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]

Group 1 - The article emphasizes the importance of combining macro quantification with factor momentum to select resonant factors, particularly focusing on growth factors while considering market conditions [1][4] - Current macro indicators show economic decline, slightly loose liquidity, and improving credit indicators, leading to a correction in the direction of economic downturn and tight liquidity [3][4] - The article identifies that the stock pools are still biased towards growth factors, especially in the CSI 300 and CSI 1000 indices, while the CSI 500 leans more towards fundamental factors [4][5] Group 2 - Economic leading indicators suggest a potential slight increase after reaching a short-term bottom in August 2025, despite recent declines in PMI and new orders [6][8] - Various leading indicators are analyzed, indicating that many are in a downward cycle, with expectations for some to reach their bottom by early 2026 [9][10] - The liquidity environment is assessed as slightly loose, with interest rates remaining stable and monetary supply indicators suggesting a continuation of this trend [12][14] Group 3 - Credit indicators are generally weak, but the overall credit environment remains positive, with some signs of recovery in recent months [15][16] - The article recommends increasing stock allocations due to improving equity trends, while reducing allocations in other asset classes [16][17] - The focus remains on liquidity as the most significant variable affecting market dynamics, with credit and inflation also being monitored [18][20] Group 4 - The article suggests industry selection based on economic sensitivity and credit sensitivity, highlighting sectors that are less sensitive to economic downturns but more responsive to credit conditions [20][21] - Industries identified as having high growth potential include electronics, media, and beauty care, which are less affected by economic fluctuations [20][21]

Group 1 - The report indicates a weakening of profit expectation indicators, leading to a preference for growth-oriented asset allocation. The economic recovery is noted, but the micro mapping shows a shift towards weaker profit expectations, resulting in a focus on factors that are less sensitive to economic changes and more sensitive to credit conditions [4][7][9] - The economic outlook is positioned at the late stage of an upward trend, with expectations of reaching a peak in June 2025 and entering a downward cycle thereafter. Recent PMI data shows a decline, indicating a potential slowdown [11][14] - Liquidity is maintained at a slightly tight level, with short-term interest rates showing a slight decline while long-term rates have decreased more significantly. Overall liquidity indicators remain neutral to slightly tight [24][27] Group 2 - The report suggests reducing commodity positions in the asset allocation strategy, with a slight increase in A-share positions and a minor recovery in US stock positions. The commodity allocation has been reduced to zero [31] - Market focus has shifted towards liquidity, which has become a significant variable influencing market performance, especially following the recent upward trends in September [32] - In terms of industry selection, the report emphasizes choosing sectors that are less sensitive to economic fluctuations but more sensitive to credit conditions, highlighting industries with growth attributes [33]

Group 1 - The core viewpoint emphasizes the continued significance of low volatility factors as resonance factors in investment strategies, integrating macroeconomic quantitative insights with factor momentum [1][2] - The analysis indicates that the economic recovery is ongoing, liquidity is returning to a neutral-tight state, and credit indicators are improving, with no need for adjustments based on micro mappings [1][2] - The stock pool configurations for various indices such as CSI 300 and CSI 1000 show a consistent preference for low volatility and growth factors, with value factors also being selected in the CSI 500 index [2] Group 2 - Economic leading indicators are positioned in the late stage of an upward trend, with expectations of reaching a peak by June 2025 and entering a downward cycle by December 2025 [3][8] - Specific indicators such as PMI and fixed asset investment are showing positive trends, suggesting continued economic growth in the near term [3][9] - The liquidity environment is tightening, with short-term interest rates rising above their moving averages, indicating a shift towards a tighter monetary policy [11][15] Group 3 - Credit indicators have shown improvement, with social financing stock increasing for two consecutive months, reflecting a more favorable credit environment [16][18] - The asset allocation strategy suggests reducing bond and US stock positions while increasing allocations in A-shares and commodities, reflecting a bullish outlook on domestic markets [18][22] - The focus on liquidity as a key variable driving market performance indicates that fluctuations in liquidity will significantly impact stock volatility and overall market dynamics [19][22]