证券研究报告 | 2025年10月11日 多因子选股周报 超额全线回暖，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，预期 EPTTM、一个月波动、BP 等因子表现较好，而单季营收同比增速、三个月机构覆盖、3 个月盈利上下 调等因子表现较差。 以中证 500 指数为选股空间。最近一周，SPTTM、预期 BP、单季 EP 等因 子表现较好，而一年动量、预期 PEG、标准化预期外收入等因子表现较差。 以中证 1000 指数为选股空间。最近一周，EPTTM、SPTTM、预期 EPTTM 等因子表现较好，而预期净利润环比、一年动量、单季营收同比增速等因子 表现较差。 以中证 A500 指数为选股空间。最近一周，单季 SP、SPTTM、一个月波动 等因子表现较好，而单季营收同比增速、一年动量、三个月机构覆盖等因子 表现较差。 以公募重仓指数为选股空间。最近一周，预期 EPTTM、单季 EP、一个月波 动等因子表现较好，而一年动量、单季营收同比增速、预期净利润环比等因 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 ...

视角下的多策略增强组合 》）为基准的增强组合，力求能稳定战胜各自基准。近期组合的表现如下 图： 国信金工指数增强组合表现如下： 二 因子表现监控 我们分别以沪深300指数、中证500指数、中证1000指数、中证A500指数及公募重仓指数为选股空间， 构造单因子MFE组合并检验其相对于各自基准的超额收益。 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-0.17%，本年超额收益16.49%。 中证500指数增强组合本周超额收益0.26%，本年超额收益8.94%。 中证1000指数增强组合本周超额收益0.91%，本年超额收益17.72%。 中证A500指数增强组合本周超额收益-0.21%，本年超额收益9.06%。 二、本周选股因子表现跟踪 沪深300成分股中单季超预期幅度、单季营收同比增速、单季ROE等因子表 现较好。 中证500成分股中三个月换手、单季营收同比增速、EPTTM一年分位点等因 子表现较好。 中证1000成分股中三个月机构覆盖、单季ROE、高管薪酬等因子表现较好。 中证A500指数成分股中单季营收同比增速、EPTTM一年分位点、单季ROE 等因子表现较好。 公募基金重仓股中高管薪酬、单 ...

证券研究报告 | 2025年09月27日 多因子选股周报 中证 1000 增强组合本周超额 0.91%，年内超额 17.72% 核心观点 金融工程周报 沪深 300 指数增强产品最近一周：超额收益最高 0.91%，最低-1.54%，中 位数-0.17%。最近一月：超额收益最高 4.81%，最低-3.36%，中位数-0.53%。 中证 500 指数增强产品最近一周：超额收益最高 1.63%，最低-1.35%，中 位数-0.01%。最近一月：超额收益最高 2.51%，最低-5.04%，中位数-0.56%。 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，单季超预期幅度、单季营收同比增 速、单季 ROE 等因子表现较好，而预期 BP、预期净利润环比、BP 等因子 表现较差。 以中证 500 指数为选股空间。最近一周，三个月换手、单季营收同比增速、 EPTTM 一年分位点等因子表现较好，而一年动量、标准化预期外收入、 SPTTM 等因子表现较差。 以中证 1000 指数为选股空间。最近一周，三个月机构覆盖、单季 ROE、高 管薪酬等因子表现较好，而一年动量、DELTAROA、预期 ...

Group 1 - The core viewpoint of the article is to track the performance of various index enhancement portfolios and the factors influencing stock selection across different indices [1][2][22] - The HuShen 300 index enhancement portfolio achieved an excess return of 0.86% this week and 16.44% year-to-date [8][22] - The CSI 500 index enhancement portfolio recorded an excess return of -0.49% this week and 9.76% year-to-date [8][22] - The CSI 1000 index enhancement portfolio had an excess return of 1.46% this week and 16.90% year-to-date [8][22] - The CSI A500 index enhancement portfolio saw an excess return of 0.69% this week and 9.70% year-to-date [8][22] Group 2 - In the HuShen 300 component stocks, factors such as single-season ROE, expected EPTTM, and single-season EP performed well [9][11] - In the CSI 500 component stocks, factors like expected PEG, single-season SP, and SPTTM showed strong performance [10][12] - For the CSI 1000 component stocks, factors including single-season revenue year-on-year growth, three-month reversal, and expected PEG performed well [14][15] - In the CSI A500 index component stocks, single-season ROE, single-season EP, and EPTTM were among the top-performing factors [17][18] Group 3 - The public fund index enhancement products showed varying excess returns, with the HuShen 300 index enhancement product having a maximum excess return of 1.44% and a minimum of -0.86% this week [25][26] - The CSI 500 index enhancement product had a maximum excess return of 1.48% and a minimum of -1.21% this week [26][28] - The CSI 1000 index enhancement product recorded a maximum excess return of 1.32% and a minimum of -0.81% this week [28][29] - The CSI A500 index enhancement product achieved a maximum excess return of 1.52% and a minimum of -0.87% this week [28][29]

Group 1 - The core viewpoint of the article is to track the performance of various index-enhanced portfolios and stock selection factors, highlighting their excess returns and the effectiveness of different factors in various indices [1][2][19]. Group 2 - The performance of the CSI 300 index-enhanced portfolio showed an excess return of 2.90% for the week and 15.46% year-to-date [7][19]. - The CSI 500 index-enhanced portfolio had an excess return of -0.67% for the week and 10.61% year-to-date [7][19]. - The CSI 1000 index-enhanced portfolio recorded an excess return of 0.18% for the week and 15.26% year-to-date [7][19]. - The CSI A500 index-enhanced portfolio experienced an excess return of -1.39% for the week and 8.91% year-to-date [7][19]. Group 3 - In the CSI 300 component stocks, factors such as single-quarter ROE, single-quarter ROA, and DELTAROE performed well [8][10]. - In the CSI 500 component stocks, factors like DELTAROA, DELTAROE, and single-quarter net profit year-on-year growth showed strong performance [10][12]. - For the CSI 1000 component stocks, standardized expected excess earnings, single-quarter net profit year-on-year growth, and DELTAROA were among the top-performing factors [12][14]. - In the CSI A500 index component stocks, DELTAROE, single-quarter ROE, and DELTAROA were the standout factors [15][16]. Group 4 - The public fund index-enhanced products for the CSI 300 had a maximum excess return of 2.81% and a minimum of -2.40% for the week, with a median of -0.38% [20][22]. - The CSI 500 index-enhanced products had a maximum excess return of 1.26% and a minimum of -2.79% for the week, with a median of -0.51% [23][24]. - The CSI 1000 index-enhanced products recorded a maximum excess return of 1.32% and a minimum of -1.44% for the week, with a median of -0.06% [24][25]. - The CSI A500 index-enhanced products had a maximum excess return of 0.71% and a minimum of -1.82% for the week, with a median of -0.51% [25][26]. Group 5 - The total number of public fund index-enhanced products for the CSI 300 is 70, with a total scale of 77 billion [19]. - The CSI 500 index-enhanced products total 71, with a total scale of 43.2 billion [19]. - The CSI 1000 index-enhanced products consist of 46, with a total scale of 15 billion [19]. - The CSI A500 index-enhanced products have 52, with a total scale of 20.5 billion [19].

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.93% this week and 12.11% year-to-date [1][6] - The CSI 500 index enhanced portfolio recorded an excess return of -0.58% this week and 10.97% year-to-date [1][6] - The CSI 1000 index enhanced portfolio had an excess return of -1.56% this week and 14.33% year-to-date [1][6] - The CSI A500 index enhanced portfolio saw an excess return of -0.15% this week and 11.56% year-to-date [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as single-season ROA, standardized expected external income, and standardized expected external profit performed well [1][9] - In the CSI 500 component stocks, factors like one-year momentum, single-season surprise magnitude, and standardized expected external profit showed strong performance [1][9] - For the CSI 1000 component stocks, one-year momentum, EPTTM one-year percentile, and standardized expected external profit were notable [1][9] - In the CSI A500 index component stocks, DELTAROA, standardized expected external income, and DELTAROE performed well [1][9] - Among public fund heavy stocks, one-year momentum, DELTAROA, and single-season revenue year-on-year growth were strong [1][9] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.91%, a minimum of -1.41%, and a median of -0.09% this week [1][20] - The CSI 500 index enhanced products recorded a maximum excess return of 0.52%, a minimum of -2.05%, and a median of -0.51% this week [1][21] - The CSI 1000 index enhanced products achieved a maximum excess return of 0.94%, a minimum of -1.70%, and a median of -0.53% this week [1][22] - The CSI A500 index enhanced products had a maximum excess return of 0.71%, a minimum of -1.10%, and a median of -0.25% this week [1][25]

- The report tracks the performance of Guosen JinGong's index enhancement portfolios and public fund index enhancement products, as well as monitors the performance of common stock selection factors across different sample spaces[11][12][15] - Guosen JinGong's index enhancement portfolios are constructed based on three main components: return prediction, risk control, and portfolio optimization. These portfolios are benchmarked against indices such as CSI 300, CSI 500, CSI 1000, and CSI A500[12][14] - The report introduces the concept of Maximized Factor Exposure (MFE) portfolios to test the effectiveness of single factors under real-world constraints. The optimization model maximizes single-factor exposure while controlling for style, industry, stock weight deviations, and other constraints[41][42][43] - The optimization model for MFE portfolios is expressed as: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style exposure, industry exposure, stock weight deviations, and component stock weight limits[41][42] - The report tracks the performance of single-factor MFE portfolios across different sample spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy positions index. Factors are evaluated based on their excess returns relative to benchmarks[15][18][26] - Common stock selection factors are categorized into valuation, reversal, growth, profitability, liquidity, company governance, and analyst dimensions. Examples include BP (Book-to-Price), ROA (Return on Assets), and one-year momentum[16][17] - In the CSI 300 sample space, factors such as single-season ROA, standardized unexpected income, and standardized unexpected earnings performed well recently, while factors like one-month volatility and three-month volatility performed poorly[19] - In the CSI 500 sample space, factors such as one-year momentum and standardized unexpected earnings showed strong performance recently, while factors like one-month turnover and three-month volatility underperformed[21] - In the CSI 1000 sample space, factors such as one-year momentum and standardized unexpected earnings performed well recently, while factors like BP and single-season SP (Sales-to-Price) performed poorly[23] - In the CSI A500 sample space, factors such as DELTAROA (Change in ROA) and standardized unexpected income performed well recently, while factors like three-month volatility and one-month turnover performed poorly[25] - In the public fund heavy positions index sample space, factors such as one-year momentum and DELTAROA performed well recently, while factors like one-month turnover and three-month turnover underperformed[27] - Public fund index enhancement products are tracked for their excess returns relative to benchmarks. For CSI 300 products, recent weekly excess returns ranged from -1.41% to 1.91%, with a median of -0.09%[32] - For CSI 500 products, recent weekly excess returns ranged from -2.05% to 0.52%, with a median of -0.51%[34] - For CSI 1000 products, recent weekly excess returns ranged from -1.70% to 0.94%, with a median of -0.53%[37] - For CSI A500 products, recent weekly excess returns ranged from -1.10% to 0.71%, with a median of -0.25%[40]

Core Viewpoint - The current market situation suggests a potential bull market, but many investors may not be adequately prepared for it [2][8]. Group 1: Historical Experiences - In the 2014-2015 bull market, the company engaged in numerous trades but struggled to keep up with the market, resulting in minimal gains [4][5]. - During the 2019-2020 bull market, the company focused on broad index products, achieving annual returns of over 30%, which were considered relatively low at the time [6]. Group 2: Investment Strategies - The company emphasizes the importance of responding to market conditions rather than attempting to predict them, suggesting that most investors should adopt a more reactive approach [9][10]. - Two key strategies for responding to a bull market include: 1. Investing a significant portion of funds in equity markets when market positions are not high, and being willing to increase investments during market pullbacks [11]. 2. Accepting average market returns rather than seeking quick profits, which may involve diversifying investments across broad indices to mitigate risks [11][12]. Group 3: Market Dynamics - The current market index levels may not accurately reflect the underlying sector performances, as value stocks have contributed significantly to index gains, while other sectors may still have room for growth [11]. - The company suggests that the best approach for ordinary investors is to increase equity allocations during market lows and focus on responding to market changes rather than making predictions [16].

Quantitative Models and Construction Methods Model Name: DFQ-FactorGCL - **Model Construction Idea**: Based on hypergraph convolutional neural networks and temporal residual contrastive learning for stock return prediction[6] - **Model Construction Process**: The model uses hypergraph convolutional neural networks to capture complex relationships between stocks and temporal residual contrastive learning to enhance prediction accuracy[6] - **Model Evaluation**: The model is effective in capturing stock trends and improving prediction accuracy[6] Model Name: Neural ODE - **Model Construction Idea**: Reconstructing time series dynamic systems for deep learning factor mining[6] - **Model Construction Process**: The model uses ordinary differential equations to model the continuous dynamics of stock prices, allowing for more accurate factor extraction[6] - **Model Evaluation**: The model provides a novel approach to factor mining, improving the robustness and accuracy of predictions[6] Model Name: DFQ-FactorVAE-pro - **Model Construction Idea**: Incorporating feature selection and environmental variable modules into the FactorVAE model[6] - **Model Construction Process**: The model uses variational autoencoders with additional modules for feature selection and environmental variables to enhance stock selection[6] - **Model Evaluation**: The model improves stock selection by considering more comprehensive factors and environmental variables[6] Quantitative Factors and Construction Methods Factor Name: Beta - **Factor Construction Idea**: Bayesian compressed market Beta[16] - **Factor Construction Process**: The factor is constructed by compressing the market Beta using Bayesian methods to capture market sensitivity[16] - **Factor Evaluation**: The factor is effective in identifying stocks with high market sensitivity[12] Factor Name: Volatility - **Factor Construction Idea**: Average logarithmic turnover rate over the past 243 days[16] - **Factor Construction Process**: The factor is calculated using the average logarithmic turnover rate and its regression with the market turnover rate over the past 243 days[16] - **Factor Evaluation**: The factor captures the demand for high volatility assets[12] Factor Name: Liquidity - **Factor Construction Idea**: Average logarithmic turnover rate over the past 243 days[16] - **Factor Construction Process**: The factor is calculated using the average logarithmic turnover rate and its regression with the market turnover rate over the past 243 days[16] - **Factor Evaluation**: The factor indicates the demand for high liquidity assets[12] Factor Name: Value - **Factor Construction Idea**: Book-to-market ratio (BP) and earnings yield (EP)[16] - **Factor Construction Process**: The factor is calculated using the book-to-market ratio and earnings yield[16] - **Factor Evaluation**: The factor shows limited recognition of value investment strategies[12] Factor Name: Growth - **Factor Construction Idea**: State-owned enterprise stock proportion[16] - **Factor Construction Process**: The factor is calculated using the proportion of state-owned enterprise stocks[16] - **Factor Evaluation**: The factor indicates the market's attention to state-owned enterprise stocks[12] Factor Name: Cubic Size - **Factor Construction Idea**: Market capitalization power term[16] - **Factor Construction Process**: The factor is calculated using the market capitalization power term[16] - **Factor Evaluation**: The factor shows the market's reduced attention to micro-cap stocks[12] Factor Name: Trend - **Factor Construction Idea**: EWMA with different half-lives[18] - **Factor Construction Process**: The factor is calculated using EWMA with half-lives of 20, 120, and 240 days, standard volatility, FF3 specific volatility, range, and maximum and minimum returns over the past 243 days[18] - **Factor Evaluation**: The factor indicates the market's reduced preference for trend investment strategies[12] Factor Name: Certainty - **Factor Construction Idea**: Sales growth, institutional holding percentage, net asset growth, analyst coverage, and listing days[18] - **Factor Construction Process**: The factor is calculated using sales growth, institutional holding percentage, net asset growth, analyst coverage, and listing days[18] - **Factor Evaluation**: The factor shows the market's reduced confidence in certainty investment strategies[12] Factor Performance Monitoring Performance in Different Index Spaces - **CSI 300 Index**: Factors like expected PEG, DELTAROE, and single-quarter EP performed well, while three-month reversal and one-month volatility performed poorly[7][24][26] - **CSI 500 Index**: Factors like one-year momentum and expected ROE change performed well, while three-month reversal and three-month institutional coverage performed poorly[7][28][30] - **CSI 800 Index**: Factors like expected ROE change and DELTAROE performed well, while one-month volatility and three-month reversal performed poorly[7][32][34] - **CSI 1000 Index**: Factors like DELTAROA and single-quarter net profit growth performed well, while public holding market value and standardized unexpected revenue performed poorly[7][36][37] - **CNI 2000 Index**: Factors like non-liquidity impact and expected PEG performed well, while public holding market value and one-month volatility performed poorly[7][39][41] - **ChiNext Index**: Factors like three-month earnings adjustment and single-quarter EP performed well, while expected net profit change and expected ROE change performed poorly[7][43][45] - **CSI All Index**: Factors like one-month UMR and one-month reversal performed well, while one-month volatility and three-month volatility performed poorly[7][47][50] Factor Backtesting Results CSI 300 Index - **Expected PEG**: 0.75% (recent week), 2.07% (recent month), 7.23% (year-to-date), 5.96% (annualized)[24] - **DELTAROE**: 0.73% (recent week), 2.19% (recent month), 7.91% (year-to-date), 5.07% (annualized)[24] - **Single-quarter EP**: 0.71% (recent week), 0.96% (recent month), 5.93% (year-to-date), 7.58% (annualized)[24] CSI 500 Index - **One-year momentum**: 0.84% (recent week), 2.33% (recent month), 3.83% (year-to-date), 3.00% (annualized)[28] - **Expected ROE change**: 0.76% (recent week), 0.28% (recent month), 6.15% (year-to-date), 7.67% (annualized)[28] - **Three-month UMR**: 0.74% (recent week), -0.38% (recent month), 0.29% (year-to-date), -1.06% (annualized)[28] CSI 800 Index - **Expected ROE change**: 0.93% (recent week), 1.76% (recent month), 2.27% (year-to-date), -3.20% (annualized)[32] - **Expected PEG**: 0.83% (recent week), 2.60% (recent month), 10.99% (year-to-date), 10.96% (annualized)[32] - **DELTAROE**: 0.79% (recent week), 2.64% (recent month), 11.60% (year-to-date), 8.99% (annualized)[32] CSI 1000 Index - **DELTAROA**: 0.63% (recent week), 1.57% (recent month), 8.06% (year-to-date), 15.10% (annualized)[36] - **Single-quarter net profit growth**: 0.57% (recent week), 1.03% (recent month), 8.04% (year-to-date), 10.77% (annualized)[36] - **One-month UMR**: 0.47% (recent week), -0.92% (recent month), 1.13% (year-to-date), -3.13% (annualized)[36] CNI 2000 Index - **Non-liquidity impact**: 1.26% (recent week), 1.99% (recent month), 12.11% (year-to-date), 21.51% (annualized)[39] - **Expected PEG**: 0.54% (recent week), 0.32% (recent month), 10.32% (year-to-date), 36.23% (annualized)[39] - **Three-month institutional coverage**: 0.54% (recent week), 4.56% (recent month), 5.41% (year-to-date), -1.19% (annualized)[39] ChiNext Index - **Three-month earnings adjustment**: 0.66% (recent week), 0.53% (recent month), -12.72% (year-to-date), -28.10% (annualized)[43] - **Single-quarter EP**: 0.66% (recent week), 0.69% (recent month), 2.90% (year-to-date), 24.70% (annualized)[43] - **PB_ROE rank difference**: 0.61% (recent week), -0.26% (

Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The MFE portfolio is designed to maximize the exposure of a single factor while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover limits. This approach ensures that the factor's predictive power is tested under realistic portfolio constraints, making it more applicable in practice [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, and \( w \) is the stock weight vector. - **Constraints**: 1. **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. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. 4. **Constituent Weight Control**: \( B_b \) is a binary vector indicating benchmark constituents, 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. 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T} w = 1 \) [39][40][41]. **Model Evaluation**: The MFE portfolio is effective in testing factor performance under realistic constraints, making it a practical tool for portfolio construction [39][40]. Quantitative Factors and Construction Methods - **Factor Name**: DELTAROE **Factor Construction Idea**: Measures the change in return on equity (ROE) over a specific period to capture improvements in profitability [16]. **Factor Construction Process**: $ \text{DELTAROE} = \text{ROE}_{\text{current quarter}} - \text{ROE}_{\text{same quarter last year}} $ Where ROE is calculated as: $ \text{ROE} = \frac{\text{Net Income} \times 2}{\text{Beginning Equity} + \text{Ending Equity}} $ [16]. **Factor Evaluation**: DELTAROE is a profitability factor that has shown strong performance in multiple sample spaces, including CSI 300, CSI 500, and CSI A500 indices [17][19][24]. - **Factor Name**: Pre-expected PEG (Pre-expected Price-to-Earnings Growth) **Factor Construction Idea**: Incorporates analysts' earnings growth expectations to evaluate valuation relative to growth potential [16]. **Factor Construction Process**: $ \text{Pre-expected PEG} = \frac{\text{Forward P/E}}{\text{Expected Earnings Growth Rate}} $ Where forward P/E is based on analysts' consensus earnings estimates [16]. **Factor Evaluation**: This factor has demonstrated strong predictive power in growth-oriented sample spaces such as CSI 300 and CSI A500 indices [17][24]. - **Factor Name**: DELTAROA **Factor Construction Idea**: Measures the change in return on assets (ROA) over a specific period to capture improvements in asset efficiency [16]. **Factor Construction Process**: $ \text{DELTAROA} = \text{ROA}_{\text{current quarter}} - \text{ROA}_{\text{same quarter last year}} $ Where ROA is calculated as: $ \text{ROA} = \frac{\text{Net Income} \times 2}{\text{Beginning Total Assets} + \text{Ending Total Assets}} $ [16]. **Factor Evaluation**: DELTAROA has shown consistent performance across multiple indices, including CSI 1000 and public fund-heavy indices [22][26]. Factor Backtesting Results - **DELTAROE**: - CSI 300: Weekly excess return 0.75%, monthly 2.28%, YTD 8.04% [17]. - CSI 500: Weekly excess return 0.07%, monthly 0.59%, YTD 6.67% [19]. - CSI A500: Weekly excess return 0.68%, monthly 3.61%, YTD 9.20% [24]. - **Pre-expected PEG**: - CSI 300: Weekly excess return 0.72%, monthly 2.10%, YTD 7.22% [17]. - CSI 500: Weekly excess return 0.15%, monthly 1.34%, YTD 9.62% [19]. - CSI A500: Weekly excess return 0.85%, monthly 2.07%, YTD 10.35% [24]. - **DELTAROA**: - CSI 300: Weekly excess return 0.44%, monthly 2.27%, YTD 7.10% [17]. - CSI 1000: Weekly excess return 0.66%, monthly 1.57%, YTD 8.57% [22]. - Public Fund Index: Weekly excess return 0.66%, monthly 1.57%, YTD 8.57% [26].