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]

Quantitative Models and Construction Methods 1. Model Name: GRU(50,2)+NN(10) Factor - **Model Construction Idea**: This factor leverages a deep learning architecture combining Gated Recurrent Units (GRU) and Neural Networks (NN) to capture high-frequency trading patterns and predict stock returns[4][55] - **Model Construction Process**: - The GRU(50,2) component processes sequential high-frequency data with 50 units and 2 layers - The NN(10) component is a fully connected neural network with 10 neurons in the output layer - The model is trained on historical high-frequency data to predict stock returns, optimizing for multi-class classification or regression tasks[4][55] - **Model Evaluation**: Demonstrates robust performance in capturing high-frequency trading signals and generating stable returns[4][55] 2. Model Name: Multi-Granularity Model (5-Day Label) - **Model Construction Idea**: This model uses multi-granularity data to predict stock returns over a 5-day horizon, leveraging bidirectional AGRU (Attention-based GRU) for feature extraction[57][60] - **Model Construction Process**: - Input data is segmented into multiple granularities (e.g., daily, intraday) - Bidirectional AGRU is applied to extract temporal features from the data - A 5-day label is used as the prediction target, and the model is trained to optimize for this horizon[57][60] - **Model Evaluation**: Effective in capturing medium-term trading patterns and generating consistent returns[57][60] 3. Model Name: Multi-Granularity Model (10-Day Label) - **Model Construction Idea**: Similar to the 5-day label model, this version extends the prediction horizon to 10 days, using bidirectional AGRU for feature extraction[60][65] - **Model Construction Process**: - Multi-granularity data is processed with bidirectional AGRU - A 10-day label is used as the prediction target, and the model is trained to optimize for this extended horizon[60][65] - **Model Evaluation**: Provides a longer-term perspective on trading patterns, with slightly lower returns compared to the 5-day model but still effective[60][65] --- Model Backtesting Results GRU(50,2)+NN(10) Factor - **IC**: Historical: 0.066, 2025: 0.039[4][55] - **e^(-RankMAE)**: Historical: 0.336, 2025: 0.334[4][55] - **Long-Short Return**: Weekly: 0.70%, June: 3.58%, 2025 YTD: 19.78%[4][55] - **Long-Only Excess Return**: Weekly: -0.30%, June: 0.92%, 2025 YTD: -1.06%[4][55] Multi-Granularity Model (5-Day Label) - **IC**: Historical: 0.081, 2025: 0.070[57][60] - **e^(-RankMAE)**: Historical: 0.344, 2025: 0.343[57][60] - **Long-Short Return**: Weekly: 1.56%, June: 5.97%, 2025 YTD: 35.45%[57][60] - **Long-Only Excess Return**: Weekly: 0.40%, June: 2.16%, 2025 YTD: 11.87%[57][60] Multi-Granularity Model (10-Day Label) - **IC**: Historical: 0.074, 2025: 0.065[60][65] - **e^(-RankMAE)**: Historical: 0.342, 2025: 0.343[60][65] - **Long-Short Return**: Weekly: 1.66%, June: 5.76%, 2025 YTD: 33.44%[60][65] - **Long-Only Excess Return**: Weekly: 0.71%, June: 2.06%, 2025 YTD: 11.11%[60][65] --- Quantitative Factors and Construction Methods 1. Factor Name: Intraday Skewness Factor - **Factor Construction Idea**: Measures the skewness of intraday returns to capture asymmetry in price movements[4][10] - **Factor Construction Process**: - Calculate intraday returns for each stock - Compute the skewness of these returns using the formula: $ Skewness = \frac{E[(X - \mu)^3]}{\sigma^3} $ where $X$ is the return, $\mu$ is the mean, and $\sigma$ is the standard deviation[4][10] - **Factor Evaluation**: Effective in identifying stocks with asymmetric return distributions, though performance varies across periods[4][10] 2. Factor Name: Downside Volatility Ratio - **Factor Construction Idea**: Focuses on the proportion of downside volatility relative to total volatility to capture risk-averse behavior[4][14] - **Factor Construction Process**: - Calculate downside volatility as the standard deviation of negative returns - Compute the ratio of downside volatility to total volatility[4][14] - **Factor Evaluation**: Useful for identifying stocks with higher downside risk, though returns are sensitive to market conditions[4][14] 3. Factor Name: Opening Buy Intensity - **Factor Construction Idea**: Measures the intensity of buy orders during the opening period to capture early trading sentiment[4][17] - **Factor Construction Process**: - Aggregate buy orders in the first 30 minutes of trading - Normalize by total trading volume during the same period[4][17] - **Factor Evaluation**: Captures short-term sentiment effectively, though performance is volatile[4][17] --- Factor Backtesting Results Intraday Skewness Factor - **IC**: Historical: 0.027, 2025: 0.047[4][10] - **e^(-RankMAE)**: Historical: 0.324, 2025: 0.330[4][10] - **Long-Short Return**: Weekly: -0.51%, June: 1.48%, 2025 YTD: 14.73%[4][10] - **Long-Only Excess Return**: Weekly: -0.03%, June: 0.18%, 2025 YTD: 2.59%[4][10] Downside Volatility Ratio - **IC**: Historical: 0.025, 2025: 0.046[4][14] - **e^(-RankMAE)**: Historical: 0.324, 2025: 0.328[4][14] - **Long-Short Return**: Weekly: -0.04%, June: 1.86%, 2025 YTD: 12.84%[4][14] - **Long-Only Excess Return**: Weekly: 0.09%, June: 0.50%, 2025 YTD: 1.07%[4][14] Opening Buy Intensity - **IC**: Historical: 0.031, 2025: 0.028[4][17] - **e^(-RankMAE)**: Historical: 0.322, 2025: 0.322[4][17] - **Long-Short Return**: Weekly: 0.77%, June: 1.85%, 2025 YTD: 11.44%[4][17] - **Long-Only Excess Return**: Weekly: 0.04%, June: 0.61%, 2025 YTD: 5.91%[4][17]