary items for the prior trailing 12-month period,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8697,126,217,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_qepsinclxo,EPS including extraordinary items - most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9415,161,253,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_qgrosmgn,Gross Margin - most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.814,123,267,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_qtanbvps,Book value (tangible) per share - most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8196,70,89,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_qtotd2eq,Total debt to total equity ratio for the most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8012,56,71,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_qtotd2eq2,"Total debt/total equity - most recent quarter, 1 year ago","{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7525,51,66,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_rasv2_atotd2eq,Total debt to total equity ratio for the most recent fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.968,241,691,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_roxlcxspea,EPS excluding extraordinary items - most recent fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9792,129,264,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_roxlcxspeq,EPS excluding extraordinary items - most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9415,205,579,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_rygnhcspe,EPS Change % - most recent quarter 1 year ago,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9039,168,522,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_spvba,Book value (Common Equity) per share - most recent fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9841,154,263,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_spvbq,Book value (Common Equity) per share - most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8196,79,98,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_ttmepsincx,EPS including extraordinary items - trailing 12 month,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9261,302,577,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_ttmgrosmgn,Gross Margin - trailing 12 month,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8059,119,221,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_xlcxspemtp,Earnings per share excluding extraordinary items for the prior trailing 12-month period,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8697,90,149,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl39_xlcxspemtt,EPS excluding extraordinary items - trailing 12 month,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9261,239,483,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG +anl4_afv4_cfps_high,Cash Flow Per Share - The highest estimation for the annual forecast,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5056,144,295,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_cfps_low,Cash Flow Per Share - The lowest estimation for the upcoming fiscal year,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5056,71,97,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_cfps_mean,Cash Flow Per Share - average of estimations for the annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5056,79,105,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_cfps_median,Cash Flow Per Share - Median value among forecasts for the annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5056,68,103,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_cfps_number,Cash Flow Per Share - number of estimations for annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5056,76,99,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_div_high,Dividend per share - The highest estimation for the annual forecast.,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9249,353,2247,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_div_low,Dividend - The lowest estimation for the annual forecast,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9249,267,1052,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_div_mean,Dividend per share - average of estimations for annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9249,345,1496,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_div_median,Dividend per share - Median value among forecasts,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9249,325,3689,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_div_number,Number of estimations for Dividend per share - annually,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9249,150,404,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_div_std,Dividend per share - standard deviation of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6713,134,211,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_dts_spe,Earnings per share - standard deviation of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6542,147,475,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_eps_high,Earnings per share - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9687,756,6633,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_eps_low,Earnings per share - The lowest estimation for annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9687,280,1314,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_eps_mean,Earnings per share - mean of estimations for annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9687,892,7471,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_eps_number,Earnings per share - number of estimations for annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9687,320,2451,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_afv4_median_eps,Earnings per share - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9687,543,5292,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_bvps_high,"Book value - the highest estimation, per share","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4719,49,75,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_bvps_low,"Book value - the lowest estimation, per share","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4719,51,76,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_bvps_median,Book value per share - Median value among forecasts,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4719,55,69,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_bvps_number,Book value per share - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4719,48,62,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_dts_ptp,Pretax income- std of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.527,65,92,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ebit_high,Earnings before interest and taxes - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9106,313,2012,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ebit_low,Earnings before interest and taxes - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9106,319,2270,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ebit_median,Earnings before interest and taxes - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9106,341,2150,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ebit_number,Earnings before interest and taxes - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9106,105,160,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ebit_std,Earnings before interest and taxes - std of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4586,66,81,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ebitda_high,"Earnings before interest, taxes, depreciation and amortization - The highest estimation","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8721,158,565,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ebitda_low,"Earnings before interest, taxes, depreciation and amortization - The lowest estimation","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8721,200,876,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ebitda_number,"Earnings before interest, taxes, depreciation and amortization - number of estimations","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8721,84,133,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ebitda_std,"Earnings before interest, taxes, depreciation and amortization - std of estimations","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4949,59,83,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_epsa_high,Earnings per share adjusted by excluding extraordinary items and stock option expenses - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.7992,67,99,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_epsa_low,Earnings per share adjusted by excluding extraordinary items and stock option expenses - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.7992,79,143,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_epsa_median,Earnings per share adjusted by excluding extraordinary items and stock option expenses - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.7992,83,152,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_epsa_number,Earnings per share adjusted by excluding extraordinary items and stock option expenses - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.7992,97,190,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_epsr_high,GAAP Earnings per share - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.651,60,99,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_epsr_low,GAAP Earnings per share - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.651,54,72,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_epsr_number,GAAP Earnings per share - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.651,54,77,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_gric_high,Gross income- The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5829,45,69,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_gric_low,Gross income- The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5829,50,69,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_gric_median,Gross income- median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5829,42,58,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_gric_number,Gross income- number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5829,117,332,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_median_epsreported,GAAP Earnings per share - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.651,72,107,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_medianepsbfam,"Earnings before interest, taxes, depreciation and amortization - median of estimations","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8721,159,755,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_netprofit_high,Net profit- The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9623,368,2829,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_netprofit_low,Net profit- The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9623,279,1511,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_netprofit_median,Net profit- median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9623,309,1021,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_netprofit_number,Net profit- number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9623,89,175,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_netprofit_std,Net profit- std of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5654,45,68,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_netprofita_high,Adjusted net income- The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8995,209,659,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_netprofita_low,Adjusted net income- The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8995,290,2070,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_netprofita_median,Adjusted net income- median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8995,217,910,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_netprofita_number,Adjusted net income- number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8995,87,165,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ptp_high,Pretax income- The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.95,155,473,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ptp_low,Pretax income- The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.95,157,490,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ptp_median,Pretax income- median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.95,167,791,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ptp_number,Pretax income- number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.95,76,138,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ptpr_high,Reported Pretax income- The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6358,114,582,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ptpr_low,Reported Pretax income- The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6358,68,142,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ptpr_median,Reported Pretax income- median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6358,58,89,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_ptpr_number,Reported Pretax income- number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6358,74,170,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_qfv4_dts_spe,Earnings per share - standard deviation of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5408,54,68,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_qfv4_eps_high,Earnings per share - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9424,258,1124,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_qfv4_eps_low,Earnings per share - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9424,533,6028,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_qfv4_eps_mean,Earnings per share - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9424,253,1098,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_qfv4_eps_number,Earnings per share - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9424,115,395,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_qfv4_median_eps,Earnings per share - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9424,422,5384,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_totassets_high,Total Assets - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4853,38,68,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_totassets_low,Total Assets - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4853,30,36,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_totassets_median,Total Assets - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4853,26,38,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl4_totassets_number,Total Assets - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4853,34,51,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +earnings_per_share_adjusted_estimate_standard_deviation,Earnings per share adjusted by excluding extraordinary items and stock option expenses - std of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.3898,47,57,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_bookvalue_ps,Book value per share - average of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4719,71,176,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_ebit,Earnings before interest and taxes - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9106,476,4982,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_ebitda,"Earnings before interest, taxes, depreciation and amortization - mean of estimations","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8721,284,2635,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_epsa,Earnings per share adjusted by excluding extraordinary items and stock option expenses - average of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.7992,131,409,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_epsr,GAAP Earnings per share - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.651,94,276,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_grossincome,Gross income- mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5829,90,177,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_netprofit,Net profit- mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9623,464,6015,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_netprofit_adj,Adjusted net income- mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8995,399,3908,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_ptp,Pretax income- mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.95,196,803,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_ptpr,Reported Pretax income- mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6358,95,236,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +est_tot_assets,Total Assets - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4853,54,94,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +highest_sales_estimate,Sales - The highest estimation for the annual period,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9672,172,454,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +lowest_sales_estimate,Sales - The lowest estimation for the annual period,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9672,109,233,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +median_sales_estimate,Sales - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9672,150,555,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +net_income_adjusted_estimate_standard_deviation,Adjusted net income- std of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4938,55,75,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +reporting_currency_code_9,Home currency of instrument,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,1.0,145,801,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +sales_estimate_average_annual,Sales - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9672,208,813,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +sales_estimate_average_quarterly,Sales - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9613,123,249,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +sales_estimate_count_2,Number of Sales estimates,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9672,81,151,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +sales_estimate_count_quarterly,Sales - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9613,79,138,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +sales_estimate_dispersion,Standard deviation of Sales estimations for the annual period.,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6465,89,150,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +sales_estimate_maximum_quarterly,Sales - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9613,132,434,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +sales_estimate_median_quarterly,Sales - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9613,132,661,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +sales_estimate_minimum_quarterly,Sales - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9613,237,2093,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +sales_estimate_stddev_quarterly,Standard deviation of Sales estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.561,61,110,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +shareholders_equity_estimate_average_qf,Average value of broker estimates for shareholders' equity (quarterly frequency).,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5119,37,57,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +shareholders_equity_estimate_count_qf,Number of broker estimates for shareholders' equity (quarterly frequency).,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5119,42,49,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +shareholders_equity_estimate_maximum_qf,Highest value among broker estimates for shareholders' equity (quarterly frequency).,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5119,50,67,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +shareholders_equity_estimate_median_qf,Median value of broker estimates for shareholders' equity (quarterly frequency).,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5119,32,38,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +shareholders_equity_estimate_minimum_qf,Lowest value among broker estimates for shareholders' equity (quarterly frequency).,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5119,40,51,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates +anl81_class,"Creditwrothiness class indices in integers: 9(AAA), 5(AA), 4(A), 1(BBB), 2(BB), 3(B), 7(CCC), 8(CC), 6(C), 10(D)","{'id': 'analyst81', 'name': 'Creditworthiness model'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-ratings', 'name': 'Analyst Ratings'}",IND,1,TOP500,MATRIX,1.0,0.2609,79,129,1.5,[],analyst81,Creditworthiness model,analyst,Analyst,analyst-analyst-ratings,Analyst Ratings +anl81_confidence_level_percent,"Confidence level of the assessment, measuring data completeness and reliability as a percentage from 0 to 100","{'id': 'analyst81', 'name': 'Creditworthiness model'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-ratings', 'name': 'Analyst Ratings'}",IND,1,TOP500,MATRIX,0.9507,0.2609,57,77,1.5,[],analyst81,Creditworthiness model,analyst,Analyst,analyst-analyst-ratings,Analyst Ratings +anl81_probability_of_default_percent,"Estimated probability of default over the rating horizon, expressed as a percentage from 0 to 100","{'id': 'analyst81', 'name': 'Creditworthiness model'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-ratings', 'name': 'Analyst Ratings'}",IND,1,TOP500,MATRIX,0.9507,0.2609,31,41,1.5,[],analyst81,Creditworthiness model,analyst,Analyst,analyst-analyst-ratings,Analyst Ratings +default_likelihood_percent,"Previous-period probability of default estimated by mode_finance, expressed as a percentage from 0 to 100","{'id': 'analyst81', 'name': 'Creditworthiness model'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-ratings', 'name': 'Analyst Ratings'}",IND,1,TOP500,MATRIX,0.9507,0.2711,24,27,1.5,[],analyst81,Creditworthiness model,analyst,Analyst,analyst-analyst-ratings,Analyst Ratings +financial_data_completeness_percent,"Confidence level for the lagged assessment, 0–100%, indicating completeness/reliability of available data","{'id': 'analyst81', 'name': 'Creditworthiness model'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-ratings', 'name': 'Analyst Ratings'}",IND,1,TOP500,MATRIX,0.9507,0.2711,32,47,1.5,[],analyst81,Creditworthiness model,analyst,Analyst,analyst-analyst-ratings,Analyst Ratings +anl9_estanalystmap,Human-readable name/description of the analyst corresponding to estimateAnalystIx,"{'id': 'analyst9', 'name': 'Analyst Estimate Daily Data'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.8467,92,159,1.5,[],analyst9,Analyst Estimate Daily Data,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_earnings.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_earnings.csv new file mode 100644 index 0000000..c4afae5 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_earnings.csv @@ -0,0 +1,9 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +ern3_all_delay_1_next_interval,"Number of trading days before the next earnings report date for all Asia (ASI) equities, according to the 1-day delayed earnings calendar","{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.7091,353,601,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates +ern3_all_delay_1_next_reptime,Scheduled time of the next upcoming earnings report announcement,"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.7091,254,430,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates +ern3_all_delay_1_pre_interval,"Number of trading days before the last earnings report date (should be negative or zero), reflecting timing relative to previous earnings for all Asia (ASI) equities, in the 1-day delayed calendar","{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.872,514,1065,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates +ern3_all_delay_1_pre_reptime,Time of the most recent previous earnings report announcement,"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.872,323,573,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates +ern3_next_interval,Number of trading days until the next scheduled earnings report date,"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.7094,316,558,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates +ern3_next_reptime,Scheduled time for the next earnings report (in HHMM or session code),"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.7094,203,341,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates +ern3_pre_interval,Number of trading days elapsed since the previous earnings report date (always non-positive),"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.8721,467,939,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates +ern3_pre_reptime,Scheduled or actual event time of the previous earnings report (in HHMM or session code),"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.8721,349,627,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_fundamental.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_fundamental.csv new file mode 100644 index 0000000..e35ff33 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_fundamental.csv @@ -0,0 +1,689 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +enterprise_value_to_ebitda_current,Current enterprise value divided by EBITDA ratio,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9452,681,2580,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +financial_reporting_currency_code_3,Reporting currency used in the company’s financial statements,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,39,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_1_reptoprcexrate,Reporting Currency to base/pricing currency exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,248,1076,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_1_usdtorepexrate,Exchange rate from reporting currency to USD,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,188,1012,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_2_reptoprcexrate,Reporting Currency to base currency exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,140,853,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_2_usdtorepexrate,Reporting Currency to USD exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,77,144,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_2anrhsfcq,Cash Flow per share - most recent quarter - 1 (not annualized),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8691,238,1121,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_2qe2dtlq,Long-term debt to equity ratio for the most recent quarter one year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7431,78,102,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_2rhsfca,Cash flow per share for the prior fiscal year (as defined by the dataset; divided by average shares outstanding),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9656,299,1391,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_2rhsfcq,"Cash Flow per share - most recent quarter, 1 year ago","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8501,123,576,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_2tcpngmpoa,Operating Margin - 2nd historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9643,337,1550,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_3_reptoprcexrate,Exchange rate from the company’s reporting currency to the dataset’s base price currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,38,52,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_3_usdtorepexrate,Exchange rate from USD to reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,47,67,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_4_reptoprcexrate,Exchange rate from reporting currency to base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,19,26,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_4_usdtorepexrate,Reporting currency to USD exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,22,26,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_5_reptoprcexrate,Reporting Currency to base currency exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,22,38,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_5_usdtorepexrate,Reporting currency to USD exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,25,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_6_reptoprcexrate,Exchange rate from reporting currency to base/price currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,31,55,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_6_usdtorepexrate,Exchange rate from reporting currency to USD,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,41,58,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_7_reptoprcexrate,Exchange rate from the company’s reporting currency to the dataset’s base/pricing currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,22,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_7_usdtorepexrate,Exchange rate from the reporting currency to USD,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,23,44,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_8_reptoprcexrate,Reporting Currency to base currency exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,22,33,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_8_usdtorepexrate,Reporting currency to USD exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,30,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_a2netmrgn,Net Profit Margin % - 2nd historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9651,62,76,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aastturn,Asset turnover - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9078,301,1625,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_abepsxclxo,Basic earnings per share excluding extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,205,1018,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_acapspps,Capital Spending per share - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9443,66,79,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_acogs,Cost of goods sold for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8727,51,83,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_acurast,Total current assets for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8585,52,72,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_acurliab,Current liabilities - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8585,57,67,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_acurratio,Current ratio for the most recent fiscal year (current assets divided by current liabilities),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8583,186,856,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_adebteps,Debt service to earnings per share ratio for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7178,38,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_adepexp,Depreciation expense from statement of cash flows - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9419,43,52,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_adeprescfz,"Accumulated depreciation, most recent fiscal year","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9042,33,42,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_adiv5yavg,Dividend per share 5-year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6901,114,607,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_adivchg,Year-over-year percentage change in annual dividends per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.713,108,154,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_adivshr,Dividend per share - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8202,238,1387,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aebit,Earnings before interest and taxes for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,53,88,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aebitd,EBITDA for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9674,54,82,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aebitd2,EBITDA for the previous fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9665,49,70,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aebitd5yr,EBITD Margin - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.889,63,85,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aebitdmg,EBITD Margin - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9666,75,99,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aebtnorm,Normalized earnings before taxes for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,40,45,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aepsinclxo,Earnings per share including extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,50,67,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aepsnorm,EPS normalized - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,91,143,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_agrosmgn,Gross Margin - 1st historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8599,53,64,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_agrosmgn2,Gross Margin - 2nd historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8606,28,34,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aintcov,"Interest coverage ratio for the most recent fiscal year, calculated as EBIT divided by interest expense","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6589,27,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aintexpz,Total interest expense for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8088,24,26,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ainventory,Inventory - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7992,41,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ainvturn,Inventory turnover - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.782,24,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_altd2ast,Long-term debt to total assets for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9116,27,45,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_altd2cap,Long-term Debt to Total Capital: long-term debt divided by total capital (short-term debt + current portion of long-term debt + long-term debt + capitalized leases + shareholders’ equity),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.911,25,28,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_altd2eq,Long-term Debt to Equity: long-term debt divided by total shareholders’ equity,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9561,35,46,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_altdps,Long-term debt per share for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9117,25,27,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ani,Net income (earnings after taxes) for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,46,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aniac,Net income available to common shareholders for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,30,37,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aniacinclx,Net income available to common including extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,19,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aniacnorm,Normalized net income available to common (excludes unusual/one-time items) for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,39,68,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_anichg,Year-over-year percentage change in net income,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9602,50,73,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aniexclxor,Net income excluding extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,17,18,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aniinclxor,Net income including extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,19,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aninorm,Net income after taxes excluding unusual or one-time items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,33,46,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aniperemp,Net income per employee for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7994,18,19,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_annperiods,Number of historical periods - Annual,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,31,40,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_anrhsfcq,"Cash flow per share for the most recent quarter, not annualized","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8698,32,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_apayratio,Payout ratio for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8654,51,75,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_apayratio2,Payout ratio for the prior fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8672,38,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_apeexclxor,Price-to-earnings ratio excluding extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8852,78,134,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_apenorm,Normalized price-to-earnings ratio for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.889,110,199,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_apr2rev,Price to sales - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9671,97,149,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_apr2tanbk,Price to Tangible Book - most fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9315,59,91,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aprice2bk,Price-to-book ratio for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9574,135,325,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aptmgnpct,Pretax margin - 1st historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9671,28,33,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aptmgnpct2,Pretax Margin - 2nd historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9651,25,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aquickrati,Quick ratio for the most recent fiscal year: (cash + short-term investments + accounts receivable) divided by current liabilities,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7934,46,58,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_arecturn,Receivables turnover - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.89,57,85,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_arecvbl,Receivables - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.906,23,32,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_arev,Total revenue (sales) for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,27,37,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_arevchg,Year-over-year percentage change in revenue,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9586,50,67,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_arevperemp,Revenue per employee - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7991,23,32,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_arevps,Revenue per share for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,45,58,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_arevstrt,"Reinvestment rate for the most recent fiscal year, defined as 100 minus the payout ratio (percent of earnings retained)","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8862,33,38,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aroa5yavg,Five-year average return on average assets,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8909,41,54,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aroapct,Return on average assets for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9664,55,98,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aroe5yavg,Five-year average return on average equity,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8791,35,59,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aroepct,Return on average equity for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9523,27,37,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aroi5yravg,Five-year average return on investment,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8204,26,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_aroipct,Return on investment for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8936,17,23,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_asga2rev,SG&A expenses / net sales - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9079,34,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ata,Total assets - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,33,48,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atachg,Year-over-year percentage change in total assets for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9663,31,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atanbvdolr,"Tangible book value in dollars, most recent fiscal year","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,33,36,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atanbvps,Tangible book value per share for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,49,60,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ataxpd,Income taxes paid in the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9675,28,36,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ataxrat5yr,Five-year average effective tax rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8186,30,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ataxrate,Effective tax rate for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8899,41,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ataxrate2,Effective tax rate for the prior fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8838,42,47,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atmtt,"Total assets, trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.748,22,28,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atotce,Common equity (book value) - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,32,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atotd,Total debt - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9712,19,21,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atotd2ast,Total debt to total assets for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9712,29,49,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atotd2cap,"Total debt to total capital percentage, most recent fiscal year","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9118,30,36,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atotd2eq,Total debt to shareholders’ equity for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.957,54,75,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atotltd,Long-term debt (total) - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9704,8,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atotse,Shareholders' equity - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,32,43,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_atq,Total assets - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,15,18,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_awcappspr,"Working capital per share divided by price, most recent fiscal year","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8583,44,52,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_beta,Beta,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.863,36,45,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_beta_down,Downside beta calculated during down-market periods,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.909,62,85,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_beta_up,Upside beta calculated during up-market periods,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9299,38,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_bvtrendgr,Five-year compound annual growth rate of book value per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8716,38,48,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_cainmtt,Net income available to common shareholders over the trailing twelve months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,118,800,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_cainq,Net income available to common shareholders for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,30,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_cftrendgr,5-year compound annual growth rate of cash flow,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8059,34,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_chpctpricemtd,Month-to-date price percent change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,45,63,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_chpctpriceqtd,Quarter-to-date price percent change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9718,68,112,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_chpctpricewtd,Week-to-date price percent change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,59,114,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_csptrendgr,Five year compound annual growth rate of capital spending,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8494,18,18,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_dai,"Dividend rate, indicated annual","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7901,73,100,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_debtcap_a,Debt-to-capitalization ratio for the last fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.913,33,36,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_debtcap_i,Debt-to-capitalization ratio for the latest interim period,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7573,9,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_div,Dividends paid from the statement of cash flows - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.799,32,34,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_divgrpct,Three-year compound annual growth rate of dividends per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6326,64,103,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_divnq,Dividend - next quarterly declared,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8009,55,100,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_divnqpdt,Dividend - next quarterly pay date,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7788,69,82,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_divnqxdt,Dividend - next quarterly ex-date,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7909,125,197,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_divtrend10,Ten-year compound annual growth rate of dividends per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5005,44,54,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_divtrendgr,Five-year compound annual growth rate of dividends per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5946,38,43,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_dvolshsout,Daily trading volume as a percentage of shares outstanding,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,27,40,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ebitda2ev,"EBITDA divided by Enterprise Value, current","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9665,206,462,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ebitda2ev_a,EBITDA to enterprise value using last fiscal year EBITDA and current enterprise value,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9631,254,1450,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ebitda2ev_ttm,Ratio of EBITDA to enterprise value for the trailing twelve months (current),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8669,58,82,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_epschngin,Percent change in EPS from the prior quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9062,23,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_epstrend10,Ten-year compound annual growth rate of earnings per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.622,28,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_epstrendgr,EPS growth rate over the past 5 years,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.754,52,73,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ev2ebitda_cur,"Enterprise value divided by EBITDA, current","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9452,292,1702,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ev2rev_cur,"Enterprise value divided by revenue, current","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8989,91,156,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ev_cur,Current Enterprise Value (market capitalization plus net debt),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,106,163,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_fcf1a,Free cash flow - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9533,54,67,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_fcfmtt,Free cash flow - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2714,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_fcfq,Free cash flow for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5027,6,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ghcspea,Year-over-year percent change in annual earnings per share compared to the previous fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9602,42,70,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ghcspemtt,Percent change in trailing-twelve-month EPS versus the prior trailing-twelve-month period,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8434,29,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_gihn,12-month high price,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,74,115,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_gmntrendgr,Five-year growth rate of gross margin,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7887,20,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_grosmgn5yr,Gross Margin - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7779,11,15,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_gvary5ep,Five-year average P/E excluding extraordinary items,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7262,37,44,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_gvay5dly,Dividend yield - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6365,44,49,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_histrelpe,Historical relative price-to-earnings ratio,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7512,58,71,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_hsca,Cash and cash equivalents at fiscal year-end,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9727,45,52,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_inmtt,Net income after taxes over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,54,73,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_inq,Net income (earnings after taxes) for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,38,55,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_lbvcerq,Receivables - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7495,11,11,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_lcxspebmtp,EPS Basic excluding extraordinary items - prior trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.861,9,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_lcxspebmtt,EPS Basic excluding extraordinary items - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,62,85,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_lta,Total liabilities - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,26,42,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ltq,"Total liabilities, most recent quarter","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,11,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_margin5yr,Net Profit Margin - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8909,23,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_mvolshsout,Monthly trading volume as a percentage of shares outstanding,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,32,38,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_netdebt_a,Net debt (total debt minus cash and equivalents) for the last fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,26,37,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_netdebt_i,Net debt (total debt minus cash and equivalents) for the latest interim period,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8103,13,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ngmpnmtp,Net Profit Margin % - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8599,20,26,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ngmpnmtt,Net Profit Margin % - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9153,25,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ngmtpmtt,Pretax margin over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9153,52,73,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_nichngin,Percent change in net income versus the prior quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9063,17,28,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_nichngyr,Percent change in net income for the most recent quarter versus the same quarter a year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8948,128,937,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_nigrpct,Growth rate % - net income,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7977,24,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_nitrendgr,Five-year compound annual growth rate of net income,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7542,25,46,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_nlow,12-month low price,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,43,61,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_npmtrendgr,Five-year growth rate of net profit margin,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7507,28,33,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_nprice,Closing price or last bid,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,57,82,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_oxlcxebepp,"Basic P/E excluding extraordinary items, prior trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7729,17,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_oxlcxspebq,Basic earnings per share excluding extraordinary items for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,28,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_payout5yr,Five-year average payout ratio,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7576,26,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_peexclxor,"P/E excluding extraordinary items, trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8433,55,84,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_pehigh,High value of P/E excluding extraordinary items,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8707,19,20,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_pelow,Low value of P/E excluding extraordinary items,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8707,30,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ppeexclxor,"P/E excluding extraordinary items, prior trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7729,16,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_pr1dayprc,Percent change in the stock price for the most recent trading day,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,50,115,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_pr2tanbk,Price to Tangible Book - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7768,12,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_pr5dayprc,Percent change in the stock price over the last 5 trading days,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,77,184,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_price2bk,Price-to-book ratio based on the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7994,12,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_price2bk2,"Price to Book - most recent quarter, 1 year ago","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7229,8,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_priceavg150day,Average price of the last 150 days,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.963,90,132,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_priceavg200day,Average price of the last 200 days,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9567,67,102,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_priceavg50day,Average price of the last 50 days,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.972,58,80,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_prytdpct,Year-to-date price percent change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9625,39,64,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_prytdpctr,Relative (S&P500) price percent change - Year to Date,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9625,58,99,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ptmepsincx,EPS including extraordinary items - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.861,11,15,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ptmintcov,"Interest coverage for the prior trailing 12 months, defined as EBIT divided by interest expense","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6945,5,5,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ptmpr2rev,Price to sales - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8344,15,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ptmrev,Revenue for the prior trailing twelve months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.861,14,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ptmroepct,Return on average equity for the prior trailing twelve months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6641,6,6,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_pxepedmtt,Depreciation expense from statement of cash flows - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.265,3,3,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_pxepedq,Depreciation expense from the statement of cash flows - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4903,3,3,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qcapspps,Capital Spending per share - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4933,3,3,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qcash,Cash and cash equivalents for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,11,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qcogs,Cost of goods sold for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8134,15,18,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qcurast,Current assets - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7119,7,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qcurliab,Current liabilities - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7115,7,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qcurratio,Current ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7115,12,19,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qcurratio2,"Current ratio - most recent quarter, 1 year ago","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6634,7,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qdebteps,Debt service to earnings per share ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.721,19,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qe2dtlq,Long-term debt to shareholder equity ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.785,14,21,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qebit,Earnings before interest and taxes for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,22,28,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qebitd,EBITDA for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8799,14,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qepsinclxo,Earnings per share including extraordinary items for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,35,43,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qgrosmgn,Gross Margin - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8064,38,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qinventory,Inventory for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6405,5,5,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qltd2ast,Long-term debt to total assets for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7486,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qltd2cap,"Long-term debt to total capital ratio for the most recent quarter (total capital includes short-term debt, current portion of long-term debt, long-term debt, capitalized leases, and shareholders’ equity)","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7482,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qniperemp,Net Income per employee - most recent quarter (annualized),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7878,9,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qpayratio,Payout ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7464,17,22,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qpr2rev,Price to sales - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9286,51,65,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qquickrat2,Quick ratio for the most recent quarter one year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5937,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qquickrati,Quick ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6362,7,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qrev,Revenue for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,16,24,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qrevperemp,Revenue per employee - most recent quarter (annualized),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7878,11,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qrevps,Revenue/share - most recent quarter (annualized),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,22,34,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qrevpsna,Revenue/share - most recent quarter (not annualized),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,19,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qrevstrt,"Reinvestment rate for the most recent quarter; 100 minus payout ratio, percent of earnings retained","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8724,16,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qsga2rev,SG&A expenses / net sales - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8624,29,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtachg,Year over year percent change in total assets as of one year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.748,3,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtanbvdolr,"Tangible book value in dollars, most recent quarter","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,5,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtanbvps,Tangible book value per share for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,15,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtaxpd,Income taxes paid in the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9306,37,54,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtaxrate,Effective tax rate for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.873,44,73,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtotce,Common equity (book value) - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,4,6,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtotce2,"Common equity (book value), most recent quarter one year ago","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7572,6,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtotd,Total debt - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8027,5,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtotd2ast,Total debt to total assets for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8026,7,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtotd2cap,Ratio of total debt to total capital for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7552,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtotd2eq,Total debt to shareholder equity ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.792,10,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtotd2eq2,"Total debt to shareholder equity ratio for the most recent quarter, measured one year ago","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7447,9,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtotltd,"Long-term debt (total), most recent quarter","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7957,6,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtotse,Shareholders' equity - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,8,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qtrperiods,Number of historical quarterly periods available for the company,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,12,19,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_qwcappspr,Working capital per share divided by current price for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7115,7,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_revchngin,Percentage change in revenue versus the immediately preceding quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9056,25,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_revchngyr,Percent change in revenue for the most recent quarter versus the same quarter a year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8939,36,45,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_revgrpct,Three-year revenue growth rate (percent),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9333,19,23,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_revps5ygr,Five-year growth rate of revenue per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8804,23,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_revtrend10,Ten-year revenue growth rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7186,28,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_revtrendgr,Five-year revenue growth rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8804,24,27,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rhsfca,Cash flow per share for the most recent fiscal year (as defined by the dataset; divided by average shares outstanding),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9668,62,88,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rhsfcf1a,Free cash flow per share for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9533,36,42,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rhsfcf2a,Free cash flow per share for the fiscal year prior to the most recent (FCF divided by average shares outstanding),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9665,30,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rhsfcmtp,Cash Flow per share - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8082,7,11,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rhsfcmtt,Cash Flow per share - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8591,28,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rhsfcq,"Cash flow per share for the most recent quarter, annualized","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8698,26,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_roxlcniep,"P/E including extraordinary items, trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8433,63,126,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_roxlcxebep,"Basic P/E excluding extraordinary items, trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8382,18,21,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_roxlcxspea,Earnings per share excluding extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,23,27,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_roxlcxspeq,Earnings per share excluding extraordinary items for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,29,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rtcpkw25rp,Relative (S&P500) price percent change - 52 week,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9395,51,63,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rtcpkw31rp,Relative (S&P500) price percent change - 13 week,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9718,14,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rtcpkw40rp,Four-week price percent change relative to the S&P 500,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,46,60,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rtcpkw62rp,Relative (S&P500) price percent change - 26 week,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9606,19,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ry5ngmpo,Operating Margin - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8893,22,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ry5ngmtp,Pretax Margin - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8909,18,32,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_rygnhcspe,Year-over-year percent change in EPS for the most recent quarter compared to the same quarter last year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8948,28,49,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_spdq,Dividend - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9245,56,72,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_spdtlq,LT debt/share - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7486,9,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_spfcfrpa,Price to Free Cash Flow per Share - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6076,40,47,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_spfcrpa,Price to Cash Flow per share - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9231,134,272,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_spfcrpmtp,Price to Cash Flow per share - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7539,11,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_spfcrpmtt,Price to Cash Flow per share - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8262,21,25,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_spfcrpq,Price to Cash Flow per share - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8387,50,65,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_sphsca,Cash per share - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9727,35,49,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_sphscq,Cash and equivalents per share for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,9,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_spvba,Book value of common equity per share for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,37,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_spvbq,Book value (common equity) per share for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,7,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tbea,Earnings before taxes for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,29,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tbemtt,Earnings before taxes over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,75,109,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tbeq,Earnings before taxes for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,53,76,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpaormtp,Return on average assets for the prior trailing twelve months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6735,4,5,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpaormtt,Return on average assets over the trailing 12 months (percentage),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7459,5,5,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpkw25rp,Percent change in the stock price over the last 52 weeks,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9395,84,173,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpkw31rp,Percent change in the stock price over the last 13 weeks,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9718,35,50,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpkw40rp,Four-week price percent change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,65,94,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpkw62rp,Percent change in the stock price over the last 26 weeks,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9606,51,111,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpngmpna,Net Profit Margin % - 1st historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9671,26,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpngmpnq,Net Profit Margin % - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9286,31,42,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpngmpoa,Operating margin - 1st historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9667,18,25,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpngmpoq,Operating margin - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.926,23,40,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcpngmtpq,Pretax margin - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9286,26,37,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_tcprgspe,Three-year compound annual growth rate of earnings per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7974,12,20,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmastturn,Asset turnover - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6996,12,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmcapspps,Capital Spending per share - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.266,3,3,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmcogs,Cost of goods sold over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8041,7,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmdebteps,Debt Service to EPS - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6978,15,18,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmdivshr,Dividends per share - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9151,35,46,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmebit,EBIT over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,30,62,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmebitd,EBITDA over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8673,7,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmebitdmg,EBITD Margin - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8667,14,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmebitdps,EBITD per share - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8673,32,40,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmepsincx,EPS including extraordinary items - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,89,173,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmgrosmgn,Gross margin over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7986,19,23,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmintcov,Interest coverage ratio (EBIT divided by interest expense) over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7424,14,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttminvturn,Inventory turnover - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5781,8,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmnichg,Percent change in trailing 12-month net income versus the prior TTM,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8434,19,19,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmniperem,Net Income per employee - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7731,8,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmopmgn,Operating margin over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9127,44,61,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmpayrat,"Payout ratio over the trailing 12 months, defined as cash dividends as a percent of earnings","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8414,19,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmpehigh,"P/E excluding extraordinary items high, trailing 12 months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6193,8,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmpelow,"P/E excluding extraordinary items low, trailing 12 months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6193,6,6,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmpr2rev,Price to sales - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9153,48,65,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmrecturn,Receivables turnover - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6834,12,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmrev,Revenue over the trailing 12 months; units: millions,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,21,25,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmrevchg,"Percent change in revenue, trailing 12 months over trailing 12 months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8428,19,19,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmrevpere,Revenue per employee - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7732,11,11,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmrevps,Revenue/share - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,36,43,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmrevstrt,Reinvestment rate over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8446,20,24,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmroepct,Return on average equity - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7357,12,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmroipct,Return on investment - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6889,7,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmsga2rev,SG&A expenses / net sales - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8521,14,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmtaxpd,Taxes paid over the trailing 12 months; units: millions,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,62,78,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_ttmtaxrate,Tax rate - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8475,35,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_voctniq,Interest coverage ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7615,20,38,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_vol10davg,Volume - avg. trading volume for the last ten days,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,31,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_vol1davg,Volume - 1 Day Average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,21,24,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_vol1dprc,Volume - 1 Day % Change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,25,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_vol3mavg,Volume - avg. trading volume for the last 3 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,21,42,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_xlcxspemtp,EPS excluding extraordinary items - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.861,22,47,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_xlcxspemtt,EPS excluding extraordinary items - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,48,75,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_yield,Dividend yield - indicated annual dividend divided by closing price,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7901,229,493,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd17_yragoprc1,Price - year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9395,29,38,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +gross_margin_trailing_twelve_months,Gross margin over the trailing 12 months (percentage),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7986,21,32,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +pricing_currency_code_4,Base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,5,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +pricing_currency_code_ras1,Base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,14,26,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +pricing_currency_code_ras2,Base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,8,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +pricing_currency_code_ras3,Base currency used for pricing in this dataset,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,5,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +pricing_currency_code_ras4,Base currency used for price or market values,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,11,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +pricing_currency_code_ras5,Base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,10,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +pricing_currency_code_ras6,base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,16,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +pricing_currency_code_ras8,base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,8,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +quarterly_free_cash_flow_value,Free Cash Flow — most recent quarter (units: millions),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5027,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +reporting_currency_code_11,Reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,7,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +reporting_currency_code_ras1,Company’s reporting currency code,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,13,15,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +reporting_currency_code_ras2,Reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,13,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +reporting_currency_code_ras3,Reporting currency code,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,20,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +reporting_currency_code_ras4,Company’s financial reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,8,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +reporting_currency_code_ras5,Reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,7,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +reporting_currency_code_ras6,Reporting currency of the company,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,6,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +reporting_currency_code_ras8,Reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,9,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +reporting_to_pricing_currency_fx,Exchange rate from the company’s reporting currency to the dataset’s base (price) currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,13,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +reporting_to_pricing_currency_fx_rate,Exchange rate from reporting currency to the base (pricing) currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,11,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +usd_to_reporting_currency_fx_rate,The exchange rate used to convert US dollars to the reporting currency for financial statements.,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,17,20,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +usd_to_reporting_fx_rate_2,USD to reporting currency exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,21,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +accounts_receivable_current_assets,Accounts receivable included in current assets for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8387,143,184,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +annual_fiscal_month_number,The fiscal month number corresponding to the end of the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9799,40,48,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +annual_fiscal_year_number,The fiscal year associated with the annual reporting period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9799,57,70,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +annual_net_income_value,Net income reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9891,145,214,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +annual_reporting_currency_code,The currency code in which annual financial values are presented after conversion.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9799,15,16,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +currency_of_converted_financials,Currency in which the financial values have been converted for reporting.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8745,15,21,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd23_pamonper,maps each rep identifier to an instrument on a given day.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.3255,16,23,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd23_repnotickermap,maps each rep to a ticker on a given day.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,1.0,50,64,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +interim_fiscal_month,The fiscal month corresponding to the interim reporting period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8745,32,39,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +interim_fiscal_year,The fiscal year corresponding to the interim reporting period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8745,42,46,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +interim_period_sequence_number_2,Sequence number of the interim period within the fiscal year.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8745,43,55,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +long_term_collateral_debt,Long-term collateralized debt reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8785,63,75,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +long_term_contract_liabilities,Long-term contract liabilities reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9087,35,45,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +long_term_debt_total,Total long-term debt reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9233,64,96,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +long_term_loans_liabilities,Long-term loans and liabilities reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9887,52,60,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +long_term_trade_debt,Long-term trade debt reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9878,93,160,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +other_accumulated_reserves,Other accumulated reserves reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9049,45,62,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +other_appropriations_balance,Other appropriations balance reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.85,49,55,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +other_interest_liabilities,Other interest-bearing liabilities reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.7976,118,168,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +other_long_term_obligations,Other long-term obligations reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9858,61,97,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +other_net_equity_total,Total of other net equity items for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9766,112,198,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +other_off_balance_liabilities,Other off-balance sheet liabilities for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8219,40,45,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +other_operating_assets,Other operating assets reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8365,27,36,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +quasi_equity_contracts,Quasi-equity contracts reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.988,29,35,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +quasi_equity_liabilities,Quasi-equity liabilities reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9887,35,54,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +retained_earnings_liabilities,Retained earnings classified as liabilities for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9254,19,23,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +securities_deferred_net_income,Deferred net income related to securities for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9894,36,50,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +share_capital_extraordinary,Extraordinary share capital reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9829,42,47,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +share_capital_ordinary,Ordinary share capital reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9058,31,35,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +share_capital_subscribed,Share capital subscribed reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9155,113,187,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +share_deposit_equity,Share deposit equity reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9766,20,21,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +share_dividend_equity,Share dividend equity reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9766,30,41,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +statement_of_cash_flows,Statement of cash flows for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9863,56,77,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +total_assets_annual_atot,Total assets reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9885,87,135,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +total_current_assets_4,Total current assets reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9086,37,42,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +valuation_market_adjustment_amount,Amount of market adjustment made to valuation.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.341,14,17,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +voting_amortized_investments,Voting amortized investments reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9011,72,92,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +voting_beneficial_equity_shares,Voting beneficial equity shares reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9906,181,281,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd4_chinascope_secmap,"Internal numeric security identifier used by Chinascope for cross-referencing securities within datasets, assigned per security per day","{'id': 'fundamental4', 'name': 'Hong Kong Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3737,31,44,1.4,[],fundamental4,Hong Kong Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd86_average_score,"Overall composite score aggregating all pillars, scaled as deciles 1–10 with higher being better","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9739,591,1934,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd86_earnings_score,"Earnings pillar score (1–10 decile, higher is better), reflecting earnings quality, revisions, and growth","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7632,360,981,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd86_fundamental_score,"Fundamental score on a 1–10 decile scale, higher indicates stronger fundamental quality","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9737,260,607,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd86_insider_trading_score,Insider trading score,"{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.0,39,90,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd86_price_momentum_score,"Price momentum score on a 1–10 decile scale, higher indicates stronger recent price trend","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9686,275,889,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd86_relative_valuation_score,"Relative valuation score on a 1–10 decile scale, higher indicates more attractive valuation versus peers in the region","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9456,269,759,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd86_risk_score,"Risk pillar score, integer decile 1–10 where higher indicates a more favorable risk profile","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9755,271,639,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_41_11,Current asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4158,130,423,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_41_12,Current asset accruals divided by time-series average total assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4156,90,403,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_41_21,Current asset accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4159,81,390,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_41_22,Current asset accruals divided by time-series average current assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4159,147,569,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_41_31,Current asset accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3538,80,252,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_42_11,Current asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2833,16,17,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_42_12,Current asset accruals divided by time-series average total assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2832,20,27,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_42_21,Current asset accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2833,17,23,1.4,[],fundamental89,Accrual based Earnings 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'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4596,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_61_21,Financial Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4139,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_61_22,Financial Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4138,6,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_61_31,Financial Asset Accruals / Sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.394,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_62_11,Financial Asset Accruals / Total Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4597,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_62_12,Financial Asset Accruals / Time Series Average Total Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4596,6,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_62_21,Financial Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4139,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_62_22,Financial Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4138,2,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_62_31,Financial asset accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.394,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_63_11,Financial asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4597,10,15,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_63_12,Financial asset accruals divided by time-series average total assets (variant 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4596,10,11,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_63_21,Financial asset accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4139,4,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_63_22,Financial asset accruals divided by time-series average current assets (variant 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4138,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_63_31,Financial asset accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.394,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_64_11,Financial asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4019,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_64_12,Financial asset accruals divided by time-series average total assets (variant 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4018,4,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_64_21,Financial asset accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3657,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_64_22,Financial asset accruals divided by time-series average current assets (variant 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3656,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_64_31,Financial asset accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3496,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_asset_d1_asset_change,Change in asset,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5007,18,22,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best11_d1_asset_double_history,"Indicator (0/1) that the company’s assets have at some point doubled in its history, signaling asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best11_d1_asset_half_history,"Indicator (0/1) that the company’s assets have at some point halved in its history, signaling asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,5,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best11_d1_length,Number of available accruals ratio observations used in the historical/rolling window for these statistics,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,6,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best21_d1_accum_4q,Sum of the firm’s DescBest21 accruals ratio over the last four fiscal quarters,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5803,6,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best21_d1_asset_double_history,"Indicator (1/0) that the company’s total assets have at some point doubled relative to a prior level in its observable history, signaling asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,9,17,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best21_d1_asset_half_history,"Indicator (1/0) that the company’s total assets have at some point halved relative to a prior level in its observable history, signaling asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,9,9,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best21_d1_current_t,"Current t-stat/z-score of the accruals ratio, computed as (current accruals ratio − historical mean) / historical standard deviation","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3642,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best21_d1_dts,Historical standard deviation of the accruals ratio over the available history up to this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6035,8,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best21_d1_length,Number of available accrual ratio observations used in the historical statistics at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,9,11,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best21_d1_max,Historical maximum of the accruals ratio over the available history up to this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,10,12,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best21_d1_mean,Historical mean of the accruals ratio over the available history up to this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,7,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_best21_d1_min,Historical minimum of the accruals ratio over the available history up to this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,9,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjones_all_r2_major_21,"R-squared value of the cross-sectional ratio of an asset-related balance-sheet field, scaled by average total assets, computed across the entire universe (All)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4822,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjones_all_r2_major_22,"R-squared value of the cross-sectional ratio of an asset-related balance-sheet field, scaled by average total assets, computed across the entire universe (All)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3163,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjones_all_r2_major_31,"R-squared value of the cross-sectional ratio of an asset-related balance-sheet field, scaled by average total assets, computed across the entire universe (All)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4751,2,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjones_all_r2_major_32,"R-squared value of the cross-sectional ratio of an asset-related balance-sheet field, scaled by average total assets, computed across the entire universe (All)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3134,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjones_all_r2_other_11,R-squared of the cross-sectional comprehensive accruals ratio (scaled by average total assets) computed across the All-universe group in Asia,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,4,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjones_all_r2_other_12,R-squared of the cross-sectional operating accruals ratio (scaled by average total assets) computed across the All-universe group in Asia,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5535,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjones_all_r2_other_13,R-squared of the cross-sectional financial accruals ratio (scaled by average total assets) computed across the All-universe group in Asia,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5535,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjones_all_r2_other_14,R-squared of the cross-sectional working capital accruals ratio (scaled by average total assets) computed across the All-universe group in Asia,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4822,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjones_all_r2_other_15,R-squared of the cross-sectional long-term operating accruals ratio (scaled by average total assets) computed across the All-universe group in Asia,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.482,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjones_d1_asset_change,Change in asset,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.376,8,9,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesallmajor13_d1_asset_double_history,"0/1 indicator that the company’s total assets have at some point at least doubled in its available history, used as an asset-volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,1,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesallmajor13_d1_asset_half_history,"0/1 indicator that the company’s total assets have at some point at least halved in its available history, used as an asset-volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesallmajor13_d1_length,Number of historical accruals-ratio observations used in the descriptive statistics at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesallother11_d1_asset_double_history,"Indicator (1/0) whether the company’s assets have doubled at any point in its history, used as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,4,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesallother11_d1_asset_half_history,"Indicator (1/0) whether the company’s assets have halved at any point in its history, used as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesallother11_d1_length,Number of accruals ratio observations available in the historical window used for the descriptive statistics,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesindustrymajor13_d1_asset_double_history,"Indicator (1/0) that the company’s assets have doubled at some point in its history, used as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,4,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesindustrymajor13_d1_asset_half_history,"Indicator (1/0) that the company’s assets have halved at some point in its history, used as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesindustrymajor13_d1_length,Number of available accrual ratio observations in the historical window used for the descriptive statistics,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesindustryother11_d1_asset_double_history,"Indicator 1 or 0 showing whether the company’s total assets have doubled at any point in its history, as a volatility flag","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesindustryother11_d1_asset_half_history,"Indicator 1 or 0 showing whether the company’s total assets have halved at any point in its history, as a volatility flag","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_csjonesindustryother11_d1_length,Count of accrual ratio observations available in the historical window used for the descriptive statistics,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_fixed1313_d1_asset_double_history,"0/1 indicator whether the company’s assets have ever doubled in its history, indicating asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_fixed1313_d1_asset_half_history,"0/1 indicator whether the company’s assets have ever halved in its history, indicating asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_fixed1313_d1_length,Number of available accruals ratio observations in the historical window (sample size),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,50,129,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_fixed1513_d1_asset_double_history,"Indicator (0/1) whether the company’s assets have doubled at any point in its history, as a volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_fixed1513_d1_asset_half_history,"Indicator (0/1) whether the company’s assets have halved at any point in its history, as a volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_fixed1513_d1_length,Number of available accruals-ratio observations used at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,3,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_pct_major_21,"Percentage share of discretionary asset-related accruals (MAJOR group), scaled by average total assets, out of total accruals","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_pct_major_22,"Percentage share of discretionary asset-related accruals (MAJOR group), scaled by average total assets, out of total accruals","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4221,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_pct_major_31,"Percentage share of discretionary asset-related accruals (MAJOR group), scaled by average total assets, out of total accruals","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4334,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_pct_major_32,"Percentage of discretionary accruals for asset-related (balance sheet) items, scaled by average total assets, as a share of total accruals within the MAJOR overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4042,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_pct_other_11,"Percentage of discretionary accruals for comprehensive accruals, scaled by average total assets, as a share of total accruals within the OTHER overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4939,6,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_pct_other_12,"Percentage of discretionary accruals for operating accruals, scaled by average total assets, as a share of total accruals within the OTHER overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4939,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_pct_other_13,"Percentage of discretionary accruals for financial accruals, scaled by average total assets, as a share of total accruals within the OTHER overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.491,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_pct_other_14,"Percentage share of discretionary working capital accruals (OTHER group), scaled by average total assets, out of total accruals","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_pct_other_15,"Percentage share of discretionary long-term operating accruals (OTHER group), scaled by average total assets, out of total accruals","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_r2_major_21,"R-squared of the Jones-model time series accrual ratio for the MAJOR group based on asset-related balance sheet items, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_r2_major_22,"R-squared of the time-series accrual ratio for asset-related (balance sheet) items, scaled by average total assets, within the MAJOR overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4221,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_r2_major_31,"R-squared of the Jones-model time series accrual ratio for the MAJOR group based on asset-related balance sheet items, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4333,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_r2_major_32,"R-squared of the Jones-model time series accrual ratio for the MAJOR group based on asset-related balance sheet items, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4042,0,0,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_r2_other_11,"R-squared of the Jones-model time series accrual ratio for the OTHER group based on comprehensive accruals, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4939,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_r2_other_12,"R-squared of the Jones-model time series accrual ratio for the OTHER group based on operating accruals, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4939,3,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_r2_other_13,"R-squared of the Jones-model time series accrual ratio for the OTHER group based on financial accruals, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4939,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_r2_other_14,"R-squared of the time-series accrual ratio for working capital accruals, scaled by average total assets, within the OTHER overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_accrual_r2_other_15,"R-squared of the time-series accrual ratio for long-term operating accruals, scaled by average total assets, within the OTHER overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jones_d1_asset_change,Change in asset,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3244,2,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesmajor13_d1_asset_double_history,"Indicator (1/0) that the company’s assets have doubled at some point in its history, as a volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesmajor13_d1_asset_half_history,"Indicator (1/0) that the company’s assets have halved at some point in its history, as a volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesmajor13_d1_length,Number of historical periods available for the accrual ratio used to compute these descriptive statistics,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesmajor31_d1_asset_double_history,"Indicator (1/0) whether the company’s total assets have doubled at any point in its available history, used as an asset-volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesmajor31_d1_asset_half_history,"Indicator (1/0) whether the company’s total assets have halved at any point in its available history, used as an asset-volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesmajor31_d1_length,Number of historical accruals-ratio observations available and used to compute the descriptive statistics at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesmajor32_d1_asset_double_history,"0 or 1 indicator whether the company’s assets have ever doubled in its history, as a proxy for asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesmajor32_d1_asset_half_history,"0 or 1 indicator whether the company’s assets have ever halved in its history, as a proxy for asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesmajor32_d1_length,Count of available accruals ratio observations used in the historical window/statistics at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother11_d1_accum_4q,Accumulated (4-quarter) value of the accrual ratio over the last four fiscal quarters,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5803,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother11_d1_asset_double_history,Indicator (1/0) whether the company’s assets have ever doubled in its history (asset volatility diagnostic),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,4,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother11_d1_asset_half_history,Indicator (1/0) whether the company’s assets have ever halved in its history (asset volatility diagnostic),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,4,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother11_d1_current_t,Current t/z-score of the accrual ratio: (current value − historical mean) / historical standard deviation,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3642,3,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother11_d1_dts,Historical standard deviation of the Jones “Other” accrual ratio (scaled by total assets),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6035,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother11_d1_length,Number of historical observations used to compute the descriptive statistics for the accrual ratio at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,41,150,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother11_d1_max,Historical maximum of the Jones “Other” accrual ratio (scaled by total assets),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,7,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother11_d1_mean,Historical mean of the Jones “Other” accrual ratio (scaled by total assets) over the available history,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother11_d1_min,Historical minimum of the Jones “Other” accrual ratio (scaled by total assets),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,9,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother14_d1_asset_double_history,Indicator (0/1) that the company’s assets have at some point doubled over its available history; used as an asset volatility diagnostic,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother14_d1_asset_half_history,Indicator (0/1) that the company’s assets have at some point halved over its available history; used as an asset volatility diagnostic,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother14_d1_length,Number of historical observations available for the accruals ratio,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother15_d1_asset_double_history,"Indicator (1/0) that the company’s assets have doubled at some point in its history, as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother15_d1_asset_half_history,"Indicator (1/0) that the company’s assets have halved at some point in its history, as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_jonesother15_d1_length,Number of available accrual ratio observations used in the historical calculation,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_21_11,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt), scaled by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3938,7,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_21_12,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt), scaled by average total assets computed as (current + 4 quarters ago)/2","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3937,9,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_21_21,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt), scaled by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3939,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_21_22,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt), scaled by average current assets computed as (current + 4 quarters ago)/2","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3939,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_21_31,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt), scaled by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3356,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_31_11,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt) − depreciation and amortization expense, scaled by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3294,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_31_12,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt) − depreciation and amortization expense, scaled by average total assets computed as (current + 4 quarters ago)/2","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3293,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_31_21,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt) − depreciation and amortization expense, scaled by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3295,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_31_22,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt) − depreciation and amortization expense, scaled by average current assets computed as (current + 4 quarters ago)/2","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3295,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_31_31,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt) − depreciation and amortization expense, scaled by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3292,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_major_d1_asset_change,Period-over-period change in total assets (Major overlay),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5007,15,18,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_11_11,"Change in common shareholders’ equity minus change in cash, scaled by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4636,7,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_11_12,"Change in common shareholders’ equity minus change in cash, divided by two-quarter average total assets (current and 4 quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4635,8,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_11_21,"Change in common shareholders’ equity minus change in cash, scaled by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4158,4,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_11_22,"Change in common shareholders’ equity minus change in cash, divided by two-quarter average current assets (current and 4 quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4157,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_11_31,"Change in common shareholders’ equity minus change in cash, divided by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3963,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_12_11,"Change in total assets excluding cash and long-term investments and advances, minus change in total liabilities excluding debt, scaled by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4532,4,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_12_12,"Change in total assets excluding cash and long-term investments and advances, minus change in total liabilities excluding debt, scaled by two-point average total assets (current and four quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4532,11,13,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_12_21,"Change in total assets excluding cash and long-term investments and advances, minus change in total liabilities excluding debt, scaled by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4067,12,14,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_12_22,"Change in total assets excluding cash and long-term investments and advances, minus change in total liabilities excluding debt, scaled by two-point average current assets (current and four quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4066,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_12_31,"Change in total assets excluding cash and long-term investments and advances, minus change in total liabilities excluding debt, scaled by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3882,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_13_11,"Comprehensive accruals minus operating accruals, divided by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.453,11,12,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_13_12,"Comprehensive accruals minus operating accruals, divided by two-quarter average total assets (current and 4 quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.453,6,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_13_21,"Comprehensive accruals minus operating accruals, divided by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4065,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_13_22,"Comprehensive accruals minus operating accruals, divided by two-quarter average current assets (current and 4 quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4064,7,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_13_31,"Comprehensive accruals minus operating accruals, divided by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3882,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_14_11,"Change in current assets excluding cash and short-term investments minus change in current liabilities excluding short-term debt, scaled by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3938,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_14_12,"Change in current assets excluding cash and short-term investments minus change in current liabilities excluding short-term debt, scaled by two-point average total assets (current and four quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3937,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_14_21,"Change in current assets excluding cash and short-term investments minus change in current liabilities excluding short-term debt, scaled by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3939,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_14_22,"Change in current assets excluding cash and short-term investments minus change in current liabilities excluding short-term debt, scaled by two-point average current assets (current and four quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3939,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_14_31,"Change in current assets excluding cash and short-term investments minus change in current liabilities excluding short-term debt, scaled by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3356,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_15_11,"Operating accruals minus working capital accruals, divided by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3936,7,16,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_15_12,"Operating accruals – working capital accruals divided by average total assets, where average total assets = (total assets at the current quarter + total assets 4 quarters earlier) / 2","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3936,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_15_21,Working capital accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3936,5,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_15_22,Working capital accruals divided by average current assets (average of current and the same quarter four quarters earlier),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3936,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_15_31,Working capital accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3355,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd89_other_d1_asset_change,Period-over-period change in total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5007,76,197,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd90_game_accruals_vol,"The volatility of accounting accruals, measuring variability in accruals over time; higher values indicate greater manipulation or lower earnings quality","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.573,48,70,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_an_cov_chng,"Period-over-period change in the number of financial analysts covering the company, positive values indicate increasing analyst attention","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5611,91,138,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_asset_turnover_chg,"The change in asset turnover ratio (typically sales/assets), reflecting improvement or deterioration in capital utilization over time","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8257,77,127,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_capex_dep,"Ratio of capital expenditures to depreciation, with higher values indicating more aggressive reinvestment relative to asset aging","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7553,44,57,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_capex_sale,"Ratio of capital expenditures to sales, indicating investment intensity compared to revenue generation","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8239,92,148,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_cash_flow_coverage,A ratio measuring how well a company's operating cash flow covers its obligations (likely total debt or interest); higher values signal stronger ability to service liabilities,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.701,65,95,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_contingent_liab,"The ratio of contingent liabilities (potential future obligations) to equity, indicating off-balance-sheet or unrecognized risk; higher values mean higher risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.486,32,37,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_deferred_sal_chg,"The period-over-period change in deferred revenue (sales received but not yet recognized), which may reflect aggressive revenue recognition practices; positive changes may warrant scrutiny","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5082,41,70,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_deferred_tax_asset_chg,"The period-over-period change in the amount of deferred tax assets on the balance sheet, reflecting adjustments or aggressive accounting in tax accruals or recognition timing","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6129,47,78,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_deferred_tax_liab_chg,"The period-over-period change in deferred tax liabilities, indicating shifts in future tax obligations possibly due to accounting policies or estimates","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6283,45,61,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_depreciation_chg,"The change in the depreciation rate (either method or level), which can indicate changes in accounting estimate or potential earnings management","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7578,43,79,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_earning_smooth,"A measure of how stable or ""smooth"" reported earnings are over time, potentially capturing both persistent profitability or possible earnings management/smoothing practices","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7041,61,99,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_eff_interest_rate,"The effective interest rate experienced by the company, representing the cost of debt factoring in all forms of financing; higher rates signal more financial leverage risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5542,18,24,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_eps_sur_vol,"Volatility of analyst EPS (earnings per share) forecast errors, reflecting the unpredictability or low quality of reported earnings versus market expectations","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6827,26,42,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_eps_vol,"Volatility (standard deviation) of earnings per share over a period, with higher values indicating unstable profitability","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7875,40,43,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_equity_dilution,"An indicator of new equity issuance or dilution, such as through new share issuance or conversion of convertibles, reflecting risk of ownership dilution to existing shareholders","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8298,74,129,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_esop_grant,ESOP grants [ ] - [Quality.Capital structure],"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.3124,16,19,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_finished_goods,"Ratio of finished goods to total inventory, indicating the proportion of inventory in final form; higher values may signal slow sales, overproduction, or potential risk in working capital management","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5529,19,21,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_gma_chg,"Change in gross margin (as a percentage or ratio) from one period to the next, capturing improvement or deterioration in efficiency","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7719,41,45,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_gross_margin_vol,"Volatility in gross margin over time; high volatility signals instability or unpredictability in profitability, indicating potential business risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.789,50,59,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_inc_cash_tax,"Ratio of tax expense per income statement to actual cash taxes paid, designed to highlight aggressive tax recognition or earnings quality red flags","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8226,50,75,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_interest_investment_inc,"The amount of income earned from interest and investments, as opposed to operating earnings, which might distort quality of earnings if high relative to core business profit","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.672,24,27,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_inventory_chg,"Change in company inventory levels over a specific period, typically quarter-over-quarter or year-over-year, reflecting working capital changes and operational efficiency; higher changes may indicate risk of inventory buildup or fluctuations in demand","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6559,40,46,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_inventory_days,"Number of days inventory remains on hand before being sold, calculated as inventory divided by average daily sales; a higher value suggests slower inventory turnover and potential working capital inefficiency or sales weakness","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6559,17,18,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_labor_efficiency_chg,"Period-to-period change in labor efficiency (e.g., revenue or profit per employee), with increases reflecting greater efficiency","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5813,22,29,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_minority_interest,The ratio of minority interest (equity attributable to non-controlling shareholders in subsidiaries) to total equity; higher values may reflect more complex ownership structures and potential earnings quality concerns,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5186,21,23,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_noa,"Net Operating Assets; represents the company's operating assets minus operating liabilities, indicating capital employed in operations","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.792,21,30,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_non_oper_inc,"Non-operating income as a share of EBITDA, highlighting the proportion of earnings derived from sources outside the core business; higher ratios may imply lower earnings quality or potential one-off items","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.685,12,12,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_non_production_asset,"Amount or ratio of company assets not directly tied to production or core operations (such as investments, property held for sale); higher values may indicate inefficient capital utilization or non-core area exposure","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6593,29,34,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_optimism_gma,Analyst Optimism Gross Margin [ Descending] - [Quality.Profitability],"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5365,21,24,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_optimism_sale,Analyst Optimism Sales [ Descending] - [Quality.Profitability],"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6952,25,32,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_other_asset_gr,Other Asset Growth [ Descending] - [Quality.Capital utilization],"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8289,27,29,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_payble_days,"Average number of days the company takes to pay its suppliers (accounts payable days); a higher value means slower payments, which could indicate cash flow management or payment risk to suppliers","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.771,22,25,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_pension_debt_equi_pbo,Value of the company's net pension benefit obligation (PBO) expressed as debt equivalent; a higher value indicates greater pension-related financial risk relative to debt,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8267,22,31,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_pension_discount_rate,The discount rate used to calculate the present value of pension obligations; higher values may indicate more aggressive accounting practices (lowering reported liabilities),"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8031,20,26,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_pension_ev,Ratio of pension and other post-employment benefit obligations to total enterprise value; higher values mean greater pension-related liability risks relative to the company's market worth,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6727,22,33,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_pension_expense,"Pension-related expense scaled by EBITDA, where higher values indicate greater pension-related liability risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8309,32,45,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_pension_interest,"Interest cost component of pension expense, representing the increase in projected pension liability due to the passage of time","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7941,23,25,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_pension_pbo,"Ratio of projected pension benefit obligations (pension liabilities) to total enterprise value, indicating pension-related financial risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8267,114,194,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_prepaid_expense,"Ratio of prepaid expenses to sales, indicating the extent of accounting items paid in advance relative to revenue, which may signal aggressive accounting practices","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5922,14,19,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_receivable_chg,"Change in receivables (typically quarter-over-quarter or year-over-year), showing how much accounts receivable increased or decreased, reflecting shifts in working capital","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8107,30,39,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_receivable_days,"Number of days, on average, it takes a company to collect receivables from customers; a measure of working capital efficiency","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8107,18,26,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_relative_rtn_vol,"Volatility of a security’s returns relative to a benchmark or peers, representing relative risk or instability of stock returns","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8325,25,29,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_serial_special,"Total of special (non-recurring/one-off) accounting items as a proportion of sales, highlighting the prevalence of such adjustments and potential earnings manipulation","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8279,24,31,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_sg_a_cost_ratio,"Selling, General, & Administrative (SG&A) expenses as a ratio to a base (likely sales or revenue), measuring company efficiency in overhead spending","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8256,22,26,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_sg_a_inefficiency,"Level of inefficiency in SG&A (Selling, General & Administrative) expenses; higher values suggest less efficient management of overhead costs","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8287,18,19,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_sg_a_vol,"Volatility or variability in SG&A expenses over time, with higher values indicating instability or unpredictability in overhead costs, which could be a risk factor for efficiency","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7988,16,16,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_short_term_debt,"Ratio of short-term (due within one year) debt to a base (likely total assets or capitalization), indicating near-term financial leverage or refinancing risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6488,22,24,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_tax_rate,"Effective tax rate, with lower rates possibly indicating aggressive accounting or tax minimization strategies","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8242,30,39,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_game_total_debt_chg,"Change in total debt balance (period-over-period), measuring increases or decreases in company leverage","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6438,22,26,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_cashflow_var,"Variability (volatility) of operating cashflows; higher values indicate more unpredictable or volatile cashflow generation, which means higher risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.819,26,28,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_chs_default,"Standardized probability of bankruptcy/default (z-score) based on the Campbell, Hilscher, Szilagyi model; higher values = greater default risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7663,62,76,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_cscore,"Montier’s C-score, an integer-based composite designed to flag accounting manipulation or fraud risk (higher is more risk)","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8325,22,26,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_earn_var,"Earnings variability, typically representing the standard deviation or volatility of a company's earnings over a period; higher values indicate less stable or more unpredictable earnings, signaling higher risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.814,31,35,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_earnigs_timeliness,"Z-score measuring the timeliness of a company's earnings releases, where higher values typically indicate quicker (more timely) earnings reporting, which may reflect better governance or information transparency","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5639,27,36,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_grossprofit_asset,"Gross profit divided by total assets, a quality/profitability metric where higher values indicate better profitability","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7751,17,18,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_icfroe_1y,Incremental Cash Flow Return on Equity over the past 1 year; higher values indicate stronger cash generation relative to equity for the recent year,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6985,10,11,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_icfroe_3y,Incremental Cash Flow Return on Equity over the past 3 years; higher values indicate stronger multi-year cash flow returns relative to equity,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.676,21,24,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_icfroe_5y,Incremental Cash Flow Return on Equity over the past 5 years; higher values indicate sustained cash flow return performance relative to equity over a longer period,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6493,10,10,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_mohanram,Mohanram “G-score” assessing risk of earnings manipulation in growth stocks (higher indicates greater fraud or earnings risk),"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8325,43,54,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_negative_earnings,Frequency or indicator of negative earnings occurrences for the entity; higher numbers suggest more frequent negative earnings results,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8322,31,34,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_ohlson,"Ohlson O-score, a bankruptcy prediction model score; higher scores indicate higher risk of bankruptcy for a company","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8285,46,66,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_pscore,"Piotroski F-score (0–9), a composite accounting quality score based on fundamental signals; higher values indicate better accounting quality and financial health","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8325,61,81,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd90_qes_gamef_score,SCORE,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8142,49,64,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +nonliquid_asset_ratio,"Ratio of illiquid assets (such as plant, equipment, intangibles, or other hard-to-sell assets) to total assets, indicating potential capital utilization or liquidity risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8318,32,49,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models +fnd93_accruals_d1_length,Count of available accruals ratio observations within the trailing twelve-month (LTM) window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.76,72,110,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_accrualsratio_d1_asset_change,"Asset change ratio = latest total assets divided by asset_ltm (e.g., Asset_2018Q3 / Asset_2017Q3)","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4956,39,49,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_expense_11,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5727,22,31,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_expense_12,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5722,22,27,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_expense_21,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5018,12,17,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_expense_22,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5011,21,30,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_expense_31,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5723,23,27,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_expense_41,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5195,16,17,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_expense_51,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5224,22,32,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_liability_11,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.455,18,22,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_liability_12,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.455,10,10,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_liability_13,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3536,17,20,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_liability_21,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4032,9,13,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_liability_22,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4031,9,11,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_liability_23,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2886,9,9,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_liability_31,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.388,5,6,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_liability_41,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3569,6,7,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_deferred_tax_liability_51,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX"" indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3607,10,10,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_expense_d1_asset_change,"YoY asset change ratio: latest total assets divided by total assets 12 months earlier (e.g., Asset_2018Q3 / Asset_2017Q3)","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5841,16,18,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_expense_d1_current_t,"Z-score (t-statistic) of the current deferred tax expense ratio relative to the LTM window, computed as (current ratio − mean) / std","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4983,22,22,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_expense_d1_dts,Standard deviation of the deferred tax expense ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6756,16,19,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_expense_d1_length,Number of available deferred tax expense ratio observations over the LTM (last twelve months) window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7381,25,33,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_expense_d1_max,Maximum value of the deferred tax expense ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7181,22,25,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_expense_d1_mean,Mean of the deferred tax expense ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7181,23,31,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_expense_d1_min,Minimum value of the deferred tax expense ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7181,16,22,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_liability_d1_asset_change,"Ratio of latest total assets to asset_ltm (e.g., Assets_current quarter / Assets_same quarter prior year)","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4954,22,25,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_liability_d1_current_t,"Z-score of the current liability ratio versus the LTM window, computed as (current ratio − mean) / std","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3428,7,8,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_liability_d1_dts,Standard deviation of the deferred tax liability ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5769,9,10,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_liability_d1_length,Number of deferred tax liability ratio observations available in the trailing 12-month (LTM) window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7594,26,30,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_liability_d1_max,Maximum value of the deferred tax liability ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6177,12,15,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_liability_d1_mean,Mean of the deferred tax liability ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6175,8,8,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data +fnd93_liability_d1_min,Minimum value of the deferred tax liability ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6175,10,12,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_imbalance.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_imbalance.csv new file mode 100644 index 0000000..3d0941e --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_imbalance.csv @@ -0,0 +1,3 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +imb5_mktcap,Market capitalization of security in regional currency units,"{'id': 'imbalance5', 'name': 'Oil Price Resilience Scores'}","{'id': 'imbalance', 'name': 'Imbalance'}","{'id': 'imbalance-imbalance-models', 'name': 'Imbalance Models'}",IND,1,TOP500,MATRIX,0.9507,0.7876,869,2509,1.0,[],imbalance5,Oil Price Resilience Scores,imbalance,Imbalance,imbalance-imbalance-models,Imbalance Models +imb5_score,SHIELD-OIL composite score (0-1) indicating resilience/advantage in oil shock regimes,"{'id': 'imbalance5', 'name': 'Oil Price Resilience Scores'}","{'id': 'imbalance', 'name': 'Imbalance'}","{'id': 'imbalance-imbalance-models', 'name': 'Imbalance Models'}",IND,1,TOP500,MATRIX,0.9507,0.7876,1546,9502,1.0,[],imbalance5,Oil Price Resilience Scores,imbalance,Imbalance,imbalance-imbalance-models,Imbalance Models diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_institutions.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_institutions.csv new file mode 100644 index 0000000..ee5daf9 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_institutions.csv @@ -0,0 +1,12 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +aggregate_equity_value_all_owners,"Aggregate dollar value of shares of the security held by all owners, with duplication between child and parent owners removed","{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,438,794,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models +aggregate_equity_value_institutions,Aggregate dollar value of shares of the security currently held by all institutional investors at the reporting date,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,343,594,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models +aggregate_share_count_all_owners,"Aggregate number of shares of the security held by all owners, with duplication between child and parent owners removed","{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,319,527,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models +aggregate_share_count_institutions,Total number of shares of the security currently held by all institutional investors at the reporting date,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,276,501,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models +count_institutional_buyers_security,Number of institutional investors who purchased shares of the security during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,517,1210,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models +count_institutional_holders_security,Number of institutional investors currently holding shares (with holdings greater than zero) for the security at the reporting date,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,340,580,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models +count_institutional_sellers_security,Number of institutional investors who sold shares of the security during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,323,576,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models +market_value_institutional_shares_acquired,Aggregate dollar value of shares of the security purchased by institutional investors during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,592,1334,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models +market_value_institutional_shares_disposed,Aggregate dollar value of shares of the security sold by institutional investors during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.9915,255,414,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models +quantity_institutional_shares_acquired,Total number of shares of the security purchased by institutional investors during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,445,873,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models +quantity_institutional_shares_disposed,Total number of shares of the security sold by institutional investors during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,294,484,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_macro.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_macro.csv new file mode 100644 index 0000000..abf2c85 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_macro.csv @@ -0,0 +1,2 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +mcr63_membership,Indicates the membership status of stocks in the WisdomTree WTIDJH Index,"{'id': 'macro63', 'name': 'Index Reconstitution Data'}","{'id': 'macro', 'name': 'Macro'}","{'id': 'macro-macroeconomic-activities', 'name': 'Macroeconomic Activities'}",IND,1,TOP500,MATRIX,0.9507,0.9968,325,1177,1.0,[],macro63,Index Reconstitution Data,macro,Macro,macro-macroeconomic-activities,Macroeconomic Activities diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_model.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_model.csv new file mode 100644 index 0000000..2d3f589 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_model.csv @@ -0,0 +1,1590 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +mdl106_country,Country of listing,"{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,124,297,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_dividend,Forecast dividend yield for the next 12 months (percent),"{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,268,522,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_fmb,Measure of a stock’s downside sensitivity in bear markets; higher values imply greater susceptibility during market downturns,"{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,80,138,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_fnb,Bad News Factor indicating the stock’s sensitivity to negative news and its tendency to underperform during adverse events,"{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,66,102,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_global_evaluation,"Composite multi-criteria evaluation score combining fundamentals, technicals, and risk; range -2 (negative) to +2 (positive)","{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,103,171,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_mkt_cap,"Market capitalization in USD billions, calculated as share price times shares outstanding","{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,135,231,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_pr,"4-week performance relative to the national or regional index, in percent","{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,53,83,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_price,Latest daily stock price in the security’s trading currency,"{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,92,133,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_risk,risk levels by historic price,"{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,81,158,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_rv,Valuation rating from -2 to +2 indicating whether the stock appears overvalued or undervalued relative to growth potential,"{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,55,93,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_stars,"Overall star rating summarizing the four pillars, on a 0–4 scale","{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,105,204,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_tre,"Earnings Revision (Revenue) Trend score in the range [-1, 1], with positive values indicating upward revisions","{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,67,94,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_tt,"Medium-term technical trend indicator: +1 positive, -1 negative; includes a tech reverse price threshold where the trend would reverse","{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,57,95,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl106_ycc,ISO 4217 currency code of the security (currency used for price and related fields),"{'id': 'model106', 'name': 'Analysts rating model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.4953,42,53,1.3,[],model106,Analysts rating model,model,Model,model-estimates-models,Estimates Models +mdl110_alternative,"Composite alpha signal generated from non-traditional sources such as alternative data, big data, and unstructured information","{'id': 'model110', 'name': 'Big data and machine learning based model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7885,445,2269,1.3,[],model110,Big data and machine learning based model,model,Model,model-mlai-models,ML/AI Models +mdl110_analyst_sentiment,"Aggregate sentiment score derived from analyst ratings, estimate revisions, and market sentiment factors","{'id': 'model110', 'name': 'Big data and machine learning based model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7885,1284,10323,1.3,[],model110,Big data and machine learning based model,model,Model,model-mlai-models,ML/AI Models +mdl110_growth,"Composite growth factor derived from forward-looking estimates and backward-looking financials, representing robust sales and earnings growth","{'id': 'model110', 'name': 'Big data and machine learning based model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7885,498,3137,1.3,[],model110,Big data and machine learning based model,model,Model,model-mlai-models,ML/AI Models +mdl110_price_momentum_reversal,"A score indicating the influence of recent price momentum and mean-reversion effects on the stock price, capturing both trends and potential reversals in price over short to medium time periods","{'id': 'model110', 'name': 'Big data and machine learning based model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7885,708,4314,1.3,[],model110,Big data and machine learning based model,model,Model,model-mlai-models,ML/AI Models +mdl110_quality,"Composite score measuring company profitability, accounting robustness, earnings quality, and risk-related financial metrics","{'id': 'model110', 'name': 'Big data and machine learning based model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7885,459,1811,1.3,[],model110,Big data and machine learning based model,model,Model,model-mlai-models,ML/AI Models +mdl110_score,"The overall alpha signal produced by a weighted combination of all underlying factor scores (sentiment, quality, value, growth, price momentum/reversion, alternative, tree)","{'id': 'model110', 'name': 'Big data and machine learning based model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7885,1943,25749,1.3,[],model110,Big data and machine learning based model,model,Model,model-mlai-models,ML/AI Models +mdl110_tree,"A composite score generated from nonlinear tree-based machine learning models, synthesizing all available individual signals and factors to capture complex interactions and enhance alpha prediction beyond linear approaches","{'id': 'model110', 'name': 'Big data and machine learning based model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7885,470,1990,1.3,[],model110,Big data and machine learning based model,model,Model,model-mlai-models,ML/AI Models +mdl110_value,"Composite valuation factor blending multiple price-based and fundamental metrics such as P/E, P/B, and price-to-sales","{'id': 'model110', 'name': 'Big data and machine learning based model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7885,866,4859,1.3,[],model110,Big data and machine learning based model,model,Model,model-mlai-models,ML/AI Models +mdl140_qes_sinc_comp,Composite factor combining both pure inflation sensitivity and interest rate sensitivity into a holistic metric of a stock's macro inflation exposure; preferred for portfolio risk modeling and thematic tilts,"{'id': 'model140', 'name': 'Sensitivity to the Inflation Change'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7367,68,94,1.3,[],model140,Sensitivity to the Inflation Change,model,Model,model-mlai-models,ML/AI Models +mdl140_qes_sinc_neut,"Stock-level sensitivity to expected inflation changes, ""neutralized"" or adjusted to remove the contemporaneous effect of benchmark interest rate changes; captures pure inflation beta orthogonal to interest rates","{'id': 'model140', 'name': 'Sensitivity to the Inflation Change'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7695,97,178,1.3,[],model140,Sensitivity to the Inflation Change,model,Model,model-mlai-models,ML/AI Models +mdl140_qes_sinc_sensitivity,"Stock-level estimated “beta” sensitivity to expected inflation, specifically to changes in the 10-year breakeven inflation rate, derived from elastic net regression","{'id': 'model140', 'name': 'Sensitivity to the Inflation Change'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,0.9507,0.7328,40,80,1.3,[],model140,Sensitivity to the Inflation Change,model,Model,model-mlai-models,ML/AI Models +mdl141_mktcap,"The market capitalization of the company, typically float-adjusted; represents total equity market value","{'id': 'model141', 'name': 'Interest Rate Sensitivity Measures'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.1961,25,38,1.3,[],model141,Interest Rate Sensitivity Measures,model,Model,model-risk-based-models,Risk Based Models +mdl141_qes_macrobeta3_wti_tr,"Beta of stock returns to oil price returns (specifically, West Texas Intermediate crude), representing sensitivity to oil as a macro factor","{'id': 'model141', 'name': 'Interest Rate Sensitivity Measures'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.1947,6,6,1.3,[],model141,Interest Rate Sensitivity Measures,model,Model,model-risk-based-models,Risk Based Models +mdl192_altmanz_di,"Altman Z-score, a measure of company credit risk or bankruptcy risk","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,38,66,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_benchmark_fee_di,Imputed or backfilled value of Benchmark_fee used when the raw benchmark fee is missing to enhance continuity,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,18,55,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_booklev_di,Book leverage ratio (likely debt to equity or related measure using book values),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,19,106,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cashburnrate_di,Imputed/backfilled version of CashBurnRate to maintain continuity when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,22,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cashc_di,Imputed/backfilled version of CashC to maintain continuity when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,18,46,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cashratio_di,Imputed or backfilled ratio of cash & equivalents to current liabilities; measure of short-term liquidity,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,19,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_1mvol_di,1-month CDS Spread Change Volatility (Tenor=5Y),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,20,47,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_1ybeta_di,Imputed or backfilled value of CDS_1YBeta used when the raw beta is missing to enhance continuity,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,36,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_beta_di,5-year (60 month) beta of CDS spread changes (measure of CDS spread sensitivity to market),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,53,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_canbeta_di,Sovereign Sensitivity - Canada,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,11,30,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_eurusd_di,Sensitivity of CDS spread to EUR/USD exchange rate movements,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,11,38,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_frabeta_di,"Sensitivity of an entity's CDS spread changes to France sovereign CDS, representing how much the entity's CDS responds to movements in France's sovereign credit risk, with values typically indicating degree of co-movement","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,30,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_gerbeta_di,"Sensitivity of an entity's CDS spread changes to German sovereign CDS, representing correlation to changes in Germany's sovereign credit risk","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,8,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_gnd_di,Imputed or backfilled gross notional CDS volume as reported by DTCC relative to total debt,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,19,83,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_gnvol5wavg_di,"Five-week moving average of weekly gross notional CDS transaction volumes from DTCC, measuring recent liquidity/activity trends","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,15,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_gnvol5wchg_di,5-week Change in DTCC Weekly Gross Notional Volumes,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_itabeta_di,Sensitivity of CDS spread changes to Italian sovereign movements,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,14,15,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_japbeta_di,Sovereign Sensitivity - Japan,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,11,12,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_op_di,Imputed or backfilled sensitivity of CDS spread to oil price movements,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,20,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_pd1y_di,"Estimated probability of default over a one-year horizon, typically derived from the entity’s CDS data (Tenor=1Y)","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,11,16,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_pd5y_di,Probability of default over a 5-year horizon implied by CDS spread,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_porbeta_di,"Imputed/backfilled version of CDS_PorBeta, the ranked sensitivity of daily CDS spread changes to Portugal sovereign CDS changes, used when raw data are missing","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,16,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_ps_rank1m_di,1-month Momentum Divergence,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,8,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_ps_rank5d_di,"Imputed/backfilled version of CDS_PS_Rank5D, the same 5-day momentum divergence measure used when raw data are missing","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_ps_reg3_di,"Imputed/backfilled version of CDS_PS_Reg3, representing the same model-based value divergence when raw data are missing","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,8,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_spabeta_di,"Imputed/backfilled version of CDS_SpaBeta, the ranked sensitivity of daily CDS spread changes to Spain sovereign CDS changes, used when raw data are missing","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_tier_di,"CDS seniority tier (e.g., senior/subordinated), imputed for missing data","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,10,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_ukbeta_di,Sensitivity of CDS spread changes to UK sovereign movements,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cds_usbeta_di,Sensitivity of CDS spread changes to US sovereign movements,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_cfleverage_di,Imputed/backfilled version of CFLeverage to maintain continuity when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_chg12mtotdebt_di,Change in total debt over the past 12 months,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,14,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_chg3ycfast_di,Imputed/backfilled version of Chg3YCFAst to maintain continuity when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_chgshare_di,"Percent change in the number of shares outstanding compared to a specified previous period, typically one year ago","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_curratio_di,Current ratio; company liquidity measured as current assets divided by current liabilities,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,9,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_days_to_cover_di,Number of days of typical volume needed for short sellers to cover positions (imputed),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_div5yg_di,5-Year Dividend Growth Rate,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,8,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_dmd_supply_di,"Imputed or backfilled demand supply ratio, i.e., borrowed stock relative to lendable inventory","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,11,16,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_earnshortfall_di,Earnings Shortfall,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_ebitdadebt_di,EBITDA-to-debt ratio (imputed/backfill value),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_ebitdadebtchg_di,Year-over-Year Change in EBITDA-to-Debt,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_ebitdaev_di,Trailing-twelve-month EBITDA divided by enterprise value (imputed/backfill value),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_fc_fqsurstd_di,"Most recent quarterly earnings surprise (difference between actual and estimated EPS), normalized by the standard deviation of analyst forecasts; measures the magnitude of surprise relative to consensus dispersion","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_fc_numrevy1_di,Net number of analyst forecast revisions for fiscal year 1,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_gear_di,"Imputed/backfilled version of GEAR, used when raw data are missing","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_2adpmoc_sdc,Relative Value - Comprehensive Debt to Total Assets,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_2vd_adpmoc_sdc,Comprehensive debt-to-assets ratio; total company debt as percentage of total assets for deep value analysis,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_2vd_voctni_sdc,Deep Value - Interest Coverage,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,4,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_2voctni_sdc,Relative value metric based on interest coverage ratio (imputed/backfill value),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_ad,Total Debt to Total Assets ratio (leverage measure),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_adpmoc,Imputed/backfilled version of CompDA to maintain continuity when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_aoer,Retained earnings divided by total assets,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_aor,Return on Assets: net income or similar measure divided by total assets,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_dcc,Current Cash Flow Debt Coverage Ratio,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_dtsm1_rd,Standard deviation of realized stock returns over the past 1 month (realized volatility),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_dtsm1_rd_sdc,"Imputed or backfilled 1-month realized volatility of stock returns, relative value signal","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_ed,"Long-term debt divided by equity (Debt-to-Equity, imputed/backfill value)","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_fcghc,"1-year change in assets-adjusted trailing twelve month cash flow, imputed","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_gtl,Estimate of long-term growth rate for the company,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_htrgm1ng_sdc,"One-month growth rate in DTCC (central clearinghouse) gross notional CDS volumes, reflecting liquidity/trading activity increase or decrease","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_htrgy1ng_sdc,"Year-on-year growth in DTCC gross notional CDS volumes, imputed","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_idb,Imputed or backfilled value of BDI used when the raw BDI is missing to enhance continuity,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_irl_sdc,"Imputed/backfilled version of CDS_LRI, representing the same leading risk measure when raw data are missing","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,8,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_md,Imputed/backfilled version of DM to maintain continuity when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_md_sdc,Imputed/backfilled value of CDS_DM used when raw data are missing to maintain series continuity,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_mrspe_cf,"Magnitude of analyst EPS estimate revisions over a recent period, possibly imputed","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,9,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_ox_sdc,Imputed/backfilled version of CDS_XO to maintain continuity when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_pu,Unexpected Profitability,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_rp,Imputed/backfilled Profitability Ratio used when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_rq,Quick Ratio (Acid Test Ratio): measure of a company's short-term liquidity calculated as (Current Assets - Inventories) / Current Liabilities,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_spe1yfvc_cf,Dispersion of analysts' FY1 EPS estimates (coefficient of variation),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_spefcn,Imputed/backfilled version of NCFEPS used when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_tni_ths,Short interest (likely number or percentage of shares shorted for the security),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,9,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_tqe_sdc,Equity price sensitivity (how the CDS spread responds to equity price movements),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_voctni,Interest coverage ratio (likely EBIT or similar over interest expense),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_xiv_sdc,"Sensitivity of the entity’s CDS spread to changes in overall market volatility (typically measured by VIX), reflecting how much credit risk correlates to equity market volatility","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_y1dpghcd1_sdc,"Change in probability of default (implied from 1-year CDS) over a single day, measuring daily credit risk movement for the immediate horizon","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_y1dpghcd5_sdc,Imputed or delta value for 5-day change in the probability of default implied by the 1-year CDS spread,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_y1dpghcm1_sdc,1-month change in probability of default for the 1-year tenor,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_y5dpghcd1_sdc,1-day change in probability of default using CDS spread at 5-year tenor,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_y5dpghcd5_sdc,"5-day change in probability of default as implied by 5-year CDS, showing short-term movement in perceived credit risk for a 5-year horizon","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,11,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_id_y5dpghcm1_sdc,1-month Change in Probability of Default (Tenor=5Y),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_indrelcroe_di,Industry-adjusted quarterly return on equity,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_indrelrtn4w_di,4-week Industry Relative Return,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_indrelrtn5d_di,"5-day relative stock return compared to industry peers, imputed for missing observations","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,8,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_netdebt_di,Net debt ratio (net debt divided by a relevant base like equity or total assets),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_nopatmargin_di,Net Operating Profit After Tax Margin; margin calculated as NOPAT divided by revenue,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_ocfratio_di,"Operating cash flow to total debt ratio (ability to cover debt with operating cash flow, imputed/backfill value)","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_ohlsonscore_di,Ohlson Bankruptcy Score; statistical measure of bankruptcy risk based on Ohlson's financial ratios,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,9,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_ollev_di,Imputed/backfilled Operating Liability Leverage used when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_opincltd_di,Change in trailing twelve months operating income compared to long-term debt,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,4,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_oplev_di,"Operating Leverage, likely percent change in operating income divided by percent change in sales","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_ratrev6m_di,Imputed/backfilled version of RatRev6M to maintain continuity when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_reinrate_di,Imputed/backfilled version of ReInRate to maintain continuity when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_rev3my2std_di,Relative dispersion (standard deviation) of 3-month revisions in next-fiscal-year-2 EPS forecasts,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,8,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_roic_di,Return on Invested Capital (profitability relative to invested capital),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,11,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_stockrating_di,Consensus stock rating from market participants or analysts,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_ticker_di,Ticker,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,14,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_totalcov_di,Imputed/backfilled version of TotalCov to maintain continuity when raw data are missing,"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_visiratio_di,"Liquidity volume ratio, calculated as trading volume divided by average volume over the last 50 days (imputed/backfill value)","{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,8,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_yoychgcr_di,Year-over-year change in current ratio (liquidity metric),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +mdl192_yoychgda_di,Year-over-year change in total debt to total assets ratio (imputed/backfill value),"{'id': 'model192', 'name': 'CDS Factor Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,16,1.3,[],model192,CDS Factor Data,model,Model,model-risk-based-models,Risk Based Models +country_relative_investment_rank,Normalized (0–100) float value for the stock’s Smart Holdings score within its country,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,86,139,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +country_relative_investment_rank_d1,Normalized (0-100) score of the stock's likelihood of institutional ownership within its country,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,56,82,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +global_change_in_preference_rank,"Normalized (float, 0–100) global rank of the Change Component, representing recent changes (momentum) in ownership signals for the stock globally","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9824,154,746,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +global_change_in_preference_rank_d1,Normalized (0-100) score of the recent change momentum for the stock globally,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9824,94,414,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +global_institutional_preference_rank,Normalized (0–100) float percentile rank of the stock’s Smart Holdings score globally. Higher value = higher likelihood of institutional ownership worldwide,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,111,646,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +global_institutional_preference_rank_d1,Normalized (0-100) score of the stock's likelihood of institutional ownership at global level,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,68,104,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +global_peer_investment_concentration_rank,"Normalized (0–100) float value measuring global peer crowding/risk, where higher values indicate higher global institutional ownership. This is a reversal score: high = high crowding risk","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9826,29,48,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +global_peer_investment_concentration_rank_d1,Normalized (0-100) peer ownership score for the stock at the global level,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9826,25,80,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +global_screening_factor_alignment_rank,"Normalized (0–100) float value indicating the stock's style-fit or screening component, globally, adjusted for peer characteristics. Higher values suggest stronger global style fit","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,37,51,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +global_screening_factor_alignment_rank_d1,"Normalized (0-100) score of the stock’s style-fit (screening component) globally, adjusted for peer ownership","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,33,41,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +industry_relative_investment_rank,Normalized (0–100) float value representing the stock’s Smart Holdings score within its industry,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,52,80,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +industry_relative_investment_rank_d1,Normalized (0-100) score of the stock's likelihood of institutional ownership within its industry,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,57,82,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_country_rank,Country rank (1–100) of Smart Holdings score. Higher rank = higher likelihood of institutional ownership within that country,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,196,827,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_country_rank_d1,Rank (1–100) representing the likelihood of institutional ownership for the stock within its country according to the Smart Holdings model,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,82,234,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_global_change_rank,"Global rank (1–100) for the momentum or recent change component, representing the degree of recent increase in institutional ownership likelihood compared to global peers","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9824,106,235,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_global_change_rank_d1,"Rank (1–100) measuring recent changes or momentum in institutional ownership for the stock, globally","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9824,86,186,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_global_owner_rank,"Global rank (1–100) of peer crowding/capacity risk. 100 = highest global institutional ownership (most crowded), 1 = lowest. Indicates liquidity/capacity risk from peer positions","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9826,39,62,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_global_owner_rank_d1,"Peer ownership score of the stock globally, ranked 1-100 where 100 indicates highest peer ownership and liquidity risk","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9826,38,67,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_global_rank,Global rank (1–100) of Smart Holdings model score. Higher ranks imply greater probability of global institutional ownership,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,147,440,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_global_rank_d1,Rank (1–100) representing the likelihood of institutional ownership for the stock across the global universe according to the Smart Holdings model,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,59,92,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_global_screening_rank,"Global rank (1–100) of the stock’s style-fit or screening component, adjusted for peer characteristics. Higher values represent better alignment with factors favored by institutional investors worldwide","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,90,283,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_global_screening_rank_d1,"Peers-adjusted screening (style fit) score for a stock, ranked 1-100 at the global level, indicating how well its characteristics match those favored by institutional investors globally","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,77,276,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_industry_rank,Industry rank (1–100) of Smart Holdings score. Higher rank means higher probability of institutional ownership within that industry,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,89,183,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_industry_rank_d1,Rank (1–100) representing the likelihood of institutional ownership for the stock within its industry according to the Smart Holdings model,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,60,97,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_region_change_rank,Regional rank (1–100) indicating the strength of recent change or momentum in institutional ownership within the region,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9824,48,72,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_region_change_rank_d1,"Rank (1-100) of recent changes or momentum in ownership signals for the stock within its region, reflecting directional flow of institutional interest","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9824,55,91,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_region_owner_rank,"Regional rank (1–100) of peer crowding. 100 = highest regional institutional ownership (most crowded), 1 = lowest. Highlights local capacity risk","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9826,27,31,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_region_owner_rank_d1,"Peer ownership score of the stock within its region, ranked 1-100 where 100 indicates highest peer ownership and liquidity risk","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9826,18,22,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_region_rank,Regional rank (1–100) of Smart Holdings score. Higher rank = higher likelihood of institutional ownership within the region,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,57,91,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_region_rank_d1,Rank (1–100) representing the likelihood of institutional ownership for the stock within its region according to the Smart Holdings model,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,36,50,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_region_screening_rank,"Regional rank (1–100) of the stock’s style-fit or screening component, peer-adjusted. Higher rank suggests stronger regional style fit","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,62,131,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_region_screening_rank_d1,"Peers-adjusted screening (style fit) score for a stock, ranked 1-100 within its region, indicating how well its characteristics match those favored by institutional investors in that region","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,120,1665,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_sector_rank,Sector rank (1–100) of Smart Holdings score. Higher values represent higher expected institutional ownership in that sector,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9827,58,148,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +mdl238_sector_rank_d1,Rank (1–100) representing the likelihood of institutional ownership for the stock within its sector according to the Smart Holdings model,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9827,55,92,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +regional_change_in_preference_rank,"Normalized (float, 0–100) regional rank of the Change Component, representing momentum in institutional ownership signals at the region level","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9824,33,41,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +regional_change_in_preference_rank_d1,Normalized (0-100) score of the recent change momentum for the stock in its region,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9824,43,50,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +regional_institutional_preference_rank,Normalized (0–100) float value for the stock’s Smart Holdings score within its region,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,34,41,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +regional_institutional_preference_rank_d1,Normalized (0-100) score of the stock's likelihood of institutional ownership within its region,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,25,29,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +regional_peer_investment_concentration_rank,Normalized (0–100) float value measuring regional peer crowding. High values mean higher institutional ownership regionally (reversed: high = high crowding risk),"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9826,12,19,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +regional_peer_investment_concentration_rank_d1,Normalized (0-100) peer ownership score for the stock in its region,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9826,17,20,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +regional_screening_factor_alignment_rank,"Normalized (0–100) float value indicating the stock's style-fit or screening component within the region, adjusted for peer characteristics","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,21,36,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +regional_screening_factor_alignment_rank_d1,"Normalized (0-100) score of the stock’s style-fit (screening component) in its region, adjusted for peer ownership","{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,48,350,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +sector_relative_investment_rank,Normalized (0–100) float value reflecting the stock’s Smart Holdings score within its sector,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9827,117,526,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +sector_relative_investment_rank_d1,Normalized (0-100) score of the stock's likelihood of institutional ownership within its sector,"{'id': 'model238', 'name': 'SmartHoldings Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.9827,62,115,1.3,[],model238,SmartHoldings Model,model,Model,model-estimates-models,Estimates Models +defensiveness_composite_score,SHIELD model output score representing the defensive (risk/quality) rating for the security (normalized 0–1),"{'id': 'model252', 'name': 'Systematic Hedging for Investors to Evade Large Drawdowns'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,1.0,0.7851,95,320,1.3,[],model252,Systematic Hedging for Investors to Evade Large Drawdowns,model,Model,model-mlai-models,ML/AI Models +total_equity_market_value_2,"Market capitalization of the security on the score's effective date, usually calculated from closing price and shares outstanding","{'id': 'model252', 'name': 'Systematic Hedging for Investors to Evade Large Drawdowns'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-mlai-models', 'name': 'ML/AI Models'}",IND,1,TOP500,MATRIX,1.0,0.7851,81,371,1.3,[],model252,Systematic Hedging for Investors to Evade Large Drawdowns,model,Model,model-mlai-models,ML/AI Models +actual_vs_predicted_earnings_surprise_pct_lastq,"Percentage surprise for the last quarter’s earnings, calculated as actual reported minus consensus estimate, expressed as a percent of the consensus","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5726,179,294,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +actual_vs_predicted_ebitda_surprise_pct_lastq,"Percentage surprise for the last quarter’s EBITDA, calculated as actual reported minus consensus estimate, expressed as a percent of the consensus","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5734,123,171,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analyst_count_prior_seven_days_quarterly,Number of analysts contributing to the mean FQ1 earnings estimate 7 days prior,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5608,65,97,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analyst_count_revised_down_thirty_days_quarterly,Count of analysts who lowered FQ1 earnings estimates in the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8406,55,107,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analyst_count_revised_up_thirty_days_quarterly,Count of analysts who raised FQ1 earnings estimates in the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8406,51,76,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analyst_recommendation_component,The portion of the score based on changes in analyst recommendations.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,240,581,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analyst_recommendation_score_component,Score component reflecting changes in analyst buy/sell/hold recommendations.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8578,105,222,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analyst_recommendation_upgrades_count_30d,Number of analyst recommendation upgrades over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,57,88,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analyst_revision_primary_earnings_score,Score component reflecting analyst revisions and surprises in the primary earnings measure.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,204,441,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analyst_revision_revenue_score,ARM revenue component percentile score reflecting revenue-related estimate revisions,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8625,92,165,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analyst_revision_revenue_score_d1,Score component reflecting analyst revisions and surprises in company revenue (daily version).,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8625,63,90,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analyst_revision_secondary_earnings_score,Score component reflecting analyst revisions and surprises in the secondary earnings measure.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7811,94,181,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analysts_count_revising_down_quarter1_earnings_7d,Number of analysts revising down earnings estimates for Fiscal Quarter 1 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,22,28,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analysts_count_revising_down_year1_earnings_14d,Count of analysts who lowered their earnings estimates for the current fiscal year (FY1) in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,20,22,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analysts_count_revising_down_year1_ebitda_14d,Count of analysts who lowered their EBITDA estimates for the current fiscal year (FY1) in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,32,56,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analysts_count_revising_up_quarter2_earnings_30d,Number of analysts who raised their FQ2 earnings estimates in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,23,31,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +analysts_count_revising_up_year2_ebitda_14d,Count of analysts who raised their FY2 EBITDA estimates in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,72,148,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +auxiliary_earnings_component,The portion of the score derived from changes in a secondary earnings metric.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,178,488,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +average_estimate_change_thirty_days_quarterly,Percent change in the consensus mean FQ1 earnings estimate over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4771,23,30,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_change_analyst_recommendation_14d,Change in the mean analyst recommendation over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8707,41,52,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_change_analyst_recommendation_30d,Change in the mean analyst recommendation over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8687,51,70,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_change_analyst_recommendation_60d,Change in the mean analyst recommendation over the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8637,20,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_change_analyst_recommendation_7d,Change in the mean analyst recommendation over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8714,36,47,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_change_analyst_recommendation_90d,Change in the mean analyst recommendation over the past 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8582,41,69,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_estimate_change_pct_f12m_ebitda_7d,Mean percentage change in analyst EBITDA estimates for the next 12 months over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7764,44,61,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_estimate_change_pct_quarter1_ebitda_14d,Mean percentage change in EBITDA estimates for Fiscal Quarter 1 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5732,18,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_estimate_change_pct_quarter1_revenue_60d,Mean percentage change in revenue estimates for Fiscal Quarter 1 over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4678,28,38,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_estimate_change_pct_quarter2_ebitda_60d,Mean percentage change in EBITDA estimates for Fiscal Quarter 2 over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2534,7,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_estimate_change_pct_year1_earnings_90d,Mean percentage change in earnings estimates for Fiscal Year 1 over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8514,66,140,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_estimate_change_pct_year1_revenue_90d,Mean percentage change in FY1 revenue estimates over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8501,30,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_earnings_est_12m_30d,Mean percentage change in next-12-months earnings estimates over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8585,43,51,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_earnings_est_12m_90d,Mean percentage change in analyst earnings estimates for the next 12 months over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8454,55,117,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_earnings_est_qtr_14d,Mean percentage change in earnings estimates for Fiscal Quarter 1 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5693,21,24,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_earnings_est_qtr_30d,Mean percentage change in earnings estimates for Fiscal Quarter 1 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4934,11,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_earnings_est_qtr_60d,Mean percentage change in earnings estimates for Fiscal Quarter 1 over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4094,16,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_earnings_est_year_14d,Mean percentage change in earnings estimates for Fiscal Year 1 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8642,95,133,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_ebitda_est_12m_14d,Mean percentage change in next 12 months (F12M) EBITDA estimates over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7753,34,49,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_ebitda_est_12m_90d,Mean percentage change in next 12 months EBITDA estimates over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7571,49,79,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_12m_earnings_14d,Mean percentage change in next 12 months (F12M) earnings consensus estimates over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8608,86,164,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_12m_earnings_60d,Mean percentage change in analyst earnings estimates for the next 12 months over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8526,30,63,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_12m_earnings_7d,Mean percentage change in next 12 months earnings estimates over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8615,54,101,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_12m_ebitda_30d,Mean percentage change in forward 12-month EBITDA estimates over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7722,16,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_12m_ebitda_60d,Mean percentage change in next 12 months (F12M) EBITDA estimates over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7653,19,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_12m_revenue_30d,Mean percentage change in revenue estimates for the next 12 months over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8574,31,58,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_12m_revenue_7d,Mean percentage change in forward 12-month revenue estimates over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8604,20,23,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_12m_revenue_90d,Mean percentage change in revenue estimates for the next 12 months over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8441,32,40,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_curr_qtr_ebitda_7d,Mean percentage change in EBITDA estimates for Fiscal Quarter 1 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5912,15,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_curr_qtr_ebitda_90d,Mean percentage change in FQ1 EBITDA estimates over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3109,12,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_curr_qtr_revenue_30d,Mean percentage change in revenue estimates for Fiscal Quarter 1 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5923,12,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_forward_revenue_14d,Mean percentage change in next 12 months revenue estimates over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8597,16,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_forward_revenue_60d,Mean percentage change in revenue estimates for the next 12 months over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8514,22,31,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_quarter_earnings_14d,Mean percentage change in earnings estimates for Fiscal Quarter 2 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3686,12,12,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_quarter_ebitda_14d,Mean percentage change in EBITDA estimates for Fiscal Quarter 2 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2934,9,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_quarter_ebitda_30d,Mean percentage change in EBITDA estimates for Fiscal Quarter 2 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2806,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_quarter_ebitda_7d,Mean percentage change in EBITDA estimates for Fiscal Quarter 2 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.299,7,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_quarter_revenue_14d,Mean percentage change in revenue estimates for Fiscal Quarter 2 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4041,11,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_quarter_revenue_60d,Mean percentage change in FQ2 revenue estimates over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3587,10,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_year_earnings_30d,Mean percentage change in earnings estimates for Fiscal Year 1 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8622,20,25,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_year_earnings_60d,Mean percentage change in FY1 earnings estimates over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8572,30,39,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_year_earnings_7d,Mean percentage change in FY1 earnings estimates over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8648,37,52,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_year_ebitda_14d,Mean percentage change in FY1 EBITDA consensus estimates over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7812,13,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_next_year_revenue_14d,Mean percentage change in FY1 revenue estimates over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8631,16,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_qtr2_earnings_30d,Mean percentage change in earnings estimates for Fiscal Quarter 2 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3599,6,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_qtr2_earnings_60d,Mean percentage change in earnings estimates for Fiscal Quarter 2 over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3434,8,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_qtr2_earnings_7d,Mean percentage change in earnings estimates for Fiscal Quarter 2 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3718,7,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_qtr2_earnings_90d,Mean percentage change in earnings estimates for Fiscal Quarter 2 over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3278,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_qtr2_revenue_30d,Mean percentage change in Fiscal Quarter 2 revenue estimates over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3891,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_qtr2_revenue_7d,Mean percentage change in FQ2 revenue estimates over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4102,13,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_qtr2_revenue_90d,Mean percentage change in FQ2 revenue estimates over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3293,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_quarter_earnings_7d,Mean percentage change in earnings estimates for Fiscal Quarter 1 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5821,17,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_quarter_earnings_90d,Mean percentage change in earnings estimates for Fiscal Quarter 1 over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3756,8,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_quarter_ebitda_30d,Mean percentage change in EBITDA estimates for Fiscal Quarter 1 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4726,6,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_quarter_ebitda_60d,Mean percentage change in EBITDA estimates for Fiscal Quarter 1 over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3484,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_quarter_revenue_14d,Mean percentage change in revenue estimates for Fiscal Quarter 1 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.6862,11,12,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_quarter_revenue_7d,Mean percentage change in revenue estimates for Fiscal Quarter 1 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7006,23,28,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_year_ebitda_30d,Mean percentage change in FY1 EBITDA consensus estimates over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7785,15,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_year_ebitda_60d,Mean percentage change in the consensus EBITDA estimate for Fiscal Year 1 (next fiscal year) over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7725,15,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_year_ebitda_7d,Mean percentage change in Fiscal Year 1 EBITDA estimates over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7821,23,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_year_ebitda_90d,Mean percentage change in Fiscal Year 1 EBITDA estimates over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.766,22,31,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_estimate_year_revenue_30d,Mean percentage change in FY1 revenue estimates over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8611,26,34,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_revenue_est_qtr_90d,Mean percentage change in revenue estimates for Fiscal Quarter 1 over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4218,16,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_revenue_est_year_60d,Mean percentage change in FY1 revenue consensus estimates over the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8558,13,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +avg_pct_change_revenue_est_year_7d,Average percentage change in Fiscal Year 1 revenue estimates over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8637,13,22,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_downward_revision_next_quarter_ebitda_30d,Count of analysts who lowered their EBITDA estimates for fiscal quarter 2 (FQ2) in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,18,25,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_downward_revision_quarter_earnings_14d,Number of analysts lowering FQ1 earnings estimates in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,13,20,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_downward_revision_quarter_ebitda_14d,Count of analysts who lowered their FQ1 EBITDA estimates in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_downward_revision_quarter_ebitda_7d,Count of analysts who lowered Fiscal Quarter 1 EBITDA estimates over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,6,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_downward_revision_quarter_revenue_30d,Count of analysts who lowered their revenue estimates for the next fiscal quarter (FQ1) in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,14,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_downward_revision_quarter_revenue_7d,Count of analysts who lowered their revenue estimates for the next fiscal quarter (FQ1) in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,14,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_downward_revision_year_ebitda_30d,Count of analysts who lowered their EBITDA estimates for the current fiscal year (FY1) in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,18,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_downward_revision_year_ebitda_7d,Count of analysts who lowered their EBITDA estimates for the current fiscal year (FY1) in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,15,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_downward_revision_year_revenue_30d,Count of analysts who lowered their revenue estimates for the current fiscal year (FY1) in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,15,25,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_recommendation_downgrades_14d,Number of analyst recommendation downgrades over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,28,34,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_recommendation_downgrades_30d,Number of analyst recommendation downgrades over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,20,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_recommendation_downgrades_60d,Number of analyst recommendation downgrades over the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,16,25,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_recommendation_downgrades_7d,Number of analyst recommendation downgrades over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,12,20,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_recommendation_downgrades_90d,Number of analyst recommendation downgrades over the past 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,30,44,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_recommendation_upgrades_14d,Number of analyst recommendation upgrades over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,16,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_recommendation_upgrades_60d,Number of analyst recommendation upgrades over the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,14,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_recommendation_upgrades_7d,Number of analyst recommendation upgrades over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,17,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_recommendation_upgrades_90d,Number of analyst recommendation upgrades over the past 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,15,20,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_next_quarter_ebitda_14d,Number of analysts who revised up FQ2 EBITDA estimates in the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,12,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_next_quarter_revenue_30d,Number of analysts who raised FQ2 revenue estimates in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_next_quarter_revenue_7d,Number of analysts raising FQ2 revenue estimates in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_next_year_earnings_14d,Count of analysts who raised Fiscal Year 2 earnings estimates over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,37,49,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_next_year_ebitda_30d,Number of analysts revising up EBITDA estimates for Fiscal Year 2 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,25,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_next_year_revenue_7d,Number of analysts who revised up Fiscal Year 2 revenue estimates over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,12,20,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_quarter_earnings_30d,Number of analysts who raised Fiscal Quarter 1 earnings estimates in the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_quarter_revenue_14d,Number of analysts who raised their FQ1 revenue estimates over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_quarter_revenue_7d,Count of analysts who raised Fiscal Quarter 1 revenue estimates over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_year_earnings_14d,Count of analysts who raised their Fiscal Year 1 earnings estimates over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,18,25,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_year_earnings_7d,Number of analysts who raised their FY1 earnings estimates in the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,10,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_year_ebitda_14d,Number of analysts raising FY1 EBITDA estimates in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,16,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_year_revenue_14d,Count of analysts who raised their FY1 revenue estimates in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,12,20,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analyst_upward_revision_year_revenue_7d,Number of analysts who revised up Fiscal Year 1 revenue estimates over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,10,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_curr_qtr_earnings_30d,Number of analysts revising down earnings estimates for Fiscal Quarter 1 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_curr_qtr_ebitda_30d,Number of analysts revising down EBITDA estimates for Fiscal Quarter 1 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_curr_qtr_revenue_14d,Count of analysts who lowered their revenue estimates for the next fiscal quarter (FQ1) in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_earnings_est_next_qtr_14d_d1,Number of sell-side analysts who lowered their earnings estimate for Fiscal Quarter 2 in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,6,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_earnings_est_next_qtr_30d,Count of analysts who lowered their earnings estimates for fiscal quarter 2 (FQ2) in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,8,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_earnings_est_next_qtr_7d,Count of analysts who lowered their earnings estimates for fiscal quarter 2 (FQ2) in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_earnings_est_next_qtr_7d_d1,Number of sell-side analysts who lowered their earnings estimate for Fiscal Quarter 2 in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_earnings_est_year_7d,Count of analysts who lowered their earnings estimates for the current fiscal year (FY1) in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,11,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_ebitda_est_next_qtr_14d,Count of analysts who lowered their EBITDA estimates for fiscal quarter 2 (FQ2) in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,6,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_ebitda_est_next_qtr_14d_d1,Number of sell-side analysts who lowered their EBITDA estimate for Fiscal Quarter 2 in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_ebitda_est_next_qtr_30d_d1,Number of sell-side analysts who lowered their EBITDA estimate for Fiscal Quarter 2 in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_ebitda_est_next_qtr_7d_d1,Number of sell-side analysts who lowered their EBITDA estimate for Fiscal Quarter 2 in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_ebitda_est_qtr_14d_d1,Number of analysts revising down EBITDA estimates for Fiscal Quarter 1 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_ebitda_est_qtr_30d_d1,Number of analysts revising down EBITDA estimates for Fiscal Quarter 1 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,10,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_ebitda_est_qtr_7d_d1,Number of analysts revising down EBITDA estimates for Fiscal Quarter 1 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_next_year_earnings_30d,Count of analysts who lowered their earnings estimates for the current fiscal year (FY1) in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,6,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_next_year_revenue_14d,Count of analysts who lowered their revenue estimates for the current fiscal year (FY1) in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_qtr2_earnings_14d,Count of analysts who lowered their earnings estimates for fiscal quarter 2 (FQ2) in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_qtr2_ebitda_7d,Count of analysts who lowered their EBITDA estimates for fiscal quarter 2 (FQ2) in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_qtr2_revenue_30d,Count of analysts who lowered their revenue estimates for fiscal quarter 2 (FQ2) in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,8,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_qtr2_revenue_7d,Count of analysts who lowered their revenue estimates for fiscal quarter 2 (FQ2) in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,6,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_revenue_est_next_qtr_14d,Count of analysts who lowered their revenue estimates for fiscal quarter 2 (FQ2) in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,8,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_revenue_est_next_qtr_7d_d1,Number of sell-side analysts who lowered their revenue estimate for Fiscal Quarter 2 in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,7,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_lower_revenue_est_year_7d,Count of analysts who lowered their revenue estimates for the current fiscal year (FY1) in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_curr_qtr_earnings_14d,Count of analysts who raised their Fiscal Quarter 1 earnings estimates over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_curr_qtr_ebitda_14d,Count of analysts who raised Fiscal Quarter 1 EBITDA estimates over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,1,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_curr_qtr_ebitda_30d,Number of analysts who raised their FQ1 EBITDA estimates in the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_earnings_est_next_qtr_14d,Number of analysts who raised their Fiscal Quarter 2 earnings estimates in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_earnings_est_next_qtr_30d_d1,Number of analysts who raised their Fiscal Quarter 2 earnings estimates in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_earnings_est_next_qtr_7d_d1,Number of analysts who raised their Fiscal Quarter 2 earnings estimates in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,2,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_earnings_est_qtr_14d_d1,Number of analysts who raised their Fiscal Quarter 1 earnings estimates in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,1,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_earnings_est_qtr_7d,Number of analysts revising up earnings estimates for Fiscal Quarter 1 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_earnings_est_qtr_7d_d1,Number of analysts who raised their Fiscal Quarter 1 earnings estimates in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_ebitda_est_7d,Count of analysts who raised Fiscal Quarter 1 EBITDA estimates over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_ebitda_est_next_qtr_14d_d1,Number of analysts revising up EBITDA estimates for Fiscal Quarter 2 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_ebitda_est_next_qtr_30d,Number of analysts who raised Fiscal Quarter 2 EBITDA estimates in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,1,1,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_ebitda_est_next_qtr_30d_d1,Number of analysts revising up EBITDA estimates for Fiscal Quarter 2 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_ebitda_est_next_qtr_7d,Count of analysts who raised Fiscal Quarter 2 EBITDA estimates over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_ebitda_est_next_qtr_7d_d1,Number of analysts revising up EBITDA estimates for Fiscal Quarter 2 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,2,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_ebitda_est_qtr_14d_d1,Number of analysts who raised their Fiscal Quarter 1 EBITDA estimates in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,7,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_ebitda_est_qtr_30d_d1,Number of analysts who raised their Fiscal Quarter 1 EBITDA estimates in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_ebitda_est_qtr_7d_d1,Number of analysts who raised their Fiscal Quarter 1 EBITDA estimates in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_next_year_earnings_30d,Count of analysts who raised their earnings estimates for Fiscal Year 1 (FY1) in the past 30 calendar days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_next_year_ebitda_30d,Count of analysts who raised Fiscal Year 1 EBITDA estimates over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,19,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_next_year_ebitda_7d,Number of analysts who revised up FY1 EBITDA estimates in the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,11,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_next_year_revenue_30d,Number of analysts who raised their FY1 revenue estimates over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,12,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_qtr2_earnings_14d,Number of analysts who revised up Fiscal Quarter 2 earnings estimates over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_qtr2_earnings_7d,Number of analysts revising up earnings estimates for Fiscal Quarter 2 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,2,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_revenue_est_next_qtr_14d,Number of analysts who revised up FQ2 revenue estimates in the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_revenue_est_next_qtr_14d_d1,Number of analysts revising up revenue estimates for Fiscal Quarter 2 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_revenue_est_next_qtr_30d_d1,Number of analysts revising up revenue estimates for Fiscal Quarter 2 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_revenue_est_next_qtr_7d_d1,Number of analysts revising up revenue estimates for Fiscal Quarter 2 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,2,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_revenue_est_qtr_30d,Number of analysts who raised Fiscal Quarter 1 revenue estimates in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,2,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_year2_earnings_30d,Count of analysts who raised their earnings estimates for Fiscal Year 2 (FY2) in the past 30 calendar days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,14,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_year2_earnings_7d,Number of analysts revising up earnings estimates for Fiscal Year 2 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,7,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_year2_ebitda_7d,Number of analysts who raised their FY2 EBITDA estimates over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,18,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_year2_revenue_14d,Number of analysts who raised FY2 revenue estimates in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +count_analysts_raise_year2_revenue_30d,Number of analysts who revised up Fiscal Year 2 revenue estimates over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +country_percentile_rank_model_score,Country-level percentile rank of the ARM headline signal (1–100),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,53,86,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +country_percentile_rank_score,"Country-level percentile rank of the ARM signal 1–100, higher indicates stronger revision prospects","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,61,122,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +country_rank_score_float,Score representing the country-based ranking for a stock.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,69,330,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +days_since_previous_financial_report,Model days since the company last reported results,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8431,17,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +earnings_surprise_previous_quarter,Percent surprise for the last reported quarter’s earnings versus the consensus at the time,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5515,27,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +earnings_surprise_previous_year,"Earnings surprise for the last fiscal year, expressed as the percent difference between actual and consensus","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7878,14,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +earnings_to_price_yield_smartest_12m,Earnings yield percentage based on the StarMine SmartEstimate for the next 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8525,70,132,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +earnings_to_price_yield_smartest_next_year,Earnings-to-price yield percentage based on SmartEstimate for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8506,46,69,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +earnings_to_price_yield_smartest_year2,Earnings-to-Price yield percentage based on StarMine SmartEstimate for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.85,46,137,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +estimate_diffusion_thirty_days_quarterly,Estimate Diffusion FQ1 Earnings 30,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2987,1,1,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +estimate_revision_component,The portion of the score based on analyst estimate revisions.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,182,397,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +expected_surprise_forward_earnings_12m,Difference between the SmartEstimate and the mean consensus for forward 12 months earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8483,29,34,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +expected_surprise_forward_ebitda_12m,Difference between the SmartEstimate and the mean consensus for forward 12 months EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7626,13,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +expected_surprise_next_quarter_earnings,StarMine predicted earnings surprise amount for Fiscal Quarter 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3601,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +expected_surprise_next_quarter_ebitda,StarMine predicted EBITDA surprise amount for Fiscal Quarter 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.293,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +expected_surprise_quarter_earnings,StarMine predicted earnings surprise amount for FQ1 (SmartEstimate minus consensus mean),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5826,8,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +expected_surprise_quarter_revenue,StarMine predicted revenue surprise amount for Fiscal Quarter 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7036,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +expected_surprise_year_earnings,StarMine predicted earnings surprise amount for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.851,18,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +expected_surprise_year_ebitda,Difference between the SmartEstimate and the mean consensus for Fiscal Year 1 EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7667,12,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_12m_revenue,StarMine predicted revenue surprise amount for forward 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8469,27,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_curr_qtr2_revenue,StarMine predicted revenue surprise amount for Fiscal Quarter 2 (SmartEstimate minus mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4044,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_earnings_next_year,StarMine predicted earnings surprise amount for fiscal year 2 (SmartEstimate minus consensus mean),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8504,16,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_ebitda_qtr,StarMine predicted EBITDA surprise amount for Fiscal Quarter 1 (SmartEstimate minus mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.596,1,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_pct_12m_revenue,StarMine predicted percentage revenue surprise for the next 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8469,21,29,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_pct_curr_qtr_ebitda,StarMine predicted percentage EBITDA surprise for Fiscal Quarter 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.596,4,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_pct_earnings_12m,Predicted surprise percentage for trailing 12 months earnings (actual minus predicted),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8483,20,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_pct_earnings_qtr,StarMine predicted percentage earnings surprise for FQ1 (SmartEstimate vs mean),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5826,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_pct_ebitda_12m,StarMine predicted percentage EBITDA surprise for the next 12 months (F12M),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7626,14,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_pct_next_year_earnings,Percentage difference between the SmartEstimate and the mean consensus for Fiscal Year 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.851,13,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_pct_qtr2_revenue,StarMine predicted percentage revenue surprise for Fiscal Quarter 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4044,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_pct_year2_revenue,"Predicted percentage revenue surprise for Fiscal Year 2, i.e., SmartEstimate versus mean consensus as a percent of the mean","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8479,43,117,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forecasted_surprise_year2_revenue,StarMine predicted revenue surprise amount for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8479,26,39,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forward_pe_ratio_mean_12m,Forward price-to-earnings ratio based on mean analyst estimates for the next 12 months (F12M),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8506,31,62,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forward_pe_ratio_mean_year,Forward price-to-earnings ratio based on mean analyst estimates for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8423,31,51,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forward_pe_ratio_mean_year2,Forward price-to-earnings ratio based on mean analyst estimates for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8518,31,58,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forward_pe_ratio_smartest_12m,Forward price-to-earnings ratio based on the SmartEstimate for the next 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8387,54,115,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forward_pe_ratio_smartest_next_year,Forward price-to-earnings ratio based on StarMine SmartEstimate for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.828,36,73,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +forward_pe_ratio_smartest_year2,Forward price-to-earnings ratio based on StarMine SmartEstimate for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8406,48,131,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +global_percentile_rank_score,Global percentile rank of the ARM headline signal (1–100),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,83,172,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +global_percentile_score,"Global percentile rank of the ARM composite signal (1–100, higher = better)","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,68,223,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +global_percentile_score_float,"Global percentile rank of the overall ARM signal, expressed as a floating-point value","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,105,342,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +growth_rate_estimate_current_vs_prior_year_earnings,Model SmartEstimate earnings growth (this year versus last year),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7613,21,24,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +growth_rate_estimate_current_vs_prior_year_ebitda,Growth rate of EBITDA from last fiscal year to this fiscal year based on the SmartEstimate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.719,9,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +growth_rate_estimate_current_vs_prior_year_revenue,"SmartEstimate growth rate for revenue, comparing this fiscal year to last fiscal year","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8159,31,49,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +growth_rate_estimate_next_vs_current_year_revenue,"SmartEstimate growth rate for revenue, comparing next fiscal year to this fiscal year","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8467,25,52,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +highest_estimate_change_thirty_days_quarterly,Percent change in the highest FQ1 earnings estimate over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4771,14,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +highest_estimate_prior_thirty_days_quarterly,Highest FQ1 earnings estimate as of 30 days prior,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4794,8,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +lowest_estimate_change_thirty_days_quarterly,Percent change in the lowest FQ1 earnings estimate over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4771,6,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +lowest_estimate_prior_thirty_days_quarterly,Lowest FQ1 earnings estimate as of 30 days prior,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4794,10,12,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +main_earnings_component,The portion of the score derived from changes in the primary earnings metric.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,182,420,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_5yr_hstrcl_grwth_rt,5year historical growth rate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7106,66,241,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_actual_last_q_earnings,Actual earnings reported for the most recent fiscal quarter,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5763,56,79,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_actual_last_q_ebitda,Actual EBITDA reported for the most recent fiscal quarter,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.6645,27,41,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_actual_last_q_revenue,Actual revenue reported for the most recent fiscal quarter,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7401,25,28,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_actual_last_y_earnings,Actual earnings reported for the most recent fiscal year,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7895,41,108,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_actual_last_y_ebitda,Actual EBITDA reported for the most recent fiscal year,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7957,25,54,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_actual_last_y_revenue,Actual revenue reported for the most recent fiscal year,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8392,22,38,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_arm_new_activity_flag,"Flag indicating newly detected analyst activity or revisions impacting ARM on this date; 1 = new activity present, 0 = none","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8435,24,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_arm_recommendations,recommendations,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,90,214,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_arm_revenue,revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,109,211,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_arm_revisions,revisions,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,193,595,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_arm_score,"Overall ARM composite score (Analyst Revisions Score) for the security, expressed as a regional percentile rank where higher is better","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,65,120,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_arm_score_change_1,Change in the ARM score over the last 1 day,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8643,37,67,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_arm_score_change_3,Change in the ARM score over the last 3 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.864,48,112,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_arm_score_change_60,Change in the ARM score over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8537,37,58,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_arm_score_change_7,Change in the ARM score over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8634,51,84,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_arm_score_change_90,Change in the ARM score over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8473,56,82,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_average_revison,average revison,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.289,6,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_bld_stmt_flg_fq1_rnngs,Flag indicating whether the current fiscal quarter 1 earnings estimate is bold (significantly different from consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8408,15,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_blndd_grwth_ndstry_prcntl,blended growth industry percentile,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8199,28,68,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_blndd_grwth_rt_smrtstmt,blended growth rate smartestimate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.82,46,72,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_blndd_grwth_sctr_prcntl,blended growth sector percentile,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8199,43,89,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_frm_52wk_hgh_prc,Percentage price change from the 52-week high price,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,44,77,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_frm_52wk_lw_prc,Percentage change from the 52-week low price to the current price,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,36,41,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_cc_40_30,Change relative to CAC 40 index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,23,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_cntry_bnchmrk_30,Percentage change relative to the country benchmark over the last 30 trading days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8423,25,42,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_dj_stxx_600_30,Change relative to DJ STOXX 600 index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,21,26,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_fts_100_30,Change relative to FTSE 100 index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,12,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_hng_sng_30,Change relative to Hang Seng index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,19,52,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_js_ll_shr_30,Change relative to JSE All Share index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,16,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_msc_f_30,Change relative to MSCI EAFE index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,8,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_msc_mf_30,Change relative to MSCI Emerging Markets Free index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,11,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_msc_pc_x_jp_30,Change relative to MSCI Pacific excluding Japan index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,17,24,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_msc_rp_30,Change relative to MSCI Europe index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,11,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_ndstry_bnchmrk_30,Percentage change relative to the industry benchmark over the last 30 trading days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8423,21,25,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_r1000_grwth_30,Change relative to Russell 1000 Growth index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,14,23,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_rssll_2000_30,Change relative to Russell 2000 index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,14,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_rssll_mdcp_30,Change relative to Russell Midcap index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,15,68,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_sp_400_30,Change relative to S&P 400 index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,12,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_sp_500_30,Change relative to S&P 500 index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,20,33,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_sp_600_30,The stock’s 30-day percentage price change minus the S&P 600 index’s 30-day percentage change,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,12,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_sx_100_30,Change relative to ASX 100 index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,12,17,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_sx_200_30,Change relative to ASX 200 index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,16,98,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_sx_300_30,Change relative to ASX 300 index over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,7,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_sx_ll_rd_30,Change relative to ASX All Ordinaries over 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,7,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_tpx_30,Stock’s 30-day percentage price change minus the TOPIX index’s 30-day percentage change,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,14,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_chng_rltv_t_tsx_cmpst_30,Stock’s 30-day percentage price change minus the TSX Composite index’s 30-day percentage change,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,22,29,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_dff_frm_200dy_mvng_vrg,Difference between the current price and the 200-day moving average price,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8423,39,78,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_dff_frm_50dy_mvng_vrg,Difference between the current price and the 50-day moving average price,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,32,43,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_dffrnc_frm_hstrcl_p,difference from historical (Price/Earnings ratio),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8153,39,62,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_dffrnc_frm_hstrcl_prc_rt_rnngs,Percent difference between the current P/E and the company’s historical median P/E,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8153,25,65,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_divisor_fq1_earnings,Scaling divisor used in calculations for FQ1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5656,11,12,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_dsnc_clstr_bgn_fq1_rnngs,Number of days since the current revision cluster for Fiscal Quarter 1 earnings began,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2335,8,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_dsnc_clstr_dtctd_fq1_rnngs,Number of days since a revision cluster was detected for fiscal quarter 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2335,6,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_ep_industry_percentile_fy1,Percentile rank within industry of the Fiscal Year 1 earnings yield (E/P),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8152,76,238,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_ep_sector_percentile_fy1,Percentile rank within sector of the Fiscal Year 1 earnings yield (E/P),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8152,88,198,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_ep_yield_smartestimate_fy1,"Earnings yield (earnings-to-price ratio) for next fiscal year based on StarMine SmartEstimate, expressed as a percentage","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8153,74,182,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_est_diffusion,estimate diffusion,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.289,13,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_forward_pe_mean_f12m,Forward price to earnings ratio based on the mean consensus estimate for the next 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8506,46,93,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_forward_pe_mean_fy1,Forward price-to-earnings ratio using the mean analyst EPS estimate for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8078,53,115,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_forward_pe_mean_fy2,Forward price to earnings ratio based on the mean consensus estimate for the next fiscal year FY2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8518,42,91,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_frwrd_p_stm_f12m,Forward price to earnings ratio based on StarMine SmartEstimate for the next 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8387,39,67,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_frwrd_p_stm_fy1,Forward price-to-earnings ratio using StarMine SmartEstimate for Fiscal Year 1 EPS,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7937,48,153,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_frwrd_p_stm_fy2,Forward price to earnings ratio based on StarMine SmartEstimate for the next fiscal year FY2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8406,41,110,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_grwth_nxt_yr_ndstry_prcntl_rnngs,Industry percentile rank of next fiscal year’s earnings growth (SmartEstimate),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7914,21,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_grwth_nxt_yr_sctr_prcntl_rnngs,Sector percentile rank of next fiscal year’s earnings growth (SmartEstimate),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7914,11,47,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_grwth_ths_yr_ndstry_prcntl_rnngs,Industry percentile rank of this fiscal year’s earnings growth (SmartEstimate),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7328,24,38,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_grwth_ths_yr_sctr_prcntl_rnngs,Percentile rank versus sector peers of expected earnings growth for the current fiscal year higher indicates faster expected growth,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7328,31,49,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_high_est,Highest analyst estimate among current estimates for the period,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4676,34,42,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_high_est_chg,high estimate chg,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4653,11,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_high_price_52_2,Highest price recorded in the past 52 weeks,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.843,38,56,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_last_fiscal_year_pe,Price-to-earnings ratio using the last fiscal year’s actual earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7465,23,55,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_low_est,Lowest analyst estimate among current estimates for the period,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4676,25,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_low_est_chg,low estimate chg,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4653,10,17,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_low_price_52_2,Lowest price recorded in the past 52 weeks,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.843,21,26,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_lt_grwth_ndstry_prcntl,Industry percentile rank of long-term growth,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.6241,18,22,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_lt_grwth_sctr_prcntl,Sector percentile rank of long-term growth,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.6241,11,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_market_cap_l_2,Market capitalization of the company in its local currency at the given date,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.843,47,95,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_market_cap_u_2,Market capitalization in USD,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.843,42,101,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mdn_hstrcl_f12m_prc_rt_rnngs,Median historical forward 12-month price-to-earnings ratio for the company,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8365,19,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mean_est,Consensus mean estimate for the period,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5483,22,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mean_est_chg,mean estimate chg,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4653,7,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_median_historical_f12m_pe,Median historical Price-to-Earnings ratio for the last twelve months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8365,15,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mn_f_rvsnclstr_nlysts_fq1_rnngs,Average revision amount among analysts in the detected revision cluster for Fiscal Quarter 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2331,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mn_stmt_prc_rt_f12m_btd,Price-to-EBITDA ratio using the mean consensus estimate for forward 12 months EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7809,22,29,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mn_stmt_prc_rt_f12m_rnngs,Price-to-earnings ratio using the mean consensus estimate for forward 12 months earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8506,37,76,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mn_stmt_prc_rt_f12m_rvn,Price-to-revenue ratio using the mean consensus estimate for forward 12 months revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8629,40,153,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mn_stmt_prc_rt_fy1_btd,Price-to-EBITDA ratio using the mean consensus estimate for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7781,13,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mn_stmt_prc_rt_fy1_rnngs,Mean analyst estimate Price-to-Earnings ratio for FY1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8078,26,50,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mn_stmt_prc_rt_fy1_rvn,Price-to-revenue ratio using the mean consensus estimate for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8628,21,29,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mn_stmt_prc_rt_fy2_btd,Price-to-EBITDA ratio using the mean consensus estimate for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7761,20,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mn_stmt_prc_rt_fy2_rnngs,Price-to-earnings ratio using the mean consensus estimate for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8518,28,66,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mn_stmt_prc_rt_fy2_rvn,Price-to-revenue ratio using the mean consensus estimate for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8598,17,26,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_btd_14,Percentage change in the consensus mean forward 12 month EBITDA estimate over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7753,15,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_btd_30,Percentage change in the consensus mean forward 12 month EBITDA estimate over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7722,12,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_btd_60,Percentage change in the consensus mean forward 12 month EBITDA estimate over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7653,12,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_btd_7,Percentage change in the consensus mean forward 12 month EBITDA estimate over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7764,16,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_btd_90,Percentage change in the consensus mean forward 12 month EBITDA estimate over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7571,30,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_rnngs_14,Percentage change in the consensus mean forward 12 month earnings estimate over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8608,36,52,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_rnngs_30,Percentage change in the consensus mean forward 12 month earnings estimate over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8585,25,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_rnngs_60,Percentage change in the consensus mean forward 12 month earnings estimate over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8526,12,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_rnngs_7,Percentage change in the consensus mean forward 12 month earnings estimate over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8615,37,61,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_rnngs_90,Percentage change in the consensus mean forward 12 month earnings estimate over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8454,30,52,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_rvn_14,Percentage change in the consensus mean forward 12 month revenue estimate over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8597,16,67,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_rvn_30,Mean percentage change in revenue estimates for the next 12 months over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8574,21,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_rvn_60,Mean percentage change in revenue estimates for the next 12 months over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8514,21,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_rvn_7,Percentage change in the consensus mean forward 12 month revenue estimate over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8604,18,30,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_f12m_rvn_90,Mean percentage change in revenue estimates for the next 12 months over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8441,29,37,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_btd_14,Mean percentage change in EBITDA estimates for Fiscal Quarter 1 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5732,6,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_btd_30,Mean percentage change in EBITDA estimates for Fiscal Quarter 1 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4726,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_btd_60,Mean percentage change in EBITDA estimates for Fiscal Quarter 1 over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3484,6,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_btd_7,Mean percentage change in EBITDA estimates for Fiscal Quarter 1 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5912,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_btd_90,Mean percentage change in EBITDA estimates for Fiscal Quarter 1 over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3109,8,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_rnngs_14,Mean percentage change in earnings estimates for Fiscal Quarter 1 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5693,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_rnngs_30,Mean percentage change in earnings estimates for Fiscal Quarter 1 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4934,4,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_rnngs_60,Mean percentage change in earnings estimates for Fiscal Quarter 1 over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4094,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_rnngs_7,Mean percentage change in earnings estimates for Fiscal Quarter 1 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5821,8,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_rnngs_90,Mean percentage change in earnings estimates for Fiscal Quarter 1 over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3756,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_rvn_14,Mean percentage change in revenue estimates for Fiscal Quarter 1 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.6862,10,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_rvn_30,Mean percentage change in revenue estimates for Fiscal Quarter 1 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5923,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_rvn_60,Mean percentage change in revenue estimates for Fiscal Quarter 1 over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4678,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_rvn_7,Mean percentage change in revenue estimates for Fiscal Quarter 1 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7006,9,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq1_rvn_90,Mean percentage change in revenue estimates for Fiscal Quarter 1 over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4218,9,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_btd_14,Mean percentage change in EBITDA estimates for Fiscal Quarter 2 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2934,4,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_btd_30,Mean percentage change in EBITDA estimates for Fiscal Quarter 2 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2806,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_btd_60,Mean percentage change in EBITDA estimates for Fiscal Quarter 2 over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2534,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_btd_7,Mean percentage change in EBITDA estimates for Fiscal Quarter 2 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.299,3,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_rnngs_14,Mean percentage change in earnings estimates for Fiscal Quarter 2 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3686,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_rnngs_30,Mean percentage change in earnings estimates for Fiscal Quarter 2 over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3599,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_rnngs_60,Mean percentage change in earnings estimates for Fiscal Quarter 2 over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3434,4,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_rnngs_7,Mean percentage change in earnings estimates for Fiscal Quarter 2 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3718,1,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_rnngs_90,Mean percentage change in earnings estimates for Fiscal Quarter 2 over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3278,5,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_rvn_14,Mean percentage change in revenue estimates for Fiscal Quarter 2 over the last 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4041,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_rvn_30,Mean percentage change in analyst revenue estimates for Fiscal Quarter 2 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3891,1,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_rvn_60,Mean percentage change in analyst revenue estimates for Fiscal Quarter 2 over the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3587,4,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_rvn_7,Mean percentage change in revenue estimates for Fiscal Quarter 2 over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4102,7,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fq2_rvn_90,Mean percentage change in analyst revenue estimates for Fiscal Quarter 2 over the past 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3293,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_btd_14,Mean percentage change in analyst EBITDA estimates for Fiscal Year 1 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7812,21,32,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_btd_30,Mean percentage change in analyst EBITDA estimates for Fiscal Year 1 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7785,6,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_btd_60,Mean percentage change in analyst EBITDA estimates for Fiscal Year 1 over the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7725,7,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_btd_7,Mean percentage change in analyst EBITDA estimates for Fiscal Year 1 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7821,26,30,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_btd_90,Mean percentage change in analyst EBITDA estimates for Fiscal Year 1 over the past 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.766,28,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_rnngs_14,Mean percentage change in analyst earnings estimates for Fiscal Year 1 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8642,18,22,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_rnngs_30,Mean percentage change in analyst earnings estimates for Fiscal Year 1 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8622,17,22,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_rnngs_60,Mean percentage change in analyst earnings estimates for Fiscal Year 1 over the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8572,14,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_rnngs_7,Mean percentage change in analyst earnings estimates for Fiscal Year 1 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8648,16,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_rnngs_90,Mean percentage change in analyst earnings estimates for Fiscal Year 1 over the past 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8514,21,37,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_rvn_14,Mean percentage change in analyst revenue estimates for Fiscal Year 1 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8631,5,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_rvn_30,Mean percentage change in analyst revenue estimates for Fiscal Year 1 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8611,7,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_rvn_60,Mean percentage change in analyst revenue estimates for Fiscal Year 1 over the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8558,10,12,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_rvn_7,Mean percentage change in analyst revenue estimates for Fiscal Year 1 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8637,6,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_mstchg_pct_fy1_rvn_90,Mean percentage change in analyst revenue estimates for Fiscal Year 1 over the past 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8501,16,23,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nm_ngtv_bld_stmts_fq1_rnngs,Count of negative bold (significantly below consensus) estimates for fiscal quarter 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8408,7,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nm_pstv_bld_stmts_fq1_rnngs,Count of positive bold estimates for fiscal quarter 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8408,6,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nm_rvsnclstr_nlysts_fq1_rnngs,Count of analysts included in the current revision cluster for Fiscal Quarter 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2331,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nm_stm_nlysts_fq1_rnngs,Number of analyst estimates included in the SmartEstimate for FQ1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8408,19,32,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fq1_rnngs_14,Number of analysts revising down earnings estimates for Fiscal Quarter 1 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fq1_rnngs_7,Number of analysts revising down earnings estimates for Fiscal Quarter 1 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fq1_rvn_14,Number of sell-side analysts who lowered their revenue estimate for Fiscal Quarter 1 in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,11,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fq1_rvn_30,Number of sell-side analysts who lowered their revenue estimate for Fiscal Quarter 1 in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fq1_rvn_7,Number of sell-side analysts who lowered their revenue estimate for Fiscal Quarter 1 in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fq2_rnngs_30,Number of sell-side analysts who lowered their earnings estimate for Fiscal Quarter 2 in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,6,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fq2_rvn_14,Number of sell-side analysts who lowered their revenue estimate for Fiscal Quarter 2 in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,6,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fq2_rvn_30,Number of sell-side analysts who lowered their revenue estimate for Fiscal Quarter 2 in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fy1_btd_14,Number of sell-side analysts who lowered their EBITDA estimate for Fiscal Year 1 in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fy1_btd_30,Number of sell-side analysts who lowered their EBITDA estimate for Fiscal Year 1 in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fy1_btd_7,Number of sell-side analysts who lowered their EBITDA estimate for Fiscal Year 1 in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,3,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fy1_rnngs_14,Number of sell-side analysts who lowered their earnings estimate for Fiscal Year 1 in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fy1_rnngs_30,Number of sell-side analysts who lowered their earnings estimate for Fiscal Year 1 in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fy1_rnngs_7,Number of sell-side analysts who lowered their earnings estimate for Fiscal Year 1 in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,6,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fy1_rvn_14,Number of sell-side analysts who lowered their revenue estimate for Fiscal Year 1 in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,7,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fy1_rvn_30,Number of sell-side analysts who lowered their revenue estimate for Fiscal Year 1 in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_dwn_fy1_rvn_7,Number of sell-side analysts who lowered their revenue estimate for Fiscal Year 1 in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fq1_rvn_14,Number of analysts who raised their Fiscal Quarter 1 revenue estimates in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fq1_rvn_30,Number of analysts who raised their Fiscal Quarter 1 revenue estimates in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,6,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fq1_rvn_7,Number of analysts who raised their Fiscal Quarter 1 revenue estimates in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,4,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy1_btd_14,Number of analysts revising up EBITDA estimates for Fiscal Year 1 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,17,25,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy1_btd_30,Number of analysts revising up EBITDA estimates for Fiscal Year 1 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,10,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy1_btd_7,Number of analysts revising up EBITDA estimates for Fiscal Year 1 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,22,28,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy1_rnngs_14,Number of analysts revising up earnings estimates for Fiscal Year 1 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy1_rnngs_30,Number of analysts revising up earnings estimates for Fiscal Year 1 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy1_rnngs_7,Number of analysts revising up earnings estimates for Fiscal Year 1 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,17,70,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy1_rvn_14,Number of analysts revising up revenue estimates for Fiscal Year 1 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy1_rvn_30,Number of analysts revising up revenue estimates for Fiscal Year 1 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,10,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy1_rvn_7,Number of analysts revising up revenue estimates for Fiscal Year 1 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,7,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy2_btd_14,Number of analysts revising up EBITDA estimates for Fiscal Year 2 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,23,30,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy2_btd_30,Number of analysts revising up EBITDA estimates for Fiscal Year 2 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,23,31,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy2_btd_7,Number of analysts revising up EBITDA estimates for Fiscal Year 2 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,16,22,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy2_rnngs_14,Number of analysts revising up earnings estimates for Fiscal Year 2 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,21,34,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy2_rnngs_30,Number of analysts revising up earnings estimates for Fiscal Year 2 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,24,73,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy2_rnngs_7,Number of analysts revising up earnings estimates for Fiscal Year 2 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,7,33,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy2_rvn_14,Number of analysts revising up revenue estimates for Fiscal Year 2 over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,12,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy2_rvn_30,Number of analysts revising up revenue estimates for Fiscal Year 2 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,9,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_nnlyst_rvsng_p_fy2_rvn_7,Number of analysts revising up revenue estimates for Fiscal Year 2 over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,13,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_downgrades_14,Number of analyst recommendation downgrades in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,31,41,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_downgrades_60,Number of analyst recommendation downgrades in the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,23,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_downgrades_7,Number of analyst recommendation downgrades in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,12,17,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_downgrades_90,Number of analyst recommendation downgrades in the past 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,25,30,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_mean_analyst,Number of analysts contributing to the mean estimate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5483,31,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_revions_up,num revions up,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8282,22,30,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_revisons_down,num revisons down,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8282,34,82,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_upgrades_14,Number of analyst recommendation upgrades in the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,36,43,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_upgrades_60,Number of analyst recommendation upgrades in the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,12,17,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_upgrades_7,Number of analyst recommendation upgrades in the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,12,17,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_num_upgrades_90,Number of analyst recommendation upgrades in the past 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,20,24,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_p_yld_pct_stm_f12m,Earnings yield percentage earnings divided by price based on SmartEstimate for the next 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8525,89,177,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_p_yld_pct_stm_fy1,Earnings yield percentage earnings divided by price based on SmartEstimate for the current fiscal year FY1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8506,54,139,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_p_yld_pct_stm_fy2,Earnings-to-Price yield percentage based on StarMine SmartEstimate for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.85,73,157,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_peg_mean_fy1,PEG ratio computed using forward FY1 P/E from mean estimates and the corresponding growth estimate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7877,40,61,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_peg_smartestimate_fy1,(Price/Earnings-to-growth) smartestimate FYEAR1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7746,93,192,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_preferred,preferred,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8294,92,172,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_price_change_30,Percentage price change over the last 30 trading days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8424,22,26,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_f12m_earnings,Predicted surprise (actual minus predicted value) for trailing 12 months earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8483,34,38,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_f12m_ebitda,Difference between the StarMine SmartEstimate and the mean consensus for forward 12 months EBITDA (Predicted Surprise amount),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7626,9,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_f12m_revenue,StarMine predicted revenue surprise amount for the next 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8469,33,48,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_flag_fq1_earnings,"Flag indicating the predicted surprise status such as positive, negative, or neutral for fiscal quarter 1 earnings","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8408,7,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fq1_earnings,"Predicted earnings surprise amount for fiscal quarter 1, i.e., SmartEstimate minus consensus mean","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5608,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fq1_ebitda,Difference between the StarMine SmartEstimate and the mean consensus for fiscal quarter 1 EBITDA (Predicted Surprise amount),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.596,9,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fq1_revenue,Difference between the StarMine SmartEstimate and the mean consensus for fiscal quarter 1 revenue (Predicted Surprise amount),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7036,6,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fq2_earnings,StarMine predicted earnings surprise amount for Fiscal Quarter 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3601,6,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fq2_ebitda,Difference between the StarMine SmartEstimate and the mean consensus for fiscal quarter 2 EBITDA (Predicted Surprise amount),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.293,3,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fq2_revenue,StarMine predicted revenue surprise amount for Fiscal Quarter 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4044,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fy1_earnings,StarMine predicted earnings surprise amount for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.851,12,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fy1_ebitda,Predicted surprise (actual minus predicted) for Fiscal Year 1 EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7667,10,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fy1_revenue,StarMine predicted revenue surprise amount for fiscal year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8491,16,24,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fy2_earnings,Difference between the StarMine SmartEstimate and the mean consensus for fiscal year 2 earnings (Predicted Surprise amount),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8504,10,73,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fy2_ebitda,Difference between the StarMine SmartEstimate and the mean consensus for fiscal year 2 EBITDA (Predicted Surprise amount),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7645,10,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_fy2_revenue,Difference between the StarMine SmartEstimate and the mean consensus for fiscal year 2 revenue (Predicted Surprise amount),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8479,30,48,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_f12m_earnings,StarMine predicted percentage earnings surprise for the next 12 months (SmartEstimate versus mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8483,16,23,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_f12m_ebitda,StarMine predicted percentage EBITDA surprise for the next 12 months (SmartEstimate versus mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7626,11,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_f12m_revenue,StarMine predicted percentage revenue surprise for the next 12 months (SmartEstimate versus mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8469,16,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fq1_earnings,StarMine predicted percentage earnings surprise for the next fiscal quarter (SmartEstimate versus mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5826,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fq1_ebitda,StarMine predicted percentage EBITDA surprise for the next fiscal quarter (SmartEstimate versus mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.596,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fq1_revenue,StarMine predicted percentage revenue surprise for the next fiscal quarter (SmartEstimate versus mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7036,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fq2_earnings,Percentage difference between the StarMine SmartEstimate and the mean consensus for fiscal quarter 2 earnings (Predicted Surprise %),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3601,1,1,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fq2_ebitda,Percentage difference between the StarMine SmartEstimate and the mean consensus for fiscal quarter 2 EBITDA (Predicted Surprise %),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.293,2,2,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fq2_revenue,Percentage difference between the StarMine SmartEstimate and the mean consensus for fiscal quarter 2 revenue (Predicted Surprise %),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4044,6,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fy1_earnings,Percentage difference between the StarMine SmartEstimate and the mean consensus for fiscal year 1 earnings (Predicted Surprise %),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.851,14,21,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fy1_ebitda,Percentage difference between the StarMine SmartEstimate and the mean consensus for fiscal year 1 EBITDA (Predicted Surprise %),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7667,8,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fy1_revenue,Percentage difference between the StarMine SmartEstimate and the mean consensus for fiscal year 1 revenue (Predicted Surprise %),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8491,12,17,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fy2_earnings,Percentage difference between the StarMine SmartEstimate and the mean consensus for fiscal year 2 earnings (Predicted Surprise %),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8504,13,24,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fy2_ebitda,Percentage difference between the StarMine SmartEstimate and the mean consensus for fiscal year 2 EBITDA (Predicted Surprise %),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7645,7,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_pct_fy2_revenue,Percentage difference between the StarMine SmartEstimate and the mean consensus for fiscal year 2 revenue (Predicted Surprise %),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8479,26,30,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_prsprise_per_fq1_earnings,predicted surprise (actual value - predicted value) per FQTR1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5608,5,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_rank,Time series of the security’s percentile rank of the overall ARM score within its universe,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8294,152,1875,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_rcmmndtn_mn_chng_14,Change in the mean analyst recommendation over the past 14 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8707,18,23,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_rcmmndtn_mn_chng_60,Change in the mean analyst recommendation over the past 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8637,12,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_rcmmndtn_mn_chng_7,Change in the mean analyst recommendation over the past 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8714,8,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_rcmmndtn_mn_chng_90,Change in the mean analyst recommendation over the past 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8582,24,42,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_recommendations,Recommendations component of ARM history capturing analyst recommendation changes and momentum,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8231,26,39,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_revenue,Revenue component of ARM history capturing analyst revenue estimate revisions,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8274,29,39,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_revisions,revisions,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8294,62,143,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_score,Time series of the overall ARM composite score (1–100) for the security,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8294,155,454,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_secondary,secondary,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7497,32,47,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_frwrd_p_stm_fy1,Forward price to earnings ratio based on StarMine SmartEstimate for the current fiscal year FY1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.828,27,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_mn_stmt_prc_rt_fy1_rnngs,Price-to-earnings ratio using the mean consensus estimate for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8423,19,33,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_nlyst_rvsns_scr,Composite ARM percentile score indicating strength of analyst estimate and recommendation revisions for the stock,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,73,144,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_nnlyst_rvsng_dwn_fq1_rnngs_30,Number of analysts revising down earnings estimates for Fiscal Quarter 1 over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,6,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_nnlyst_rvsng_p_fq1_rnngs_30,Number of analysts who raised their Fiscal Quarter 1 earnings estimates in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_rcmmndtn_mn_chng_30,Change in the mean analyst recommendation over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8687,14,17,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_rm_prfrrd_rnngs_cmpnnt,"Preferred earnings subcomponent of the ARM score (portion of final score, scaled 1–100)","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,45,65,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_rm_rcmmndtns_cmpnnt,Recommendations subcomponent score contributing to the overall ARM score,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8578,32,54,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_rm_scndry_rnngs_cmpnnt,Secondary earnings component contributing to the final ARM score,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7811,22,28,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_smrtst_grwth_nxt_yrths_yr_rnngs,SmartEstimate growth rate ratio of next year’s to this year’s earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8258,7,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_smrtst_grwth_ths_yrlst_yr_rnngs,SmartEstimate growth rate ratio of this year’s to last year’s earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7613,14,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_stm_fq1_rnngs,StarMine SmartEstimate (proprietary weighted consensus) for fiscal quarter 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5826,10,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sm_stm_prc_rt_fy1_rnngs,Price-to-earnings ratio using StarMine SmartEstimate for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.828,21,44,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_actual_last_q_earnings,Actual earnings reported for the most recent fiscal quarter,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5985,10,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_actual_last_y_earnings,Actual earnings reported for the most recent fiscal year,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8202,11,12,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_arm_country_rank,Country-level percentile rank of the ARM signal (integer 1–100),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,66,124,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_arm_global_rank,Global percentile rank of the ARM signal (integer 1–100),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,63,129,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_arm_new_activity_flag,Flag indicating new ARM-related analyst activity,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.88,9,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_arm_revenue_component,"Revenue subcomponent of the ARM score, reflecting revenue-related analyst signals (scaled as a component score)","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8625,28,39,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_arm_score_5,Five-day average of the composite ARM score,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,57,82,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_arm_score_change_30,Change in the ARM score over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8595,26,87,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_days_since_last_report,Number of model days since the last analyst report or estimate activity,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8791,15,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_forward_pe_mean_fy1,Forward price to earnings ratio based on the mean consensus estimate for the current fiscal year FY1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8423,29,46,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_last_fiscal_year_pe,Price to earnings ratio using earnings from the last completed fiscal year,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7753,21,38,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_num_downgrades_30,Number of analyst recommendation downgrades in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,23,29,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_num_upgrades_30,Number of analyst recommendation upgrades in the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8763,15,17,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_prsprise_fq1_earnings,StarMine predicted earnings surprise amount for fiscal quarter 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5826,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_sma_trailing_4_quarter_pe,Price to earnings ratio using earnings from the most recent four reported quarters,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4072,43,66,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_f12m_ebitda,StarMine SmartEstimate (proprietary weighted consensus) for forward 12 months EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7626,11,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_f12m_revenue,StarMine SmartEstimate (proprietary weighted consensus) for forward 12 months revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8469,9,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fq1_earnings,StarMine SmartEstimate for fiscal Q1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5608,13,29,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fq1_ebitda,StarMine SmartEstimate (proprietary weighted consensus) for fiscal quarter 1 EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.596,12,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fq1_revenue,StarMine SmartEstimate (proprietary weighted consensus) for fiscal quarter 1 revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7036,17,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fq2_earnings,StarMine SmartEstimate (proprietary weighted consensus) for earnings for Fiscal Quarter 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3601,6,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fq2_ebitda,StarMine SmartEstimate (proprietary weighted consensus) for EBITDA for Fiscal Quarter 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.293,7,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fq2_revenue,StarMine SmartEstimate (proprietary weighted consensus) for revenue for Fiscal Quarter 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4044,6,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fy1_earnings,StarMine SmartEstimate (proprietary weighted consensus) for earnings for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.851,27,59,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fy1_ebitda,StarMine SmartEstimate (proprietary weighted consensus) for EBITDA for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7667,7,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fy1_revenue,StarMine SmartEstimate (proprietary weighted consensus) for revenue for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8491,14,22,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fy2_earnings,StarMine SmartEstimate (proprietary weighted consensus) for earnings for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8504,21,33,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fy2_ebitda,StarMine SmartEstimate (proprietary weighted consensus) for EBITDA for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7645,17,28,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smartestimate_fy2_revenue,StarMine SmartEstimate (proprietary weighted consensus) for revenue for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8479,14,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smrtst_grwth_f12mt12m_rnngs,Expected earnings growth from trailing 12 months to forward 12 months based on SmartEstimate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7416,14,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smrtst_grwth_f24mf12m_rnngs,Expected earnings growth from forward 12 months to forward 24 months based on SmartEstimate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7196,19,60,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smrtst_grwth_nxt_yrths_yr_btd,SmartEstimate growth rate ratio of next year’s to this year’s EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7587,13,13,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smrtst_grwth_nxt_yrths_yr_rnngs,Expected earnings growth from this fiscal year to next fiscal year based on the StarMine SmartEstimate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7915,11,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smrtst_grwth_nxt_yrths_yr_rvn,SmartEstimate growth rate ratio of next year’s to this year’s revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8467,22,28,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smrtst_grwth_ths_yrlst_yr_btd,SmartEstimate growth rate ratio of this year’s to last year’s EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.719,5,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smrtst_grwth_ths_yrlst_yr_rnngs,Earnings growth from last fiscal year to this fiscal year based on the StarMine SmartEstimate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7329,18,26,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smrtst_grwth_ths_yrlst_yr_rvn,SmartEstimate growth rate ratio of this year’s to last year’s revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8159,23,30,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smrtst_pg_ndstry_prcntl_f12m,Industry percentile ranking of the PEG ratio based on SmartEstimate and forward 12-month growth,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7794,19,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_smrtst_pg_sctr_prcntl_f12m,Sector percentile ranking of the PEG ratio based on SmartEstimate and forward 12-month growth,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7794,44,64,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_srprs_pct_lst_q_rnngs,"Percentage surprise for last quarter earnings: (actual minus predicted) divided by predicted, expressed as a percentage","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5726,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_srprs_pct_lst_q_rvn,"Percentage surprise for last quarter revenue: (actual minus predicted) divided by predicted, expressed as a percentage","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.6801,8,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_srprs_pct_lst_y_rnngs,"Percentage surprise for last year earnings: (actual minus predicted) divided by predicted, expressed as a percentage","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8182,9,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_srprs_pct_lst_y_rvn,"Percentage surprise for last year revenue: (actual minus predicted) divided by predicted, expressed as a percentage","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8187,4,4,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stdv_rvsnclstr_nlysts_fq1_rnngs,Standard deviation of revision amounts among analysts in the revision cluster for Fiscal Quarter 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2331,6,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stdv_stm_nlysts_fq1_rnngs,Standard deviation of analyst estimates contributing to the FQ1 earnings SmartEstimate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5608,9,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_chng_fq1_rnngs_30,Change in the SmartEstimate for FQ1 earnings over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4674,7,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_f12m_rnngs,StarMine SmartEstimate (proprietary weighted consensus) for forward 12 months earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8483,28,32,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_prc_rt_f12m_btd,Price-to-EBITDA ratio using StarMine SmartEstimate for forward 12 months EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7658,26,39,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_prc_rt_f12m_rnngs,Price-to-earnings ratio using StarMine SmartEstimate for forward 12 months earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8387,31,59,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_prc_rt_f12m_rvn,Price-to-revenue ratio using StarMine SmartEstimate for forward 12 months revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8493,38,51,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_prc_rt_fy1_btd,Price-to-EBITDA ratio using StarMine SmartEstimate for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7622,28,67,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_prc_rt_fy1_rnngs,Price-to-earnings ratio based on the StarMine SmartEstimate for FY1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7937,30,45,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_prc_rt_fy1_rvn,Price-to-revenue ratio using StarMine SmartEstimate for Fiscal Year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8484,26,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_prc_rt_fy2_btd,Price-to-EBITDA ratio using StarMine SmartEstimate for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7624,24,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_prc_rt_fy2_rnngs,Price-to-earnings ratio using StarMine SmartEstimate for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8405,36,71,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_prc_rt_fy2_rvn,Price-to-revenue ratio using StarMine SmartEstimate for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8473,23,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_stm_prr_fq1_rnngs_7,The StarMine SmartEstimate for Fiscal Quarter 1 earnings as of seven days prior,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5554,5,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_surprise_60_q,surprise (actual value - predicted value) 60 q,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3787,12,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_surprise_60_y,surprise (actual value - predicted value) 60 y,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7673,11,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_surprise_pct_last_q_ebitda,"Percentage surprise for last quarter EBITDA: (actual minus predicted) divided by predicted, expressed as a percentage","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5734,21,32,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_surprise_pct_last_y_ebitda,"Percentage surprise for last year EBITDA: (actual minus predicted) divided by predicted, expressed as a percentage","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7372,10,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_surprise_q,surprise (actual value - predicted value) q,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.54,19,22,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_surprise_y,surprise (actual value - predicted value) y,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7747,11,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_traded_volume_2,traded volume (in 1000s),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.843,12,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_traded_volume_l_2,traded volume (in 1000s)-Local Currency,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.843,15,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_traded_volume_u_2,traded volume (in 1000s)-USD,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.843,13,14,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_trailing_4_quarter_pe,Price-to-earnings ratio using the sum of earnings per share over the most recent four reported quarters,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3954,35,67,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_yld_ndstry_prcntl_fy1_rnngs,Industry percentile ranking of the dividend yield based on FY1 estimates,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8152,48,112,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_yld_sctr_prcntl_fy1_rnngs,Sector percentile ranking of the dividend yield based on FY1 estimates,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8152,53,105,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl26_yld_stm_fy1_rnngs,Dividend yield percentage estimated using StarMine SmartEstimate for fiscal year 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8153,66,194,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mean_estimate_seven_days_ago_quarterly,Mean FQ1 earnings estimate as of 7 days prior,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5608,8,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mean_estimate_to_price_ratio_year1_ebitda,Consensus mean price-to-EBITDA ratio for FY1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7781,19,29,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mean_revision_thirty_days_quarterly,Average percent change of individual analyst FQ1 earnings estimate revisions over the past 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.2987,7,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +model_score_delta_3d,Change in the StarMine ARM score over the last 3 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.864,23,29,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +model_score_delta_60d,Change in the StarMine ARM composite score over the last 60 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8537,26,37,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +new_model_activity_indicator,Flag indicating newly detected activity or updates in the StarMine ARM dataset for the company since the prior day,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.88,11,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +parent_entity_indicator,Indicator flag showing whether the company is a parent company in a corporate group,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8435,6,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_expected_surprise_next_quarter_earnings,StarMine predicted percentage earnings surprise for Fiscal Quarter 2 (SmartEstimate vs mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3601,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_expected_surprise_next_quarter_ebitda,StarMine predicted percentage EBITDA surprise for Fiscal Quarter 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.293,1,1,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_expected_surprise_next_year_earnings,"Predicted percentage earnings surprise for Fiscal Year 2, i.e., SmartEstimate versus mean consensus as a percent of the mean","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8504,20,28,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_expected_surprise_next_year_ebitda,StarMine predicted percentage EBITDA surprise for FY2 (SmartEstimate vs mean),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7645,13,29,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_expected_surprise_quarter_revenue,StarMine predicted percentage revenue surprise for Fiscal Quarter 1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7036,13,22,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_expected_surprise_year_ebitda,"StarMine predicted percentage surprise for FY1 EBITDA, i.e., (SmartEstimate − mean) / mean","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7667,21,48,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_expected_surprise_year_revenue,Predicted percentage revenue surprise for Fiscal Year 1 (SmartEstimate vs mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8491,17,37,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_surprise_reported_earnings_previous_year,"Percentage surprise for the last year’s earnings, calculated as actual reported minus consensus estimate, expressed as a percent of the consensus","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8182,12,15,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_surprise_reported_ebitda_previous_year,"Percentage surprise for the last year’s EBITDA, calculated as actual reported minus consensus estimate, expressed as a percent of the consensus","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7372,10,18,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_surprise_reported_revenue_previous_quarter,"Percentage surprise for the last quarter’s revenue, calculated as actual reported minus consensus estimate, expressed as a percent of the consensus","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.6801,17,26,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pct_surprise_reported_revenue_previous_year,"Percentage surprise for the last year’s revenue, calculated as actual reported minus consensus estimate, expressed as a percent of the consensus","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8187,9,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pe_ratio_previous_fiscal_year,Price-to-earnings ratio using earnings from the last completed fiscal year,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7753,23,36,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +pe_ratio_trailing_4q_2,Price-to-earnings ratio using earnings from the most recent four reported quarters,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4072,39,59,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +predicted_surprise_pct_year1_revenue,StarMine predicted revenue surprise amount for Fiscal Year 1 (SmartEstimate minus mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8491,20,31,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +predicted_surprise_pct_year2_ebitda,StarMine predicted EBITDA surprise amount for Fiscal Year 2 (SmartEstimate minus mean consensus),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7645,18,38,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_mean_earnings_ratio_12m,Mean price-to-earnings estimate ratio for the next 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8506,33,53,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_mean_earnings_ratio_year,Price-to-earnings ratio using the mean (consensus) estimate for Fiscal Year 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8423,23,39,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_mean_estimate_next_year_earnings_ratio,Mean price-to-earnings estimate ratio for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8518,35,78,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_mean_estimate_ratio_12m_ebitda,Mean price-to-EBITDA estimate ratio for the next 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7809,27,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_mean_estimate_ratio_next_year_revenue,Price-to-revenue ratio using the mean (consensus) estimate for Fiscal Year 1 revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8628,33,50,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_mean_estimate_ratio_year2_ebitda,Mean price-to-EBITDA estimate ratio for Fiscal Year 2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7761,34,61,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_mean_estimate_ratio_year2_revenue,Consensus mean price-to-revenue ratio for FY2,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8598,22,98,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_mean_revenue_ratio_12m,Mean price-to-revenue estimate ratio for forward 12 months,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8629,32,52,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_smartest_earnings_ratio_next_year,Price-to-earnings ratio using the SmartEstimate for Fiscal Year 2 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8405,45,77,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_smartest_ebitda_ratio_year,StarMine SmartEstimate price-to-EBITDA ratio for FY1,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7622,27,50,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_smartest_estimate_ratio_12m_earnings,Price-to-earnings ratio for the next 12 months based on the SmartEstimate of forward 12-month earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8387,48,75,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_smartest_estimate_ratio_next_year_earnings,Price-to-earnings ratio using the StarMine SmartEstimate for Fiscal Year 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.828,27,34,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_smartest_estimate_ratio_next_year_revenue,Price-to-revenue ratio using the StarMine SmartEstimate for Fiscal Year 1 revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8484,33,57,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_smartest_estimate_ratio_year2_revenue,Price-to-revenue ratio using the StarMine SmartEstimate for Fiscal Year 2 revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8473,34,58,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_smartest_forward_ebitda_ratio,Price-to-EBITDA ratio using StarMine SmartEstimate for forward 12 months EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7658,24,47,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_smartest_forward_revenue_ratio,Price-to-revenue ratio using StarMine SmartEstimate for forward 12 months revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8493,29,67,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +price_to_smartest_next_year_ebitda_ratio,Price-to-EBITDA ratio using the StarMine SmartEstimate for Fiscal Year 2 EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7624,29,48,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +primary_earnings_measure_type_4,Preferred methodology or measure for earnings used in analysis,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8408,4,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +recommendation_change_score,Recommendations component portion of the final ARM score expressed as a percentile 1–100,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8578,33,150,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +recommendation_change_score_component,ARM recommendations component percentile score reflecting recommendation momentum,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8578,27,44,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +region_rank_decimal_score,"Region-specific relative rank of the ARM signal, expressed in decimal form","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,91,626,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +region_rank_score_decimal,Region-specific relative rank of the ARM signal expressed as a decimal percentile,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,84,455,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +region_score_component,Score component reflecting the regional ranking of the stock.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,7,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +region_score_float,Score representing the region-based ranking for a stock.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,6,10,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +reported_earnings_last_fiscal_year,Actual earnings reported for the most recent fiscal year,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8202,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +reported_earnings_previous_quarter,Actual earnings reported for the most recent fiscal quarter,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5985,7,7,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +reported_ebitda_last_quarter,Actual EBITDA reported for the most recent fiscal quarter,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.6645,6,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +reported_ebitda_previous_year,Actual EBITDA reported for the most recent fiscal year,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7957,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +reported_revenue_last_fiscal_year,Actual revenue reported for the most recent fiscal year,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8392,8,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +reported_revenue_last_quarter,Actual revenue reported for the most recent fiscal quarter,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7401,5,5,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +revenue_component_score,The portion of the score based on changes in revenue estimates.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,46,73,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +revenue_revision_score_3,Revenue component portion of the final ARM score expressed as a percentile 1–100,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8625,30,46,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +score_analyst_estimate_revisions,"Composite StarMine ARM percentile score reflecting the direction, breadth, and momentum of analyst estimate revisions","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,109,199,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +score_avg_5d,ARM score averaged over the past 5 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,104,309,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +score_change_model_30d,Change in the StarMine ARM composite score over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8595,29,35,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +score_change_model_7d,Change in the StarMine ARM composite score over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8634,35,58,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +score_component_primary_earnings,Preferred earnings component portion of the final ARM score,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,59,104,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +score_component_primary_earnings_float,ARM preferred earnings component percentile score,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8645,47,72,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +score_delta_1d,Change in the ARM composite score over the last 1 day,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8643,36,55,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +score_delta_90d,Change in the StarMine ARM score over the last 90 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8473,74,207,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +secondary_earnings_score,Secondary earnings component portion of the final ARM score expressed as a percentile 1–100,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7811,36,60,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +secondary_earnings_score_component,Score component reflecting revisions and surprises in secondary earnings measures.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7811,35,53,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +seven_day_mean_estimate_change_flag_quarterly,Flag indicating a significant change in mean quarterly earnings estimates over seven days.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3316,8,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +sixty_day_earnings_surprise_quarter,Earnings surprise for the last quarter measured over the past 60 days.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.389,8,8,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +sixty_day_earnings_surprise_year,Earnings surprise for the last year measured over the past 60 days.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7808,9,9,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +smartest_growth_ebitda_next_vs_this_year,Growth rate of EBITDA from this fiscal year to next fiscal year based on the SmartEstimate,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7587,10,16,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +smartest_growth_rate_next_vs_current_year_earnings,"SmartEstimate growth rate for earnings, comparing next fiscal year to this fiscal year","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8258,13,17,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_country_rank,Country-level percentile rank of the ARM signal (higher means stronger revisions vs country peers),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.829,509,3909,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_global_rank,Global percentile rank of the ARM signal (higher means stronger revisions vs global peers),"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.829,543,5926,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_pref_earnings_score,Percentile sub-score for the company’s preferred earnings metric revisions used by ARM; contributes to the overall ARM score,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.829,244,1171,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_rec_change_flag,Flag indicating whether the mean recommendation changed over the last 7 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.301,54,79,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_rec_days_since_2lv_change,Number of days since a two-level change in analyst recommendation occurred,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7342,49,115,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_rec_days_since_new,Number of calendar days since the most recent new analyst recommendation was issued,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.843,55,123,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_rec_days_since_newsell,Number of calendar days since the latest new Sell recommendation was issued for the stock,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.724,86,250,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_rec_mean_change30,Change in the average analyst recommendation over the past 30 calendar days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8334,67,179,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_rec_mean_prior7,The mean analyst recommendation as of 7 calendar days ago,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8359,95,188,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_rec_ndown_30,Count of analyst rating downgrades in the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8406,68,165,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_rec_nup_30,Count of analyst rating upgrades in the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8406,49,104,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_recommendations_score,Percentile sub-score capturing analyst recommendation momentum; contributes to the overall ARM score,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8226,186,1057,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_region_rank_decimal,Regional percentile rank expressed as a decimal between 0 and 1 for the ARM signal,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.829,135,368,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_revenue_score,Percentile sub-score for revenue estimate revisions; contributes to the overall ARM score,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.827,107,220,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_score,Headline ARM composite score summarizing analyst estimate and recommendation revisions,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.829,359,2060,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_score_5,Five-day moving average of the ARM composite (Analyst Revisions) score,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.829,120,287,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_score_change_1m,Change in the ARM composite score over the last 30 days,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8245,79,246,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +star_arm_secondary_earnings_score,Percentile sub-score for the secondary earnings metric revisions; contributes to the overall ARM score,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7491,103,231,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +stock_model_score,Overall score assigned to the stock by the model.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,32,47,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +stock_percentile_ranking,Percentile rank of the stock based on the model score.,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,170,428,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +suspect_data_indicator_any_earnings,"Indicator flag showing whether any earnings-related data (across forecast/reporting periods) is considered suspect; 1 = suspect, 0 = not suspect","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8408,4,6,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +suspect_data_indicator_model,"Indicator that the ARM calculation/data for this record are suspect (1=flagged, 0=clean)","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8432,9,30,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +suspect_data_indicator_quarter1_earnings,"Indicator that FQ1 earnings data are suspect (1=flagged, 0=clean)","{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8408,3,3,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_12m_earnings,StarMine weighted consensus estimate for forward 12 months earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8483,35,55,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_12m_ebitda,StarMine weighted consensus estimate for forward 12 months EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7626,35,66,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_curr_qtr_ebitda,StarMine weighted consensus estimate for Fiscal Quarter 1 EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.596,18,42,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_curr_qtr_revenue,StarMine weighted consensus estimate for Fiscal Quarter 1 revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7036,21,34,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_forward_12m_revenue,StarMine weighted consensus estimate for forward 12 months revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8469,31,69,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_next_quarter_earnings,StarMine SmartEstimate (weighted consensus) for Fiscal Quarter 2 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.3601,14,23,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_next_quarter_revenue,StarMine SmartEstimate (proprietary weighted analyst consensus) for Fiscal Quarter 2 revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.4044,19,28,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_next_year_earnings,StarMine SmartEstimate (proprietary weighted analyst consensus) for next fiscal year (FY1) earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.851,29,67,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_next_year_earnings_2,StarMine SmartEstimate (proprietary weighted analyst consensus) for the following fiscal year (FY2) earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8504,34,50,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_next_year_ebitda,StarMine SmartEstimate (proprietary weighted analyst consensus) for next fiscal year (FY1) EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7667,22,52,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_next_year_ebitda_2,SmartEstimate consensus for FY2 EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.7645,37,65,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_next_year_revenue,StarMine SmartEstimate (proprietary weighted analyst consensus) for next fiscal year (FY1) revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8491,25,31,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_qtr2_ebitda,StarMine SmartEstimate (proprietary weighted analyst consensus) for Fiscal Quarter 2 EBITDA,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.293,9,11,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_quarter_earnings,StarMine weighted consensus estimate for Fiscal Quarter 1 earnings,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.5826,14,19,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +weighted_estimate_year2_revenue,StarMine SmartEstimate (proprietary weighted analyst consensus) for the following fiscal year (FY2) revenue,"{'id': 'model26', 'name': 'Analyst Revisions'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-revisions-models', 'name': 'Revisions Models'}",IND,1,TOP500,MATRIX,0.9507,0.8479,23,27,1.3,[],model26,Analyst Revisions,model,Model,model-revisions-models,Revisions Models +mdl262_cur_mae,Mean absolute error of the Enterprise Value prediction/model,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9758,65,234,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_cur_predict,Predicted Enterprise Value (market-implied EV) from the model with 1-day delay,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,71,368,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebit_a_mae,Mean absolute error (MAE) of the annual EBIT prediction for this model,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.2572,12,12,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebit_a_predict,Predicted (market-implied) annual EBIT,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,59,271,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebit_q_mae,Mean absolute error (MAE) of the quarterly EBIT prediction for this model,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.2572,3,3,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebit_q_predict,Predicted (market-implied) quarterly EBIT,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,41,117,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebit_ttm_mae,Mean absolute error (MAE) of the trailing-twelve-month EBIT prediction for this model,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.2572,7,8,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebit_ttm_predict,Predicted (market-implied) trailing-twelve-month EBIT,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,47,116,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebitd_a_predict,"Market-implied annual Earnings before Interest, Tax, and Depreciation (EBITD) prediction, one-day delayed (D1)","{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,29,58,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebitd_q_predict,"Market-implied quarterly Earnings before Interest, Tax, and Depreciation (EBITD) prediction, one-day delayed (D1)","{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,25,49,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebitd_ttm_predict,"Market-implied trailing-twelve-month Earnings before Interest, Tax, and Depreciation (EBITD) prediction, one-day delayed (D1)","{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,44,283,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebt_a_predict,"Market-implied forecast of annual earnings before tax (EBT), with 1-day delay","{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,32,80,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebt_q_predict,"Market-implied forecast of quarterly earnings before tax (EBT), with 1-day delay","{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,17,43,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ebt_ttm_predict,"Market-implied forecast of trailing-twelve-month earnings before tax (EBT), with 1-day delay","{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,23,47,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_fcf_a_predict,Predict value of Free Cash Flow,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,50,254,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_fcf_q_predict,Predict value of Free Cash Flow,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,43,212,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_fcf_ttm_predict,Predict value of Free Cash Flow,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,51,241,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ni_a_predict,"Market-implied forecast of annual Net Income, as of D0","{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,35,208,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ni_q_predict,"Market-implied forecast of quarterly Net Income, as of D0","{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,33,103,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_ni_ttm_predict,"Market-implied forecast of Net Income on a trailing-twelve-month (TTM) basis, as of D0","{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,50,290,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_rev_a_predict,Predict value of Revenue - Total,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,14,45,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_rev_q_predict,Predict value of Revenue - Total,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,29,113,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_rev_ttm_predict,"Market‑implied predicted Total Revenue on a trailing‑twelve‑month basis (as of close, D0)","{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,42,236,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_totd_a_predict,Market implied point in time prediction of annual Total Debt with a 1 trading day delay,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,37,108,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +mdl262_totd_q_predict,Market implied point in time prediction of quarterly Total Debt with a 1 trading day delay,"{'id': 'model262', 'name': 'DNN prediction of fundamentals'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.9893,43,255,1.3,[],model262,DNN prediction of fundamentals,model,Model,model-estimates-models,Estimates Models +annualized_asset_drift_pct,Annualized rate of change in asset value used in default modeling.,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9696,52,72,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +annualized_asset_volatility_pct,Annualized volatility of asset value used in default modeling.,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9689,14,15,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +asset_drift_rate_pct,Annualized rate of change in asset value used in default modeling (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9695,28,31,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +asset_volatility_annualized_pct,Annualized volatility of asset value used in default modeling (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9688,20,22,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +country_default_risk_percentile,Percentile rank of default likelihood within the same country.,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9687,16,21,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +country_default_risk_percentile_backfill,Percentile rank of default likelihood within the same country (backfill version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,13,18,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +country_default_risk_percentile_float,Percentile rank of default likelihood within the same country (float version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,6,7,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +country_default_risk_percentile_v1,Percentile rank of default likelihood within the same country (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,8,16,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +default_probability_one_year_pct,"Estimated probability of default over the next year, expressed as a percent (original version).","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,17,22,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_country_percentile_d1,Percentile rank of default likelihood within the same country (d1 version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,9,9,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_global_percentile_d1,Percentile rank of default likelihood compared to all companies worldwide (d1 version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,13,18,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_global_percentile_v1,Percentile rank of default likelihood compared to all companies worldwide (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,12,19,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_industry_percentile_d1,Percentile rank of default likelihood within the same industry group (d1 version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9684,4,5,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_region_percentile_d1,Percentile rank of default likelihood within the same geographic region (d1 version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,8,13,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_sector_percentile_d1,Percentile rank of default likelihood within the same economic sector (d1 version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,7,18,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +default_threshold_to_asset_ratio,Ratio comparing the default threshold to total asset value (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9808,33,39,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +distance_to_default_standard_deviation,Number of standard deviations between asset value and default threshold (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,12,44,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +distance_to_default_stddev,Number of standard deviations between asset value and default threshold.,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9687,54,280,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +global_default_risk_percentile,Percentile rank of default likelihood compared to all companies globally.,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9687,33,72,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +global_default_risk_percentile_backfill,Percentile rank of default likelihood compared to all companies globally (backfill version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,29,43,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +global_default_risk_percentile_v1,Percentile rank of default likelihood compared to all companies globally (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,13,20,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +industry_default_risk_percentile,Percentile rank of default likelihood within the same industry.,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9685,33,80,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +industry_default_risk_percentile_backfill,Percentile rank of default likelihood within the same industry (backfill version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.969,23,47,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +industry_default_risk_percentile_float,Percentile rank of default likelihood within the same industry (float version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9684,15,20,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +industry_default_risk_percentile_v1,Percentile rank of default likelihood within the same industry (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9684,15,26,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +leverage_ratio_to_assets,Ratio comparing the default threshold to total asset value.,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9811,11,12,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +model_based_letter_grade_backfill,Agency-style rating derived from model-estimated default probability (backfill version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,12,39,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +model_based_letter_grade_d1,Agency-style rating derived from model-estimated default probability (d1 version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,4,11,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +model_based_letter_grade_v1,Agency-style rating derived from model-estimated default probability (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,2,2,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +one_year_default_probability_pct,"Estimated probability of default over the next year, expressed as a percent.","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9687,19,24,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +region_default_risk_percentile,Percentile rank of default likelihood within the same region.,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9687,16,42,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +region_default_risk_percentile_backfill,Percentile rank of default likelihood within the same region (backfill version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,22,38,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +region_default_risk_percentile_float,Percentile rank of default likelihood within the same region (float version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,9,13,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +region_default_risk_percentile_v1,Percentile rank of default likelihood within the same region (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,5,8,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +sector_default_risk_percentile,Percentile rank of default likelihood within the same sector.,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9687,11,39,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +sector_default_risk_percentile_backfill,Percentile rank of default likelihood within the same sector (backfill version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,11,36,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +sector_default_risk_percentile_float,Percentile rank of default likelihood within the same sector (float version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,13,18,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +sector_default_risk_percentile_v1,Percentile rank of default likelihood within the same sector (original version).,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,6,13,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_asset_drift_pct,1-100 rank of the annualized rate of change of the market value of the firm’s assets,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9695,63,105,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_asset_drift_pct_bfl,"The current 1-100 percentile rank, among other firms in the same region and industry, of a company's 1.year default probability","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9701,51,72,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_asset_volatility_pct,annualized volatility of the market value of the firm’s assets,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9688,19,22,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_asset_volatility_pct_bfl,1-100 rank of the annualized rate of change of the market value of the firm’s assets,"{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9694,24,51,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_country_rank,"""The current 1-100 percentile rank, by country, of a company's 1.year default probability based on the Structural Component of the Thomson Reuters StarMine Credit Risk Model. Higher scores indicate companies that are less likely to go bankrupt, or default on their debt obligations, within the next 1.year period."",,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,42,60,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_country_rank_bfl,"""The current 1-100 percentile rank, by country, of a company's 1.year default probability based on the Structural Component of the Thomson Reuters StarMine Credit Risk Model. Higher scores indicate companies that are less likely to go bankrupt, or default on their debt obligations, within the next 1.year period."",,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,19,26,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_dist_to_default,"Number of standard deviations difference between the market value of the firm's assets and its estimated default point.,,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,24,52,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_dist_to_default_bfl,"Number of standard deviations difference between the market value of the firm's assets and its estimated default point.,,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,14,32,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_global_rank,"""The current global 1-100 percentile rank of a company's 1-year default probability based on the Structural Component of the Thomson Reuters StarMine Credit Risk Model. Higher scores indicate companies that are less likely to go bankrupt, or default on their debt obligations, within the next 1-year period."",,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,50,143,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_global_rank_bfl,"""The current global 1-100 percentile rank of a company's 1-year default probability based on the Structural Component of the Thomson Reuters StarMine Credit Risk Model. Higher scores indicate companies that are less likely to go bankrupt, or default on their debt obligations, within the next 1-year period."",,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,13,16,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_implied_rating,"Agency.equivalent credit rating implied by the current estimated forward 1.year SCR default probability.,,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9867,29,96,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_industry_rank,"""The current 1-100 percentile rank, among other firms in the same region and industry, of a company's 1.year default probability based on the Structural Component of the Thomson Reuters StarMine Credit Risk Model. Higher scores indicate companies that are less likely to go bankrupt, or default on their debt obligations, within the next 1.year period."",,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9684,18,23,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_industry_rank_bfl,"""The current 1-100 percentile rank, among other firms in the same region and industry, of a company's 1.year default probability based on the Structural Component of the Thomson Reuters StarMine Credit Risk Model. Higher scores indicate companies that are less likely to go bankrupt, or default on their debt obligations, within the next 1.year period."",,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.969,11,13,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_leverage,"The current 1-100 percentile rank, among other firms in the same region and industry, of a company's 1.year default probability","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9808,33,58,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_leverage_bfl,"the current 1-100 percentile rank, among other firms in the same region and industry, of a company's 1.year default probability b","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9815,25,45,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_pd_pct,"A percentage that indicates the probability,that the company will go bankrupt, or default on its debt obligations, +over the next 1-year period","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,17,20,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_pd_pct_bfl,"A percentage that indicates the probability,that the company will go bankrupt, or default on its debt obligations, +over the next 1-year period","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,8,8,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_region_rank,"""The current 1-100 percentile rank, among other firms in the same region, of a company's 1.year default probability based on the Structural Component of the Thomson Reuters StarMine Credit Risk Model. Higher scores indicate companies that are less likely to go bankrupt, or default on their debt obligations, within the next 1.year period."",,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,10,10,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_region_rank_bfl,"""The current 1-100 percentile rank, among other firms in the same region, of a company's 1.year default probability based on the Structural Component of the Thomson Reuters StarMine Credit Risk Model. Higher scores indicate companies that are less likely to go bankrupt, or default on their debt obligations, within the next 1.year period."",,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,18,20,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_sector_rank,"""The current 1-100 percentile rank, among other firms in the same region and industrial sector, of a company's 1.year default probability based on the Structural Component of the Thomson Reuters StarMine Credit Risk Model. Higher scores indicate companies that are less likely to go bankrupt, or default on their debt obligations, within the next 1.year period."",,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,17,17,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +star_cr_sector_rank_bfl,"""The current 1-100 percentile rank, among other firms in the same region and industrial sector, of a company's 1.year default probability based on the Structural Component of the Thomson Reuters StarMine Credit Risk Model. Higher scores indicate companies that are less likely to go bankrupt, or default on their debt obligations, within the next 1.year period."",,,","{'id': 'model28', 'name': 'Structural Credit Risk Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.9692,14,15,1.3,[],model28,Structural Credit Risk Model,model,Model,model-risk-based-models,Risk Based Models +analyst_count_quarter1_new,Number of analysts contributing to the weighted estimate for current fiscal quarter (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.877,36,48,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +analyst_count_quarter2_new,Number of analysts contributing to the weighted estimate for next fiscal quarter (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.877,31,48,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +analyst_count_year1_new,Number of analysts contributing to the weighted estimate for current fiscal year (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.877,33,46,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +analyst_count_year2_new,Number of analysts contributing to the weighted estimate for next fiscal year (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.877,32,37,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +entity_parent_indicator,Indicator showing whether the entity is a parent company.,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.88,6,8,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +entity_parent_indicator_new,Indicator showing whether the entity is a parent company (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.8803,4,4,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +forecast_surprise_percent_forward12m_new,Percent difference between weighted and consensus estimates for forward 12 months (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7628,8,10,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +forecast_surprise_percent_quarter1_new,Percent difference between weighted and consensus estimates for current quarter (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.5961,7,8,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +forecast_surprise_percent_quarter2_new,Percent difference between weighted and consensus estimates for next quarter (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.2931,2,2,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +forecast_surprise_percent_year1_new,Percent difference between weighted and consensus estimates for current fiscal year (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.767,11,11,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +forecast_surprise_percent_year2_new,Percent difference between weighted and consensus estimates for next fiscal year (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7647,9,9,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl29_parent_flag_d1,Parent Flag,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.88,18,107,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_analyst_number_fq1,FQ1 Number of SE Analysts,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.8767,31,47,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_analyst_number_fq1_d1,FQ1 Number of SE Analysts,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.8767,13,18,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_analyst_number_fq2,FQ2 Number of SE Analysts,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.8767,6,6,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_analyst_number_fq2_d1,FQ2 Number of SE Analysts,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.8767,16,18,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_analyst_number_fy1,FY1 Number of SE Analysts,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.8767,21,24,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_analyst_number_fy1_d1,FY1 Number of SE Analysts,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.8767,16,19,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_analyst_number_fy2,FY2 Number of SE Analysts,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.8767,15,15,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_analyst_number_fy2_d1,FY2 Number of SE Analysts,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.8767,13,13,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_smart_estimate_12m,F12M SmartEstimate,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7626,47,63,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_smart_estimate_12m_d1,F12M SmartEstimate,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7626,32,41,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_smart_estimate_fq1,FQ1 SmartEstimate,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.596,24,42,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_smart_estimate_fq1_d1,FQ1 SmartEstimate,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.596,24,27,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_smart_estimate_fq2,FQ2 SmartEstimate,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.293,9,9,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_smart_estimate_fq2_d1,FQ2 SmartEstimate,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.293,8,9,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_smart_estimate_fy1,FY1 SmartEstimate,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7667,24,53,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_smart_estimate_fy1_d1,FY1 SmartEstimate,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7667,17,22,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_smart_estimate_fy2,FY2 SmartEstimate,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7645,23,39,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_smart_estimate_fy2_d1,FY2 SmartEstimate,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7645,32,40,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_surprise_prediction_12m,Surprise between estimate & consensus for 12m look ahead period,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7626,21,31,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_surprise_prediction_12m_d1,Surprise between estimate & consensus for 12m look ahead period,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7626,8,13,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_surprise_prediction_fq1,Surprise between estimate & consensus for most recent quarter,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.596,10,28,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_surprise_prediction_fq1_d1,Surprise between estimate & consensus for most recent quarter,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.596,7,10,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_surprise_prediction_fq2,Surprise between estimate & consensus for next quarter,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.293,6,7,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_surprise_prediction_fq2_d1,Surprise between estimate & consensus for next quarter,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.293,5,5,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_surprise_prediction_fy1,Surprise between estimate & consensus for current fiscal year,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7667,16,21,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_surprise_prediction_fy1_d1,Surprise between estimate & consensus for current fiscal year,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7667,13,13,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_surprise_prediction_fy2,Surprise between estimate & consensus for next fiscal year,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7645,13,17,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +star_ebitda_surprise_prediction_fy2_d1,Surprise between estimate & consensus for next fiscal year,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7645,12,15,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +weighted_estimate_forward12m_new,Weighted financial estimate for the forward 12 months period (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7628,30,55,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +weighted_estimate_quarter1_new,Weighted financial estimate for the current fiscal quarter (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.5961,15,17,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +weighted_estimate_quarter2_new,Weighted financial estimate for the next fiscal quarter (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.2931,5,5,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +weighted_estimate_year1_new,Weighted financial estimate for the current fiscal year (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.767,13,29,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +weighted_estimate_year2_new,Weighted financial estimate for the next fiscal year (new data).,"{'id': 'model29', 'name': 'EBITDA Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,1.0,0.7647,20,39,1.3,[],model29,EBITDA Estimate Model,model,Model,model-estimates-models,Estimates Models +is_parent_company_flag,"Indicator whether the entity is a parent company (1 = parent, 0 = otherwise)","{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.88,11,11,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_numnly_fq1_eps,Number of analysts included in the StarMine SmartEstimate for EPS for Fiscal Quarter 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.877,42,75,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_numnly_fq2_eps,Number of analysts included in the StarMine SmartEstimate for EPS for Fiscal Quarter 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.877,31,59,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_numnly_fy1_eps,Number of analysts included in the StarMine SmartEstimate for EPS for Fiscal Year 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.877,39,152,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_numnly_fy2_eps,Number of analysts included in the StarMine SmartEstimate for EPS for Fiscal Year 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.877,27,37,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_parent_flag_d1,Indicator if the entity is a parent company where 1 indicates parent and 0 otherwise,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.88,12,25,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_psprise_pct_f12m_eps,Predicted percent surprise in EPS for forward 12 months (SmartEstimate versus consensus mean),"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8486,51,87,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_psprise_pct_fq1_eps,Predicted percent surprise in EPS for Fiscal Quarter 1 (SmartEstimate versus consensus mean),"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.5828,22,28,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_psprise_pct_fq2_eps,Predicted percent surprise in EPS for Fiscal Quarter 2 (SmartEstimate versus consensus mean),"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.3602,22,27,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_psprise_pct_fy1_eps,Predicted percent surprise in EPS for Fiscal Year 1 (SmartEstimate versus consensus mean),"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8513,86,137,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_psprise_pct_fy2_eps,Predicted percent surprise in EPS for Fiscal Year 2 (SmartEstimate versus consensus mean),"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8507,30,49,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_smartestimate_f12m_eps,StarMine SmartEstimate (analyst-weighted) EPS forecast for Forward 12 Months,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8486,52,91,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_smartestimate_fq1_eps,StarMine SmartEstimate (analyst-weighted) EPS forecast for Fiscal Quarter 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.5828,35,55,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_smartestimate_fq2_eps,StarMine SmartEstimate (analyst-weighted) EPS forecast for Fiscal Quarter 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.3602,19,23,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_smartestimate_fy1_eps,StarMine SmartEstimate (analyst-weighted) EPS forecast for Fiscal Year 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8513,127,218,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl30_smartestimate_fy2_eps,StarMine SmartEstimate (analyst-weighted) EPS forecast for Fiscal Year 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8507,86,129,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +parent_company_indicator,"Indicator if the entity is a parent company (1 parent, 0 otherwise)","{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8803,18,29,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_analyst_number_fq1_d1,Number of analysts included in the SmartEstimate for EPS for fiscal quarter 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8767,25,30,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_analyst_number_fq2_d1,Number of analysts included in the SmartEstimate for EPS for fiscal quarter 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8767,13,15,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_analyst_number_fy1_d1,Number of analysts included in the SmartEstimate for EPS for fiscal year 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8767,15,35,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_analyst_number_fy2_d1,Number of analysts included in the SmartEstimate for EPS for fiscal year 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8767,8,9,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_smart_estimate_12m_d1,StarMine SmartEstimate of EPS for the forward 12 months,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8483,60,99,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_smart_estimate_fq1_d1,StarMine SmartEstimate of EPS for fiscal quarter 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.5826,17,22,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_smart_estimate_fq2_d1,StarMine SmartEstimate of EPS for fiscal quarter 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.3601,14,16,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_smart_estimate_fy1_d1,StarMine SmartEstimate of EPS for fiscal year 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.851,47,56,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_smart_estimate_fy2_d1,StarMine SmartEstimate of EPS for fiscal year 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8504,70,97,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_surprise_prediction_12m_d1,Percentage difference between the SmartEstimate and the consensus mean for EPS for the forward 12 months,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8483,39,44,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_surprise_prediction_fq1_d1,Percentage difference between the SmartEstimate and the consensus mean for EPS for fiscal quarter 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.5826,7,9,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_surprise_prediction_fq2_d1,Percentage difference between the SmartEstimate and the consensus mean for EPS for fiscal quarter 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.3601,7,8,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_surprise_prediction_fy1_d1,Percentage difference between the SmartEstimate and the consensus mean for EPS for fiscal year 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.851,20,25,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_eps_surprise_prediction_fy2_d1,Percentage difference between the SmartEstimate and the consensus mean for EPS for fiscal year 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8504,22,43,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_analyst_number_fq1,Number of analysts included in the StarMine SmartEstimate for EPS for Fiscal Quarter 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8767,14,17,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_analyst_number_fq2,Number of analysts included in the StarMine SmartEstimate for EPS for Fiscal Quarter 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8767,11,23,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_analyst_number_fy1,Number of analysts included in the StarMine SmartEstimate for EPS for Fiscal Year 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8767,12,17,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_analyst_number_fy2,Number of analysts included in the StarMine SmartEstimate for EPS for Fiscal Year 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8767,16,21,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_smart_estimate_12m,StarMine SmartEstimate (analyst-weighted) EPS forecast for the forward 12 months,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8483,55,93,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_smart_estimate_fq1,StarMine SmartEstimate (analyst-weighted) EPS forecast for Fiscal Quarter 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.5826,20,22,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_smart_estimate_fq2,StarMine SmartEstimate (analyst-weighted) EPS forecast for Fiscal Quarter 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.3601,13,19,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_smart_estimate_fy1,StarMine SmartEstimate (analyst-weighted) EPS forecast for Fiscal Year 1,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.851,43,56,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_smart_estimate_fy2,StarMine SmartEstimate (analyst-weighted) EPS forecast for Fiscal Year 2,"{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8504,60,94,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_surprise_prediction_12m,"Predicted surprise percentage for EPS over the forward 12 months, as the deviation of SmartEstimate from the consensus mean","{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8483,28,38,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_surprise_prediction_fq1,"Predicted surprise percentage for EPS for Fiscal Quarter 1, as the deviation of SmartEstimate from the consensus mean","{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.5826,11,14,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_surprise_prediction_fq2,"Predicted surprise percentage for EPS for Fiscal Quarter 2, as the deviation of SmartEstimate from the consensus mean","{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.3601,6,6,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_surprise_prediction_fy1,"Predicted surprise percentage for EPS for Fiscal Year 1, as the deviation of SmartEstimate from the consensus mean","{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.851,16,26,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +star_new_eps_surprise_prediction_fy2,"Predicted surprise percentage for EPS for Fiscal Year 2, as the deviation of SmartEstimate from the consensus mean","{'id': 'model30', 'name': 'EPS Estimate Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.8504,18,23,1.3,[],model30,EPS Estimate Model,model,Model,model-estimates-models,Estimates Models +mdl313_iai,"Intangible Asset Intensity Score (composite of KCI and OCI), normalized [0,1]","{'id': 'model313', 'name': 'Intangible Asset Factors'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.6749,31,40,1.3,[],model313,Intangible Asset Factors,model,Model,model-estimates-models,Estimates Models +mdl313_ico,"Organizational Capital Intensity Score, calculated from capitalized SG&A, normalized [0,1]","{'id': 'model313', 'name': 'Intangible Asset Factors'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.6749,17,22,1.3,[],model313,Intangible Asset Factors,model,Model,model-estimates-models,Estimates Models +mdl313_mktcap_usd,Market capitalization of the company in US dollars as of the observation date,"{'id': 'model313', 'name': 'Intangible Asset Factors'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-estimates-models', 'name': 'Estimates Models'}",IND,1,TOP500,MATRIX,0.9507,0.6749,14,14,1.3,[],model313,Intangible Asset Factors,model,Model,model-estimates-models,Estimates Models +backfill_global_momentum_rank,Global peer-relative price momentum percentile rank (integer 1–100); higher = stronger momentum vs global peers,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9568,72,184,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_global_momentum_rank_float,Floating-point percentile (0–100) of global price momentum rank; higher = stronger momentum vs global peers,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9568,49,129,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_industry_momentum_score,Industry-relative price momentum percentile rank (integer 1–100) comparing the stock’s momentum to its TRBC industry peers; higher = stronger momentum vs industry,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9833,33,55,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_industry_momentum_score_float,Floating-point percentile (0–100) of industry-relative price momentum vs TRBC industry peers; higher = stronger industry-relative momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9833,31,40,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_long_term_momentum_score,Long-term (about 12 months) price momentum component as a percentile rank (integer 1–100); higher = stronger long-term momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.957,20,35,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_long_term_momentum_score_float,Floating-point percentile (0–100) for long-term (about 12 months) price momentum; higher = stronger long-term momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.957,20,35,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_mid_term_momentum_score,Mid-term (about 3–6 months) price momentum component as a percentile rank (integer 1–100); higher = stronger medium-term momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9831,23,33,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_mid_term_momentum_score_float,Floating-point percentile (0–100) for mid-term (about 3–6 months) price momentum; higher = stronger medium-term momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9831,16,27,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_regional_momentum_rank,Regional (Asia) peer-relative price momentum percentile rank (integer 1–100); higher = stronger momentum vs regional peers,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9568,43,75,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_regional_momentum_rank_float,Floating-point percentile (0–100) of regional (Asia) price momentum rank; higher = stronger momentum vs regional peers,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9568,20,26,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_short_term_momentum_score,"Short-term (about 1 week) price momentum component as a percentile rank (integer 1–100), adjusted for volatility/autocorrelation; higher = stronger short-term momentum","{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9834,70,215,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +backfill_short_term_momentum_score_float,Floating-point percentile (0–100) for short-term (about 1 week) price momentum; higher = stronger short-term momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9834,96,348,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +d1_global_momentum_rank_float,Float percentile value 0–100 ranking the stock’s price momentum versus global peers; higher indicates stronger momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9561,38,64,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +d1_industry_momentum_score_float,Float percentile (0–100) of industry-relative price momentum within the stock’s TRBC industry,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9827,26,33,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +d1_long_term_momentum_score_float,Float percentile (0–100) of long-term (~12 months) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9564,21,36,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +d1_mid_term_momentum_score_float,Float percentile (0–100) of mid-term (~3–6 months) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,21,26,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +d1_regional_momentum_rank_float,Float percentile value 0–100 ranking the stock’s price momentum versus regional peers in Asia; higher indicates stronger momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9561,23,35,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +d1_short_term_momentum_score_float,Float percentile (0–100) of short-term (~1 week) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,72,169,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +global_momentum_rank_float,Floating-point percentile value (0–100) of overall price momentum versus all global stocks,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9561,50,91,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +industry_momentum_score_float,Floating-point percentile value (0–100) of price momentum relative to TRBC industry peers,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9827,40,74,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +long_horizon_momentum_score_float,Floating-point percentile value (0–100) for long-term (~12 months) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9564,20,24,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +mid_horizon_momentum_score_float,Floating-point percentile value (0–100) for mid-term (~3–6 months) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,16,20,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +regional_momentum_rank_float,Float percentile value (0–100) of the stock’s price momentum relative to peers in the USA region; higher values indicate stronger momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9561,22,31,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +short_horizon_momentum_score_float,Floating-point percentile value (0–100) for short-term (~1 week) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,186,1154,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_global_rank,Percentile rank of overall price momentum versus all global stocks,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9561,35,44,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_global_rank_d1,Integer percentile rank 1–100 of the stock’s price momentum versus global peers; higher indicates stronger momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9561,29,69,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_industry,Percentile rank of price momentum relative to TRBC industry peers (industry-specific momentum),"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9827,19,22,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_industry_d1,Integer percentile rank (1–100) of industry-relative price momentum within the stock’s TRBC industry,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9827,19,41,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_longterm,Percentile rank of long-term (~12 months) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9564,20,22,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_longterm_d1,Integer percentile rank (1–100) of long-term (~12 months) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9564,15,18,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_midterm,Percentile rank of mid-term (~3–6 months) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,22,24,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_midterm_d1,Integer percentile rank (1–100) of mid-term (~3–6 months) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,17,19,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_region_rank,Integer percentile rank (1–100) of the stock’s price momentum relative to peers in the USA region; higher values indicate stronger momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9561,28,46,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_region_rank_d1,Integer percentile rank 1–100 of the stock’s price momentum versus regional peers in Asia; higher indicates stronger momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9561,29,70,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_shortterm,Percentile rank of short-term (~1 week) price momentum; higher values indicate stronger recent momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,74,127,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +star_pm_shortterm_d1,Integer percentile rank (1–100) of short-term (~1 week) price momentum,"{'id': 'model32', 'name': 'Price Momentum Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-nlp-models', 'name': 'NLP Models'}",IND,1,TOP500,MATRIX,0.9507,0.9828,55,75,1.3,[],model32,Price Momentum Model,model,Model,model-nlp-models,NLP Models +credit_risk_coverage_score_d1,"Numeric score for coverage factors (e.g., ability to cover interest/debt); higher indicates better coverage","{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9789,41,54,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +credit_risk_coverage_score_main,Float percentile score (0–100) for the coverage component; higher indicates better coverage and lower credit risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9789,18,29,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +credit_risk_growth_score_d1,Numeric score for growth-related factors affecting credit health; higher indicates stronger growth profile,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9757,20,25,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +credit_risk_growth_score_main,Float percentile score (0–100) for the growth and stability component; higher indicates better growth/stability and lower credit risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9757,11,14,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +credit_risk_leverage_score_d1,Percentile score reflecting leverage factors in credit risk assessment (daily version).,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,15,19,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +credit_risk_leverage_score_main,Float percentile score (0–100) for the leverage component; higher indicates better leverage profile and lower credit risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,11,14,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +credit_risk_liquidity_score_d1,Numeric score for liquidity-related factors; higher indicates stronger liquidity,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9801,9,10,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +credit_risk_liquidity_score_main,Float percentile score (0–100) for the liquidity component; higher indicates stronger liquidity and lower credit risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9801,10,11,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +credit_risk_profitability_score_d1,Numeric score for profitability-related factors; higher indicates better profitability,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,17,19,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +credit_risk_profitability_score_main,Float percentile score (0–100) for the profitability component; higher indicates stronger profitability and lower credit risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,20,21,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +creditworthiness_letter_rating_numeric_d1,Continuous implied rating score aligned to the letter-grade scale (not a PD%); higher indicates stronger credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,1,1,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +creditworthiness_letter_rating_numeric_main,Continuous numeric implied rating score aligned to the letter-grade scale (not a PD%); higher is safer,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,4,5,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_country_percentile_d1_2,Float percentile rank (0–100) of the company’s financial health within its country peer group; higher indicates better credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,21,31,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_country_percentile_main,Float 0–100 percentile rank of the company’s 1-year default probability within its country; higher values indicate lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,18,28,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_global_percentile_d1_2,Float percentile rank (0–100) of financial health globally; higher indicates better credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,20,30,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_global_percentile_main,Float 0–100 global percentile rank of the company’s 1-year default probability; higher values indicate lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,18,32,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_industry_percentile_d1_2,Float percentile rank (0–100) within the company’s industry; higher indicates better credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9806,11,15,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_industry_percentile_main,Float 0–100 percentile rank of the company’s 1-year default probability within its region and industry; higher values indicate lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9806,15,21,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_region_percentile_d1_2,Float percentile rank (0–100) of financial health within the region; higher indicates better credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,30,42,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_region_percentile_main,Float 0–100 regional percentile rank of the company’s 1-year default probability; higher values indicate lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,26,38,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_sector_percentile_d1_2,Float percentile rank (0–100) within the company’s sector; higher indicates better credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9808,11,19,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +default_risk_sector_percentile_main,Float percentile rank (0–100) within the same region and sector by 1-year default probability; higher means safer,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9808,8,19,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_country_rank,Current 1–100 percentile rank by country of the company’s 1-year default probability; higher values indicate lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,42,75,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_country_rank_d1,Percentile rank (1–100) within the company’s country of its 1‑year default probability; higher means lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,22,24,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_coverage,"Percentile rank reflecting only the coverage factors of the model; 100 best, 1 worst","{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9789,21,45,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_coverage_d1,Percentile rank (1–100) reflecting only the coverage factors; higher indicates better credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9789,25,31,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_global_rank,Current global 1–100 percentile rank of the company’s 1-year default probability based on SmartRatios; higher values indicate lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,32,52,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_global_rank_d1,Global percentile rank (1–100) of the company’s 1‑year default probability from SmartRatios; higher means lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,39,92,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_growth,"Percentile rank reflecting only the growth and stability factors of the model; 100 best, 1 worst","{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9757,28,37,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_growth_d1,Percentile rank (1–100) reflecting only the growth and stability factors; higher indicates better credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9757,24,30,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_industr_rank,Current 1–100 percentile rank of the company’s 1-year default probability among firms in the same region and industry; higher values indicate lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9806,19,20,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_industr_rank_d1,Percentile rank (1–100) within the same region and industry of the company’s 1‑year default probability; higher means lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9806,18,21,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_leverage,"Percentile rank reflecting only the leverage factors of the model; 100 best, 1 worst","{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,25,34,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_leverage_d1,Percentile rank (1–100) reflecting only the leverage factors; higher indicates better credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,13,15,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_liquidity,"Percentile rank reflecting only the liquidity factors of the model; 100 best, 1 worst","{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9801,13,19,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_liquidity_d1,Percentile rank (1–100) reflecting only the liquidity factors; higher indicates better credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9801,12,13,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_profitability,"Percentile rank reflecting only the profitability factors of the model; 100 best, 1 worst","{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,30,52,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_profitability_d1,Percentile rank (1–100) reflecting only the profitability factors in the SmartRatios Credit Risk Model; higher indicates better credit quality,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,27,34,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_region_rank,Current regional 1–100 percentile rank of the company’s 1-year default probability; higher values indicate lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,17,40,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_region_rank_d1,Regional percentile rank (1–100) of the company’s 1‑year default probability; higher means lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9809,13,13,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_sector_rank,Current 1–100 percentile rank of the company’s 1-year default probability among firms in the same region and sector; higher values indicate lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9808,33,60,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +star_sr_sector_rank_d1,Percentile rank (1–100) within the same region and sector of the company’s 1‑year default probability; higher means lower default risk,"{'id': 'model36', 'name': 'SmartRatios Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.9507,0.9808,21,28,1.3,[],model36,SmartRatios Model,model,Model,model-risk-based-models,Risk Based Models +buyback_yield_relative_score,Percentile rank (1-100) of the stock’s valuation attractiveness based solely on its buyback yield,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9642,63,100,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +buyback_yield_relative_score_float,Percentile rank (floating-point) of buyback yield’s contribution to relative valuation within peer group,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9642,44,71,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +dividend_yield_relative_score,Global percentile rank (integer 1-100) of the stock for Dividend Yield component of the Relative Valuation model — higher value = better value on this measure,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8273,179,502,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +dividend_yield_relative_score_float,Percentile rank (floating-point) for dividend yield’s contribution to relative valuation within peer group,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8273,106,160,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +earnings_measure_type_float,"Indicator (floating-point) specifying the type of earnings measure used (e.g., EPS, FFO) in the intrinsic valuation","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,22,25,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +ev_ebitda_relative_score_float,Floating-point normalized score showing the contribution of the EV/EBITDA ratio to the relative valuation component,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8201,122,374,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +ev_ebitda_relative_score_main,Global percentile rank (integer 1-100) of the stock for the EV/EBITDA component in the Relative Valuation model — higher value means better value on this measure,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8201,88,142,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +ev_sales_relative_score,Percentile rank (1-100) of the stock’s valuation attractiveness based solely on its EV/Sales ratio,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.7944,91,153,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +ev_sales_relative_score_float,"Percentile rank (floating-point, global) for valuation based on EV/Sales component in the Relative Valuation Model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.7944,84,139,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +forward_10yr_eps_growth_rate,"Compound annual growth rate of EPS from last reported EPS to fiscal year 10, projected by StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.841,44,62,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +forward_5yr_eps_growth_rate,"Compound annual growth rate of EPS from last reported EPS to fiscal year 5, projected by StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.841,81,247,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +global_relative_valuation_rank_main,"Current global 1–100 percentile rank of the security’s valuation attractiveness in the Relative Valuation Model, combining all measures, with higher scores indicating better value","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,103,272,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +global_relative_valuation_score,"Current global percentile rank (floating-point) in the StarMine Relative Valuation Model, reflecting overall valuation attractiveness compared to all global peers","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,161,510,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +industry_rank_price_intrinsic_value,"A 1-100 percentile score ranking the stock’s price/intrinsic value ratio among stocks in its industry within the same region, higher scores indicating greater undervaluation","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.7803,64,94,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +industry_rank_price_to_intrinsic_value,Percentile rank (floating-point) of a stock’s price-to-intrinsic value ratio within its industry; higher scores suggest more undervaluation,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.7803,42,61,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +industry_relative_valuation_rank_main,"Industry percentile rank (1-100) of the stock's valuation attractiveness within its industry, higher is better","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9818,76,144,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +industry_relative_valuation_score,Floating-point percentile rank of the stock in the relative valuation model within its industry; higher scores signal more attractive valuation,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9818,78,121,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +intrinsic_value_in_currency,"Intrinsic value of the security (fair value estimate) calculated by StarMine, expressed in the chosen projection currency","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,32,42,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +justified_forward_pe_ratio_2,"Forward 12-month P/E ratio warranted by the intrinsic valuation model, calculated as intrinsic value divided by year-ahead EPS projection","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.841,88,272,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +long_term_assumed_annual_dividends,Year in which the company is projected to reach steady state dividend payout,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,28,43,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +long_term_assumed_annual_earnings_main,Year in which the company is projected to reach steady state earnings growth,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,26,38,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +long_term_dividend_payout_ratio_2,Dividend payout rate the company is projected to maintain at steady state in future years,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,14,25,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +long_term_earnings_growth_rate_2,Earnings growth rate the company is projected to maintain at steady state in future years,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,28,50,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +market_implied_10yr_eps_growth,Market-implied compound annual growth rate of earnings per share over the next 10 years based on current market price,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8384,72,93,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +market_implied_5yr_eps_growth,Forward 5-year compound annual growth rate (CAGR) of EPS implied by the market price (adjusted EPS over FY1-FY5 so the calculated intrinsic value matches current market price),"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8384,52,82,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +model_discount_rate,Discount rate used by the StarMine Intrinsic Valuation model when discounting future cash flows (primarily dividends),"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,17,22,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +pb_ratio_relative_score,Contribution of price-to-book ratio to the overall relative valuation attractiveness score,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.983,56,97,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +pb_ratio_relative_score_float,Floating-point percentile rank for the Price/Book ratio component in the relative valuation model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.983,56,105,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +pcf_ratio_relative_score,Percentile rank (1-100) of the stock’s valuation attractiveness based solely on its price-to-cash-flow (P/CF) ratio,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.7932,50,66,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +pcf_ratio_relative_score_float,"Percentile score (floating-point) for Price/Cash Flow ratio, measuring valuation attractiveness globally in StarMine’s model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.7932,38,53,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +pe_ratio_relative_score_float,"Percentile score (floating-point) for Price/Earnings ratio, measuring valuation attractiveness globally in StarMine’s model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.983,158,418,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +pe_ratio_relative_score_main,Percentile rank (1-100) of the stock’s valuation attractiveness based solely on its price-to-earnings (P/E) ratio,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.983,183,484,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +price_to_intrinsic_value_ratio,Ratio of current market price to model-estimated intrinsic value; values below 1 indicate possible undervaluation,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8396,129,299,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy10_2,"Estimate of dividends per share (DPS) for fiscal year 10, projected by StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,22,35,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy11_2,Estimated dividends per share (DPS) for fiscal year 11 according to the StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,21,33,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy12_2,Dividend per share estimate for fiscal year 12 from the StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,22,25,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy13_2,Dividend per share estimate for fiscal year 13 from the StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,21,34,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy14_2,"Estimate of dividends per share (DPS) for fiscal year 14, projected by StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,27,50,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy15_2,Estimated dividends per share (DPS) for fiscal year 15 projected by the StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,17,22,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy1_2,"Estimate of dividends per share (DPS) for fiscal year 1, projected by StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9857,77,113,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy2_2,"Estimate of dividends per share (DPS) for fiscal year 2, projected by StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,23,44,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy3_2,"Estimate of dividends per share (DPS) for fiscal year 3, projected by StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,39,55,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy4_2,StarMine SmartGrowth model’s estimated dividends per share (DPS) for fiscal year 4 for the security,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,22,32,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy5_2,"Estimate of dividends per share (DPS) for fiscal year 5, projected by StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,24,34,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy6_2,Estimated dividends per share for fiscal year 6 based on the intrinsic valuation model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,40,60,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy7_2,Dividend per share estimate for fiscal year 7 from the StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,40,52,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy8_2,"Estimate of dividends per share (DPS) for fiscal year 8, projected by StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,34,57,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_dividends_fy9_2,Dividend per share estimate for fiscal year 9 from the StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,26,36,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy10_2,StarMine SmartGrowth model’s estimated earnings per share (EPS) for fiscal year 10 for the security,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,15,27,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy11_2,"Projected EPS estimate for fiscal year 11 for the company, based on the StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,25,35,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy12_2,Fiscal year 12 EPS estimate from the StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,14,23,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy13_2,StarMine SmartGrowth model’s estimated earnings per share (EPS) for fiscal year 13 for the security,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,20,43,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy14_2,Projected earnings per share (EPS) for fiscal year 14 from StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,21,38,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy15_2,StarMine SmartGrowth model’s estimated earnings per share (EPS) for fiscal year 15 for the security,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,23,42,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy1_2,StarMine SmartGrowth model’s estimated earnings per share (EPS) for fiscal year 1 for the security,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,72,117,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy2_2,StarMine SmartGrowth model’s estimated earnings per share (EPS) for fiscal year 2 for the security,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,31,55,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy3_2,StarMine SmartGrowth model’s estimated earnings per share (EPS) for fiscal year 3 for the security,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,31,45,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy4_2,Projected earnings per share (EPS) for fiscal year 4 from StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,27,41,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy5_2,StarMine SmartGrowth model’s estimated earnings per share (EPS) for fiscal year 5 for the security,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,29,46,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy6_2,"Projected EPS estimate for fiscal year 6 for the company, based on the StarMine SmartGrowth model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,32,58,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy7_2,Estimated EPS for fiscal year 7 projected by the StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,17,39,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy8_2,"Projected adjusted earnings for fiscal year 8, based on analyst estimates and StarMine modeling","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,22,35,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +projected_earnings_fy9_2,Estimated EPS for fiscal year 9 projected by the StarMine SmartGrowth model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8411,16,23,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +region_rank_price_intrinsic_value,"A 1-100 percentile score ranking the stock’s price/intrinsic value ratio relative to all stocks in its region, where a lower ratio (cheaper stock) results in a higher rank","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8396,63,123,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +region_rank_price_to_intrinsic_value_float,Floating-point percentile rank of price/intrinsic value within the region,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8396,74,136,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +region_relative_valuation_score,"Percentile rank (1-100) for valuation attractiveness within the given region, reflecting how the stock compares to peers according to the Relative Valuation Model","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,74,171,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +region_relative_valuation_score_float,Floating-point regional percentile rank of the stock in the Relative Valuation Model,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9825,88,228,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +sector_rank_price_intrinsic_value,"A 1-100 percentile score ranking the stock’s price/intrinsic value ratio relative to all stocks in its sector within the same region, higher scores indicating greater undervaluation","{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8394,87,316,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +sector_rank_price_to_intrinsic_value_float,Floating-point version of the sector-relative percentile ranking for price/intrinsic value,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.8394,108,387,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +sector_relative_valuation_rank_main,Current 1–100 percentile rank of the stock in its sector within the overall Relative Valuation Model; higher scores mean the stock is viewed as better value in its sector,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9822,112,368,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +sector_relative_valuation_score_float,Floating-point version of the sector-relative percentile rank for valuation attractiveness,"{'id': 'model38', 'name': 'Growth Valuation Model'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9822,186,644,1.3,[],model38,Growth Valuation Model,model,Model,model-valuation-models,Valuation Models +global_price_momentum_percentile_2,The current global 1-100 percentile rank of the security based on the Price Momentum Model,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9568,310,1455,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +global_value_momentum_rank,The current global 1-100 percentile rank of the security based on the Value-Momentum Model,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.976,861,8858,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +global_value_momentum_rank_float,Float (non-integer) value of the global 1-100 percentile rank for the Value-Momentum Model,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.976,709,6506,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +industry_price_momentum_score_2,A percentile rank reflecting only the industry aspect of the Price Momentum Model; scores of 100 indicate the highest rated stocks and 1 the lowest,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9833,257,1239,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +industry_value_momentum_rank,The current industry 1-100 percentile rank of the security based on the Value-Momentum Model,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9756,703,5405,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +industry_value_momentum_rank_float,Float (non-integer) value of the industry 1-100 percentile rank for the Value-Momentum Model,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9756,803,8105,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +long_term_price_momentum_score_2,A percentile rank reflecting only the long term aspect of the Price Momentum Model; scores of 100 indicate the highest rated stocks and 1 the lowest,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.957,255,1305,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +mid_term_price_momentum_score_2,A percentile rank reflecting only the mid term aspect of the Price Momentum Model; scores of 100 indicate the highest rated stocks and 1 the lowest,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9831,216,1180,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +region_value_momentum_rank,The current regional 1-100 percentile rank of the security based on the Value-Momentum Model,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.976,544,3800,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +region_value_momentum_rank_float,Float (non-integer) value of the regional 1-100 percentile rank for the Value-Momentum Model,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.976,560,6032,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +regional_price_momentum_percentile_2,The current regional 1-100 percentile rank of the security based on the Price Momentum Model,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9568,255,1223,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +sector_value_momentum_rank,The current sector 1-100 percentile rank of the security based on the Value-Momentum Model,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.976,1092,16263,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +sector_value_momentum_rank_float,Float (non-integer) value of the sector 1-100 percentile rank for the Value-Momentum Model,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.976,924,11932,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +short_term_price_momentum_score_2,A percentile rank reflecting only the short term aspect of the Price Momentum Model; scores of 100 indicate the highest rated stocks and 1 the lowest,"{'id': 'model39', 'name': 'Valuation Momentum Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-valuation-models', 'name': 'Valuation Models'}",IND,1,TOP500,MATRIX,0.9507,0.9834,555,4543,1.3,[],model39,Valuation Momentum Data,model,Model,model-valuation-models,Valuation Models +aggregate_downside_risk_score_2,Composite downside-risk score indicating likelihood of underperforming over the next 3–24 months,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6481,23,28,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +analyst_count_fy1_forecasts,Number of analysts providing fiscal year 1 earnings forecasts,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6674,21,24,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +analyst_forecast_revision_score_3,Indicator of analyst estimate revisions momentum,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6054,13,14,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +avg_daily_volume,Average daily trading volume in shares,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6956,18,38,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +beta_relative_to_country,Market beta relative to the stock’s country benchmark,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6956,7,9,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +beta_vs_global_ex_us,Stock’s beta relative to the MSCI ACWI ex-US index,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6956,11,16,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +capital_investment_score,"Capital investment metric (e.g., investment rate or capex intensity)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6435,19,25,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +closing_share_price,Current share price,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6956,11,14,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_analyst_forecast_revision_score,Country-normalized indicator of analyst estimate revisions momentum for the stock,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6054,8,8,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_capital_investment_score,Country-normalized capital investment measure for the company,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6435,8,8,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_earnings_quality_score,Country-normalized earnings quality measure,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6456,6,7,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_earnings_valuation_score,Country-normalized earnings-based valuation measure,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6103,12,12,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_external_financing_score,Country-normalized external financing indicator,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6481,8,8,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_financial_leverage_score,Leverage ratio normalized within country,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6481,7,9,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_forecast_dispersion_score,Country-normalized measure of analyst estimate uncertainty/dispersion,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6273,9,9,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_free_cash_flow_yield_score,Country-normalized free cash flow valuation metric,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6436,8,8,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_fundamental_growth_score,"Fundamental growth measure (e.g., EPS or dividend growth) normalized within country (country-neutralized)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.5233,7,7,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_fundamental_stability_score,Country-normalized fundamental stability indicator,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.4882,3,3,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_operating_income_yield_score,Country-normalized enterprise value-based valuation metric,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6488,18,32,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_price_momentum_score,Country-normalized stock price momentum measure,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6071,9,9,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_price_volatility_score,Country-normalized rank of the stock’s realized price volatility,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6498,6,6,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_profitability_consistency_score,Profitability consistency measure normalized within country,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.339,6,6,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_profitability_level_score,Profitability measure normalized within country,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6453,7,7,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +country_shareholder_yield_score,Country-normalized shareholder yield (dividends and buybacks minus issuance),"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6413,6,6,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +debt_equity_ratio_2,Debt-to-equity ratio,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6949,9,9,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +dividend_yield_forecast,Forward dividend yield provided as a continuous float,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6956,9,17,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +earnings_quality_score,"Earnings quality indicator (e.g., accruals/quality diagnostics)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6456,13,19,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +earnings_valuation_score,"Earnings-based valuation factor (e.g., earnings yield), indicating how cheap or expensive the stock is","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6103,9,10,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +external_financing_score,External financing indicator measuring reliance on net equity or debt issuance,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6481,8,8,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +financial_leverage_score_2,"Financial leverage measure (e.g., debt burden)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6481,9,11,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +five_year_eps_growth_forecast,Forecasted EPS growth over the next five years for the company,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.4093,6,6,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +forecast_dispersion_score,Measure of dispersion or uncertainty in analysts’ estimates for the stock,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6273,6,6,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +free_cash_flow_yield_score,"Free cash flow valuation metric (e.g., FCF yield)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6436,14,17,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +fundamental_growth_score_3,"Fundamental growth metric (e.g., EPS or dividend growth)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.5233,9,11,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +fundamental_stability_score,"Fundamental stability measure (variability of revenues, earnings, or cash flows)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.4882,3,3,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +latest_quarterly_eps_surprise,Most recent quarterly EPS surprise,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.4893,12,14,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +mdl50_bk_adr_ticker,American Depositary Receipt ticker symbol for the stock,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6532,20,21,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +mdl50_bk_debt_to_equity,Debt-to-equity ratio,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6975,17,20,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +mdl50_bk_eps_growth_f_5year,Analyst forecast for EPS growth over the next five years,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.4128,11,23,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +mdl50_bk_exchange_country,Country of the exchange where the security is listed,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6532,9,48,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +mdl50_bk_msci_eafe_member,"Flag indicating MSCI EAFE index membership (1=member, 0=non-member)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6982,11,14,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +mdl50_bk_recent_eps_sur,Most recent quarterly EPS surprise versus consensus (reported minus expected),"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.4925,31,39,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +operating_income_yield_score,"Enterprise value–based valuation metric (e.g., EV ratios)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6488,17,23,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +price_momentum_score,Stock price momentum score (range typically -5 to 5),"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6071,17,20,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +price_volatility_metric,Continuous measure of the stock’s realized price volatility,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6956,8,9,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +price_volatility_score,Ranked measure of the stock’s realized price volatility,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6498,14,14,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +profitability_consistency_score,Measure of profitability consistency/persistence across time,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.339,2,2,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +profitability_level_score,"Composite profitability metric (e.g., margins or return measures) used in the risk model","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6453,11,15,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +risk_decile_country_rank,"Decile rank of the downside-risk score within the country (1=lowest, 10=highest)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6481,19,21,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +risk_decile_sector_rank,"Decile rank of the downside-risk score within the company’s sector (1=lowest, 10=highest)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6481,13,23,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +risk_decile_universe_rank,"Decile rank of overall downside risk across all covered stocks, 1=lowest and 10=highest risk","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6481,15,19,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +risk_score_change_13w,Change in the downside-risk score over the past 13 weeks,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6696,28,51,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +risk_score_change_1w,Change in the downside-risk score over the past 1 week,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6906,14,21,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +roe_ratio,Return on equity (ROE),"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.5844,6,6,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +shareholder_yield_score,"Shareholder yield (net payout to shareholders, e.g., dividends plus buybacks minus issuance)","{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,1.0,0.6413,7,7,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +six_month_price_change,Stock price change over the past six months,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6944,6,7,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +total_market_capitalization,Total market capitalization of the company,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6956,7,11,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +trailing_pe_ratio_forecast,Forecasted trailing price-to-earnings ratio.,"{'id': 'model50', 'name': 'International Scorings Data'}","{'id': 'model', 'name': 'Model'}","{'id': 'model-risk-based-models', 'name': 'Risk Based Models'}",IND,1,TOP500,MATRIX,0.6243,0.6953,3,3,1.3,[],model50,International Scorings Data,model,Model,model-risk-based-models,Risk Based Models +average_trading_volume_preceding_6m_india,Average Trading Volume in Preceding 6-month,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8381,200,902,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +board_compensation_policy_score,Board composition score (Corporate Governance - structure and makeup of company's board),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,1,1,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +board_functionality_score,"Board functioning score (Corporate Governance - effectiveness, independence, and quality of board operation)","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,4,5,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +board_structure_governance_score,"Board structure score (Corporate Governance - structure, organization, committees of the board)","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,4,4,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +book_leverage_ratio_india,Book leverage: most recent total assets divided by book equity,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8384,179,796,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +capex_to_depreciation_linkage_asia,Capital Expenditures to Depreciation Linkage,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9051,60,81,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +composite_score_qsg_india,QSG India Model,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8402,392,3235,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +core_earnings_to_price_ratio_2,"It is defined as the trailing 12-month earnings per share from operations for a stock deflated by its trading price. This item excludes cumulative effect of accounting change, discounted operations, extraordinary items and special items","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9545,226,1112,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +corporate_governance_score,Asset4 ESG Corporate Governance overall rating,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,5,5,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +customer_product_responsibility_score,"Customer and product responsibility score (Social/ESG - quality, safety, and customer protection)","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,1,2,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +distress_metric_asia,Distress Measure,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8243,134,605,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +dividends_to_gross_profit_2,Total dividends paid divided by gross profit (revenue minus cost of goods sold).,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8304,22,27,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +earnings_shortfall_vs_free_cashflow,Earnings Shortfall,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9543,155,778,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +earnings_torpedo_metric,Next four quarter EPS estimate plus trailing 12-month real earnings surprise divided by trailing EPS.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8236,34,41,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +economic_market_performance_score,Market performance and penetration score (Economic dimension - company’s competitive market position),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,1,1,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +economic_product_sustainability_score,Product/service labeling score (Economic dimension - quality and transparency in product/service labeling),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,1,1,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +economic_resource_circularity_score,Regional economic cluster score (Economic dimension - assessment of company’s position in economic clusters),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,1,1,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +economic_score_asset4,Economic Rating score,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,2,2,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +environmental_emissions_reduction_score,Emission reduction efforts score (ESG theme - company actions to reduce environmental emissions),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,2,3,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +environmental_policy_initiative_score,Environmental policy initiatives score (ESG theme - company efforts/strategy towards environmental policy and innovation),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,2,2,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +environmental_resource_reduction_score,Environmental reporting quality score (ESG - resource reduction and reporting standards),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,2,2,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +environmental_score_asset4,Overall environmental score (ESG theme - environmental performance and practices),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,2,2,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +five_year_eps_stability_2,Standard deviation of last five years’ trailing twelve-month EPS divided by their mean.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9137,29,34,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +fixed_cash_ratio,"Cash and equivalents divided by current liabilities, using a fixed calculation method.","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7998,19,23,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +forward_book_value_to_price_2,Forward-looking book value per share divided by close price.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8723,16,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +forward_cash_flow_to_price_2,Forward-looking cash flows per share divided by stock price.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.6125,17,33,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +forward_ebitda_to_enterprise_value_3,Forward-looking EBITDA divided by enterprise value.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8349,129,533,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +forward_sales_to_price_2,Forward-looking sales per share divided by trading price.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8806,17,43,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +forward_two_year_eps_growth_asia,2-Year Projected EPS Growth,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7828,37,45,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +governance_integrity_vision_score,"Investor voting structure score (Corporate Governance - transparency, fairness of shareholder voting rights/process)","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,1,1,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +gross_profit_margin_ttm_3,"Gross profit margin over the trailing twelve months, calculated as gross profit divided by sales.","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8439,17,33,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +gross_profit_to_assets_ratio_2,Trailing 12-month gross profit to assets ratio.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8431,11,17,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +industry_rel_ttm_sales_to_ev_asia,Industry-relative TTM Sales to Enterprise Value,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9578,207,1213,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +inventory_change_avg_assets_asia,Change in Inventory to Average Assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8767,20,30,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +investor_relations_score_asset4,Investor relations communication score (quality and frequency of company-to-investor communication),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,1,1,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +management_quality_score_india,Mgmt. Quality- QSG India,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8402,152,525,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_aci,Abnormal capital investment,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9277,208,821,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_acp,Average Collection Period; average of trailing 12-month accounts receivable times 365 divided by trailing 12-month sales,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8892,198,1611,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_acqmul,Acquisition Multiple: It is defined as the most recent quarterly reported invested capital divided by the trailing 12-month EBITDA.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9399,142,492,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_actrtn12m,"Twelve-month active return with one-month lag, price change from month t-13 to t-1","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9284,185,1349,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_actrtn18m,"18-Month Active Return with 1-Month Lag, percent price change from month t-19 to t-1","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9089,45,61,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_actrtn1m,"1-Month Active Return, percent price change from t-1 to t","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9708,47,100,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_actrtn24m,24-month active return with 1-month lag; percent price change from month t-25 to t-1,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8941,32,49,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_actrtn2m,2-month active return; percent price change from month t-2 to t,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9708,139,666,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_actrtn36m,36-month active return,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8654,42,68,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_actrtn3m,"Three-month active return, percent change in stock price from month t-3 to t","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.971,152,1084,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_actrtn60m,"60-Month Active Return with 1-Month Lag, percent price change from month t-61 to t-1","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8157,41,60,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_actrtn6m,Six-month active return; percent change in stock price over the last six months,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9523,23,35,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_actrtn9m,9-month active return with 1-month lag; percent price change from month t-10 to t-1,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.94,20,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_alpha60m,60-Month Alpha: It is defined as the intercept of the regression line which best fits a stock's monthly price return against the S&P 500 index monthly return over last 60-month period.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8181,26,36,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_app,Average Payable Period; average of trailing 12-month accounts payable times 365 divided by trailing 12-month cost of goods sold,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7916,32,48,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_aqi,Asset Quality Index,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7929,34,45,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_aspanratio,"Attention Span Ratio: quarterly operating assets minus operating liabilities, deflated by total assets","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8862,31,38,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_astcomp,Most recent quarter current assets divided by most recent quarter total assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.798,34,53,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_astto,Assets turnover ratio; trailing 12-month sales divided by most recent quarterly assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9678,44,73,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_bdi,Baltic Dry Index (shipping and freight activity index),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.5046,2,2,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_beta,Beta coefficient indicating market sensitivity,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8605,23,32,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_betasigma,Product of adjusted 60-month beta and 60-month sigma (standard deviation of monthly returns),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8177,13,20,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_booklev,Book leverage ratio: total assets divided by book equity,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9686,134,683,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_bp,Book value to price ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9682,22,28,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_capacq,Capital acquisition ratio (management quality measure),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9604,26,40,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_capexast,Trailing 12-month capital expenditures divided by total assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9626,30,40,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_capexsale,Trailing 12-month capital expenditures-to-sales,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9629,27,35,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cashburnrate,Rate at which the company burns cash; lower indicates better long-term cash generation,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8882,43,59,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cashc,Cash Conversion Cycle; inventory days plus collection days minus payable days,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.79,23,57,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cashp,Cash-to-Price; most recent cash and equivalents per share divided by trading price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8906,22,30,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cashratio,"Cash ratio, cash divided by current liabilities","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7998,22,24,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cashsale,"Cash-to-sales ratio, average cash and equivalents over TTM divided by TTM sales","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8893,169,1030,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cashsev,Cash to Enterprise Value; total cash and equivalents divided by enterprise value,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8908,25,39,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ccd,Current cash flow debt coverage ratio; cash flow from operations minus dividends divided by interest-bearing debt,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9007,24,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cfita,Trailing 12-month cash flow from investing divided by average total assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9658,32,54,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cfleverage,Cash flow leverage; total liabilities divided by trailing 12-month operating cash flow,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8263,27,29,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cfroi,Cash Flow Return on Invested Capital; TTM cash flow divided by average invested capital,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9658,23,50,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cg3ysales,3-year compound annual sales growth; geometric growth rate of sales per share over the last 12 quarters,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9504,32,44,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chg3ycfast,Three-year change in cash flow-to-assets ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9516,29,53,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chg3ycfp,"Three-year change in price-adjusted trailing 12-month cash flow per share, divided by price","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9516,33,43,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chg3yepsast,"3-year change in assets-adjusted trailing twelve-month EPS, EPS before extraordinary items minus value 12 quarters ago scaled by assets","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.953,109,905,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chg3yepsp,"Three-year change in price-adjusted trailing 12-month EPS (current EPS minus EPS 12 quarters ago, divided by price)","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9531,38,64,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chg3yfcfast,"3-year change in assets-adjusted TTM free cash flow (current TTM FCF minus 12 quarters ago, scaled by assets)","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9495,28,43,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chg3yfcfp,3-year change in price-adjusted trailing 12-month free cash flow; FCF minus that of 12 quarters ago divided by price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9494,25,29,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chg3yocfast,Three-year change in TTM operating cash flow scaled by average total assets (current minus 12-quarters ago),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9514,28,42,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chg3yocfp,The difference between the trailing 12-month operating cash flow per share and that of 12 quarters ago for a stock divided by its trading price.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9509,23,38,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chg6malpha18m,"6-Month Nominal Change in 18-Month Alpha: It is defined as the 6-month change in a stock's 18-month alpha, which equals the intercept from the regression of a stock price's monthly return against the S&P 500 index monthly return over the last 18-month period.","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.896,31,46,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chg6mltg,6-month percent change in long-term growth estimates,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.5459,32,36,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgalpha12m,Six-month nominal change in 12-month alpha (regression intercept over the past 12 months),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9122,28,60,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgalpha36m,6-month nominal change in 36-month alpha; change in the regression intercept of the stock’s monthly returns against a market index over the past 36 months,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8554,26,34,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgars,1-year change in accounts receivable as a percentage of sales,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8857,24,37,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgcf,One-year change in trailing twelve-month cash flow scaled by assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9648,17,21,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgcfp,1-year change in price-adjusted trailing 12-month cash flow; difference in cash flow per share over the last year divided by price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9639,24,41,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgeps,Change in earnings per share,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9654,16,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgepsp,1-year change in price-adjusted TTM EPS; EPS difference over last year divided by price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9666,33,64,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgfcf,Change in free cash flow (historical growth),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9601,19,24,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgfcfp,One-year change in price-adjusted trailing 12-month free cash flow,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.962,17,24,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgis,Year-over-year change in quarterly inventory as a percent of sales (inventory-to-sales ratio),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8843,25,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgnoa,Year-over-year change in net operating assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8887,26,41,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgnpm,Change in net profit margin,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.965,33,53,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgocf,One-year change in assets-adjusted trailing 12-month operating cash flow,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9638,19,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgocfp,1-year change in price-adjusted TTM operating cash flow,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9632,15,20,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgollev,Change in operating liability leverage over the last four quarters,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9548,30,37,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgopm,One-year change in operating profit margin over the last four quarters,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9553,30,64,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgreccast,Year-over-year change in accounts receivable to current assets ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7941,26,40,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgsgasale,Change in quarterly SG&A expenses versus sales; difference in SG&A-to-sales ratios,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.515,3,3,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgshare,Change in shares outstanding,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9541,33,43,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_chgvolpre4y,4-year change in the average trading volume; change in 6-month moving average of monthly turnover over 4 years,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.818,29,39,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cogsinvt,Change in TTM COGS vs. Inventory Level; absolute difference between yearly percent change in cost of goods sold and inventory level,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7763,21,40,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_covol,"60-Month trading volume trend, slope of regression over the last 60 months","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8495,22,24,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_curindbp_,Industry-relative book-to-market ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9663,23,43,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_curindcfp_,Industry-relative trailing 12-month cash flow-to-price; stock’s TTM cash flow-to-price compared to industry,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9665,17,20,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_curinddivp_,"Industry relative trailing 12-month dividend yield, deflated by industry standard deviation","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9504,27,44,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_curindebitdap_,"Industry-relative trailing 12-month EBITDA-to-price ratio minus industry average, deflated by industry standard deviation","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9644,35,57,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_curindep_,Industry-relative TTM EPS-to-price ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9661,123,551,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_curindfcfp_,Industry-relative trailing 12-month free cash flow-to-price ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9638,28,56,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_curindocfp_,Industry-relative trailing 12-month operating cash flow-to-price ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9642,21,42,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_curindocfta_,Industry-relative TTM Operating Cash Flow-to-Total Assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9641,16,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_curindsp_,Industry relative trailing 12-month sales-to-price ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9661,23,35,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_curratio,"Current ratio, current assets divided by current liabilities","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.798,27,56,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cvpre90dp,CV of Prior 90-Day Closing Prices: It is defined as the standard deviation of a stock's last 90 days closing prices divided by the mean of these prices.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9663,31,128,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_cvvolp20d,20-Day Volume Volatility to Price Volatility: It is defined as the coefficient of variation of the last 20 days of closing prices divided by the coefficient of variation of daily trading volume for the same time period.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9688,28,94,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_de,Long-term debt-to-equity ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9679,37,56,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_debtcf,Long-term debt to cash flow (long- and short-term interest-bearing debt divided by trailing 12-month cash flow),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8275,146,517,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_dfl,Financial leverage; sum of pretax income and interest expense over pretax income,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9585,53,92,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_div5yg,5-year dividend growth rate,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7399,29,48,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_divcf,"Dividends-to-cash flow ratio, trailing 12-month dividends divided by cash flow","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7115,30,41,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_divcov,Dividend coverage ratio (TTM operating earnings divided by dividends),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7585,25,35,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_divyield,Dividend yield,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9522,167,2138,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_dpcapex,Absolute difference between percent change in TTM depreciation and percent change in capital expenditures,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9401,26,39,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ebitdaev,Trailing 12-month EBITDA-to-enterprise value ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9653,109,550,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ebitdap,"TTM EBITDA-to-Price: It is defined as the trailing 12-month earnings before interest, taxes, depreciation and amortization for a company divided by its month-end trading price.","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9655,30,69,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_epschgetr,EPS from Change in Effective Tax Rate: It is defined as the trailing 12-month pre-tax income per share times the difference between most recent trailing 12-month effective tax rate and that of 4 quarters ago. The effective tax rate is defined as total tax expense divided by pre-tax income.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9261,26,35,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_equityto,"Equity Turnover Ratio, sales divided by average book equity","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9678,40,87,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_curindfwdep_,"Industry relative leading 4-quarters EPS-to-price ratio, compared to industry average and deflated by industry standard deviation","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9187,23,29,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_cvfy1eps,Dispersion of next fiscal year EPS estimates (coefficient of variation),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7222,30,44,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_cvfy2eps,FY2 EPS forecast dispersion: standard deviation divided by mean,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.72,26,37,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_dypeg,Reciprocal of the dividend yield-adjusted PEG ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.6225,28,35,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_ebop,Edwards-Bell-Ohlson value-to-price; EBO model valuation divided by market price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8218,31,38,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_egp,"Inverse of PEG ratio; next 4-quarter earnings estimate times long-term growth rate, scaled by price","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.6327,118,477,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_epsrm,"Street revision magnitude: 3-month change in median FY1 earnings estimate, scaled","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8812,53,69,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_estep,Leading 12-month median earnings yield,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9199,132,566,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_f12mepssev,Forward 12-month EPS-to-Enterprise Value ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9192,242,1759,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_fcfp,"Forward free cash flow-to-price; next 4-quarter mean earnings estimate plus depreciation minus capex, scaled by price","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.915,193,1493,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_fcfroey1p,"Product of TTM free cash flow yield and forward ROE; TTM FCF per share times next 4-quarter earnings forecast divided by most recent book value per share, scaled by price","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9114,26,50,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_fwdep,"Leading 12-Month Mean Earnings Yield, next 4-quarter mean earnings estimate divided by price","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.92,109,499,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_fwdroe,Forward Return on Equity; next 4-quarter earnings estimate divided by recent common equity,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9094,28,41,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_hlep,"Street revision confidence: sum of 3-month high and low FY1 estimate changes, scaled","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9035,32,53,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_numest,Number of analyst estimates covering fiscal year 1,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.92,18,20,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_numrevy1,Net number of revisions for FY1; weighted average of analyst forecast increases minus decreases in FY1,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.916,169,1682,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_pdy,Predicted dividend yield,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.5629,21,27,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_rel5yfwdep,"5-year relative leading 12-month earnings yield vs 60-month average, scaled","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8245,26,46,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_rev3y1,Three-month revision in next fiscal year (FY1) EPS forecasts,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9106,33,79,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_rev3y2,"Three-month revision in FY2 EPS forecasts; change in FY2 earnings forecast versus three months ago, scaled by price","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9076,30,47,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_rev6,Six-month revision in consensus EPS estimates,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.903,18,24,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_stdevfy1epsp,Standard deviation of FY1 EPS estimates scaled by price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8,29,38,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_stdevfy2epsp,Standard deviation of FY2 EPS estimates scaled by price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7961,166,683,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fc_y2aepsg,"2-Year Ahead EPS Growth: It is defined as the most recent consensus earnings forecast for fiscal year 2 minus the consensus earnings forecast for fiscal year 1, divided by trading price.","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9194,37,50,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fcfequity,Trailing twelve-month free cash flow to equity,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9587,19,27,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fcfroi,Free Cash Flow Return on Investment,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9627,21,33,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fcfsale,Free cash flow to sales ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9646,20,35,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ff10mrtn,Fama-French Momentum: It is defined as the percent change in a stock's price from month t-12 to month t-2.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.932,29,56,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_fixastto,Fixed Assets Turnover; TTM sales divided by total fixed assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9675,28,52,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_flowratio,"Flow Ratio, current assets minus cash and equivalents divided by current liabilities minus short-term debt","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7975,24,34,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_gear,"Capital gearing ratio, long-term debt divided by (total assets minus current liability)","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9667,22,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_globalemgasiashortsentimentfactor_inv_conc,"Inventory value concentration in the securities lending market, measuring how lendable shares’ value is concentrated among lenders for the stock","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3669,108,344,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_globalemgasiashortsentimentfactor_lend_supply,Lending supply: total quantity of shares available to lend,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3669,4,4,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_high52w,52-week high ratio; month-end price divided by highest monthly closing price in past 12 months,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9706,188,1660,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_indrelcroe_,"Industry-adjusted quarterly return on equity: stock’s lagged quarterly ROE minus industry average, scaled by the industry standard deviation","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9531,22,34,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_indrelrecd_,Industry-adjusted Doubtful Account Receivables: It is defined as a stock's asset-adjusted annual doubtful receivables minus the average of the receivables of all stocks in the same industry deflated by the standard deviation of these receivables.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.6984,14,16,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_indrelrtn4w_,"Four-week industry-relative return, stock’s 4-week return minus industry average, deflated by industry standard deviation","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9685,14,30,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_indrelrtn5d_,"Five-day industry-relative return, stock’s 5-day return minus industry average, deflated by industry standard deviation","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9683,253,2788,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_intcov,Interest Coverage,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9405,26,55,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_invast,Inventory-to-total assets ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.89,20,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_invto,Inventory Turnover Ratio: It is defined as the trailing 12-month cost of goods sold divided by the average of inventory in the same period.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7856,11,20,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_lagegp,"Lagged Inverse of PEG Ratio: It is defined as the trailing 12-month earnings per share before extraordinary items times the change in yearly trailing 12-month sales per share growth rate, divided by trading price.","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.965,32,43,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_liqcoeff,Liquidity Coefficient: It is the slope of the regression between the monthly stock trading turnover ratio (X) and the contemporary monthly stock price returns (Y).,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9132,23,30,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ltg,Long-term growth rate forecast from I/B/E/S,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.6369,25,33,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_milliq,Stock Illiquidity: It is defined as the monthly average of the daily absolute return to the daily dollar trading volume ratio.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9708,47,95,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_mktcappera,Market capitalization per covering analyst,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9206,241,3470,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_mktlev,Stock's total market value plus the most recent reported quarterly book debt then divided by the market value.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9675,31,55,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_navp,Net Asset Value price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9682,24,34,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ncfeps,Change in TTM EPS vs. Oper Cash Flows: It is defined as the absolute value of the difference between the yearly percent change in trailing 12-month operating cash flow per share and the yearly percent change in trailing 12-month diluted EPS before extraordinary items.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7928,28,37,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_netcashp,Net Cash to Equity,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8878,37,73,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_netdebt,"Net debt, total debt minus cash and cash equivalents","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9673,27,65,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_nfaldebt,Net fixed assets to long-term debt ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8871,25,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_niper,Net income per employee,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8984,24,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_nlassets,Natural logarithm of total assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.968,30,39,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_nlmktcap,Natural logarithm of market capitalization,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9708,14,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_nlprice,Natural logarithm of closing price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9708,20,35,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_nlsales,Natural logarithm of trailing 12-month sales,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9671,21,43,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_nlvolcap,Net monthly liquidity volatility based on market-cap-weighted liquidity and risk,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9535,21,33,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_nnastp,Net Current Assets-to-Price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7979,155,755,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_noato,Net operating asset turnover,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9678,14,27,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_nopatmargin,Net Operating Profit After Tax margin,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9443,27,59,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_npm,Net profit margin,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9672,29,50,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ocfast,Operating cash flow to total assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9659,19,29,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ocfmargin,Operating cash flow profit margin,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.965,14,37,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ocfratio,Operating Cash Flow Ratio: It is defined as a stock's most recently reported quarterly cash flow from operations divided by its current liabilities.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7968,13,19,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ocfroi,Operating cash flow return on invested capital (operating cash flow divided by invested capital),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9651,14,60,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ollev,Operating liability leverage,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9579,25,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_opincltd,Change in TTM Oper Income vs LT Debt: It is defined as the difference between the yearly percent change in operating income and the yearly percent change in long-term debt.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.852,24,36,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_oplev,Operating Leverage: It is defined as the percent change in the trailing 12-month operating income from the previous quarter divided by the percent change in trailing 12-month sales from the previous quarter.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9414,19,27,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_opmb,Operating profit margin,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.965,33,53,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_p50_200ratio,50-200 day stock price ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9409,26,68,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_past,Price-to-Total Assets ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9682,169,1347,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctabv260low,Price above last 260-day lowest trading price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9711,37,69,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchg3ycf,3-year growth in trailing 12-month cash flow,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9427,26,50,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchg3yeps,3-year growth in trailing 12-month earnings per share,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.933,20,46,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchg3yfcf,3-year growth in trailing 12-month free cash flow,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7611,18,23,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchg3yocf,3-year growth in trailing twelve-month operating cash flow,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8739,17,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchgastto,Percent change in the asset turnover ratio over 1 year (vs. 4 quarters ago),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9636,20,32,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchgcf,One-year growth in trailing 12-month cash flow,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9477,17,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchgeps,One-year growth in trailing twelve-month earnings per share,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9318,164,2241,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchgfcf,Percent change in Free Cash Flow (historical growth),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7783,11,11,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchgocf,1-year growth in trailing 12-month operating cash flow,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8812,10,11,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchgqtrast,Year-over-year change in total assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9659,30,40,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pctchgqtrsales,1-Yr Change in Sales: It is defined as the growth in the most recent reported quarterly sales per share as compared to 4 quarters ago.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9638,21,27,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pcurlia,Current liabilities-to-price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7993,21,34,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_perg,Risk-adjusted PEG ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.594,9,10,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pinoa,Pretax return on net operating assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7938,19,21,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pr_1536,15/36 Week Stock Price Ratio: It is defined as the moving average of a stock's prices in the last 15 weeks divided by the moving average of its prices in the last 36 weeks.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9475,29,53,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_pr_30w75w,30-75 week stock price ratio; moving average of last 30 weeks' prices divided by last 75 weeks' prices,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9158,17,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_qr,Quick Ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7979,29,55,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rationalalpha,Rational Decay Alpha: It evaluates stocks based on their historical 12-month market (S&P 500) adjusted excess return (the Y-intercept from an OLS regression equation) using a proprietary rational decay function.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9281,28,42,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ratrev6m,Street Rating Revision; 6-month average change in analyst recommendations,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9038,24,39,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rdsale,R&D intensity (research and development expense relative to sales),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.5454,15,17,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_reinrate,Reinvestment Rate: It is defined as the trailing 12-month earnings per share before extra items less the trailing 12-month dividends per share by ex-date divided by the average book equity per share in the same period.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9458,22,28,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rel5ybp,5-Yr Relative Book-to-Market: It is defined as a stock's current book-to-market ratio (BP) minus the average of the BPs in the last 60 months scaled by the standard deviation of the BPs over the same period.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7899,20,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rel5ycfp,5-year relative trailing cash flow-to-price ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7865,12,19,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rel5ydivp,5-year Relative trailing twelve-month Dividend Yield,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7168,4,5,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rel5yebitdap,5-year relative trailing 12-month EBITDA-to-price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7767,16,22,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rel5yep,5-year relative trailing twelve-month earnings-to-price ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7907,11,18,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rel5yfcfp,5-year relative TTM free cash flow-to-price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7802,11,23,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rel5yocfp,5-year relative trailing 12-month operating cash flow-to-price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7815,15,19,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rel5ysp,5-year relative trailing 12-month sales-to-price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7904,11,32,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_relpricestrength_,"Industry-adjusted 12-month relative price strength (minus industry average, scaled by industry std dev)","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9274,36,87,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_reoa,Retained earnings-to-total assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.955,14,29,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rerror60m,Regression error of 60-month CAPM,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8185,7,10,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_revper,Revenue per employee; trailing 12-month sales divided by number of employees,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8984,16,18,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_roa,Return on Assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9678,16,35,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_roe,Return on Equity,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9544,15,20,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_roic,Return on Invested Capital,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9569,22,48,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rsi26w,26-week relative price strength; most recent weekly closing price divided by the weekly closing price 26 weeks ago,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9581,22,47,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rtn2nd6m,Price return over the second six-month period,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.932,33,69,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_rtn39w,39-week return with a 4-week lag,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9405,25,45,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_saleeps,Change in TTM Sales vs EPS: It is defined as the absolute value of the difference between the yearly percent change in trailing 12-month sales per share and the yearly percent change in trailing 12-month diluted earnings per share before extraordinary items.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8979,24,29,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_saleg5y,5-year Sales Growth,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9035,30,38,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_salegpm,Change in QTR Sales vs Gross Margin: It is defined as the difference between the yearly change in most recent reported quarterly sales and the yearly change in the quarterly gross profit margin.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8646,21,25,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_salerec,Change in trailing twelve-month sales relative to accounts receivable,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8862,20,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_apsales,Proportion of company sales from the Asia-Pacific region,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8173,21,27,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_ceroe,Cash Earnings Return on Equity,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9404,15,17,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_crp,Credit Risk Premium: change in yield spread between Moody's BAA and AAA bond indices (CRP_t − CRP_t−1),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.945,21,35,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_da,Total debt to total assets leverage ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.96,110,493,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_emeasales,"Proportion of company sales from the Europe, Middle East and Africa region","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7757,16,18,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_hs,Housing Starts: percentage change in new residential housing units ((HS_t / HS_t−1) − 1) × 100,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.945,22,27,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_inflation,Inflation Sensitivity,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.945,146,886,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_ip,Industrial Production: percentage change in industrial output ((IP_t / IP_t−1) − 1) × 100,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.945,32,57,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_lasales,Proportion of company sales from the Latin America region,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.748,11,19,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_nasales,Proportion of company sales from the North America region,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7876,16,21,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_oilprice,Beta coefficient measuring the stock’s sensitivity to oil price changes,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.945,26,36,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_usd,US Dollar Value Sensitivity,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.945,25,37,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_vix,CBOE Volatility Index (market volatility measure),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.945,24,30,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sensitivityfactor_yieldsprd,Sensitivity to the yield curve slope (beta to yield spread),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.945,25,38,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sga,"Selling, General, and Administrative Expenses","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.6779,4,7,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_sigma,Standard deviation of returns,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8185,16,18,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_skew90cortn,Skewness of 90-day daily excess returns,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.966,23,38,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_skew90drtn,Skewness of 90-day stock daily returns,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9661,21,44,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_slope52wp,Slope of 52-week price trend line,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.93,18,34,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_slope66wp,Slope of 66-week price trend line; 4-week lagged slope coefficient of least squares regression of last 66 weeks' weekly closing price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9197,16,23,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_stockrating,Street Consensus Rating: It is defined as the consensus recommendation for a company.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9264,205,2337,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_surp,"Earnings surprise; actual EPS minus forecast, scaled by price","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.83,27,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_susgrowth,The maximum growth rate a firm can sustain without having to increase financial leverage.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9457,16,25,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_swc,Working capital-to-trailing 12-month sales,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7971,135,1725,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_tobinq,"Tobin’s Q, market value of equity plus debt divided by total assets","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9652,27,44,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_totalcov,Total coverage ratio; TTM operating income before depreciation divided by annualized cash dividends plus interest expense,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9142,18,22,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_totalsaleg,Year-over-year growth rate of total sales based on trailing twelve months,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9648,28,36,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_tstalp,1-Year Price Momentum Indicator,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9301,28,59,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmaccu,Trailing twelve months accruals; earnings quality measure based on accruals,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9659,18,34,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmcapexp,Trailing twelve-month capital expenditures divided by price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9637,17,30,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmcfp,Trailing twelve-month cash flow from operations,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9676,22,29,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmepa,An indicator that standardizes and compares relative share price between time periods and among companies.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9678,129,809,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmepb,"The company's performance in trailing 1-year before taking into account non-recurring gain or loss, divided by its month-end trading price.","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9681,25,63,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmfcfev,Trailing 12-month Free Cash Flow-to-Enterprise Value ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9634,117,543,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmfcfp,Trailing 12-month free cash flow-to-price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9655,21,59,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmgfp,Trailing twelve-month Growth Flow-to-Price ratio; last four quarters GAAP earnings plus R&D expenses scaled by market value,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9679,25,45,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmocfp,Trailing twelve-month operating cash flow to price,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9659,20,25,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmpiqp,Trailing 12-month pretax income-to-price (pretax income per share scaled by price),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.968,199,1552,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmsaleev,Trailing 12-month Sales-to-Enterprise Value ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9678,138,839,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_ttmsp,Trailing twelve-month sales-to-price ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9679,24,43,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_tw_ep,Time-weighted earnings yield,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9196,153,813,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_uap,Unexpected Change in Accounts Payable: It is defined as the difference between current accounts payable and the expected level of accounts payable (multiplying the prior year's closing account balance by the growth in costs of goods sold in the trailing 12 months) scaled by the total assets.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7906,19,21,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_uar,"Unexpected change in accounts receivable relative to expected level, scaled by total assets","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8857,10,11,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_udep,Unexpected Change in Depreciation: It is defined as the difference between the trailing 12-month depreciation expenses and the expected level of depreciation expenses (multiplying the prior year's closing account balance by the growth in total fixed assets) scaled by the total assets.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9624,19,33,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_uinv,The difference of inventory between present and expected levels.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8551,23,33,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_var24m,24-month Value at Risk,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.896,10,13,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_varresirtn,24-Month residual return variance,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7985,14,17,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_visiratio,Visibility Ratio; most recent daily trading volume divided by average daily volume over the last 50 days,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9708,16,21,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_volpre6m,Volatility of trading volume over the past six months,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9668,17,19,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_volto,Trading Turnover Ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9708,24,45,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_wcacc,Working capital accruals (earnings quality measure),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.78,31,45,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_wcast,Working capital-to-total assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7977,171,1805,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_wcinv,Working capital to inventory ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7549,127,967,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_yoychgaa,Year-over-year change in accruals-adjusted assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9634,28,39,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_yoychgcr,Year-over-year change in current ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7954,156,762,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_yoychgda,Year-over-year Change in Leverage: It is defined as the difference between the most recent reported quarterly total liabilities as a percentage of the reported total assets and the percentage for the same quarter one year ago.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9658,21,31,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_yoychggpm,Year-over-year change in gross profit margin,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8679,19,34,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +mdl177_yoychgroa,Year-over-year change in return on assets,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9655,26,46,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +net_fy2_analyst_revisions,Net number of analyst forecast revisions for the next fiscal year (upgrades minus downgrades).,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8992,22,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +normalized_earnings_yield_composite,Normalized Earnings Yield,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.4889,98,1132,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +ohlson_bankruptcy_risk_score_2,Ohlson Bankruptcy Score,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7852,137,712,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +one_month_return_interquartile_range,1-Month Stock Return Interquartile Range,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9623,24,34,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +one_month_return_stddev,1-Month Realized Stock Return Volatility,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9618,97,362,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +one_year_gross_profit_to_assets_change,1-Year Change in Gross Profit to Assets. It is defined as the year-on-year change in gross profit to assets.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8347,17,24,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +one_year_price_momentum_indicator_india,1-Year Price Momentum Indicator,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8069,126,1273,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +percent_change_quarterly_sales_india,1-yr Change in Sales,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8359,19,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +price_to_book_roe_residual,Price-to-Book Return-on-Equity Combination (PB-ROE),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8668,18,22,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +profitability_ratio_operating_income_to_assets_2,Profitability Ratio,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9553,19,30,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +quarterly_earnings_surprise_stddev_2,Most Recent Quarterly Earnings Surprise,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7081,29,46,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +quarterly_earnings_surprise_stddev_60d_lag,60-Day Lagged Quarterly Earnings Surprise,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.6553,33,39,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +quarterly_eps_surprise_variation,Change in Real Earnings Surprise,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7461,26,41,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +r_squared_five_year_eps_trend,R-squared of five-year trailing 12-month EPS trend line.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9206,117,425,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +shareholder_rights_score,Shareholder rights score (Corporate Governance - protections and rights afforded to shareholders),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,1,1,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +six_month_avg_fy2_earnings_revision,"Average of the prior six months' changes in consensus FY2 earnings forecasts, scaled by standard deviation.","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8842,19,20,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +slope_five_year_eps_trend,Slope coefficient of five-year trailing 12-month EPS trend line.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9206,23,32,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +social_community_score,Community responsibility score (Social/ESG theme - company’s impact and engagement with local communities),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,2,3,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +social_human_rights_score,Human rights and social responsibility score (Social/ESG theme - policies and practices regarding human rights),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,3,3,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +social_score_asset4,Total shares outstanding,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,2,2,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +three_month_fy1_eps_revision_stddev,3-M Revision in FY1 EPS Forecasts: Dispersion Relative,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7876,142,1020,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +three_month_fy2_earnings_revision_stddev,3-M Revision in FY2 EPS Forecasts: Dispersion Relative,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7851,164,1153,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +three_month_revision_fy1_earnings_india,"3-month revision in FY1 EPS forecasts; change in current FY1 forecast versus three months ago, scaled","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7937,243,2905,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +three_month_revision_fy2_earnings_india,"3-month revision in FY2 EPS forecasts; change in FY2 earnings forecast since three months ago, scaled by price","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.7924,165,1553,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +three_year_gross_margin_change,3 Year Change in Gross Profit Margin. It is defined as the 3 Year on Year Change in Gross Profit Margin.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8125,15,20,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +three_year_gross_profit_to_assets_change,3-Year Change in Gross Profit to Assets. It is defined as the 3-Year on Year Change in Gross Profit to Assets.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8136,15,26,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +time_weighted_book_value_to_price_2,Time-weighted average of book value per share estimates for FY1 and FY2 divided by close price.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8703,98,628,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +time_weighted_cashflow_to_price,Time-weighted average of cash flows per share estimates for FY1 and FY2 divided by close price.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.6072,14,19,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +time_weighted_ebitda_enterprise_value,Time-weighted average of EBITDA to enterprise value for FY1 and FY2.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8306,23,24,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +time_weighted_eps_revision_six_month,Time Weighted Earnings Revision,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.6267,26,30,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +time_weighted_eps_stddev_revision_2,"Six-month average of time-weighted FY1 and FY2 earnings estimate revisions, adjusted by standard deviation.","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.6275,18,20,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +time_weighted_sales_to_price_2,Time-weighted sales per share for FY1 and FY2 divided by price.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.88,10,11,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +ttm_operating_cash_flow_to_ev_asia,TTM Operating Cash Flow-to-Enterprise Value,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9578,34,104,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +ttm_operating_income_to_ev_asia,TTM Operating Income to Enterprise Value,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9575,139,1032,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +twelve_month_total_debt_change_asia,12 Month Change in Total Debt,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9574,18,22,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +two_year_eps_growth_pct_change_asia,2-Year Ahead EPS Growth Percentage Change,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9121,34,48,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +unexpected_profitability_metric,Unexpected Profitability,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8813,39,50,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +value_score_india,Value - QSG India,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.8402,259,1194,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +workforce_diversity_opportunity_score,Workforce diversity and opportunity score (Social/ESG theme - diversity and equal opportunity in hiring/promotions),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,1,1,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +workforce_equity_score,"Workforce employment quality score (Social/ESG theme - labor practices, pay, stability, satisfaction)","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,1,1,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +workforce_training_development_score,Workforce training and development score (Social/ESG theme - quality and investment in employee training),"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,3,4,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +workplace_health_safety_score,"Workforce health and safety score (Social/ESG theme - occupational health, accident prevention, and safety policies)","{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.3978,2,4,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +yearly_arithmetic_change_return_on_equity,The absolute change in return on equity (ROE) over the past year.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,0.8508,0.9293,37,52,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models +yearly_percentage_change_return_on_equity,The percentage change in return on equity (ROE) over the past year.,"{'id': 'model77', 'name': ""Analysts' Factor Model""}","{'id': 'model', 'name': 'Model'}","{'id': 'model-technical-models', 'name': 'Technical Models'}",IND,1,TOP500,MATRIX,1.0,0.9,41,53,1.3,[],model77,Analysts' Factor Model,model,Model,model-technical-models,Technical Models diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_news.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_news.csv new file mode 100644 index 0000000..c986a86 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_news.csv @@ -0,0 +1,73 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +estimated_next_shareholder_meeting_date,"Tentative or estimated date (as an integer date, e.g., YYYYMMDD) for the next shareholder annual meeting, provided with next-trading-day (D1) availability for the USA region","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.6319,125,161,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +event_frequency_label,Integer count of StreetEvents mentions or hits for the company during the represented period or date,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2625,13,16,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +next_earnings_call_date,"Next scheduled or confirmed earnings conference call date for the US region module, encoded as an integer date (typically YYYYMMDD); data available with D1 (next trading day) delay","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.4176,32,43,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +next_earnings_estimate_date,"Estimated next earnings release date when the actual date is not yet confirmed (D1 module; next-day availability), stored as an integer date (e.g., YYYYMMDD)","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.5964,75,99,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +next_ex_dividend_event_date,"The next confirmed ex-dividend date for the company in the USA module, delivered with next-day (D1) timeliness and encoded as an integer date","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3362,65,124,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +next_shareholder_meeting_date,"Confirmed next scheduled date (as an integer date, e.g., YYYYMMDD) for the upcoming shareholder annual meeting as of today, provided with next-trading-day (D1) availability for the USA region","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2696,24,26,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +nws21_tonexiguome1_tone_sc,(number of positive words - number of negative words) divided by (number of positive words + number of negative words),"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,4,4,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +nws21_xiguo_me1_event_freq,number of events,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,5,5,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +nws21_xiguo_me1_neg_sc,number of negative words divided by number of all words,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,4,6,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +nws21_xiguo_me1_pos_sc,number of positive words divided by number of all words,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,2,2,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +nws21_xiguo_me1_qerf_gen,number of negative words,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,3,3,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +nws21_xiguo_me1_qerf_sop,number of positive words,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,3,3,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +previous_corporate_presentation_date,"Most recent past corporate presentation date before today (for a China-listed company), available on a next-trading-day delay, stored as an integer date (e.g., YYYYMMDD)","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2785,13,13,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +previous_earnings_call_date,"Most recent past earnings conference call date prior to today for the US region module, encoded as an integer date (typically YYYYMMDD); data available with D1 delay","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.5211,73,123,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +previous_ex_dividend_event_date,"The most recent past ex-dividend date before today for the company in the USA module, encoded as an integer date","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.547,87,110,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +previous_shareholder_meeting_date,"Most recent past date (as an integer date, e.g., YYYYMMDD) when the shareholder annual meeting last occurred before today, provided with next-trading-day (D1) availability for the USA region","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.4653,26,30,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News +mws52_chars_in_presentation,Number of characters in the presentation section,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,18,22,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_chars_in_qa,Number of characters in the Q&A section,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,17,33,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_conference_call_participants,"Number of non-corporate participants (analysts, third parties) in the conference call","{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,12,38,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_corporate_participants,Number of representatives from the company (issuer) participating in the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,14,22,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_eventtype,"Type/category of the conference call event (e.g., earnings call, guidance update, etc.)","{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,12,30,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_expirationtime,Time when the event or record is considered expired or no longer current,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,14,30,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_lastuptime,Time when the record was last updated,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,9,17,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_paragraphs_in_presentation,Number of paragraphs in the presentation section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,13,31,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_pparagraphs_in_qa,Number of paragraphs in the Q&A section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,14,20,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_questions_in_presentation,Number of questions asked during the presentation section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,12,15,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_questions_in_qa,Number of questions asked in the Q&A section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,7,10,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_sentences_in_presentation,Number of sentences in the presentation section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,10,19,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_sentences_in_qa,Number of sentences in the Q&A section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,13,25,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_starttime,Time the conference call event started,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,11,16,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_talks_in_presentation,Number of talks (distinct speaker turns or interventions) in the presentation section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,18,36,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_talks_in_qa,Number of individual talks in the Q&A section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,10,13,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_words_in_presentation,Number of words in the presentation section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,10,19,1.5,[],news52,Conference call data,news,News,news-news,News +mws52_words_in_qa,Number of words in the Q&A section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,18,39,1.5,[],news52,Conference call data,news,News,news-news,News +headline_mention_count,Total number of news items for the security/date in the dataset,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,54,80,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_mention_count_2,Number of news headlines for the given security and date,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,94,200,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_negative_mention_count,Count of negative words in headlines across all news for a security/date,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,26,43,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_negative_mention_count_2,Total count of negative words appearing in all news headlines for the day,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,69,135,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_negative_tone_score,"Average proportion of negative words in news headlines, calculated as negative words divided by total words, across all news for the security/date","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,30,46,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_negative_tone_score_2,The average ratio of negative words to all words in all news headlines for the day,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,55,84,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_overall_tone_score,average tone score calculated as (positive word count - negative word count) divided by (positive word count + negative word count) for headlines of news stories,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,31,43,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_overall_tone_score_2,"average normalized headline tone score across all news, calculated as (positive words - negative words) divided by (positive words + negative words) per headline","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,57,98,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_positive_mention_count,Count of positive words in headlines across all news for a security/date,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,14,21,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_positive_mention_count_2,Total count of positive words appearing in all news headlines for the day,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,35,57,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_positive_tone_score,"Average proportion of positive words in news headlines, calculated as positive words divided by total words, across all news for the security/date","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,18,32,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +headline_positive_tone_score_2,The average ratio of positive words to all words in all news headlines for the day,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,34,45,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +news_mention_frequency_1,"Count of news stories per security and date in the EUR region for the real-time (D0) module, excluding stories containing the keyword ""insider""","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9755,45,96,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +news_mention_frequency_d0,The number of news events or articles detected for each security and date in the Asia region on a real-time (intraday) basis,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9761,44,74,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +news_mention_frequency_d0_twn,"Count of news events per Taiwan-listed security on a given date, captured at real-time/intraday (D0) timing within the Asia module","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9761,27,45,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +news_mention_frequency_d0_twn_alt,Total number of news stories per security and date for Taiwan-listed companies in the Asia news frequency module at real-time/intraday (D0) delay,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9757,46,57,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +news_mention_frequency_simple,"Count of news articles related to a security on a given date in China, excluding those containing ""insider"" keywords","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9755,43,79,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +news_sales_mention_count,"Daily count of news articles for European securities that mention sales-related keywords (e.g., sale, sales) or earnings, aggregated per security per date","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3677,23,40,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News +max_primary_sentiment_score_transfer,Daily maximum of variant-1 sentiment scores for the stock-day,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,51,66,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +max_secondary_sentiment_score_transfer,"Daily maximum of variant 2 sentiment across PR events; scale [-1, 1]","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,27,37,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +max_sentiment_score_transfer,Daily maximum of base sentiment scores across PR events mapped to the stock,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6973,20,35,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +mean_primary_sentiment_score_transfer,Daily mean of sentiment1 scores across PR events (may be weighted by article importance),"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,13,15,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +mean_secondary_sentiment_score_transfer,Daily mean of sentiment2 scores across PR events (may be weighted by article importance),"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,9,12,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +mean_sentiment_score_transfer,Daily mean of base sentiment scores across PR events (often weighted by article importance),"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,10,11,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +median_primary_sentiment_score_transfer,Daily median of sentiment1 scores across PR events,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,14,17,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +median_secondary_sentiment_score_transfer,"Median of the TRNA-style sentiment2 scores from all RavenPack press-release events linked to the stock on that day (USA region, D1 delay)","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,10,11,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +median_sentiment_score_transfer,Daily median of event-level sentiment scores for the stock-day,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,10,11,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +min_primary_sentiment_score_transfer,"Daily minimum of sentiment1 scores across PR events mapped to the stock; range [-1, 1]","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,12,18,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +min_secondary_sentiment_score_transfer,"Daily minimum of variant 2 sentiment across PR events; scale [-1, 1]","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,18,21,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +min_sentiment_score_transfer,"Daily minimum of main sentiment across PR events; scale [-1, 1]","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,9,10,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +news_item_count_transfer,"Total number of PR headlines mapped to the stock on that day (USA, D1)","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,27,28,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +normalized_news_item_count_transfer_2,"Normalized daily news article count (unitless), scaled by 90-day rolling mean for cross-sectional comparability","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,17,20,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +skew_primary_sentiment_score_transfer,The skewness of the primary transferred sentiment score distribution for the period.,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,11,14,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +skew_secondary_sentiment_score_transfer,"Skewness of the distribution of TRNA-style sentiment2 scores across all RavenPack press-release events linked to the stock on that day (USA region, D1 delay)","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,11,18,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +skew_sentiment_score_transfer,Skewness of the distribution of event-level sentiment scores for the stock-day,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,18,49,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +sum_primary_sentiment_score_transfer,The sum of all primary transferred sentiment scores for the period.,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,24,28,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +sum_secondary_sentiment_score_transfer,Daily sum of variant-2 sentiment scores across all PR events for the stock-day,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,11,11,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment +sum_sentiment_score_transfer,"Sum of the main TRNA-style sentiment scores across all RavenPack press-release events linked to the stock on that day (USA region, D1 delay)","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6973,15,30,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_other.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_other.csv new file mode 100644 index 0000000..8b9fa85 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_other.csv @@ -0,0 +1,31 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +oth571_views14d,Total Wikipedia company page views in the USA module over the trailing 14-day window ending 14 days ago (inclusive; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4536,65,97,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views1y,Total Wikipedia company page views in the USA module over the trailing 365-day window (366 in leap years) ending 364 days ago (inclusive; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,14,15,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views1yafternoon,Wikipedia company page views in the USA module from 12:00 to 17:59 on the day 364 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,17,17,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views1ydesktop,Total Wikipedia company page views in the USA module via desktop devices on the day 364 days ago (desktop web; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,15,30,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views1yevening,Wikipedia company page views in the USA module from 18:00 to 23:59 on the day 364 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,6,6,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views1ymobile,Total Wikipedia company page views in the USA module via mobile devices on the day 364 days ago (mobile web and apps; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,33,51,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views1ymorning,Wikipedia company page views in the USA module from 06:00 to 11:59 on the day 364 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,19,24,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views1ynight,Wikipedia company page views in the USA module from 00:00 to 05:59 on the day 364 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,5,6,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views28d,Total Wikipedia company page views in the USA module over the trailing 28-day window ending 28 days ago (inclusive; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,17,19,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views28dafternoon,Wikipedia company page views in the USA module from 12:00 to 17:59 on the day 28 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,16,22,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views28ddesktop,Total Wikipedia company page views in the USA module via desktop devices on the day 28 days ago (desktop web; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,23,46,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views28devening,Wikipedia company page views in the USA module from 18:00 to 23:59 on the day 28 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,18,24,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views28dmobile,Total Wikipedia company page views in the USA module via mobile devices on the day 28 days ago (mobile web and apps; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,12,12,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views28dmorning,Wikipedia company page views in the USA module from 06:00 to 11:59 on the day 28 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,15,19,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views28dnight,Wikipedia company page views in the USA module from 00:00 to 05:59 on the day 28 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,10,10,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views7d,Total Wikipedia page views over the past 7 natural days.,"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,18,20,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views7dafternoon,Wikipedia company page views in the USA module from 12:00 to 17:59 on the day 7 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,13,16,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views7ddesktop,Wikipedia page views from desktop devices over the past 7 natural days.,"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,11,15,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views7devening,Wikipedia company page views in the USA module from 18:00 to 23:59 on the day 7 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,48,66,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views7dmobile,Wikipedia page views from mobile devices over the past 7 natural days.,"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,23,32,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views7dmorning,Wikipedia company page views in the USA module from 06:00 to 11:59 on the day 7 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,9,9,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views7dnight,Wikipedia company page views in the USA module from 00:00 to 05:59 on the day 7 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,8,8,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_views84d,Total Wikipedia company page views in the USA module over the trailing 84-day window ending 84 days ago (inclusive; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4426,12,28,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_viewstoday,Total Wikipedia company page views in the USA module today (calendar day; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,10,10,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_viewstodayafternoon,Wikipedia company page views in the USA module from 12:00 to 17:59 today (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,5,5,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_viewstodaydesktop,Total Wikipedia company page views in the USA module via desktop devices today (desktop web; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,10,10,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_viewstodayevening,Wikipedia company page views in the USA module from 18:00 to 23:59 today (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,16,19,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_viewstodaymobile,Total Wikipedia company page views in the USA module via mobile devices today (mobile web and apps; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,21,26,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_viewstodaymorning,Wikipedia company page views in the USA module from 06:00 to 11:59 today (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,10,13,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models +oth571_viewstodaynight,Wikipedia company page views in the USA module from 00:00 to 05:59 today (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,6,6,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_pv.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_pv.csv new file mode 100644 index 0000000..104d95f --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_pv.csv @@ -0,0 +1,525 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +adjfactor,Adjustment factor applied to historical prices and dividends to account for splits and other corporate actions,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,0,0,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +adv20,Average daily volume in past 20 days,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,807,5362,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +cap,Daily market capitalization (in millions),"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,1343,13996,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +close,Daily close price,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,1979,35257,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +dividend,Dividend,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,282,3636,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +high,Daily high price,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,718,6168,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +low,Daily low price,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,559,5053,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +open,Daily open price,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,917,16295,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +returns,Daily returns,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,1929,33747,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +sharesout,Daily outstanding shares (in millions),"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,372,3302,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +split,Stock split ratio,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,316,3390,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +volume,Daily volume,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,1217,8816,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +vwap,Daily volume weighted average price,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,715,5610,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume +session_1430to1430_final_trade_price,Last trade price recorded at the end of the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,339,1068,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume +session_1430to1430_initial_trade_price,First trade price recorded at the start of the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,236,726,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume +session_1430to1430_market_value,Aggregate market value of all trades during the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,243,926,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume +session_1430to1430_max_trade_price,Highest trade price recorded during the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,213,781,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume +session_1430to1430_min_trade_price,Lowest trade price recorded during the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,209,762,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume +session_1430to1430_total_traded_volume,Total number of shares or contracts traded during the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,143,299,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume +session_1430to1430_volume_weighted_avg_price,Volume-weighted average price for trades during the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,223,801,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume +aggregated_slippage_metric,Comprehensive measure of slippage across multiple trades or venues.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,93,181,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +asia_trade_cost_buy,Estimated cost incurred when buying in Asian markets.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,30,37,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +asia_trade_cost_sell,Estimated cost incurred when selling in Asian markets.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,16,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +asian_market_slippage,Slippage metric specific to Asian market trades.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +average_spread_slippage,"Estimated portion of trade slippage attributable to crossing the bid-ask spread, i.e., the extra transaction cost versus mid-price execution when trading futures","{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,23,24,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +bid_ask_price_gap,Difference between the best bid and ask prices for a security.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.989,49,62,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +group_buy_slippage,Slippage incurred when executing grouped buy orders.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,16,25,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +group_order_slippage,Slippage experienced when executing grouped orders.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,1,1,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +group_sell_slippage,Slippage incurred when executing grouped sell orders.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,16,26,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +korean_market_slippage,"Korea-specific modeled trading slippage overlay that estimates expected execution cost for Korean equities, derived from microstructure and spread data and masked for eligibility","{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,59,105,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +price_difference_bid_ask,Unknown,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.989,49,64,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +pv106_wli_lastspread,Bid-ask spread averaged over the last 30 minutes,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.957,47,105,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +pv106_wli_lastspreadbp,Bid-ask spread over the last 30 minutes expressed in basis points,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.957,39,56,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +pv106_wli_spread,Difference between bid and ask price (raw bid-ask spread),"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9867,83,142,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +pv106_wli_spreadbp,Bid-ask spread expressed in basis points,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9867,63,111,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +slippage_at_spread_20,Slippage value calculated at a spread threshold of 20 units.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,31,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +slippage_commission_2025,Estimated slippage and commission costs for the year 2025.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,16,21,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +slippage_commission_estimate,Estimated slippage and commission costs for trades.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,25,43,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +transaction_cost_estimate,Estimated cost incurred when executing a trade.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,15,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +transaction_cost_maximum,Maximum estimated transaction cost for a trade.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,31,42,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +transaction_cost_median,Median estimated transaction cost for a trade.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,24,34,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +transaction_cost_percentile_10,Estimated transaction cost at the 10th percentile for a trade.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,23,24,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +transaction_cost_percentile_25,Estimated transaction cost at the 25th percentile for a trade.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,14,15,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume +pv149_status_5,"Status code representing current state of data/record, e.g., valid, missing, suspended","{'id': 'pv149', 'name': 'Holidays and Trading Hours Calendar'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-relationship', 'name': 'Relationship'}",IND,1,TOP500,MATRIX,0.9507,1.0,94,224,1.2,[],pv149,Holidays and Trading Hours Calendar,pv,Price Volume,pv-relationship,Relationship +industry_grouping_level10_all_instruments_variant513,"Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 10 adaptive clusters based on co-movement of returns; value is cluster label (1 to 10), or -1/10000 for unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,152,274,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level10_india500_xjp513,"For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 10 statistically determined clusters based on historical stock return correlations, representing adaptive industry classification labels. Values from 1 to 10 indicate cluster membership; -1 or 10000 means unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9261,80,141,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level10_top800_xjp513,"Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 10 groups","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.1674,9,9,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level20_all_instruments_variant513,"Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 20 adaptive clusters based on co-movement of returns; value is cluster label (1 to 20), or -1/10000 for unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,86,169,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level20_india500_xjp513,"For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 20 statistically determined clusters based on historical stock return correlations, representing fine-grained industry classification labels. Values from 1 to 20 indicate cluster membership; -1 or 10000 means unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9261,70,122,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level20_top800_xjp513,"Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 20 groups","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.1674,9,10,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level2_all_instruments_variant513,"Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 2 adaptive clusters based on co-movement of returns; value is cluster label (1 or 2), or -1/10000 for unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,53,78,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level2_india500_xjp513,"For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 2 statistically determined clusters based on historical stock return correlations, representing broad statistical industry classification labels. Values from 1 to 2 indicate cluster membership; -1 or 10000 means unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9261,103,309,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level2_top800_xjp513,"Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 2 groups","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.1674,0,0,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level50_all_instruments_variant513,"Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 50 adaptive clusters based on co-movement of returns; value is cluster label (1 to 50), or -1/10000 for unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,100,240,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level50_india500_xjp513,"For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 50 statistically determined clusters based on historical stock return correlations, representing very fine-grained industry classification labels. Values from 1 to 50 indicate cluster membership; -1 or 10000 means unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9261,58,89,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level50_top800_xjp513,"Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 50 groups","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.1674,7,7,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level5_all_instruments_variant513,"Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 5 adaptive clusters based on co-movement of returns; value is cluster label (1 to 5), or -1/10000 for unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,60,113,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level5_india500_xjp513,"For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 5 statistically determined clusters based on historical stock return correlations, representing intermediate-level statistical industry classification labels. Values from 1 to 5 indicate cluster membership; -1 or 10000 means unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9261,64,99,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +industry_grouping_level5_top800_xjp513,"Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 5 groups","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.1674,7,12,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method1_group10,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,35,52,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method1_group2,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,14,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method1_group20,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,20,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method1_group5,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method1_group50,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,20,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method2_group10,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,10,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method2_group2,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method2_group20,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method2_group5,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method2_group50,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method3_group10,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method3_group2,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method3_group20,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method3_group5,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method3_group50,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method4_group10,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method4_group2,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method4_group20,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method4_group5,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_minvol1m_pca_method4_group50,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method1_group10,Asia equity principal component grouping using method 1 with 10 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method1_group2,Asia equity principal component grouping using method 1 with 2 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method1_group20,Asia equity principal component grouping using method 1 with 20 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method1_group5,Asia equity principal component grouping using method 1 with 5 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method1_group50,Asia equity principal component grouping using method 1 with 50 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method2_group10,Asia equity principal component grouping using method 2 with 10 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method2_group2,Asia equity principal component grouping using method 2 with 2 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method2_group20,Asia equity principal component grouping using method 2 with 20 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method2_group5,Asia equity principal component grouping using method 2 with 5 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method2_group50,Asia equity principal component grouping using method 2 with 50 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method3_group10,Asia equity principal component grouping using method 3 with 10 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method3_group2,Asia equity principal component grouping using method 3 with 2 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method3_group20,Asia equity principal component grouping using method 3 with 20 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method3_group5,Asia equity principal component grouping using method 3 with 5 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method3_group50,Asia equity principal component grouping using method 3 with 50 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method4_group10,Asia equity principal component grouping using method 4 with 10 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method4_group2,Asia equity principal component grouping using method 4 with 2 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method4_group20,Asia equity principal component grouping using method 4 with 20 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method4_group5,Asia equity principal component grouping using method 4 with 5 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +asia_equity_pca_method4_group50,Asia equity principal component grouping using method 4 with 50 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method1_group10,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,10,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method1_group2,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method1_group20,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method1_group5,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method1_group50,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method2_group10,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method2_group2,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method2_group20,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method2_group5,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method2_group50,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method3_group10,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method3_group2,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method3_group20,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method3_group5,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method3_group50,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method4_group10,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method4_group2,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method4_group20,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method4_group5,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +ex_japan_equity_pca_method4_group50,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method1_group10,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method1_group2,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method1_group20,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method1_group5,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method1_group50,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method2_group10,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method2_group2,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method2_group20,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method2_group5,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method2_group50,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method3_group10,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method3_group2,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method3_group20,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method3_group5,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method3_group50,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method4_group10,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method4_group2,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method4_group20,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method4_group5,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +exjapan_minvol_method4_group50,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor1_group10_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,20,48,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor1_group20_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,21,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor1_group2_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor1_group50_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor1_group5_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,20,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor2_group10_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,16,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor2_group20_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor2_group2_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor2_group50_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor2_group5_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor3_group10_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,12,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor3_group20_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor3_group2_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor3_group50_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,11,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor3_group5_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor4_group10_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor4_group20_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor4_group2_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor4_group50_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor4_group5_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method1_10clusters,Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method1_20clusters,Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method1_2clusters,Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method1_50clusters,Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method1_5clusters,Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method2_10clusters,Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method2_20clusters,Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method2_2clusters,Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method2_50clusters,Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method2_5clusters,Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method3_10clusters,Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method3_20clusters,Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method3_2clusters,Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method3_50clusters,Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method3_5clusters,Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method4_10clusters,Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method4_20clusters,Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method4_2clusters,Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method4_50clusters,Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +factor_based_industry_method4_5clusters,Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +first_method_cluster10_all,Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT1 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,12,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +first_method_cluster5_all,Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT1 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +fourth_method_cluster10_all,Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT4 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +fourth_method_cluster5_all,Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT4 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method1_group2,Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT1 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method1_group20,Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT1 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method1_group50,Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT1 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method2_group2,Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT2 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method2_group20,Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT2 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,10,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method2_group50,Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT2 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method3_group2,Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT3 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method3_group20,Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT3 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method3_group50,Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT3 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method4_group2,Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT4 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method4_group20,Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT4 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +global_method4_group50,Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT4 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method1_group2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method1_group20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,15,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method1_group5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method1_group50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method2_group10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method2_group2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method2_group20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method2_group50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method3_group10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method3_group2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method3_group5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method3_group50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method4_group10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method4_group20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_equity_pca_method4_group5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method1_grouping10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 10 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method1_grouping2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 2 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method1_grouping20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 20 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method1_grouping5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 5 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method1_grouping50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 50 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method2_grouping10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 10 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method2_grouping2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 2 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method2_grouping20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 20 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method2_grouping5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 5 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method2_grouping50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 50 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method3_grouping10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 10 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method3_grouping2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 2 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method3_grouping20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 20 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method3_grouping5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 5 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method3_grouping50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 50 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method4_grouping10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 10 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method4_grouping2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 2 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method4_grouping20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 20 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method4_grouping5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 5 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_region_method4_grouping50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 50 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method1_group10,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method1_group2,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method1_group20,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method1_group5,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method1_group50,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method2_group10,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method2_group2,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method2_group20,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method2_group5,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method2_group50,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method3_group10,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method3_group2,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method3_group20,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method3_group5,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method3_group50,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method4_group10,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method4_group2,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method4_group20,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method4_group5,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hk_top200_pca_method4_group50,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hongkong_pca_grouping_method1_10clusters,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hongkong_pca_grouping_method2_5clusters,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hongkong_pca_grouping_method3_20clusters,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hongkong_pca_grouping_method4_2clusters,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +hongkong_pca_grouping_method4_50clusters,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method1_group10,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,56,134,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method1_group2,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,28,50,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method1_group20,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,26,84,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method1_group5,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,14,37,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method1_group50,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,25,58,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method2_group10,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,39,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method2_group2,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,44,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method2_group20,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,21,71,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method2_group5,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,67,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method2_group50,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,20,41,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method3_group10,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,21,67,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method3_group2,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,11,30,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method3_group20,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,46,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method3_group5,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,10,35,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method3_group50,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,44,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method4_group10,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,14,25,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method4_group2,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,27,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method4_group20,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,25,60,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method4_group5,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,24,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +india_top500_method4_group50,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,28,63,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method1_grouping10,"Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,23,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method1_grouping2,"Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method1_grouping20,"Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method1_grouping5,"Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method1_grouping50,"Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method2_grouping10,"Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method2_grouping2,"Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method2_grouping20,"Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method2_grouping5,"Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method2_grouping50,"Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method3_grouping10,"Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method3_grouping2,"Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method3_grouping20,"Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method3_grouping5,"Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method3_grouping50,"Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method4_grouping10,"Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method4_grouping2,"Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method4_grouping20,"Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method4_grouping5,"Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +jp_minvol_method4_grouping50,"Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method1_10clusters,Industry group assignment using first PCA method and 10 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method1_20clusters,Industry group assignment using first PCA method and 20 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method1_2clusters,Industry group assignment using first PCA method and 2 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method1_50clusters,Industry group assignment using first PCA method and 50 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method1_5clusters,Industry group assignment using first PCA method and 5 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method2_10clusters,Industry group assignment using second PCA method and 10 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method2_20clusters,Industry group assignment using second PCA method and 20 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method2_2clusters,Industry group assignment using second PCA method and 2 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method2_50clusters,Industry group assignment using second PCA method and 50 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method2_5clusters,Industry group assignment using second PCA method and 5 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method3_10clusters,Industry group assignment using third PCA method and 10 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method3_20clusters,Industry group assignment using third PCA method and 20 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method3_2clusters,Industry group assignment using third PCA method and 2 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method3_50clusters,Industry group assignment using third PCA method and 50 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method3_5clusters,Industry group assignment using third PCA method and 5 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method4_10clusters,Industry group assignment using fourth PCA method and 10 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method4_20clusters,Industry group assignment using fourth PCA method and 20 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method4_2clusters,Industry group assignment using fourth PCA method and 2 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method4_50clusters,Industry group assignment using fourth PCA method and 50 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +minvol_pca_grouping_method4_5clusters,Industry group assignment using fourth PCA method and 5 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method1_10clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT1 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method1_20clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT1 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method1_2clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT1 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method1_50clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT1 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method1_5clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT1 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method2_10clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT2 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method2_20clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT2 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method2_2clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT2 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method2_50clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT2 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method2_5clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT2 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method3_10clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT3 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method3_20clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT3 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method3_2clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT3 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method3_50clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT3 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method3_5clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT3 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method4_10clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT4 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method4_20clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT4 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method4_2clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT4 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method4_50clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT4 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +pca_industry_grouping_method4_5clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT4 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_0_all513,"Continuous loading on the 1st robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,14,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_0_top1200_xjp_513,First principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_10_all513,"Continuous loading on the 11th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_10_top1200_xjp_513,Eleventh principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_11_all513,"Continuous loading on the 12th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_11_top1200_xjp_513,Twelfth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_12_all513,"Continuous loading on the 13th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,8,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_12_top1200_xjp_513,Thirteenth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_13_all513,"Continuous loading on the 14th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_13_top1200_xjp_513,Fourteenth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_14_all513,"Continuous loading on the 15th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_14_top1200_xjp_513,Fifteenth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_1_all513,"Continuous loading on the 2nd robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,14,15,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_1_top1200_xjp_513,Second principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_2_all513,"Continuous loading on the 3rd robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,19,24,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_2_top1200_xjp_513,Third principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_3_all513,"Continuous loading on the 4th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,15,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_3_top1200_xjp_513,Fourth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_4_all513,"Continuous loading on the 5th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,35,39,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_4_top1200_xjp_513,Fifth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_5_all513,"Continuous loading on the 6th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,10,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_5_top1200_xjp_513,Sixth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_6_all513,"Continuous loading on the 7th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,11,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_6_top1200_xjp_513,Seventh principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_7_all513,"Continuous loading on the 8th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,19,25,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_7_top1200_xjp_513,Eighth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_8_all513,"Continuous loading on the 9th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,13,14,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_8_top1200_xjp_513,Ninth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_9_all513,"Continuous loading on the 10th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_9_top1200_xjp_513,Tenth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method1_10clusters,"Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method1_20clusters,"Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method1_2clusters_2,"Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method1_50clusters,"Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method1_5clusters,"Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method2_10clusters,"Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method2_20clusters,"Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method2_2clusters_2,"Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method2_50clusters_2,"Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method2_5clusters,"Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method3_10clusters,Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method3_10clusters_2,"Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method3_20clusters,Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method3_20clusters_2,"Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method3_2clusters,"Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method3_50clusters,Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method3_50clusters_3,"Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method3_5clusters,"Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method4_10clusters,Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method4_10clusters_2,"Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method4_20clusters,Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method4_20clusters_3,"Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method4_2clusters,Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method4_2clusters_3,"Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method4_50clusters,Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method4_50clusters_2,"Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method4_5clusters,Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +principal_component_grouping_method4_5clusters_2,"Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_0,First robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,24,28,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_1,Second robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,12,16,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_10,Eleventh robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,9,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_11,Twelfth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_12,Thirteenth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,12,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_13,Fourteenth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_14,Fifteenth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,11,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_2,Third robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_3,Fourth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_4,Fifth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,10,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_5,Sixth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_6,Seventh robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,11,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_7,Eighth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,9,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_8,Ninth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,11,15,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +robust_factor_component_9,Tenth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,3,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +second_method_cluster10_all,Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT2 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,16,32,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +second_method_cluster5_all,Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT2 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,17,29,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_equity_pca_method1_group10,"Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,10,37,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_equity_pca_method1_group2,"Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_equity_pca_method1_group50,"Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_equity_pca_method3_group20,"Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,12,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_equity_pca_method4_group2,"Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method1_group2,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method1_group20,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method1_group50,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method2_group10,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method2_group2,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method2_group20,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method2_group50,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method3_group2,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method3_group5,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method3_group50,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method4_group10,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method4_group20,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top100_method4_group5,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method1_group20,"Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method1_group5,"Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method2_group10,"Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method2_group2,"Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method2_group20,"Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method2_group5,"Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method2_group50,"Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method3_group10,"Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method3_group2,"Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method3_group5,"Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method3_group50,"Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method4_group10,"Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method4_group20,"Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method4_group5,"Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top300_method4_group50,"Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method1_group10,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method1_group2,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method1_group20,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method1_group5,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method1_group50,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method2_group10,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method2_group2,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method2_group20,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method2_group5,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method2_group50_500,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method3_group10,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method3_group2,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method3_group20,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method3_group5,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method3_group50,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method4_group10_500,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method4_group2,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method4_group20,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method4_group5,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +taiwan_top500_method4_group50,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +third_method_cluster10_all,Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT3 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,37,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +third_method_cluster5_all,Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT3 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor1_grouping10,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT1 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor1_grouping2,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT1 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor1_grouping20,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT1 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor1_grouping5,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT1 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor1_grouping50,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT1 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor2_grouping10,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT2 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor2_grouping2,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT2 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor2_grouping20,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT2 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor2_grouping5,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT2 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor2_grouping50,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT2 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor3_grouping10,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT3 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor3_grouping2,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT3 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor3_grouping20,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT3 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor3_grouping5,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT3 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor3_grouping50,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT3 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor4_grouping10,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT4 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor4_grouping2,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT4 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor4_grouping20,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT4 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor4_grouping5,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT4 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1000_pca_factor4_grouping50,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT4 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor1_grouping10,Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor1_grouping2,Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor1_grouping20,Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor1_grouping5,Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor1_grouping50,Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor2_grouping10,Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor2_grouping2,Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor2_grouping20,Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor2_grouping5,Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor2_grouping50,Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor3_grouping2,Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top1500_pca_factor3_grouping5,Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method1_10,Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT1 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method1_2,Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT1 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method1_20,Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT1 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method1_5,Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT1 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method1_50,Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT1 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method2_10,Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT2 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method2_2,Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT2 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method2_20,Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT2 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method2_5,Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT2 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method2_50,Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT2 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method3_10,Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT3 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method3_2,Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT3 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method3_20,Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT3 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method3_5,Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT3 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method3_50,Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT3 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method4_10,Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT4 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method4_2,Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT4 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method4_20,Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT4 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method4_5,Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT4 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +top500_industry_grouping_method4_50,Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT4 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +tw_region_method1_grouping10,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +tw_region_method1_grouping5,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +tw_region_method2_grouping5,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +tw_region_method3_grouping10,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +tw_region_method3_grouping20,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +tw_region_method4_grouping2,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume +tw_region_method4_grouping50,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_risk.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_risk.csv new file mode 100644 index 0000000..81ec643 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_risk.csv @@ -0,0 +1,7 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +rsk68_beta,"Unitless stock beta for a European asset relative to the regional liquidity-weighted market_return, typically estimated over a long rolling window (e.g., ~504 trading days)","{'id': 'risk68', 'name': 'Forecast Data'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,0.9507,0.9589,436,826,1.0,[],risk68,Forecast Data,risk,Risk,risk-risk-models,Risk Models +rsk68_residual_return,"Idiosyncratic 1-day return for a European asset unexplained by the market factor, computed as actual return minus beta times market_return, expressed in decimal","{'id': 'risk68', 'name': 'Forecast Data'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,0.9507,0.9587,821,2301,1.0,[],risk68,Forecast Data,risk,Risk,risk-risk-models,Risk Models +rsk68_weight_dadv,"Simple moving average dollar average daily trading volume for European instruments, used as a liquidity weight (dollar units)","{'id': 'risk68', 'name': 'Forecast Data'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,0.9507,0.9884,294,500,1.0,[],risk68,Forecast Data,risk,Risk,risk-risk-models,Risk Models +rsk68_weight_edadv,"Exponentially decayed dollar average daily trading volume for European instruments, used as a liquidity weight (dollar units)","{'id': 'risk68', 'name': 'Forecast Data'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,0.9507,0.989,282,476,1.0,[],risk68,Forecast Data,risk,Risk,risk-risk-models,Risk Models +rsk68_weight_volatility_short,"Short-term volatility estimate for European instruments, expressed in decimal terms","{'id': 'risk68', 'name': 'Forecast Data'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,0.9507,0.998,434,912,1.0,[],risk68,Forecast Data,risk,Risk,risk-risk-models,Risk Models +alternative_market_cap_usd,Alternative calculation of market capitalization in US dollars.,"{'id': 'risk88', 'name': 'Other Multi-Factor Risk Models'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,1.0,1.0,548,1351,1.0,[],risk88,Other Multi-Factor Risk Models,risk,Risk,risk-risk-models,Risk Models diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_sentiment.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_sentiment.csv new file mode 100644 index 0000000..05e8889 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_sentiment.csv @@ -0,0 +1,211 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +snt21_2neg_conf_low,Lower bound of the confidence interval for the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,351,797,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neg_conf_up,Upper bound of the confidence interval for the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,212,541,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neg_max,Maximum value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,221,488,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neg_mean,Mean value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,123,249,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neg_median,Median value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,126,306,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neg_min,Minimum value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,110,254,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neg_std,Standard deviation of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,132,313,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neut_conf_low,Lower bound of the confidence interval for the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,96,198,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neut_conf_up,Upper bound of the confidence interval for the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,74,130,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neut_max,Maximum value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,85,165,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neut_mean,Mean value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,72,135,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neut_median,Median value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,75,128,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neut_min,Minimum value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,107,221,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2neut_std,Standard deviation of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,53,68,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2pos_conf_low,Lower bound of the confidence interval for the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,58,109,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2pos_conf_up,Upper bound of the confidence interval for the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,68,101,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2pos_max,Maximum value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,78,129,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2pos_mean,Mean value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,87,155,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2pos_median,Median value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,62,92,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2pos_min,Minimum value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,99,191,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_2pos_std,Standard deviation of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,118,305,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neg_conf_low,Lower bound of the confidence interval for the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,50,124,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neg_conf_up,Upper bound of the confidence interval for the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,36,52,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neg_max,Maximum value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,38,64,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neg_mean,Mean value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,40,54,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neg_median,Median value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,35,60,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neg_min,Minimum value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,35,64,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neg_std,Standard deviation of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,38,61,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neut_conf_low,Lower bound of the confidence interval for the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,36,50,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neut_conf_up,Upper bound of the confidence interval for the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,25,30,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neut_max,Maximum value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,34,36,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neut_mean,Mean value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,32,49,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neut_median,Median value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,31,45,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neut_min,Minimum value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,31,77,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3neut_std,Standard deviation of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,36,61,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3pos_conf_low,Lower bound of the confidence interval for the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,30,45,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3pos_conf_up,Upper bound of the confidence interval for the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,30,31,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3pos_max,Maximum value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,25,43,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3pos_mean,Mean value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,24,25,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3pos_median,Median value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,32,51,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3pos_min,Minimum value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,29,39,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_3pos_std,Standard deviation of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,81,161,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neg_conf_low,"Lower bound of the approximate 95% confidence interval for the daily mean negative sentiment score, aggregated from TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,38,55,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neg_conf_up,"Upper bound of the approximate 95% confidence interval for the daily mean negative sentiment score, aggregated from TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,31,39,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neg_max,Daily maximum of negative sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,42,48,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neg_mean,"Daily mean negative sentiment score from TRNA equity-linked news linked to the stock, computed by the in-house transformer in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,39,51,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neg_median,Daily median negative sentiment score from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,26,46,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neg_min,Daily minimum of negative sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,30,70,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neg_std,Daily standard deviation of negative sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,25,32,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neut_conf_low,"Daily lower bound of the approximate 95% confidence interval for the mean neutral sentiment score, aggregated from that day's TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,23,33,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neut_conf_up,"Daily upper bound of the approximate 95% confidence interval for the mean neutral sentiment score, aggregated from TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,21,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neut_max,Daily maximum of neutral sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,21,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neut_mean,"Daily mean neutral sentiment score from TRNA equity-linked news linked to the stock, computed by the in-house transformer in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,22,43,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neut_median,Daily median neutral sentiment score from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,30,34,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neut_min,Daily minimum of neutral sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,29,45,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4neut_std,Daily standard deviation of neutral sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,22,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4pos_conf_low,"Daily lower bound of the approximate 95% confidence interval for the mean positive sentiment score, aggregated from TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,27,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4pos_conf_up,"Daily upper bound of the approximate 95% confidence interval for the mean positive sentiment score, aggregated from that day's TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,43,61,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4pos_max,Daily maximum of positive sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,47,101,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4pos_mean,"Daily mean positive sentiment score from TRNA equity-linked news linked to the stock, computed by the in-house transformer in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,34,57,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4pos_median,Daily median positive sentiment score from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,26,33,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4pos_min,Daily minimum of positive sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,22,27,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_4pos_std,Daily standard deviation of positive sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,41,57,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neg_conf_low,Lower bound of the confidence interval for the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,33,44,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neg_conf_up,Upper bound of the confidence interval for the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,46,84,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neg_max,Maximum value of the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,55,128,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neg_mean,Mean value of the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,35,57,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neg_median,Median value of the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,24,30,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neg_min,Minimum value of the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,32,52,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neg_std,Standard deviation of the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,54,89,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neut_conf_low,Lower bound of the confidence interval for the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,26,31,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neut_conf_up,Upper bound of the confidence interval for the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,14,28,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neut_max,Maximum value of the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,29,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neut_mean,Mean value of the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,28,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neut_median,Median value of the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,22,33,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neut_min,Minimum value of the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,38,104,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5neut_std,Standard deviation of the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,20,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5pos_conf_low,Lower bound of the confidence interval for the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,23,36,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5pos_conf_up,Upper bound of the confidence interval for the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,21,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5pos_max,Maximum value of the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,44,96,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5pos_mean,Mean value of the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,25,36,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5pos_median,Median value of the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,16,19,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5pos_min,Minimum value of the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,31,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_5pos_std,Standard deviation of the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,22,33,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neg_conf_low,Lower bound of the approximate 95% confidence interval for the daily mean negative sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,21,35,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neg_conf_up,Upper bound of the approximate 95% confidence interval for the daily mean negative sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,18,22,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neg_max,Maximum value of the daily negative sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,24,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neg_mean,Daily mean of the negative sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,33,46,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neg_median,Daily median of the negative sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,13,29,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neg_min,Minimum value of the daily negative sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,16,23,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neg_std,Daily standard deviation of the negative sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,16,22,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neut_conf_low,Lower bound of the approximate 95% confidence interval for the daily mean neutral sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,22,38,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neut_conf_up,Upper bound of the approximate 95% confidence interval for the daily mean neutral sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,26,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neut_max,Maximum value of the daily neutral sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,21,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neut_mean,Daily mean of the neutral sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,27,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neut_median,Daily median of the neutral sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,17,22,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neut_min,Minimum value of the daily neutral sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,26,37,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_neut_std,Daily standard deviation of the neutral sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,23,29,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_pos_conf_low,Lower bound of the approximate 95% confidence interval for the daily mean positive sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,21,22,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_pos_conf_up,Upper bound of the approximate 95% confidence interval for the daily mean positive sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,25,28,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_pos_max,Maximum value of the daily positive sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,28,32,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_pos_mean,Daily mean of the positive sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,69,143,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_pos_median,Daily median of the positive sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,27,45,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_pos_min,"Minimum value of the daily positive sentiment scores (0–1) for a stock in the USA D0 module, aggregated from TRNA equity-linked events","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,35,45,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt21_pos_std,Daily standard deviation of the positive sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,37,46,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neg_conf_low,Lower bound of the 95% confidence interval for the mean negative sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,100,159,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neg_conf_up,Upper bound of the 95% confidence interval for the mean negative sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,82,198,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neg_max,Maximum negative sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,57,110,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neg_mean,Average negative sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,53,77,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neg_median,Median negative sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,55,85,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neg_min,Minimum negative sentiment score across all Inferess-linked news stories for the stock on that date (same-day aggregation); unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,40,48,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neg_std,Standard deviation of negative sentiment scores across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,67,122,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neut_conf_low,Lower bound of the 95% confidence interval for the mean neutral sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,36,39,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neut_conf_up,Upper bound of the 95% confidence interval for the mean neutral sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,31,38,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neut_max,Maximum neutral sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,47,114,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neut_mean,Average neutral sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,44,60,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neut_median,Median neutral sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,26,52,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neut_min,Minimum neutral sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,25,36,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2neut_std,Standard deviation of neutral sentiment scores across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,28,30,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2pos_conf_low,Lower bound of the 95% confidence interval for the mean positive sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,27,30,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2pos_conf_up,Upper bound of the 95% confidence interval for the mean positive sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,26,35,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2pos_max,Maximum positive sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,25,31,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2pos_mean,Average positive sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,31,48,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2pos_median,Median positive sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,48,105,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2pos_min,Minimum positive sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,52,63,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_2pos_std,Standard deviation of positive sentiment scores across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,24,26,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neg_conf_low,"Lower bound of the 95% confidence interval for the mean negative sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly be below 0","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,14,17,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neg_conf_up,"Upper bound of the 95% confidence interval for the mean negative sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly exceed 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,14,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neg_max,"Maximum negative sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,17,18,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neg_mean,"Average negative sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neg_median,"Median negative sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,14,19,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neg_min,"Minimum negative sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neg_std,"Standard deviation of negative sentiment scores across all stories for the stock on the day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neut_conf_low,"Lower bound of the 95% confidence interval for the mean neutral sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly be below 0","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,17,24,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neut_conf_up,"Upper bound of the 95% confidence interval for the mean neutral sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly exceed 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neut_max,"Maximum neutral sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neut_mean,"Average neutral sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neut_median,"Median neutral sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,16,20,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neut_min,"Minimum neutral sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,19,56,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3neut_std,"Standard deviation of neutral sentiment scores across all stories for the stock on the day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3pos_conf_low,"Lower bound of the 95% confidence interval for the mean positive sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly be below 0","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,11,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3pos_conf_up,"Upper bound of the 95% confidence interval for the mean positive sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly exceed 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3pos_max,"Maximum positive sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,13,15,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3pos_mean,"Average positive sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3pos_median,"Median positive sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,45,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3pos_min,"Minimum positive sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_3pos_std,"Standard deviation of positive sentiment scores across all stories for the stock on the day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,11,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neg_conf_low,"Lower bound of the 95% confidence interval for the mean negative sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,24,30,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neg_conf_up,"Upper bound of the 95% confidence interval for the mean negative sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neg_max,"Maximum negative sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,18,22,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neg_mean,"Average negative sentiment score across all stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,11,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neg_median,"Median negative sentiment score across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,15,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neg_min,"Minimum negative sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,15,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neg_std,"Standard deviation of negative sentiment scores across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neut_conf_low,"Lower bound of the 95% confidence interval for the mean neutral sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,19,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neut_conf_up,"Upper bound of the 95% confidence interval for the mean neutral sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,6,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neut_max,"Maximum neutral sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,17,21,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neut_mean,Average neutral sentiment score across stories for the stock on the date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neut_median,"Median neutral sentiment score across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neut_min,"Minimum neutral sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4neut_std,"Standard deviation of neutral sentiment scores across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4pos_conf_low,"Lower bound of the 95% confidence interval for the mean positive sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4pos_conf_up,"Upper bound of the 95% confidence interval for the mean positive sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,18,21,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4pos_max,"Maximum positive sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,13,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4pos_mean,"Average positive sentiment score across all stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4pos_median,"Median positive sentiment score across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4pos_min,"Minimum positive sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,16,18,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_4pos_std,"Standard deviation of positive sentiment scores across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,14,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neg_conf_low,"Lower bound of the 95% confidence interval for the mean negative sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neg_conf_up,"Upper bound of the 95% confidence interval for the mean negative sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neg_max,"Maximum negative sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,16,18,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neg_mean,"Average negative sentiment score across all stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,17,19,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neg_median,"Median negative sentiment score across stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,14,16,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neg_min,"Minimum negative sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,16,18,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neg_std,"Standard deviation of negative sentiment scores across stories for the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neut_conf_low,"Lower bound of the 95% confidence interval for the mean neutral sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neut_conf_up,"Upper bound of the 95% confidence interval for the mean neutral sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,5,6,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neut_max,"Maximum neutral sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neut_mean,"Average neutral sentiment score across all stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,11,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neut_median,"Median neutral sentiment score across stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neut_min,"Minimum neutral sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,5,5,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5neut_std,"Standard deviation of neutral sentiment scores across stories for the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5pos_conf_low,"Lower bound of the 95% confidence interval for the mean positive sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5pos_conf_up,"Upper bound of the 95% confidence interval for the mean positive sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5pos_max,"Maximum positive sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,16,21,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5pos_mean,"Average positive sentiment score across news stories linked to the stock on the day (USA, D0, variant 5); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5pos_median,"Median positive sentiment score across stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,4,4,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5pos_min,"Minimum positive sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,6,6,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_5pos_std,"Standard deviation of positive sentiment scores across stories for the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neg_conf_low,"Lower bound of the 95% confidence interval for the negative sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,7,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neg_conf_up,"Upper bound of the 95% confidence interval for the negative sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neg_max,"Maximum negative sentiment score across all news stories for the stock on the day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,7,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neg_mean,"Average negative sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,13,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neg_median,"Median negative sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neg_min,"Minimum negative sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,5,5,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neg_std,"Standard deviation of negative sentiment scores across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neut_conf_low,"Lower bound of the 95% confidence interval for the neutral sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,6,6,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neut_conf_up,"Upper bound of the 95% confidence interval for the neutral sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neut_max,"Maximum neutral sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,6,6,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neut_mean,"Average neutral sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,18,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neut_median,"Median neutral sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neut_min,"Minimum neutral sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,4,4,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_neut_std,"Standard deviation of neutral sentiment scores across all stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_pos_conf_low,"Lower bound of the 95% confidence interval for the positive sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,11,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_pos_conf_up,"Upper bound of the 95% confidence interval for the positive sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,15,15,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_pos_max,"Maximum positive sentiment score across all stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,19,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_pos_mean,"Average positive sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,31,38,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_pos_median,"Median positive sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,32,64,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_pos_min,"Minimum positive sentiment score across all stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,77,161,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment +snt23_pos_std,"Standard deviation of positive sentiment scores across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,24,29,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment diff --git a/simple72/dataset/datafields_cache_IND_TOP500_D1_shortinterest.csv b/simple72/dataset/datafields_cache_IND_TOP500_D1_shortinterest.csv new file mode 100644 index 0000000..e174951 --- /dev/null +++ b/simple72/dataset/datafields_cache_IND_TOP500_D1_shortinterest.csv @@ -0,0 +1,4 @@ +id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name +max_price_decrease_ratio,"The daily proportionate lower price limit ratio (as a decimal or percentage), representing how much a stock’s price can fall in a single day according to exchange rules or special regimes. Used to define price-down limit constraints for each stock on each analytic date","{'id': 'shortinterest5', 'name': 'Daily Price Limit Data'}","{'id': 'shortinterest', 'name': 'Short Interest'}","{'id': 'shortinterest-short-sale-models', 'name': 'Short Sale Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,251,576,1.0,[],shortinterest5,Daily Price Limit Data,shortinterest,Short Interest,shortinterest-short-sale-models,Short Sale Models +max_price_increase_ratio,"The daily proportionate upper price limit ratio (as a decimal or percentage), representing how much a stock’s price can rise in a single day according to exchange rules or special regimes. Used to define price-up limit constraints for each stock on each analytic date","{'id': 'shortinterest5', 'name': 'Daily Price Limit Data'}","{'id': 'shortinterest', 'name': 'Short Interest'}","{'id': 'shortinterest-short-sale-models', 'name': 'Short Sale Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,214,463,1.0,[],shortinterest5,Daily Price Limit Data,shortinterest,Short Interest,shortinterest-short-sale-models,Short Sale Models +price_limit_condition,"Numeric code indicating the current tradability regime for the security, such as fully tradable, limit up, limit down, or halted/suspended; requires codebook decoding for exact regime meaning","{'id': 'shortinterest5', 'name': 'Daily Price Limit Data'}","{'id': 'shortinterest', 'name': 'Short Interest'}","{'id': 'shortinterest-short-sale-models', 'name': 'Short Sale Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,207,373,1.0,[],shortinterest5,Daily Price Limit Data,shortinterest,Short Interest,shortinterest-short-sale-models,Short Sale Models diff --git a/simple72/main.py b/simple72/main.py index 10e4e9e..c16bcaa 100644 --- a/simple72/main.py +++ b/simple72/main.py @@ -206,6 +206,10 @@ async def generate_alpha(request: Request): user_category = data.get('user_category', []) categories_to_check = user_category if isinstance(user_category, list) else [user_category] if user_category else [] + # 输出用户选择的类别 + logger.info(f"[Generate] 用户选择的类别: {categories_to_check}") + logger.info(f"[Generate] Region: {data.get('user_region')}, Delay: {data.get('user_delay')}, Universe: {data.get('user_universe')}") + # 创建会话来获取数据集信息 session = SingleSession() session.auth = (data.get('brain_username'), data.get('brain_password')) @@ -655,6 +659,32 @@ async def download_datafields(request: Request): dataset_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dataset') os.makedirs(dataset_dir, exist_ok=True) + # 检查哪些类别已经存在缓存文件 + categories_to_download_filtered = [] + already_exists = [] + for cat in categories_to_download: + cache_filename = f"datafields_cache_{region}_{universe}_D{delay}_{cat}.csv" + cache_path = os.path.join(dataset_dir, cache_filename) + if os.path.exists(cache_path): + already_exists.append(cat) + logger.info(f"类别 '{cat}' 的缓存文件已存在，跳过下载: {cache_filename}") + else: + categories_to_download_filtered.append(cat) + + if already_exists: + logger.info(f"以下类别缓存已存在，将跳过: {already_exists}") + + if not categories_to_download_filtered: + logger.info("所有类别的缓存文件都已存在，无需下载") + return JSONResponse(content={ + "success": True, + "count": 0, + "message": "所有类别的缓存文件都已存在", + "skipped_categories": already_exists + }) + + categories_to_download = categories_to_download_filtered + logger.info(f"开始下载数据字段: region={region}, delay={delay}, universe={universe}, type={data_type}, categories={categories_to_download}") total_count = 0 @@ -740,7 +770,11 @@ async def download_results(alpha_id: str): # 生成时间戳 timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") zip_filename = f"{alpha_id}_{timestamp}.zip" - zip_path = os.path.join(output_dir, zip_filename) + + # 保存到 save_zip 文件夹（不会被清空） + save_zip_dir = os.path.join(transformer_dir, 'save_zip') + os.makedirs(save_zip_dir, exist_ok=True) + zip_path = os.path.join(save_zip_dir, zip_filename) # 创建 zip 文件 with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf: diff --git a/simple72/running_error.txt b/simple72/running_error.txt index 5255c86..c6771a7 100644 --- a/simple72/running_error.txt +++ b/simple72/running_error.txt @@ -1,7 +1,7 @@ { "success": true, "alpha_id": "MP5gWQva", - "stdout": "✓ 已加载模板总结文件: /Users/jack/source/mySpace/mycode/my_project/py/alpha_odoo/alpha_tools/simple72/Tranformer/template_summary.md\n✓ 已从命令行参数加载配置: /Users/jack/source/mySpace/mycode/my_project/py/alpha_odoo/alpha_tools/simple72/Tranformer/config_51a505a0-322f-45f2-9180-98c25b2e731c.json\n✓ 使用内置模板总结\n--- 正在启动 BRAIN 会话... ---\nNew session created (ID: 12659217744) with authentication response: 201, {'user': {'id': 'YC93384'}, 'token': {'expiry': 14400.0}, 'permissions': ['BEFORE_AND_AFTER_PERFORMANCE_V2', 'BRAIN_LABS', 'BRAIN_LABS_JUPYTER_LAB', 'CONSULTANT', 'MULTI_SIMULATION', 'PROD_ALPHAS', 'REFERRAL', 'VISUALIZATION', 'WORKDAY']} (新会话已创建)\n--- 正在认证 LLM Gateway... ---\n✓ LLM Gateway 认证成功\n\n--- 正在获取 Alpha ID: MP5gWQva 的详情... ---\nNew session created (ID: 12659217744) with authentication response: 201, {'user': {'id': 'YC93384'}, 'token': {'expiry': 14400.0}, 'permissions': ['BEFORE_AND_AFTER_PERFORMANCE_V2', 'BRAIN_LABS', 'BRAIN_LABS_JUPYTER_LAB', 'CONSULTANT', 'MULTI_SIMULATION', 'PROD_ALPHAS', 'REFERRAL', 'VISUALIZATION', 'WORKDAY']} (新会话已创建)\nstatus_code 429, sleep 3 seconds\nLLM Gateway Authentication successful. (LLM网关认证成功)\n--- Calling LLM to propose templates... (正在调用LLM生成模板...) ---\nLLM Gateway Authentication successful. (LLM网关认证成功)\n--- Calling LLM to propose templates... (正在调用LLM生成模板...) ---\nAlpha MP5gWQva description updated on platform. (Alpha描述已在平台更新)\nNew session created (ID: 12659217744) with authentication response: 201, {'user': {'id': 'YC93384'}, 'token': {'expiry': 14400.0}, 'permissions': ['BEFORE_AND_AFTER_PERFORMANCE_V2', 'BRAIN_LABS', 'BRAIN_LABS_JUPYTER_LAB', 'CONSULTANT', 'MULTI_SIMULATION', 'PROD_ALPHAS', 'REFERRAL', 'VISUALIZATION', 'WORKDAY']} (新会话已创建)\n✓ LLM Gateway 认证成功\nAlpha Details Retrieved (已获取Alpha详情):\n{\n \"settings\": {\n \"instrumentType\": \"EQUITY\",\n \"region\": \"IND\",\n \"universe\": \"TOP500\",\n \"delay\": 1,\n \"decay\": 12,\n \"neutralization\": \"SLOW_AND_FAST\",\n \"truncation\": 0.02,\n \"pasteurization\": \"ON\",\n \"unitHandling\": \"VERIFY\",\n \"nanHandling\": \"ON\",\n \"maxTrade\": \"OFF\",\n \"maxPosition\": \"OFF\",\n \"language\": \"FASTEXPR\",\n \"visualization\": false,\n \"startDate\": \"2014-01-01\",\n \"endDate\": \"2023-12-31\"\n },\n \"expression\": {\n \"code\": \"divide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))\",\n \"description\": \"{\\n \\\"text\\\": \\\"\\\\nWe need to generate a new, improved description for the alpha code.\\\\n\\\\nThe code:\\\\n\\\\ndivide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))\\\\n\\\\nSo the alpha is dividing the average percent change in next-year earnings estimates over the past 7 days by the number of analysts revising up Q2 earnings over the last 30 days plus a small constant.\\\\n\\\\nWe need to produce an improved description: explain investment idea, rationale for data used, rationale for operators used.\\\\n\\\\nWe need to format as:\\\\n\\\\n\\\\\\\"Idea: xxxxx\\\\\\\\nRationale for data used: xxxxx\\\\\\\\nRationale for operators used: xxxxx\\\\\\\"\\\\n\\\\nWe should produce a description that clarifies the alpha: The alpha tries to measure the momentum in earnings estimate revisions relative to the breadth of analyst revisions, possibly indicating the strength of upward sentiment. By dividing the short-term (7d) average percentage change in next-year earnings estimates by the count of analysts revising up Q2 earnings (with a small floor), it normalizes the magnitude of estimate changes by the number of analysts, adjusting for market breadth. The small constant avoids division by zero.\\\\n\\\\nRationale for data used: avg_pct_change_estimate_next_year_earnings_7d captures recent changes in forward earnings expectations; analysts_count_revising_up_quarter2_earnings_30d captures recent positive revisions for near-term quarter; using next-year vs quarter provides longer horizon vs near-term; combining them reflects both magnitude and breadth.\\\\n\\\\nRationale for operators: divide normalizes the magnitude by breadth; add ensures non-zero denominator; maybe we can mention the constant 0.0001 is to avoid division by zero.\\\\n\\\\nThe description should be clear and concise, suitable for a quantitative alpha description.\\\\n\\\\nPotential nuance: The alpha may be interpreted as a measure of how large the per-analyst estimate change is, relative to the number of analysts revising up; if many analysts revise up but the average change is small, the ratio may be low; if a few analysts raise estimates dramatically, the ratio may be high. This can signal either concentrated strong conviction or broad moderate revisions.\\\\n\\\\nWe might also mention that the 0.0001 constant ensures stability and prevents division by zero while having negligible effect on scaling.\\\\n\\\\nWe need to incorporate the idea that the alpha uses a small denominator offset to avoid extreme values.\\\\n\\\\nThus the description will be something like:\\\\n\\\\nIdea: The alpha captures the intensity of recent upward revisions in earnings estimates by dividing the short\\\\u2011term percentage change in next\\\\u2011year consensus earnings by the count of analysts raising Q2 earnings. It thus reflects the average magnitude of upward revisions per revising analyst, combining the speed of estimate moves with the breadth of positive revisions.\\\\n\\\\nRationale for data used: ... etc.\\\\n\\\\nRationale for operators used: ... etc.\\\\n\\\\nLet's produce that. Use proper punctuation.\\\\n\\\\nMake sure to mention the constant 0.0001 for stability.\\\\n\\\\nOk.\\\\n\\\\n\\\\nIdea: The alpha measures the intensity of recent upward earnings revisions by taking the short\\\\u2011term percentage change in consensus estimates for the next fiscal year and scaling it by the breadth of analysts that have raised their second\\\\u2011quarter estimates. In other words, it reflects how large the average upward revision is per revising analyst, combining the momentum of estimate changes with the number of analysts turning bullish.\\\\n\\\\nRationale for data used: \\\\n- **avg_pct_change_estimate_next_year_earnings_7d** captures the most recent (7\\\\u2011day) directional momentum in forward\\\\u2011year earnings expectations, giving a timely signal of how fast analysts are revising their views. \\\\n- **analysts_count_revising_up_quarter2_earnings_30d** measures the breadth of positive sentiment over a slightly longer (30\\\\u2011day) window for the nearer\\\\u2011term quarter, indicating how many analysts are turning optimistic. By pairing a long\\\\u2011horizon momentum metric with a near\\\\u2011term breadth metric, the alpha blends the strength of the revision trend with the consensus behind it.\\\\n\\\\nRationale for operators used: \\\\n- **divide** normalizes the magnitude of the estimate change by the count of revising analysts, producing a \\\\u201cper\\\\u2011analyst\\\\u201d revision intensity that is comparable across stocks regardless of how many analysts cover them. \\\\n- **add(..., 0.0001)** introduces a tiny constant to the denominator to avoid division\\\\u2011by\\\\u2011zero when no analysts have revised up, ensuring numerical stability without materially affecting the ratio\\\\u2019s scaling. The small offset is negligible in normal conditions but prevents extreme values or errors in thin\\\\u2011coverage names.\\\"\\n}\",\n \"operatorCount\": 2\n }\n}\n\n============================================================\n[Step 2/5] 正在生成 Alpha 模板提议...\n============================================================\ncurrent seed alpha detail (当前种子Alpha详情): {'code': 'divide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))', 'description': '{\\n \"text\": \"\\\\nWe need to generate a new, improved description for the alpha code.\\\\n\\\\nThe code:\\\\n\\\\ndivide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))\\\\n\\\\nSo the alpha is dividing the average percent change in next-year earnings estimates over the past 7 days by the number of analysts revising up Q2 earnings over the last 30 days plus a small constant.\\\\n\\\\nWe need to produce an improved description: explain investment idea, rationale for data used, rationale for operators used.\\\\n\\\\nWe need to format as:\\\\n\\\\n\\\\\"Idea: xxxxx\\\\\\\\nRationale for data used: xxxxx\\\\\\\\nRationale for operators used: xxxxx\\\\\"\\\\n\\\\nWe should produce a description that clarifies the alpha: The alpha tries to measure the momentum in earnings estimate revisions relative to the breadth of analyst revisions, possibly indicating the strength of upward sentiment. By dividing the short-term (7d) average percentage change in next-year earnings estimates by the count of analysts revising up Q2 earnings (with a small floor), it normalizes the magnitude of estimate changes by the number of analysts, adjusting for market breadth. The small constant avoids division by zero.\\\\n\\\\nRationale for data used: avg_pct_change_estimate_next_year_earnings_7d captures recent changes in forward earnings expectations; analysts_count_revising_up_quarter2_earnings_30d captures recent positive revisions for near-term quarter; using next-year vs quarter provides longer horizon vs near-term; combining them reflects both magnitude and breadth.\\\\n\\\\nRationale for operators: divide normalizes the magnitude by breadth; add ensures non-zero denominator; maybe we can mention the constant 0.0001 is to avoid division by zero.\\\\n\\\\nThe description should be clear and concise, suitable for a quantitative alpha description.\\\\n\\\\nPotential nuance: The alpha may be interpreted as a measure of how large the per-analyst estimate change is, relative to the number of analysts revising up; if many analysts revise up but the average change is small, the ratio may be low; if a few analysts raise estimates dramatically, the ratio may be high. This can signal either concentrated strong conviction or broad moderate revisions.\\\\n\\\\nWe might also mention that the 0.0001 constant ensures stability and prevents division by zero while having negligible effect on scaling.\\\\n\\\\nWe need to incorporate the idea that the alpha uses a small denominator offset to avoid extreme values.\\\\n\\\\nThus the description will be something like:\\\\n\\\\nIdea: The alpha captures the intensity of recent upward revisions in earnings estimates by dividing the short\\\\u2011term percentage change in next\\\\u2011year consensus earnings by the count of analysts raising Q2 earnings. It thus reflects the average magnitude of upward revisions per revising analyst, combining the speed of estimate moves with the breadth of positive revisions.\\\\n\\\\nRationale for data used: ... etc.\\\\n\\\\nRationale for operators used: ... etc.\\\\n\\\\nLet\\'s produce that. Use proper punctuation.\\\\n\\\\nMake sure to mention the constant 0.0001 for stability.\\\\n\\\\nOk.\\\\n\\\\n\\\\nIdea: The alpha measures the intensity of recent upward earnings revisions by taking the short\\\\u2011term percentage change in consensus estimates for the next fiscal year and scaling it by the breadth of analysts that have raised their second\\\\u2011quarter estimates. In other words, it reflects how large the average upward revision is per revising analyst, combining the momentum of estimate changes with the number of analysts turning bullish.\\\\n\\\\nRationale for data used: \\\\n- **avg_pct_change_estimate_next_year_earnings_7d** captures the most recent (7\\\\u2011day) directional momentum in forward\\\\u2011year earnings expectations, giving a timely signal of how fast analysts are revising their views. \\\\n- **analysts_count_revising_up_quarter2_earnings_30d** measures the breadth of positive sentiment over a slightly longer (30\\\\u2011day) window for the nearer\\\\u2011term quarter, indicating how many analysts are turning optimistic. By pairing a long\\\\u2011horizon momentum metric with a near\\\\u2011term breadth metric, the alpha blends the strength of the revision trend with the consensus behind it.\\\\n\\\\nRationale for operators used: \\\\n- **divide** normalizes the magnitude of the estimate change by the count of revising analysts, producing a \\\\u201cper\\\\u2011analyst\\\\u201d revision intensity that is comparable across stocks regardless of how many analysts cover them. \\\\n- **add(..., 0.0001)** introduces a tiny constant to the denominator to avoid division\\\\u2011by\\\\u2011zero when no analysts have revised up, ensuring numerical stability without materially affecting the ratio\\\\u2019s scaling. The small offset is negligible in normal conditions but prevents extreme values or errors in thin\\\\u2011coverage names.\"\\n}', 'operatorCount': 2}\n\n[Step 1/5] 正在调用 LLM 生成 Alpha 模板...\n - 模型: MiniMax-M2.7\n - 数据类型: MATRIX\nAn error occurred while calling the LLM (调用LLM时发生错误): unhashable type: 'slice'\nFailed to generate proposed alpha templates. (生成提议模板失败)\n", + "stdout": "✓ 已加载模板总结文件: /Users/jack/source/mySpace/mycode/my_project/py/alpha_odoo/alpha_tools/simple72/Tranformer/template_summary.md\n✓ 已从命令行参数加载配置: /Users/jack/source/mySpace/mycode/my_project/py/alpha_odoo/alpha_tools/simple72/Tranformer/config_51a505a0-322f-45f2-9180-98c25b2e731c.json\n✓ 使用内置模板总结\n--- 正在启动 BRAIN 会话... ---\nNew session created (ID: 12659217744) with authentication response: 201, {'user': {'id': 'YC93384'}, 'token': {'expiry': 14400.0}, 'permissions': ['BEFORE_AND_AFTER_PERFORMANCE_V2', 'BRAIN_LABS', 'BRAIN_LABS_JUPYTER_LAB', 'CONSULTANT', 'MULTI_SIMULATION', 'PROD_ALPHAS', 'REFERRAL', 'VISUALIZATION', 'WORKDAY']} (新会话已创建)\n--- 正在认证 LLM Gateway... ---\n✓ LLM Gateway 认证成功\n\n--- 正在获取 Alpha ID: MP5gWQva 的详情... ---\nNew session created (ID: 12659217744) with authentication response: 201, {'user': {'id': 'YC93384'}, 'token': {'expiry': 14400.0}, 'permissions': ['BEFORE_AND_AFTER_PERFORMANCE_V2', 'BRAIN_LABS', 'BRAIN_LABS_JUPYTER_LAB', 'CONSULTANT', 'MULTI_SIMULATION', 'PROD_ALPHAS', 'REFERRAL', 'VISUALIZATION', 'WORKDAY']} (新会话已创建)\nstatus_code 429, sleep 3 seconds\nLLM Gateway Authentication successful. (LLM网关认证成功)\n--- Calling LLM to propose templates... (正在调用LLM生成模板...) ---\nLLM Gateway Authentication successful. (LLM网关认证成功)\n--- Calling LLM to propose templates... (正在调用LLM生成模板...) ---\nAlpha MP5gWQva description updated on platform. (Alpha描述已在平台更新)\nNew session created (ID: 12659217744) with authentication response: 201, {'user': {'id': 'YC93384'}, 'token': {'expiry': 14400.0}, 'permissions': ['BEFORE_AND_AFTER_PERFORMANCE_V2', 'BRAIN_LABS', 'BRAIN_LABS_JUPYTER_LAB', 'CONSULTANT', 'MULTI_SIMULATION', 'PROD_ALPHAS', 'REFERRAL', 'VISUALIZATION', 'WORKDAY']} (新会话已创建)\n✓ LLM Gateway 认证成功\nAlpha Details Retrieved (已获取Alpha详情):\n{\n \"settings\": {\n \"instrumentType\": \"EQUITY\",\n \"region\": \"IND\",\n \"universe\": \"TOP500\",\n \"delay\": 1,\n \"decay\": 12,\n \"neutralization\": \"SLOW_AND_FAST\",\n \"truncation\": 0.02,\n \"pasteurization\": \"ON\",\n \"unitHandling\": \"VERIFY\",\n \"nanHandling\": \"ON\",\n \"maxTrade\": \"OFF\",\n \"maxPosition\": \"OFF\",\n \"language\": \"FASTEXPR\",\n \"visualization\": false,\n \"startDate\": \"2014-01-01\",\n \"endDate\": \"2023-12-31\"\n },\n \"expression\": {\n \"code\": \"divide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))\",\n \"description\": \"{\\n \\\"text\\\": \\\"\\\\nWe need to generate a new, improved description for the alpha code.\\\\n\\\\nThe code:\\\\n\\\\ndivide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))\\\\n\\\\nSo the alpha is dividing the average percent change in next-year earnings estimates over the past 7 days by the number of analysts revising up Q2 earnings over the last 30 days plus a small constant.\\\\n\\\\nWe need to produce an improved description: explain investment idea, rationale for data used, rationale for operators used.\\\\n\\\\nWe need to format as:\\\\n\\\\n\\\\\\\"Idea: xxxxx\\\\\\\\nRationale for data used: xxxxx\\\\\\\\nRationale for operators used: xxxxx\\\\\\\"\\\\n\\\\nWe should produce a description that clarifies the alpha: The alpha tries to measure the momentum in earnings estimate revisions relative to the breadth of analyst revisions, possibly indicating the strength of upward sentiment. By dividing the short-term (7d) average percentage change in next-year earnings estimates by the count of analysts revising up Q2 earnings (with a small floor), it normalizes the magnitude of estimate changes by the number of analysts, adjusting for market breadth. The small constant avoids division by zero.\\\\n\\\\nRationale for data used: avg_pct_change_estimate_next_year_earnings_7d captures recent changes in forward earnings expectations; analysts_count_revising_up_quarter2_earnings_30d captures recent positive revisions for near-term quarter; using next-year vs quarter provides longer horizon vs near-term; combining them reflects both magnitude and breadth.\\\\n\\\\nRationale for operators: divide normalizes the magnitude by breadth; add ensures non-zero denominator; maybe we can mention the constant 0.0001 is to avoid division by zero.\\\\n\\\\nThe description should be clear and concise, suitable for a quantitative alpha description.\\\\n\\\\nPotential nuance: The alpha may be interpreted as a measure of how large the per-analyst estimate change is, relative to the number of analysts revising up; if many analysts revise up but the average change is small, the ratio may be low; if a few analysts raise estimates dramatically, the ratio may be high. This can signal either concentrated strong conviction or broad moderate revisions.\\\\n\\\\nWe might also mention that the 0.0001 constant ensures stability and prevents division by zero while having negligible effect on scaling.\\\\n\\\\nWe need to incorporate the idea that the alpha uses a small denominator offset to avoid extreme values.\\\\n\\\\nThus the description will be something like:\\\\n\\\\nIdea: The alpha captures the intensity of recent upward revisions in earnings estimates by dividing the short\\\\u2011term percentage change in next\\\\u2011year consensus earnings by the count of analysts raising Q2 earnings. It thus reflects the average magnitude of upward revisions per revising analyst, combining the speed of estimate moves with the breadth of positive revisions.\\\\n\\\\nRationale for data used: ... etc.\\\\n\\\\nRationale for operators used: ... etc.\\\\n\\\\nLet's produce that. Use proper punctuation.\\\\n\\\\nMake sure to mention the constant 0.0001 for stability.\\\\n\\\\nOk.\\\\n\\\\n\\\\nIdea: The alpha measures the intensity of recent upward earnings revisions by taking the short\\\\u2011term percentage change in consensus estimates for the next fiscal year and scaling it by the breadth of analysts that have raised their second\\\\u2011quarter estimates. In other words, it reflects how large the average upward revision is per revising analyst, combining the momentum of estimate changes with the number of analysts turning bullish.\\\\n\\\\nRationale for data used: \\\\n- **avg_pct_change_estimate_next_year_earnings_7d** captures the most recent (7\\\\u2011day) directional momentum in forward\\\\u2011year earnings expectations, giving a timely signal of how fast analysts are revising their views. \\\\n- **analysts_count_revising_up_quarter2_earnings_30d** measures the breadth of positive sentiment over a slightly longer (30\\\\u2011day) window for the nearer\\\\u2011term quarter, indicating how many analysts are turning optimistic. By pairing a long\\\\u2011horizon momentum metric with a near\\\\u2011term breadth metric, the alpha blends the strength of the revision trend with the consensus behind it.\\\\n\\\\nRationale for operators used: \\\\n- **divide** normalizes the magnitude of the estimate change by the count of revising analysts, producing a \\\\u201cper\\\\u2011analyst\\\\u201d revision intensity that is comparable across stocks regardless of how many analysts cover them. \\\\n- **add(..., 0.0001)** introduces a tiny constant to the denominator to avoid division\\\\u2011by\\\\u2011zero when no analysts have revised up, ensuring numerical stability without materially affecting the ratio\\\\u2019s scaling. The small offset is negligible in normal conditions but prevents extreme values or errors in thin\\\\u2011coverage names.\\\"\\n}\",\n \"operatorCount\": 2\n }\n}\n\n============================================================\n[Step 2/5] 正在生成 Alpha 模板提议...\n============================================================\ncurrent seed alpha detail (当前种子Alpha详情): {'code': 'divide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))', 'description': '{\\n \"text\": \"\\\\nWe need to generate a new, improved description for the alpha code.\\\\n\\\\nThe code:\\\\n\\\\ndivide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))\\\\n\\\\nSo the alpha is dividing the average percent change in next-year earnings estimates over the past 7 days by the number of analysts revising up Q2 earnings over the last 30 days plus a small constant.\\\\n\\\\nWe need to produce an improved description: explain investment idea, rationale for data used, rationale for operators used.\\\\n\\\\nWe need to format as:\\\\n\\\\n\\\\\"Idea: xxxxx\\\\\\\\nRationale for data used: xxxxx\\\\\\\\nRationale for operators used: xxxxx\\\\\"\\\\n\\\\nWe should produce a description that clarifies the alpha: The alpha tries to measure the momentum in earnings estimate revisions relative to the breadth of analyst revisions, possibly indicating the strength of upward sentiment. By dividing the short-term (7d) average percentage change in next-year earnings estimates by the count of analysts revising up Q2 earnings (with a small floor), it normalizes the magnitude of estimate changes by the number of analysts, adjusting for market breadth. The small constant avoids division by zero.\\\\n\\\\nRationale for data used: avg_pct_change_estimate_next_year_earnings_7d captures recent changes in forward earnings expectations; analysts_count_revising_up_quarter2_earnings_30d captures recent positive revisions for near-term quarter; using next-year vs quarter provides longer horizon vs near-term; combining them reflects both magnitude and breadth.\\\\n\\\\nRationale for operators: divide normalizes the magnitude by breadth; add ensures non-zero denominator; maybe we can mention the constant 0.0001 is to avoid division by zero.\\\\n\\\\nThe description should be clear and concise, suitable for a quantitative alpha description.\\\\n\\\\nPotential nuance: The alpha may be interpreted as a measure of how large the per-analyst estimate change is, relative to the number of analysts revising up; if many analysts revise up but the average change is small, the ratio may be low; if a few analysts raise estimates dramatically, the ratio may be high. This can signal either concentrated strong conviction or broad moderate revisions.\\\\n\\\\nWe might also mention that the 0.0001 constant ensures stability and prevents division by zero while having negligible effect on scaling.\\\\n\\\\nWe need to incorporate the idea that the alpha uses a small denominator offset to avoid extreme values.\\\\n\\\\nThus the description will be something like:\\\\n\\\\nIdea: The alpha captures the intensity of recent upward revisions in earnings estimates by dividing the short\\\\u2011term percentage change in next\\\\u2011year consensus earnings by the count of analysts raising Q2 earnings. It thus reflects the average magnitude of upward revisions per revising analyst, combining the speed of estimate moves with the breadth of positive revisions.\\\\n\\\\nRationale for data used: ... etc.\\\\n\\\\nRationale for operators used: ... etc.\\\\n\\\\nLet\\'s produce that. Use proper punctuation.\\\\n\\\\nMake sure to mention the constant 0.0001 for stability.\\\\n\\\\nOk.\\\\n\\\\n\\\\nIdea: The alpha measures the intensity of recent upward earnings revisions by taking the short\\\\u2011term percentage change in consensus estimates for the next fiscal year and scaling it by the breadth of analysts that have raised their second\\\\u2011quarter estimates. In other words, it reflects how large the average upward revision is per revising analyst, combining the momentum of estimate changes with the number of analysts turning bullish.\\\\n\\\\nRationale for data used: \\\\n- **avg_pct_change_estimate_next_year_earnings_7d** captures the most recent (7\\\\u2011day) directional momentum in forward\\\\u2011year earnings expectations, giving a timely signal of how fast analysts are revising their views. \\\\n- **analysts_count_revising_up_quarter2_earnings_30d** measures the breadth of positive sentiment over a slightly longer (30\\\\u2011day) window for the nearer\\\\u2011term quarter, indicating how many analysts are turning optimistic. By pairing a long\\\\u2011horizon momentum metric with a near\\\\u2011term breadth metric, the alpha blends the strength of the revision trend with the consensus behind it.\\\\n\\\\nRationale for operators used: \\\\n- **divide** normalizes the magnitude of the estimate change by the count of revising analysts, producing a \\\\u201cper\\\\u2011analyst\\\\u201d revision intensity that is comparable across stocks regardless of how many analysts cover them. \\\\n- **add(..., 0.0001)** introduces a tiny constant to the denominator to avoid division\\\\u2011by\\\\u2011zero when no analysts have revised up, ensuring numerical stability without materially affecting the ratio\\\\u2019s scaling. The small offset is negligible in normal conditions but prevents extreme values or errors in thin\\\\u2011coverage names.\"\\n}', 'operatorCount': 2}\n\n[Step 1/5] 正在调用 LLM 生成 Alpha 模板...\n - 模型: MiniMax-M2.7-highspeed\n - 数据类型: MATRIX\nAn error occurred while calling the LLM (调用LLM时发生错误): unhashable type: 'slice'\nFailed to generate proposed alpha templates. (生成提议模板失败)\n", "stderr": "", "return_code": 1, "expressions_success": [], diff --git a/simple72/templates/app.js b/simple72/templates/app.js index 3a93c01..db4b955 100644 --- a/simple72/templates/app.js +++ b/simple72/templates/app.js @@ -179,63 +179,65 @@ function populateCategoryButtons(categories) { }); } -function toggleCategory(btn) { - const allBtn = document.getElementById('cat-all'); - const isAllBtn = (btn === allBtn); - - if (isAllBtn) { - // 点击"不筛选"按钮：选中所有具体类别 - allBtn.style.backgroundColor = '#000080'; - allBtn.style.color = 'white'; - - const otherBtns = categoryButtons.querySelectorAll('button:not(#cat-all)'); +// 全选/反选功能 +function toggleAllCategories() { + const otherBtns = categoryButtons.querySelectorAll('button:not(#cat-all)'); + + // 检查是否全部选中 + let allSelected = true; + otherBtns.forEach(b => { + if (!isCategoryButtonSelected(b)) { + allSelected = false; + } + }); + + if (allSelected) { + // 全部选中 -> 全不选 otherBtns.forEach(b => { - // 选中所有具体类别按钮 - b.style.backgroundColor = '#000080'; - b.style.color = 'white'; + b.style.backgroundColor = 'white'; + b.style.color = 'black'; }); } else { - // 点击具体类别按钮 - if (btn.style.backgroundColor === 'rgb(0, 0, 128)') { - btn.style.backgroundColor = '#c0c0c0'; - btn.style.color = 'black'; - } else { - btn.style.backgroundColor = '#000080'; - btn.style.color = 'white'; - } - - // 检查是否有选中的具体类别 - const anySelected = categoryButtons.querySelectorAll('button:not(#cat-all)'); - let hasSelected = false; - anySelected.forEach(b => { - if (b.style.backgroundColor === 'rgb(0, 0, 128)') { - hasSelected = true; - } + // 不是全部选中 -> 全选 + otherBtns.forEach(b => { + b.style.backgroundColor = '#87CEEB'; // 浅蓝色 + b.style.color = 'black'; }); + } +} - // 如果没有选中任何具体类别，恢复"不筛选"按钮 - if (!hasSelected) { - allBtn.style.backgroundColor = '#000080'; - allBtn.style.color = 'white'; - // 自动选中所有类别 - anySelected.forEach(b => { - b.style.backgroundColor = '#000080'; - b.style.color = 'white'; - }); - } else { - allBtn.style.backgroundColor = '#c0c0c0'; - allBtn.style.color = 'black'; - } +// 点击具体类别按钮 +function toggleCategory(btn) { + if (isCategoryButtonSelected(btn)) { + btn.style.backgroundColor = 'white'; + btn.style.color = 'black'; + } else { + btn.style.backgroundColor = '#87CEEB'; // 浅蓝色 + btn.style.color = 'black'; } } +// 检查类别按钮是否被选中的辅助函数 +function isCategoryButtonSelected(btn) { + const bg = btn.style.backgroundColor; + // 浅蓝色或 rgb(135, 206, 235) 都表示选中 + return bg === '#87CEEB' || bg === 'rgb(135, 206, 235)' || bg === 'lightblue'; +} + // 获取选中的类别列表 function getSelectedCategories() { - const allBtn = document.getElementById('cat-all'); const otherBtns = categoryButtons.querySelectorAll('button:not(#cat-all)'); - // 如果"不筛选"按钮被选中，返回所有具体类别的值 - if (allBtn.style.backgroundColor === 'rgb(0, 0, 128)') { + // 获取选中的具体类别（使用新的选中判断） + const selected = []; + otherBtns.forEach(b => { + if (isCategoryButtonSelected(b)) { + selected.push(b.dataset.value); + } + }); + + // 如果没有选中任何具体类别，返回所有类别（默认全选） + if (selected.length === 0) { const allCategories = []; otherBtns.forEach(b => { allCategories.push(b.dataset.value); @@ -243,13 +245,6 @@ function getSelectedCategories() { return allCategories; } - // 否则返回选中的具体类别 - const selected = []; - otherBtns.forEach(b => { - if (b.style.backgroundColor === 'rgb(0, 0, 128)') { - selected.push(b.dataset.value); - } - }); return selected; } @@ -323,20 +318,10 @@ form.addEventListener('submit', async (e) => { if (delay) formData.user_delay = parseInt(delay); if (universe) formData.user_universe = universe; - const allBtn = document.getElementById('cat-all'); - let selectedCategories = []; - - if (allBtn.style.backgroundColor !== 'rgb(0, 0, 128)') { - const categoryBtns = categoryButtons.querySelectorAll('button:not(#cat-all)'); - categoryBtns.forEach(btn => { - if (btn.style.backgroundColor === 'rgb(0, 0, 128)') { - selectedCategories.push(btn.dataset.value); - } - }); - - if (selectedCategories.length > 0) { - formData.user_category = selectedCategories; - } + // 使用 getSelectedCategories 获取选中的类别 + const selectedCategories = getSelectedCategories(); + if (selectedCategories.length > 0) { + formData.user_category = selectedCategories; } submitBtn.disabled = true; diff --git a/simple72/templates/index.html b/simple72/templates/index.html index fa6c378..e08be98 100644 --- a/simple72/templates/index.html +++ b/simple72/templates/index.html @@ -107,6 +107,13 @@ +
+ + +
+
-
- - -
-
-