高频选股因子周报（20260104-20260109）：买入意愿因子开年强势，多粒度因子表现一般。AI增强组合超额开年不利，出现大幅回撤。-20260111

• The "Buy Intention Factor" showed strong performance at the beginning of the year, with intraday high-frequency skewness factor, intraday downside volatility proportion factor, post-opening buy intention proportion factor, post-opening buy intention strength factor, post-opening large order net buy proportion factor, post-opening large order net buy strength factor, intraday return factor, end-of-day trading proportion factor, average single outflow amount proportion factor, and large order push-up factor all being evaluated[5][6][9] - The "Multi-Granularity Factor" showed average performance, with GRU(10,2)+NN(10) factor, GRU(50,2)+NN(10) factor, multi-granularity model (5-day label) factor, and multi-granularity model (10-day label) factor being evaluated[5][6][9] - The "AI Enhanced Portfolio" had a poor start to the year, with significant drawdowns observed in the weekly rebalanced CSI 500 AI enhanced wide constraint portfolio, CSI 500 AI enhanced strict constraint portfolio, CSI 1000 AI enhanced wide constraint portfolio, and CSI 1000 AI enhanced strict constraint portfolio[5][6][9] Quantitative Factors and Construction Methods 1. Factor Name: Intraday High-Frequency Skewness Factor - Construction Idea: Measures the skewness of intraday returns to capture the asymmetry in return distribution[5][6] - Construction Process: Calculated using high-frequency data to determine the skewness of returns within a trading day[5][6] - Evaluation: Demonstrated strong performance at the beginning of the year[5][6] 2. Factor Name: Intraday Downside Volatility Proportion Factor - Construction Idea: Measures the proportion of downside volatility in intraday returns[5][6] - Construction Process: Calculated using high-frequency data to determine the proportion of downside volatility within a trading day[5][6] - Evaluation: Showed moderate performance[5][6] 3. Factor Name: Post-Opening Buy Intention Proportion Factor - Construction Idea: Measures the proportion of buy intentions after market opening[5][6] - Construction Process: Calculated using high-frequency data to determine the proportion of buy intentions after the market opens[5][6] - Evaluation: Demonstrated strong performance at the beginning of the year[5][6] 4. Factor Name: Post-Opening Buy Intention Strength Factor - Construction Idea: Measures the strength of buy intentions after market opening[5][6] - Construction Process: Calculated using high-frequency data to determine the strength of buy intentions after the market opens[5][6] - Evaluation: Showed moderate performance[5][6] 5. Factor Name: Post-Opening Large Order Net Buy Proportion Factor - Construction Idea: Measures the proportion of net buy orders of large size after market opening[5][6] - Construction Process: Calculated using high-frequency data to determine the proportion of net buy orders of large size after the market opens[5][6] - Evaluation: Demonstrated weak performance[5][6] 6. Factor Name: Post-Opening Large Order Net Buy Strength Factor - Construction Idea: Measures the strength of net buy orders of large size after market opening[5][6] - Construction Process: Calculated using high-frequency data to determine the strength of net buy orders of large size after the market opens[5][6] - Evaluation: Showed weak performance[5][6] 7. Factor Name: Intraday Return Factor - Construction Idea: Measures the return within a trading day[5][6] - Construction Process: Calculated using high-frequency data to determine the return within a trading day[5][6] - Evaluation: Demonstrated strong performance at the beginning of the year[5][6] 8. Factor Name: End-of-Day Trading Proportion Factor - Construction Idea: Measures the proportion of trading activity at the end of the day[5][6] - Construction Process: Calculated using high-frequency data to determine the proportion of trading activity at the end of the day[5][6] - Evaluation: Showed strong performance[5][6] 9. Factor Name: Average Single Outflow Amount Proportion Factor - Construction Idea: Measures the proportion of average single outflow amounts[5][6] - Construction Process: Calculated using high-frequency data to determine the proportion of average single outflow amounts[5][6] - Evaluation: Demonstrated moderate performance[5][6] 10. Factor Name: Large Order Push-Up Factor - Construction Idea: Measures the impact of large orders on price increases[5][6] - Construction Process: Calculated using high-frequency data to determine the impact of large orders on price increases[5][6] - Evaluation: Showed moderate performance[5][6] 11. Factor Name: GRU(10,2)+NN(10) Factor - Construction Idea: Combines GRU and neural network models to capture complex patterns in data[5][6] - Construction Process: Utilizes GRU with 10 units and 2 layers, followed by a neural network with 10 units[5][6] - Evaluation: Demonstrated average performance[5][6] 12. Factor Name: GRU(50,2)+NN(10) Factor - Construction Idea: Combines GRU and neural network models to capture complex patterns in data[5][6] - Construction Process: Utilizes GRU with 50 units and 2 layers, followed by a neural network with 10 units[5][6] - Evaluation: Showed weak performance[5][6] 13. Factor Name: Multi-Granularity Model (5-Day Label) Factor - Construction Idea: Uses multi-granularity approach to capture patterns over different time frames[5][6] - Construction Process: Trained using a 5-day label to capture short-term patterns[5][6] - Evaluation: Demonstrated average performance[5][6] 14. Factor Name: Multi-Granularity Model (10-Day Label) Factor - Construction Idea: Uses multi-granularity approach to capture patterns over different time frames[5][6] - Construction Process: Trained using a 10-day label to capture longer-term patterns[5][6] - Evaluation: Showed weak performance[5][6] Factor Backtest Results 1. Intraday High-Frequency Skewness Factor: IC -0.007, e^(-rank mae) 0.312, long-short return 0.29%, long-only excess return 0.99%, monthly win rate 1/1[9][10] 2. Intraday Downside Volatility Proportion Factor: IC -0.001, e^(-rank mae) 0.313, long-short return 0.22%, long-only excess return 0.95%, monthly win rate 1/1[9][10] 3. Post-Opening Buy Intention Proportion Factor: IC 0.032, e^(-rank mae) 0.324, long-short return 1.04%, long-only excess return -0.41%, monthly win rate 0/1[9][10] 4. Post-Opening Buy Intention Strength Factor: IC 0.027, e^(-rank mae) 0.323, long-short return 0.65%, long-only excess return 0.62%, monthly win rate 1/1[9][10] 5. Post-Opening Large Order Net Buy Proportion Factor: IC -0.006, e^(-rank mae) 0.306, long-short return -0.52%, long-only excess return -0.53%, monthly win rate 0/1[9][10] 6. Post-Opening Large Order Net Buy Strength Factor: IC 0.004, e^(-rank mae) 0.308, long-short return -0.07%, long-only excess return -0.66%, monthly win rate 0/1[9][10] 7. Intraday Return Factor: IC 0.037, e^(-rank mae) 0.328, long-short return 1.77%, long-only excess return 1.89%, monthly win rate 1/1[9][10] 8. End-of-Day Trading Proportion Factor: IC 0.084, e^(-rank mae) 0.334, long-short return 2.67%, long-only excess return 1.35%, monthly win rate 1/1[9][10] 9. Average Single Outflow Amount Proportion Factor: IC 0.013, e^(-rank mae) 0.319, long-short return 0.45%, long-only excess return 0.14%, monthly win rate 1/1[9][10] 10. Large Order Push-Up Factor: IC -0.007, e^(-rank mae) 0.327, long-short return 0.22%, long-only excess return 0.43%, monthly win rate 1/1[9][10] 11. **GRU(10,2