多因子选股周报：动量因子表现出色，四大指增组合本周均战胜基准-20251130

Quantitative Models and Construction Methods 1. 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 constraints. This approach ensures that factors deemed effective can genuinely contribute to return prediction in the final portfolio[41][42]. - Model Construction Process: - The optimization model aims to maximize single-factor exposure while adhering to various constraints: $\begin{array}{ll} \text{max} & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T}w = 1 \end{array}$ - Objective Function: Maximize single-factor exposure, where $f$ represents factor values, $f^{T}w$ is the weighted exposure of the portfolio to the factor, and $w$ is the stock weight vector[42]. - Constraints: - 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[42]. - Industry Exposure: $H$ is the industry exposure matrix, and $h_l, h_h$ are the lower and upper bounds for industry deviations[42]. - Stock Weight Deviation: $w_l, w_h$ are the lower and upper bounds for individual stock weight deviations from the benchmark[42]. - Constituent Stock Weight: $B_b$ is a 0-1 vector indicating whether a stock is a benchmark constituent, and $b_l, b_h$ are the lower and upper bounds for constituent stock weights[42]. - No Short Selling: Ensures non-negative weights and limits individual stock weights to $l$[42]. - Full Investment: Ensures the portfolio is fully invested with $\mathbf{1}^{T}w = 1$[43]. - The MFE portfolio is constructed monthly, and historical returns are calculated after accounting for transaction costs of 0.3% on both sides[45]. - Model Evaluation: The MFE portfolio effectively tests factor performance under realistic constraints, making it a robust tool for evaluating factor predictability in practical scenarios[41][42]. --- Factor Construction and Methods 1. Factor Name: Momentum (1-Year Momentum) - Factor Construction Idea: Measures the momentum effect by capturing the price trend over the past year, excluding the most recent month[18]. - Factor Construction Process: - Formula: $\text{1-Year Momentum} = \text{Cumulative Return over the past 12 months (excluding the last month)}$[18]. - Factor Evaluation: Momentum factors generally perform well in capturing price trends, as evidenced by their positive performance in multiple sample spaces[20][22][24]. 2. Factor Name: DELTAROE - Factor Construction Idea: Measures the change in return on equity (ROE) compared to the same quarter in the previous year, reflecting profitability improvement[18]. - Factor Construction Process: - Formula: $\text{DELTAROE} = \text{Current Quarter ROE} - \text{ROE of the Same Quarter Last Year}$[18]. - Factor Evaluation: DELTAROE is effective in identifying companies with improving profitability, as shown by its strong performance in various sample spaces[22][24][26]. 3. Factor Name: Standardized Unexpected Earnings (SUE) - Factor Construction Idea: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings, to capture earnings surprises[18]. - Factor Construction Process: - Formula: $\text{SUE} = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}}$[18]. - Factor Evaluation: SUE is a reliable indicator of earnings surprises and is particularly effective in growth-oriented sample spaces[18][24]. --- Factor Backtesting Results 1. 1-Year Momentum - Performance in Different Sample Spaces: - CSI 300: Positive performance in the past week but underperformed in the past month and year-to-date[20]. - CSI 500: Strong performance in the past week and year-to-date, with weaker results in the past month[22]. - CSI 1000: Underperformed year-to-date but showed strong weekly performance[24]. - CSI A500: Mixed results, with strong weekly performance but weaker year-to-date performance[26]. - Public Fund Heavyweight Index: Positive weekly performance but underperformed year-to-date[28]. 2. DELTAROE - Performance in Different Sample Spaces: - CSI 300: Strong year-to-date performance, with mixed results in the past week and month[20]. - CSI 500: Positive weekly and year-to-date performance, with weaker results in the past month[22]. - CSI 1000: Strong weekly and year-to-date performance, with weaker results in the past month[24]. - CSI A500: Positive weekly and year-to-date performance, with weaker results in the past month[26]. - Public Fund Heavyweight Index: Positive weekly and year-to-date performance, with weaker results in the past month[28]. 3. SUE - Performance in Different Sample Spaces: - CSI 300: Not explicitly mentioned in the report[18]. - CSI 500: Not explicitly mentioned in the report[18]. - CSI 1000: Not explicitly mentioned in the report[18]. - CSI A500: Not explicitly mentioned in the report[18]. - Public Fund Heavyweight Index: Not explicitly mentioned in the report[18]. --- Quantitative Model Backtesting Results 1. MFE Portfolio - Performance in Different Sample Spaces: - CSI 300: Weekly excess return of 0.64%, year-to-date excess return of 17.85%[15]. - CSI 500: Weekly excess return of 0.00%, year-to-date excess return of 7.07%[15]. - CSI 1000: Weekly excess return of 0.21%, year-to-date excess return of 14.89%[15]. - CSI A500: Weekly excess return of 0.44%, year-to-date excess return of 8.26%[15].