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FieldDownloader/reference_fields/model16_data-fields.json

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[
{
"id": "analyst_revision_rank_derivative",
"description": "Change in ranking for analyst revisions and momentum compared to previous period.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "cashflow_efficiency_rank_derivative",
"description": "Change in ranking for cash flow generation and profitability compared to previous period.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "composite_factor_score_derivative",
"description": "Change in overall composite factor score from the prior period.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "earnings_certainty_rank_derivative",
"description": "Change in ranking for earnings sustainability and certainty compared to previous period.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_bfl_growth",
"description": "The purpose of this metric is to qualify the expected MT growth potential of the stock.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_bfl_momentum",
"description": "The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_bfl_profitability",
"description": "The purpose of this metric is to rank stock based on their ability to generate cash flows.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_bfl_quality",
"description": "The purpose of this metric is to measure both the sustainability and certainty of earnings.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_bfl_surface",
"description": "The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_bfl_surface_accel",
"description": "The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_bfl_total",
"description": "The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_bfl_value",
"description": "The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_growth",
"description": "The purpose of this metric is to qualify the expected MT growth potential of the stock.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_momentum",
"description": "The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_profitability",
"description": "The purpose of this metric is to rank stock based on their ability to generate cash flows.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_quality",
"description": "The purpose of this metric is to measure both the sustainability and certainty of earnings.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_surface",
"description": "The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_surface_accel",
"description": "The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_total",
"description": "The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "fscore_value",
"description": "The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "growth_potential_rank_derivative",
"description": "Change in ranking for medium-term growth potential compared to previous period.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "multi_factor_acceleration_score_derivative",
"description": "Change in the acceleration of multi-factor score compared to previous period.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "multi_factor_static_score_derivative",
"description": "Change in static multi-factor score compared to previous period.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
},
{
"id": "relative_valuation_rank_derivative",
"description": "Change in ranking for valuation metrics compared to previous period.",
"dataset_id": "model16",
"dataset_name": "Fundamental Scores",
"category_id": "model",
"category_name": "Model",
"region": "USA",
"delay": 1,
"universe": "TOP3000",
"type": "MATRIX"
}
]