Quantitative Models and Construction Methods 1. Model Name: High-frequency "Gold" Combination CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This model combines three types of high-frequency factors (price range, price-volume divergence, and regret avoidance) with equal weights to enhance the CSI 1000 Index. It aims to leverage the predictive power of high-frequency factors for stock selection[3][62][66] - **Model Construction Process**: 1. Combine the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with weights of 25%, 25%, and 50%, respectively[36][42][51] 2. Neutralize the combined factor by industry market capitalization[36][42][51] 3. Implement weekly rebalancing with a turnover buffer mechanism to reduce transaction costs[62][66] - **Model Evaluation**: The model demonstrates strong excess return performance both in-sample and out-of-sample, with a stable upward trend in the net value curve[39][66] 2. Model Name: High-frequency & Fundamental Resonance Combination CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This model integrates high-frequency factors with fundamental factors (consensus expectations, growth, and technical factors) to improve the performance of multi-factor investment portfolios[67][69] - **Model Construction Process**: 1. Combine the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with fundamental factors (consensus expectations, growth, and technical factors) using equal weights[67][69] 2. Neutralize the combined factor by industry market capitalization[67][69] 3. Implement weekly rebalancing with a turnover buffer mechanism to reduce transaction costs[67][69] - **Model Evaluation**: The model shows improved performance metrics compared to the high-frequency-only strategy, with higher annualized returns and Sharpe ratios[69][71] --- Model Backtesting Results 1. High-frequency "Gold" Combination CSI 1000 Index Enhanced Strategy - Annualized Return: 9.63% - Annualized Volatility: 23.82% - Sharpe Ratio: 0.40 - Maximum Drawdown: 47.77% - Annualized Excess Return: 9.85% - Tracking Error: 4.32% - IR: 2.28 - Maximum Excess Drawdown: 6.04%[63][66] 2. High-frequency & Fundamental Resonance Combination CSI 1000 Index Enhanced Strategy - Annualized Return: 13.80% - Annualized Volatility: 23.44% - Sharpe Ratio: 0.59 - Maximum Drawdown: 39.60% - Annualized Excess Return: 13.93% - Tracking Error: 4.20% - IR: 3.31 - Maximum Excess Drawdown: 4.52%[69][71] --- Quantitative Factors and Construction Methods 1. Factor Name: Price Range Factor - **Factor Construction Idea**: Measures the activity of stock transactions in different price ranges during the day, reflecting investors' expectations of future stock trends[3][33] - **Factor Construction Process**: 1. Use high-frequency snapshot data to calculate transaction volume and number of transactions in high (80%) and low (10%) price ranges[33][36] 2. Combine sub-factors with weights of 25%, 25%, and 50%[36] 3. Neutralize the combined factor by industry market capitalization[36] - **Factor Evaluation**: The factor shows strong predictive power and stable performance, with a steadily upward excess net value curve[39] 2. Factor Name: Price-Volume Divergence Factor - **Factor Construction Idea**: Measures the correlation between stock price and trading volume. Lower correlation indicates a higher probability of future price increases[3][40] - **Factor Construction Process**: 1. Use high-frequency snapshot data to calculate the correlation between price and trading volume, as well as price and transaction count[40][42] 2. Combine sub-factors with equal weights[42] 3. Neutralize the combined factor by industry market capitalization[42] - **Factor Evaluation**: The factor's performance has been relatively flat in recent years but has shown good excess return this year[44] 3. Factor Name: Regret Avoidance Factor - **Factor Construction Idea**: Based on behavioral finance, this factor captures investors' regret avoidance emotions, such as the impact of selling stocks that later rebound[3][46] - **Factor Construction Process**: 1. Use tick-by-tick transaction data to identify active buy/sell directions[46] 2. Construct sub-factors like sell rebound ratio and sell rebound deviation, and apply restrictions on small orders and closing trades[46] 3. Combine sub-factors with equal weights and neutralize by industry market capitalization[46][51] - **Factor Evaluation**: The factor shows stable upward performance and strong excess return levels out-of-sample[53] 4. Factor Name: Slope Convexity Factor - **Factor Construction Idea**: Captures the impact of order book slope and convexity on expected returns, reflecting investor patience and supply-demand elasticity[3][54] - **Factor Construction Process**: 1. Use order book data to calculate the slope of buy and sell orders at different levels[54] 2. Construct sub-factors for low-level slope and high-level convexity, and combine them[54][58] 3. Neutralize the combined factor by industry market capitalization[58] - **Factor Evaluation**: The factor has shown stable performance since 2016, with relatively flat out-of-sample results[61] --- Factor Backtesting Results 1. Price Range Factor - Annualized Excess Return: 4.90% - IR: 1.13 - Maximum Excess Drawdown: 1.89%[36][39] 2. Price-Volume Divergence Factor - Annualized Excess Return: 5.59% - IR: 1.29 - Maximum Excess Drawdown: 2.13%[42][44] 3. Regret Avoidance Factor - Annualized Excess Return: -2.62% - IR: -0.61 - Maximum Excess Drawdown: 1.69%[46][53] 4. Slope Convexity Factor - Annualized Excess Return: -10.40% - IR: -2.35 - Maximum Excess Drawdown: 2.42%[58][61]

上周（特指 20251215-20251219，下同）高频因子走势分化持续，多粒度因子表现反 弹。AI 增强组合均一定程度反弹。 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 黄金继续上涨，国内资产 BL 策略 2 本周上涨 0.1% 2025.12.20 绝对收益产品及策略周报（251208-251212） 2025.12.18 大额买入与资金流向跟踪（20251208-20251212） 2025.12.16 上周大市值风格占优，分析师、盈利因子表现较 好 2025.12.16 风格 Smart beta 组合跟踪周报（2025.12.08- 2025.12.12） 2025.12.15 证 ...

- The report tracks the performance of high-frequency stock selection factors, including Price Range Factor, Price-Volume Divergence Factor, Regret Avoidance Factor, and Slope Convexity Factor. These factors are evaluated based on their excess returns and predictive capabilities[2][3][11] - **Price Range Factor**: This factor measures the activity of stock transactions in different price ranges during the day, reflecting investors' expectations for future stock trends. It includes sub-factors such as high-price range transaction volume (VH80TAW), high-price range transaction count (MIH80TAW), and low-price range average transaction volume (VPML10TAW). The factor shows a strong predictive effect and stable performance this year[3][12][14] - **Price-Volume Divergence Factor**: This factor evaluates the correlation between stock prices and trading volumes. A lower correlation indicates a higher likelihood of future price increases. Sub-factors include price-to-transaction count correlation (CorrPM) and price-to-volume correlation (CorrPV). The factor has shown relatively stable performance this year, despite a declining trend since 2020[3][20][22] - **Regret Avoidance Factor**: Based on behavioral finance, this factor examines the proportion and degree of stock price rebounds after being sold by investors. Sub-factors include sell-rebound proportion (LCVOLESW) and sell-rebound deviation (LCPESW). The factor demonstrates stable out-of-sample excess returns, indicating that regret avoidance sentiment significantly impacts stock price expectations[3][23][31] - **Slope Convexity Factor**: Derived from the elasticity of supply and demand, this factor uses order book data to calculate the slope and convexity of buy and sell orders. Sub-factors include low-level slope (Slope_abl) and high-level convexity (Slope_alh). The factor's performance has been relatively flat in recent years, with some fluctuations in recent weeks[3][32][35] - The report constructs two enhanced strategies: the "High-Frequency Gold" portfolio and the "High-Frequency & Fundamental Resonance" portfolio. The "High-Frequency Gold" portfolio combines the three high-frequency factors with equal weights, achieving an annualized excess return of 10.11% and an IR of 2.36. The "High-Frequency & Fundamental Resonance" portfolio integrates high-frequency factors with fundamental factors (e.g., consensus expectations, growth, and technical factors), achieving an annualized excess return of 14.21% and an IR of 3.39[3][39][44] - **Performance Metrics for High-Frequency Gold Portfolio**: Annualized return: 9.49%, Annualized volatility: 23.87%, Sharpe ratio: 0.40, Maximum drawdown: 47.77%, Annualized excess return: 10.11%, IR: 2.36, Maximum excess drawdown: 6.04%[40][43] - **Performance Metrics for High-Frequency & Fundamental Resonance Portfolio**: Annualized return: 13.66%, Annualized volatility: 23.49%, Sharpe ratio: 0.58, Maximum drawdown: 39.60%, Annualized excess return: 14.21%, IR: 3.39, Maximum excess drawdown: 4.52%[47][48]

程 20251128） 高频因子普遍反弹，多粒度因子多头表现明显改善。AI 增 强组合表现平稳，多数组合获得正收益。 本报告导读： 上周（特指 20251124-20251128，下同）高频因子普遍反弹，多粒度因子多头表现明 显改善。AI 增强组合表现平稳，多数组合获得正收益。 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 量化择时和拥挤度预警周报（20251128） 2025.11.30 低频选股因子周报（2025.11.21-2025.11.28） 2025.11.29 权益黄金尽墨，全球资产 BL 模型 2 本周微录正 收益 2025.11.28 绝对收益产品及策略周报（251117-251121） 2025.11.27 上周 ...

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]

- The report tracks high-frequency stock selection factors, including Price Range Factor, Price-Volume Divergence Factor, Regret Avoidance Factor, and Slope Convexity Factor, with their respective excess returns detailed for different periods [2][3][13] - Price Range Factor measures the activity level of stocks in different intraday price ranges, reflecting investor expectations for future stock trends. It shows strong predictive performance and stable results this year [3][11][17] - Price-Volume Divergence Factor evaluates the correlation between stock price and trading volume. Lower correlation indicates higher potential for future stock price increases. However, its performance has been unstable in recent years [3][22][24] - Regret Avoidance Factor examines the proportion and degree of stock rebound after being sold by investors, leveraging behavioral finance theories. It demonstrates stable excess returns out-of-sample, indicating significant influence of regret avoidance sentiment on stock price expectations [3][25][34] - Slope Convexity Factor is constructed using high-frequency order book data, analyzing the slope and convexity of order books to assess the impact of investor patience and supply-demand elasticity on expected returns. It includes High-Level Slope Factor and High-Level Convexity Factor [3][36][39] - A high-frequency "Gold" portfolio strategy was created by equally combining the three high-frequency factors, achieving an annualized excess return of 10.09% and an IR of 2.36 [3][43][46] - A combined high-frequency and fundamental factor strategy was developed, integrating high-frequency factors with fundamental factors like consensus expectations, growth, and technical factors. This strategy achieved an annualized excess return of 14.28% and an IR of 3.41 [3][47][50]

- The report tracks high-frequency stock selection factors, including price range factor, price-volume divergence factor, regret avoidance factor, and slope convexity factor, with their out-of-sample performance being generally strong[2][3][11] - **Price Range Factor**: Measures the activity of stock transactions within different intraday price ranges, reflecting investors' expectations of future stock trends. High price range transaction volume and transaction count factors are negatively correlated with future stock returns, while low price range average transaction volume factor is positively correlated with future stock returns. The factor is constructed by combining three sub-factors: high price 80% range transaction volume factor (VH80TAW), high price 80% range transaction count factor (MIH80TAW), and low price 10% range average transaction volume factor (VPML10TAW). These sub-factors are weighted at 25%, 25%, and 50%, respectively, and are industry market value neutralized[12][14][17] - **Price-Volume Divergence Factor**: Measures the correlation between stock price and trading volume. When price and volume diverge, the likelihood of future price increases is higher, while convergence indicates a higher likelihood of price decreases. The factor is constructed using high-frequency snapshot data to calculate the correlation between snapshot transaction price and snapshot trading volume, as well as snapshot transaction price and transaction count. Two sub-factors are used: price and transaction count correlation factor (CorrPM) and price and trading volume correlation factor (CorrPV). These sub-factors are equally weighted and industry market value neutralized[22][23][25] - **Regret Avoidance Factor**: Based on behavioral finance theory, this factor utilizes investors' regret avoidance emotions to construct effective stock selection factors. It examines the proportion and degree of stock price rebound after being sold by investors. The factor is constructed using transaction data to identify active buy/sell directions, with additional restrictions on small orders and closing trades to enhance performance. Two sub-factors are used: sell rebound proportion factor (LCVOLESW) and sell rebound deviation factor (LCPESW). These sub-factors are equally weighted and industry market value neutralized[26][32][35] - **Slope Convexity Factor**: Derived from the elasticity of supply and demand, this factor uses high-frequency snapshot data from limit order books to calculate the slope and convexity of buy and sell orders. The factor is constructed by aggregating order volume data by level and calculating the slope of buy and sell order books. Two sub-factors are used: low-level slope factor (Slope_abl) and high-level seller convexity factor (Slope_alh). These sub-factors are equally weighted and industry market value neutralized[36][41][43] - **High-frequency "Gold" Portfolio Strategy**: Combines the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with equal weights to construct an enhanced strategy for the CSI 1000 Index. The strategy includes mechanisms to reduce transaction costs, such as weekly rebalancing and turnover rate buffering. The strategy's annualized excess return is 10.20%, with an IR of 2.38 and maximum excess drawdown of 6.04%[44][46][47] - **High-frequency & Fundamental Resonance Portfolio Strategy**: Combines high-frequency factors with fundamental factors (consensus expectations, growth, and technical factors) to construct an enhanced strategy for the CSI 1000 Index. The strategy's annualized excess return is 14.49%, with an IR of 3.46 and maximum excess drawdown of 4.52%[48][50][52]

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]

- The report tracks high-frequency stock selection factors, including Price Range Factor, Price-Volume Divergence Factor, Regret Avoidance Factor, and Slope Convexity Factor, with their out-of-sample performance showing overall excellence[2][3][11] - Price Range Factor measures the activity level of stocks traded within different intraday price ranges, reflecting investors' expectations for future stock trends. It demonstrates strong predictive power and stable performance this year[3][11][17] - Price-Volume Divergence Factor evaluates the correlation between stock prices and trading volumes. Lower correlation typically indicates higher potential for future stock price increases. However, its performance has been unstable in recent years, with multi-long net value curves flattening[3][22][26] - Regret Avoidance Factor examines the proportion and degree of stock rebounds after being sold by investors, showcasing good predictive power. Its out-of-sample excess returns are stable, indicating that A-share investors' regret avoidance sentiment significantly impacts stock price expectations[3][27][36] - Slope Convexity Factor analyzes the slope and convexity of order books to assess the impact of investor patience and supply-demand elasticity on expected returns. It is constructed using high-frequency snapshot data from limit order books[3][37][42] - The report combines three high-frequency factors into an equal-weighted "Gold" portfolio for CSI 1000 Index enhancement strategy, achieving an annualized excess return rate of 10.51% and a maximum excess drawdown of 6.04%[3][44][45] - To further enhance strategy performance, the report integrates high-frequency factors with three effective fundamental factors (Consensus Expectations, Growth, and Technical Factors) to construct a high-frequency & fundamental resonance portfolio for CSI 1000 Index enhancement strategy. This strategy achieves an annualized excess return rate of 14.57% and a maximum excess drawdown of 4.52%[4][49][51] Factor Backtesting Results - Price Range Factor: Weekly excess return 0.40%, monthly excess return 0.51%, annual excess return 5.86%[2][13][17] - Price-Volume Divergence Factor: Weekly excess return -0.24%, monthly excess return 1.53%, annual excess return 9.00%[2][13][26] - Regret Avoidance Factor: Weekly excess return 0.27%, monthly excess return -0.49%, annual excess return 2.32%[2][13][36] - Slope Convexity Factor: Weekly excess return -1.74%, monthly excess return -2.46%, annual excess return -5.90%[2][13][42] Strategy Performance Metrics - "Gold" Portfolio: Annualized return 9.49%, annualized excess return 10.51%, Sharpe ratio 0.39, IR 2.47, maximum excess drawdown 6.04%[45][47][48] - High-frequency & Fundamental Resonance Portfolio: Annualized return 13.62%, annualized excess return 14.57%, Sharpe ratio 0.58, IR 3.50, maximum excess drawdown 4.52%[51][53][55]

Core Insights - The report highlights the significant performance of the A-share market driven by passive investment and leveraged funds, with the total margin financing and securities lending balance exceeding 1.99 trillion as of August 4, 2025, marking a historical high since 2024 [5][8][6] - The automotive sector, particularly the company North Car Blue Valley (600733.SH), has launched a "Three-Year Leap Plan" aimed at enhancing profitability through sales growth, structural optimization, cost control, and expanding its profit ecosystem [4][16] - The company reported a 151% year-on-year increase in revenue for Q1 2025, with a gross margin improvement of 4.1 percentage points, and a reduction in net loss by 60 million [4][16] Industry Overview - The automotive industry is focusing on high-end market penetration, with North Car Blue Valley collaborating with Huawei to enhance its brand image and product offerings, particularly in the high-end vehicle segment [18][17] - The report indicates a notable increase in sales for the "Extreme Fox" brand due to comprehensive adjustments in product positioning, marketing strategies, and channel expansion [17] - The "Enjoy" brand, under the Huawei partnership, aims to redefine high-end sedans with innovative features and improved range, which is expected to boost sales significantly [18] Market Dynamics - The report discusses the microstructure of the market, emphasizing the importance of early trading concentration and the dynamics between institutional and retail investors [9][10][12] - It notes that the market's profitability effect has increased retail participation, contrasting with the trend of rising institutional ownership since 2017 [6][8] - The report tracks high-frequency factors, indicating strong performance in various trading strategies, with notable returns from specific factors such as the high-dimensional memory factor yielding 29.3% since 2023 [14]