Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure Portfolio (MFE) - **Model Construction Idea**: The MFE portfolio is designed to maximize the exposure to a single factor while controlling for various constraints such as industry exposure, style exposure, stock weight deviations, and turnover limits. This approach ensures that the factor's predictive power is tested under realistic portfolio constraints [41][42]. - **Model Construction Process**: - The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T}w = 1 \end{array} $$ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( w \) is the stock weight vector [42]. - **Constraints**: - **Style Exposure**: \( X \) is the style factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure [42]. - **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviations [42]. - **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviations relative to the benchmark [42]. - **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 [42]. - **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \) [42]. - **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T}w = 1 \) [43]. - The MFE portfolio is constructed monthly, and historical returns are backtested with a 0.3% transaction cost applied to both sides of trades [45]. - **Model Evaluation**: The MFE approach is effective in testing factor efficacy under realistic constraints, ensuring that factors deemed "effective" are more likely to contribute to actual portfolio performance [41][42]. --- Factor Construction and Methodology 1. Factor Name: Book-to-Price Ratio (BP) - **Factor Construction Idea**: Measures valuation by comparing a company's book value to its market value [17]. - **Factor Construction Process**: - Formula: \( \text{BP} = \frac{\text{Net Assets}}{\text{Market Value}} \) [17]. 2. Factor Name: Single-Quarter ROE - **Factor Construction Idea**: Evaluates profitability by calculating the return on equity for a single quarter [17]. - **Factor Construction Process**: - Formula: \( \text{ROE} = \frac{\text{Net Profit (Quarterly)} \times 2}{\text{(Beginning Equity + Ending Equity)}} \) [17]. 3. Factor Name: Single-Quarter Revenue Growth (YoY) - **Factor Construction Idea**: Measures growth by comparing quarterly revenue to the same quarter in the previous year [17]. - **Factor Construction Process**: - Formula: \( \text{Revenue Growth (YoY)} = \frac{\text{Revenue (Current Quarter)} - \text{Revenue (Same Quarter Last Year)}}{\text{Revenue (Same Quarter Last Year)}} \) [17]. 4. Factor Name: DELTAROA - **Factor Construction Idea**: Captures changes in return on assets (ROA) compared to the same quarter in the previous year [17]. - **Factor Construction Process**: - Formula: \( \text{DELTAROA} = \text{ROA (Current Quarter)} - \text{ROA (Same Quarter Last Year)} \) [17]. 5. Factor Name: Non-Liquidity Shock - **Factor Construction Idea**: Measures the impact of illiquidity on stock prices over a 20-day period [17]. - **Factor Construction Process**: - Formula: \( \text{Non-Liquidity Shock} = \frac{\text{Absolute Daily Returns}}{\text{Average Trading Volume (20 Days)}} \) [17]. --- Factor Backtesting Results 1. Single-Quarter ROE - **Performance**: - Recent Week: 1.07% - Recent Month: 1.80% - Year-to-Date: 2.56% - Historical Annualized: 5.38% [19]. 2. DELTAROA - **Performance**: - Recent Week: 0.95% - Recent Month: 0.09% - Year-to-Date: 1.15% - Historical Annualized: 4.99% [19]. 3. Single-Quarter Revenue Growth (YoY) - **Performance**: - Recent Week: 0.94% - Recent Month: 0.78% - Year-to-Date: 1.50% - Historical Annualized: 4.65% [19]. 4. Non-Liquidity Shock - **Performance**: - Recent Week: 0.54% - Recent Month: -0.21% - Year-to-Date: -0.49% - Historical Annualized: 0.34% [19]. 5. Book-to-Price Ratio (BP) - **Performance**: - Recent Week: -0.87% - Recent Month: -0.34% - Year-to-Date: -0.49% - Historical Annualized: 2.56% [19]. --- Model Backtesting Results 1. CSI 300 Enhanced Portfolio - Weekly Excess Return: 1.90% - Year-to-Date Excess Return: 5.84% [5][14]. 2. CSI 500 Enhanced Portfolio - Weekly Excess Return: 1.94% - Year-to-Date Excess Return: 3.61% [5][14]. 3. CSI 1000 Enhanced Portfolio - Weekly Excess Return: 1.13% - Year-to-Date Excess Return: 4.68% [5][14]. 4. CSI A500 Enhanced Portfolio - Weekly Excess Return: -0.90% - Year-to-Date Excess Return: 3.71% [5][14].

Performance of Index Enhancement Portfolios - The CSI 300 index enhancement portfolio achieved an excess return of 0.31% this week and 3.36% year-to-date [1][6] - The CSI 500 index enhancement portfolio recorded an excess return of 1.11% this week but a negative return of -1.15% year-to-date [1][6] - The CSI 1000 index enhancement portfolio had an excess return of 1.60% this week and 3.40% year-to-date [1][6] - The CSI A500 index enhancement portfolio saw an excess return of 0.05% this week and 3.77% year-to-date [1][6] Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as EPTTM, expected EPTTM, and single-quarter EP performed well [1][9] - In the CSI 500 component stocks, expected EPTTM, single-quarter EP, and EPTTM were the top-performing factors [1][11] - For the CSI 1000 component stocks, expected EPTTM, BP, and expected BP showed strong performance [1][13] - In the CSI A500 index component stocks, expected EPTTM, EPTTM, and single-quarter EP were the best-performing factors [1][15] - Among publicly offered fund heavy stocks, single-quarter EP, EPTTM, and expected EPTTM also performed well [1][17] Public Fund Index Enhancement Product Performance Tracking - The CSI 300 index enhancement products had a maximum excess return of 1.02% and a minimum of -2.08% this week, with a median of -0.08% [1][20] - The CSI 500 index enhancement products achieved a maximum excess return of 1.65% and a minimum of -0.86% this week, with a median of 0.01% [1][23] - The CSI 1000 index enhancement products recorded a maximum excess return of 1.32% and a minimum of -1.01% this week, with a median of 0.04% [1][24] - The CSI A500 index enhancement products had a maximum excess return of 1.14% and a minimum of -0.88% this week, with a median of 0.03% [1][25]

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]

Group 1 - The core viewpoint of the article is to track the performance of various index enhancement portfolios and stock selection factors across different indices, highlighting their excess returns and factor performance over recent weeks and the year to date [1][2][22]. Group 2 - The performance of the CSI 300 index enhancement portfolio showed an excess return of -0.14% for the week and 3.07% year-to-date [8][22]. - The CSI 500 index enhancement portfolio had an excess return of -0.27% for the week and -0.57% year-to-date [8][22]. - The CSI 1000 index enhancement portfolio reported an excess return of -0.69% for the week and 3.24% year-to-date [8][22]. - The CSI A500 index enhancement portfolio achieved an excess return of 0.12% for the week and 3.43% year-to-date [8][22]. Group 3 - In the CSI 300 component stocks, factors such as standardized unexpected revenue, quarterly revenue growth year-on-year, and one-year momentum performed well [9][11]. - In the CSI 500 component stocks, factors like quarterly surprise magnitude, three-month earnings adjustments, and quarterly net profit growth year-on-year showed strong performance [11][12]. - For the CSI 1000 component stocks, factors including expected net profit quarter-on-quarter, quarterly net profit growth year-on-year, and standardized unexpected revenue performed well [11][14]. - In the CSI A500 index component stocks, factors such as standardized unexpected revenue, quarterly revenue growth year-on-year, and one-year momentum were notable [11][18]. Group 4 - The public fund index enhancement products for the CSI 300 had a maximum excess return of 1.44% and a minimum of -0.58% for the week, with a median of 0.10% [22][28]. - The CSI 500 index enhancement products had a maximum excess return of 1.02% and a minimum of -1.67% for the week, with a median of -0.04% [22][30]. - The CSI 1000 index enhancement products reported a maximum excess return of 1.21% and a minimum of -1.30% for the week, with a median of 0.20% [22][31]. - The CSI A500 index enhancement products had a maximum excess return of 1.02% and a minimum of -0.77% for the week, with a median of 0.05% [22][30].

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].

Group 1 - The core viewpoint of the article is to track and analyze the performance of various index enhancement portfolios and the factors influencing stock selection across different indices [1][2][5][18]. Group 2 - The performance of the CSI 300 index enhancement portfolio this week showed an excess return of 0.00%, with a year-to-date excess return of 3.00% [6][22]. - The CSI 500 index enhancement portfolio had an excess return of 0.01% this week, but a year-to-date excess return of -0.88% [6][24]. - The CSI 1000 index enhancement portfolio achieved an excess return of 0.90% this week, with a year-to-date excess return of 2.17% [6][25]. - The CSI A500 index enhancement portfolio reported an excess return of -0.53% this week, with a year-to-date excess return of 2.90% [6][28]. Group 3 - In the CSI 300 component stocks, factors such as expected PEG, quarterly ROA, and EPTTM performed well [7][9]. - In the CSI 500 component stocks, factors like dividend yield, EPTTM, and BP showed strong performance [11][10]. - For the CSI 1000 component stocks, factors including quarterly ROA, quarterly ROE, and standardized expected non-operating income performed well [13][12]. - In the CSI A500 index component stocks, factors such as dividend yield, quarterly revenue growth year-on-year, and quarterly ROA were notable [15][14]. Group 4 - The public fund index enhancement products for the CSI 300 had a maximum excess return of 1.08% and a minimum of -1.05% this week, with a median of -0.02% [22][20]. - The CSI 500 index enhancement products had a maximum excess return of 1.72% and a minimum of -0.67% this week, with a median of 0.29% [24][23]. - The CSI 1000 index enhancement products reported a maximum excess return of 0.96% and a minimum of -0.97% this week, with a median of 0.18% [26][25]. - The CSI A500 index enhancement products had a maximum excess return of 1.14% and a minimum of -1.73% this week, with a median of -0.00% [28][27].

Quantitative Models and Factor Analysis Summary 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] - **Model Construction Process**: - **Return Prediction**: Predicting the returns of stocks within the benchmark index - **Risk Control**: Implementing risk control measures to manage portfolio risk - **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate[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: 0.00% - Year-to-Date Excess Return: 3.00%[5][14] - **CSI 500 Index Enhanced Portfolio**: - Weekly Excess Return: 0.01% - Year-to-Date Excess Return: -0.88%[5][14] - **CSI 1000 Index Enhanced Portfolio**: - Weekly Excess Return: 0.90% - Year-to-Date Excess Return: 2.17%[5][14] - **CSI A500 Index Enhanced Portfolio**: - Weekly Excess Return: -0.53% - Year-to-Date Excess Return: 2.90%[5][14] Quantitative Factors and Construction Methods Factor Name: Dividend Yield - **Factor Construction Idea**: Measures the dividend income relative to the stock price, indicating the return on investment from dividends[17] - **Factor Construction Process**: - Formula: $\text{Dividend Yield} = \frac{\text{Dividend per Share}}{\text{Stock Price}}$ - The factor is calculated as the sum of the dividends declared over the last four quarters divided by the current market capitalization[17] - **Factor Evaluation**: The dividend yield factor is effective in identifying stocks with high dividend returns, which can be attractive to income-focused investors[17] Factor Name: EPTTM (Earnings to Price TTM) - **Factor Construction Idea**: Measures the earnings relative to the stock price over the trailing twelve months, indicating the profitability of the company[17] - **Factor Construction Process**: - Formula: $\text{EPTTM} = \frac{\text{Earnings TTM}}{\text{Market Capitalization}}$ - The factor is calculated as the total earnings over the trailing twelve months divided by the current market capitalization[17] - **Factor Evaluation**: The EPTTM factor is useful for identifying undervalued stocks with strong earnings performance[17] Factor Name: BP (Book to Price) - **Factor Construction Idea**: Measures the book value relative to the stock price, indicating the intrinsic value of the company[17] - **Factor Construction Process**: - Formula: $\text{BP} = \frac{\text{Book Value}}{\text{Market Capitalization}}$ - The factor is calculated as the book value of equity divided by the current market capitalization[17] - **Factor Evaluation**: The BP factor helps in identifying stocks that are potentially undervalued based on their book value[17] Factor Backtesting Results CSI 300 Index Sample Space - **Best Performing Factors (Recent Week)**: Expected PEG, Single Quarter ROA, EPTTM[1][19] - **Worst Performing Factors (Recent Week)**: Three-Month Reversal, Expected Net Profit QoQ, One-Month Turnover[1][19] CSI 500 Index Sample Space - **Best Performing Factors (Recent Week)**: Dividend Yield, EPTTM, BP[1][20] - **Worst Performing Factors (Recent Week)**: Expected Net Profit QoQ, Single Quarter Operating Profit YoY Growth, Three-Month Institutional Coverage[1][20] CSI 1000 Index Sample Space - **Best Performing Factors (Recent Week)**: Single Quarter ROA, Single Quarter ROE, Standardized Unexpected Revenue[1][22] - **Worst Performing Factors (Recent Week)**: One-Month Reversal, Single Quarter Net Profit YoY Growth, Three-Month Reversal[1][22] CSI A500 Index Sample Space - **Best Performing Factors (Recent Week)**: Dividend Yield, Single Quarter Revenue YoY Growth, Single Quarter ROA[1][24] - **Worst Performing Factors (Recent Week)**: Expected Net Profit QoQ, Single Quarter Net Profit YoY Growth, Single Quarter Operating Profit YoY Growth[1][24] Public Fund Heavy Index Sample Space - **Best Performing Factors (Recent Week)**: Dividend Yield, Single Quarter ROA, DELTAROA[1][27] - **Worst Performing Factors (Recent Week)**: Three-Month Reversal, Three-Month Institutional Coverage, Single Quarter Net Profit YoY Growth[1][27]

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed using multi-factor stock selection models targeting benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices[11][12][14] - The construction process of the index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization[12] - Single-factor Maximized Factor Exposure (MFE) portfolios are constructed for each factor within different stock selection spaces (e.g., CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy positions index) to evaluate the effectiveness of individual factors under real-world constraints[15][40][41] - The optimization model for MFE portfolios aims to maximize single-factor exposure while controlling for constraints such as style exposure, industry exposure, individual stock weight deviation, component stock weight proportion, and individual stock weight limits[40][41][42] - 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, industry, and stock-specific limits[40][41] - Factors are categorized into valuation, reversal, growth, profitability, liquidity, volatility, corporate governance, and analyst dimensions, with over 30 factors tracked, such as BP, EPTTM, ROA, ROE, and dividend yield[16][17] - Factor performance varies across different stock selection spaces. For example, in the CSI 300 space, factors like expected PEG, single-quarter ROA, and EPTTM performed well recently, while factors like three-month reversal and one-month turnover performed poorly[19] - In the CSI 500 space, factors such as dividend yield, EPTTM, and BP showed strong performance recently, while factors like three-month reversal and single-quarter profit growth rate underperformed[20][21] - In the CSI 1000 space, factors like single-quarter ROA, single-quarter ROE, and standardized unexpected income performed well, while factors like one-month reversal and three-month reversal lagged[22][23] - In the CSI A500 space, factors such as dividend yield, single-quarter revenue growth rate, and single-quarter ROA performed well, while factors like expected net profit quarter-on-quarter and single-quarter profit growth rate underperformed[24][25] - In the public fund heavy positions index space, factors like dividend yield, single-quarter ROA, and DELTAROA performed well recently, while factors like three-month reversal and single-quarter profit growth rate lagged[26][27] - The report also tracks the performance of public fund index enhancement products, including CSI 300, CSI 500, CSI 1000, and CSI A500 enhancement products, with metrics such as excess returns over different periods[28][32][35][37][39]

Group 1 - The core viewpoint of the article is to track and analyze the performance of various index enhancement portfolios and the factors influencing stock selection across different indices [1][2][5][18]. Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of 0.88% for the week and 2.99% year-to-date [6][18]. - The performance of the Zhongzheng 500 index enhancement portfolio indicated an excess return of 0.68% for the week but a negative return of -0.92% year-to-date [6][18]. - The Zhongzheng 1000 index enhancement portfolio achieved an excess return of 1.44% for the week and 1.18% year-to-date [6][18]. - The Zhongzheng A500 index enhancement portfolio reported an excess return of 1.76% for the week and 3.50% year-to-date [6][18]. Group 3 - In the HuShen 300 component stocks, factors such as three-month volatility, illiquidity shock, and BP performed well [7][9]. - In the Zhongzheng 500 component stocks, factors like SPTTM, single-quarter SP, and single-quarter surprise magnitude showed strong performance [8][10]. - For Zhongzheng 1000 component stocks, standardized expected external income, single-quarter EP, and expected BP were among the top-performing factors [12][13]. - In the Zhongzheng A500 index component stocks, factors such as three-month turnover, three-month volatility, and one-month turnover performed well [15][16]. Group 4 - The public fund index enhancement products for HuShen 300 had a maximum excess return of 2.44%, a minimum of -0.52%, and a median of 0.38% for the week [22][24]. - The Zhongzheng 500 index enhancement products had a maximum excess return of 1.77%, a minimum of -1.45%, and a median of 0.07% for the week [24]. - The Zhongzheng 1000 index enhancement products achieved a maximum excess return of 3.29%, a minimum of 0.00%, and a median of 0.86% for the week [23][27]. - The Zhongzheng A500 index enhancement products reported a maximum excess return of 2.50%, a minimum of -0.54%, and a median of 0.31% for the week [25][28].

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios and common stock selection factors in different stock selection spaces[10][11] - The Guosen Financial Engineering team constructs index enhancement portfolios benchmarked against the CSI 300, CSI 500, CSI 1000, and CSI A500 indices, aiming to consistently outperform their respective benchmarks[10][11] - The construction process of the Guosen Financial Engineering index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization[11] - The report monitors the performance of common stock selection factors in different stock selection spaces, including the CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices[10][14] - The report constructs single-factor Maximized Factor Exposure (MFE) portfolios for each factor in the different stock selection spaces and tracks their performance relative to their respective benchmarks[10][14] - The factor library includes over 30 common factors from dimensions such as valuation, reversal, growth, profitability, liquidity, corporate governance, and analysts[15] - The construction of the MFE portfolios involves an optimization model with the objective function of maximizing single-factor exposure while controlling for various constraints such as style exposure, industry exposure, individual stock weight deviation, component stock weight proportion, and individual stock weight limits[40][41][42] - The report provides detailed performance tracking of public fund index enhancement products, including those benchmarked against the CSI 300, CSI 500, CSI 1000, and CSI A500 indices[27][28] - The report includes the weekly, monthly, and year-to-date excess returns of the Guosen Financial Engineering index enhancement portfolios and public fund index enhancement products[13][31][34][37][39]