Group 1 - The core viewpoint of the article is to track and analyze the performance of various index enhancement portfolios and the factors influencing stock selection across different indices [1][2][5][18]. Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of 0.88% for the week and 2.99% year-to-date [6][18]. - The performance of the Zhongzheng 500 index enhancement portfolio indicated an excess return of 0.68% for the week but a negative return of -0.92% year-to-date [6][18]. - The Zhongzheng 1000 index enhancement portfolio achieved an excess return of 1.44% for the week and 1.18% year-to-date [6][18]. - The Zhongzheng A500 index enhancement portfolio reported an excess return of 1.76% for the week and 3.50% year-to-date [6][18]. Group 3 - In the HuShen 300 component stocks, factors such as three-month volatility, illiquidity shock, and BP performed well [7][9]. - In the Zhongzheng 500 component stocks, factors like SPTTM, single-quarter SP, and single-quarter surprise magnitude showed strong performance [8][10]. - For Zhongzheng 1000 component stocks, standardized expected external income, single-quarter EP, and expected BP were among the top-performing factors [12][13]. - In the Zhongzheng A500 index component stocks, factors such as three-month turnover, three-month volatility, and one-month turnover performed well [15][16]. Group 4 - The public fund index enhancement products for HuShen 300 had a maximum excess return of 2.44%, a minimum of -0.52%, and a median of 0.38% for the week [22][24]. - The Zhongzheng 500 index enhancement products had a maximum excess return of 1.77%, a minimum of -1.45%, and a median of 0.07% for the week [24]. - The Zhongzheng 1000 index enhancement products achieved a maximum excess return of 3.29%, a minimum of 0.00%, and a median of 0.86% for the week [23][27]. - The Zhongzheng A500 index enhancement products reported a maximum excess return of 2.50%, a minimum of -0.54%, and a median of 0.31% for the week [25][28].

证券研究报告 | 2026年01月24日 多因子选股周报 超额全线回暖，中证 A500 增强组合年内超额 3.50% 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，三个月波动、非流动性冲击、BP 等因子表现较好，而标准化预期外盈利、单季超预期幅度、一年动量等因子 表现较差。 以中证 500 指数为选股空间。最近一周，SPTTM、单季 SP、单季超预期幅 度等因子表现较好，而三个月反转、高管薪酬、非流动性冲击等因子表现较 差。 以中证 1000 指数为选股空间。最近一周，标准化预期外收入、单季 EP、预 期 BP 等因子表现较好，而一个月波动、高管薪酬、三个月波动等因子表现 较差。 以中证 A500 指数为选股空间。最近一周，三个月换手、三个月波动、一个 月换手等因子表现较好，而一年动量、一个月反转、标准化预期外盈利等因 子表现较差。 以公募重仓指数为选股空间。最近一周，BP、预期 BP、单季 SP 等因子表 现较好，而一年动量、高管薪酬、单季 ROE 等因子表现较差。 公募基金指数增强产品表现跟踪 目前，公募基金沪深 300 指数增强产品共有 7 ...

Group 1 - The core viewpoint of the article is to track the performance of various index-enhanced portfolios and the factors influencing stock selection across different indices [2][3][19] Group 2 - The performance of the CSI 300 index-enhanced portfolio showed an excess return of 1.60% for the week and 2.09% year-to-date [7][19] - The CSI 500 index-enhanced portfolio had an excess return of 0.23% for the week but a negative return of -1.59% year-to-date [7][19] - The CSI 1000 index-enhanced portfolio achieved an excess return of 1.77% for the week and -0.36% year-to-date [7][19] - The CSI A500 index-enhanced portfolio reported an excess return of 0.97% for the week and 1.63% year-to-date [7][19] Group 3 - In the CSI 300 component stocks, factors such as standardized unexpected earnings, quarterly earnings surprises, and DELTAROE performed well [8][10] - In the CSI 500 component stocks, factors like year-on-year revenue growth, specificity, and expected net profit quarter-on-quarter showed strong performance [10][12] - For the CSI 1000 component stocks, factors such as illiquidity shock, one-month turnover, and three-month turnover performed well [10][14] - In the CSI A500 index component stocks, factors like three-month earnings adjustments, standardized unexpected revenue, and specificity showed good performance [10][16] Group 4 - The public fund index-enhanced products for the CSI 300 had a maximum excess return of 2.12% and a minimum of -0.45% for the week, with a median of 0.44% [21][23] - The CSI 500 index-enhanced products had a maximum excess return of 0.42% and a minimum of -2.18% for the week, with a median of -0.14% [25] - The CSI 1000 index-enhanced products reported a maximum excess return of 1.18% and a minimum of -0.52% for the week, with a median of 0.49% [24][25] - The CSI A500 index-enhanced products had a maximum excess return of 2.00% and a minimum of -0.52% for the week, with a median of 0.37% [26]

Quantitative Models and Construction Methods - **Model Name**: CSI 300 Enhanced Portfolio **Model Construction Idea**: The model aims to enhance the performance of the CSI 300 Index by leveraging quantitative strategies to generate excess returns over the benchmark[4][8][14] **Model Construction Process**: The portfolio is constructed by selecting stocks from the CSI 300 Index based on quantitative factors and optimization techniques. The model seeks to maximize excess returns while controlling tracking error relative to the benchmark[8][14] **Model Evaluation**: The model demonstrates strong performance in generating consistent excess returns over the CSI 300 Index, indicating its effectiveness in capturing alpha[14] - **Model Name**: CSI 500 Enhanced Portfolio **Model Construction Idea**: Similar to the CSI 300 Enhanced Portfolio, this model focuses on enhancing the performance of the CSI 500 Index by applying quantitative strategies[8][14] **Model Construction Process**: Stocks are selected from the CSI 500 Index using quantitative factors, and the portfolio is optimized to achieve excess returns while maintaining a controlled tracking error[8][14] **Model Evaluation**: The model shows mixed results, with some periods of underperformance relative to the benchmark, suggesting room for improvement in factor selection or optimization[14] - **Model Name**: CSI 1000 Enhanced Portfolio **Model Construction Idea**: This model targets the CSI 1000 Index, aiming to generate excess returns through quantitative strategies tailored to small-cap stocks[8][14] **Model Construction Process**: The portfolio is constructed by selecting stocks from the CSI 1000 Index based on quantitative factors and optimizing for excess returns while managing tracking error[8][14] **Model Evaluation**: The model performs well, particularly in capturing alpha from small-cap stocks, with positive excess returns over the benchmark[14] - **Model Name**: GARP Portfolio **Model Construction Idea**: The GARP (Growth at a Reasonable Price) portfolio combines growth and valuation factors to identify stocks with strong growth potential at reasonable valuations[32] **Model Construction Process**: Stocks are selected based on a combination of growth metrics (e.g., earnings growth) and valuation metrics (e.g., PE ratio). The portfolio is optimized to balance growth and valuation considerations[32] **Model Evaluation**: The portfolio demonstrates strong performance, with significant excess returns over the CSI 300 Index, indicating the effectiveness of the GARP strategy[32] - **Model Name**: Small-Cap Value Portfolio **Model Construction Idea**: This portfolio focuses on small-cap stocks with attractive valuation metrics, aiming to capture value premiums in the small-cap segment[34][36] **Model Construction Process**: Stocks are selected based on valuation factors such as PB and PE ratios. The portfolio is optimized to maximize exposure to value factors while maintaining diversification[34][36] **Model Evaluation**: The portfolio shows mixed results, with one version underperforming the benchmark and another version generating positive excess returns, highlighting the importance of factor selection and portfolio construction[34][36] - **Model Name**: Small-Cap Growth Portfolio **Model Construction Idea**: This portfolio targets small-cap stocks with strong growth potential, leveraging growth factors to identify high-growth opportunities[38] **Model Construction Process**: Stocks are selected based on growth metrics such as earnings growth and revenue growth. The portfolio is optimized to maximize exposure to growth factors while maintaining diversification[38] **Model Evaluation**: The portfolio underperforms the benchmark, suggesting challenges in capturing growth premiums in the small-cap segment[38] Model Backtesting Results - **CSI 300 Enhanced Portfolio**: Weekly return 0.91%, monthly return 5.64%, annual return 5.64%, excess return over benchmark 3.44%[8][14] - **CSI 500 Enhanced Portfolio**: Weekly return 1.54%, monthly return 7.98%, annual return 7.98%, excess return over benchmark -2.30%[8][14] - **CSI 1000 Enhanced Portfolio**: Weekly return 2.56%, monthly return 8.89%, annual return 8.89%, excess return over benchmark 0.50%[8][14] - **GARP Portfolio**: Weekly return 1.23%, monthly return 4.89%, annual return 4.89%, excess return over benchmark 2.69%[32] - **Small-Cap Value Portfolio 1**: Weekly return 0.64%, monthly return 5.91%, annual return 5.91%, excess return over benchmark -0.60%[34] - **Small-Cap Value Portfolio 2**: Weekly return 2.84%, monthly return 7.92%, annual return 7.92%, excess return over benchmark 1.40%[36] - **Small-Cap Growth Portfolio**: Weekly return 1.20%, monthly return 6.21%, annual return 6.21%, excess return over benchmark -0.31%[38] Quantitative Factors and Construction Methods - **Factor Name**: Size Factor **Factor Construction Idea**: Captures the performance difference between small-cap and large-cap stocks[42] **Factor Construction Process**: Stocks are ranked by market capitalization, and the top 10% (small-cap) and bottom 10% (large-cap) are selected to form long and short portfolios, respectively. The size factor return is calculated as the difference between the long and short portfolio returns[42] **Factor Evaluation**: The size factor shows positive returns in the short term but mixed results over longer periods, indicating variability in its effectiveness[42] - **Factor Name**: PB Factor **Factor Construction Idea**: Measures the valuation premium or discount of stocks based on their price-to-book ratio[42] **Factor Construction Process**: Stocks are ranked by PB ratio, and the top 10% (low PB) and bottom 10% (high PB) are selected to form long and short portfolios, respectively. The PB factor return is calculated as the difference between the long and short portfolio returns[42] **Factor Evaluation**: The PB factor shows negative returns, suggesting challenges in capturing valuation premiums[42] - **Factor Name**: ROE Factor **Factor Construction Idea**: Identifies stocks with high profitability based on return on equity[53] **Factor Construction Process**: Stocks are ranked by ROE, and the top 10% (high ROE) and bottom 10% (low ROE) are selected to form long and short portfolios, respectively. The ROE factor return is calculated as the difference between the long and short portfolio returns[53] **Factor Evaluation**: The ROE factor demonstrates strong positive returns, indicating its effectiveness in identifying profitable stocks[53] Factor Backtesting Results - **Size Factor**: Weekly return 0.91%, annual return 0.16% (all-market), 5.33% (CSI 300), -9.74% (CSI 500), -2.90% (CSI 1000)[42][43] - **PB Factor**: Weekly return -1.83%, annual return -5.94% (all-market), -8.16% (CSI 300), -12.18% (CSI 500), -8.70% (CSI 1000)[42][43] - **ROE Factor**: Weekly return 2.47%, annual return 1.10% (all-market), 0.13% (CSI 300), -2.02% (CSI 500), 1.16% (CSI 1000)[53][54]

证券研究报告 | 2026年01月17日 多因子选股周报 景气类因子表现出色，四大指增组合本周均跑赢基准 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，标准化预期外盈利、单季超预期幅 度、DELTAROE 等因子表现较好，而股息率、一个月反转、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，单季营收同比增速、特异度、预期 净利润环比等因子表现较好，而三个月换手、一个月换手、三个月波动等因 子表现较差。 以中证 1000 指数为选股空间。最近一周，非流动性冲击、一个月换手、三 个月换手等因子表现较好，而单季营利同比增速、SPTTM、BP 等因子表现 较差。 以中证 A500 指数为选股空间。最近一周，3 个月盈利上下调、标准化预期 外收入、特异度等因子表现较好，而非流动性冲击、一个月换手、一个月反 转等因子表现较差。 以公募重仓指数为选股空间。最近一周，一年动量、DELTAROA、3 个月盈 利上下调等因子表现较好，而三个月换手、一个月换手、股息率等因子表现 较差。 公募基金指数增强产品表现跟踪 目前，公募基金沪深 300 ...

Core Viewpoint - The AI Fund Debang Quantitative Optimal Stock A (167702) reported a profit of 20.84 thousand yuan for Q4 2025, with a weighted average profit per fund share of 0.0271 yuan, and a net value growth rate of 2.14% during the reporting period [2]. Fund Performance - As of January 15, the fund's unit net value was 1.302 yuan, with a one-year cumulative growth rate of 26.43%, the highest among its peers [2]. - The fund's performance over different time frames includes a three-month growth rate of 4.89% (79/121 among comparable funds), a six-month growth rate of 14.80% (85/121), and a three-year growth rate of 0.65% (69/89) [3]. - The fund's Sharpe ratio over the past three years is 0.313, ranking 67 out of 86 comparable funds [9]. - The maximum drawdown over the past three years is 41.09%, with the largest quarterly drawdown occurring in Q1 2024 at 36.83% [11]. Investment Strategy - The fund employs a combination of AI-driven factor models and fundamental multi-factor stock selection models to identify quality investment opportunities and achieve excess returns [2]. Market Outlook - The fund manager indicates that global economic threats include trade protectionism and geopolitical conflicts, while China's economy is expected to show resilience supported by policy measures and innovation [2]. - The A-share market is anticipated to enter a profit upturn cycle due to policy support, with expectations for a slow bull market driven by resilience in the Chinese economy [2]. Fund Holdings - As of December 31, the fund's top ten holdings include Ningde Times, China Ping An, Kweichow Moutai, Zhongji Xuchuang, Zijin Mining, China Merchants Bank, Midea Group, Industrial and Commercial Bank of China, Luxshare Precision, and BYD [18]. Fund Size - The fund's total size as of Q4 2025 was 908.57 thousand yuan [15]. Stock Positioning - The average stock position over the past three years was 88.12%, slightly below the peer average of 88.3% [14]. - The fund reached its highest stock position of 93.94% at the end of Q1 2020 and its lowest of 69.22% in the first half of 2019 [14].

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.44% this week, with the same excess return for the year [8] - The CSI 500 index enhanced portfolio recorded an excess return of -1.80% this week, matching the year-to-date performance [8] - The CSI 1000 index enhanced portfolio saw an excess return of -2.20% this week, consistent with the year-to-date performance [8] - The CSI A500 index enhanced portfolio achieved an excess return of 0.61% this week, with the same year-to-date performance [8] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month institutional coverage, DELTAROA, and DELTAROE performed well [9] - In the CSI 500 component stocks, factors like quarterly net profit year-on-year growth, expected net profit quarter-on-quarter, and specificity showed strong performance [11] - In the CSI 1000 component stocks, factors such as one-year momentum, quarterly revenue year-on-year growth, and standardized expected external income performed well [14] - In the CSI A500 index component stocks, factors like quarterly net profit year-on-year growth, DELTAROE, and quarterly profit year-on-year growth showed strong performance [17] - In public fund heavy stocks, factors like quarterly net profit year-on-year growth, expected net profit quarter-on-quarter, and three-month reversal performed well [20] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.48%, a minimum of -1.42%, and a median of 0.09% this week [28] - The CSI 500 index enhanced products had a maximum excess return of 0.06%, a minimum of -3.87%, and a median of -1.38% this week [30] - The CSI 1000 index enhanced products had a maximum excess return of 1.11%, a minimum of -1.84%, and a median of -0.38% this week [29] - The CSI A500 index enhanced products had a maximum excess return of 2.21%, a minimum of -1.13%, and a median of -0.26% this week [28]

Quantitative Models and Factor Analysis Quantitative Models and Construction Methods Model Name: Guosen JinGong Index Enhanced Portfolio - **Model Construction Idea**: The model aims to outperform its respective benchmarks by constructing enhanced portfolios based on multiple factors[11][12] - **Model Construction Process**: 1. **Return Prediction**: Predicting the returns of stocks within the benchmark index 2. **Risk Control**: Implementing risk control measures to manage the portfolio's risk exposure 3. **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to risk constraints[12] - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors[11][12] Model Backtesting Results - **Guosen JinGong Index Enhanced Portfolio**: - **CSI 300 Index Enhanced Portfolio**: Weekly excess return 0.44%, annual excess return 0.44%[5][14] - **CSI 500 Index Enhanced Portfolio**: Weekly excess return -1.80%, annual excess return -1.80%[5][14] - **CSI 1000 Index Enhanced Portfolio**: Weekly excess return -2.20%, annual excess return -2.20%[5][14] - **CSI A500 Index Enhanced Portfolio**: Weekly excess return 0.61%, annual excess return 0.61%[5][14] Quantitative Factors and Construction Methods Factor Name: Single Factor MFE (Maximized Factor Exposure) Portfolio - **Factor Construction Idea**: The factor aims to maximize the exposure to a single factor while controlling for various constraints such as industry exposure, style exposure, and stock weight deviations[40][41] - **Factor Construction Process**: 1. **Optimization Model**: The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T} w \\ \text{s.t.} & s_{l} \leq X(w - w_{b}) \leq s_{h} \\ & h_{l} \leq H(w - w_{b}) \leq h_{h} \\ & w_{l} \leq w - w_{b} \leq w_{h} \\ & b_{l} \leq B_{b} w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $$ where \( f \) represents the factor values, \( w \) is the stock weight vector, and the constraints include style exposure, industry exposure, stock weight deviations, and component stock weight limits[40][41] 2. **Constraints**: The constraints include: - **Style Exposure**: \( X \) is the factor exposure matrix, \( w_{b} \) is the benchmark weight vector, \( s_{l} \) and \( s_{h} \) are the lower and upper bounds for style exposure[41] - **Industry Exposure**: \( H \) is the industry exposure matrix, \( h_{l} \) and \( h_{h} \) are the lower and upper bounds for industry exposure[41] - **Stock Weight Deviations**: \( w_{l} \) and \( w_{h} \) are the lower and upper bounds for stock weight deviations[41] - **Component Stock Weight Limits**: \( B_{b} \) is the 0-1 vector indicating whether a stock is a benchmark component, \( b_{l} \) and \( b_{h} \) are the lower and upper bounds for component stock weights[41] - **No Short Selling**: The weights are non-negative and sum to 1[41] 3. **Portfolio Construction**: The MFE portfolio is constructed by maximizing the factor exposure while adhering to the constraints[42][44] - **Factor Evaluation**: The MFE portfolio is used to test the effectiveness of single factors under realistic constraints, making it more likely to reflect the true predictive power of the factors in the final portfolio[40][41] Factor Backtesting Results - **CSI 300 Index**: - **Best Performing Factors (Weekly)**: Three-month institutional coverage (0.86%), DELTAROA (0.61%), DELTAROE (0.52%)[19] - **Worst Performing Factors (Weekly)**: Expected net profit QoQ (-0.78%), one-year momentum (-0.45%), idiosyncratic volatility (-0.42%)[19] - **CSI 500 Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (0.06%), expected net profit QoQ (0.33%), idiosyncratic volatility (0.22%)[21] - **Worst Performing Factors (Weekly)**: One-month volatility (-2.47%), EPTTM (-3.56%), single-quarter ROE (-0.67%)[21] - **CSI 1000 Index**: - **Best Performing Factors (Weekly)**: One-year momentum (1.94%), single-quarter revenue YoY growth (1.31%), standardized unexpected income (0.92%)[23] - **Worst Performing Factors (Weekly)**: EPTTM (-3.56%), dividend yield (-3.27%), expected EPTTM (-3.22%)[23] - **CSI A500 Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (1.14%), DELTAROE (0.88%), single-quarter operating profit YoY growth (0.70%)[25] - **Worst Performing Factors (Weekly)**: EPTTM (-1.29%), one-month volatility (-1.22%), three-month volatility (-1.09%)[25] - **Public Fund Heavy Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (1.14%), expected net profit QoQ (0.88%), three-month reversal (0.29%)[27] - **Worst Performing Factors (Weekly)**: Expected EPTTM (-0.74%), EPTTM (-1.29%), one-month volatility (-1.22%)[27]

- The report highlights that in December 2025, the top-performing factors in the broker's gold stock pool were single-quarter ROE, net analyst upgrades, and net operating cash flow, while volatility, single-quarter revenue growth, and intraday returns performed poorly[3][27] - Throughout 2025, the best-performing factors were total market capitalization, single-quarter revenue growth, and single-quarter ROE, while EPTTM, expected dividend yield, and volatility performed poorly[3][27] - The broker's gold stock performance enhancement portfolio achieved an absolute return of 5.24% in December 2025, with an excess return of 2.18% relative to the mixed equity fund index[5][41] - For the year 2025, the broker's gold stock performance enhancement portfolio achieved an absolute return of 40.66%, with an excess return of 7.47% relative to the mixed equity fund index, ranking in the 32.60th percentile among active equity funds[5][41] - The construction of the broker's gold stock performance enhancement portfolio involves selecting stocks from the broker's gold stock pool, optimizing the portfolio to control deviations in individual stocks and styles, and using the industry distribution of all public funds as the industry allocation benchmark[42] - The historical performance of the broker's gold stock performance enhancement portfolio from 2018 to 2025 shows an annualized return of 21.71%, with an annualized excess return of 14.18% relative to the mixed equity fund index, consistently ranking in the top 30% of active equity funds each year[43][46]

一、本周指数增强组合表现 沪深300指数增强组合本周超额收益-0.59%，本年超额收益20.90%。 中证500指数增强组合本周超额收益-0.54%，本年超额收益5.45%。 中证1000指数增强组合本周超额收益-0.19%，本年超额收益15.64%。 中证A500指数增强组合本周超额收益-0.24%，本年超额收益10.26%。 二、本周选股因子表现跟踪 沪深300成分股中标准化预期外盈利、DELTAROA、DELTAROE等因子表现 较好。 中证500成分股中SPTTM、单季SP、单季营收同比增速等因子表现较好。 中证1000成分股中非流动性冲击、三个月机构覆盖、三个月反转等因子表现 较好。 中证A500指数成分股中特异度、SPTTM、标准化预期外盈利等因子表现较 好。 公募基金重仓股中一年动量、单季EP、股息率等因子表现较好。 三、本周公募基金指数增强产品表现跟踪 沪深300指数增强产品本周超额收益最高0.59%，最低-0.68%，中位 数-0.01%。 中证500指数增强产品本周超额收益最高0.28%，最低-0.84%，中位 数-0.39%。 中证1000指数增强产品本周超额收益最高0.52%，最低-1 ...