Group 1: Performance of Index Enhancement Portfolios - The CSI 300 index enhancement portfolio achieved an excess return of 0.68% this week and 18.98% year-to-date [8] - The CSI 500 index enhancement portfolio recorded an excess return of 0.13% this week and 7.30% year-to-date [8] - The CSI 1000 index enhancement portfolio had an excess return of 0.77% this week and 15.97% year-to-date [8] - The CSI A500 index enhancement portfolio reported an excess return of 0.87% this week and 9.47% year-to-date [8] Group 2: Factor Performance Tracking - In the CSI 300 component stocks, factors such as quarterly ROE, three-month institutional coverage, and EPTTM performed well [9] - In the CSI 500 component stocks, factors like BP, three-month turnover, and expected BP showed strong performance [12] - For the CSI 1000 component stocks, factors including quarterly EP, expected EPTTM, and EPTTM were notable [15] - In the CSI A500 index component stocks, factors such as quarterly ROE, three-month institutional coverage, and quarterly ROA performed well [18] Group 3: Public Fund Index Enhancement Products Performance - The CSI 300 index enhancement products had a maximum excess return of 1.01%, a minimum of -0.79%, and a median of 0.18% this week [24] - The CSI 500 index enhancement products achieved a maximum excess return of 1.26%, a minimum of -0.76%, and a median of 0.38% this week [27] - The CSI 1000 index enhancement products recorded a maximum excess return of 1.20%, a minimum of -0.85%, and a median of 0.54% this week [29] - The CSI A500 index enhancement products had a maximum excess return of 0.98%, a minimum of -1.14%, and a median of 0.16% this week [33] Group 4: Factor Performance in Public Fund Heavyweight Index - In the public fund heavyweight index, factors such as quarterly ROE, three-month institutional coverage, and quarterly ROA performed well recently [20] - Over the past month, three-month institutional coverage, three-month turnover, and one-month turnover showed strong performance [20] - Year-to-date, factors like quarterly ROE, DELTAROE, and quarterly revenue growth performed well [20]

- The report tracks the performance of Guosen's quantitative enhanced index portfolios and public fund enhanced index products, as well as the performance of common stock selection factors in different stock selection spaces[11][12][15] - Guosen's quantitative enhanced index portfolios are constructed based on multi-factor stock selection, targeting benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices, aiming to consistently outperform their respective benchmarks[11][12] - The construction process of Guosen's enhanced index portfolios includes three main components: return prediction, risk control, and portfolio optimization[12] - The MFE (Maximized Factor Exposure) portfolio is used to test the effectiveness of individual factors under real-world constraints, such as industry exposure, style exposure, stock weight deviation, and turnover rate. The optimization model maximizes single-factor exposure while adhering to these constraints[41][42][43] - The MFE optimization model is expressed as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where $f$ represents factor values, $w$ is the stock weight vector, and the constraints include limits on style factor exposure ($X$), industry exposure ($H$), stock weight deviation, and component stock weight proportions ($B_b$)[41][42][43] - The report also evaluates the performance of single-factor MFE portfolios across different stock selection spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices[15][18][20][22][24][26] - The factor library includes over 30 factors categorized into valuation, reversal, growth, profitability, liquidity, volatility, corporate governance, and analyst-related dimensions. Examples include BP (Book-to-Price), single-quarter ROE, one-month reversal, and three-month turnover[16][17] - The public fund heavy-holding index is constructed using the holdings of ordinary stock funds and equity-biased hybrid funds. Stocks are selected based on cumulative weight reaching 90% of the average fund holdings[44] - The report tracks the excess returns of public fund enhanced index products for CSI 300, CSI 500, CSI 1000, and CSI A500 indices. For example, in the CSI 300 enhanced products, the highest weekly excess return was 1.01%, and the highest annual excess return was 11.97%[28][32][35][37][40]

Quantitative Models and Construction Methods - **Model Name**: Securities Firm Golden Stock Performance Enhancement Portfolio **Model Construction Idea**: The model aims to optimize the selection of stocks from the securities firm's golden stock pool to outperform the benchmark index, specifically the actively managed equity fund index. It leverages a multi-factor approach and portfolio optimization techniques to control deviations in individual stocks and styles while aligning with the industry distribution of public funds[37][42]. **Model Construction Process**: 1. Use the securities firm's golden stock pool as the stock selection space and constraint benchmark. 2. Apply a multi-factor model to further refine the stock selection within the pool. 3. Optimize the portfolio to control deviations in individual stocks and styles relative to the golden stock pool. 4. Use the industry distribution of all public funds as the industry allocation benchmark. 5. Adjust the portfolio at the beginning of each month based on the latest recommendations and market data[37][42]. **Model Evaluation**: The model demonstrates strong alpha generation potential and consistently outperforms the benchmark index, reflecting the research strength of securities firms[42]. Model Backtesting Results - **Securities Firm Golden Stock Performance Enhancement Portfolio**: - **Absolute Return (Monthly)**: -1.06% (20251103-20251128)[41] - **Excess Return (Monthly)**: +1.39% relative to the actively managed equity fund index[41] - **Absolute Return (Year-to-Date)**: +33.65% (20250102-20251128)[41] - **Excess Return (Year-to-Date)**: +4.42% relative to the actively managed equity fund index[41] - **Ranking in Actively Managed Equity Funds (Year-to-Date)**: 35.37th percentile (1227/3469)[41] - **Annualized Return (2018-2025)**: +19.34%[43] - **Annualized Excess Return (2018-2025)**: +14.38% relative to the actively managed equity fund index[43] - **Performance Ranking (2018-2025)**: Top 30% of actively managed equity funds every year[43] Quantitative Factors and Construction Methods - **Factor Name**: Total Market Capitalization **Factor Construction Idea**: Reflects the size of a company, often used to capture the size effect in stock returns[26][27]. **Factor Construction Process**: Calculate the total market capitalization of each stock in the golden stock pool. Group stocks into quintiles based on their market capitalization and calculate the long-short portfolio returns for each group[26][27]. **Factor Evaluation**: Demonstrates strong performance in both the recent month and year-to-date periods[26][27]. - **Factor Name**: Single-Quarter Revenue Growth **Factor Construction Idea**: Measures the growth in revenue over a single quarter, capturing the growth potential of a company[26][27]. **Factor Construction Process**: Compute the quarter-over-quarter revenue growth for each stock. Group stocks into quintiles based on their growth rates and calculate the long-short portfolio returns for each group[26][27]. **Factor Evaluation**: Exhibits strong performance year-to-date[26][27]. - **Factor Name**: SUR (Surprise) **Factor Construction Idea**: Captures the degree to which a company's earnings or revenue exceed market expectations[26][27]. **Factor Construction Process**: Calculate the difference between actual and expected earnings or revenue for each stock. Group stocks into quintiles based on their surprise levels and calculate the long-short portfolio returns for each group[26][27]. **Factor Evaluation**: Performs well in both the recent month and year-to-date periods[26][27]. - **Factor Name**: EPTTM (Earnings to Price Trailing Twelve Months) **Factor Construction Idea**: A valuation factor that measures the earnings yield of a stock[26][27]. **Factor Construction Process**: Compute the ratio of trailing twelve-month earnings to the current stock price for each stock. Group stocks into quintiles based on their EPTTM values and calculate the long-short portfolio returns for each group[26][27]. **Factor Evaluation**: Underperforms year-to-date[26][27]. - **Factor Name**: Expected Dividend Yield **Factor Construction Idea**: Reflects the expected return from dividends, often used as a value factor[26][27]. **Factor Construction Process**: Calculate the expected dividend yield for each stock. Group stocks into quintiles based on their yields and calculate the long-short portfolio returns for each group[26][27]. **Factor Evaluation**: Underperforms year-to-date[26][27]. - **Factor Name**: BP (Book-to-Price Ratio) **Factor Construction Idea**: A valuation factor that measures the book value relative to the stock price[26][27]. **Factor Construction Process**: Compute the ratio of book value to stock price for each stock. Group stocks into quintiles based on their BP values and calculate the long-short portfolio returns for each group[26][27]. **Factor Evaluation**: Underperforms year-to-date[26][27]. Factor Backtesting Results - **Total Market Capitalization**: Strong performance in the recent month and year-to-date[26][27] - **Single-Quarter Revenue Growth**: Strong performance year-to-date[26][27] - **SUR (Surprise)**: Strong performance in the recent month and year-to-date[26][27] - **EPTTM (Earnings to Price Trailing Twelve Months)**: Weak performance year-to-date[26][27] - **Expected Dividend Yield**: Weak performance year-to-date[26][27] - **BP (Book-to-Price Ratio)**: Weak performance year-to-date[26][27]

Quantitative Models and Construction Methods - **Model Name**: Securities Firm Golden Stock Performance Enhancement Portfolio **Model Construction Idea**: The model aims to optimize the selection of stocks from the securities firm golden stock pool, using a multi-factor approach to achieve stable outperformance relative to the benchmark index (Active Equity Hybrid Fund Index) [37][42] **Model Construction Process**: 1. The securities firm golden stock pool is used as the stock selection space and constraint benchmark [42] 2. The portfolio optimization method is applied to control deviations in individual stocks and styles between the portfolio and the golden stock pool [42] 3. The industry allocation is based on the distribution of all public funds [42] 4. The portfolio's benchmark is the Active Equity Hybrid Fund Index, and the portfolio's position last month was 90.48% [37] **Model Evaluation**: The model demonstrates strong alpha generation potential and stable performance, consistently outperforming the benchmark index over multiple years [42][43] Model Backtesting Results - **Securities Firm Golden Stock Performance Enhancement Portfolio**: - **Absolute Return (Monthly)**: -1.06% [41] - **Excess Return (Monthly)**: 1.39% relative to the Active Equity Hybrid Fund Index [41] - **Absolute Return (Year-to-Date)**: 33.65% [41] - **Excess Return (Year-to-Date)**: 4.42% relative to the Active Equity Hybrid Fund Index [41] - **Ranking in Active Equity Funds (Year-to-Date)**: 35.37% percentile (1227/3469) [41] - **Annualized Return (2018-2025)**: 19.34% [43] - **Annualized Excess Return (2018-2025)**: 14.38% relative to the Active Equity Hybrid Fund Index [43] - **Performance Ranking (2018-2025)**: Top 30% of active equity funds every year [43] Quantitative Factors and Construction Methods - **Factor Name**: Total Market Capitalization **Factor Construction Idea**: Reflects the size of a company, often used to capture size-related effects in stock returns [26][27] **Factor Evaluation**: Demonstrated strong performance both in the past month and year-to-date [26][27] - **Factor Name**: Single-Quarter Surprise (SUR) **Factor Construction Idea**: Measures the degree of earnings surprise in a single quarter, capturing the market's reaction to unexpected earnings [26][27] **Factor Evaluation**: Performed well in both the past month and year-to-date [26][27] - **Factor Name**: Single-Quarter Revenue Growth **Factor Construction Idea**: Tracks the growth rate of revenue in a single quarter, reflecting a company's operational growth [26][27] **Factor Evaluation**: Strong performance year-to-date [26][27] - **Factor Name**: EPTTM (Earnings to Price Trailing Twelve Months) **Factor Construction Idea**: A valuation factor that measures earnings relative to price over the trailing twelve months [26][27] **Factor Evaluation**: Underperformed year-to-date [26][27] - **Factor Name**: Expected Dividend Yield **Factor Construction Idea**: Captures the expected dividend income relative to the stock price, often used as an income-focused valuation metric [26][27] **Factor Evaluation**: Underperformed year-to-date [26][27] - **Factor Name**: BP (Book-to-Price Ratio) **Factor Construction Idea**: A valuation factor that measures the book value of equity relative to the stock price [26][27] **Factor Evaluation**: Underperformed year-to-date [26][27] Factor Backtesting Results - **Total Market Capitalization**: Strong performance in both the past month and year-to-date [26][27] - **Single-Quarter Surprise (SUR)**: Strong performance in both the past month and year-to-date [26][27] - **Single-Quarter Revenue Growth**: Strong performance year-to-date [26][27] - **EPTTM**: Weak performance year-to-date [26][27] - **Expected Dividend Yield**: Weak performance year-to-date [26][27] - **BP (Book-to-Price Ratio)**: Weak performance year-to-date [26][27]

- The report highlights that in November 2025, the top-performing factors in the broker's gold stock pool were total market capitalization, single-quarter revenue surprise, and SUR, while factors like intraday return, analyst net upgrade magnitude, and analyst net upgrade ratio performed poorly[3][26] - For the year 2025, the best-performing factors were total market capitalization, single-quarter revenue growth, and SUR, whereas EPTTM, expected dividend yield, and BP underperformed[3][26] - The broker's gold stock performance enhancement portfolio achieved an absolute return of -1.06% for the month (20251103-20251128) and an excess return of 1.39% relative to the mixed equity fund index[5][41] - For the year (20250102-20251128), the portfolio achieved an absolute return of 33.65% and an excess return of 4.42% relative to the mixed equity fund index, ranking in the 35.37% percentile among active equity funds[5][41] - The broker's gold stock performance enhancement portfolio has consistently outperformed the mixed equity fund index from 2018 to 2022, ranking in the top 30% of active equity funds each year[12][37][43]

Group 1 - The performance of the HuShen 300 index enhanced portfolio achieved an excess return of 0.64% this week and 17.85% year-to-date [5][17] - The performance of the Zhongzheng 500 index enhanced portfolio recorded an excess return of 0.00% this week and 7.07% year-to-date [5][17] - The Zhongzheng 1000 index enhanced portfolio had an excess return of 0.21% this week and 14.89% year-to-date [5][17] - The Zhongzheng A500 index enhanced portfolio achieved an excess return of 0.44% this week and 8.26% year-to-date [5][17] Group 2 - In the HuShen 300 constituent stocks, factors such as three-month institutional coverage, one-year momentum, and single-quarter ROE performed well [6][8] - In the Zhongzheng 500 constituent stocks, factors like one-year momentum, expected net profit month-on-month, and DELTAROE showed strong performance [6][8] - For Zhongzheng 1000 constituent stocks, factors such as single-quarter revenue year-on-year growth, DELTAROA, and standardized expected external income performed well [6][8] - In the Zhongzheng A500 index constituent stocks, one-year momentum, standardized expected external profit, and standardized expected external income were strong factors [6][8] Group 3 - The HuShen 300 index enhanced products had a maximum excess return of 2.01%, a minimum of -0.78%, and a median of 0.19% this week [21] - The Zhongzheng 500 index enhanced products recorded a maximum excess return of 0.93%, a minimum of -2.16%, and a median of 0.05% this week [22] - The Zhongzheng 1000 index enhanced products achieved a maximum excess return of 1.47%, a minimum of -0.59%, and a median of 0.39% this week [23] - The Zhongzheng A500 index enhanced products had a maximum excess return of 1.47%, a minimum of -0.59%, and a median of 0.39% this week [24]

Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure Portfolio (MFE) - **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of single factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, and turnover constraints. This approach ensures that factors deemed effective can genuinely contribute to return prediction in the final portfolio[41][42]. - **Model Construction Process**: - The optimization model aims to maximize single-factor exposure while adhering to various constraints: $$ \begin{array}{ll} \text{max} & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T}w = 1 \end{array} $$ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, \( f^{T}w \) is the weighted exposure of the portfolio to the factor, and \( w \) is the stock weight vector[42]. - **Constraints**: - **Style Exposure**: \( X \) is 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]. - **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 individual stock weight deviations from the benchmark[42]. - **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[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 calculated after accounting for transaction costs of 0.3% on both sides[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]. --- Factor Construction and Methods 1. Factor Name: Momentum (1-Year Momentum) - **Factor Construction Idea**: Measures the momentum effect by capturing the price trend over the past year, excluding the most recent month[18]. - **Factor Construction Process**: - Formula: \( \text{1-Year Momentum} = \text{Cumulative Return over the past 12 months (excluding the last month)} \)[18]. - **Factor Evaluation**: Momentum factors generally perform well in capturing price trends, as evidenced by their positive performance in multiple sample spaces[20][22][24]. 2. Factor Name: DELTAROE - **Factor Construction Idea**: Measures the change in return on equity (ROE) compared to the same quarter in the previous year, reflecting profitability improvement[18]. - **Factor Construction Process**: - Formula: \( \text{DELTAROE} = \text{Current Quarter ROE} - \text{ROE of the Same Quarter Last Year} \)[18]. - **Factor Evaluation**: DELTAROE is effective in identifying companies with improving profitability, as shown by its strong performance in various sample spaces[22][24][26]. 3. 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[18]. - **Factor Construction Process**: - Formula: \( \text{SUE} = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}} \)[18]. - **Factor Evaluation**: SUE is a reliable indicator of earnings surprises and is particularly effective in growth-oriented sample spaces[18][24]. --- Factor Backtesting Results 1. 1-Year Momentum - **Performance in Different Sample Spaces**: - **CSI 300**: Positive performance in the past week but underperformed in the past month and year-to-date[20]. - **CSI 500**: Strong performance in the past week and year-to-date, with weaker results in the past month[22]. - **CSI 1000**: Underperformed year-to-date but showed strong weekly performance[24]. - **CSI A500**: Mixed results, with strong weekly performance but weaker year-to-date performance[26]. - **Public Fund Heavyweight Index**: Positive weekly performance but underperformed year-to-date[28]. 2. DELTAROE - **Performance in Different Sample Spaces**: - **CSI 300**: Strong year-to-date performance, with mixed results in the past week and month[20]. - **CSI 500**: Positive weekly and year-to-date performance, with weaker results in the past month[22]. - **CSI 1000**: Strong weekly and year-to-date performance, with weaker results in the past month[24]. - **CSI A500**: Positive weekly and year-to-date performance, with weaker results in the past month[26]. - **Public Fund Heavyweight Index**: Positive weekly and year-to-date performance, with weaker results in the past month[28]. 3. SUE - **Performance in Different Sample Spaces**: - **CSI 300**: Not explicitly mentioned in the report[18]. - **CSI 500**: Not explicitly mentioned in the report[18]. - **CSI 1000**: Not explicitly mentioned in the report[18]. - **CSI A500**: Not explicitly mentioned in the report[18]. - **Public Fund Heavyweight Index**: Not explicitly mentioned in the report[18]. --- Quantitative Model Backtesting Results 1. MFE Portfolio - **Performance in Different Sample Spaces**: - **CSI 300**: Weekly excess return of 0.64%, year-to-date excess return of 17.85%[15]. - **CSI 500**: Weekly excess return of 0.00%, year-to-date excess return of 7.07%[15]. - **CSI 1000**: Weekly excess return of 0.21%, year-to-date excess return of 14.89%[15]. - **CSI A500**: Weekly excess return of 0.44%, year-to-date excess return of 8.26%[15].

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio recorded a weekly excess return of -0.71% and a year-to-date excess return of 16.74% [1][6] - The CSI 500 index enhanced portfolio achieved a weekly excess return of 0.12% and a year-to-date excess return of 6.85% [1][6] - The CSI 1000 index enhanced portfolio experienced a weekly excess return of -0.94% and a year-to-date excess return of 14.08% [1][6] - The CSI A500 index enhanced portfolio had a weekly excess return of -1.37% and a year-to-date excess return of 7.55% [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as one-month volatility, one-month turnover, and three-month volatility performed well [1][9] - In the CSI 500 component stocks, factors like three-month institutional coverage, one-month reversal, and three-month reversal showed strong performance [1][9] - For the CSI 1000 component stocks, one-month turnover, three-month institutional coverage, and single-season ROA were among the best-performing factors [1][9] - In the CSI A500 index component stocks, one-month turnover, three-month turnover, and one-month volatility were notable performers [1][9] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a weekly excess return ranging from a maximum of 0.70% to a minimum of -1.26%, with a median of 0.09% [1][22] - The CSI 500 index enhanced products recorded a weekly excess return with a maximum of 1.17%, a minimum of -1.13%, and a median of 0.11% [1][24] - The CSI 1000 index enhanced products had a weekly excess return ranging from a maximum of 0.89% to a minimum of -1.38%, with a median of -0.05% [1][26] - The CSI A500 index enhanced products showed a weekly excess return with a maximum of 0.71%, a minimum of -0.86%, and a median of -0.04% [1][27]

Quantitative Models and Construction Methods 1. Model Name: Guosen Quantitative Index Enhanced Portfolio - **Model Construction Idea**: The model aims to construct enhanced portfolios benchmarked against indices such as CSI 300, CSI 500, CSI 1000, and CSI A500, with the goal of consistently outperforming their respective benchmarks [10][11]. - **Model Construction Process**: 1. **Revenue Prediction**: Predict stock returns using multiple factors. 2. **Risk Control**: Apply constraints on industry exposure, style exposure, stock weight deviation, and turnover rate. 3. **Portfolio Optimization**: Optimize the portfolio to maximize single-factor exposure while adhering to constraints. The optimization model is as follows: $ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, \( w \) is the stock weight vector, and \( f^{T}w \) is the weighted exposure to the factor. - **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 Stock Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark component, and \( b_l, b_h \) are the lower and upper bounds for component stock weight. - **No Short Selling**: Ensure non-negative weights and limit individual stock weights. - **Full Investment**: Ensure the portfolio is fully invested with weights summing to 1 [40][41][42]. 4. **Backtesting**: Rebalance the portfolio monthly, calculate historical returns, and evaluate performance metrics such as excess returns and risk statistics [44]. 2. Model Name: Public Fund Heavyweight Index - **Model Construction Idea**: Construct an index based on the holdings of public funds to evaluate factor performance under "institutional style" [42][43]. - **Model Construction Process**: 1. **Sample Selection**: Include ordinary equity funds and partial equity hybrid funds with a minimum size of 50 million RMB and at least six months of listing history. Exclude recently transformed funds or those with insufficient data. 2. **Data Collection**: Use fund periodic reports (annual, semi-annual, or quarterly) to gather holding information. 3. **Weight Calculation**: Average the stock weights across eligible funds. 4. **Index Construction**: Sort stocks by weight in descending order and select those accounting for 90% of cumulative weight to form the index [43]. --- Model Backtesting Results 1. Guosen Quantitative Index Enhanced Portfolio - **CSI 300 Enhanced Portfolio**: - Weekly excess return: -0.71% - Year-to-date excess return: 16.74% [13] - **CSI 500 Enhanced Portfolio**: - Weekly excess return: 0.12% - Year-to-date excess return: 6.85% [13] - **CSI 1000 Enhanced Portfolio**: - Weekly excess return: -0.94% - Year-to-date excess return: 14.08% [13] - **CSI A500 Enhanced Portfolio**: - Weekly excess return: -1.37% - Year-to-date excess return: 7.55% [13] 2. Public Fund Heavyweight Index - **CSI 300 Index Enhanced Products**: - Weekly excess return: Max 0.70%, Min -1.26%, Median 0.09% - Year-to-date excess return: Max 9.92%, Min -4.53%, Median 2.58% [31] - **CSI 500 Index Enhanced Products**: - Weekly excess return: Max 1.17%, Min -1.13%, Median 0.11% - Year-to-date excess return: Max 13.14%, Min -9.17%, Median 3.94% [33] - **CSI 1000 Index Enhanced Products**: - Weekly excess return: Max 0.89%, Min -1.38%, Median -0.05% - Year-to-date excess return: Max 19.12%, Min -1.84%, Median 8.24% [36] - **CSI A500 Index Enhanced Products**: - Weekly excess return: Max 0.71%, Min -0.86%, Median -0.04% - Year-to-date excess return: Max 2.67%, Min -4.14%, Median -0.76% [39] --- Quantitative Factors and Construction Methods 1. Factor Name: Maximized Factor Exposure (MFE) - **Factor Construction Idea**: Evaluate factor effectiveness under real-world constraints by maximizing single-factor exposure in a portfolio [40][41]. - **Factor Construction Process**: 1. Define constraints for style exposure, industry exposure, stock weight deviation, and component stock weight. 2. Optimize the portfolio to maximize single-factor exposure while adhering to constraints. 3. Rebalance monthly and calculate historical returns [40][41][44]. 2. Factor Name: Public Fund Heavyweight Factors - **Factor Construction Idea**: Test factor performance in the public fund heavyweight index to reflect institutional preferences [42][43]. - **Factor Construction Process**: 1. Use public fund holdings to construct the index. 2. Evaluate factor performance within this index using metrics such as excess returns and risk-adjusted returns [42][43]. --- Factor Backtesting Results 1. Maximized Factor Exposure (MFE) - **CSI 300 Sample Space**: - Best-performing factors (weekly): One-month volatility (0.83%), one-month turnover (0.68%), three-month volatility (0.65%) - Worst-performing factors (weekly): Single-quarter profit growth (-0.26%), three-month institutional coverage (-0.24%), one-year momentum (-0.24%) [18] - **CSI 500 Sample Space**: - Best-performing factors (weekly): Three-month institutional coverage (1.09%), one-month reversal (1.01%), three-month reversal (0.99%) - Worst-performing factors (weekly): Standardized unexpected earnings (-1.00%), DELTAROA (-0.81%), DELTAROE (-0.81%) [20] - **CSI 1000 Sample Space**: - Best-performing factors (weekly): One-month turnover (1.08%), three-month institutional coverage (1.06%), single-quarter ROA (1.04%) - Worst-performing factors (weekly): Single-quarter SP (-1.29%), expected PEG (-1.25%), SPTTM (-1.22%) [22] - **CSI A500 Sample Space**: - Best-performing factors (weekly): One-month turnover (0.82%), three-month turnover (0.75%), one-month volatility (0.74%) - Worst-performing factors (weekly): Expected net profit QoQ (-0.91%), single-quarter net profit growth (-0.61%), expected PEG (-0.41%) [24] - **Public Fund Heavyweight Index**: - Best-performing factors (weekly): One-month volatility (1.32%), one-month turnover (1.23%), three-month turnover (0.89%) - Worst-performing factors (weekly): Single-quarter revenue growth (-0.89%), single-quarter profit growth (-0.88%), single-quarter ROE (-0.81%) [26]

Performance of Index Enhancement Portfolios - The CSI 300 index enhancement portfolio recorded an excess return of -0.22% for the week and 18.41% year-to-date [1][6] - The CSI 500 index enhancement portfolio had an excess return of -0.52% for the week and 7.09% year-to-date [1][6] - The CSI 1000 index enhancement portfolio showed an excess return of -0.12% for the week and 16.38% year-to-date [1][6] - The CSI A500 index enhancement portfolio achieved an excess return of 0.01% for the week and 9.75% year-to-date [1][6] Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month volatility, one-month volatility, and three-month reversal performed well [1][9] - In the CSI 500 component stocks, factors like one-month turnover, BP, and illiquidity shock showed strong performance [1][9] - For the CSI 1000 component stocks, factors such as illiquidity shock, expected net profit month-on-month, and EPTTM one-year percentile performed well [1][9] - In the CSI A500 index component stocks, factors like three-month volatility, one-month volatility, and one-month turnover performed well [1][9] Public Fund Index Enhancement Products Performance Tracking - The CSI 300 index enhancement products had a maximum excess return of 1.15%, a minimum of -2.04%, and a median of 0.19% for the week [1][20] - The CSI 500 index enhancement products recorded a maximum excess return of 2.03%, a minimum of -0.65%, and a median of 0.27% for the week [1][21] - The CSI 1000 index enhancement products had a maximum excess return of 1.84%, a minimum of -0.95%, and a median of 0.00% for the week [1][23] - The CSI A500 index enhancement products achieved a maximum excess return of 0.94%, a minimum of -0.47%, and a median of 0.16% for the week [1][25] Public Fund Index Enhancement Product Quantity and Scale - There are currently 76 CSI 300 index enhancement products with a total scale of 77.9 billion [1][19] - There are 74 CSI 500 index enhancement products with a total scale of 50.5 billion [1][19] - There are 46 CSI 1000 index enhancement products with a total scale of 21.4 billion [1][19] - There are 68 CSI A500 index enhancement products with a total scale of 25.3 billion [1][19]