多因子选股周报：量价因子表现出色，沪深300增强组合年内超额16.74%-20251122

Quantitative Models and Construction Methods 1. Model Name: Guosen Quantitative Index Enhanced Portfolio - Model Construction Idea: The model aims to construct enhanced portfolios benchmarked against indices such as CSI 300, CSI 500, CSI 1000, and CSI A500, with the goal of consistently outperforming their respective benchmarks [10][11]. - Model Construction Process: 1. Revenue Prediction: Predict stock returns using multiple factors. 2. Risk Control: Apply constraints on industry exposure, style exposure, stock weight deviation, and turnover rate. 3. Portfolio Optimization: Optimize the portfolio to maximize single-factor exposure while adhering to constraints. The optimization model is as follows: $ \begin{array}{ll} max & f^{T} w \ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \ & h_{l} \leq H(w-w_{b}) \leq h_{h} \ & w_{l} \leq w-w_{b} \leq w_{h} \ & b_{l} \leq B_{b}w \leq b_{h} \ & \mathbf{0} \leq w \leq l \ & \mathbf{1}^{T} w = 1 \end{array} $ - Objective Function: Maximize single-factor exposure, where $f$ represents factor values, $w$ is the stock weight vector, and $f^{T}w$ is the weighted exposure to the factor. - Constraints: - Style Exposure: $X$ is the factor exposure matrix, $w_b$ is the benchmark weight vector, and $s_l, s_h$ are the lower and upper bounds for style exposure. - Industry Exposure: $H$ is the industry exposure matrix, and $h_l, h_h$ are the lower and upper bounds for industry deviation. - Stock Weight Deviation: $w_l, w_h$ are the lower and upper bounds for stock weight deviation. - Component Stock Weight: $B_b$ is a 0-1 vector indicating whether a stock is a benchmark component, and $b_l, b_h$ are the lower and upper bounds for component stock weight. - No Short Selling: Ensure non-negative weights and limit individual stock weights. - Full Investment: Ensure the portfolio is fully invested with weights summing to 1 [40][41][42]. 4. Backtesting: Rebalance the portfolio monthly, calculate historical returns, and evaluate performance metrics such as excess returns and risk statistics [44]. 2. Model Name: Public Fund Heavyweight Index - Model Construction Idea: Construct an index based on the holdings of public funds to evaluate factor performance under "institutional style" [42][43]. - Model Construction Process: 1. Sample Selection: Include ordinary equity funds and partial equity hybrid funds with a minimum size of 50 million RMB and at least six months of listing history. Exclude recently transformed funds or those with insufficient data. 2. Data Collection: Use fund periodic reports (annual, semi-annual, or quarterly) to gather holding information. 3. Weight Calculation: Average the stock weights across eligible funds. 4. Index Construction: Sort stocks by weight in descending order and select those accounting for 90% of cumulative weight to form the index [43]. --- Model Backtesting Results 1. Guosen Quantitative Index Enhanced Portfolio - CSI 300 Enhanced Portfolio: - Weekly excess return: -0.71% - Year-to-date excess return: 16.74% [13] - CSI 500 Enhanced Portfolio: - Weekly excess return: 0.12% - Year-to-date excess return: 6.85% [13] - CSI 1000 Enhanced Portfolio: - Weekly excess return: -0.94% - Year-to-date excess return: 14.08% [13] - CSI A500 Enhanced Portfolio: - Weekly excess return: -1.37% - Year-to-date excess return: 7.55% [13] 2. Public Fund Heavyweight Index - CSI 300 Index Enhanced Products: - Weekly excess return: Max 0.70%, Min -1.26%, Median 0.09% - Year-to-date excess return: Max 9.92%, Min -4.53%, Median 2.58% [31] - CSI 500 Index Enhanced Products: - Weekly excess return: Max 1.17%, Min -1.13%, Median 0.11% - Year-to-date excess return: Max 13.14%, Min -9.17%, Median 3.94% [33] - CSI 1000 Index Enhanced Products: - Weekly excess return: Max 0.89%, Min -1.38%, Median -0.05% - Year-to-date excess return: Max 19.12%, Min -1.84%, Median 8.24% [36] - CSI A500 Index Enhanced Products: - Weekly excess return: Max 0.71%, Min -0.86%, Median -0.04% - Year-to-date excess return: Max 2.67%, Min -4.14%, Median -0.76% [39] --- Quantitative Factors and Construction Methods 1. Factor Name: Maximized Factor Exposure (MFE) - Factor Construction Idea: Evaluate factor effectiveness under real-world constraints by maximizing single-factor exposure in a portfolio [40][41]. - Factor Construction Process: 1. Define constraints for style exposure, industry exposure, stock weight deviation, and component stock weight. 2. Optimize the portfolio to maximize single-factor exposure while adhering to constraints. 3. Rebalance monthly and calculate historical returns [40][41][44]. 2. Factor Name: Public Fund Heavyweight Factors - Factor Construction Idea: Test factor performance in the public fund heavyweight index to reflect institutional preferences [42][43]. - Factor Construction Process: 1. Use public fund holdings to construct the index. 2. Evaluate factor performance within this index using metrics such as excess returns and risk-adjusted returns [42][43]. --- Factor Backtesting Results 1. Maximized Factor Exposure (MFE) - CSI 300 Sample Space: - Best-performing factors (weekly): One-month volatility (0.83%), one-month turnover (0.68%), three-month volatility (0.65%) - Worst-performing factors (weekly): Single-quarter profit growth (-0.26%), three-month institutional coverage (-0.24%), one-year momentum (-0.24%) [18] - CSI 500 Sample Space: - Best-performing factors (weekly): Three-month institutional coverage (1.09%), one-month reversal (1.01%), three-month reversal (0.99%) - Worst-performing factors (weekly): Standardized unexpected earnings (-1.00%), DELTAROA (-0.81%), DELTAROE (-0.81%) [20] - CSI 1000 Sample Space: - Best-performing factors (weekly): One-month turnover (1.08%), three-month institutional coverage (1.06%), single-quarter ROA (1.04%) - Worst-performing factors (weekly): Single-quarter SP (-1.29%), expected PEG (-1.25%), SPTTM (-1.22%) [22] - CSI A500 Sample Space: - Best-performing factors (weekly): One-month turnover (0.82%), three-month turnover (0.75%), one-month volatility (0.74%) - Worst-performing factors (weekly): Expected net profit QoQ (-0.91%), single-quarter net profit growth (-0.61%), expected PEG (-0.41%) [24] - Public Fund Heavyweight Index: - Best-performing factors (weekly): One-month volatility (1.32%), one-month turnover (1.23%), three-month turnover (0.89%) - Worst-performing factors (weekly): Single-quarter revenue growth (-0.89%), single-quarter profit growth (-0.88%), single-quarter ROE (-0.81%) [26]