以沪深 300 指数为选股空间。最近一周，标准化预期外收入、预期 PEG、 DELTAROE 等因子表现较好，而非流动性冲击、一个月反转、三个月波动 等因子表现较差。 证券研究报告 | 2026年02月28日 多因子选股周报 估值因子表现出色，中证 A500 增强组合年内超额 3.78% 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 以中证 500 指数为选股空间。最近一周，预期 BP、BP、SPTTM 等因子表 现较好，而预期净利润环比、三个月反转、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，SPTTM、一年动量、单季 SP 等 因子表现较好，而 DELTAROA、一个月换手、一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，特异度、标准化预期外收入、预 期 PEG 等因子表现较好，而 EPTTM 一年分位点、三个月换手、预期净利 润环比等因子表现较差。 以公募重仓指数为选股空间。最近一周，一年动量、单季 SP、单季营利同 比增速等因子表现较好，而非流动性冲击、三个月反转、一个月波动等因子 表现较差。 公募基金指数增强产品表现跟踪 目前 ...

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of single factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, and turnover rate. This approach ensures that factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[39][40]. **Model Construction Process**: The MFE portfolio is constructed using the following optimization model: $ \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, \( f^{T}w \) is the weighted exposure of the portfolio to the factor, and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix for stocks, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style factor exposure. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, where \( H_{ij} = 1 \) if stock \( i \) belongs to industry \( j \), and \( h_l, h_h \) are the lower and upper bounds for industry deviation. 3. **Stock Deviation**: \( w_l, w_h \) are the lower and upper bounds for individual stock deviations from the benchmark. 4. **Constituent Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark constituent, and \( b_l, b_h \) are the lower and upper bounds for constituent weights. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \). 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T}w = 1 \)[39][40][41]. **Model Evaluation**: The MFE portfolio effectively tests factor performance under realistic constraints, making it a robust tool for evaluating factor predictability in practical scenarios[39][40]. --- Quantitative Factors and Construction Methods - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual quarterly net profit from expected profit, standardized by the standard deviation of expected profit. This factor captures earnings surprises[17]. **Factor Construction Process**: $ SUE = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Quarterly Net Profit}}{\text{Standard Deviation of Expected Net Profit}} $ **Factor Evaluation**: SUE is a growth-related factor and has shown strong performance in certain market conditions, particularly in capturing earnings surprises[17]. - **Factor Name**: One-Year Momentum **Factor Construction Idea**: Measures the momentum of stock prices over the past year, excluding the most recent month, to avoid short-term reversals[17]. **Factor Construction Process**: $ \text{One-Year Momentum} = \text{Cumulative Return Over the Past Year (Excluding the Last Month)} $ **Factor Evaluation**: This factor is widely used in momentum strategies and has demonstrated consistent performance in various market environments[17]. - **Factor Name**: Three-Month Earnings Revision **Factor Construction Idea**: Tracks the net number of analyst upgrades versus downgrades over the past three months, normalized by the total number of analysts covering the stock[17]. **Factor Construction Process**: $ \text{Three-Month Earnings Revision} = \frac{\text{Number of Upgrades} - \text{Number of Downgrades}}{\text{Total Number of Analysts}} $ **Factor Evaluation**: This factor reflects changes in market sentiment and has shown strong predictive power for short-term stock performance[17]. --- Backtesting Results of Models - **MFE Portfolio Performance**: - **CSI 300 Index**: Weekly excess return: -0.14%; YTD excess return: 3.07%[5][14]. - **CSI 500 Index**: Weekly excess return: -0.27%; YTD excess return: -0.57%[5][14]. - **CSI 1000 Index**: Weekly excess return: -0.69%; YTD excess return: 3.24%[5][14]. - **CSI A500 Index**: Weekly excess return: 0.12%; YTD excess return: 3.43%[5][14]. --- Backtesting Results of Factors - **Standardized Unexpected Earnings (SUE)**: - **CSI 300 Index**: Weekly excess return: 0.31%; Monthly excess return: -0.50%; YTD excess return: 0.16%[19]. - **CSI 500 Index**: Weekly excess return: 0.77%; Monthly excess return: -0.02%; YTD excess return: 0.11%[21]. - **CSI 1000 Index**: Weekly excess return: 0.31%; Monthly excess return: 0.40%; YTD excess return: -1.04%[23]. - **CSI A500 Index**: Weekly excess return: 0.65%; Monthly excess return: -0.68%; YTD excess return: 0.46%[25]. - **One-Year Momentum**: - **CSI 300 Index**: Weekly excess return: 0.54%; Monthly excess return: 0.74%; YTD excess return: 0.36%[19]. - **CSI 500 Index**: Weekly excess return: 0.08%; Monthly excess return: -0.56%; YTD excess return: -1.95%[21]. - **CSI 1000 Index**: Weekly excess return: -0.33%; Monthly excess return: -0.12%; YTD excess return: 1.52%[23]. - **CSI A500 Index**: Weekly excess return: 0.66%; Monthly excess return: -0.96%; YTD excess return: -1.32%[25]. - **Three-Month Earnings Revision**: - **CSI 300 Index**: Weekly excess return: 0.19%; Monthly excess return: -0.47%; YTD excess return: -0.04%[19]. - **CSI 500 Index**: Weekly excess return: 1.02%; Monthly excess return: 2.06%; YTD excess return: 0.80%[21]. - **CSI 1000 Index**: Weekly excess return: 0.31%; Monthly excess return: 2.78%; YTD excess return: 3.88%[23]. - **CSI A500 Index**: Weekly excess return: 0.02%; Monthly excess return: 0.53%; YTD excess return: 0.56%[25].

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed based on benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices, aiming to consistently outperform their respective benchmarks [11][12][14] - The construction process of the index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization [12] - The report monitors the performance of common stock selection factors across different stock selection spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices, by constructing single-factor Maximized Factor Exposure (MFE) portfolios and tracking their relative excess returns [11][15][42] - The MFE portfolio construction process involves optimizing the portfolio to maximize single-factor exposure while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, turnover rate, and component stock weight proportion [42][43][44] - The optimization model for MFE portfolios is expressed as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style factor deviation, industry deviation, stock weight deviation, component stock weight proportion, and stock weight limits [42][43] - The report provides detailed performance tracking of single-factor MFE portfolios across different stock selection spaces, highlighting factors such as SP, SPTTM, EP, and others that performed well in specific indices like CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices [15][18][20][22][24][26] - The report also tracks the excess returns of public fund index enhancement products, including CSI 300, CSI 500, CSI 1000, and CSI A500, with detailed statistics on maximum, minimum, and median excess returns over different time periods [28][32][35][38][41]

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 the risk constraints[12]. - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors[11]. Model Backtesting Results - **Guosen JinGong Index Enhanced Portfolio**: - **CSI 300 Index Enhanced Portfolio**: Weekly excess return 0.24%, annual excess return 3.21%[5][14]. - **CSI 500 Index Enhanced Portfolio**: Weekly excess return 0.53%, annual excess return -0.27%[5][14]. - **CSI 1000 Index Enhanced Portfolio**: Weekly excess return 1.63%, annual excess return 3.92%[5][14]. - **CSI A500 Index Enhanced Portfolio**: Weekly excess return 0.40%, annual excess return 3.28%[5][14]. Quantitative Factors and Construction Methods Factor Name: Single-Season SP - **Factor Construction Idea**: This factor measures the ratio of single-quarter operating revenue to total market value[17]. - **Factor Construction Process**: - Formula: $ \text{Single-Season SP} = \frac{\text{Single-Quarter Operating Revenue}}{\text{Total Market Value}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 300 and public fund heavy index selection spaces[1][26]. Factor Name: Single-Season EP - **Factor Construction Idea**: This factor measures the ratio of single-quarter net profit attributable to the parent company to total market value[17]. - **Factor Construction Process**: - Formula: $ \text{Single-Season EP} = \frac{\text{Single-Quarter Net Profit Attributable to Parent}}{\text{Total Market Value}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 300 and CSI A500 index selection spaces[1][24]. Factor Name: SPTTM - **Factor Construction Idea**: This factor measures the ratio of trailing twelve months (TTM) operating revenue to total market value[17]. - **Factor Construction Process**: - Formula: $ \text{SPTTM} = \frac{\text{TTM Operating Revenue}}{\text{Total Market Value}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 300 and public fund heavy index selection spaces[1][26]. Factor Name: One-Month Reversal - **Factor Construction Idea**: This factor measures the price change over the past 20 trading days[17]. - **Factor Construction Process**: - Formula: $ \text{One-Month Reversal} = \text{Price Change over Past 20 Trading Days} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 500 and CSI 1000 index selection spaces[1][20][22]. Factor Name: Three-Month Reversal - **Factor Construction Idea**: This factor measures the price change over the past 60 trading days[17]. - **Factor Construction Process**: - Formula: $ \text{Three-Month Reversal} = \text{Price Change over Past 60 Trading Days} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 500 and CSI 1000 index selection spaces[1][20][22]. Factor Name: Non-Liquidity Shock - **Factor Construction Idea**: This factor measures the average absolute value of daily price changes over the past 20 trading days divided by the average trading volume[17]. - **Factor Construction Process**: - Formula: $ \text{Non-Liquidity Shock} = \frac{\text{Average Absolute Daily Price Change}}{\text{Average Trading Volume}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 1000 index selection space[1][22]. Factor Backtesting Results - **Single-Season SP**: - **CSI 300**: Weekly excess return 1.33%, monthly excess return 0.89%, annualized historical return 2.80%[19]. - **Public Fund Heavy Index**: Weekly excess return 1.45%, monthly excess return 1.56%, annualized historical return 1.98%[26]. - **Single-Season EP**: - **CSI 300**: Weekly excess return 0.99%, monthly excess return 1.48%, annualized historical return 5.37%[19]. - **CSI A500**: Weekly excess return 1.29%, monthly excess return 1.59%, annualized historical return 5.16%[24]. - **SPTTM**: - **CSI 300**: Weekly excess return 1.11%, monthly excess return 0.81%, annualized historical return 2.03%[19]. - **Public Fund Heavy Index**: Weekly excess return 1.44%, monthly excess return 1.09%, annualized historical return 0.76%[26]. - **One-Month Reversal**: - **CSI 500**: Weekly excess return 1.19%, monthly excess return -0.32%, annualized historical return -1.60%[20]. - **CSI 1000**: Weekly excess return 1.77%, monthly excess return -0.37%, annualized historical return -4.29%[22]. - **Three-Month Reversal**: - **CSI 500**: Weekly excess return 1.42%, monthly excess return -3.14%, annualized historical return -2.38%[20]. - **CSI 1000**: Weekly excess return 1.56%, monthly excess return 0.76%, annualized historical return -1.95%[22]. - **Non-Liquidity Shock**: - **CSI 1000**: Weekly excess return 1.52%, monthly excess return 2.73%, annualized historical return 2.48%[22].

Core Insights - An emerging investment tool, index-enhanced funds, is gaining popularity among investors, with a significant increase in new products and total fundraising in 2025, surpassing previous years' totals [1] - Index-enhanced funds aim not only to track indices but also to outperform them by actively managing a portion of their assets through quantitative models and stock selection [1] Fund Performance - In 2025, the top-performing index-enhanced funds achieved substantial excess returns, particularly those tracking mid and small-cap indices like the Guozhen 2000 and Zhongzheng 1000, with the leading fund, Huaitianfu Guozhen 2000 Index Enhanced A, achieving a net value growth rate of 25.22% [2] - Other notable funds, such as the ICBC Zhongzheng 1000 Index Enhanced A and Baodao Zhongzheng 1000 Index Enhanced A, also reported excess returns exceeding 20% [2] Characteristics of Mid and Small-Cap Funds - Mid and small-cap index-enhanced funds are more successful in generating excess returns due to their index characteristics and the suitability of mainstream enhancement strategies [3] - The large number of constituent stocks in mid-cap indices, such as the Zhongzheng 2000, allows for a broad selection pool, facilitating the identification of potential stocks through quantitative models [3] - Information asymmetry and pricing inefficiencies in mid and small-cap stocks provide opportunities for quantitative strategies to discover mispriced assets and generate alpha returns [3] - The active trading and liquidity of small-cap stocks create a favorable environment for efficient trading execution and short-term price capture, enhancing overall returns [3]

Group 1 - The core viewpoint of the news is that the Xingyin CSI 500 Index Enhanced A fund has shown strong performance, with a recent net value of 1.3146 yuan and a six-month return of 27.88%, ranking 127th out of 757 in its category [1] - As of December 31, 2025, the fund achieved a one-year return of 34.82%, exceeding the benchmark return by 6.01%, and a three-year return of 32.63%, surpassing the benchmark return rate by 6.38% [1][2] Group 2 - The Xingyin Enhanced A fund is positioned as an index-enhanced equity fund, closely tracking the CSI 500 Index while optimizing component stock weights through quantitative models [2] - The fund's asset allocation shows a stock position of 92.18%, with only 0.71% in bonds and 6.53% in cash [2] - The manufacturing sector dominates the fund's industry allocation, accounting for 60.39% of net value, followed by information technology and finance at 4.53% and 4.34%, respectively [2] Group 3 - The top ten holdings of the fund are all CSI 500 Index component stocks, collectively representing approximately 7.24% of the fund's net asset value, indicating a diversified overall holding [2] - The fund actively invests in growth sectors, including information technology (e.g., Giant Network, Crystal Optoelectronics), high-end manufacturing (e.g., Lead Intelligent, Goldwind Technology), aerospace (e.g., China Satellite, Aerospace Electronics), and electronics (e.g., Jingwang Electronics, Xingsen Technology) [2] Group 4 - Fund manager Weng Zichen noted that the CSI 500 Index performed strongly in the fourth quarter, and despite the pressure on enhanced index products, the fund achieved stable excess returns through strict style exposure control and optimization using the Barra multi-factor model [5] - The strategy emphasizes controlling the volatility of excess returns and improving the Sharpe ratio to ensure consistent and stable performance across different market cycles [5] - Looking ahead to 2026, the CSI 500 Index will remain a key tool for investing in quality mid-cap growth stocks, with the fund continuing to leverage its quantitative model for risk control and alpha generation [5]

Core Viewpoint - The increasing popularity of index investing has led to a rise in interest in enhanced index funds, which have shown a trend of outperforming traditional index funds in recent years [1][7]. Group 1: Differences Between Enhanced Index Funds and Traditional Index Funds - Traditional index funds aim to closely track the performance of an index with strict control over tracking error, while enhanced index funds, although also index funds, do not fully replicate the index and allow for some active optimization to seek excess returns [2]. - Enhanced index funds are required to invest at least 80% of their non-cash assets in index constituents and their alternatives, meaning their performance is largely driven by the index itself, with some room for active management [2]. Group 2: Common Enhancement Strategies - The three main strategies for enhancing index funds include: 1. **Quantitative Model Stock Selection**: This strategy involves using quantitative factors to build enhancement models, scoring stocks across multiple dimensions to optimize expected performance [3]. 2. **Fundamental Enhancement**: Similar to active fund stock selection, this strategy involves comparing and adjusting constituent stocks based on financial quality, profitability, and valuation metrics [5]. 3. **Portfolio and Trading Optimization**: This strategy focuses on adjusting trading frequency, controlling transaction costs, and managing risks to minimize unnecessary volatility that could erode long-term returns [6]. Group 3: Performance Comparison - Over the past 5 and 10 years, enhanced index funds have consistently outperformed traditional index funds in terms of returns, with the performance gap widening over time, while maximum drawdowns have not significantly increased [7][8]. Group 4: Selection Criteria for Enhanced Index Funds - Investors are advised to adopt a "two-step" approach for selecting enhanced index funds: first, evaluate the underlying index, and then consider the enhancement strategy employed [11]. - The choice of index is crucial, as the performance of enhanced index funds largely depends on the characteristics of the benchmark index, with broader indices generally providing more opportunities for enhancement [11]. - Different enhancement strategies vary among funds, with many incorporating AI and machine learning to capture non-linear relationships between factors for excess returns [11]. Group 5: Target Audience for Enhanced Index Funds - Enhanced index funds are positioned as a hybrid investment tool suitable for investors willing to accept index volatility while seeking long-term excess returns [12]. - Conversely, traditional index funds may be more appropriate for investors who prefer to closely track an index without the risk of underperformance [13].

Core Viewpoint - The article emphasizes the growing importance of achieving sustainable and robust excess returns in passive investment, highlighting the capabilities of Penghua Fund's quantitative index enhancement family as a tool that combines index transparency with active management advantages [1][6]. Performance and Strategy - Penghua's quantitative index enhancement family has consistently outperformed benchmarks, with all five funds launched for over a year achieving excess returns as of January 30, 2026. Notably, the Penghua Guozhen 2000 Index Enhanced A fund recorded a net value growth rate of 61.80%, surpassing its benchmark by 18.16% [2]. - The small-cap style products have shown particularly strong performance, with the Penghua CSI 1000 Index Enhanced A fund achieving a net value growth rate of 55.83%, exceeding its benchmark by 16.86% [2]. Comprehensive Layout - The Penghua quantitative index enhancement family includes eight funds, covering various market capitalizations and sectors, catering to diverse investor needs and risk preferences. This includes broad market indices like the CSI 300 and niche indices focusing on small and mid-cap stocks [3]. - The family also strategically invests in emerging sectors, such as the Penghua CSI A500 Index Enhanced fund and several funds targeting technology innovation, thereby providing a multi-faceted approach to market opportunities [3]. Fusion Concept - The core advantage of Penghua's index and quantitative teams lies in their "fusion" approach, combining fundamental, price-volume, and alternative data factors to enhance strategy stability and reduce volatility [4]. - The integration of classic multi-factor models with machine learning techniques allows for a balance between strategy stability and flexibility, addressing the limitations of single-factor models [4]. Research and Technology Support - Penghua has developed a robust research ecosystem that supports the implementation of quantitative strategies, including a self-developed financial technology platform that enables high-speed data processing and model training [5]. - The team continuously updates its factor library, incorporating 200-300 effective factors while staying aligned with academic advancements and market dynamics [5]. Market Potential - The index enhancement funds are positioned for growth, benefiting from policy guidance and a shift of household savings towards investment, creating a favorable environment for these products [6]. - The Penghua quantitative index enhancement family offers a blend of passive investment's diversification and discipline with the potential for active management's excess returns, aligning with investor desires for balance and control [7].

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 CSI 300 index enhancement portfolio this week showed an excess return of 0.00%, with a year-to-date excess return of 3.00% [6][22]. - The CSI 500 index enhancement portfolio had an excess return of 0.01% this week, but a year-to-date excess return of -0.88% [6][24]. - The CSI 1000 index enhancement portfolio achieved an excess return of 0.90% this week, with a year-to-date excess return of 2.17% [6][25]. - The CSI A500 index enhancement portfolio reported an excess return of -0.53% this week, with a year-to-date excess return of 2.90% [6][28]. Group 3 - In the CSI 300 component stocks, factors such as expected PEG, quarterly ROA, and EPTTM performed well [7][9]. - In the CSI 500 component stocks, factors like dividend yield, EPTTM, and BP showed strong performance [11][10]. - For the CSI 1000 component stocks, factors including quarterly ROA, quarterly ROE, and standardized expected non-operating income performed well [13][12]. - In the CSI A500 index component stocks, factors such as dividend yield, quarterly revenue growth year-on-year, and quarterly ROA were notable [15][14]. Group 4 - The public fund index enhancement products for the CSI 300 had a maximum excess return of 1.08% and a minimum of -1.05% this week, with a median of -0.02% [22][20]. - The CSI 500 index enhancement products had a maximum excess return of 1.72% and a minimum of -0.67% this week, with a median of 0.29% [24][23]. - The CSI 1000 index enhancement products reported a maximum excess return of 0.96% and a minimum of -0.97% this week, with a median of 0.18% [26][25]. - The CSI A500 index enhancement products had a maximum excess return of 1.14% and a minimum of -1.73% this week, with a median of -0.00% [28][27].

Quantitative Models and Factor Analysis Summary 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] - **Model Construction Process**: - **Return Prediction**: Predicting the returns of stocks within the benchmark index - **Risk Control**: Implementing risk control measures to manage portfolio risk - **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate[12] - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors[11] Model Backtesting Results Guosen JinGong Index Enhanced Portfolio - **CSI 300 Index Enhanced Portfolio**: - Weekly Excess Return: 0.00% - Year-to-Date Excess Return: 3.00%[5][14] - **CSI 500 Index Enhanced Portfolio**: - Weekly Excess Return: 0.01% - Year-to-Date Excess Return: -0.88%[5][14] - **CSI 1000 Index Enhanced Portfolio**: - Weekly Excess Return: 0.90% - Year-to-Date Excess Return: 2.17%[5][14] - **CSI A500 Index Enhanced Portfolio**: - Weekly Excess Return: -0.53% - Year-to-Date Excess Return: 2.90%[5][14] Quantitative Factors and Construction Methods Factor Name: Dividend Yield - **Factor Construction Idea**: Measures the dividend income relative to the stock price, indicating the return on investment from dividends[17] - **Factor Construction Process**: - Formula: $\text{Dividend Yield} = \frac{\text{Dividend per Share}}{\text{Stock Price}}$ - The factor is calculated as the sum of the dividends declared over the last four quarters divided by the current market capitalization[17] - **Factor Evaluation**: The dividend yield factor is effective in identifying stocks with high dividend returns, which can be attractive to income-focused investors[17] Factor Name: EPTTM (Earnings to Price TTM) - **Factor Construction Idea**: Measures the earnings relative to the stock price over the trailing twelve months, indicating the profitability of the company[17] - **Factor Construction Process**: - Formula: $\text{EPTTM} = \frac{\text{Earnings TTM}}{\text{Market Capitalization}}$ - The factor is calculated as the total earnings over the trailing twelve months divided by the current market capitalization[17] - **Factor Evaluation**: The EPTTM factor is useful for identifying undervalued stocks with strong earnings performance[17] Factor Name: BP (Book to Price) - **Factor Construction Idea**: Measures the book value relative to the stock price, indicating the intrinsic value of the company[17] - **Factor Construction Process**: - Formula: $\text{BP} = \frac{\text{Book Value}}{\text{Market Capitalization}}$ - The factor is calculated as the book value of equity divided by the current market capitalization[17] - **Factor Evaluation**: The BP factor helps in identifying stocks that are potentially undervalued based on their book value[17] Factor Backtesting Results CSI 300 Index Sample Space - **Best Performing Factors (Recent Week)**: Expected PEG, Single Quarter ROA, EPTTM[1][19] - **Worst Performing Factors (Recent Week)**: Three-Month Reversal, Expected Net Profit QoQ, One-Month Turnover[1][19] CSI 500 Index Sample Space - **Best Performing Factors (Recent Week)**: Dividend Yield, EPTTM, BP[1][20] - **Worst Performing Factors (Recent Week)**: Expected Net Profit QoQ, Single Quarter Operating Profit YoY Growth, Three-Month Institutional Coverage[1][20] CSI 1000 Index Sample Space - **Best Performing Factors (Recent Week)**: Single Quarter ROA, Single Quarter ROE, Standardized Unexpected Revenue[1][22] - **Worst Performing Factors (Recent Week)**: One-Month Reversal, Single Quarter Net Profit YoY Growth, Three-Month Reversal[1][22] CSI A500 Index Sample Space - **Best Performing Factors (Recent Week)**: Dividend Yield, Single Quarter Revenue YoY Growth, Single Quarter ROA[1][24] - **Worst Performing Factors (Recent Week)**: Expected Net Profit QoQ, Single Quarter Net Profit YoY Growth, Single Quarter Operating Profit YoY Growth[1][24] Public Fund Heavy Index Sample Space - **Best Performing Factors (Recent Week)**: Dividend Yield, Single Quarter ROA, DELTAROA[1][27] - **Worst Performing Factors (Recent Week)**: Three-Month Reversal, Three-Month Institutional Coverage, Single Quarter Net Profit YoY Growth[1][27]