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。 1 沪深300样本空间中的因子表现 我们以沪深300指数为样本空间，对常见选股因子构造其相对于沪深300指数的MFE组合并跟踪其表 现，具体表现如下图。 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0.82%，本年超额收益6.67%。 中证500指数增强组合本周超额收益0.04%，本年超额收益7.84%。 中证1000指数增强组合本周超额收益0.34%，本年超额收益12.61%。 中证A500指数增强组合本周超额收益-0.89%，本年超额收益7.43%。 二、本周选股因子表现跟踪 沪深300成分股中预期EPTTM、单季EP、EPTTM等因子表现较好。 中证500成分股中BP、预期BP、预期EPTTM等因子表现较好。 中证1000成分股中BP、一个月换手、三个月波动等因子表现较好。 中证A500指数成分股中单季EP、预期EPTTM、预期PEG等因子表现较好。 公募基金重仓股中预期EPTTM、单季EP、预期PEG等因子表现较好。 三、本周公 ...

Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure Portfolio (MFE) - **Model Construction Idea**: The MFE model is designed to test the effectiveness of individual factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, and turnover constraints. By maximizing single-factor exposure while adhering to these constraints, the model evaluates the predictive power of factors in a controlled environment [40][41]. - **Model Construction Process**: - The optimization model aims to maximize single-factor exposure: $ \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 $f^{T}w$, where $f$ represents factor values and $w$ represents stock weights [40][41]. - **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 [41]. 2. **Industry Exposure**: $H$ is the industry exposure matrix, and $h_l$, $h_h$ are the lower and upper bounds for industry deviation [41]. 3. **Stock Weight Deviation**: $w_l$, $w_h$ are the lower and upper bounds for stock weight deviation [41]. 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 [41]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights [41]. 6. **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [42]. - **Implementation**: - At the end of each month, MFE portfolios are constructed for each factor under the specified constraints. - Historical returns are calculated for the MFE portfolios, adjusted for transaction costs (0.3% per side), and compared to the benchmark [44]. - **Model Evaluation**: The MFE model is effective in testing factor performance under realistic constraints, making it a practical tool for portfolio construction and factor validation [40][41]. --- Quantitative Factors and Construction Methods 1. Factor Name: Book-to-Price Ratio (BP) - **Factor Construction Idea**: Measures valuation by comparing book value to market capitalization [18]. - **Factor Construction Process**: - Formula: $ BP = \frac{\text{Net Assets}}{\text{Market Capitalization}} $ [18]. 2. Factor Name: Earnings-to-Price Ratio (EP) - **Factor Construction Idea**: Evaluates profitability relative to market capitalization [18]. - **Factor Construction Process**: - Formula: $ EP = \frac{\text{Net Income (Quarterly)}}{\text{Market Capitalization}} $ [18]. 3. Factor Name: Earnings-to-Price TTM (EPTTM) - **Factor Construction Idea**: Tracks trailing twelve-month earnings relative to market capitalization [18]. - **Factor Construction Process**: - Formula: $ EPTTM = \frac{\text{Net Income (TTM)}}{\text{Market Capitalization}} $ [18]. 4. Factor Name: Momentum (1-Year Momentum) - **Factor Construction Idea**: Captures price trends by measuring returns over the past year, excluding the most recent month [18]. - **Factor Construction Process**: - Formula: $ \text{1-Year Momentum} = \text{Cumulative Return (Year)} - \text{Return (Last Month)} $ [18]. 5. Factor Name: Analyst Coverage (3-Month Coverage) - **Factor Construction Idea**: Measures the number of analysts covering a stock over the past three months [18]. - **Factor Construction Process**: - Formula: $ \text{3-Month Coverage} = \text{Number of Analysts Covering Stock (Last 3 Months)} $ [18]. --- Factor Backtesting Results 1. Factor Performance in CSI 300 Universe - **Best-Performing Factors (Recent Week)**: EPTTM, Single-Quarter EP, EPTTM Percentile [20]. - **Worst-Performing Factors (Recent Week)**: 1-Year Momentum, Executive Compensation, Illiquidity Shock [20]. 2. Factor Performance in CSI 500 Universe - **Best-Performing Factors (Recent Week)**: BP, Expected BP, Expected EPTTM [22]. - **Worst-Performing Factors (Recent Week)**: 1-Year Momentum, 3-Month Coverage, Illiquidity Shock [22]. 3. Factor Performance in CSI 1000 Universe - **Best-Performing Factors (Recent Week)**: BP, 1-Month Turnover, 3-Month Volatility [24]. - **Worst-Performing Factors (Recent Week)**: 1-Year Momentum, 3-Month Coverage, Single-Quarter ROE [24]. 4. Factor Performance in CSI A500 Universe - **Best-Performing Factors (Recent Week)**: Single-Quarter EP, Expected EPTTM, Expected PEG [26]. - **Worst-Performing Factors (Recent Week)**: 3-Month Reversal, 1-Year Momentum, 1-Month Reversal [26]. 5. Factor Performance in Public Fund Heavyweight Index - **Best-Performing Factors (Recent Week)**: Expected EPTTM, Single-Quarter EP, Expected PEG [28]. - **Worst-Performing Factors (Recent Week)**: 1-Year Momentum, 3-Month Coverage, Expected Net Profit QoQ [28].

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0.74%，本年超额收益5.84%。 中证500指数增强组合本周超额收益0.20%，本年超额收益7.93%。 中证1000指数增强组合本周超额收益0.75%，本年超额收益12.43%。 中证A500指数增强组合本周超额收益0.65%，本年超额收益8.45%。 二、本周选股因子表现跟踪 沪深300成分股中高管薪酬、预期EPTTM、预期PEG等因子表现较好。 中证500成分股中单季ROE、单季ROA、单季营收同比增速等因子表现较 好。 中证1000成分股中高管薪酬、预期EPTTM、单季ROE等因子表现较好。 中证A500指数成分股中单季ROE、单季ROA、预期EPTTM等因子表现较 好。 公募基金重仓股中单季营收同比增速、一年动量、预期PEG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0.71%，最低-0.43%，中位数 0.22%。 中证500指数增强产品本周超额收益最高1.46%，最低-0.57%，中位数 0.29%。 中证1000指数增强产品本周超额收益最高1.26%，最低-0.68%，中位数 0 ...

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0.83%，本年超额收益5.09%。 中证500指数增强组合本周超额收益1.13%，本年超额收益7.75%。 中证1000指数增强组合本周超额收益1.86%，本年超额收益11.66%。 中证A500指数增强组合本周超额收益1.24%，本年超额收益7.78%。 二、本周选股因子表现跟踪 沪深300成分股中三个月机构覆盖、单季ROA、单季ROE等因子表现较好。 中证500成分股中标准化预期外盈利、一个月反转、DELTAROE等因子表现 较好。 中证1000成分股中单季营收同比增速、DELTAROE、单季ROE等因子表现较 好。 中证A500指数成分股中单季ROE、预期PEG、DELTAROE等因子表现较好。 公募基金重仓股中DELTAROE、一年动量、单季营收同比增速等因子表现较 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1.14%，最低-0.35%，中位数 0.07%。 中证500指数增强产品本周超额收益最高0.88%，最低-0.75%，中位数 0.07%。 中证1000指数增强产品本周超额收益最高0.89%，最 ...

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[12][13] - **Model Construction Process**: The construction process includes three main components: return prediction, risk control, and portfolio optimization. The model uses the following optimization formula to construct the Maximized Factor Exposure (MFE) portfolio: $$ \begin{array}{ll} \text{max} & f^{T} w \\ \text{s.t.} & s_{l} \leq X(w - w_{b}) \leq s_{h} \\ & h_{l} \leq H(w - w_{b}) \leq h_{h} \\ & w_{l} \leq w - w_{b} \leq w_{h} \\ & b_{l} \leq B_{b} w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $$ where \( f \) represents the factor values, \( w \) is the stock weight vector, \( X \) is the factor exposure matrix, \( H \) is the industry exposure matrix, and \( B_{b} \) is the 0-1 vector indicating whether a stock is part of the benchmark index[41][42][43] - **Model Evaluation**: The model is designed to control various exposures and constraints, making it more likely to achieve stable and reliable performance in real-world conditions[41][42][43] Model Backtesting Results - **Guosen JinGong Index Enhanced Portfolio**: - **CSI 300 Index Enhanced Portfolio**: Weekly excess return 0.83%, annual excess return 5.09%[6][15] - **CSI 500 Index Enhanced Portfolio**: Weekly excess return 1.13%, annual excess return 7.75%[6][15] - **CSI 1000 Index Enhanced Portfolio**: Weekly excess return 1.86%, annual excess return 11.66%[6][15] - **CSI A500 Index Enhanced Portfolio**: Weekly excess return 1.24%, annual excess return 7.78%[6][15] Quantitative Factors and Construction Methods Factor Name: Single-Quarter ROE - **Factor Construction Idea**: Measures the return on equity for a single quarter to evaluate a company's profitability[18] - **Factor Construction Process**: $$ \text{Single-Quarter ROE} = \frac{\text{Net Income} \times 2}{\text{Beginning Equity} + \text{Ending Equity}} $$ where net income is the net profit attributable to shareholders, and equity is the shareholders' equity at the beginning and end of the quarter[18] - **Factor Evaluation**: This factor is effective in capturing the profitability of companies and has shown good performance in various index spaces[18] Factor Name: Delta ROE - **Factor Construction Idea**: Measures the change in return on equity compared to the same quarter of the previous year to capture improvements or deteriorations in profitability[18] - **Factor Construction Process**: $$ \text{Delta ROE} = \text{Current Quarter ROE} - \text{ROE of the Same Quarter Last Year} $$ where ROE is calculated as above[18] - **Factor Evaluation**: This factor is useful for identifying companies with improving or deteriorating profitability trends[18] Factor Backtesting Results - **CSI 300 Index Space**: - **Single-Quarter ROE**: Weekly excess return 0.64%, monthly excess return 1.78%, annual excess return 3.31%, historical annualized return 4.34%[20] - **Delta ROE**: Weekly excess return 0.41%, monthly excess return 1.59%, annual excess return 3.58%, historical annualized return 3.62%[20] - **CSI 500 Index Space**: - **Single-Quarter ROE**: Weekly excess return 0.35%, monthly excess return 1.14%, annual excess return 1.39%, historical annualized return 5.12%[22] - **Delta ROE**: Weekly excess return 0.89%, monthly excess return 1.41%, annual excess return 2.54%, historical annualized return 7.27%[22] - **CSI 1000 Index Space**: - **Single-Quarter ROE**: Weekly excess return 1.75%, monthly excess return 2.65%, annual excess return -1.60%, historical annualized return 7.78%[24] - **Delta ROE**: Weekly excess return 1.78%, monthly excess return 1.50%, annual excess return 3.83%, historical annualized return 8.66%[24] - **CSI A500 Index Space**: - **Single-Quarter ROE**: Weekly excess return 0.84%, monthly excess return 2.37%, annual excess return 3.03%, historical annualized return 2.79%[26] - **Delta ROE**: Weekly excess return 0.75%, monthly excess return 1.77%, annual excess return 3.11%, historical annualized return 3.47%[26] - **Public Fund Heavy Index Space**: - **Single-Quarter ROE**: Weekly excess return 0.57%, monthly excess return 1.41%, annual excess return 0.99%, historical annualized return 2.52%[28] - **Delta ROE**: Weekly excess return 1.03%, monthly excess return 1.71%, annual excess return 3.79%, historical annualized return 3.64%[28]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 1.06% this week and 4.21% year-to-date [1][5] - The CSI 500 index enhanced portfolio recorded an excess return of -0.05% this week and 6.45% year-to-date [1][5] - The CSI 1000 index enhanced portfolio had an excess return of 0.72% this week and 9.41% year-to-date [1][5] - The CSI A500 index enhanced portfolio reported an excess return of 0.36% this week and 6.44% year-to-date [1][5] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month volatility, one-month volatility, and standardized unexpected earnings performed well [1][6] - In the CSI 500 component stocks, factors like quarterly revenue growth year-on-year, standardized unexpected revenue, and non-liquidity shocks showed strong performance [1][6] - For the CSI 1000 component stocks, factors such as EPTTM one-year percentile, SPTTM, and BP performed well [1][6] - In the CSI A500 index component stocks, factors like BP, quarterly EP, and three-month volatility showed good performance [1][6] - Among publicly offered fund heavy stocks, factors like quarterly unexpected magnitude, standardized unexpected earnings, and standardized unexpected revenue performed well [1][6] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.37%, a minimum of -0.21%, and a median of 0.32% this week [1][19] - The CSI 500 index enhanced products had a maximum excess return of 0.92%, a minimum of -0.09%, and a median of 0.35% this week [1][20] - The CSI 1000 index enhanced products had a maximum excess return of 0.98%, a minimum of -0.21%, and a median of 0.24% this week [1][22] - The CSI A500 index enhanced products had a maximum excess return of 0.70%, a minimum of -0.19%, and a median of 0.36% this week [1][24]

Core Viewpoint - The article discusses the evolution and optimization of dividend strategies in investment, highlighting the increasing popularity of dividend-focused index funds and the emergence of various optimized dividend indices in the market [2][3]. Group 1: Overview of Optimized Dividend Indices - There are currently 10 optimized dividend indices available in the market, which are categorized alongside traditional high-dividend indices like the CSI Dividend Index and Low Volatility Dividend Index [5]. - The performance of these optimized indices has shown that many have outperformed traditional indices over various time frames, indicating their potential as investment options [7][8]. Group 2: Performance Comparison - From 2015 to the present, several optimized indices, such as the New China Trust Dividend Value Index and the Leading Dividend 50 Index, have achieved significant returns, with the Leading Dividend 50 Index showing a return of 307.35% [7]. - The analysis indicates that most optimized indices have provided excess returns compared to traditional indices, particularly in specific time frames [8][9]. Group 3: Optimization Logic - The article outlines several key optimization angles for dividend indices, including the introduction of multi-factor selection criteria that incorporate market indicators like low volatility and low beta to enhance returns [13][14]. - Timeliness in reflecting a company's operational and stock performance is emphasized, with some indices adjusting their selection criteria based on the latest dividend announcements [16][18]. - The importance of assessing the stability of a company's earnings, such as using ROE stability as a filter, is highlighted to ensure consistent performance [19]. Group 4: Identifying Value Traps - The article discusses methods to identify potential value traps in high-dividend strategies, suggesting that indices should exclude companies that have recently underperformed in the market [20]. - The approach of quarterly adjustments to remove underperforming stocks is recommended as a straightforward method to mitigate risks associated with value traps [20]. Group 5: Growth Considerations - Some optimized indices aim to identify companies with strong profitability and growth potential, reflecting a more aggressive investment strategy [21]. - The article suggests that as market risk appetite increases, these growth-oriented dividend indices may gain more attention and perform well [22]. Group 6: Conclusion - The article concludes that the various optimization strategies for dividend indices focus on enhancing the reflection of a company's fundamentals, aiming to select stocks with favorable future prospects while excluding those with deteriorating fundamentals [25].

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0.32%，本年超额收益3.16%。 中证500指数增强组合本周超额收益0.64%，本年超额收益6.49%。 中证1000指数增强组合本周超额收益0.52%，本年超额收益8.57%。 中证A500指数增强组合本周超额收益0.35%，本年超额收益6.13%。 二、本周选股因子表现跟踪 沪深300成分股中预期净利润环比、3个月盈利上下调、单季超预期幅度等因 子表现较好。 中证500成分股中非流动性冲击、单季ROE、预期PEG等因子表现较好。 中证1000成分股中三个月换手、一个月换手、非流动性冲击等因子表现较 好。 中证A500指数成分股中3个月盈利上下调、单季超预期幅度、DELTAROE等 因子表现较好。 公募基金重仓股中非流动性冲击、三个月换手、一个月换手等因子表现较 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1.07%，最低-0.38%，中位数 0.11%。 中证500指数增强产品本周超额收益最高0.90%，最低-0.43%，中位数 0.45%。 中证1000指数增强产品本周超额收益最高1.00%，最低-0. ...

- The report tracks the performance of Guosen JinGong's index enhancement portfolios and public fund index enhancement products, as well as the performance of common stock selection factors in different stock selection spaces[10][11][14] - Guosen JinGong's index enhancement portfolios are constructed using a multi-factor stock selection approach, targeting benchmarks such as the CSI 300, CSI 500, CSI 1000, and CSI A500 indices[10][11] - The construction process of these portfolios includes earnings forecasting, risk control, and portfolio optimization[11] - The report monitors the performance of factors in different stock selection spaces, including the CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices[14][15] - The factor library includes over 30 common factors from dimensions such as valuation, reversal, growth, profitability, liquidity, corporate governance, and analysts[15][16] - The report constructs single-factor MFE (Maximized Factor Exposure) portfolios for each factor in the respective stock selection spaces and tracks their performance relative to their benchmarks[14][17][19][21][23][25] - The construction of the MFE portfolios involves an optimization model with the objective function of maximizing single-factor exposure while controlling for various constraints such as style exposure, industry exposure, individual stock weight deviation, and component stock weight proportion[41][42][43] - The report provides detailed performance tracking of public fund index enhancement products, including those based on the CSI 300, CSI 500, CSI 1000, and CSI A500 indices[27][28][29][32][35][38] - The performance metrics for these products include excess returns over different periods, such as the past week, past month, past quarter, and year-to-date[31][34][37][40] Factor Performance Monitoring - In the CSI 300 index space, factors such as expected net profit QoQ, 3-month earnings revisions, and single-quarter surprise magnitude performed well recently, while 3-month reversal, single-quarter EP, and expected EPTTM performed poorly[1][17] - In the CSI 500 index space, factors such as illiquidity shock, single-quarter ROE, and expected PEG performed well recently, while 1-year momentum, idiosyncratic volatility, and single-quarter SP performed poorly[1][19] - In the CSI 1000 index space, factors such as 3-month turnover, 1-month turnover, and illiquidity shock performed well recently, while 1-year momentum, EPTTM 1-year percentile, and single-quarter operating profit growth YoY performed poorly[1][21] - In the CSI A500 index space, factors such as 3-month earnings revisions, single-quarter surprise magnitude, and DELTAROE performed well recently, while expected BP, 1-month reversal, and expected EPTTM performed poorly[1][23] - In the public fund heavy-holding index space, factors such as illiquidity shock, 3-month turnover, and 1-month turnover performed well recently, while 1-year momentum, expected EPTTM, and 1-month reversal performed poorly[1][25] Public Fund Index Enhancement Product Performance - CSI 300 index enhancement products: highest weekly excess return 1.07%, lowest -0.38%, median 0.11%; highest monthly excess return 2.89%, lowest -0.64%, median 0.49%[2][31] - CSI 500 index enhancement products: highest weekly excess return 0.90%, lowest -0.43%, median 0.45%; highest monthly excess return 2.93%, lowest -0.45%, median 1.07%[2][34] - CSI 1000 index enhancement products: highest weekly excess return 1.00%, lowest -0.41%, median 0.26%; highest monthly excess return 3.22%, lowest -0.16%, median 1.35%[2][37] - CSI A500 index enhancement products: highest weekly excess return 0.41%, lowest -0.19%, median 0.09%; highest monthly excess return 1.08%, lowest -0.46%, median 0.33%[3][40]

Group 1 - The core viewpoint of the article is to track the performance of index enhancement portfolios and the effectiveness of various stock selection factors across different indices [1][2][3] Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of 0.37% for the week and 2.84% year-to-date [5] - The performance of the Zhongzheng 500 index enhancement portfolio showed an excess return of 1.06% for the week and 5.87% year-to-date [5] - The Zhongzheng 1000 index enhancement portfolio had an excess return of 1.73% for the week and 8.10% year-to-date [5] - The Zhongzheng A500 index enhancement portfolio reported an excess return of 0.53% for the week and 5.78% year-to-date [5] Group 3 - In the HuShen 300 component stocks, factors such as one-month reversal, expected PEG, and expected EPTTM performed well [6] - In the Zhongzheng 500 component stocks, one-month reversal, single-quarter SP, and SPTTM factors showed strong performance [6] - For Zhongzheng 1000 component stocks, factors like DELTAROA, executive compensation, and standardized expected external earnings performed well [6] - In the Zhongzheng A500 index component stocks, three-month reversal, single-quarter ROE, and one-month reversal factors were effective [6] - Among public fund heavy stocks, one-month reversal, three-month reversal, and single-quarter EP factors performed well [6] Group 4 - The HuShen 300 index enhancement products had a maximum excess return of 1.10%, a minimum of -0.76%, and a median of 0.06% for the week [19] - The Zhongzheng 500 index enhancement products had a maximum excess return of 0.99%, a minimum of -0.08%, and a median of 0.40% for the week [21] - The Zhongzheng 1000 index enhancement products had a maximum excess return of 0.81%, a minimum of -0.28%, and a median of 0.26% for the week [20] - The Zhongzheng A500 index enhancement products had a maximum excess return of 0.39%, a minimum of -0.52%, and a median of 0.23% for the week [22] Group 5 - The total number of public fund HuShen 300 index enhancement products is 67, with a total scale of 778 billion [16] - There are 70 Zhongzheng 500 index enhancement products with a total scale of 454 billion [16] - The Zhongzheng 1000 index enhancement products consist of 46 products with a total scale of 150 billion [16] - The Zhongzheng A500 index enhancement products have 35 products with a total scale of 223 billion [16]