Quantitative Models and Construction Methods 1. Model Name: High-frequency "Gold" Portfolio CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This strategy combines three categories of high-frequency factors (price range, price-volume divergence, regret avoidance) equally weighted to enhance the CSI 1000 Index. It aims to leverage high-frequency data to capture microstructure insights and improve stock selection performance[4][38][39] - **Model Construction Process**: 1. Combine the three high-frequency factors (price range, price-volume divergence, regret avoidance) with equal weights (25%, 25%, 50%) 2. Apply industry and market capitalization neutralization to the combined factor 3. Implement weekly rebalancing with a transaction cost rate of 0.2% per side 4. Introduce turnover buffering mechanisms to reduce transaction costs[14][38][39] - **Model Evaluation**: The strategy demonstrates strong out-of-sample performance with stable excess returns, though it has experienced some recent adjustments[42] 2. Model Name: High-frequency & Fundamental Resonance Portfolio CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This strategy integrates high-frequency factors with fundamental factors (consensus expectations, growth, and technical factors) to improve multi-factor portfolio performance. The low correlation between high-frequency and traditional fundamental factors enhances diversification[43][45] - **Model Construction Process**: 1. Combine the three high-frequency factors and three fundamental factors equally weighted 2. Apply industry and market capitalization neutralization to the combined factor 3. Implement weekly rebalancing with a transaction cost rate of 0.2% per side 4. Introduce turnover buffering mechanisms to reduce transaction costs[43][45] - **Model Evaluation**: The strategy shows improved performance metrics compared to the high-frequency-only strategy, with higher annualized returns and lower maximum drawdowns[45][47] --- Model Backtesting Results 1. High-frequency "Gold" Portfolio CSI 1000 Index Enhanced Strategy - Annualized Return: 10.56% - Annualized Volatility: 23.75% - Sharpe Ratio: 0.44 - Maximum Drawdown: 47.77% - Annualized Excess Return: 9.58% - Tracking Error: 4.36% - Information Ratio (IR): 2.20 - Maximum Excess Drawdown: 6.53%[39] 2. High-frequency & Fundamental Resonance Portfolio CSI 1000 Index Enhanced Strategy - Annualized Return: 14.80% - Annualized Volatility: 23.39% - Sharpe Ratio: 0.63 - Maximum Drawdown: 39.60% - Annualized Excess Return: 13.70% - Tracking Error: 4.23% - Information Ratio (IR): 3.24 - Maximum Excess Drawdown: 4.97%[45] --- Quantitative Factors and Construction Methods 1. Factor Name: Price Range Factor - **Factor Construction Idea**: Measures the activity of stock transactions in different intraday price ranges, reflecting investor expectations for future stock trends[3] - **Factor Construction Process**: 1. Use 3-second snapshot data to calculate transaction volume and count in high (80%) and low (10%) price ranges 2. Construct sub-factors: - High price range transaction volume factor (VH80TAW) - High price range transaction count factor (MIH80TAW) - Low price range average transaction volume factor (VPML10TAW) 3. Combine sub-factors with weights of 25%, 25%, and 50%, respectively 4. Apply industry and market capitalization neutralization[11][14][16] - **Factor Evaluation**: Demonstrates strong predictive power and stable performance out-of-sample[3][16] 2. Factor Name: Price-Volume Divergence Factor - **Factor Construction Idea**: Measures the correlation between stock price and trading volume. Lower correlation indicates higher potential for future price increases[3][19] - **Factor Construction Process**: 1. Use high-frequency snapshot data to calculate correlations: - Price and transaction count correlation (CorrPM) - Price and transaction volume correlation (CorrPV) 2. Combine sub-factors equally weighted 3. Apply industry and market capitalization neutralization[19][22][23] - **Factor Evaluation**: Performance has declined since 2020 due to widespread adoption but remains stable with positive excess returns in 2023[23] 3. Factor Name: Regret Avoidance Factor - **Factor Construction Idea**: Based on behavioral finance, this factor captures investor regret avoidance emotions, such as the impact of selling stocks that later rebound[3][24] - **Factor Construction Process**: 1. Use tick-by-tick transaction data to identify active buy/sell directions 2. Construct sub-factors: - Sell rebound proportion factor (LCVOLESW) - Sell rebound deviation factor (LCPESW) 3. Combine sub-factors equally weighted 4. Apply industry and market capitalization neutralization[24][28][30] - **Factor Evaluation**: Exhibits stable out-of-sample performance, indicating significant influence of regret avoidance on stock returns[31] 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][32] - **Factor Construction Process**: 1. Calculate order book slope using cumulative order volume and price at different levels 2. Construct sub-factors: - Low-level slope factor (Slope_abl) - High-level convexity factor (Slope_alh) 3. Combine sub-factors equally weighted 4. Apply industry and market capitalization neutralization[32][35][37] - **Factor Evaluation**: Performance has been stable since 2016, though recent results are relatively flat[35] --- Factor Backtesting Results 1. Price Range Factor - Annualized Excess Return: 3.24% (VH80TAW), 4.45% (MIH80TAW), -0.77% (VPML10TAW)[12][14][16] 2. Price-Volume Divergence Factor - Annualized Excess Return: 2.56% (CorrPM), 2.61% (CorrPV)[19][22][23] 3. Regret Avoidance Factor - Annualized Excess Return: -2.67% (LCVOLESW), 0.33% (LCPESW)[24][26][31] 4. Slope Convexity Factor - Annualized Excess Return: -2.35% (Slope_abl), 0.02% (Slope_alh)[34][35][37]

- 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]

- 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]