东方因子周报：Growth风格登顶，EPTTM一年分位点因子表现出色-20250602

Quantitative Models and Factor Construction Factor Names and Construction - Factor Name: EPTTM One-Year Percentile - Construction Idea: Measures the percentile rank of the earnings-to-price ratio (EPTTM) over the past year to capture valuation trends[6][17] - Construction Process: - Calculate the earnings-to-price ratio (EPTTM) for each stock - Determine the percentile rank of the current EPTTM relative to its distribution over the past year[17] - Evaluation: Demonstrated strong performance in certain indices like CSI 1000 and CSI All Share, indicating its effectiveness in capturing valuation signals[6][33][47] - Factor Name: Pre-Expected PEG - Construction Idea: Combines price-to-earnings ratio with expected growth rates to evaluate valuation adjusted for growth[17] - Construction Process: - Calculate the price-to-earnings ratio (PE) - Divide PE by the expected growth rate of earnings to derive the PEG ratio - Use analyst consensus forecasts for expected growth rates[17] - Evaluation: Exhibited strong performance in indices like CSI 800 and CSI 500, suggesting its utility in growth-adjusted valuation analysis[6][29][33] - Factor Name: Six-Month UMR - Construction Idea: Captures momentum adjusted for risk over a six-month window[17] - Construction Process: - Calculate the cumulative return over the past six months - Adjust for risk using a volatility or beta-based measure - Normalize the adjusted return to derive the UMR score[17] - Evaluation: Consistently effective across multiple indices, including CSI 500 and CSI 1000, highlighting its robustness in momentum strategies[6][25][33] - Factor Name: Standardized Unexpected Earnings (SUE) - Construction Idea: Measures the deviation of actual earnings from analyst expectations, standardized by the forecast error[17] - Construction Process: - Calculate the difference between actual and expected earnings - Standardize this difference using the standard deviation of forecast errors - Derive the SUE score for each stock[17] - Evaluation: Effective in identifying earnings surprises, with strong performance in CSI 500 and CSI All Share indices[6][25][47] Factor Backtesting Results - EPTTM One-Year Percentile - CSI 1000: Weekly return of 1.09%, monthly return of 0.83%, annualized return of 5.54%[6][33] - CSI All Share: Weekly return of 1.09%, monthly return of -0.34%, annualized return of -4.16%[6][47] - Pre-Expected PEG - CSI 800: Weekly return of 0.66%, monthly return of 2.66%, annualized return of 3.11%[6][29] - CSI 500: Weekly return of 0.27%, monthly return of 1.02%, annualized return of -1.79%[6][25] - Six-Month UMR - CSI 500: Weekly return of 0.76%, monthly return of 1.14%, annualized return of -3.98%[6][25] - CSI 1000: Weekly return of 0.32%, monthly return of 0.08%, annualized return of 2.58%[6][33] - Standardized Unexpected Earnings (SUE) - CSI 500: Weekly return of 0.55%, monthly return of -0.06%, annualized return of 1.46%[6][25] - CSI All Share: Weekly return of 0.32%, monthly return of -0.46%, annualized return of -4.36%[6][47] MFE Portfolio Construction - Model Description: Maximized Factor Exposure (MFE) portfolios are constructed to maximize exposure to a single factor while controlling for constraints such as industry and style exposures, stock weight limits, and turnover[61][62] - Optimization Formula: $\begin{array}{ll} \max & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & 0 \leq w \leq l \\ & 1^{T}w = 1 \\ & \Sigma|w-w_{0}| \leq to_{h} \end{array}$ - Explanation: The objective function maximizes factor exposure, subject to constraints on style, industry, stock weights, and turnover[61][62] - Evaluation: Effective in isolating factor performance under realistic portfolio constraints, providing a robust framework for factor validation[61][65]