- The report discusses high-frequency stock selection factors, deep learning factors, and AI-enhanced portfolios, summarizing their historical and 2026 performance in terms of IC, RankMAE, long-short returns, long-only excess returns, and monthly win rates[9][10][11] - High-frequency factors include intraday skewness, downside volatility proportion, post-opening buying intention proportion, post-opening buying intensity, net large-order buying proportion, net large-order buying intensity, improved reversal, end-of-day trading proportion, average single-order outflow proportion, and large-order-driven price increase[7][9][10] - Deep learning factors include GRU(50,2)+NN(10), residual attention LSTM(48,2)+NN(10), multi-granularity models with 5-day and 10-day labels, which are trained using advanced machine learning techniques like AGRU[7][9][10] - AI-enhanced portfolios are constructed based on deep learning factors, specifically the multi-granularity 10-day label model, and include four combinations: CSI 500 AI-enhanced wide constraint, CSI 500 AI-enhanced strict constraint, CSI 1000 AI-enhanced wide constraint, and CSI 1000 AI-enhanced strict constraint. These portfolios aim to maximize expected returns under specific constraints such as turnover, industry, and market cap limits[73][74][75] - The optimization objective for AI-enhanced portfolios is defined as maximizing the expected excess return, represented by the formula: $$\operatorname*{max}_{w_{i}}\sum\mu_{i}w_{i}$$ where \(w_i\) is the weight of stock \(i\) in the portfolio, and \(\mu_i\) is the expected excess return of stock \(i\)[74][75] - Performance results for high-frequency factors show positive long-short returns for most factors in January and 2026, with notable results for factors like intraday skewness (1.55%), downside volatility proportion (1.65%), and post-opening buying intensity (2.86%)[5][9][10] - Deep learning factors also demonstrate strong performance, with GRU(50,2)+NN(10) achieving a long-short return of 2.79% in 2026, and the multi-granularity 5-day label model achieving 2.13%[5][9][10] - AI-enhanced portfolios show mixed results, with the CSI 500 AI-enhanced wide constraint portfolio recording a -4.47% excess return in 2026, while the CSI 1000 AI-enhanced strict constraint portfolio achieved a relatively better performance of -1.57%[5][14][73]

- 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

- The high-frequency skewness factor had long-short returns of 0.67% last week, -1.18% in December, and 22.39% year-to-date 2025[5] - The intraday downside volatility factor had long-short returns of 0.87% last week, -1.33% in December, and 19.08% year-to-date 2025[5] - The post-open buying intention proportion factor had long-short returns of 0.66% last week, 0.61% in December, and 21.12% year-to-date 2025[5] - The post-open buying intention intensity factor had long-short returns of 0.46% last week, 0.94% in December, and 28.09% year-to-date 2025[5] - The post-open large order net buying proportion factor had long-short returns of -0.21% last week, 0.17% in December, and 22.11% year-to-date 2025[5] - The post-open large order net buying intensity factor had long-short returns of -0.25% last week, 0.38% in December, and 12.5% year-to-date 2025[5] - The intraday return factor had long-short returns of 0.35% last week, 0.91% in December, and 22.33% year-to-date 2025[5] - The end-of-day trading proportion factor had long-short returns of -0.94% last week, 1.04% in December, and 16.73% year-to-date 2025[5] - The average single transaction outflow proportion factor had long-short returns of -1.15% last week, -2.15% in December, and -8.11% year-to-date 2025[5] - The large order push-up factor had long-short returns of 0.41% last week, -0.93% in December, and 7.19% year-to-date 2025[5] - The GRU(10,2)+NN(10) factor had long-short returns of 1.13% last week, -0.47% in December, and 47.04% year-to-date 2025, with long-only excess returns of -0.2% last week, -0.26% in December, and 7.1% year-to-date 2025[5] - The GRU(50,2)+NN(10) factor had long-short returns of 1.66% last week, 0.19% in December, and 47.39% year-to-date 2025, with long-only excess returns of 0.15% last week, 0.06% in December, and 8.92% year-to-date 2025[5] - The multi-granularity model (5-day label) factor had long-short returns of 2.46% last week, 1.12% in December, and 68.13% year-to-date 2025, with long-only excess returns of 0.74% last week, -0.18% in December, and 24.48% year-to-date 2025[5] - The multi-granularity model (10-day label) factor had long-short returns of 2.26% last week, 1.11% in December, and 62.71% year-to-date 2025, with long-only excess returns of 0.76% last week, -0.5% in December, and 24.3% year-to-date 2025[5] - The weekly rebalanced CSI 500 AI-enhanced wide constraint portfolio had excess returns of 0.41% last week, -2.64% in December, and 5.46% year-to-date 2025[5] - The weekly rebalanced CSI 500 AI-enhanced strict constraint portfolio had excess returns of 0.92% last week, -1.62% in December, and 9.23% year-to-date 2025[5] - The weekly rebalanced CSI 1000 AI-enhanced wide constraint portfolio had excess returns of 1.55% last week, -2.69% in December, and 15.39% year-to-date 2025[5] - The weekly rebalanced CSI 1000 AI-enhanced strict constraint portfolio had excess returns of 1.48% last week, -1.45% in December, and 19.02% year-to-date 2025[5]

Core Insights - The report indicates a general rebound in high-frequency factors, with significant improvement in multi-granularity factor long positions, and AI-enhanced portfolios showing stable performance with positive returns across multiple combinations [2][5]. High-Frequency Factors and Deep Learning Factors Summary - High-frequency skew factor returns for the last week, November, and 2025 YTD are 1.93%, 1.29%, and 23.56% respectively [5][10]. - Downward volatility proportion factor returns for the last week, November, and 2025 YTD are 1.63%, 1.44%, and 20.42% respectively [5][10]. - Opening buy intention proportion factor returns for the last week, November, and 2025 YTD are 1.21%, 1.17%, and 20.51% respectively [5][10]. - Opening buy intention strength factor returns for the last week, November, and 2025 YTD are 1.17%, 1.36%, and 27.15% respectively [5][10]. - Opening large order net buy proportion factor returns for the last week, November, and 2025 YTD are 1.35%, 1.00%, and 21.94% respectively [5][10]. - Opening large order net buy strength factor returns for the last week, November, and 2025 YTD are 0.97%, -0.49%, and 12.12% respectively [5][10]. - Daily return factor for the last week, November, and 2025 YTD is 0.01%, -0.60%, and 21.42% respectively [5][10]. - End-of-day transaction proportion factor returns for the last week, November, and 2025 YTD are 1.64%, -0.07%, and 15.70% respectively [5][10]. - Average single outflow amount proportion factor returns for the last week, November, and 2025 YTD are 0.02%, -2.91%, and -5.96% respectively [5][10]. - Large order driving increase factor returns for the last week, November, and 2025 YTD are -0.34%, -0.49%, and 8.12% respectively [5][10]. AI Enhanced Portfolio Performance - The weekly rebalancing of the CSI 500 AI enhanced wide constraint portfolio shows excess returns of -0.08%, 4.36%, and 8.33% for the last week, November, and 2025 YTD respectively [5][13]. - The weekly rebalancing of the CSI 500 AI enhanced strict constraint portfolio shows excess returns of 0.19%, 2.75%, and 11.02% for the last week, November, and 2025 YTD respectively [5][13]. - The weekly rebalancing of the CSI 1000 AI enhanced wide constraint portfolio shows excess returns of 0.11%, 4.58%, and 18.58% for the last week, November, and 2025 YTD respectively [5][13]. - The weekly rebalancing of the CSI 1000 AI enhanced strict constraint portfolio shows excess returns of 0.11%, 1.93%, and 20.77% for the last week, November, and 2025 YTD respectively [5][13].

Quantitative Models and Construction Methods Model Name: GRU(10,2)+NN(10) - **Model Construction Idea**: This model combines Gated Recurrent Units (GRU) with a neural network (NN) to capture temporal dependencies in high-frequency data[4] - **Model Construction Process**: The model uses a GRU with 10 units and 2 layers, followed by a neural network with 10 units. The GRU processes sequential data, and the NN captures non-linear relationships[4] - **Model Evaluation**: The model shows strong performance in capturing temporal patterns and generating significant returns[4] Model Name: GRU(50,2)+NN(10) - **Model Construction Idea**: Similar to the GRU(10,2)+NN(10) model but with more GRU units to capture more complex temporal dependencies[4] - **Model Construction Process**: The model uses a GRU with 50 units and 2 layers, followed by a neural network with 10 units. This setup allows for deeper temporal feature extraction[4] - **Model Evaluation**: The model is effective in capturing complex temporal patterns and generating significant returns[4] Model Name: Multi-Granularity Model (5-day label) - **Model Construction Idea**: This model uses multiple granularities of data to improve prediction accuracy[4] - **Model Construction Process**: The model labels data with a 5-day horizon and uses a combination of features from different time scales to enhance prediction[4] - **Model Evaluation**: The model shows strong performance in capturing multi-scale patterns and generating significant returns[4] Model Name: Multi-Granularity Model (10-day label) - **Model Construction Idea**: Similar to the 5-day label model but with a 10-day horizon to capture longer-term dependencies[4] - **Model Construction Process**: The model labels data with a 10-day horizon and combines features from different time scales to enhance prediction[4] - **Model Evaluation**: The model is effective in capturing longer-term patterns and generating significant returns[4] Model Backtesting Results - **GRU(10,2)+NN(10)**: - Multi-Period Return: -1.32% (last week), -0.71% (November), 44.83% (2025)[4] - Excess Return: -0.77% (last week), -1.01% (November), 7.21% (2025)[4] - **GRU(50,2)+NN(10)**: - Multi-Period Return: -1.5% (last week), -1.23% (November), 44.56% (2025)[4] - Excess Return: -0.83% (last week), -0.92% (November), 7.9% (2025)[4] - **Multi-Granularity Model (5-day label)**: - Multi-Period Return: 0.75% (last week), 2.56% (November), 63.15% (2025)[4] - Excess Return: 1.07% (last week), 2.36% (November), 24.44% (2025)[4] - **Multi-Granularity Model (10-day label)**: - Multi-Period Return: 0.91% (last week), 2.55% (November), 57.7% (2025)[4] - Excess Return: 0.98% (last week), 2.27% (November), 24.14% (2025)[4] Quantitative Factors and Construction Methods Factor Name: Intraday Skewness Factor - **Factor Construction Idea**: This factor captures the skewness of intraday returns to identify asymmetric return distributions[4] - **Factor Construction Process**: The factor is calculated using the skewness of intraday returns over a specified period[4] - **Factor Evaluation**: The factor is effective in identifying stocks with asymmetric return distributions[4] Factor Name: Downside Volatility Proportion Factor - **Factor Construction Idea**: This factor measures the proportion of downside volatility to capture risk characteristics[4] - **Factor Construction Process**: The factor is calculated as the proportion of downside volatility relative to total volatility over a specified period[4] - **Factor Evaluation**: The factor is effective in identifying stocks with higher downside risk[4] Factor Name: Post-Open Buy Intention Proportion Factor - **Factor Construction Idea**: This factor measures the proportion of buy intentions after market open to capture investor sentiment[4] - **Factor Construction Process**: The factor is calculated as the proportion of buy orders relative to total orders after market open[4] - **Factor Evaluation**: The factor is effective in capturing investor sentiment and predicting stock movements[4] Factor Name: Post-Open Buy Intensity Factor - **Factor Construction Idea**: This factor measures the intensity of buy intentions after market open to capture investor sentiment strength[4] - **Factor Construction Process**: The factor is calculated as the intensity of buy orders relative to total orders after market open[4] - **Factor Evaluation**: The factor is effective in capturing the strength of investor sentiment and predicting stock movements[4] Factor Backtesting Results - **Intraday Skewness Factor**: - Multi-Period Return: -0.26% (last week), 0.49% (November), 22.76% (2025)[4] - Excess Return: 0.42% (last week), 1.46% (November), 6.14% (2025)[4] - **Downside Volatility Proportion Factor**: - Multi-Period Return: 0.38% (last week), 1.35% (November), 20.32% (2025)[4] - Excess Return: 0.41% (last week), 1.08% (November), 3.54% (2025)[4] - **Post-Open Buy Intention Proportion Factor**: - Multi-Period Return: 0.28% (last week), -0.01% (November), 19.33% (2025)[4] - Excess Return: 0.47% (last week), 0.28% (November), 8.78% (2025)[4] - **Post-Open Buy Intensity Factor**: - Multi-Period Return: 0.27% (last week), 0.57% (November), 26.36% (2025)[4] - Excess Return: -0.22% (last week), -0.55% (November), 10.06% (2025)[4]

Core Viewpoint - The article highlights the strong performance of the GF Quantitative Multi-Factor Mixed Fund (005225), which has achieved a cumulative return of 109.93% since its inception on March 21, 2018, significantly outperforming its benchmark across various time frames [1]. Group 1: Fund Performance - The GF Quantitative Multi-Factor Fund has a high equity position of 91.75%, with a diversified portfolio that includes six stocks with a total market capitalization below 10 billion, accounting for 8.35% of the fund's net asset value [2]. - Over the past year, the GF Quantitative Multi-Factor Fund has outperformed the National Securities 2000 Index by 30 percentage points, achieving a return of 54.33% compared to the index's performance [2]. - The fund's monthly win rate against the National Securities 2000 Index is 81%, with an average monthly excess return of 1.20% since the current fund managers took over [3]. Group 2: Investment Strategy - The fund employs a dual-engine model combining traditional quantitative multi-factor models with advanced machine learning techniques to enhance factor discovery and integration [4][5]. - The fund managers utilize AI tools to identify hidden pricing patterns and improve the efficiency of alpha factor extraction, addressing the limitations of traditional models [5]. Group 3: Other Quantitative Funds - The article also discusses other quantitative funds under GF, such as the GF Multi-Factor Mixed Fund (002943), which has consistently outperformed major indices over the past seven years [6][7]. - GF has a diverse range of quantitative products, including Smart Beta strategies, which focus on small-cap style enhancement [7]. Group 4: Dividend and Value Strategies - The GF Stable Strategy Fund (006780) employs a combination of subjective and quantitative approaches to capture dividend opportunities, achieving a return of 25.93% in 2024, outperforming the benchmark by 7.17% [10]. - The GF High Dividend Preferred Fund (008704) focuses on high-dividend, low-valuation stocks, achieving a year-to-date return of 12.10%, significantly surpassing the performance of the benchmark indices [14][15].

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