- The report highlights the strong performance of high-frequency factors, with notable multi-granularity factor returns and differentiated excess returns in AI-enhanced portfolios [6][10][11] - High-frequency factors such as intraday skewness, downside volatility proportion, and opening buy intention proportion recorded significant multi-long-short returns and excess returns across weekly, monthly, and YTD periods [6][10][11] - Multi-granularity models (5-day and 10-day labels) achieved robust multi-long-short returns, with the 5-day label model yielding 0.91% (weekly), 2.6% (monthly), and 10.22% (YTD), while the 10-day label model delivered 0.71% (weekly), 3.06% (monthly), and 8.9% (YTD) [6][10][11] - AI-enhanced portfolios, including the Air Index Increment and CSI 500/1000 AI Enhanced portfolios, demonstrated varying excess and absolute returns under both wide and strict constraints, with weekly and daily rebalancing strategies [6][15][73] - The optimization objective for AI-enhanced portfolios is to maximize expected returns, represented by the function: $$max\sum\mu_{i}w_{i}$$ where \(w_i\) is the weight of stock \(i\) in the portfolio, and \(\mu_i\) is the expected excess return of stock \(i\) [75][76]

Quantitative Models and Construction Methods 1. Model Name: Multi-Granularity Model (5-Day Label) - **Model Construction Idea**: This model leverages deep learning techniques to capture multi-granularity features of stock data over a 5-day horizon[66] - **Model Construction Process**: The factor is trained using a bidirectional AGRU (Attention Gated Recurrent Unit) model, which processes sequential data to extract temporal dependencies and patterns[66] - **Model Evaluation**: The model demonstrates stable performance across different time periods, indicating its robustness in capturing market dynamics[66] 2. Model Name: Multi-Granularity Model (10-Day Label) - **Model Construction Idea**: Similar to the 5-day label model, this model extends the horizon to 10 days to capture longer-term patterns in stock data[70] - **Model Construction Process**: The factor is also trained using a bidirectional AGRU model, with adjustments to accommodate the extended time horizon[70] - **Model Evaluation**: The model shows consistent performance, with slightly different characteristics compared to the 5-day label model, making it suitable for longer-term strategies[70] 3. Model Name: AI-Enhanced Index Strategies - **Model Construction Idea**: Combines multiple deep learning factors (e.g., 5-day and 10-day multi-granularity models) to construct AI-enhanced index strategies with risk constraints[72] - **Model Construction Process**: - The combined factor is a weighted sum: `0.5 * Multi-Granularity Model (5-Day Label) + 0.5 * Multi-Granularity Model (10-Day Label)`[72] - Optimization objective: Maximize expected returns, represented by the function: $$ max \sum \mu_{i} w_{i} $$ where \( w_{i} \) is the weight of stock \( i \), and \( \mu_{i} \) is the expected excess return of stock \( i \)[75] - Risk control constraints include limits on individual stock weights, industry weights, market capitalization, and turnover rates[73][75] - Backtesting assumes next-day average price execution and deducts a 0.3% transaction cost[76] - **Model Evaluation**: The model effectively balances return maximization and risk control, with different configurations (e.g., wide vs. strict constraints) tailored to specific index benchmarks[72][73] --- Model Backtesting Results 1. Multi-Granularity Model (5-Day Label) - **IC**: Historical: 0.079; 2026: 0.040[14] - **e^(-RankMAE)**: Historical: 0.343; 2026: 0.334[14] - **Long-Short Return**: March: 1.68%; 2026 YTD: 9.31%[14] - **Long-Only Excess Return**: March: 1.21%; 2026 YTD: 4.95%[14] - **Monthly Win Rate**: 9/10[14] 2. Multi-Granularity Model (10-Day Label) - **IC**: Historical: 0.072; 2026: 0.040[14] - **e^(-RankMAE)**: Historical: 0.342; 2026: 0.336[14] - **Long-Short Return**: March: 2.35%; 2026 YTD: 8.19%[14] - **Long-Only Excess Return**: March: 1.48%; 2026 YTD: 4.72%[14] - **Monthly Win Rate**: 8/10[14] 3. AI-Enhanced Index Strategies - **AI Air Quality Index Strategy**: - **Weekly Rebalancing**: Excess Return: -0.12% (last week), 0.65% (March), 4.17% (2026 YTD); Absolute Return: -5.47% (last week), -7.86% (March), 6.70% (2026 YTD)[15][81] - **Daily Rebalancing**: Excess Return: -0.78% (last week), -0.08% (March), 4.41% (2026 YTD); Absolute Return: -6.12% (last week), -8.59% (March), 6.94% (2026 YTD)[15][81] - **CSI 500 AI Enhanced (Wide Constraint)**: - **Weekly Rebalancing**: Excess Return: 1.43% (last week), 5.62% (March), 2.71% (2026 YTD); Absolute Return: -4.40% (last week), -4.76% (March), 6.66% (2026 YTD)[15][83] - **Daily Rebalancing**: Excess Return: 0.60% (last week), 1.79% (March), -2.71% (2026 YTD); Absolute Return: -5.23% (last week), -8.58% (March), 1.24% (2026 YTD)[15][83] - **CSI 500 AI Enhanced (Strict Constraint)**: - **Weekly Rebalancing**: Excess Return: 0.35% (last week), 3.51% (March), 2.73% (2026 YTD); Absolute Return: -5.47% (last week), -6.87% (March), 6.68% (2026 YTD)[15][89] - **Daily Rebalancing**: Excess Return: 0.31% (last week), 2.10% (March), 1.42% (2026 YTD); Absolute Return: -5.52% (last week), -8.27% (March), 5.37% (2026 YTD)[15][89] - **CSI 1000 AI Enhanced (Wide Constraint)**: - **Weekly Rebalancing**: Excess Return: 0.79% (last week), 3.52% (March), 4.19% (2026 YTD); Absolute Return: -4.46% (last week), -5.56% (March), 6.67% (2026 YTD)[15][91] - **Daily Rebalancing**: Excess Return: -0.20% (last week), 1.81% (March), 1.92% (2026 YTD); Absolute Return: -5.44% (last week), -7.27% (March), 4.40% (2026 YTD)[15][91] - **CSI 1000 AI Enhanced (Strict Constraint)**: - **Weekly Rebalancing**: Excess Return: 0.57% (last week), 2.55% (March), 3.67% (2026 YTD); Absolute Return: -4.68% (last week), -6.53% (March), 6.15% (2026 YTD)[15][97] - **Daily Rebalancing**: Excess Return: 0.75% (last week), 1.87% (March), 3.72% (2026 YTD); Absolute Return: -4.49% (last week), -7.21% (March), 6.20% (2026 YTD)[15][97]

Investment Rating - The report indicates an overall strong performance in high-frequency trading factors, with various multi-granularity factors maintaining positive returns [2][3]. Core Insights - High-frequency trading factors exhibited excellent performance, with daily high-frequency skew factors showing long-short returns of 0.49% for the last week, 0.31% for February, and 4.31% for the year 2026 [9]. - The AI-enhanced portfolio has experienced continuous excess drawdown, indicating potential areas for improvement in strategy [2][3]. Summary by Sections High-Frequency Factors and Deep Learning Factors Performance - Daily high-frequency skew factor returns were 0.49% last week, 0.31% in February, and 4.31% year-to-date [11]. - Daily downside volatility factor returns were 0.23%, 1.06%, and 5.81% respectively [11]. - The opening buy intention ratio factor showed returns of 0.01%, -0.90%, and 2.97% [11]. - The opening large net buy ratio factor had returns of 0.73%, 2.26%, and 4.81% [11]. - The average single outflow amount factor showed returns of -0.56%, -1.33%, and -2.89% [11]. AI Enhanced Portfolio Performance - Weekly rebalancing of the AI-enhanced portfolio showed excess/absolute returns of -1.13%/2.25%, 1.34%/5.23%, and 3.74%/15.79% for the last week, February, and year-to-date respectively [14]. - The CSI 500 AI enhanced portfolio with wide constraints showed excess/absolute returns of -2.69%/1.63%, -1.14%/2.30%, and -3.99%/11.99% [14]. - The CSI 1000 AI enhanced portfolio with strict constraints showed excess/absolute returns of -1.03%/3.31%, 0.06%/3.76%, and 0.86%/13.57% [14]. Multi-Granularity Factor Analysis - Multi-granularity models (5-day label) showed long-short returns of 0.22%, 2.13%, and 7.62% [11]. - Multi-granularity models (10-day label) showed long-short returns of -0.37%, 1.76%, and 5.83% [11]. - The deep learning high-frequency factors demonstrated varying performance, with the improved GRU and LSTM models showing positive returns in different time frames [11][12].

- The report highlights the performance of high-frequency factors, deep learning factors, and AI-enhanced portfolios, summarizing their historical and 2026 metrics such as IC, RankMAE, multi-long-short returns, and monthly win rates[9][10][11] - High-frequency factors include intraday skewness, downside volatility proportion, post-open buying intention proportion, post-open buying intention intensity, post-open large-order net buying proportion, post-open large-order net buying intensity, improved reversal factor, end-of-day transaction proportion, average single-order outflow proportion, and large-order-driven price increase factor[7][9][10] - Deep learning factors include improved GRU(50,2)+NN(10), residual attention LSTM(48,2)+NN(10), multi-granularity model with 5-day labels, and multi-granularity model with 10-day labels[7][9][10] - AI-enhanced portfolios are constructed based on deep learning factors, combining multi-granularity models (5-day labels and 10-day labels) with constraints such as stock weight, industry weight, market cap, PB, ROE, SUE, volatility, turnover rate, and constituent stock limits. The optimization goal is to maximize expected returns using the formula $$max\sum\mu_{i}w_{i}$$, where \(w_i\) is the stock weight and \(\mu_i\) is the expected excess return[73][74][76] - The report provides detailed backtesting results for high-frequency factors, deep learning factors, and AI-enhanced portfolios, including weekly, monthly, and annual returns, as well as win rates across different timeframes[9][10][11]

- High-frequency factors showed differentiation, with large-order factors performing well and multi-granularity factors continuing to perform stably[1][2][5] - AI-enhanced portfolios continued to perform strongly[1][2][5] - The intra-day high-frequency skewness factor had a long-short return of -1.11% for the past week and February, and 2.89% for 2026[5] - The intra-day downside volatility proportion factor had a long-short return of -0.61% for the past week and February, and 4.14% for 2026[5] - The post-opening buying intention proportion factor had a long-short return of -0.04% for the past week and February, and 3.82% for 2026[5] - The post-opening buying intention intensity factor had a long-short return of -0.79% for the past week and February, and 3.56% for 2026[5] - The post-opening large-order net buying proportion factor had a long-short return of 0.34% for the past week and February, and 2.89% for 2026[5] - The post-opening large-order net buying intensity factor had a long-short return of 0.29% for the past week and February, and 2.15% for 2026[5] - The intra-day return factor had a long-short return of 0.19% for the past week and February, and 2.93% for 2026[5] - The end-of-day trading proportion factor had a long-short return of -0.4% for the past week and February, and 3.9% for 2026[5] - The average single outflow amount proportion factor had a long-short return of -0.43% for the past week and February, and -1.99% for 2026[5] - The large-order driven rise factor had a long-short return of -0.99% for the past week and February, and 0.41% for 2026[5] - The multi-granularity model (5-day label) factor had a long-short return of 0.65% for the past week and February, and 6.15% for 2026, with a long-only excess return of 0.18% for the past week and February, and 3.45% for 2026[5] - The multi-granularity model (10-day label) factor had a long-short return of 0.53% for the past week and February, and 4.6% for 2026, with a long-only excess return of 0.26% for the past week and February, and 3.01% for 2026[5] - The weekly rebalanced AI air value enhancement portfolio had an excess/absolute return of 3.63%/3.29% for the past week and February, and 6.18%/13.66% for 2026[5] - The daily rebalanced AI air value enhancement portfolio had an excess/absolute return of 3.83%/3.48% for the past week and February, and 6.60%/14.08% for 2026[5] - The weekly rebalanced CSI 500 AI-enhanced wide constraint portfolio had an excess/absolute return of 2.25%/-0.42% for the past week and February, and -0.11%/9.01% for 2026[5] - The daily rebalanced CSI 500 AI-enhanced wide constraint portfolio had an excess/absolute return of 2.08%/-0.59% for the past week and February, and -1.01%/8.11% for 2026[5] - The weekly rebalanced CSI 500 AI-enhanced strict constraint portfolio had an excess/absolute return of 1.29%/-1.38% for the past week and February, and -0.21%/8.91% for 2026[5] - The daily rebalanced CSI 500 AI-enhanced strict constraint portfolio had an excess/absolute return of 1.46%/-1.22% for the past week and February, and 0.44%/9.56% for 2026[5] - The weekly rebalanced CSI 1000 AI-enhanced wide constraint portfolio had an excess/absolute return of 2.66%/0.20% for the past week and February, and 4.21%/10.22% for 2026[5] - The daily rebalanced CSI 1000 AI-enhanced wide constraint portfolio had an excess/absolute return of 2.43%/-0.03% for the past week and February, and 3.40%/9.40% for 2026[5] - The weekly rebalanced CSI 1000 AI-enhanced strict constraint portfolio had an excess/absolute return of 1.65%/-0.82% for the past week and February, and 2.55%/8.56% for 2026[5] - The daily rebalanced CSI 1000 AI-enhanced strict constraint portfolio had an excess/absolute return of 1.46%/-1.00% for the past week and February, and 3.90%/9.90% for 2026[5]

- 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]

- The report discusses the performance of high-frequency factors, deep learning factors, and AI-enhanced portfolios, highlighting significant drawdowns in multi-granularity factors and AI-enhanced combinations during the week of December 8-12, 2025 [1][2][5] - High-frequency factors include intraday skewness, downside volatility proportion, post-open buying intention proportion, post-open buying intention intensity, post-open large-order net buying proportion, post-open large-order net buying intensity, improved reversal, end-of-day transaction proportion, average single-outflow amount proportion, and large-order-driven price increase [6][8][9] - 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, trained using bidirectional AGRU [6][8][65][66] - AI-enhanced portfolios are constructed based on deep learning factors, including CSI 500 and CSI 1000 indices under wide and strict constraint conditions. Optimization aims to maximize expected returns using the formula: $$max\sum\mu_{i}w_{i}$$, where \(w_i\) is the weight of stock \(i\) and \(\mu_i\) is the expected excess return of stock \(i\) [70][71][72] - Intraday skewness factor achieved historical IC of 0.026 and 2025 IC of 0.030, with 2025 multi-long-short returns of 16.44% and monthly win rate of 7/12 [9][10] - Downside volatility proportion factor achieved historical IC of 0.024 and 2025 IC of 0.025, with 2025 multi-long-short returns of 12.84% and monthly win rate of 8/12 [9][10] - Post-open buying intention proportion factor achieved historical IC of 0.031 and 2025 IC of 0.023, with 2025 multi-long-short returns of 10.49% and monthly win rate of 7/12 [9][10] - Post-open buying intention intensity factor achieved historical IC of 0.035 and 2025 IC of 0.025, with 2025 multi-long-short returns of 13.52% and monthly win rate of 10/12 [9][10] - Post-open large-order net buying proportion factor achieved historical IC of 0.040 and 2025 IC of 0.029, with 2025 multi-long-short returns of 17.93% and monthly win rate of 10/12 [9][10] - Post-open large-order net buying intensity factor achieved historical IC of 0.032 and 2025 IC of 0.023, with 2025 multi-long-short returns of 13.05% and monthly win rate of 9/12 [9][10] - Improved reversal factor achieved historical IC of 0.031 and 2025 IC of 0.011, with 2025 multi-long-short returns of 4.38% and monthly win rate of 6/12 [9][10] - End-of-day transaction proportion factor achieved historical IC of 0.049 and 2025 IC of 0.027, with 2025 multi-long-short returns of 16.82% and monthly win rate of 9/12 [9][10] - Average single-outflow amount proportion factor achieved historical IC of 0.018 and 2025 IC of -0.006, with 2025 multi-long-short returns of -2.57% and monthly win rate of 5/12 [9][10] - Large-order-driven price increase factor achieved historical IC of 0.015 and 2025 IC of 0.001, with 2025 multi-long-short returns of 4.72% and monthly win rate of 7/12 [9][10] - GRU(50,2)+NN(10) factor achieved historical IC of 0.066 and 2025 IC of 0.045, with 2025 multi-long-short returns of 45.90% and weekly win rate of 40/50 [12][60] - Residual attention LSTM(48,2)+NN(10) factor achieved historical IC of 0.062 and 2025 IC of 0.043, with 2025 multi-long-short returns of 45.73% and weekly win rate of 45/50 [12][62] - Multi-granularity model (5-day label) factor achieved historical IC of 0.080 and 2025 IC of 0.064, with 2025 multi-long-short returns of 65.67% and weekly win rate of 44/50 [12][65] - Multi-granularity model (10-day label) factor achieved historical IC of 0.073 and 2025 IC of 0.059, with 2025 multi-long-short returns of 60.45% and weekly win rate of 44/50 [12][66] - CSI 500 AI-enhanced wide constraint portfolio achieved weekly returns of -2.80%, December returns of -3.04%, and 2025 YTD returns of 5.03%, with weekly win rate of 28/50 [13][76] - CSI 500 AI-enhanced strict constraint portfolio achieved weekly returns of -2.42%, December returns of -2.51%, and 2025 YTD returns of 8.24%, with weekly win rate of 32/50 [13][79] - CSI 1000 AI-enhanced wide constraint portfolio achieved weekly returns of -3.54%, December returns of -4.18%, and 2025 YTD returns of 13.63%, with weekly win rate of 33/50 [13][80] - CSI 1000 AI-enhanced strict constraint portfolio achieved weekly returns of -2.35%, December returns of -2.88%, and 2025 YTD returns of 17.29%, with weekly win rate of 32/50 [13][86]

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].