- The report tracks the performance of 48 representative factors across 12 major styles, including scale, Beta, volatility, value, liquidity, momentum & reversal, technical, profitability, growth, quality, dividend, and consensus expectations. These factors were selected based on their relative performance over a 10-year backtest period[19][20] - Factor data preprocessing includes outlier removal, industry and market capitalization neutralization, and z-score standardization. For industry neutralization, OLS regression with industry dummy variables is used, while market capitalization neutralization involves regression with logarithmic market capitalization. The residuals from these regressions represent the neutralized factor values[199][202][203] - IC analysis measures the correlation between factor exposure and future stock returns using Spearman rank correlation coefficients. Positive IC values indicate logical and expected factor performance. Factors are also tested through layered backtesting and regression analysis to evaluate their effectiveness[204][205][206] - Layered backtesting involves sorting stocks by factor scores, dividing them into layers, and observing the cumulative returns of each layer. This method evaluates the linear and non-linear relationships between factors and stock returns[206][207] - Regression analysis controls for industry and market capitalization effects, using a linear model to assess the relationship between factor exposure and stock returns. The regression coefficients reflect the factor's predictive power, with significant t-values indicating robust factor performance[207][208] - The report highlights the monthly performance of factors across different stock pools (Wind All A, CSI 300, CSI 500, CSI 1000). Factors such as growth, quality, and consensus expectations showed strong performance, while volatility and liquidity factors experienced significant drawdowns[2][3][21] - Specific factors like turnover rate standard deviation (1-month), reversal (1-month), and turnover rate-price correlation (1-month) performed well among volume-price factors. Financial factors such as quarterly revenue growth and return on invested capital (ROIC) also showed notable performance[4] - In the Wind All A stock pool, the expectation factor achieved an IC of 5.19%, a long-short return of 1.37%, and a long-only excess return of 0.32%. The reversal factor had an IC of 4.83%, a long-short return of -0.65%, and a long-only excess return of 0.37%[2][21] - In the CSI 300 stock pool, the expectation factor achieved an IC of 25.94%, a long-short return of 11.07%, and a long-only excess return of 3.44%. The quality factor had an IC of 18.24%, a long-short return of 4.64%, and a long-only excess return of 1.73%[2][21] - In the CSI 500 stock pool, the growth factor achieved an IC of 0.70%, a long-short return of 2.90%, and a long-only excess return of 1.24%. The expectation factor had an IC of 0.50%, a long-short return of 2.01%, and a long-only excess return of 0.93%[2][21] - In the CSI 1000 stock pool, the technical factor achieved an IC of 2.02%, a long-short return of -1.27%, and a long-only excess return of 0.02%. The growth factor had an IC of 0.88%, a long-short return of 1.68%, and a long-only excess return of 1.13%[3][21] - Factors such as Beta, small-cap, and volatility showed negative performance overall, with significant drawdowns in specific stock pools. Dividend and value factors also underperformed across all stock pools during the month[3][46][97][174] - The expectation factor demonstrated strong performance in large-cap stock pools, particularly in the CSI 300 pool, with positive excess returns and long-short returns. Growth factors showed consistent positive performance across all stock pools, with more pronounced results in large-cap pools[3][143][186]

Group 1 - The report indicates that the small-cap style outperformed last week, with the CSI 2000 index enhancement strategy achieving an excess return of 18.92% year-to-date [1] - The report tracks the performance of public index enhancement funds for major indices, including CSI 300, CSI 500, CSI 1000, and CSI 2000, as of July 11, 2025 [8] - The top three public funds for the CSI 300 index enhancement this year are: Anxin Quantitative Selected CSI 300 Index Enhancement A (003957.OF) with an excess return of 8.86%, Changxin CSI 300 Index Enhancement A (005137.OF) with 5.91%, and Changcheng Jiutai CSI 300 A (200002.OF) with 5.33% [9] Group 2 - For the CSI 500 index enhancement, the top three funds this year are: Zhongou CSI 500 Index Enhancement A (015453.OF) with 9.15%, Penghua CSI 500 Index Enhancement A (014344.OF) with 7.72%, and Baodao CSI 500 Index Enhancement A (006593.OF) with 7.46% [16] - The CSI 1000 index enhancement funds show the best performers as: Guojin CSI 1000 Index Enhancement A (017846.OF) with 13.65%, ICBC Credit Suisse CSI 1000 Index Enhancement A (016942.OF) with 13.62%, and Huitianfu CSI 1000 Index Enhancement A (017953.OF) with 11.89% [22] - The top three funds for the CSI 2000 index enhancement this year are: Huitianfu CSI 2000 Index Enhancement A (019318.OF) with 14.1%, Penghua CSI 2000 Index Enhancement A (017892.OF) with 13.04%, and Tianhong CSI 2000 Index Enhancement A (017547.OF) with 10.94% [27] Group 3 - The report highlights that the excess returns of various factors within the CSI indices are tracked, with significant factors identified for each index [7] - For the CSI 300, the best-performing factors last week were market capitalization, high-frequency minute data, and valuation [34] - In the CSI 500, the top factors were high-frequency minute data, growth, and market capitalization [42]

Quantitative Factors and Construction Methods 1. Factor Name: Original ACT Factor - **Construction Idea**: The ACT factor measures the "active net buying intensity" by comparing the active buying amount with the active selling amount, reflecting the future price movement expectations of different types of traders [13][14] - **Construction Process**: - The ACT factor is calculated as follows: $$ \mathrm{ACT} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Active Buy Amount} + \text{Active Sell Amount}} $$ where "Active Buy Amount" and "Active Sell Amount" represent the total amounts of active buying and selling transactions, respectively [13] - The factor value for each stock is computed as the average ACT value over the past 20 trading days at the end of each month [14] - Stocks with insufficient trading days, suspension, or ST status are excluded from the sample [14] - **Evaluation**: The IC values of the original ACT factor are low, and its stock selection ability does not meet expectations, which has led to reduced research interest in this factor in recent years [14] 2. Factor Name: ACT Factor with Segmentation - **Construction Idea**: The segmentation method divides the ACT factor based on different market conditions (e.g., high-return and low-return days) to better capture the nuanced behavior of different trader groups [6][15] - **Construction Process**: - Calculate the daily ACT value over the past 20 trading days [20] - Identify the highest-return days as "high-return days" and the lowest-return days as "low-return days" [20] - Compute the average ACT value for high-return days (ACT_high) and low-return days (ACT_low) [20] - **Evaluation**: - Large and medium-sized orders show strong positive stock selection effects on high-return days, while small orders exhibit strong negative stock selection effects on low-return days [6][20] - The segmentation approach aligns with intuition, as institutional investors dominate large and medium orders, while retail investors dominate small orders [21] 3. Factor Name: ACT Positive and ACT Negative Factors - **Construction Idea**: Based on the segmentation analysis, ACT Positive focuses on large and medium orders with positive stock selection effects, while ACT Negative focuses on small orders with negative stock selection effects [7][24] - **Construction Process**: - ACT Positive: $$ \mathrm{ACT\ Positive} = \frac{\text{Active Buy Amount (Large + Medium Orders)} - \text{Active Sell Amount (Large + Medium Orders)}}{\text{Active Buy Amount (Large + Medium Orders)} + \text{Active Sell Amount (Large + Medium Orders)}} $$ - ACT Negative: $$ \mathrm{ACT\ Negative} = \frac{\text{Active Buy Amount (Small Orders)} - \text{Active Sell Amount (Small Orders)}}{\text{Active Buy Amount (Small Orders)} + \text{Active Sell Amount (Small Orders)}} $$ - The factor values are averaged over the past 20 trading days [26] - **Evaluation**: - ACT Positive demonstrates superior stock selection ability, particularly in smaller segmentation ratios (e.g., λ=10%), with a high return-to-volatility ratio [7][26] - ACT Negative shows stable return-to-volatility ratios but declining returns in recent years, reflecting the increasing dominance of large funds [7][31] --- Factor Backtesting Results 1. Original ACT Factor - **IC Values**: Gradually increase from negative to positive as the order size increases (small → medium → large → extra-large) [14] - **Stock Selection Ability**: Poor, as evidenced by the unsatisfactory multi-long-short net value curve [14] 2. ACT Positive Factor - **λ=10%**: - Multi-long-short return-to-volatility ratio: 3.06 - Multi-long return-to-volatility ratio: 0.87 [26][28] - **Performance in Neutralized Conditions**: - Multi-long-short return-to-volatility ratio: 2.40 - Multi-long return-to-volatility ratio: 0.66 [38] 3. ACT Negative Factor - **λ=10%**: - Multi-long-short return-to-volatility ratio: 0.82 - Multi-long return-to-volatility ratio: 0.73 [33] 4. ACT Positive Factor in Different Sample Spaces - **CSI 300**: - λ=20%: Multi-long-short return-to-volatility ratio: 1.32; Multi-long return-to-volatility ratio: 0.63 [44] - **CSI 500**: - λ=20%: Multi-long-short return-to-volatility ratio: 1.78; Multi-long return-to-volatility ratio: 0.80 [44]