Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.42% this week and 8.31% year-to-date [1][4] - The CSI 500 index enhanced portfolio recorded an excess return of 0.63% this week and 10.17% year-to-date [1][4] - The CSI 1000 index enhanced portfolio had an excess return of 0.48% this week and 15.24% year-to-date [1][4] - The CSI A500 index enhanced portfolio saw an excess return of 0.28% this week and 9.48% year-to-date [1][4] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as quarterly revenue growth rate, DELTAROA, and quarterly ROE performed well [1][7] - In the CSI 500 component stocks, factors like one-year momentum, standardized unexpected revenue, and standardized unexpected earnings showed strong performance [1][7] - In the CSI 1000 component stocks, factors such as three-month reversal, standardized unexpected revenue, and quarterly earnings surprise performed well [1][7] - In the CSI A500 index component stocks, factors like DELTAROA, standardized unexpected earnings, and quarterly ROA performed well [1][7] - Among publicly offered fund heavy stocks, factors like one-year momentum, standardized unexpected revenue, and expected net profit quarter-on-quarter performed well [1][7] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced product had a maximum excess return of 2.14%, a minimum of -0.62%, and a median of -0.06% this week [1][20] - The CSI 500 index enhanced product recorded a maximum excess return of 0.73%, a minimum of -1.10%, and a median of -0.09% this week [1][22] - The CSI 1000 index enhanced product achieved a maximum excess return of 0.91%, a minimum of -0.81%, and a median of 0.13% this week [1][21] - The CSI A500 index enhanced product had a maximum excess return of 1.06%, a minimum of -0.90%, and a median of -0.02% this week [1][23]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure (MFE) Portfolio **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 limits, and turnover constraints. This approach ensures that the factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[41][42]. **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 \( f^{T} w \) represents the weighted exposure of the portfolio to the factor \( f \), and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) represents the factor exposure matrix for stocks, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style factor exposure[42]. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviations[42]. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviations relative to the benchmark[42]. 4. **Constituent Weight**: \( B_b \) is a binary vector indicating whether a stock is part of the benchmark, and \( b_l, b_h \) are the lower and upper bounds for constituent weights[42]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \)[42]. 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T} w = 1 \)[43]. - **Implementation**: 1. Define constraints for style, industry, and stock weights. For example, for CSI 500 and CSI 300 indices, industry exposure is neutralized, and stock weight deviations are capped at 1%[45]. 2. Construct the MFE portfolio at the end of each month based on the constraints[45]. 3. Backtest the portfolio, accounting for transaction costs (0.3% per side), and calculate performance metrics relative to the benchmark[45]. **Model Evaluation**: The MFE portfolio effectively tests factor performance under realistic constraints, making it a robust tool for evaluating factor predictability in practical scenarios[41][42]. Quantitative Factors and Construction Methods - **Factor Name**: DELTAROA **Factor Construction Idea**: Measures the change in return on assets (ROA) compared to the same quarter in the previous year, capturing improvements in asset utilization efficiency[16]. **Factor Construction Process**: $ DELTAROA = ROA_{current\ quarter} - ROA_{same\ quarter\ last\ year} $ Where \( ROA = \frac{Net\ Income}{Total\ Assets} \)[16]. **Factor Evaluation**: DELTAROA is a growth-oriented factor that has shown strong performance in multiple sample spaces, particularly in the CSI A500 index[19][25]. - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings, to capture earnings surprises[16]. **Factor Construction Process**: $ SUE = \frac{Actual\ Earnings - Expected\ Earnings}{Standard\ Deviation\ of\ Expected\ Earnings} $[16]. **Factor Evaluation**: SUE is a profitability factor that performs well in growth-oriented indices like CSI 1000 and CSI A500[19][23][25]. - **Factor Name**: One-Year Momentum **Factor Construction Idea**: Captures the trend-following behavior of stocks by measuring price momentum over the past year, excluding the most recent month[16]. **Factor Construction Process**: $ Momentum = \frac{Price_{t-12} - Price_{t-1}}{Price_{t-1}} $ Where \( t-12 \) and \( t-1 \) represent the stock price 12 months and 1 month ago, respectively[16]. **Factor Evaluation**: Momentum is a widely used factor that has shown consistent performance in large-cap indices like CSI 300 and CSI 500[19][21]. Factor Backtesting Results - **CSI 300 Sample Space**: - **Best-Performing Factors (1 Week)**: Single-quarter revenue growth, DELTAROA, single-quarter ROE[19]. - **Worst-Performing Factors (1 Week)**: Three-month volatility, one-month volatility, three-month turnover[19]. - **CSI 500 Sample Space**: - **Best-Performing Factors (1 Week)**: One-year momentum, standardized unexpected revenue, standardized unexpected earnings[21]. - **Worst-Performing Factors (1 Week)**: SPTTM, single-quarter SP, dividend yield[21]. - **CSI 1000 Sample Space**: - **Best-Performing Factors (1 Week)**: Three-month reversal, standardized unexpected revenue, single-quarter surprise magnitude[23]. - **Worst-Performing Factors (1 Week)**: Dividend yield, one-month volatility, BP[23]. - **CSI A500 Sample Space**: - **Best-Performing Factors (1 Week)**: DELTAROA, standardized unexpected earnings, single-quarter ROA[25]. - **Worst-Performing Factors (1 Week)**: Three-month volatility, one-month turnover, one-month volatility[25]. - **Public Fund Heavyweight Index Sample Space**: - **Best-Performing Factors (1 Week)**: One-year momentum, standardized unexpected revenue, expected net profit QoQ[27]. - **Worst-Performing Factors (1 Week)**: Dividend yield, one-month volatility, three-month volatility[27].

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of -0.30% this week, with a year-to-date excess return of 7.76% [5] - The CSI 500 index enhanced portfolio recorded an excess return of 0.31% this week, with a year-to-date excess return of 9.34% [5] - The CSI 1000 index enhanced portfolio had an excess return of 0.39% this week, with a year-to-date excess return of 14.45% [5] - The CSI A500 index enhanced portfolio posted an excess return of 0.71% this week, with a year-to-date excess return of 9.03% [5] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 constituent stocks, factors such as standardized unexpected income, specificity, and quarterly EP performed well [6] - In the CSI 500 constituent stocks, factors like standardized unexpected earnings, specificity, and SPTTM showed strong performance [6] - In the CSI 1000 constituent stocks, factors such as DELTAROE, quarterly profit growth year-on-year, and DELTAROA performed well [6] - In the CSI A500 index constituent stocks, factors like specificity, expected EPTTM, and quarterly profit growth year-on-year showed good performance [6] - In public fund heavy stocks, factors like specificity, DELTAROE, and DELTAROA performed well [6] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced product had a maximum excess return of 0.87%, a minimum of -0.57%, and a median of 0.24% this week [19] - The CSI 500 index enhanced product recorded a maximum excess return of 0.90%, a minimum of -0.68%, and a median of 0.24% this week [22] - The CSI 1000 index enhanced product achieved a maximum excess return of 1.06%, a minimum of -0.31%, and a median of 0.29% this week [24] - The CSI A500 index enhanced product had a maximum excess return of 0.80%, a minimum of -0.35%, and a median of 0.20% this week [25]

Quantitative Models and Construction Methods Model Name: MFE (Maximized Factor Exposure) Portfolio - **Model Construction Idea**: The MFE portfolio aims to maximize the exposure to a single factor while controlling for various constraints such as industry exposure, style exposure, and individual stock weight deviations[40][41]. - **Model Construction Process**: - The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T} w \\ \text{s.t.} & s_{l} \leq X(w - w_{b}) \leq s_{h} \\ & h_{l} \leq H(w - w_{b}) \leq h_{h} \\ & w_{l} \leq w - w_{b} \leq w_{h} \\ & b_{l} \leq B_{b} w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $$ - **Explanation of Parameters**: - \( f \): Factor values - \( w \): Stock weight vector - \( X \): Factor exposure matrix for style factors - \( w_{b} \): Benchmark index component weights - \( s_{l}, s_{h} \): Lower and upper bounds for style factor exposure - \( H \): Industry exposure matrix - \( h_{l}, h_{h} \): Lower and upper bounds for industry exposure - \( w_{l}, w_{h} \): Lower and upper bounds for individual stock weight deviations - \( B_{b} \): 0-1 vector indicating whether a stock is a benchmark component - \( b_{l}, b_{h} \): Lower and upper bounds for component stock weight - \( l \): Upper limit for individual stock weight - The model aims to maximize the factor exposure while satisfying constraints on style, industry, and individual stock weights[40][41][42]. Factor Construction and Performance Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings[17]. - **Factor Construction Process**: - Formula: $$ \text{SUE} = \frac{\text{Actual Earnings} - \text{Expected Earnings}}{\text{Standard Deviation of Expected Earnings}} $$ - **Explanation**: This factor captures the surprise in earnings relative to market expectations, which can indicate potential stock price movements[17]. Factor Name: DELTAROE - **Factor Construction Idea**: Measures the change in Return on Equity (ROE) from the same quarter of the previous year[17]. - **Factor Construction Process**: - Formula: $$ \text{DELTAROE} = \text{Current Quarter ROE} - \text{ROE of the Same Quarter Last Year} $$ - **Explanation**: This factor indicates the improvement or deterioration in a company's profitability over time[17]. Factor Performance Monitoring Performance in Different Index Spaces - **CSI 300 Index**: - Recent week: Factors like Standardized Unexpected Earnings, Specificity, and Single Quarter EP performed well, while factors like 3-Month Earnings Revisions, 1-Month Turnover, and 1-Year Momentum performed poorly[1][18]. - Recent month: Factors like Single Quarter EP, Expected EPTTM, and EPTTM performed well, while factors like 1-Year Momentum, Illiquidity Shock, and 1-Month Turnover performed poorly[18]. - Year-to-date: Factors like Single Quarter Earnings Growth, Single Quarter Revenue Growth, and DELTAROE performed well, while factors like 1-Year Momentum, 1-Month Turnover, and 3-Month Turnover performed poorly[18]. - **CSI 500 Index**: - Recent week: Factors like Standardized Unexpected Earnings, Specificity, and SPTTM performed well, while factors like Single Quarter ROA, Single Quarter ROE, and 3-Month Institutional Coverage performed poorly[1][20]. - Recent month: Factors like DELTAROE, Single Quarter Earnings Growth, and Single Quarter Net Profit Growth performed well, while factors like 3-Month Institutional Coverage, 1-Month Turnover, and Illiquidity Shock performed poorly[20]. - Year-to-date: Factors like Single Quarter Revenue Growth, 1-Month Reversal, and Expected PEG performed well, while factors like EPTTM, 3-Month Volatility, and 1-Month Turnover performed poorly[20]. - **CSI 1000 Index**: - Recent week: Factors like DELTAROE, Single Quarter Earnings Growth, and DELTAROA performed well, while factors like Expected EPTTM, 3-Month Institutional Coverage, and 3-Month Turnover performed poorly[1][22]. - Recent month: Factors like Standardized Unexpected Earnings, BP, and Single Quarter Net Profit Growth performed well, while factors like Illiquidity Shock, 3-Month Institutional Coverage, and 3-Month Turnover performed poorly[22]. - Year-to-date: Factors like Standardized Unexpected Earnings, Standardized Unexpected Revenue, and Illiquidity Shock performed well, while factors like 3-Month Volatility, 1-Month Volatility, and Single Quarter ROE performed poorly[22]. - **CSI A500 Index**: - Recent week: Factors like Specificity, Expected EPTTM, and Single Quarter Earnings Growth performed well, while factors like 3-Month Earnings Revisions, 1-Month Turnover, and 3-Month Turnover performed poorly[1][24]. - Recent month: Factors like Expected EPTTM, Single Quarter Earnings Growth, and Single Quarter EP performed well, while factors like 1-Month Turnover, 3-Month Turnover, and Illiquidity Shock performed poorly[24]. - Year-to-date: Factors like Expected PEG, Single Quarter Earnings Growth, and DELTAROE performed well, while factors like 1-Year Momentum, 1-Month Turnover, and SPTTM performed poorly[24]. - **Public Fund Heavyweight Index**: - Recent week: Factors like Specificity, DELTAROE, and DELTAROA performed well, while factors like 3-Month Earnings Revisions, 3-Month Turnover, and 1-Month Turnover performed poorly[1][26]. - Recent month: Factors like Expected EPTTM, Single Quarter Earnings Growth, and Single Quarter EP performed well, while factors like Illiquidity Shock, 1-Year Momentum, and 3-Month Institutional Coverage performed poorly[26]. - Year-to-date: Factors like DELTAROA, DELTAROE, and Standardized Unexpected Earnings performed well, while factors like 1-Month Volatility, BP, and Expected BP performed poorly[26]. Model Backtesting Results CSI 300 Index Enhanced Portfolio - Weekly excess return: -0.30% - Year-to-date excess return: 7.76%[5][14] CSI 500 Index Enhanced Portfolio - Weekly excess return: 0.31% - Year-to-date excess return: 9.34%[5][14] CSI 1000 Index Enhanced Portfolio - Weekly excess return: 0.39% - Year-to-date excess return: 14.45%[5][14] CSI A500 Index Enhanced Portfolio - Weekly excess return: 0.71% - Year-to-date excess return: 9.03%[5][14] Public Fund Index Enhanced Product Performance CSI 300 Index Enhanced Products - Weekly excess return: Highest 0.87%, Lowest -0.57%, Median 0.24% - Monthly excess return: Highest 2.06%, Lowest -0.45%, Median 0.63%[2][31] CSI 500 Index Enhanced Products - Weekly excess return: Highest 0.90%, Lowest -0.68%, Median 0.24% - Monthly excess return: Highest 2.46%, Lowest -0.12%, Median 1.02%[2][34] CSI 1000 Index Enhanced Products - Weekly excess return: Highest 1.06%, Lowest -0.31%, Median 0.29% - Monthly excess return: Highest 2.98%, Lowest -0.74%, Median 1.21%[2][37] CSI A500 Index Enhanced Products - Weekly excess return: Highest 0.80%, Lowest -0.35%, Median 0.20% - Monthly excess return: Highest 1.81%, Lowest -0.34%, Median 1.13%[3][39]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 1.17% this week and 8.03% year-to-date [1][2] - The CSI 500 index enhanced portfolio recorded an excess return of 0.73% this week and 8.82% year-to-date [1][2] - The CSI 1000 index enhanced portfolio had an excess return of 1.10% this week and 13.66% year-to-date [1][2] - The CSI A500 index enhanced portfolio saw an excess return of 0.69% this week and 8.18% year-to-date [1][2] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as single-season EP, EPTTM, and expected EPTTM performed well [1] - In the CSI 500 component stocks, factors like single-season ROE, DELTAROE, and single-season EP showed strong performance [1] - In the CSI 1000 component stocks, standardized expected external profit, EPTTM, and single-season EP were among the top performers [1] - In the CSI A500 index component stocks, expected EPTTM, EPTTM, and single-season ROE were notable factors [1] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.02%, a minimum of -0.37%, and a median of 0.08% this week [1] - The CSI 500 index enhanced products achieved a maximum excess return of 1.87%, a minimum of -0.44%, and a median of 0.38% this week [1] - The CSI 1000 index enhanced products recorded a maximum excess return of 1.06%, a minimum of -0.43%, and a median of 0.38% this week [1] - The CSI A500 index enhanced products had a maximum excess return of 0.73%, a minimum of -0.19%, and a median of 0.17% this week [1]

- The report tracks the performance of Guosen JinGong's index enhancement portfolios and public fund index enhancement products, alongside monitoring the performance of common stock selection factors across different stock selection spaces [12][13][16] - 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 [13][15] - The MFE (Maximized Factor Exposure) portfolio is used to test the effectiveness of individual factors under real-world constraints. The optimization model maximizes single-factor exposure while controlling for style, industry, stock weight deviations, and other constraints. The formula for the optimization model is: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style exposure (`X`), industry exposure (`H`), stock weight deviation (`w`), and component stock weight limits (`B_b`) [40][41][42] - The factor library includes over 30 factors categorized into valuation, reversal, growth, profitability, liquidity, corporate governance, and analyst dimensions. Examples include BP (Net Asset/Market Cap), single-quarter EP (Net Profit/Market Cap), and EPTTM (TTM Net Profit/Market Cap) [17][18] - Factor performance varies across different stock selection spaces. For CSI 300, factors like single-quarter EP, EPTTM, and expected EPTTM performed well recently, while factors like three-month volatility and expected net profit QoQ performed poorly [19][20] - For CSI 500, factors such as single-quarter ROE, DELTAROE, and single-quarter EP showed strong performance recently, whereas factors like one-year momentum and three-month reversal underperformed [21][22] - In the CSI 1000 space, factors like standardized unexpected earnings, EPTTM, and single-quarter EP performed well, while factors like non-liquidity impact and three-month institutional coverage lagged [23][24] - For CSI A500, factors such as expected EPTTM, single-quarter ROE, and expected PEG showed strong performance, while factors like one-year momentum and expected net profit QoQ underperformed [25][26] - In the public fund heavy index space, factors like expected PEG, expected EPTTM, and single-quarter EP performed well recently, while factors like one-month reversal and one-month volatility performed poorly [27][28] - Public fund index enhancement products are tracked for their excess returns relative to benchmarks. For CSI 300 products, the highest weekly excess return was 1.02%, and the lowest was -0.37%, with a median of 0.08% [29][33] - CSI 500 products showed a weekly excess return range of 1.87% to -0.44%, with a median of 0.38% [34][35] - CSI 1000 products had a weekly excess return range of 1.06% to -0.43%, with a median of 0.38% [36][37] - CSI A500 products showed a weekly excess return range of 0.73% to -0.19%, with a median of 0.17% [38][39]

- The quant report highlights that the factors "single-quarter revenue growth," "SUR," and "analyst net upgrade" performed well in the past month, while "EPTTM," "volatility," and "stripped limit-up momentum" performed poorly[3][28] - For the year to date, the factors "total market capitalization," "SUE," and "SUR" have shown strong performance, whereas "expected dividend yield," "volatility," and "EPTTM" have underperformed[3][28] - The "brokerage gold stock performance enhancement portfolio" achieved an absolute return of 5.34% for the month (20250603-20250630) and an excess return of 1.00% relative to the mixed equity fund index[5][43] - Year-to-date (20250102-20250630), the "brokerage gold stock performance enhancement portfolio" achieved an absolute return of 10.59% and an excess return of 2.73% relative to the mixed equity fund index[5][43] - The "brokerage gold stock performance enhancement portfolio" ranked in the 28.16th percentile among active equity funds for the year to date (977/3469)[5][43] - The portfolio's annualized return from 2018 to 2025 was 19.34%, with an annualized excess return of 14.38% relative to the mixed equity fund index[45] - The portfolio consistently ranked in the top 30% of active equity funds each year from 2018 to 2025[45]

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 actual investment scenarios [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**: - **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. - **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. - **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. - **Component Weight Control**: \( B_b \) is a binary vector indicating benchmark components, and \( b_l, b_h \) are the lower and upper bounds for component weights. - **No Short Selling**: Ensures non-negative weights and limits individual stock weights. - **Full Investment**: Ensures the portfolio is fully invested (\( \mathbf{1}^{T}\ w=1 \)) [40][41]. **Model Evaluation**: The MFE portfolio effectively tests factor efficacy under realistic constraints, making it a robust tool for factor validation in enhanced index strategies [39][40]. --- Quantitative Factors and Construction Methods - **Factor Name**: Three-Month Reversal **Factor Construction Idea**: Measures the reversal effect by calculating the return over the past 60 trading days, assuming stocks with recent underperformance may outperform in the future [17]. **Factor Construction Process**: $ \text{Three-Month Reversal} = \text{Cumulative Return over the Past 60 Trading Days} $ **Factor Evaluation**: Demonstrates strong performance in certain index spaces, such as CSI 1000 and CSI A500, but underperforms in others like CSI 500 [17][22][25]. - **Factor Name**: One-Year Momentum **Factor Construction Idea**: Captures the momentum effect by excluding the most recent month and calculating the cumulative return over the prior 11 months [17]. **Factor Construction Process**: $ \text{One-Year Momentum} = \text{Cumulative Return over the Past 11 Months (Excluding the Most Recent Month)} $ **Factor Evaluation**: Performs well in CSI 500 and CSI 1000 spaces but shows mixed results in other index spaces [17][21][23]. - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings [17]. **Factor Construction Process**: $ \text{SUE} = \frac{\text{Actual Earnings} - \text{Expected Earnings}}{\text{Standard Deviation of Expected Earnings}} $ **Factor Evaluation**: Consistently performs well across multiple index spaces, indicating its robustness as a predictive factor [17][22][25]. - **Factor Name**: Delta ROE (DELTAROE) **Factor Construction Idea**: Measures the change in return on equity (ROE) compared to the same quarter in the previous year [17]. **Factor Construction Process**: $ \text{DELTAROE} = \text{Current Quarter ROE} - \text{ROE from the Same Quarter Last Year} $ **Factor Evaluation**: Demonstrates strong predictive power in CSI 500 and CSI A500 spaces, with moderate performance in other index spaces [17][21][25]. --- Factor Backtesting Results - **Three-Month Reversal**: - CSI 300: Weekly excess return 0.66%, monthly excess return 0.65%, YTD excess return 3.01% [19]. - CSI 500: Weekly excess return 0.79%, monthly excess return 1.17%, YTD excess return 4.07% [21]. - CSI 1000: Weekly excess return 1.09%, monthly excess return 1.40%, YTD excess return 0.38% [23]. - CSI A500: Weekly excess return 1.08%, monthly excess return 0.36%, YTD excess return 3.64% [25]. - **One-Year Momentum**: - CSI 300: Weekly excess return 0.46%, monthly excess return 0.36%, YTD excess return -1.85% [19]. - CSI 500: Weekly excess return 1.26%, monthly excess return 1.18%, YTD excess return 2.77% [21]. - CSI 1000: Weekly excess return 1.45%, monthly excess return 1.73%, YTD excess return 0.26% [23]. - CSI A500: Weekly excess return 0.74%, monthly excess return 0.87%, YTD excess return -2.03% [25]. - **SUE**: - CSI 300: Weekly excess return 0.51%, monthly excess return 2.15%, YTD excess return 3.03% [19]. - CSI 500: Weekly excess return -0.41%, monthly excess return 0.13%, YTD excess return 2.86% [21]. - CSI 1000: Weekly excess return -0.08%, monthly excess return 2.77%, YTD excess return 4.41% [23]. - CSI A500: Weekly excess return 0.47%, monthly excess return 1.63%, YTD excess return 2.04% [25]. - **Delta ROE (DELTAROE)**: - CSI 300: Weekly excess return 0.26%, monthly excess return 2.27%, YTD excess return 5.32% [19]. - CSI 500: Weekly excess return 0.58%, monthly excess return 2.49%, YTD excess return 4.03% [21]. - CSI 1000: Weekly excess return -1.15%, monthly excess return 0.74%, YTD excess return 3.01% [23]. - CSI A500: Weekly excess return 0.52%, monthly excess return 2.82%, YTD excess return 5.13% [25].

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 ...

证券研究报告 | 2025年06月21日 多因子选股周报 估值因子表现出色，中证 1000 增强组合年内超额 12.61% 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、单季 EP、EPTTM 等因子表现较好，而一年动量、高管薪酬、非流动性冲击等因子表现较差。 以中证 500 指数为选股空间。最近一周，BP、预期 BP、预期 EPTTM 等因 子表现较好，而一年动量、三个月机构覆盖、非流动性冲击等因子表现较差。 以中证 1000 指数为选股空间。最近一周，BP、一个月换手、三个月波动等 因子表现较好，而一年动量、三个月机构覆盖、单季 ROE 等因子表现较差。 以中证 A500 指数为选股空间。最近一周，单季 EP、预期 EPTTM、预期 PEG 等因子表现较好，而三个月反转、一年动量、一个月反转等因子表现 较差。 以公募重仓指数为选股空间。最近一周，预期 EPTTM、单季 EP、预期 PEG 等因子表现较好，而一年动量、三个月机构覆盖、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 300 指 ...