金融工程 | 动态跟踪 Beta 风格领衔，一年动量因子表现出色， 建议关注高弹性和高特异性波动的资产 ——东方因子周报 研究结论 投资建议 风格表现监控 本周市场正收益风格集中在 Beta 风格上，负收益风格表现在 Value 风格上。一年动 量是中证全指成分股中，本周表现最好的因子。 因子表现监控 公募基金指数增强产品表现跟踪 风险提示 | 重点关注科技医药双主线和中小盘高成长 | 2025-07-24 | | --- | --- | | 主题基金：——主动权益基金 2025 年二季 | | | 报全解析 | | | DFQ-FactorGCL：基于超图卷积神经网络 | 2025-07-21 | | 和时间残差对比学习的股票收益预测模 | | | 型：——因子选股系列之一一七 | | | 工银瑞信基金股权变更，九泰基金获增资 | 2025-06-15 | | 6000 万元 | | | ESG 基金规模突破 8200 亿，首只浮动费 | 2025-06-08 | | 率基金结募 | | | Neural ODE：时序动力系统重构下深度学 | 2025-05-27 | | 习因子挖掘模型：——因子选股系列之 ...

Quantitative Factors and Construction Methods 1. Factor Name: Beta - **Construction Idea**: Measures the sensitivity of a stock's return to market movements, capturing the market's preference for high Beta stocks [11] - **Construction Process**: Beta is calculated using Bayesian shrinkage to compress the market Beta [16] - **Evaluation**: Beta factor showed strong performance this week, with a return of 1.94%, indicating a sustained market preference for high Beta stocks [11][13] 2. Factor Name: Volatility - **Construction Idea**: Captures the market's preference for high-volatility assets [11] - **Construction Process**: Includes multiple metrics such as: - Stdvol: Standard deviation of daily returns over the past 243 days - Ivff: Idiosyncratic volatility from Fama-French 3-factor model over 243 days - Range: High/low price range over 243 days - MaxRet_6: Average return of the six highest-return days in the past 243 days - MinRet_6: Average return of the six lowest-return days in the past 243 days [16] - **Evaluation**: Volatility factor rebounded significantly this week, with a return of 0.82%, reflecting increased demand for high-volatility assets [11][13] 3. Factor Name: One-Year Momentum - **Construction Idea**: Measures the cumulative return over the past year, excluding the most recent month, to capture momentum effects [20] - **Construction Process**: Calculated as the cumulative return over the past 12 months, excluding the most recent month [20] - **Evaluation**: One-year momentum factor performed well in multiple indices, including: - CSI 500: Weekly return of 0.90% [27] - CSI 1000: Weekly return of 0.81% [35] - CSI All Share: Weekly return of 2.25% [47] 4. Factor Name: Standardized Unexpected Revenue (SUR) - **Construction Idea**: Measures the deviation of actual revenue from analyst expectations, standardized by the standard deviation of expected revenue [20] - **Construction Process**: $ SUR = \frac{\text{Actual Revenue} - \text{Expected Revenue}}{\text{Standard Deviation of Expected Revenue}} $ [20] - **Evaluation**: SUR factor showed strong performance across indices: - CSI 800: Weekly return of 1.37% [31] - CSI 1000: Weekly return of 0.86% [35] - CSI All Share: Weekly return of 1.53% [47] 5. Factor Name: Three-Month Reversal - **Construction Idea**: Captures short-term mean-reversion effects in stock prices [20] - **Construction Process**: Calculated as the cumulative return over the past three months, with a negative sign to reflect reversal [20] - **Evaluation**: Three-month reversal factor performed well in: - CSI 1000: Weekly return of 1.04% [35] - CNI 2000: Weekly return of 1.76% [39] --- Factor Backtesting Results 1. Beta Factor - Weekly Return: 1.94% - Monthly Return: 7.88% - Year-to-Date Return: 17.34% - Annualized Return (1 Year): 51.27% [13] 2. Volatility Factor - Weekly Return: 0.82% - Monthly Return: 1.86% - Year-to-Date Return: 5.96% - Annualized Return (1 Year): 27.16% [13] 3. One-Year Momentum Factor - CSI 500 Weekly Return: 0.90% [27] - CSI 1000 Weekly Return: 0.81% [35] - CSI All Share Weekly Return: 2.25% [47] 4. Standardized Unexpected Revenue Factor - CSI 800 Weekly Return: 1.37% [31] - CSI 1000 Weekly Return: 0.86% [35] - CSI All Share Weekly Return: 1.53% [47] 5. Three-Month Reversal Factor - CSI 1000 Weekly Return: 1.04% [35] - CNI 2000 Weekly Return: 1.76% [39] --- Factor Portfolio Construction: Maximized Factor Exposure (MFE) Construction Process - **Objective Function**: Maximize single-factor exposure $ \text{max } f^{T}w $ - **Constraints**: - Style exposure limits: $ s_{l} \leq X(w-w_{b}) \leq s_{h} $ - Industry exposure limits: $ h_{l} \leq H(w-w_{b}) \leq h_{h} $ - Stock weight deviation limits: $ w_{l} \leq w-w_{b} \leq w_{h} $ - Component stock weight limits: $ b_{l} \leq B_{b}w \leq b_{h} $ - No short-selling: $ 0 \leq w \leq l $ - Full investment: $ 1^{T}w = 1 $ - Turnover limits: $ \Sigma|w-w_{0}| \leq to_{h} $ [59][60][62] Backtesting Process 1. Set constraints for style, industry, and stock weight deviations 2. Construct MFE portfolios monthly 3. Calculate historical returns and risk metrics, adjusting for transaction costs [63][64]

- The Beta factor showed a significant positive return of 6.95% this week, indicating a strong market preference for high Beta stocks [10] - The Liquidity factor also performed well with a return of 5.53%, reflecting increased demand for highly liquid assets [10] - The Volatility factor improved significantly with a return of 4.19%, showing heightened market interest in high-volatility assets [10] - The Trend factor experienced a notable decline, with a return of -1.76%, indicating a reduced market preference for trend-following strategies [11] - The Size factor showed a significant drop with a return of -3.30%, indicating a decreased market focus on small-cap stocks [11] - The Value factor also declined sharply, with a return of -3.55%, reflecting a reduced market preference for value investment strategies [11] - The one-year momentum factor performed well across various indices, including the CSI 500 and CSI 1000, indicating strong performance in the past year [7][24][30] - The DELTAROE factor showed strong performance in indices like the CSI 800 and CSI 2000, indicating robust profitability growth [27][33] - The three-month reversal factor also performed well in multiple indices, reflecting a strong short-term reversal trend [7][24][27] - The UMR factors, including one-month, three-month, and six-month UMR, generally performed poorly across various indices, indicating weak momentum [7][24][27][30] - The public fund index enhancement products for the CSI 300, CSI 500, and CSI 1000 showed varying levels of excess returns, with the CSI 300 products generally outperforming the others [7][46][48][50] - The MFE (Maximized Factor Exposure) portfolio construction method was used to evaluate the effectiveness of individual factors under various constraints, ensuring controlled industry and style exposures [51][52][54][55]

Group 1 - The core viewpoint of the article is to track the performance of various index enhancement portfolios and stock selection factors across different indices, highlighting their excess returns and factor performance over the recent week and year-to-date [1][2][3]. Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of -1.26% for the week and 1.88% year-to-date [5]. - The performance of the ZhongZheng 500 index enhancement portfolio showed an excess return of -0.50% for the week and 3.40% year-to-date [5]. - The ZhongZheng 1000 index enhancement portfolio had an excess return of -0.78% for the week and 4.40% year-to-date [5]. - The ZhongZheng A500 index enhancement portfolio reported an excess return of -0.22% for the week and 4.88% year-to-date [5]. Group 3 - In the HuShen 300 component stocks, factors such as one-year momentum, one-month reversal, and DELTAROE performed well [6]. - In the ZhongZheng 500 component stocks, factors like one-month reversal, SPTTM, and executive compensation showed strong performance [6]. - For ZhongZheng 1000 component stocks, factors such as non-liquidity shock, three-month earnings adjustments, and expected net profit month-on-month performed well [6]. - In the ZhongZheng A500 component stocks, one-month reversal, one-year momentum, and executive compensation were among the top-performing factors [6]. Group 4 - The HuShen 300 index enhancement products had a maximum excess return of 0.44%, a minimum of -0.66%, and a median of -0.06% for the week [19]. - The ZhongZheng 500 index enhancement products had a maximum excess return of 0.48%, a minimum of -1.30%, and a median of -0.35% for the week [19]. - The ZhongZheng 1000 index enhancement products had a maximum excess return of 1.09%, a minimum of -0.82%, and a median of -0.06% for the week [19]. - The ZhongZheng A500 index enhancement products had a maximum excess return of 0.46%, a minimum of -0.38%, and a median of -0.22% for the week [19]. Group 5 - The total number of public fund index enhancement products includes 67 for HuShen 300 with a total scale of 77.8 billion, 69 for ZhongZheng 500 with 45.2 billion, 46 for ZhongZheng 1000 with 15 billion, and 35 for ZhongZheng A500 with 22.3 billion [16].