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][3][17]. Group 2 - The performance of the CSI 300 index enhancement portfolio showed an excess return of -0.65% for the week and 16.53% year-to-date [5][21]. - The CSI 500 index enhancement portfolio had an excess return of -0.37% for the week and 8.50% year-to-date [5][23]. - The CSI 1000 index enhancement portfolio recorded an excess return of -0.53% for the week and 16.52% year-to-date [5][26]. - The CSI A500 index enhancement portfolio achieved an excess return of 0.02% for the week and 9.22% year-to-date [5][27]. Group 3 - In the CSI 300 component stocks, factors such as one-year momentum, quarterly revenue growth year-on-year, and three-month institutional coverage performed well [6][8]. - In the CSI 500 component stocks, factors like executive compensation, standardized expected non-operating income, and quarterly revenue growth year-on-year showed strong performance [6][10]. - For the CSI 1000 component stocks, factors such as expected PEG, standardized expected non-operating income, and three-month institutional coverage performed well [6][12]. - In the CSI A500 index component stocks, factors like executive compensation, three-month institutional coverage, and quarterly revenue growth year-on-year were notable [6][14]. Group 4 - The public fund index enhancement products for the CSI 300 had a maximum excess return of 1.16% and a minimum of -1.26% for the week, with a median of -0.17% [21][19]. - The CSI 500 public fund index enhancement products had a maximum excess return of 1.09% and a minimum of -1.70% for the week, with a median of -0.25% [23][20]. - The CSI 1000 public fund index enhancement products recorded a maximum excess return of 0.96% and a minimum of -1.05% for the week, with a median of -0.08% [26][24]. - The CSI A500 public fund index enhancement products achieved a maximum excess return of 0.77% and a minimum of -0.96% for the week, with a median of -0.07% [27][25].

Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure Portfolio (MFE) - **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of individual factors under realistic constraints, such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures that the factors deemed "effective" can genuinely contribute to the portfolio's predictive power in real-world scenarios [39][40]. - **Model Construction Process**: - The optimization model maximizes single-factor exposure while adhering to constraints such as style and industry neutrality, stock weight limits, and turnover control. - The objective function 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} $ - **Explanation**: - \( f \): Factor values - \( w \): Stock weight vector - \( X \): Style factor exposure matrix - \( H \): Industry exposure matrix - \( w_b \): Benchmark stock weights - \( s_l, s_h \): Lower and upper bounds for style exposure - \( h_l, h_h \): Lower and upper bounds for industry exposure - \( w_l, w_h \): Lower and upper bounds for stock weight deviation - \( b_l, b_h \): Lower and upper bounds for benchmark stock weight proportions [39][40] - The process involves: 1. Setting constraints for style, industry, and stock weight deviations 2. Constructing the MFE portfolio at the end of each month 3. Backtesting the portfolio with historical data, accounting for transaction costs [41][43] - **Model Evaluation**: The MFE model is effective in testing factor performance under realistic constraints, ensuring that selected factors contribute to portfolio returns in practical scenarios [39][40] --- Factor Construction and Methods 1. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: SUE measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings. It captures the market's reaction to earnings surprises [17]. - **Factor Construction Process**: - Formula: $ SUE = \frac{(Actual\ Net\ Profit - Expected\ Net\ Profit)}{Standard\ Deviation\ of\ Expected\ Net\ Profit} $ - Parameters: - Actual Net Profit: Reported earnings for the quarter - Expected Net Profit: Consensus analyst estimates for the quarter - Standard Deviation of Expected Net Profit: Variability in analyst estimates [17] 2. Factor Name: Momentum (1-Year Momentum) - **Factor Construction Idea**: Momentum captures the tendency of stocks with strong past performance to continue outperforming in the near term [17]. - **Factor Construction Process**: - Formula: $ Momentum = \text{Cumulative Return over the Past Year (Excluding the Most Recent Month)} $ - Parameters: - Cumulative Return: Total return over the specified period, excluding the most recent month to avoid short-term reversal effects [17] 3. Factor Name: Single-Quarter Revenue Growth (YoY) - **Factor Construction Idea**: This factor measures the year-over-year growth in quarterly revenue, reflecting a company's growth potential [17]. - **Factor Construction Process**: - Formula: $ Revenue\ Growth = \frac{(Current\ Quarter\ Revenue - Revenue\ from\ Same\ Quarter\ Last\ Year)}{Revenue\ from\ Same\ Quarter\ Last\ Year} $ - Parameters: - Current Quarter Revenue: Revenue reported for the current quarter - Revenue from Same Quarter Last Year: Revenue reported for the same quarter in the previous year [17] --- Factor Backtesting Results 1. Factor: 1-Year Momentum - **Performance**: - **CSI 300 Universe**: Weekly excess return of 0.67%, monthly excess return of 3.06%, annualized historical return of 2.70% [19] - **CSI 500 Universe**: Weekly excess return of 0.92%, monthly excess return of 0.21%, annualized historical return of 3.07% [21] - **CSI 1000 Universe**: Weekly excess return of -0.27%, monthly excess return of -2.23%, annualized historical return of -0.46% [23] 2. Factor: Single-Quarter Revenue Growth (YoY) - **Performance**: - **CSI 300 Universe**: Weekly excess return of 0.66%, monthly excess return of 4.36%, annualized historical return of 4.93% [19] - **CSI 500 Universe**: Weekly excess return of 1.05%, monthly excess return of 2.95%, annualized historical return of 3.70% [21] - **CSI 1000 Universe**: Weekly excess return of -0.16%, monthly excess return of 4.94%, annualized historical return of 5.11% [23] 3. Factor: Standardized Unexpected Earnings (SUE) - **Performance**: - **CSI 300 Universe**: Weekly excess return of 0.02%, monthly excess return of 1.49%, annualized historical return of 3.98% [19] - **CSI 500 Universe**: Weekly excess return of 0.35%, monthly excess return of 0.22%, annualized historical return of 9.14% [21] - **CSI 1000 Universe**: Weekly excess return of -1.37%, monthly excess return of 0.77%, annualized historical return of 10.44% [23] --- Model Backtesting Results 1. CSI 300 Enhanced Portfolio - Weekly excess return: -0.65% - Year-to-date excess return: 16.53% [5][14] 2. CSI 500 Enhanced Portfolio - Weekly excess return: -0.37% - Year-to-date excess return: 8.50% [5][14] 3. CSI 1000 Enhanced Portfolio - Weekly excess return: -0.53% - Year-to-date excess return: 16.52% [5][14] 4. CSI A500 Enhanced Portfolio - Weekly excess return: 0.02% - Year-to-date excess return: 9.22% [5][14]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.61% this week and 17.47% year-to-date [1][6] - The CSI 500 index enhanced portfolio recorded an excess return of -0.85% this week and 8.97% year-to-date [1][6] - The CSI 1000 index enhanced portfolio had an excess return of -0.05% this week and 17.24% year-to-date [1][6] - The CSI A500 index enhanced portfolio reported an excess return of -0.55% this week and 9.19% year-to-date [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month institutional coverage, one-month volatility, and one-year momentum performed well [1][7] - In the CSI 500 component stocks, one-year momentum, expected net profit month-on-month, and single-quarter revenue year-on-year growth showed strong performance [1][7] - In the CSI 1000 component stocks, single-quarter EP, three-month earnings adjustments, and single-quarter ROE were notable factors [1][7] - In the CSI A500 index component stocks, one-year momentum, standardized expected external income, and single-quarter revenue year-on-year growth performed well [1][7] - Among public fund heavy stocks, one-year momentum, single-quarter revenue year-on-year growth, and expected net profit month-on-month were strong factors [1][7] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 2.13%, a minimum of -1.11%, and a median of 0.05% this week [1][20] - The CSI 500 index enhanced products had a maximum excess return of 0.44%, a minimum of -1.86%, and a median of -0.42% this week [1][22] - The CSI 1000 index enhanced products had a maximum excess return of 0.63%, a minimum of -1.37%, and a median of 0.03% this week [1][26] - The CSI A500 index enhanced products had a maximum excess return of 0.84%, a minimum of -0.79%, and a median of -0.02% this week [1][27]

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]

Core Viewpoint - The article emphasizes the importance of Return on Equity (ROE) as a key indicator of a company's profitability and the need to identify stocks with stable future ROE to enhance investment returns [1][90]. Group 1: ROE Downward Risk - ROE is a critical measure of a company's ability to generate profit from its equity, with higher ROE indicating stronger profitability and potential returns for investors [1][90]. - Historical data shows that selecting high ROE stocks based solely on past performance does not guarantee future returns, as evidenced by backtesting from April 2010 to April 2024 [1][3]. - The analysis indicates that stocks with high ROE in previous years often experience declines in future ROE, which negatively impacts overall portfolio returns [6][9]. Group 2: Financial Stability Assessment - To identify companies with stable future ROE, the article outlines four financial dimensions: profitability stability, growth stability, leverage stability, and cash flow stability [10][91]. - Specific indicators are used to measure these dimensions, such as the standard deviation of sales net profit margin and ROE over the past nine quarters [11][12]. - The stability factors derived from these dimensions show varying degrees of effectiveness in stock selection across different indices, with notable results in the CSI All Share Index [16][22][31]. Group 3: Stability Factor Application - The article discusses the application of stability factors to filter high ROE stocks, aiming to identify those likely to maintain their ROE above 10% in the future [58][92]. - A significant proportion of stocks (73.44%) in the high ROE category are expected to maintain their ROE, with this percentage increasing to 84.33% for the most stable stocks [62][92]. - The performance of portfolios constructed from stocks with high stability factors shows improved returns compared to general high ROE stock portfolios, with annualized returns reaching 15.80% for the most stable stocks [93][83]. Group 4: Multi-Factor Selection in High ROE Stocks - The article suggests further enhancing returns by applying multi-factor selection within the high ROE and high stability stock pool, focusing on factors such as growth, profitability, and volatility [79][78]. - The multi-factor optimized portfolio demonstrates superior performance, achieving an annualized return of 22.36% compared to the benchmark index [83][94]. - The analysis indicates that the optimized portfolio not only outperforms the high ROE stock pool but also maintains a favorable risk-return profile, as reflected in its Sharpe ratio [83][94].

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 - The core viewpoint of the article is to track and analyze the performance of index-enhanced portfolios and common stock selection factors across various indices, including CSI 300, CSI 500, CSI 1000, and CSI A500 [2][3][19] - The CSI 300 index-enhanced portfolio recorded an excess return of -0.87% for the week and 11.58% year-to-date [7][19] - The CSI 500 index-enhanced portfolio had an excess return of -0.22% for the week and 11.11% year-to-date [7][19] - The CSI 1000 index-enhanced portfolio achieved an excess return of 0.02% for the week and 14.85% year-to-date [7][19] - The CSI A500 index-enhanced portfolio experienced an excess return of -1.49% for the week and 10.27% year-to-date [7][19] Group 2 - In the CSI 300 component stocks, factors such as standardized unexpected revenue, one-year momentum, and quarterly revenue year-on-year growth performed well [8][10] - In the CSI 500 component stocks, factors like EPTTM one-year percentile, executive compensation, and DELTAROA showed strong performance [8][10] - For the CSI 1000 component stocks, factors such as standardized unexpected revenue, three-month reversal, and quarterly revenue year-on-year growth were notable [8][10] - In the CSI A500 index component stocks, factors like quarterly revenue year-on-year growth, three-month reversal, and one-year momentum performed well [8][10] Group 3 - The performance of public fund index-enhanced products was tracked, with the CSI 300 index-enhanced product showing a maximum excess return of 0.69% and a minimum of -1.53% for the week [23][25] - The CSI 500 index-enhanced product had a maximum excess return of 0.78% and a minimum of -1.40% for the week [25] - The CSI 1000 index-enhanced product recorded a maximum excess return of 1.65% and a minimum of -0.96% for the week [28] - The CSI A500 index-enhanced product achieved a maximum excess return of 0.65% and a minimum of -1.59% for the week [29]

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The model aims to maximize single-factor exposure while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach tests the effectiveness of factors under real-world constraints, ensuring their predictive power in portfolio construction [39][40][41] **Model Construction Process**: The optimization model is formulated as follows: $ \begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array} $ - **Objective Function**: Maximize single-factor exposure, where $f$ represents factor values, and $f^{T}w$ is the weighted exposure of the portfolio to the factor. $w$ is the stock weight vector to be optimized. - **Constraints**: - **Style Exposure**: $X$ is the matrix of stock exposures to style factors, $w_b$ is the benchmark weight vector, and $s_l$, $s_h$ are the lower and upper bounds for style factor exposure deviation. - **Industry Exposure**: $H$ is the industry exposure matrix, $h_l$, $h_h$ are the lower and upper bounds for industry deviation. - **Stock Weight Deviation**: $w_l$, $w_h$ are the lower and upper bounds for individual stock weight deviation. - **Component Weight Control**: $B_b$ is a binary vector indicating whether a stock belongs to the benchmark index, $b_l$, $b_h$ are the lower and upper bounds for component stock weight. - **No Short Selling**: Ensures non-negative weights and limits individual stock weights. - **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [39][40][41] **Model Evaluation**: The model effectively tests factor validity under real-world constraints, ensuring factors contribute to portfolio returns in practical scenarios [39][40][41] Factor Construction and Methods - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual quarterly net profit from expected net profit, standardized by the standard deviation of expected net profit [17] **Factor Construction Process**: $ SUE = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}} $ **Factor Evaluation**: Useful for identifying stocks with earnings surprises, which may lead to price adjustments [17] - **Factor Name**: One-Month Reversal **Factor Construction Idea**: Captures short-term price reversal by measuring the return over the past 20 trading days [17] **Factor Construction Process**: $ \text{One-Month Reversal} = \text{Return over the past 20 trading days} $ **Factor Evaluation**: Effective in detecting short-term mean-reverting behavior in stock prices [17] - **Factor Name**: Delta ROA **Factor Construction Idea**: Measures the change in return on assets (ROA) compared to the same quarter of the previous year [17] **Factor Construction Process**: $ \Delta ROA = \text{Current Quarter ROA} - \text{ROA of the Same Quarter Last Year} $ **Factor Evaluation**: Indicates improvement or deterioration in asset efficiency, which can signal fundamental changes [17] Factor Backtesting Results **Performance in CSI 300 Sample Space** - **Standardized Unexpected Earnings**: Weekly excess return 1.35%, monthly excess return 3.78%, annual excess return 8.35% [19] - **One-Year Momentum**: Weekly excess return 1.27%, monthly excess return 1.98%, annual excess return -1.17% [19] - **Single-Quarter Revenue Growth**: Weekly excess return 1.08%, monthly excess return 3.86%, annual excess return 11.82% [19] **Performance in CSI 500 Sample Space** - **EPTTM Year Percentile**: Weekly excess return 1.69%, monthly excess return 1.74%, annual excess return 3.77% [21] - **Delta ROA**: Weekly excess return 1.00%, monthly excess return 2.43%, annual excess return 9.72% [21] - **Standardized Unexpected Earnings**: Weekly excess return 0.87%, monthly excess return 3.32%, annual excess return 7.87% [21] **Performance in CSI 1000 Sample Space** - **Standardized Unexpected Earnings**: Weekly excess return 0.75%, monthly excess return 3.69%, annual excess return 7.64% [23] - **Three-Month Reversal**: Weekly excess return 1.34%, monthly excess return 0.24%, annual excess return 5.36% [23] - **Single-Quarter Revenue Growth**: Weekly excess return 1.43%, monthly excess return 4.58%, annual excess return 11.12% [23] **Performance in CSI A500 Sample Space** - **Single-Quarter Revenue Growth**: Weekly excess return 1.43%, monthly excess return 4.58%, annual excess return 11.12% [25] - **Delta ROA**: Weekly excess return 0.63%, monthly excess return 4.33%, annual excess return 10.97% [25] - **Three-Month Reversal**: Weekly excess return 1.34%, monthly excess return 0.24%, annual excess return 5.36% [25] **Performance in Public Fund Heavy Index Sample Space** - **One-Year Momentum**: Weekly excess return 1.11%, monthly excess return 3.36%, annual excess return 1.15% [27] - **Delta ROA**: Weekly excess return 0.63%, monthly excess return 4.33%, annual excess return 10.97% [27] - **Standardized Unexpected Earnings**: Weekly excess return 0.75%, monthly excess return 3.69%, annual excess return 7.64% [27]

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]