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多因子选股周报:成长因子表现出色,四大指增组合年内超额均超10%-20250823
Guoxin Securities·2025-08-23 07:21

Quantitative Models and Construction Methods - Model Name: Maximized Factor Exposure Portfolio (MFE) Model Construction Idea: The model aims to maximize single-factor exposure while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach tests the effectiveness of factors under real-world constraints, ensuring their predictive power in portfolio construction [39][40][41] Model Construction Process: The optimization model is formulated 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, and $f^{T}w$ is the weighted exposure of the portfolio to the factor. $w$ is the stock weight vector to be optimized. - Constraints: - Style Exposure: $X$ is the matrix of stock exposures to style factors, $w_b$ is the benchmark weight vector, and $s_l$, $s_h$ are the lower and upper bounds for style factor exposure deviation. - Industry Exposure: $H$ is the industry exposure matrix, $h_l$, $h_h$ are the lower and upper bounds for industry deviation. - Stock Weight Deviation: $w_l$, $w_h$ are the lower and upper bounds for individual stock weight deviation. - Component Weight Control: $B_b$ is a binary vector indicating whether a stock belongs to the benchmark index, $b_l$, $b_h$ are the lower and upper bounds for component stock weight. - No Short Selling: Ensures non-negative weights and limits individual stock weights. - Full Investment: Ensures the portfolio is fully invested with weights summing to 1 [39][40][41] Model Evaluation: The model effectively tests factor validity under real-world constraints, ensuring factors contribute to portfolio returns in practical scenarios [39][40][41] Factor Construction and Methods - Factor Name: Standardized Unexpected Earnings (SUE) Factor Construction Idea: Measures the deviation of actual quarterly net profit from expected net profit, standardized by the standard deviation of expected net profit [17] Factor Construction Process: $ SUE = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}} $ Factor Evaluation: Useful for identifying stocks with earnings surprises, which may lead to price adjustments [17] - Factor Name: One-Month Reversal Factor Construction Idea: Captures short-term price reversal by measuring the return over the past 20 trading days [17] Factor Construction Process: $ \text{One-Month Reversal} = \text{Return over the past 20 trading days} $ Factor Evaluation: Effective in detecting short-term mean-reverting behavior in stock prices [17] - Factor Name: Delta ROA Factor Construction Idea: Measures the change in return on assets (ROA) compared to the same quarter of the previous year [17] Factor Construction Process: $ \Delta ROA = \text{Current Quarter ROA} - \text{ROA of the Same Quarter Last Year} $ Factor Evaluation: Indicates improvement or deterioration in asset efficiency, which can signal fundamental changes [17] Factor Backtesting Results Performance in CSI 300 Sample Space - Standardized Unexpected Earnings: Weekly excess return 1.35%, monthly excess return 3.78%, annual excess return 8.35% [19] - One-Year Momentum: Weekly excess return 1.27%, monthly excess return 1.98%, annual excess return -1.17% [19] - Single-Quarter Revenue Growth: Weekly excess return 1.08%, monthly excess return 3.86%, annual excess return 11.82% [19] Performance in CSI 500 Sample Space - EPTTM Year Percentile: Weekly excess return 1.69%, monthly excess return 1.74%, annual excess return 3.77% [21] - Delta ROA: Weekly excess return 1.00%, monthly excess return 2.43%, annual excess return 9.72% [21] - Standardized Unexpected Earnings: Weekly excess return 0.87%, monthly excess return 3.32%, annual excess return 7.87% [21] Performance in CSI 1000 Sample Space - Standardized Unexpected Earnings: Weekly excess return 0.75%, monthly excess return 3.69%, annual excess return 7.64% [23] - Three-Month Reversal: Weekly excess return 1.34%, monthly excess return 0.24%, annual excess return 5.36% [23] - Single-Quarter Revenue Growth: Weekly excess return 1.43%, monthly excess return 4.58%, annual excess return 11.12% [23] Performance in CSI A500 Sample Space - Single-Quarter Revenue Growth: Weekly excess return 1.43%, monthly excess return 4.58%, annual excess return 11.12% [25] - Delta ROA: Weekly excess return 0.63%, monthly excess return 4.33%, annual excess return 10.97% [25] - Three-Month Reversal: Weekly excess return 1.34%, monthly excess return 0.24%, annual excess return 5.36% [25] Performance in Public Fund Heavy Index Sample Space - One-Year Momentum: Weekly excess return 1.11%, monthly excess return 3.36%, annual excess return 1.15% [27] - Delta ROA: Weekly excess return 0.63%, monthly excess return 4.33%, annual excess return 10.97% [27] - Standardized Unexpected Earnings: Weekly excess return 0.75%, monthly excess return 3.69%, annual excess return 7.64% [27]