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 rate. This approach ensures that the factor's predictive power is tested under realistic constraints, making it more applicable in actual portfolio construction [40][41]. **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 factor 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 Stock 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 stock weights. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights. 6. **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [40][41][42]. **Model Evaluation**: The MFE portfolio effectively tests factor validity under realistic constraints, making it a robust tool for practical portfolio construction [40][41]. --- Quantitative Factors and Construction Methods - **Factor Name**: BP (Book-to-Price Ratio) **Factor Construction Idea**: Measures valuation by comparing book value to market value [18]. **Factor Construction Process**: $ BP = \frac{\text{Net Asset}}{\text{Market Value}} $ **Factor Evaluation**: BP is a widely used valuation factor and has shown consistent performance in various market conditions [18]. - **Factor Name**: SPTTM (Sales-to-Price Ratio, Trailing Twelve Months) **Factor Construction Idea**: Evaluates valuation by comparing sales to market value over the trailing twelve months [18]. **Factor Construction Process**: $ SPTTM = \frac{\text{TTM Sales}}{\text{Market Value}} $ **Factor Evaluation**: SPTTM is effective in identifying undervalued stocks with strong sales performance [18]. - **Factor Name**: DELTAROE (Change in Return on Equity) **Factor Construction Idea**: Captures growth by measuring the change in ROE compared to the same period last year [18]. **Factor Construction Process**: $ DELTAROE = \text{ROE}_{\text{current quarter}} - \text{ROE}_{\text{same quarter last year}} $ **Factor Evaluation**: DELTAROE is a strong indicator of improving profitability and growth potential [18]. - **Factor Name**: One-Month Reversal **Factor Construction Idea**: Measures short-term price reversal by calculating the return over the past 20 trading days [18]. **Factor Construction Process**: $ \text{One-Month Reversal} = \text{Return over the past 20 trading days} $ **Factor Evaluation**: This factor is effective in capturing mean-reversion effects in the short term [18]. --- Backtesting Results of Models - **MFE Portfolio (HS300)**: - Weekly excess return: -0.04% - Year-to-date excess return: 3.07% [6][15] - **MFE Portfolio (CSI500)**: - Weekly excess return: -1.72% - Year-to-date excess return: -2.50% [6][15] - **MFE Portfolio (CSI1000)**: - Weekly excess return: -1.58% - Year-to-date excess return: 1.63% [6][15] - **MFE Portfolio (CSI A500)**: - Weekly excess return: 0.26% - Year-to-date excess return: 3.78% [6][15] --- Backtesting Results of Factors - **BP Factor (CSI500)**: - Weekly excess return: 1.39% - Monthly excess return: 3.39% - Year-to-date excess return: 1.81% [21][22] - **SPTTM Factor (CSI1000)**: - Weekly excess return: 0.85% - Monthly excess return: 0.15% - Year-to-date excess return: -1.81% [23][24] - **DELTAROE Factor (CSI A500)**: - Weekly excess return: 0.10% - Monthly excess return: 0.72% - Year-to-date excess return: 1.20% [25][26] - **One-Month Reversal Factor (HS300)**: - Weekly excess return: -0.72% - Monthly excess return: -1.41% - Year-to-date excess return: -2.52% [19][20]

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].

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

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.62% this week and 20.75% year-to-date [1][9] - The CSI 500 index enhanced portfolio recorded an excess return of -0.37% this week and 6.86% year-to-date [1][9] - The CSI 1000 index enhanced portfolio had an excess return of 0.97% this week and 16.87% year-to-date [1][9] - The CSI A500 index enhanced portfolio reported an excess return of 0.85% this week and 10.70% year-to-date [1][9] Group 2: Factor Performance Tracking - In the CSI 300 component stocks, factors such as DELTAROE, dividend yield, and DELTAROA performed well [1][10] - In the CSI 500 component stocks, factors like expected BP, BP, and three-month institutional coverage showed strong performance [1][12] - For the CSI 1000 component stocks, factors such as expected PEG, single-quarter SP, and SPTTM performed well [1][15] - In the CSI A500 index component stocks, factors like three-month turnover, dividend yield, and DELTAROE showed good performance [1][19] - In public fund heavy stocks, factors like BP, SPTTM, and expected BP performed well [1][23] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.38%, a minimum of -0.44%, and a median of 0.41% this week [1][28] - The CSI 500 index enhanced products achieved a maximum excess return of 1.55%, a minimum of -0.51%, and a median of 0.46% this week [1][30] - The CSI 1000 index enhanced products recorded a maximum excess return of 1.57%, a minimum of -0.32%, and a median of 0.57% this week [1][31] - The CSI A500 index enhanced products had a maximum excess return of 1.29%, a minimum of -0.35%, and a median of 0.43% this week [2][36]

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 real-world constraints, such as controlling for industry exposure, style exposure, stock weight limits, and turnover rates. The goal is to maximize the exposure of a single factor while adhering to these constraints[39][40] - **Model Construction Process**: - The objective function is to maximize single-factor exposure, where $f$ represents the factor values, $f^T w$ is the weighted exposure of the portfolio to the single factor, and $w$ is the vector of stock weights - The optimization model is as follows: $$ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $$ - The first constraint limits the portfolio's style exposure relative to the benchmark index, where $X$ is the factor exposure matrix, $w_b$ is the weight vector of the benchmark index constituents, and $s_l$ and $s_h$ are the lower and upper bounds of style factor exposure, respectively - The second constraint limits the portfolio's industry deviation, where $H$ is the industry exposure matrix, and $h_l$ and $h_h$ are the lower and upper bounds of industry deviation, respectively - The third constraint limits individual stock deviations relative to the benchmark index constituents, where $w_l$ and $w_h$ are the lower and upper bounds of individual stock deviations - The fourth constraint limits the weight proportion of the portfolio within the benchmark index constituents, where $B_b$ is a 0-1 vector indicating whether a stock belongs to the benchmark index, and $b_l$ and $b_h$ are the lower and upper bounds of the weight proportion - The fifth constraint prohibits short selling and limits the upper bound of individual stock weights - The sixth constraint ensures that the portfolio is fully invested, with the sum of weights equal to 1[39][40][41] - The MFE portfolio is constructed for a given benchmark index by applying the above optimization model. To avoid excessive concentration, the deviation of individual stock weights relative to the benchmark is typically set between 0.5% and 1%[41][43] - **Model Evaluation**: The MFE portfolio is used to evaluate the effectiveness of individual factors under realistic constraints, ensuring that the selected factors can contribute to the actual return prediction in the final portfolio[39][40] --- Model Backtesting Results 1. National Trust Quantitative Engineering Index Enhanced Portfolio - **CSI 300 Index Enhanced Portfolio**: - Weekly excess return: 0.63% - Year-to-date excess return: 17.65%[13] - **CSI 500 Index Enhanced Portfolio**: - Weekly excess return: 0.30% - Year-to-date excess return: 8.35%[13] - **CSI 1000 Index Enhanced Portfolio**: - Weekly excess return: 0.77% - Year-to-date excess return: 18.22%[13] - **CSI A500 Index Enhanced Portfolio**: - Weekly excess return: 1.57% - Year-to-date excess return: 11.17%[13] --- Quantitative Factors and Construction Methods 1. Factor Name: BP - **Factor Construction Idea**: Measures valuation by comparing book value to market value[16] - **Factor Construction Process**: - Formula: $BP = \frac{\text{Net Asset}}{\text{Total Market Value}}$[16] 2. Factor Name: Single Quarter EP - **Factor Construction Idea**: Measures profitability by comparing quarterly net profit to market value[16] - **Factor Construction Process**: - Formula: $Single\ Quarter\ EP = \frac{\text{Quarterly Net Profit}}{\text{Total Market Value}}$[16] 3. Factor Name: Single Quarter SP - **Factor Construction Idea**: Measures valuation by comparing quarterly revenue to market value[16] - **Factor Construction Process**: - Formula: $Single\ Quarter\ SP = \frac{\text{Quarterly Revenue}}{\text{Total Market Value}}$[16] 4. Factor Name: EPTTM - **Factor Construction Idea**: Measures profitability by comparing trailing twelve months (TTM) net profit to market value[16] - **Factor Construction Process**: - Formula: $EPTTM = \frac{\text{TTM Net Profit}}{\text{Total Market Value}}$[16] 5. Factor Name: SPTTM - **Factor Construction Idea**: Measures valuation by comparing TTM revenue to market value[16] - **Factor Construction Process**: - Formula: $SPTTM = \frac{\text{TTM Revenue}}{\text{Total Market Value}}$[16] 6. Factor Name: One-Month Volatility - **Factor Construction Idea**: Measures risk by calculating the average intraday true range over the past 20 trading days[16] - **Factor Construction Process**: - Formula: $One\ Month\ Volatility = \text{Average of Intraday True Range over 20 trading days}$[16] 7. Factor Name: Three-Month Volatility - **Factor Construction Idea**: Measures risk by calculating the average intraday true range over the past 60 trading days[16] - **Factor Construction Process**: - Formula: $Three\ Month\ Volatility = \text{Average of Intraday True Range over 60 trading days}$[16] 8. Factor Name: One-Year Momentum - **Factor Construction Idea**: Measures momentum by calculating the return over the past year, excluding the most recent month[16] - **Factor Construction Process**: - Formula: $One\ Year\ Momentum = \text{Return over the past year excluding the most recent month}$[16] 9. Factor Name: Expected EPTTM - **Factor Construction Idea**: Measures profitability based on rolling expected earnings per share (EPS)[16] - **Factor Construction Process**: - Formula: $Expected\ EPTTM = \text{Rolling Expected EPS}$[16] 10. Factor Name: Expected BP - **Factor Construction Idea**: Measures valuation based on rolling expected book-to-price ratio[16] - **Factor Construction Process**: - Formula: $Expected\ BP = \text{Rolling Expected Book-to-Price Ratio}$[16] 11. Factor Name: Expected PEG - **Factor Construction Idea**: Measures valuation by comparing expected price-to-earnings ratio to growth rate[16] - **Factor Construction Process**: - Formula: $Expected\ PEG = \text{Expected PE Ratio / Growth Rate}$[16] 12. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Measures earnings surprise by comparing actual quarterly net profit to expected net profit, normalized by the standard deviation of expected net profit[16] - **Factor Construction Process**: - Formula: $SUE = \frac{\text{Actual Quarterly Net Profit - Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}}$[16] --- Factor Backtesting Results 1. CSI 300 Index - **Best-performing factors (recent week)**: Expected EPTTM (1.19%), One-Month Volatility (1.17%), BP (1.15%)[18] - **Worst-performing factors (recent week)**: Single Quarter Revenue YoY Growth (-0.61%), Three-Month Institutional Coverage (-0.38%), Three-Month Earnings Revisions (-0.26%)[18] 2. CSI 500 Index - **Best-performing factors (recent week)**: SPTTM (1.69%), Expected BP (1.58%), Single Quarter EP (1.56%)[20] - **Worst-performing factors (recent week)**: One-Year Momentum (-1.01%), Expected PEG (-0.38%), Standardized Unexpected Revenue (-0.29%)[20] 3. CSI 1000 Index - **Best-performing factors (recent week)**: EPTTM (2.36%), SPTTM (2.14%), Expected EPTTM (2.10%)[22] - **Worst-performing factors (recent week)**: Expected Net Profit QoQ (-0.65%), One-Year Momentum (-0.48%), Single Quarter Revenue YoY Growth (-0.39%)[22] 4. CSI A500 Index - **Best-performing factors (recent week)**: Single Quarter SP (1.99%), SPTTM (1.89%), One-Month Volatility (1.69%)[24] - **Worst-performing factors (recent week)**: Single Quarter Revenue YoY Growth (-1.07%), One-Year Momentum (-0.86%), Three-Month Institutional Coverage (-0

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 - 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, highlighting the excess returns achieved by these portfolios [1][2][3]. Group 2 - The performance of the HuShen 300 index enhancement portfolio this week showed an excess return of 0.78%, with a year-to-date excess return of 9.31% [5]. - The performance of the Zhongzheng 500 index enhancement portfolio this week showed an excess return of -0.52%, with a year-to-date excess return of 9.90% [5]. - The Zhongzheng 1000 index enhancement portfolio had an excess return of 0.07% this week, with a year-to-date excess return of 15.69% [5]. - The Zhongzheng A500 index enhancement portfolio reported an excess return of 0.26% this week, with a year-to-date excess return of 9.96% [5]. Group 3 - In the HuShen 300 component stocks, factors such as specificity, EPTTM one-year quantile, and quarterly net profit year-on-year growth performed well [8]. - In the Zhongzheng 500 component stocks, factors like three-month volatility, EPTTM one-year quantile, and expected BP showed good performance [8]. - For Zhongzheng 1000 component stocks, factors such as three-month institutional coverage, three-month reversal, and expected BP performed well [8]. - In the Zhongzheng A500 index component stocks, factors like specificity, three-month reversal, and expected net profit month-on-month growth performed well [8]. Group 4 - The HuShen 300 index enhancement products had a maximum excess return of 1.28%, a minimum of -0.98%, and a median of 0.12% this week [21]. - The Zhongzheng 500 index enhancement products had a maximum excess return of 1.41%, a minimum of -1.31%, and a median of 0.04% this week [21]. - The Zhongzheng 1000 index enhancement products had a maximum excess return of 0.82%, a minimum of -0.47%, and a median of 0.15% this week [21]. - The Zhongzheng A500 index enhancement products had a maximum excess return of 1.16%, a minimum of -0.57%, and a median of -0.04% this week [21].