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]

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

Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The MFE portfolio is designed to maximize the exposure of a single factor while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover limits. This approach ensures that the factor's predictive power is tested under realistic portfolio constraints, making it more applicable in practice [39][40]. **Model Construction Process**: The MFE portfolio is constructed using the following optimization model: $ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. 4. **Constituent Weight Control**: \( B_b \) is a binary vector indicating benchmark constituents, and \( b_l, b_h \) are the lower and upper bounds for constituent weights. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights. 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T} w = 1 \) [39][40][41]. **Model Evaluation**: The MFE portfolio is effective in testing factor performance under realistic constraints, making it a practical tool for portfolio construction [39][40]. Quantitative Factors and Construction Methods - **Factor Name**: DELTAROE **Factor Construction Idea**: Measures the change in return on equity (ROE) over a specific period to capture improvements in profitability [16]. **Factor Construction Process**: $ \text{DELTAROE} = \text{ROE}_{\text{current quarter}} - \text{ROE}_{\text{same quarter last year}} $ Where ROE is calculated as: $ \text{ROE} = \frac{\text{Net Income} \times 2}{\text{Beginning Equity} + \text{Ending Equity}} $ [16]. **Factor Evaluation**: DELTAROE is a profitability factor that has shown strong performance in multiple sample spaces, including CSI 300, CSI 500, and CSI A500 indices [17][19][24]. - **Factor Name**: Pre-expected PEG (Pre-expected Price-to-Earnings Growth) **Factor Construction Idea**: Incorporates analysts' earnings growth expectations to evaluate valuation relative to growth potential [16]. **Factor Construction Process**: $ \text{Pre-expected PEG} = \frac{\text{Forward P/E}}{\text{Expected Earnings Growth Rate}} $ Where forward P/E is based on analysts' consensus earnings estimates [16]. **Factor Evaluation**: This factor has demonstrated strong predictive power in growth-oriented sample spaces such as CSI 300 and CSI A500 indices [17][24]. - **Factor Name**: DELTAROA **Factor Construction Idea**: Measures the change in return on assets (ROA) over a specific period to capture improvements in asset efficiency [16]. **Factor Construction Process**: $ \text{DELTAROA} = \text{ROA}_{\text{current quarter}} - \text{ROA}_{\text{same quarter last year}} $ Where ROA is calculated as: $ \text{ROA} = \frac{\text{Net Income} \times 2}{\text{Beginning Total Assets} + \text{Ending Total Assets}} $ [16]. **Factor Evaluation**: DELTAROA has shown consistent performance across multiple indices, including CSI 1000 and public fund-heavy indices [22][26]. Factor Backtesting Results - **DELTAROE**: - CSI 300: Weekly excess return 0.75%, monthly 2.28%, YTD 8.04% [17]. - CSI 500: Weekly excess return 0.07%, monthly 0.59%, YTD 6.67% [19]. - CSI A500: Weekly excess return 0.68%, monthly 3.61%, YTD 9.20% [24]. - **Pre-expected PEG**: - CSI 300: Weekly excess return 0.72%, monthly 2.10%, YTD 7.22% [17]. - CSI 500: Weekly excess return 0.15%, monthly 1.34%, YTD 9.62% [19]. - CSI A500: Weekly excess return 0.85%, monthly 2.07%, YTD 10.35% [24]. - **DELTAROA**: - CSI 300: Weekly excess return 0.44%, monthly 2.27%, YTD 7.10% [17]. - CSI 1000: Weekly excess return 0.66%, monthly 1.57%, YTD 8.57% [22]. - Public Fund Index: Weekly excess return 0.66%, monthly 1.57%, YTD 8.57% [26].

Quantitative Models and Construction Methods Style Factors 1. **Factor Name**: Beta Factor - **Construction Idea**: Captures the market sensitivity of stocks - **Construction Process**: - Calculate the daily returns of individual stocks and the market index (CSI All Share Index) over the past 252 trading days - Perform an exponentially weighted regression with a half-life of 63 trading days - The regression coefficient is taken as the Beta factor - **Evaluation**: High Beta stocks outperformed low Beta stocks in the recent week, indicating a preference for market-sensitive stocks[15][16] 2. **Factor Name**: Leverage Factor - **Construction Idea**: Measures the financial leverage of companies - **Construction Process**: - Calculate three sub-factors: Market Leverage (MLEV), Debt to Assets (DTOA), and Book Leverage (BLEV) - MLEV = Non-current liabilities / Total market value - DTOA = Total liabilities / Total assets - BLEV = Non-current liabilities / Shareholders' equity - Combine the three sub-factors equally to form the Leverage factor - **Evaluation**: Low leverage companies outperformed high leverage companies, indicating a market preference for financially stable companies[15][16] 3. **Factor Name**: Growth Factor - **Construction Idea**: Measures the growth potential of companies - **Construction Process**: - Calculate two sub-factors: Sales Growth (SGRO) and Earnings Growth (EGRO) - SGRO = Regression slope of past five years' annual sales per share divided by the average sales per share - EGRO = Regression slope of past five years' annual earnings per share divided by the average earnings per share - Combine the two sub-factors equally to form the Growth factor - **Evaluation**: The Growth factor showed a negative return, indicating a decline in market preference for high-growth stocks[15][16] Stock Selection Factors 1. **Factor Name**: Single Quarter ROA - **Construction Idea**: Measures the return on assets for a single quarter - **Construction Process**: - Single Quarter ROA = Net income attributable to parent company / Total assets - **Evaluation**: Performed well in the CSI 300 stock pool over the past week[21][24] 2. **Factor Name**: 240-Day Skewness - **Construction Idea**: Measures the skewness of daily returns over the past 240 trading days - **Construction Process**: - Calculate the skewness of daily returns over the past 240 trading days - **Evaluation**: Performed well in the CSI 300 stock pool over the past week[21][24] 3. **Factor Name**: Single Quarter ROE - **Construction Idea**: Measures the return on equity for a single quarter - **Construction Process**: - Single Quarter ROE = Net income attributable to parent company / Shareholders' equity - **Evaluation**: Performed well in the CSI 300 stock pool over the past week[21][24] Factor Backtesting Results 1. **Beta Factor**: Weekly long-short return: 1.86%, Monthly long-short return: 1.64%[17] 2. **Leverage Factor**: Weekly long-short return: -3.07%, Monthly long-short return: -1.58%[17] 3. **Growth Factor**: Weekly long-short return: -1.73%, Monthly long-short return: -5.13%[17] Stock Selection Factor Backtesting Results 1. **Single Quarter ROA**: Weekly excess return: 0.98%, Monthly excess return: 2.61%, Annual excess return: 9.49%, Ten-year annualized return: 3.69%[22] 2. **240-Day Skewness**: Weekly excess return: 0.75%, Monthly excess return: 2.48%, Annual excess return: 6.40%, Ten-year annualized return: 2.85%[22] 3. **Single Quarter ROE**: Weekly excess return: 0.74%, Monthly excess return: 1.55%, Annual excess return: 8.96%, Ten-year annualized return: 3.46%[22]

Group 1 - The core viewpoint of the article is to track and analyze the performance of various index enhancement portfolios and stock selection factors across different indices, highlighting their excess returns and factor performance [2][3][20]. Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of 0.47% for the week and 9.69% year-to-date [8][24]. - The performance of the Zhongzheng 500 index enhancement portfolio showed an excess return of 0.92% for the week and 10.86% year-to-date [8][26]. - The Zhongzheng 1000 index enhancement portfolio had an excess return of 0.08% for the week and 15.70% year-to-date [8][30]. - The Zhongzheng A500 index enhancement portfolio reported an excess return of 1.00% for the week and 10.95% year-to-date [8][31]. Group 3 - In the HuShen 300 component stocks, factors such as single-season ROA, standardized expected external income, and single-season revenue year-on-year growth performed well [9][11]. - For Zhongzheng 500 component stocks, factors like standardized expected external income, single-season net profit year-on-year growth, and standardized expected external profit showed strong performance [11][12]. - In the Zhongzheng 1000 component stocks, standardized expected external income, standardized expected external profit, and single-season revenue year-on-year growth were notable [11][14]. - The Zhongzheng A500 index component stocks had strong performances in single-season ROA, DELTAROA, and DELTAROE [11][17]. Group 4 - The public fund index enhancement products for HuShen 300 showed a maximum excess return of 1.58% and a minimum of -0.61% for the week, with a median of 0.13% [24]. - The Zhongzheng 500 index enhancement products had a maximum excess return of 1.06% and a minimum of -0.83% for the week, with a median of 0.16% [26]. - The Zhongzheng 1000 index enhancement products reported a maximum excess return of 1.08% and a minimum of -0.54% for the week, with a median of 0.21% [30]. - The Zhongzheng A500 index enhancement products had a maximum excess return of 0.86% and a minimum of -0.58% for the week, with a median of 0.09% [31].

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].

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Construction Idea**: The MFE portfolio is designed to maximize single-factor exposure while controlling for various real-world constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures the factor's effectiveness under practical constraints [39][40][41] **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, $f^{T}w$ is the weighted exposure of the portfolio to the factor, and $w$ is the stock weight vector to be solved [39][40] - **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 [40] - **Industry Exposure**: $H$ is the matrix of stock exposures to industries, $h_l$, $h_h$ are the lower and upper bounds for industry exposure [40] - **Stock Weight Deviation**: $w_l$, $w_h$ are the lower and upper bounds for stock weight deviation relative to the benchmark [40] - **Component Weight Control**: $B_b$ is a 0-1 vector indicating whether a stock belongs to the benchmark, $b_l$, $b_h$ are the lower and upper bounds for component weight control [40] - **No Short Selling**: Ensures non-negative weights and limits individual stock weights [40] - **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [41] **Evaluation**: This model effectively tests factor validity under real-world constraints, ensuring the factor's predictive power in practical portfolio construction [39][40][41] Quantitative Factors and Construction Methods - **Factor Name**: Specificity **Construction Idea**: Measures the uniqueness of stock returns by evaluating the residuals from a Fama-French three-factor regression [16][19][23] **Construction Process**: - Formula: $1 - R^2$ from the Fama-French three-factor regression, where $R^2$ represents the goodness-of-fit of the regression model [16] **Evaluation**: Demonstrates strong performance in multiple sample spaces, indicating its effectiveness in capturing unique stock characteristics [19][23][25] - **Factor Name**: EPTTM Year Percentile **Construction Idea**: Represents the percentile rank of trailing twelve-month earnings-to-price ratio (EPTTM) over the past year [16][19][23] **Construction Process**: - Formula: Percentile rank of $EPTTM = \frac{\text{Net Income (TTM)}}{\text{Market Cap}}$ over the past year [16] **Evaluation**: Performs well in various sample spaces, particularly in growth-oriented indices [19][23][25] - **Factor Name**: Three-Month Reversal **Construction Idea**: Captures short-term price reversal by measuring the return over the past 60 trading days [16][19][23] **Construction Process**: - Formula: $\text{Return}_{60\text{days}} = \frac{\text{Price}_{t} - \text{Price}_{t-60}}{\text{Price}_{t-60}}$ [16] **Evaluation**: Effective in identifying short-term reversal opportunities, especially in volatile indices [19][23][25] Factor Backtesting Results - **Specificity Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 1.18% - Monthly Excess Return: 2.02% - Year-to-Date Excess Return: 4.23% - Historical Annualized Return: 0.51% [19] - **Sample Space**: CSI A500 - Weekly Excess Return: 1.43% - Monthly Excess Return: 2.14% - Year-to-Date Excess Return: 2.71% - Historical Annualized Return: 1.72% [25] - **EPTTM Year Percentile Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 0.54% - Monthly Excess Return: 2.01% - Year-to-Date Excess Return: 6.74% - Historical Annualized Return: 3.26% [19] - **Sample Space**: CSI 500 - Weekly Excess Return: 1.01% - Monthly Excess Return: 1.54% - Year-to-Date Excess Return: 1.90% - Historical Annualized Return: 5.24% [21] - **Three-Month Reversal Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 0.49% - Monthly Excess Return: 1.35% - Year-to-Date Excess Return: 4.31% - Historical Annualized Return: 1.13% [19] - **Sample Space**: CSI 1000 - Weekly Excess Return: 1.10% - Monthly Excess Return: 2.15% - Year-to-Date Excess Return: 2.59% - Historical Annualized Return: -0.67% [23] Index Enhancement Portfolio Backtesting Results - **CSI 300 Enhanced Portfolio**: - Weekly Excess Return: 0.78% - Year-to-Date Excess Return: 9.31% [5][14] - **CSI 500 Enhanced Portfolio**: - Weekly Excess Return: -0.52% - Year-to-Date Excess Return: 9.90% [5][14] - **CSI 1000 Enhanced Portfolio**: - Weekly Excess Return: 0.07% - Year-to-Date Excess Return: 15.69% [5][14] - **CSI A500 Enhanced Portfolio**: - Weekly Excess Return: 0.26% - Year-to-Date Excess Return: 9.96% [5][14] Public Fund Index Enhancement Product Performance - **CSI 300 Public Fund Products**: - Weekly Excess Return: Max 1.28%, Min -0.98%, Median 0.12% - Monthly Excess Return: Max 4.10%, Min -0.99%, Median 0.61% - Quarterly Excess Return: Max 5.71%, Min -0.90%, Median 1.52% - Year-to-Date Excess Return: Max 9.84%, Min -0.77%, Median 2.87% [31] - **CSI 500 Public Fund Products**: - Weekly Excess Return: Max 1.41%, Min -1.31%, Median 0.04% - Monthly Excess Return: Max 2.56%, Min -0.60%, Median 0.60% - Quarterly Excess Return: Max 5.51%, Min -0.10%, Median 2.60% - Year-to-Date Excess Return: Max 9.88%, Min -0.77%, Median 4.19% [34] - **CSI 1000 Public Fund Products**: - Weekly Excess Return: Max 0.82%, Min -0.47%, Median 0.15% - Monthly Excess Return: Max 3.55%, Min -0.67%, Median 1.07% - Quarterly Excess Return: Max 7.14%, Min -0.58%, Median 3.21% - Year-to-Date Excess Return: Max 15.34%, Min 0.49%, Median 6.75% [36] - **CSI A500 Public Fund Products**: - Weekly Excess Return: Max 1.16%, Min -0.57%, Median -0.04% - Monthly Excess Return: Max 1.89%, Min -1.55%, Median 0.68% - Quarterly Excess Return: Max 3.76%, Min -1.67%, Median 2.20% [38]

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 Factors and Construction Methods 1. Factor Name: Beta - **Construction Idea**: Measures the sensitivity of a stock's return to market movements, capturing the market's preference for high Beta stocks [11] - **Construction Process**: Beta is calculated using Bayesian shrinkage to compress the market Beta [16] - **Evaluation**: Beta factor showed strong performance this week, with a return of 1.94%, indicating a sustained market preference for high Beta stocks [11][13] 2. Factor Name: Volatility - **Construction Idea**: Captures the market's preference for high-volatility assets [11] - **Construction Process**: Includes multiple metrics such as: - Stdvol: Standard deviation of daily returns over the past 243 days - Ivff: Idiosyncratic volatility from Fama-French 3-factor model over 243 days - Range: High/low price range over 243 days - MaxRet_6: Average return of the six highest-return days in the past 243 days - MinRet_6: Average return of the six lowest-return days in the past 243 days [16] - **Evaluation**: Volatility factor rebounded significantly this week, with a return of 0.82%, reflecting increased demand for high-volatility assets [11][13] 3. Factor Name: One-Year Momentum - **Construction Idea**: Measures the cumulative return over the past year, excluding the most recent month, to capture momentum effects [20] - **Construction Process**: Calculated as the cumulative return over the past 12 months, excluding the most recent month [20] - **Evaluation**: One-year momentum factor performed well in multiple indices, including: - CSI 500: Weekly return of 0.90% [27] - CSI 1000: Weekly return of 0.81% [35] - CSI All Share: Weekly return of 2.25% [47] 4. Factor Name: Standardized Unexpected Revenue (SUR) - **Construction Idea**: Measures the deviation of actual revenue from analyst expectations, standardized by the standard deviation of expected revenue [20] - **Construction Process**: $ SUR = \frac{\text{Actual Revenue} - \text{Expected Revenue}}{\text{Standard Deviation of Expected Revenue}} $ [20] - **Evaluation**: SUR factor showed strong performance across indices: - CSI 800: Weekly return of 1.37% [31] - CSI 1000: Weekly return of 0.86% [35] - CSI All Share: Weekly return of 1.53% [47] 5. Factor Name: Three-Month Reversal - **Construction Idea**: Captures short-term mean-reversion effects in stock prices [20] - **Construction Process**: Calculated as the cumulative return over the past three months, with a negative sign to reflect reversal [20] - **Evaluation**: Three-month reversal factor performed well in: - CSI 1000: Weekly return of 1.04% [35] - CNI 2000: Weekly return of 1.76% [39] --- Factor Backtesting Results 1. Beta Factor - Weekly Return: 1.94% - Monthly Return: 7.88% - Year-to-Date Return: 17.34% - Annualized Return (1 Year): 51.27% [13] 2. Volatility Factor - Weekly Return: 0.82% - Monthly Return: 1.86% - Year-to-Date Return: 5.96% - Annualized Return (1 Year): 27.16% [13] 3. One-Year Momentum Factor - CSI 500 Weekly Return: 0.90% [27] - CSI 1000 Weekly Return: 0.81% [35] - CSI All Share Weekly Return: 2.25% [47] 4. Standardized Unexpected Revenue Factor - CSI 800 Weekly Return: 1.37% [31] - CSI 1000 Weekly Return: 0.86% [35] - CSI All Share Weekly Return: 1.53% [47] 5. Three-Month Reversal Factor - CSI 1000 Weekly Return: 1.04% [35] - CNI 2000 Weekly Return: 1.76% [39] --- Factor Portfolio Construction: Maximized Factor Exposure (MFE) Construction Process - **Objective Function**: Maximize single-factor exposure $ \text{max } f^{T}w $ - **Constraints**: - Style exposure limits: $ s_{l} \leq X(w-w_{b}) \leq s_{h} $ - Industry exposure limits: $ h_{l} \leq H(w-w_{b}) \leq h_{h} $ - Stock weight deviation limits: $ w_{l} \leq w-w_{b} \leq w_{h} $ - Component stock weight limits: $ b_{l} \leq B_{b}w \leq b_{h} $ - No short-selling: $ 0 \leq w \leq l $ - Full investment: $ 1^{T}w = 1 $ - Turnover limits: $ \Sigma|w-w_{0}| \leq to_{h} $ [59][60][62] Backtesting Process 1. Set constraints for style, industry, and stock weight deviations 2. Construct MFE portfolios monthly 3. Calculate historical returns and risk metrics, adjusting for transaction costs [63][64]