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**中文版本** |
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**名称** |
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机构投资者持仓分歧度因子 |
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**假设** |
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当同一行业内多家重要机构投资者（如顶级公募、社保基金、QFII等）对某只股票的持仓方向出现显著分歧（例如部分大幅增持而部分大幅减持）时，表明市场对该股票的未来预期存在巨大不确定性。这种分歧通常在基本面或消息面出现重大但尚未被市场充分消化的潜在变化时产生。短期内，分歧可能导致股价波动加剧且缺乏明确方向；但从中期看，随着信息逐步明朗，股价将向代表正确预期的一方回归。因此，可以构建策略，在分歧度达到极值时建立仓位，押注于后续的信息明朗化和价格收敛。 |
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**实施方案** |
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1. **数据准备**：收集并整合上市公司定期报告（如季报、年报）中披露的前十大流通股东中的机构投资者持仓变动数据。识别出具有市场影响力的机构类型。 |
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2. **计算分歧度**： |
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* 对每个报告期，计算每只股票在特定行业内，不同类型重要机构投资者持仓变动（例如，持股数量环比变化率）的标准差或基尼系数，作为“机构持仓分歧度”的原始指标。 |
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* 使用 `ts_zscore` 算子对该原始分歧度进行横截面标准化，以消除市场整体情绪波动的影响。 |
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* 应用 `ts_decay_linear` 或类似的时序衰减算子，赋予近期报告期的分歧度更高权重，以捕捉最新动态。 |
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3. **信号生成**：当某股票的标准化的时序加权分歧度突破历史阈值（例如，过去两年内的90%分位数）时，生成信号。可考虑在信号出现后的一段时间内（如下一报告期数据披露前）建立仓位。 |
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4. **仓位方向**：此因子本身不直接指示多空方向。方向的确立需要结合其他辅助指标，例如： |
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* 结合同期股价表现（分歧期内股价相对强势，可能预示增持方观点正确，考虑多头；反之考虑空头）。 |
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* 或结合基本面趋势（如盈利预测修正方向）来判断哪方机构可能更具前瞻性。 |
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**阿尔法因子优化建议** |
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1. **动态阈值调整**：分歧度的显著性可能随市场波动率变化。建议使用波动率调整阈值，例如在市场波动率高的时期，适当提高触发信号的阈值，以减少假信号。 |
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2. **机构权重差异化**：不同机构的投资能力和市场影响力不同。可尝试根据机构的历史业绩或管理规模为其持仓变动赋予不同的权重，而不是简单平均计算分歧度，从而使分歧度指标更能反映“聪明钱”的博弈。 |
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3. **结合事件驱动**：将分歧度因子与特定公司事件（如重大资产重组、核心技术突破、监管调查等）的发布时点结合。在事件窗口期内，若出现高分歧度，其信号强度可能更强。可使用条件算子，在事件发生后的特定时间段内激活或增强该因子的权重。 |
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4. **行业与市值中性化**：分歧度水平可能受行业特性和公司市值影响。强烈建议在因子构建的最后阶段，采用横截面分组中性化处理（如按行业和市值分组），确保因子收益来源于个股选择而非风格暴露。 |
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--- |
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**English Version** |
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**Name** |
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Institutional Investor Holding Divergence Factor |
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**Hypothesis** |
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When major institutional investors within the same industry exhibit significant divergence in their positioning towards a specific stock, it indicates substantial uncertainty regarding the stock's future prospects. This divergence often arises when there are material, yet not fully digested, changes in fundamentals or news flow. In the short term, such divergence can lead to increased price volatility without a clear trend. However, over the medium term, as information becomes clearer, the stock price tends to converge towards the side representing the correct expectation. Therefore, a strategy can be constructed to establish positions when divergence reaches an extreme, betting on the subsequent information clarification and price convergence. |
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**Implementation Plan** |
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1. **Data Preparation**: Collect and integrate institutional holding change data from regular company reports, focusing on top circulating shareholders identified as influential market participants. |
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2. **Calculate Divergence**: |
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* For each reporting period, calculate a divergence metric for each stock within its specific industry. This could be the standard deviation or Gini coefficient of the holding changes among different types of key institutions. |
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* Use the `ts_zscore` operator to cross-sectionally standardize this raw divergence metric, mitigating the impact of overall market sentiment swings. |
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* Apply a time-series decay operator to assign greater weight to more recent reporting periods, capturing the latest dynamics. |
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3. **Signal Generation**: Generate a signal when a stock's normalized, time-weighted divergence breaches a historical threshold. Consider establishing positions within a defined window after the signal appears. |
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4. **Position Direction**: This factor does not directly indicate long/short direction. Direction determination requires auxiliary indicators, such as: |
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* Concurrent stock price performance during the divergence period. |
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* Underlying fundamental trend to judge which institutional view might be more prescient. |
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**Alpha Factor Optimization Suggestions** |
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1. **Dynamic Threshold Adjustment**: The significance of divergence may vary with market volatility. Consider adjusting the signal threshold dynamically. |
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2. **Differentiated Institutional Weighting**: Assign different weights to holding changes based on an institution's historical performance or AUM, refining the divergence metric to better reflect the博弈 of "smart money". |
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3. **Integration with Event Drivers**: Combine the divergence factor with the announcement timing of specific corporate events. High divergence during event windows might indicate stronger signals. |
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4. **Industry and Size Neutralization**: Divergence levels might be influenced by industry characteristics and company size. It is strongly recommended to apply cross-sectional neutralization in the final step of factor construction. |
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ts_rank(ts_delta(divide(fnd6_oiadps, sales_ps), 63), 252) |
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reverse(ts_rank(ts_sum(ts_decay_linear(divide(fnd6_ciother, fnd6_capxv), 42), 126))) |
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multiply(ts_zscore(ts_delta(fnd6_newa2v1300_oiadp, 21), 63), reverse(ts_rank(ts_mean(fnd6_newqeventv110_gdwliaq, 84), 126))) |
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subtract(ts_rank(ts_product(ts_delay(fnd6_newqeventv110_spcedq, 5), 63), ts_rank(ts_sum(fnd6_newqv1300_ciotherq, 42), 84))) |
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if_else(ts_delta(forward_price_120, 10) > 0, ts_rank(ts_av_diff(multi_factor_acceleration_score_derivative, 21), 63), reverse(ts_rank(ts_corr(anl4_netdebt_flag, min_reported_eps_guidance, 42), 84))) |
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multiply(ts_decay_linear(pv13_com_rk_au, 21), reverse(ts_rank(ts_std_dev(pv13_h2_min2_1k_sector, 42), 126))) |
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subtract(ts_mean(ts_delta(pv13_h_min22_1000_sector, 10), 21), ts_rank(ts_covariance(pv13_h_min24_500_sector, pv13_h_min2_focused_sector, 42), 84)) |
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if_else(ts_zscore(pv13_h_min52_1k_sector, 21) > 1, reverse(ts_rank(ts_backfill(news_max_up_amt, 42), 63)), ts_rank(ts_count_nans(nws18_sse, 21), 84)) |
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multiply(ts_sum(ts_delay(rp_nip_ratings, 5), 21), reverse(ts_rank(ts_arg_max(fn_comp_not_rec_stock_options_a, 42), 126))) |
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subtract(ts_rank(ts_arg_min(fn_comp_not_rec_stock_options_q, 21), 42), ts_mean(ts_delta(fn_def_tax_liab_a, 10), 63)) |
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if_else(ts_delta(fn_op_lease_min_pay_due_in_2y_a, 5) > 0, reverse(ts_rank(ts_step(1), 21)), ts_rank(ts_scale(fn_treasury_stock_shares_a, 42), 84)) |
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multiply(ts_decay_linear(fnd2_dbplanepdfbnfpnext12m, 21), reverse(ts_rank(ts_quantile(forward_price_120, 42), 126))) |
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subtract(ts_mean(ts_regression(fnd6_oiadps, sales_ps, 21, 0, 0), 42), ts_rank(ts_zscore(multi_factor_acceleration_score_derivative, 21), 84)) |
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if_else(ts_delta(anl4_netdebt_flag, 10) > 0, reverse(ts_rank(ts_sum(min_reported_eps_guidance, 21), 63)), ts_rank(ts_av_diff(pv13_com_rk_au, 42), 84)) |
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multiply(ts_decay_linear(pv13_h2_min2_1k_sector, 21), reverse(ts_rank(ts_corr(pv13_h_min22_1000_sector, pv13_h_min24_500_sector, 42), 126))) |
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subtract(ts_rank(ts_delta(pv13_h_min2_focused_sector, 10), 21), ts_mean(ts_product(pv13_h_min52_1k_sector, 42), 84)) |
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if_else(ts_zscore(news_max_up_amt, 21) > 1, reverse(ts_rank(ts_backfill(nws18_sse, 42), 63)), ts_rank(ts_count_nans(rp_nip_ratings, 21), 84)) |
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multiply(ts_sum(ts_delay(fn_comp_not_rec_stock_options_a, 5), 21), reverse(ts_rank(ts_arg_max(fn_comp_not_rec_stock_options_q, 42), 126))) |
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subtract(ts_rank(ts_arg_min(fn_def_tax_liab_a, 21), 42), ts_mean(ts_delta(fn_op_lease_min_pay_due_in_2y_a, 10), 63)) |
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ts_rank(ts_delta(forward_price_120, 63), 252) |
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ts_rank(ts_sum(ts_delta(fnd6_capxv, 63), 252), 252) |
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ts_rank(ts_av_diff(fnd6_ciother, 63), 252) |
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ts_rank(ts_mean(fnd6_newa2v1300_oiadp, 63), 252) |
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ts_rank(ts_std_dev(fnd6_newqeventv110_gdwliaq, 63), 252) |
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ts_rank(ts_corr(fnd6_newqeventv110_spcedq, fnd6_newqv1300_ciotherq, 63), 252) |
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ts_rank(ts_zscore(fnd6_oiadps, 63), 252) |
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ts_rank(ts_delay(sales_ps, 63), 252) |
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ts_rank(ts_product(multi_factor_acceleration_score_derivative, 63), 252) |
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ts_rank(ts_quantile(anl4_netdebt_flag, 63), 252) |
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ts_rank(ts_backfill(min_reported_eps_guidance, 63), 252) |
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ts_rank(ts_covariance(pv13_com_rk_au, forward_price_120, 63), 252) |
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ts_rank(ts_decay_linear(pv13_h2_min2_1k_sector, 63), 252) |
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ts_rank(ts_arg_max(pv13_h_min22_1000_sector, 63), 252) |
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ts_rank(ts_arg_min(pv13_h_min24_500_sector, 63), 252) |
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ts_rank(ts_count_nans(pv13_h_min2_focused_sector, 63), 252) |
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ts_rank(ts_scale(pv13_h_min52_1k_sector, 63), 252) |
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ts_rank(ts_regression(news_max_up_amt, nws18_sse, 63), 252) |
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ts_rank(ts_step(1), 252) |
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ts_rank(ts_delta(rp_nip_ratings, 63), 252) |
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负债增值 |
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[[假设]] |
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负债公允价值上升可能意味着公司承担了高于预期的成本。这种情况可能恶化公司的财务健康状况，进而导致盈利能力下降或引发财务困境。 |
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[[实施方案]] |
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基于基本面数据，当负债公允价值在一年内出现上升时建立空头仓位，反之则建立多头仓位。 |
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[[实施优化建议]] |
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能否通过缩短观测周期（例如季度数据）来提升策略准确性？ |
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递延收入 |
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[[假设]] |
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递延收入较高的公司在未来确认收入时，往往会给市场带来超预期表现。 |
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[[实施方案]] |
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fnd6_drc字段代表递延收入。为提高数据字段覆盖度，需使用时序回填算子。将递延收入除以总资产以消除企业规模影响。 |
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[[实施优化建议]] |
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建议采用横截面算子代替原始比率值来确定股票权重。可运用分组算子对同类股票进行横向比较。 |
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降低负债水平 |
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[[假设]] |
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对负债相较过往有所减少的公司建立多头仓位，反之对负债增加的公司建立空头仓位。 |
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[[实施方案]] |
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采用基础数据“负债”验证该假设，使用时序分位数算子分析该字段过去六个月的变化趋势。 |
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[[阿尔法因子优化建议]] |
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利用算子的驱动参数对负债数据进行分布形态转换。 |
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财务杠杆效应 |
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[[假设]] |
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高负债资产比率的企业——若财务状况稳健、现金流充裕——常将债务作为战略工具推动扩张性增长。通过有效运用财务杠杆，这类公司将借贷资金投入高潜力项目，从而更可能获得超额收益。 |
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[[实施方案]] |
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采用"负债"与"资产"数据构建比率指标。 |
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[[阿尔法因子优化建议]] |
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该比率在不同行业间差异显著，是否值得采用替代性行业中性化处理方案？ |
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社交媒体效应 |
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[[假设]] |
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表现不佳的股票在社交媒体平台上通常会被更频繁地讨论，因此可能产生更高的相对情绪讨论量，建议考虑对这类股票建立空头仓位。 |
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[[实施方案]] |
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若监测到情绪讨论量增加，则通过社交媒体数据字段施加负权重配置。 |
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估值偏离波动空头策略 |
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[[假设]] |
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价值因子与动量因子在股票中通常不相关，若某只股票同时呈现高动量与高价值评分相关性，则表明其股价与内在价值存在偏离——据此我们对这类股票建立空头仓位。 |
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[[实施方案]] |
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使用ts_corr()函数测算过去三年间估值评分与动量评分的相关性，通过ts_backfill()算子对前期缺失数据进行回填。 |
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[[阿尔法因子优化建议]] |
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建议运用分组算子与模型数据，在同类股票组内进行横向比较分析。 |
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网络依存度 |
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[[假设]] |
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数据字段中较高的枢纽评分意味着企业客户拥有广泛业务连接，而较低评分则表明合作伙伴集中度较高。若企业的客户枢纽评分偏低，说明其客户合作伙伴较少，可能更依赖该企业。这对企业股票具有积极意义，意味着被替代风险较低，因此长期持有此类股票或是明智选择。 |
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[[实施方案]] |
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基于价量数据，对过去两年客户枢纽评分持续较低的企业股票建立多头仓位。 |
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[[阿尔法因子优化建议]] |
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在现有表达式基础上叠加横截面算子是否有助于提升策略表现？ |
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新闻驱动型波动率 |
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[[假设]] |
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新闻发布后股价波动剧烈的公司股票，往往受到市场情绪分化影响，可能引发剧烈波动行情。建议在近期新闻发布导致的极端波动阶段回避交易。 |
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[[实施方案]] |
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使用'news_session_range'新闻数据字段，通过'ts_backfill'算子提升数据覆盖度。结合'ts_arg_max'算子识别过去一季度内达到最大波动值的交易天数距离当前的时间间隔。该间隔天数越短，股票在阿尔法向量中的配置权重越低。 |
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[[阿尔法因子优化建议]] |
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近期新闻事件的影响时效有限，采用对接近零值数据快速调整阿尔法权重的算子（而非均等处理远期数据）能否提升策略表现？同时，使用交易条件限定算子是否有助于降低换手率？ |
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隐含波动率价差预测因子 |
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[[假设]] |
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若看涨期权未平仓量高于看跌期权未平仓量，基于隐含波动率价差强度指标，股票可能出现上涨行情；反之则可能下跌。 |
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[[实施方案]] |
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使用'trade_when'算子，以看涨-看跌期权未平仓量比值为条件。当该比值小于1时，基于隐含波动率价差强度指标建立股票多头仓位（采用期权数据）。 |
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[[阿尔法因子优化建议]] |
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基于自建分组（如历史波动率分组）对阿尔法因子进行自定义中性化处理，能否提升细分市场的策略表现？可结合分档算子或分层算子与排序算子实现自定义中性化。 |
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研发转化滞后效应 |
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[[假设]] |
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高研发投入的公司若未能及时将研发成果转化为商业产出，可能面临资本效率低下和未来增长动力不足的风险。这类公司通常具有较高的研发费用资本化率，但专利产出或新产品收入占比偏低，市场可能在一段时间后重新评估其研发效率，导致股价回调。 |
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[[实施方案]] |
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基于研发费用资本化金额与专利授权数量（或新产品销售收入）构建“研发转化效率比”。使用时序滞后算子将专利数据滞后6-12个月以匹配研发投入周期，再通过ts_mean计算过去三年的平均转化效率。对效率持续偏低（后30%）的公司建立空头仓位，对效率显著提升（前30%）的公司建立多头仓位。 |
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[[阿尔法因子优化建议]] |
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不同行业的研发周期和转化模式差异巨大（如医药vs软件）。是否可采用行业动态分档，在各自行业队列内计算转化效率的分位数排名，以消除行业特性带来的偏差？ |
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供应链韧性溢价 |
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--- |
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[[假设]] |
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拥有多元化、抗冲击供应链的公司在面临全球性或区域性供应链中断时，表现出更强的营收稳定性和更快的恢复能力。这种韧性在危机期间被市场低估，但在多次冲击后会被逐步定价，形成长期超额收益。 |
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[[实施方案]] |
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整合供应商集中度、关键物料替代源数量、物流枢纽分布广度等数据字段，构建“供应链韧性评分”。使用时序波动率算子计算该评分在过往供应链危机事件期（如疫情、地缘冲突）内的稳定性，并对稳定性高的公司赋予正向权重。采用ts_zscore对评分进行标准化，结合横截面排序动态调整仓位。 |
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[[实施优化建议]] |
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建议引入事件驱动算子，仅在监测到供应链压力指数（如全球物流延误指数）突破阈值时触发该因子，以提升策略的敏锐度和稀疏性，降低非危机时期的无效换手。 |
||||
|
||||
--- |
||||
|
||||
碳排放强度收敛交易 |
||||
|
||||
[[假设]] |
||||
在强监管或碳税预期升温的背景下，碳排放强度显著高于行业平均的公司将面临更大的合规成本或声誉风险，其估值可能受压；而低于平均的公司则可能获得政策红利或绿色溢价。两者间的差距会随着政策推进而逐步收敛。 |
||||
|
||||
[[实施方案]] |
||||
使用公司碳排放总量与营收的比率，计算其与行业平均值的偏离度（横截面z值）。通过ts_decay_linear算子对偏离度进行时间加权，赋予近期偏差更高权重。对偏离度持续扩大且处于高位的公司建立空头，对偏离度收窄或持续低位的公司建立多头。 |
||||
|
||||
[[阿尔法因子优化建议]] |
||||
能否结合舆情数据，对“碳税立法进展”“ESG基金流入”等主题新闻进行情感分析，构建动态加权系数？当政策舆情升温时，放大该因子的权重分配，以捕捉事件催化下的收敛加速行情。 |
||||
|
||||
--- |
||||
|
||||
管理层薪酬激励错配因子 |
||||
|
||||
[[假设]] |
||||
管理层薪酬结构中若短期激励占比过高，可能导致决策短视，损害长期价值；而长期股权激励与公司长期业绩（如3年ROIC、客户留存率）挂钩较好的公司，更可能实现可持续增长。市场可能逐步识别并奖励这种治理优势。 |
||||
|
||||
[[实施方案]] |
||||
提取管理层薪酬明细字段，计算“长期激励占比”（股权激励价值/总薪酬）与“长期业绩挂钩强度”（薪酬条款中与3年以上业绩指标挂钩的比例）。使用ts_corr计算两者在过去五年的滚动相关性。对相关性高且持续提升的公司赋予正向阿尔法权重。 |
||||
|
||||
[[实施优化建议]] |
||||
建议引入股东治理数据（如机构股东持股比例、投票权集中度），作为调节变量。在治理监督较强的公司群体中，该因子的预测力可能更显著，可采用条件算子进行分层处理。 |
||||
|
||||
--- |
||||
|
||||
客户生命周期价值动量 |
||||
|
||||
[[假设]] |
||||
订阅制或高复购率行业（如SaaS、消费会员）中，客户生命周期价值（LTV）的加速增长或衰减，领先于营收变化。LTV增长动能强的公司，其未来现金流稳定性更高，应享有估值溢价；动能衰减则预示基本面临顶。 |
||||
|
||||
[[实施方案]] |
||||
基于客户新增数量、流失率、客单价等字段，通过ts_growth算子计算LTV的季度环比增长率。使用时序动量算子（如ts_rank对过去4个季度的增长率进行排序），对动量处于持续上升通道的公司建立多头仓位。采用行业中性化处理，仅在细分赛道（如企业级SaaS）内进行横截面比较。 |
||||
|
||||
[[阿尔法因子优化建议]] |
||||
为降低噪声，可结合财报电话会文本分析，提取管理层对“客户健康度”“留存趋势”的定性评论，构建情绪调整因子。当量化LTV动量与文本情绪一致时，增强信号权重；当背离时，降低权重或屏蔽信号。 |
||||
|
||||
--- |
||||
|
||||
ESG争议事件修复效应 |
||||
[[假设]] |
||||
|
||||
ESG争议事件爆发后，市场往往存在短期过度反应，导致股价偏离内在价值。若公司在争议后及时采取有效整改措施（如完善合规体系、发布整改报告、引入第三方监督），其ESG表现将逐步修复，市场情绪也会随之回暖，股价大概率回归合理区间，此类公司可建立多头仓位；反之，对争议后无整改动作、ESG表现持续恶化的公司，应建立空头仓位以规避风险。 |
||||
|
||||
[[实施方案]] |
||||
|
||||
筛选过去12个月内发生ESG争议事件的标的，跟踪事件爆发后3个月、6个月、12个月的整改进展及ESG表现变化趋势。通过时序分析工具量化整改措施的落地成效，结合股价波动数据，构建“争议严重程度-整改效率-股价修复幅度”的关联指标，对整改成效显著且股价尚未充分修复的标的配置正向权重。 |
||||
|
||||
[[阿尔法因子优化建议]] |
||||
|
||||
建议按ESG争议类型（环境违规、社会责任缺失、公司治理缺陷）进行分组，不同类型争议的修复周期与市场敏感度差异较大，分组后可提升因子针对性。同时，引入行业ESG基准值做中性化处理，消除行业属性对ESG表现及整改难度的影响；可叠加流动性因子过滤，避免因股价修复过程中流动性不足导致的交易成本上升。帮我写一份类似这样的金融逻辑的描述, |
||||
如果你不清楚我有什么数据集字段, 可以不用说, 只需要逻辑描述清晰就行 |
||||
输出的格式和我上面给你的例子一样, |
||||
但是要创新一个金融逻辑研究方向 |
||||
|
||||
先生成一份中文的, 格式如下: |
||||
|
||||
输出格式: |
||||
**名称** |
||||
... |
||||
**假设** |
||||
... |
||||
**实施方案** |
||||
... |
||||
**阿尔法因子优化建议** |
||||
|
||||
然后再将生成出来的内容, 翻译一份英文,格式和中文一样 |
||||
@ -0,0 +1,229 @@ |
||||
任务指令 |
||||
**Name** |
||||
Institutional Herding Reversal Effect |
||||
**Hypothesis** |
||||
When a large number of institutional investors concentrate on buying a particular stock within a short period, it can create a "herding effect," potentially driving the stock price away from its fundamental value. However, such concentrated positioning is often accompanied by diminishing informational advantages and increasing liquidity needs. Once market sentiment reverses or a negative catalyst emerges, institutions might be forced to sell simultaneously, leading to sharp price reversals. Therefore, establishing short positions in stocks with a significant recent surge in institutional concentration and long positions in stocks where institutional ownership has stabilized after a period of disorderly selling may capture the alpha generated by this behavioral finance phenomenon. |
||||
**Implementation Plan** |
||||
Utilize data on changes in institutional ownership holdings. Calculate the quarterly change rate of institutional ownership and its cross-sectional percentile rank. Employ the `ts_rank` operator to identify stocks with abnormally high growth in institutional ownership (e.g., top 10%). Simultaneously, use the `ts_decay_linear` operator to apply time decay to selling pressure, identifying stocks where ownership has stabilized after a period of selling. Assign negative weights to the former and positive weights to the latter. |
||||
**Alpha Factor Optimization Suggestions** |
||||
The impact of institutional behavior varies across market cap styles (e.g., persistence might be stronger in large-caps, while reversal effects could be more violent in small-caps). Would it be beneficial to group stocks by market capitalization and calculate the abnormality of institutional behavior within each group to enhance the factor's robustness? Furthermore, incorporating overall market liquidity conditions (e.g., using the `trade_when` operator) to overweight this factor specifically during periods of tightening liquidity might better capture reversal opportunities arising from forced institutional selling. |
||||
|
||||
*=========================================================================================* |
||||
输出格式： |
||||
输出必须是且仅是纯文本。 |
||||
每一行是一个完整、独立、语法正确的WebSim表达式。 |
||||
严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。 |
||||
===================== !!! 重点(输出方式) !!! ===================== |
||||
现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。 |
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 |
||||
**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)： |
||||
表达式 |
||||
表达式 |
||||
表达式 |
||||
... |
||||
表达式 |
||||
================================================================= |
||||
重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。 |
||||
以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 20 个alpha： |
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 |
||||
|
||||
================================================================= |
||||
以下是错误的组合, 请勿类似的操作： |
||||
Operator ts_product does not support event inputs |
||||
Operator ts_zscore does not support event inputs |
||||
Operator ts_mean does not support event inputs |
||||
Operator ts_scale does not support event inputs |
||||
Operator add does not support event inputs |
||||
Operator sign does not support event inputs |
||||
Operator subtract does not support event inputs |
||||
Operator ts_delta does not support event inputs |
||||
Operator ts_rank does not support event inputs |
||||
Operator greater does not support event inputs |
||||
Operator ts_av_diff does not support event inputs |
||||
Operator ts_quantile does not support event inputs |
||||
Operator ts_count_nans does not support event inputs |
||||
Operator ts_covariance does not support event inputs |
||||
Operator ts_arg_min does not support event inputs |
||||
Operator divide does not support event inputs |
||||
Operator ts_corr does not support event inputs |
||||
Operator multiply does not support event inputs |
||||
Operator if_else does not support event inputs |
||||
Operator ts_sum does not support event inputs |
||||
Operator ts_delay does not support event inputs |
||||
Operator group_zscore does not support event inputs |
||||
Operator ts_arg_max does not support event inputs |
||||
Operator ts_std_dev does not support event inputs |
||||
Operator ts_backfill does not support event inputs |
||||
|
||||
以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子 |
||||
|
||||
========================= 操作符开始 ======================================= |
||||
注意: Operator: 后面的是操作符(是可以使用的), |
||||
Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符 |
||||
特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x) |
||||
Description: Absolute value of x |
||||
Operator: add(x, y, filter = false) |
||||
Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding |
||||
Operator: densify(x) |
||||
Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient |
||||
Operator: divide(x, y) |
||||
Description: x / y |
||||
Operator: inverse(x) |
||||
Description: 1 / x |
||||
Operator: log(x) |
||||
Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights. |
||||
Operator: max(x, y, ..) |
||||
Description: Maximum value of all inputs. At least 2 inputs are required |
||||
Operator: min(x, y ..) |
||||
Description: Minimum value of all inputs. At least 2 inputs are required |
||||
Operator: multiply(x ,y, ... , filter=false) |
||||
Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1 |
||||
Operator: power(x, y) |
||||
Description: x ^ y |
||||
Operator: reverse(x) |
||||
Description: - x |
||||
Operator: sign(x) |
||||
Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN; |
||||
Operator: signed_power(x, y) |
||||
Description: x raised to the power of y such that final result preserves sign of x |
||||
Operator: sqrt(x) |
||||
Description: Square root of x |
||||
Operator: subtract(x, y, filter=false) |
||||
Description: x-y. If filter = true, filter all input NaN to 0 before subtracting |
||||
Operator: and(input1, input2) |
||||
Description: Logical AND operator, returns true if both operands are true and returns false otherwise |
||||
Operator: if_else(input1, input2, input 3) |
||||
Description: If input1 is true then return input2 else return input3. |
||||
Operator: input1 < input2 |
||||
Description: If input1 < input2 return true, else return false |
||||
Operator: input1 <= input2 |
||||
Description: Returns true if input1 <= input2, return false otherwise |
||||
Operator: input1 == input2 |
||||
Description: Returns true if both inputs are same and returns false otherwise |
||||
Operator: input1 > input2 |
||||
Description: Logic comparison operators to compares two inputs |
||||
Operator: input1 >= input2 |
||||
Description: Returns true if input1 >= input2, return false otherwise |
||||
Operator: input1!= input2 |
||||
Description: Returns true if both inputs are NOT the same and returns false otherwise |
||||
Operator: is_nan(input) |
||||
Description: If (input == NaN) return 1 else return 0 |
||||
Operator: not(x) |
||||
Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1). |
||||
Operator: or(input1, input2) |
||||
Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise |
||||
Operator: days_from_last_change(x) |
||||
Description: Amount of days since last change of x |
||||
Operator: hump(x, hump = 0.01) |
||||
Description: Limits amount and magnitude of changes in input (thus reducing turnover) |
||||
Operator: kth_element(x, d, k) |
||||
Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1 |
||||
Operator: last_diff_value(x, d) |
||||
Description: Returns last x value not equal to current x value from last d days |
||||
Operator: ts_arg_max(x, d) |
||||
Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1 |
||||
Operator: ts_arg_min(x, d) |
||||
Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1. |
||||
Operator: ts_av_diff(x, d) |
||||
Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation |
||||
Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN") |
||||
Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value) |
||||
Operator: ts_corr(x, y, d) |
||||
Description: Returns correlation of x and y for the past d days |
||||
Operator: ts_count_nans(x ,d) |
||||
Description: Returns the number of NaN values in x for the past d days |
||||
Operator: ts_covariance(y, x, d) |
||||
Description: Returns covariance of y and x for the past d days |
||||
Operator: ts_decay_linear(x, d, dense = false) |
||||
Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not. |
||||
Operator: ts_delay(x, d) |
||||
Description: Returns x value d days ago |
||||
Operator: ts_delta(x, d) |
||||
Description: Returns x - ts_delay(x, d) |
||||
Operator: ts_mean(x, d) |
||||
Description: Returns average value of x for the past d days. |
||||
Operator: ts_product(x, d) |
||||
Description: Returns product of x for the past d days |
||||
Operator: ts_quantile(x,d, driver="gaussian" ) |
||||
Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default. |
||||
Operator: ts_rank(x, d, constant = 0) |
||||
Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0. |
||||
Operator: ts_regression(y, x, d, lag = 0, rettype = 0) |
||||
Description: Returns various parameters related to regression function |
||||
Operator: ts_scale(x, d, constant = 0) |
||||
Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space |
||||
Operator: ts_std_dev(x, d) |
||||
Description: Returns standard deviation of x for the past d days |
||||
Operator: ts_step(1) |
||||
Description: Returns days' counter |
||||
Operator: ts_sum(x, d) |
||||
Description: Sum values of x for the past d days. |
||||
Operator: ts_zscore(x, d) |
||||
Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown. |
||||
Operator: normalize(x, useStd = false, limit = 0.0) |
||||
Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element |
||||
Operator: quantile(x, driver = gaussian, sigma = 1.0) |
||||
Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector |
||||
Operator: rank(x, rate=2) |
||||
Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0 |
||||
Operator: scale(x, scale=1, longscale=1, shortscale=1) |
||||
Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator |
||||
Operator: winsorize(x, std=4) |
||||
Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std. |
||||
Operator: zscore(x) |
||||
Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean |
||||
Operator: vec_avg(x) |
||||
Description: Taking mean of the vector field x |
||||
Operator: vec_sum(x) |
||||
Description: Sum of vector field x |
||||
Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10") |
||||
Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input |
||||
Operator: trade_when(x, y, z) |
||||
Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition |
||||
Operator: group_backfill(x, group, d, std = 4.0) |
||||
Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days |
||||
Operator: group_mean(x, weight, group) |
||||
Description: All elements in group equals to the mean |
||||
Operator: group_neutralize(x, group) |
||||
Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant |
||||
Operator: group_rank(x, group) |
||||
Description: Each elements in a group is assigned the corresponding rank in this group |
||||
Operator: group_scale(x, group) |
||||
Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin) |
||||
Operator: group_zscore(x, group) |
||||
Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group. |
||||
========================= 操作符结束 ======================================= |
||||
|
||||
========================= 数据字段开始 ======================================= |
||||
注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用), description_cn字段后面的内容是中文使用说明(不能使用) |
||||
|
||||
{'data_set_name': '可以使用:forward_price_120', 'description': '不可使用,仅供参考:Forward price at 120 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.'} |
||||
{'data_set_name': '可以使用:fnd6_capxv', 'description': '不可使用,仅供参考:Capital Expend Property, Plant and Equipment Schd V'} |
||||
{'data_set_name': '可以使用:fnd6_ciother', 'description': '不可使用,仅供参考:Comp. Inc. - Other Adj.'} |
||||
{'data_set_name': '可以使用:fnd6_newa2v1300_oiadp', 'description': '不可使用,仅供参考:Operating Income After Depreciation'} |
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_gdwliaq', 'description': '不可使用,仅供参考:Impairment of Goodwill After-tax'} |
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_spcedq', 'description': '不可使用,仅供参考:S&P Core Earnings EPS Diluted'} |
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{'data_set_name': '可以使用:fnd6_newqv1300_ciotherq', 'description': '不可使用,仅供参考:Comp Inc - Other Adj'} |
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{'data_set_name': '可以使用:fnd6_oiadps', 'description': '不可使用,仅供参考:Operating Income after Depreciation'} |
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{'data_set_name': '可以使用:sales_ps', 'description': '不可使用,仅供参考:Sales per Share (Quarterly)'} |
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{'data_set_name': '可以使用:multi_factor_acceleration_score_derivative', 'description': '不可使用,仅供参考:Change in the acceleration of multi-factor score compared to previous period.'} |
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{'data_set_name': '可以使用:anl4_netdebt_flag', 'description': '不可使用,仅供参考:Net debt - forecast type (revision/new/...)'} |
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{'data_set_name': '可以使用:min_reported_eps_guidance', 'description': '不可使用,仅供参考:Reported Earnings Per Share - Minimum guidance value for the annual period'} |
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{'data_set_name': '可以使用:pv13_com_rk_au', 'description': '不可使用,仅供参考:the HITS authority score of competitors'} |
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{'data_set_name': '可以使用:pv13_h2_min2_1k_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'} |
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{'data_set_name': '可以使用:pv13_h_min22_1000_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'} |
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{'data_set_name': '可以使用:pv13_h_min24_500_sector', 'description': '不可使用,仅供参考:Grouping fields for top 500'} |
||||
{'data_set_name': '可以使用:pv13_h_min2_focused_sector', 'description': '不可使用,仅供参考:Grouping fields for top 200'} |
||||
{'data_set_name': '可以使用:pv13_h_min52_1k_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'} |
||||
{'data_set_name': '可以使用:news_max_up_amt', 'description': '不可使用,仅供参考:The after the news high minus the price at the time of the news'} |
||||
{'data_set_name': '可以使用:nws18_sse', 'description': '不可使用,仅供参考:Sentiment of phrases impacting the company'} |
||||
{'data_set_name': '可以使用:rp_nip_ratings', 'description': '不可使用,仅供参考:News impact projection of analyst ratings-related news'} |
||||
{'data_set_name': '可以使用:fn_comp_not_rec_stock_options_a', 'description': '不可使用,仅供参考:Unrecognized cost of unvested stock option awards.'} |
||||
{'data_set_name': '可以使用:fn_comp_not_rec_stock_options_q', 'description': '不可使用,仅供参考:Unrecognized cost of unvested stock option awards.'} |
||||
{'data_set_name': '可以使用:fn_def_tax_liab_a', 'description': '不可使用,仅供参考:Amount, after deferred tax asset, of deferred tax liability attributable to taxable differences without jurisdictional netting.'} |
||||
{'data_set_name': '可以使用:fn_op_lease_min_pay_due_in_2y_a', 'description': '不可使用,仅供参考:Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of 1 year due in the 2nd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.'} |
||||
{'data_set_name': '可以使用:fn_treasury_stock_shares_a', 'description': '不可使用,仅供参考:Number of common and preferred shares that were previously issued and that were repurchased by the issuing entity and held in treasury on the financial statement date. This stock has no voting rights and receives no dividends.'} |
||||
{'data_set_name': '可以使用:fnd2_dbplanepdfbnfpnext12m', 'description': '不可使用,仅供参考:Amount of benefits from a defined benefit plan expected to be paid in the next fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.'} |
||||
========================= 数据字段结束 ======================================= |
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|
||||
以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合 |
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@ -1,11 +1,22 @@ |
||||
**Name** |
||||
Institutional Herding Reversal Effect |
||||
**Name** |
||||
Institutional Investor Holding Divergence Factor |
||||
|
||||
**Hypothesis** |
||||
When a large number of institutional investors concentrate on buying a particular stock within a short period, it can create a "herding effect," potentially driving the stock price away from its fundamental value. However, such concentrated positioning is often accompanied by diminishing informational advantages and increasing liquidity needs. Once market sentiment reverses or a negative catalyst emerges, institutions might be forced to sell simultaneously, leading to sharp price reversals. Therefore, establishing short positions in stocks with a significant recent surge in institutional concentration and long positions in stocks where institutional ownership has stabilized after a period of disorderly selling may capture the alpha generated by this behavioral finance phenomenon. |
||||
**Hypothesis** |
||||
When major institutional investors within the same industry exhibit significant divergence in their positioning towards a specific stock, it indicates substantial uncertainty regarding the stock's future prospects. This divergence often arises when there are material, yet not fully digested, changes in fundamentals or news flow. In the short term, such divergence can lead to increased price volatility without a clear trend. However, over the medium term, as information becomes clearer, the stock price tends to converge towards the side representing the correct expectation. Therefore, a strategy can be constructed to establish positions when divergence reaches an extreme, betting on the subsequent information clarification and price convergence. |
||||
|
||||
**Implementation Plan** |
||||
Utilize data on changes in institutional ownership holdings. Calculate the quarterly change rate of institutional ownership and its cross-sectional percentile rank. Employ the `ts_rank` operator to identify stocks with abnormally high growth in institutional ownership (e.g., top 10%). Simultaneously, use the `ts_decay_linear` operator to apply time decay to selling pressure, identifying stocks where ownership has stabilized after a period of selling. Assign negative weights to the former and positive weights to the latter. |
||||
**Implementation Plan** |
||||
1. **Data Preparation**: Collect and integrate institutional holding change data from regular company reports, focusing on top circulating shareholders identified as influential market participants. |
||||
2. **Calculate Divergence**: |
||||
* For each reporting period, calculate a divergence metric for each stock within its specific industry. This could be the standard deviation or Gini coefficient of the holding changes among different types of key institutions. |
||||
* Use the `ts_zscore` operator to cross-sectionally standardize this raw divergence metric, mitigating the impact of overall market sentiment swings. |
||||
* Apply a time-series decay operator to assign greater weight to more recent reporting periods, capturing the latest dynamics. |
||||
3. **Signal Generation**: Generate a signal when a stock's normalized, time-weighted divergence breaches a historical threshold. Consider establishing positions within a defined window after the signal appears. |
||||
4. **Position Direction**: This factor does not directly indicate long/short direction. Direction determination requires auxiliary indicators, such as: |
||||
* Concurrent stock price performance during the divergence period. |
||||
* Underlying fundamental trend to judge which institutional view might be more prescient. |
||||
|
||||
**Alpha Factor Optimization Suggestions** |
||||
The impact of institutional behavior varies across market cap styles (e.g., persistence might be stronger in large-caps, while reversal effects could be more violent in small-caps). Would it be beneficial to group stocks by market capitalization and calculate the abnormality of institutional behavior within each group to enhance the factor's robustness? Furthermore, incorporating overall market liquidity conditions (e.g., using the `trade_when` operator) to overweight this factor specifically during periods of tightening liquidity might better capture reversal opportunities arising from forced institutional selling. |
||||
**Alpha Factor Optimization Suggestions** |
||||
1. **Dynamic Threshold Adjustment**: The significance of divergence may vary with market volatility. Consider adjusting the signal threshold dynamically. |
||||
2. **Differentiated Institutional Weighting**: Assign different weights to holding changes based on an institution's historical performance or AUM, refining the divergence metric to better reflect the博弈 of "smart money". |
||||
3. **Integration with Event Drivers**: Combine the divergence factor with the announcement timing of specific corporate events. High divergence during event windows might indicate stronger signals. |
||||
4. **Industry and Size Neutralization**: Divergence levels might be influenced by industry characteristics and company size. It is strongly recommended to apply cross-sectional neutralization in the final step of factor construction. |
||||
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Reference in new issue