高频选股因子周报（20260224- 20260227） 高频整体表现优异，多粒度因子维持正收益。AI 增强组合 超额持续回撤。 本报告导读： 上周（20260224-20260227，下同）高频整体表现优异，多粒度因子多头回撤。AI增 强组合超额持续回撤。 投资要点： | [Table_Authors] | 郑雅斌(分析师) | | --- | --- | | | 021-23219395 | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 余浩淼(分析师) | | | 021-23185650 | | | yuhaomiao@gtht.com | | 登记编号 | S0880525040013 | [Table_Report] 相关报告 低频选股因子周报（2026.02.13-2026.02.27） 2026.03.01 量化择时和拥挤度预警周报（20260227） 2026.02.28 红利风格择时周报（0209-0213） 2026.02.24 量化择时和拥挤度预警周报（20260220） 2026.02.22 高频选股因子周报（2026 ...

高频选股因子周报（20260224- 20260227） 高频整体表现优异，多粒度因子维持正收益。AI 增强组合 超额持续回撤。 本报告导读： 上周（20260224-20260227，下同）高频整体表现优异，多粒度因子多头回撤。AI增 强组合超额持续回撤。 投资要点： | [Table_Authors] | 郑雅斌(分析师) | | --- | --- | | | 021-23219395 | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 余浩淼(分析师) | | | 021-23185650 | | | yuhaomiao@gtht.com | | 登记编号 | S0880525040013 | [Table_Report] 相关报告 低频选股因子周报（2026.02.13-2026.02.27） 2026.03.01 量化择时和拥挤度预警周报（20260227） 2026.02.28 红利风格择时周报（0209-0213） 2026.02.24 量化择时和拥挤度预警周报（20260220） 2026.02.22 高频选股因子周报（2026 ...

高频选股因子周报（20260202- 20260206） 高频因子分化，大单因子表现较好，多粒度因子继续稳定表 现。AI 增强组合继续强势表现。 本报告导读： 上周（20260202-20260206，下同）高频因子分化，大单因子表现较好，多粒度因子 继续稳定表现。AI 增强组合继续强势表现。 投资要点： | | 金融工程 | /[Table_Date] 2026.02.10 | | --- | --- | --- | | [Table_Authors] | | 郑雅斌(分析师) | | | 021-23219395 | | --- | --- | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 余浩淼(分析师) | | | 021-23185650 | | | yuhaomiao@gtht.com | | 登记编号 | S0880525040013 | [Table_Report] 相关报告 量化择时和拥挤度预警周报（20260206） 2026.02.08 红利风格择时周报（0202-0206） 2026.02.07 低频选股因子周报（202 ...

- 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]

高频选股因子周报（20251208- 20251212） 高频因子走势分化，多粒度因子显著回撤。AI 增强组合均 大幅度回撤。 本报告导读： 上周（特指 20251208-20251212，下同）高频因子走势分化，多粒度因子显著回撤。 AI 增强组合均大幅度回撤。 投资要点： | | | | [Table_Authors] | 郑雅斌(分析师) | | --- | --- | | | 021-23219395 | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 余浩淼(分析师) | | | 021-23185650 | | | yuhaomiao@gtht.com | | 登记编号 | S0880525040013 | [Table_Report] 相关报告 低频选股因子周报（2025.12.05-2025.12.12） 2025.12.13 绝对收益产品及策略周报（251201-251205） 2025.12.10 上周估值因子表现较好，本年中证 2000 指数增强 策略超额收益为 28.22% 2025.12.10 红利风格择时周报（1201 ...

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 Insights - The report indicates that high-frequency factors continued to retract, while multi-granularity factors showed some rebound. The AI-enhanced portfolios have sustained a rebound, with the strictly constrained 1000 enhanced portfolio achieving a record high in excess returns [2][5]. Summary by Sections 1. High-Frequency Factors, Deep Learning Factors, and AI Enhanced Portfolio Performance Summary - The report summarizes the historical and 2025 performance of high-frequency stock selection factors, including multi-factor returns and excess returns for October and year-to-date [8]. - The high-frequency skew factor had a multi-directional return of -0.54% for the last week, -2.03% for October, and 20.66% year-to-date [10]. - The deep learning high-frequency factor (improved GRU(50,2)+NN(10)) reported a multi-directional return of 0.62% for the last week, 0.38% for October, and 43.14% year-to-date [12]. 2. Weekly Rebalancing of AI Index Enhanced Portfolios - The weekly rebalancing of the CSI 500 AI enhanced wide constraint portfolio achieved excess returns of 3.51%, 4.71%, and 4.65% for the last week, October, and year-to-date respectively [13]. - The weekly rebalancing of the CSI 1000 AI enhanced strict constraint portfolio achieved excess returns of 2.21%, 3.99%, and 17.63% for the last week, October, and year-to-date respectively [13]. 3. Performance of Specific Factors - The opening buy intention strength factor had a multi-directional return of -0.98% for the last week, -2.72% for October, and 23.09% year-to-date [10]. - The average single outflow amount factor reported a multi-directional return of -0.90% for the last week, -1.90% for October, and -2.44% year-to-date [10]. - The deep learning factor (multi-granularity model - 5-day label) achieved a multi-directional return of 2.04% for the last week, 2.53% for October, and 55.62% year-to-date [12].