多因子选股周报：年度收官，沪深 300 增强组合年内超额 20.90%-20260103

Quantitative Models and Construction Methods - Model Name: Maximized Factor Exposure Portfolio (MFE) Model Construction Idea: The MFE portfolio is designed to test the effectiveness of single factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, and turnover rate. This approach ensures that factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[40][41]. Model Construction Process: The MFE portfolio is constructed using the following optimization model: $ \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, and $f^{T}w$ is the weighted exposure of the portfolio to the factor. $w$ is the stock weight vector to be optimized. - Constraints: 1. Style Exposure: $X$ is the factor exposure matrix for stocks, $w_b$ is the benchmark weight vector, and $s_l, s_h$ are the lower and upper bounds for style factor exposure[41]. 2. Industry Exposure: $H$ is the industry exposure matrix, where $H_{ij} = 1$ if stock $i$ belongs to industry $j$, otherwise $H_{ij} = 0$. $h_l, h_h$ are the lower and upper bounds for industry deviation[41]. 3. Stock Deviation: $w_l, w_h$ are the lower and upper bounds for individual stock deviations from the benchmark[41]. 4. Constituent Weight: $B_b$ is a 0-1 vector indicating whether a stock is a benchmark constituent. $b_l, b_h$ are the lower and upper bounds for constituent weights[41]. 5. No Short Selling: Ensures non-negative weights and limits individual stock weights to $l$[41]. 6. Full Investment: Ensures the portfolio is fully invested with $\mathbf{1}^{T}w = 1$[42]. - Implementation: At the end of each month, MFE portfolios are constructed for each factor under the defined constraints. Historical returns are calculated during the backtest period, accounting for a 0.3% transaction cost on both sides[44]. Model Evaluation: The MFE portfolio effectively tests factor performance under realistic constraints, ensuring that selected factors contribute to return prediction in practical applications[40][41]. Quantitative Factors and Construction Methods - Factor Name: Standardized Unexpected Earnings (SUE) Factor Construction Idea: Measures the deviation of actual quarterly net profit from expected profit, standardized by the standard deviation of expected profit. It captures earnings surprises[17]. Factor Construction Process: $ SUE = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Quarterly Net Profit}}{\text{Standard Deviation of Expected Quarterly Net Profit}} $ Factor Evaluation: SUE is a widely used factor for capturing earnings surprises and has shown effectiveness in predicting stock returns[17]. - Factor Name: DELTAROE Factor Construction Idea: Measures the change in return on equity (ROE) compared to the same quarter of the previous year, reflecting profitability improvement[17]. Factor Construction Process: $ DELTAROE = \text{Quarterly ROE} - \text{ROE of the Same Quarter Last Year} $ Factor Evaluation: DELTAROE is effective in identifying companies with improving profitability, which can lead to positive stock performance[17]. - Factor Name: Non-Liquidity Shock Factor Construction Idea: Measures the average absolute daily return over the past 20 trading days, divided by the average trading volume, capturing liquidity risk[17]. Factor Construction Process: $ \text{Non-Liquidity Shock} = \frac{\text{Average Absolute Daily Return (20 Days)}}{\text{Average Trading Volume (20 Days)}} $ Factor Evaluation: This factor is useful for identifying stocks with higher liquidity risks, which may impact their returns[17]. Factor Backtest Results - Standardized Unexpected Earnings (SUE): - CSI 300 Universe: Weekly return: 0.43%, monthly return: 2.55%, YTD return: 12.65%, historical annualized return: 4.22%[19]. - CSI 500 Universe: Weekly return: 0.07%, monthly return: 1.02%, YTD return: 7.47%, historical annualized return: 5.50%[21]. - CSI 1000 Universe: Weekly return: -0.36%, monthly return: 1.55%, YTD return: 20.90%, historical annualized return: 6.47%[23]. - CSI A500 Universe: Weekly return: -0.07%, monthly return: 1.17%, YTD return: 11.28%, historical annualized return: 4.55%[25]. - DELTAROE: - CSI 300 Universe: Weekly return: 0.33%, monthly return: 2.78%, YTD return: 18.51%, historical annualized return: 4.52%[19]. - CSI 500 Universe: Weekly return: -0.58%, monthly return: -0.75%, YTD return: 8.13%, historical annualized return: 7.56%[21]. - CSI 1000 Universe: Weekly return: -0.56%, monthly return: 1.36%, YTD return: 12.58%, historical annualized return: 8.77%[23]. - CSI A500 Universe: Weekly return: 0.01%, monthly return: 2.94%, YTD return: 20.42%, historical annualized return: 4.48%[25]. - Non-Liquidity Shock: - CSI 300 Universe: Weekly return: -0.06%, monthly return: -0.29%, YTD return: -1.78%, historical annualized return: 0.40%[19]. - CSI 500 Universe: Weekly return: -0.35%, monthly return: 0.79%, YTD return: -2.82%, historical annualized return: 0.18%[21]. - CSI 1000 Universe: Weekly return: 0.47%, monthly return: -1.66%, YTD return: 5.34%, historical annualized return: 2.23%[23]. - CSI A500 Universe: Weekly return: 0.13%, monthly return: -0.34%, YTD return: -3.95%, historical annualized return: 1.50%[25].