Core Insights - The article discusses the significant differences in factor performance across various stock selection domains, indicating that specific stock selection domains do not exhibit the same long/short performance as the overall market [1] - The composite factor's performance can be enhanced by matching style factors to the target stock selection domain, leading to improved outcomes [1] Group 1: Market Capitalization Reweighting - Reweighting factors by market capitalization shows stable performance in the overall market but enhances stock selection effects in specific index domains, particularly when small-cap stocks are given higher weight [1] - In the case of the CSI 300 index, extreme weighting towards large-cap stocks historically yields better performance, with an annualized excess return improvement exceeding 1% [2] - For the CSI 500 index, similar improvements are observed with market capitalization weighting, effectively enhancing performance by around 1% annually [2] - The CSI 1000 index does not show significant performance improvements regardless of the weighting approach used [2] Group 2: Style Reweighting - Reweighting factors by style leads to greater instability in enhancement effects across different combinations, with significant improvements noted in the strict constraint CSI 1000 enhancement combination [3] - The composite factor's enhancement effect for the CSI 300 is slightly less effective than when using market capitalization reweighting [3] - For the CSI 500, the enhancement effect is also marginally less effective compared to market capitalization reweighting [3] - The strict constraint approach for the CSI 1000 enhancement combination shows a potential annualized improvement of around 1% when using component reweighting [3]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: Market Cap Segmented Linear Regression Model **Construction Idea**: Adjust the weights of factor regressions based on market cap segmentation to address the performance differences of factors across different market cap groups [7][10][12] **Construction Process**: 1. Factors are divided into five categories: Dividend, ROE_SUE, Daily Volume-Price, High-Frequency Volume-Price, and a final composite factor [7][10] 2. Use OLS regression with IC or ICIR weighting to combine sub-factors into composite factors [7] 3. Apply KMedian clustering on the log of market cap to divide stocks into 11 groups [7] 4. Assign weights to each group using the formula: $ w_{i}=w_{base}+(1-w_{base})*|i-I|/n $ where $w_{base}$ is the minimum weight (set to 0.9, 0.5, or 0), $n$ is the number of groups, and $I$ is the group with the highest weight [7] 5. Train 11 models with different weight assignments and evaluate the composite factor's IC, RankMAE, long-short returns, and long-only returns [7] **Evaluation**: This model improves factor performance in specific market cap segments, particularly for small-cap stocks, but extreme weighting can increase volatility [7][12] - **Model Name**: Market Cap Weighted Composite Factor Model **Construction Idea**: Reweight composite factors based on market cap distribution to enhance factor performance in specific indices [48][49][65] **Construction Process**: 1. Use market cap weights from benchmark indices (e.g., CSI 300, CSI 500, CSI 1000) to reweight composite factors [48] 2. Construct enhanced portfolios with weekly rebalancing and constraints on individual stock weights, industry weights, and turnover [48] **Evaluation**: Significant performance improvement in CSI 300 and CSI 500 indices, with annualized excess returns increasing by over 1% in some cases. However, the method is less effective for CSI 1000 [49][65][79] - **Model Name**: Market Cap Weighted Cross-Composite Factor Model **Construction Idea**: Match factor weights to the market cap group of each stock to reduce parameter sensitivity [80][81] **Construction Process**: 1. Assign factor values based on the stock's market cap group: $ F_{i}=F_{l_{i}}\;\;i\in I $ where $i$ belongs to market cap group $I$ [80] 2. Evaluate single-factor performance and construct enhanced portfolios for different indices [81][85] **Evaluation**: Performance improvement is observed in CSI 300 and CSI 500 indices, but the method is less effective for CSI 1000. Parameter sensitivity is reduced compared to other methods [85][92][96] - **Model Name**: Multi-Style Factor Weighted Composite Factor Model **Construction Idea**: Incorporate style factors (e.g., value-growth, industry) into the weighting process to address factor performance differences across styles [98][99] **Construction Process**: 1. Cluster stocks based on style factors using Manhattan distance [98] 2. Construct 11 composite factor models centered on each style cluster [98] 3. Use cross-composite and component-composite methods to evaluate performance in enhanced portfolios [100][101] **Evaluation**: Performance improvement is limited compared to market cap-based methods. Cross-composite weighting shows better results than component-composite weighting in some cases [101][115][132] Backtest Results of Models - **Market Cap Segmented Linear Regression Model**: - IC: 0.057 (all-market), 0.037 (CSI 300), 0.040 (CSI 500), 0.052 (CSI 1000), 0.060 (small-cap) [7][81][84] - RankMAE: 1.090 (all-market), 1.119 (CSI 300), 1.111 (CSI 500), 1.106 (CSI 1000), 1.092 (small-cap) [7][81][84] - Long-Short Returns: 1.07% (all-market), 0.38% (CSI 300), 0.49% (CSI 500), 0.92% (CSI 1000), 1.19% (small-cap) [7][81][84] - **Market Cap Weighted Composite Factor Model**: - CSI 300: Annualized Return 8.21%, IR 0.966, Max Drawdown 15.67% (base_w=0) [49] - CSI 500: Annualized Return 14.64%, IR 1.385, Max Drawdown 12.60% (base_w=0.5) [59] - CSI 1000: Annualized Return 18.95%, IR 1.585, Max Drawdown 16.59% (equal weight) [70] - **Market Cap Weighted Cross-Composite Factor Model**: - CSI 300: Annualized Return 7.36%, IR 0.901, Max Drawdown 16.33% (base_w=0) [85] - CSI 500: Annualized Return 15.06%, IR 1.409, Max Drawdown 13.14% (base_w=0.5) [92] - CSI 1000: Annualized Return 18.95%, IR 1.585, Max Drawdown 16.59% (equal weight) [92] - **Multi-Style Factor Weighted Composite Factor Model**: - CSI 300: Annualized Return 7.24%, IR 0.926, Max Drawdown 16.32% (base_w=0.9, component-composite) [103] - CSI 500: Annualized Return 14.17%, IR 1.377, Max Drawdown 12.65% (base_w=0, cross-composite) [115] - CSI 1000: Annualized Return 18.63%, IR 1.570, Max Drawdown 16.47% (base_w=0, component-composite) [132]