丨证券研究报告丨 金融工程丨点评报告 [Table_Title] 2025 年有效选股因子 报告要点 [Table_Summary] 本文主要回顾 2025 年选股因子在全市场的表现情况。 分析师及联系人 [Table_Author] 郑起 覃川桃 SAC：S0490520060001 SAC：S0490513030001 SFC：BUT353 请阅读最后评级说明和重要声明 %% %% %% %% research.95579.com 1 [Table_Title2] 2025 年有效选股因子 [Table_Summary2] 2025 年因子选股全市场范围内表现较好 收益能力上看，2025 年全市场范围内选股以成交笔数、流动性、拥挤度、价格稳定和反转为代 表的量价因子选股能力更强，成长因子有一定盈利能力，但在失效区间回撤较大。 收益来源上看，分为量价和成长两大类，量价内又以价格稳定、反转为两类主要代表。 2025 年因子收益有着较为明显的区间特征 2025 年 8 月（不含）以前是所有因子的主要收益区间，8 月为因子的第一次分化，表现为量价 因子（除反转）回撤，成长因子收益正常，9 月为所有因子回撤区间，1 ...

Quantitative Factors and Construction Methods - **Factor Name**: TPS (Turn20 conformed by PLUS) **Factor Construction Idea**: The TPS factor is designed to improve the traditional turnover rate factor by incorporating price information, specifically the shadow difference (representing intraday market sentiment), to address the limitations of the traditional turnover rate factor[6][9] **Factor Construction Process**: 1. The traditional turnover rate factor (Turn20) is calculated as the average turnover rate over the past 20 trading days, followed by cross-sectional market capitalization neutralization[6] 2. The TPS factor integrates the shadow difference as a price factor to complement the Turn20 factor, leveraging intraday price movements (e.g., opening and closing prices) to better capture market sentiment[9] **Factor Evaluation**: The TPS factor significantly outperforms traditional turnover rate factors in terms of performance and maintains strong stock selection ability even after removing common style and industry effects[6][9] - **Factor Name**: SPS (STR conformed by PLUS) **Factor Construction Idea**: Similar to TPS, the SPS factor improves the STR (Stable Turnover Rate) factor by incorporating price information (shadow difference) to enhance its effectiveness in stock selection[9] **Factor Construction Process**: 1. The STR factor was initially developed to address the limitations of the Turn20 factor, focusing on turnover rate stability rather than magnitude[7] 2. The SPS factor further integrates the shadow difference as a price factor to complement the STR factor, using intraday price movements to better reflect market sentiment[9] **Factor Evaluation**: The SPS factor demonstrates superior performance compared to both the traditional Turn20 and STR factors, with strong stock selection capabilities even after removing style and industry biases[9] Factor Backtesting Results - **TPS Factor**: - Annualized Return: 39.30% - Annualized Volatility: 15.71% - IR: 2.50 - Monthly Win Rate: 77.64% - Maximum Drawdown: 18.19%[1][11] - **SPS Factor**: - Annualized Return: 42.98% - Annualized Volatility: 13.15% - IR: 3.27 - Monthly Win Rate: 83.54% - Maximum Drawdown: 11.58%[1][12][14]

证券研究报告 | 2025年11月29日 2025年11月30日 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 核心观点 金融工程周报 国信金工指数增强组合表现跟踪 因子表现监控 以沪深 300 指数为选股空间。最近一周，三个月机构覆盖、一年动量、单季 ROE 等因子表现较好，而一个月波动、三个月反转、一个月换手等因子表 现较差。 以中证 500 指数为选股空间。最近一周，一年动量、预期净利润环比、 DELTAROE 等因子表现较好，而三个月波动、一个月波动、三个月换手等 因子表现较差。 以中证 1000 指数为选股空间。最近一周，单季营收同比增速、DELTAROA、 标准化预期外收入等因子表现较好，而三个月波动、一个月波动、三个月反 转等因子表现较差。 以中证 A500 指数为选股空间。最近一周，一年动量、标准化预期外盈利、 标准化预期外收入等因子表现较好，而一个月波动、三个月波动、预期 EPTTM 等因子表现较差。 以公募重仓指数为选股空间。最近一周，一年动量、预期净利润环比、单季 超预期幅度等因子表现较好，而一个月波动、三个月波动、一个月换手等因 子表现较差。 公募基金指数增强产品表现跟 ...

Research Background - The stock market exhibits overnight correlation characteristics, where daily returns can be decomposed into overnight and intraday returns. This report characterizes the correlation features of similar stocks based on recent academic findings [1][9]. Overnight Price Change Correlation Research - The study separates long and short signals from trading execution to capture cross-stock information effects. A correlation matrix is constructed based on overnight and intraday returns, identifying leading (Leader) and lagging (Lagger) groups. Trading strategies are developed to generate signals only from the leading group and trade within the lagging group [2][10][16]. Empirical Research - The analysis shows that the leading-lagging effect in A-shares presents a reversal effect, where a bullish signal from the leading group results in stronger performance from the short positions, and vice versa. The strategy is particularly applicable to small-cap stocks [2][35][44]. Factor Research - Weekly and monthly stock selection factors are constructed based on overnight correlation information. The introduction of conventional correlation improves the distinction of stock selection, with the combined factor showing a monthly RANK_IC of 8.13% and an annualized return of 18.2% [2][57][79]. Correlation Analysis - The internal correlation among factors is relatively low, indicating that the correlation factors provide marginal incremental value. The correlation factor shows some similarity with style factors, such as residual volatility [2][90]. Group Identification - The report attempts to identify groups within the A-share market, including the CSI 300 and the CSI 1000. The results indicate that the method of classifying leading and lagging groups based on correlation matrix features yields stable results [30][34]. Portfolio Construction Process - The portfolio construction framework separates signal generation from execution, capturing cross-stock information effects. The process includes constructing a correlation matrix, identifying leading and lagging groups, and extracting trading signals based on the leading group's average impact score [27][35]. Factor Construction and Backtesting - The report explores the performance of factors based on overnight correlation, with results indicating that conventional correlation factors outperform overnight correlation factors in terms of predictive effectiveness [57][72]. Performance Metrics - The backtesting results show that the strategy can achieve an annualized return of approximately 10.51% when focusing on small-cap stocks, while the distinction between long and short groups is less pronounced in large-cap stocks [44][72].

Quantitative Models and Construction Methods 1. Model Name: Guosen Quantitative Index Enhanced Portfolio - **Model Construction Idea**: The model aims to construct enhanced portfolios benchmarked against indices such as CSI 300, CSI 500, CSI 1000, and CSI A500, with the goal of consistently outperforming their respective benchmarks [10][11]. - **Model Construction Process**: 1. **Revenue Prediction**: Predict stock returns using multiple factors. 2. **Risk Control**: Apply constraints on industry exposure, style exposure, stock weight deviation, and turnover rate. 3. **Portfolio Optimization**: Optimize the portfolio to maximize single-factor exposure while adhering to constraints. The optimization model is 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, \( w \) is the stock weight vector, and \( f^{T}w \) is the weighted exposure to the factor. - **Constraints**: - **Style Exposure**: \( X \) is the factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure. - **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. - **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. - **Component Stock Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark component, and \( b_l, b_h \) are the lower and upper bounds for component stock weight. - **No Short Selling**: Ensure non-negative weights and limit individual stock weights. - **Full Investment**: Ensure the portfolio is fully invested with weights summing to 1 [40][41][42]. 4. **Backtesting**: Rebalance the portfolio monthly, calculate historical returns, and evaluate performance metrics such as excess returns and risk statistics [44]. 2. Model Name: Public Fund Heavyweight Index - **Model Construction Idea**: Construct an index based on the holdings of public funds to evaluate factor performance under "institutional style" [42][43]. - **Model Construction Process**: 1. **Sample Selection**: Include ordinary equity funds and partial equity hybrid funds with a minimum size of 50 million RMB and at least six months of listing history. Exclude recently transformed funds or those with insufficient data. 2. **Data Collection**: Use fund periodic reports (annual, semi-annual, or quarterly) to gather holding information. 3. **Weight Calculation**: Average the stock weights across eligible funds. 4. **Index Construction**: Sort stocks by weight in descending order and select those accounting for 90% of cumulative weight to form the index [43]. --- Model Backtesting Results 1. Guosen Quantitative Index Enhanced Portfolio - **CSI 300 Enhanced Portfolio**: - Weekly excess return: -0.71% - Year-to-date excess return: 16.74% [13] - **CSI 500 Enhanced Portfolio**: - Weekly excess return: 0.12% - Year-to-date excess return: 6.85% [13] - **CSI 1000 Enhanced Portfolio**: - Weekly excess return: -0.94% - Year-to-date excess return: 14.08% [13] - **CSI A500 Enhanced Portfolio**: - Weekly excess return: -1.37% - Year-to-date excess return: 7.55% [13] 2. Public Fund Heavyweight Index - **CSI 300 Index Enhanced Products**: - Weekly excess return: Max 0.70%, Min -1.26%, Median 0.09% - Year-to-date excess return: Max 9.92%, Min -4.53%, Median 2.58% [31] - **CSI 500 Index Enhanced Products**: - Weekly excess return: Max 1.17%, Min -1.13%, Median 0.11% - Year-to-date excess return: Max 13.14%, Min -9.17%, Median 3.94% [33] - **CSI 1000 Index Enhanced Products**: - Weekly excess return: Max 0.89%, Min -1.38%, Median -0.05% - Year-to-date excess return: Max 19.12%, Min -1.84%, Median 8.24% [36] - **CSI A500 Index Enhanced Products**: - Weekly excess return: Max 0.71%, Min -0.86%, Median -0.04% - Year-to-date excess return: Max 2.67%, Min -4.14%, Median -0.76% [39] --- Quantitative Factors and Construction Methods 1. Factor Name: Maximized Factor Exposure (MFE) - **Factor Construction Idea**: Evaluate factor effectiveness under real-world constraints by maximizing single-factor exposure in a portfolio [40][41]. - **Factor Construction Process**: 1. Define constraints for style exposure, industry exposure, stock weight deviation, and component stock weight. 2. Optimize the portfolio to maximize single-factor exposure while adhering to constraints. 3. Rebalance monthly and calculate historical returns [40][41][44]. 2. Factor Name: Public Fund Heavyweight Factors - **Factor Construction Idea**: Test factor performance in the public fund heavyweight index to reflect institutional preferences [42][43]. - **Factor Construction Process**: 1. Use public fund holdings to construct the index. 2. Evaluate factor performance within this index using metrics such as excess returns and risk-adjusted returns [42][43]. --- Factor Backtesting Results 1. Maximized Factor Exposure (MFE) - **CSI 300 Sample Space**: - Best-performing factors (weekly): One-month volatility (0.83%), one-month turnover (0.68%), three-month volatility (0.65%) - Worst-performing factors (weekly): Single-quarter profit growth (-0.26%), three-month institutional coverage (-0.24%), one-year momentum (-0.24%) [18] - **CSI 500 Sample Space**: - Best-performing factors (weekly): Three-month institutional coverage (1.09%), one-month reversal (1.01%), three-month reversal (0.99%) - Worst-performing factors (weekly): Standardized unexpected earnings (-1.00%), DELTAROA (-0.81%), DELTAROE (-0.81%) [20] - **CSI 1000 Sample Space**: - Best-performing factors (weekly): One-month turnover (1.08%), three-month institutional coverage (1.06%), single-quarter ROA (1.04%) - Worst-performing factors (weekly): Single-quarter SP (-1.29%), expected PEG (-1.25%), SPTTM (-1.22%) [22] - **CSI A500 Sample Space**: - Best-performing factors (weekly): One-month turnover (0.82%), three-month turnover (0.75%), one-month volatility (0.74%) - Worst-performing factors (weekly): Expected net profit QoQ (-0.91%), single-quarter net profit growth (-0.61%), expected PEG (-0.41%) [24] - **Public Fund Heavyweight Index**: - Best-performing factors (weekly): One-month volatility (1.32%), one-month turnover (1.23%), three-month turnover (0.89%) - Worst-performing factors (weekly): Single-quarter revenue growth (-0.89%), single-quarter profit growth (-0.88%), single-quarter ROE (-0.81%) [26]

Group 1 - The report aims to track large purchases and net active purchases through transaction detail data, building relevant indicators [1] - The top five industries for large purchases in the last five trading days are: Banking, Real Estate, Steel, Comprehensive, and Textile & Apparel [2] - The top five industries for net active purchases in the last five trading days are: Banking, Transportation, Pharmaceuticals, Real Estate, and Oil & Petrochemicals [2] Group 2 - The top five ETFs for large purchases in the last five trading days are: Guotai CSI A500 ETF, Guotai SSE 10-Year Treasury ETF, Harvest S&P Oil & Gas Exploration and Production Selected Industry ETF, Southern Growth Enterprise Board AI ETF, and Hai Futong SSE Urban Investment Bond ETF [2] - The top five ETFs for net active purchases in the last five trading days are: Guotai SSE 10-Year Treasury ETF, E Fund CSI 300 Non-Bank ETF, Yinhua SSE Sci-Tech Innovation Board 100 ETF, Huabao CSI Nonferrous Metals ETF, and Penghua CSI Liquor ETF [2]

Quantitative Factors and Construction Methods - **Factor Name**: UTR2.0 (Upgraded Turnover Rate 2.0) **Factor Construction Idea**: The UTR2.0 factor is an upgraded version of the original UTR factor. It combines the "volume stability factor" (STR) and the "small volume factor" (Turn20) using a new methodology. The key improvement involves transitioning from ordinal scale to ratio scale for factor values, which retains more information and adjusts the impact of the small volume factor based on the stability of the volume[6][7]. **Factor Construction Process**: 1. At the end of each month, calculate the small volume factor (Turn20) and the volume stability factor (STR) for all stocks[6]. 2. Sort all samples by STR in ascending order and assign scores (1, 2, ..., N), where N is the total number of samples. This is recorded as "Score 1"[6]. 3. For the top 50% of samples ranked by STR, sort them by Turn20 in descending order and assign scores (1, 2, ..., N/2). This is recorded as "Score 2". The final score for these stocks is "Score 1 + Score 2"[6]. 4. For the bottom 50% of samples ranked by STR, sort them by Turn20 in ascending order and assign scores (1, 2, ..., N/2). This is recorded as "Score 3". The final score for these stocks is "Score 1 + Score 3"[6]. 5. Transition from ordinal scale to ratio scale by introducing a coefficient for Turn20, which is a function of STR. The coefficient reflects the impact of Turn20 on returns: the more stable the volume, the stronger the positive impact; the less stable the volume, the stronger the negative impact. The formula for UTR2.0 is: $$ \mathrm{UTR2.0} = \mathrm{STR} + \text{softsign}(\mathrm{STR}) \cdot \mathrm{Turn20} $$ where $\text{softsign}(x) = \frac{x}{1 + |x|}$[7]. **Factor Evaluation**: The UTR2.0 factor improves upon the original UTR factor by achieving better performance in terms of volatility, information ratio (IR), and monthly win rate, although its returns are slightly lower[6][7]. --- Factor Backtesting Results - **UTR2.0 Factor**: - Annualized Return: 40.48% - Annualized Volatility: 14.98% - Information Ratio (IR): 2.70 - Monthly Win Rate: 75.53% - Maximum Drawdown: 11.03%[8][12] - **October 2025 Performance**: - Long Portfolio Return: 4.64% - Short Portfolio Return: -1.50% - Long-Short Portfolio Return: 6.14%[10]

Group 1 - The core viewpoint of the article emphasizes the performance of the "brokerage golden stocks" and their ability to track the performance of mixed equity funds, showcasing the analytical capabilities of brokerage firms [2][10][31] - In October 2025, the top-performing stocks in the brokerage golden stock pool included GuoDun Quantum, Rongxin Culture, and JiangBolong, with significant monthly increases [1][3][4] - The top three brokerages in terms of monthly returns were Western Securities, Great Wall Securities, and Guoyuan Securities, with returns of 5.84%, 5.43%, and 4.03% respectively, while the mixed equity fund index returned -2.14% [6][8] Group 2 - As of November 3, 2025, a total of 42 brokerages released their golden stocks for the month, resulting in 275 unique A-shares after deduplication [21][27] - The sectors with the highest allocation in the current golden stock pool were electronics (15.26%), non-ferrous metals (8.68%), and basic chemicals (6.84%) [27] - The brokerage golden stock performance enhancement portfolio had an absolute return of -0.77% for the month and a relative excess return of 1.37% compared to the mixed equity fund index [35] Group 3 - The article highlights the performance of various selection factors within the brokerage golden stock pool, noting that total market capitalization and quarterly revenue growth rates performed well recently [18][16] - The article also discusses the stocks that received multiple recommendations from analysts, indicating higher market attention, with stocks like Industrial Fulian and Kingsoft receiving recommendations from five or more analysts [22][23] - The brokerage golden stock index showed a year-to-date return of 28.59%, compared to the mixed equity fund index's return of 32.47% [14][35]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.53% this week and 18.86% year-to-date [1][7] - The CSI 500 index enhanced portfolio recorded an excess return of 0.45% this week and 9.03% year-to-date [1][7] - The CSI 1000 index enhanced portfolio had an excess return of 0.34% this week and 19.00% year-to-date [1][7] - The CSI A500 index enhanced portfolio experienced an excess return of -0.46% this week and 8.18% year-to-date [1][7] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as quarterly ROA, quarterly ROE, and one-year momentum performed well [1][10] - In the CSI 500 component stocks, factors like SPTTM, executive compensation, and three-month institutional coverage showed strong performance [1][10] - For the CSI 1000 component stocks, factors such as three-month earnings revisions, standardized unexpected revenue, and standardized unexpected earnings performed well [1][10] - In the CSI A500 index component stocks, factors like one-year momentum, quarterly revenue year-on-year growth, and DELTAROA showed good performance [1][10] - Among publicly offered fund heavy stocks, factors like one-year momentum, standardized unexpected revenue, and three-month earnings revisions performed well [1][10] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 2.02%, a minimum of -1.13%, and a median of 0.06% this week [1][23] - The CSI 500 index enhanced products recorded a maximum excess return of 1.24%, a minimum of -1.61%, and a median of 0.19% this week [1][25] - The CSI 1000 index enhanced products achieved a maximum excess return of 1.52%, a minimum of -1.23%, and a median of 0.45% this week [1][29] - The CSI A500 index enhanced products had a maximum excess return of 0.84%, a minimum of -0.53%, and a median of 0.03% this week [1][30]

Quantitative Factor and Construction Methodology - **Factor Name**: Turnover Distribution Uniformity (UTD) Factor [1][6][7] - **Factor Construction Idea**: The UTD factor is an improvement over the traditional turnover rate factor, leveraging minute-level transaction volume data to reduce misclassification of stock samples and enhance stock selection performance [1][6][7] - **Factor Construction Process**: 1. Collect minute-level transaction volume data for individual stocks [1][7] 2. Calculate the turnover rate distribution uniformity based on the dispersion of turnover rates across different time intervals [7] 3. Construct the UTD factor by quantifying the uniformity of turnover rate distribution [7] 4. Perform style, industry, and proprietary factor neutralization to ensure the purity of the UTD factor [1] - **Factor Evaluation**: The UTD factor significantly reduces the misclassification of stock samples and demonstrates superior stock selection performance compared to traditional turnover rate factors [1][6][7] --- Factor Backtesting Results - **Traditional Turnover Rate Factor (Turn20)**: - Monthly IC Mean: -0.072 [6] - Annualized ICIR: -2.10 [6] - Annualized Return: 33.41% [6] - IR: 1.90 [6] - Monthly Win Rate: 71.58% [6] - **UTD Factor (2014/01-2025/09)**: - Annualized Return: 19.82% [1][7][12] - Annualized Volatility: 7.39% [1][7][12] - IR: 2.68 [1][7][12] - Monthly Win Rate: 77.30% [1][7][12] - Maximum Drawdown: 5.51% [1][7][12] - **UTD Factor (September 2025)**: - 10-group long portfolio return: 0.91% [1][11] - 10-group short portfolio return: 0.52% [1][11] - 10-group long-short portfolio return: 0.39% [1][11]