- 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]