高频选股因子周报：高频因子表现分化，深度学习因子依然强势。AI 增强组合分化，500 增强依然大幅回撤，1000 增强回撤收窄。-20250928

Quantitative Models and Construction Methods 1. Model Name: Weekly Rebalancing AI-Enhanced CSI 500 Wide Constraint Portfolio - Model Construction Idea: This model aims to enhance the CSI 500 index performance by leveraging AI-based factors while applying wide constraints on portfolio construction [72][73] - Model Construction Process: - The model uses deep learning factors (e.g., multi-granularity model with 10-day labels) as the basis for stock selection [72] - Constraints include: - Stock weight: 1% - Industry weight: 1% - Market cap weight: 0.3 - Turnover rate constraint: 0.3 - The optimization objective is to maximize expected returns, represented by the formula: $max \sum \mu_{i}w_{i}$ where $w_{i}$ is the weight of stock $i$ in the portfolio, and $\mu_{i}$ is the expected excess return of stock $i$ [73][74] - Model Evaluation: The model demonstrates moderate performance under wide constraints, with cumulative excess returns shown over time [75][77] 2. Model Name: Weekly Rebalancing AI-Enhanced CSI 500 Strict Constraint Portfolio - Model Construction Idea: Similar to the wide constraint model but applies stricter constraints to control risk and enhance robustness [72][73] - Model Construction Process: - Constraints include: - Stock weight: 1% - Industry weight: 1% - Market cap weight: 0.1 - Additional constraints: - Market cap squared: 0.1 - ROE: 0.3 - SUE: 0.3 - Volatility: 0.3 - Component stock constraint: 0.8 - Optimization objective remains the same as the wide constraint model [73][74] - Model Evaluation: The stricter constraints result in a more stable performance, with cumulative excess returns displayed over time [76][80] 3. Model Name: Weekly Rebalancing AI-Enhanced CSI 1000 Wide Constraint Portfolio - Model Construction Idea: This model targets the CSI 1000 index, applying wide constraints while leveraging AI-based factors for enhanced returns [72][73] - Model Construction Process: - Constraints are similar to the CSI 500 wide constraint model, with a focus on smaller-cap stocks [73] - Model Evaluation: The model shows significant cumulative excess returns, particularly in recent years [79][86] 4. Model Name: Weekly Rebalancing AI-Enhanced CSI 1000 Strict Constraint Portfolio - Model Construction Idea: Similar to the wide constraint model but applies stricter constraints to manage risk and improve consistency [72][73] - Model Construction Process: - Constraints are similar to the CSI 500 strict constraint model, tailored for the CSI 1000 index [73] - Model Evaluation: The model demonstrates strong performance under strict constraints, with cumulative excess returns highlighted [85][87] --- Model Backtesting Results 1. Weekly Rebalancing AI-Enhanced CSI 500 Wide Constraint Portfolio - Weekly Excess Return: -1.36% (last week), -3.85% (September), 0.94% (YTD 2025) [13][78] - Weekly Win Rate: 23/39 weeks [13] 2. Weekly Rebalancing AI-Enhanced CSI 500 Strict Constraint Portfolio - Weekly Excess Return: -1.35% (last week), -1.33% (September), 3.70% (YTD 2025) [13][81] - Weekly Win Rate: 24/39 weeks [13] 3. Weekly Rebalancing AI-Enhanced CSI 1000 Wide Constraint Portfolio - Weekly Excess Return: 0.40% (last week), 0.42% (September), 9.15% (YTD 2025) [13][83] - Weekly Win Rate: 26/39 weeks [13] 4. Weekly Rebalancing AI-Enhanced CSI 1000 Strict Constraint Portfolio - Weekly Excess Return: -0.19% (last week), 0.67% (September), 14.01% (YTD 2025) [13][90] - Weekly Win Rate: 25/39 weeks [13] --- Quantitative Factors and Construction Methods 1. Factor Name: Intraday Skewness Factor - Factor Construction Idea: Captures the skewness of intraday stock returns to identify potential outperformers [6][8] - Factor Construction Process: Referenced in the report "Stock Selection Factor Series Research (19)" [13] - Factor Evaluation: Demonstrates strong performance with IC values of 0.027 (historical) and 0.042 (2025) [9][10] 2. Factor Name: Downside Volatility Proportion Factor - Factor Construction Idea: Measures the proportion of downside volatility in realized volatility to assess risk-adjusted returns [6][8] - Factor Construction Process: Referenced in the report "Stock Selection Factor Series Research (25)" [18][20] - Factor Evaluation: Shows moderate performance with IC values of 0.025 (historical) and 0.036 (2025) [9][10] 3. Factor Name: Post-Open Buying Intensity Factor - Factor Construction Idea: Quantifies the intensity of buying activity after market open to identify short-term momentum [6][8] - Factor Construction Process: Referenced in the report "Stock Selection Factor Series Research (64)" [22][26] - Factor Evaluation: Displays stable performance with IC values of 0.035 (historical) and 0.030 (2025) [9][10] 4. Factor Name: Deep Learning Factor (Improved GRU(50,2)+NN(10)) - Factor Construction Idea: Utilizes a gated recurrent unit (GRU) and neural network (NN) architecture to predict stock returns [6][8] - Factor Construction Process: Combines GRU with NN to capture temporal dependencies in high-frequency data [61][62] - Factor Evaluation: Strong performance with IC values of 0.066 (historical) and 0.050 (2025) [12][61] --- Factor Backtesting Results 1. Intraday Skewness Factor - IC: 0.027 (historical), 0.042 (2025) [9][10] - Multi-Long-Short Return: 3.82% (September), 16.22% (YTD 2025) [9][10] 2. Downside Volatility Proportion Factor - IC: 0.025 (historical), 0.036 (2025) [9][10] - Multi-Long-Short Return: 2.86% (September), 13.58% (YTD 2025) [9][10] 3. Post-Open Buying Intensity Factor - IC: 0.035 (historical), 0.030 (2025) [9][10] - Multi-Long-Short Return: 0.65% (September), 11.29% (YTD 2025) [9][10] 4. Deep Learning Factor (Improved GRU(50,2)+NN(10)) - IC: 0.066 (historical), 0.050 (2025) [12][61] - Multi-Long-Short Return: 2.13% (September), 7.40% (YTD 2025) [12][61]