高频选股因子周报（20250519- 20250523）：高频因子表现有所分化，大单与买入意愿因子明显反弹， AI 增强组合继续强势表现-20250525

Quantitative Models and Construction Methods Quantitative Factors and Their Construction 1. Factor Name: Intraday Skewness Factor Construction Idea: Captures the skewness of intraday stock returns to identify potential return asymmetry[3][6] Construction Process: Referenced in the report "Stock Selection Factor Series Research (19) - High-Frequency Factors on Stock Return Distribution Characteristics"[11] Evaluation: Demonstrates mixed performance with positive returns in some periods but underperformance in others[3][6] 2. Factor Name: Downside Volatility Proportion Factor Construction Idea: Measures the proportion of downside volatility in intraday price movements to assess risk[3][6] Construction Process: Referenced in the report "Stock Selection Factor Series Research (25) - High-Frequency Factors on Realized Volatility Decomposition"[16] Evaluation: Shows consistent positive returns in certain periods but limited robustness in others[3][6] 3. Factor Name: Post-Open Buy Intention Proportion Factor Construction Idea: Quantifies the proportion of buy orders after market open to gauge investor sentiment[3][6] Construction Process: Referenced in the report "Stock Selection Factor Series Research (64) - Low-Frequency Applications of High-Frequency Data Using Intuitive Logic and Machine Learning"[20] Evaluation: Exhibits moderate performance with occasional strong returns[3][6] 4. Factor Name: Post-Open Buy Intention Intensity Factor Construction Idea: Measures the intensity of buy orders after market open to reflect market momentum[3][6] Construction Process: Referenced in the report "Stock Selection Factor Series Research (64) - Low-Frequency Applications of High-Frequency Data Using Intuitive Logic and Machine Learning"[24] Evaluation: Performance is inconsistent, with periods of underperformance[3][6] 5. Factor Name: Post-Open Large Order Net Buy Proportion Factor Construction Idea: Tracks the proportion of large net buy orders after market open to identify institutional activity[3][6] Construction Process: Derived from high-frequency trading data[30] Evaluation: Generally positive performance with strong returns in specific periods[3][6] 6. Factor Name: Post-Open Large Order Net Buy Intensity Factor Construction Idea: Measures the intensity of large net buy orders after market open to capture market trends[3][6] Construction Process: Derived from high-frequency trading data[35] Evaluation: Mixed results with moderate returns in some periods[3][6] 7. Factor Name: Improved Reversal Factor Construction Idea: Enhances traditional reversal factors by incorporating high-frequency data[3][6] Construction Process: Derived from intraday price reversals[40] Evaluation: Limited performance improvement over traditional reversal factors[3][6] 8. Factor Name: Tail-End Trading Proportion Factor Construction Idea: Measures the proportion of trading activity near market close to capture end-of-day effects[3][6] Construction Process: Derived from high-frequency trading data[45] Evaluation: Underperformance in most periods[3][6] 9. Factor Name: Average Single Transaction Outflow Proportion Factor Construction Idea: Tracks the proportion of outflows in single transactions to assess liquidity[3][6] Construction Process: Derived from high-frequency trading data[50] Evaluation: Limited effectiveness in predicting returns[3][6] 10. Factor Name: Large Order Push-Up Factor Construction Idea: Measures the impact of large orders on price increases to identify market movers[3][6] Construction Process: Derived from high-frequency trading data[55] Evaluation: Moderate performance with occasional strong returns[3][6] 11. Factor Name: Deep Learning High-Frequency Factor (Improved GRU(50,2)+NN(10)) Construction Idea: Combines GRU and neural networks to capture complex patterns in high-frequency data[3][6] Construction Process: Utilizes GRU(50,2) and NN(10) architectures for feature extraction and prediction[59] Evaluation: Strong performance in certain periods but underperformance in others[3][6] 12. Factor Name: Deep Learning High-Frequency Factor (Residual Attention LSTM(48,2)+NN(10)) Construction Idea: Incorporates residual attention mechanisms with LSTM and neural networks for enhanced prediction[3][6] Construction Process: Utilizes LSTM(48,2) and NN(10) architectures with residual attention layers[61] Evaluation: Consistently strong performance across multiple periods[3][6] 13. Factor Name: Deep Learning Factor (Multi-Granularity Model - 5-Day Label) Construction Idea: Uses multi-granularity modeling with 5-day labels for short-term predictions[3][6] Construction Process: Trained using bidirectional AGRU[64] Evaluation: Strong performance with high returns in most periods[3][6] 14. Factor Name: Deep Learning Factor (Multi-Granularity Model - 10-Day Label) Construction Idea: Uses multi-granularity modeling with 10-day labels for medium-term predictions[3][6] Construction Process: Trained using bidirectional AGRU[65] Evaluation: Consistently strong performance across multiple periods[3][6] AI-Enhanced Portfolio Construction 1. Portfolio Name: CSI 500 AI Enhanced Wide Constraint Portfolio Construction Idea: Maximizes expected returns under wide constraints using deep learning factors[69][70] Construction Process: - Weekly rebalancing - Constraints on individual stocks, industries, market cap, and other factors - Objective function: $max\sum\mu_{i}w_{i}$ where ( w_i ) is the weight of stock ( i ) and ( \mu_i ) is its expected excess return[71] Evaluation: Strong cumulative excess returns since 2017[72] 2. Portfolio Name: CSI 500 AI Enhanced Strict Constraint Portfolio Construction Idea: Similar to the wide constraint portfolio but with stricter constraints[69][70] Construction Process: Same as above with stricter constraints on market cap, ROE, SUE, and volatility[71] Evaluation: Moderate cumulative excess returns since 2017[73] 3. Portfolio Name: CSI 1000 AI Enhanced Wide Constraint Portfolio Construction Idea: Maximizes expected returns under wide constraints using deep learning factors for smaller-cap stocks[69][70] Construction Process: Same as CSI 500 portfolios but applied to CSI 1000 index[71] Evaluation: Strong cumulative excess returns since 2017[76] 4. Portfolio Name: CSI 1000 AI Enhanced Strict Constraint Portfolio Construction Idea: Similar to the wide constraint portfolio but with stricter constraints for smaller-cap stocks[69][70] Construction Process: Same as above with stricter constraints on market cap, ROE, SUE, and volatility[71] Evaluation: Strong cumulative excess returns since 2017[79] Backtest Results for Factors 1. Intraday Skewness Factor: IC (2025): 0.057, Multi-Period Returns: 14.35% (2025)[3][6] 2. Downside Volatility Proportion Factor: IC (2025): 0.055, Multi-Period Returns: 11.77% (2025)[3][6] 3. Post-Open Buy Intention Proportion Factor: IC (2025): 0.033, Multi-Period Returns: 10.32% (2025)[3][6] 4. Post-Open Buy Intention Intensity Factor: IC (2025): 0.026, Multi-Period Returns: 11.19% (2025)[3][6] 5. Post-Open Large Order Net Buy Proportion Factor: IC (2025): 0.039, Multi-Period Returns: 12.32% (2025)[3][6] 6. Post-Open Large Order Net Buy Intensity Factor: IC (2025): 0.028, Multi-Period Returns: 6.78% (2025)[3][6] 7. Improved Reversal Factor: IC (2025): 0.003, Multi-Period Returns: 9.34% (2025)[3][6] 8. Tail-End Trading Proportion Factor: IC (2025): 0.022, Multi-Period Returns: 5.43% (2025)[3][6] 9. Average Single Transaction Outflow Proportion Factor: IC (2025): 0.012, Multi-Period Returns: 0.82% (2025)[3][6] 10. **Large Order Push-Up Factor