========= - The Guosen JinGong Index Enhanced Portfolio is constructed using a multi-factor stock selection approach, targeting to outperform benchmarks such as the CSI 300, CSI 500, CSI 1000, and CSI A500 indices[10][11] - The construction process of the Guosen JinGong Index Enhanced Portfolio includes three main components: return prediction, risk control, and portfolio optimization[11] - The performance of the Guosen JinGong Index Enhanced Portfolio for the week is as follows: CSI 300 Index Enhanced Portfolio achieved an excess return of 0.53%, CSI 500 Index Enhanced Portfolio achieved an excess return of 0.45%, CSI 1000 Index Enhanced Portfolio achieved an excess return of 0.34%, and CSI A500 Index Enhanced Portfolio achieved an excess return of -0.46%[4][13] Factor Construction and Performance - Factors are monitored across different stock selection spaces, including the CSI 300, CSI 500, CSI 1000, and CSI A500 indices, as well as the Public Fund Heavyweight Index[10][14] - The factor library includes over 30 common factors categorized into valuation, reversal, growth, profitability, liquidity, corporate governance, and analyst dimensions[15][16] Factor Performance in Different Indices - **CSI 300 Index**: Factors such as single-quarter ROA, single-quarter ROE, and one-year momentum performed well in the past week, while three-month reversal, one-month volatility, and three-month turnover performed poorly[1][18] - **CSI 500 Index**: Factors such as SPTTM, executive compensation, and three-month institutional coverage performed well in the past week, while EPTTM one-year percentile, illiquidity shock, and three-month reversal performed poorly[1][19] - **CSI 1000 Index**: Factors such as three-month earnings revisions, standardized unexpected revenue, and standardized unexpected earnings performed well in the past week, while three-month reversal, BP, and expected EPTTM performed poorly[1][21] - **CSI A500 Index**: Factors such as one-year momentum, single-quarter revenue growth, and DELTAROA performed well in the past week, while three-month reversal, one-month volatility, and EPTTM one-year percentile performed poorly[1][23] - **Public Fund Heavyweight Index**: Factors such as one-year momentum, standardized unexpected revenue, and three-month earnings revisions performed well in the past week, while three-month reversal, one-month volatility, and three-month volatility performed poorly[1][25] Public Fund Index Enhanced Product Performance - **CSI 300 Index Enhanced Products**: In the past week, the highest excess return was 2.02%, the lowest was -1.13%, and the median was 0.06%. In the past month, the highest excess return was 1.87%, the lowest was -1.02%, and the median was 0.02%[2][31] - **CSI 500 Index Enhanced Products**: In the past week, the highest excess return was 1.24%, the lowest was -1.61%, and the median was 0.19%. In the past month, the highest excess return was 2.66%, the lowest was -1.07%, and the median was 1.33%[2][34] - **CSI 1000 Index Enhanced Products**: In the past week, the highest excess return was 1.52%, the lowest was -1.23%, and the median was 0.45%. In the past month, the highest excess return was 3.71%, the lowest was -0.71%, and the median was 1.90%[2][36] - **CSI A500 Index Enhanced Products**: In the past week, the highest excess return was 0.84%, the lowest was -0.53%, and the median was 0.03%. In the past month, the highest excess return was 2.16%, the lowest was -1.28%, and the median was 0.53%[3][39] Factor MFE Portfolio Construction - The MFE (Maximized Factor Exposure) portfolio is constructed using an optimization model to maximize 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] - The optimization model's objective function is to maximize single-factor exposure, with constraints including style factor deviation, industry deviation, individual stock deviation, component stock weight limits, and no short selling[41][42] - The construction process involves setting constraints, constructing MFE portfolios at the end of each month, and calculating historical returns and risk statistics for the MFE portfolios[44] =========

Performance of Index Enhancement Portfolios - The CSI 300 index enhancement portfolio achieved an excess return of 0.24% this week and 17.58% year-to-date [1][7] - The CSI 500 index enhancement portfolio recorded an excess return of 0.17% this week and 8.16% year-to-date [1][7] - The CSI 1000 index enhancement portfolio had an excess return of 0.39% this week and 17.94% year-to-date [1][7] - The CSI A500 index enhancement portfolio experienced an excess return of -1.77% this week and 8.45% year-to-date [1][7] Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as one-month reversal, three-month reversal, and EPTTM one-year percentile performed well [1][10] - In the CSI 500 component stocks, factors like three-month volatility, three-month reversal, and EPTTM one-year percentile showed strong performance [1][10] - For the CSI 1000 component stocks, factors including one-month volatility, one-month turnover, and three-month reversal performed well [1][10] - In the CSI A500 index component stocks, factors such as one-month reversal, EPTTM one-year percentile, and one-month volatility were notable [1][10] - Among public fund heavy stocks, factors like dividend yield, three-month reversal, and EPTTM performed well [1][10] Public Fund Index Enhancement Products Performance Tracking - The CSI 300 index enhancement products had a maximum excess return of 0.92%, a minimum of -3.08%, and a median of 0.01% this week [1][23] - The CSI 500 index enhancement products achieved a maximum excess return of 3.20%, a minimum of -0.48%, and a median of 0.49% this week [1][25] - The CSI 1000 index enhancement products recorded a maximum excess return of 1.58%, a minimum of -0.82%, and a median of 0.37% this week [1][28] - The CSI A500 index enhancement products had a maximum excess return of 1.20%, a minimum of -0.84%, and a median of 0.23% this week [1][29]

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed based on multi-factor stock selection models targeting benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices. The goal is to consistently outperform the respective benchmarks [11][12][14] - The construction process of the index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization. The optimization model maximizes single-factor exposure while controlling for constraints such as industry exposure, style exposure, stock weight deviation, turnover rate, and component stock weight ratio [12][41][42] - The Maximized Factor Exposure (MFE) portfolio is used to test the effectiveness of individual factors under real-world constraints. The 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 constraints include style exposure (\(X\)), industry exposure (\(H\)), stock weight deviation (\(w_b\)), and component stock weight ratio (\(B_b\)) [41][42][43] - The report monitors the performance of common stock selection factors across different sample spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices. Factors are tested using MFE portfolios to evaluate their excess return relative to benchmarks [11][15][18] - The factor library includes over 30 factors categorized into valuation, reversal, growth, profitability, liquidity, volatility, corporate governance, and analyst-related factors. Examples include BP (Book-to-Price), EPTTM (Earnings-to-Price TTM), one-month reversal, three-month reversal, one-year momentum, and others [16][17] - The report highlights the performance of specific factors in different sample spaces: - **CSI 300**: One-month reversal, three-month reversal, and EPTTM one-year percentile performed well recently, while three-month institutional coverage and standardized unexpected earnings performed poorly [1][18] - **CSI 500**: Three-month volatility, three-month reversal, and EPTTM one-year percentile performed well recently, while one-year momentum and standardized unexpected revenue performed poorly [1][20] - **CSI 1000**: One-month volatility, one-month turnover, and three-month reversal performed well recently, while executive compensation and three-month earnings revisions performed poorly [1][22] - **CSI A500**: One-month reversal, EPTTM one-year percentile, and one-month volatility performed well recently, while three-month institutional coverage and one-year momentum performed poorly [1][24] - **Public fund heavy-holding index**: Dividend yield, three-month reversal, and EPTTM performed well recently, while standardized unexpected revenue and three-month earnings revisions performed poorly [1][26][27] - The report tracks the excess returns of public fund index enhancement products, including CSI 300, CSI 500, CSI 1000, and CSI A500. For CSI 300 products, the highest weekly excess return was 0.92%, while the lowest was -3.08%, with a median of 0.01% [3][32][31] - For CSI 500 products, the highest weekly excess return was 3.20%, while the lowest was -0.48%, with a median of 0.49% [3][35][34] - For CSI 1000 products, the highest weekly excess return was 1.58%, while the lowest was -0.82%, with a median of 0.37% [3][37][36] - For CSI A500 products, the highest weekly excess return was 1.20%, while the lowest was -0.84%, with a median of 0.23% [3][40][39]

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 their excess returns and factor performance [1][2][5][18]. Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of 0.63% for the week and 17.65% year-to-date [6][21]. - The performance of the Zhongzheng 500 index enhancement portfolio indicated an excess return of 0.30% for the week and 8.35% year-to-date [6][23]. - The Zhongzheng 1000 index enhancement portfolio achieved an excess return of 0.77% for the week and 18.22% year-to-date [6][24]. - The Zhongzheng A500 index enhancement portfolio reported an excess return of 1.57% for the week and 11.17% year-to-date [6][30]. Group 3 - In the HuShen 300 component stocks, factors such as expected EPTTM, one-month volatility, and BP performed well [7][9]. - For Zhongzheng 500 component stocks, factors like SPTTM, expected BP, and single-season EP showed strong performance [11][10]. - In Zhongzheng 1000 component stocks, EPTTM, SPTTM, and expected EPTTM were among the top-performing factors [13][12]. - The Zhongzheng A500 index component stocks saw single-season SP, SPTTM, and one-month volatility as the best-performing factors [15][14]. Group 4 - The public fund index enhancement products for HuShen 300 had a maximum excess return of 1.50%, a minimum of -1.62%, and a median of 0.22% for the week [21][20]. - The Zhongzheng 500 index enhancement products had a maximum excess return of 1.75%, a minimum of -0.67%, and a median of 0.49% for the week [25][22]. - The Zhongzheng 1000 index enhancement products reported a maximum excess return of 1.27%, a minimum of -0.86%, and a median of 0.45% for the week [24][29]. - The Zhongzheng A500 index enhancement products had a maximum excess return of 1.54%, a minimum of -1.64%, and a median of 0.34% for the week [30][26].

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

Quantitative Models and Construction Methods 1. **Model Name**: Securities Firms' Golden Stock Performance Enhancement Portfolio - **Model Construction Idea**: The model aims to optimize the selection of stocks from the securities firms' golden stock pool to outperform the benchmark, which is the median of equity-biased hybrid fund indices. The model leverages a multi-factor approach to select stocks with high alpha potential while controlling for deviations in individual stocks and style factors from the golden stock pool [39][43]. - **Model Construction Process**: - The securities firms' golden stock pool is used as the stock selection universe and constraint benchmark. - A multi-factor model is applied to further optimize the selection of stocks from the pool. - The portfolio is constructed by controlling the deviation of individual stocks and style factors from the golden stock pool. - The industry allocation is based on the distribution of all public funds [43]. - **Model Evaluation**: The model demonstrates strong alpha generation potential and consistently outperforms the equity-biased hybrid fund index. It reflects the research strength of securities firms and their ability to capture market trends effectively [43]. --- Model Backtesting Results 1. **Securities Firms' Golden Stock Performance Enhancement Portfolio** - **Absolute Return (Monthly)**: -0.55% (2025/09/01 - 2025/09/30) [42] - **Excess Return (Monthly)**: -3.50% relative to equity-biased hybrid fund index (2025/09/01 - 2025/09/30) [42] - **Absolute Return (Year-to-Date)**: 33.26% (2025/01/02 - 2025/09/30) [42] - **Excess Return (Year-to-Date)**: 1.19% relative to equity-biased hybrid fund index (2025/01/02 - 2025/09/30) [42] - **Ranking in Active Equity Funds (Year-to-Date)**: 43.07% percentile (1494/3469) [42] - **Historical Performance (2018-2025)**: - Annualized Return: 19.34% - Annualized Excess Return: 14.38% relative to equity-biased hybrid fund index - Consistently ranked in the top 30% of active equity funds each year [44][47] --- Quantitative Factors and Construction Methods 1. **Factor Name**: Intraday Return - **Factor Construction Idea**: Measures the return generated within a single trading day to capture short-term price movements [27][28]. - **Factor Evaluation**: Demonstrated strong performance in the most recent month [27][28]. 2. **Factor Name**: BP (Book-to-Price Ratio) - **Factor Construction Idea**: Reflects the valuation of a stock by comparing its book value to its market price [27][28]. - **Factor Evaluation**: Performed well in the most recent month but underperformed year-to-date [27][28]. 3. **Factor Name**: Volatility - **Factor Construction Idea**: Measures the degree of variation in a stock's price over a specific period, capturing risk and uncertainty [27][28]. - **Factor Evaluation**: Showed strong performance in the most recent month but underperformed year-to-date [27][28]. 4. **Factor Name**: Total Market Capitalization - **Factor Construction Idea**: Represents the total market value of a company's outstanding shares, often used to gauge company size [27][28]. - **Factor Evaluation**: Underperformed in the most recent month but performed well year-to-date [27][28]. 5. **Factor Name**: SUE (Standardized Unexpected Earnings) - **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of past earnings surprises [27][28]. - **Factor Evaluation**: Underperformed in the most recent month [27][28]. 6. **Factor Name**: Single-Quarter Earnings Surprise - **Factor Construction Idea**: Captures the magnitude of earnings surprises in a single quarter [27][28]. - **Factor Evaluation**: Underperformed in the most recent month but performed well year-to-date [27][28]. 7. **Factor Name**: Single-Quarter Revenue Growth - **Factor Construction Idea**: Measures the growth in revenue over a single quarter, reflecting a company's sales performance [27][28]. - **Factor Evaluation**: Performed well year-to-date [27][28]. 8. **Factor Name**: Analyst Net Upward Revision - **Factor Construction Idea**: Tracks the net number of upward revisions in analysts' earnings estimates for a stock [27][28]. - **Factor Evaluation**: Performed well year-to-date [27][28]. 9. **Factor Name**: Expected Dividend Yield - **Factor Construction Idea**: Represents the expected annual dividend payments as a percentage of the stock price [27][28]. - **Factor Evaluation**: Underperformed year-to-date [27][28]. --- Factors' Backtesting Results 1. **Intraday Return Factor** - **Recent Month Performance**: Strong [27][28] - **Year-to-Date Performance**: Not specified [27][28] 2. **BP Factor** - **Recent Month Performance**: Strong [27][28] - **Year-to-Date Performance**: Weak [27][28] 3. **Volatility Factor** - **Recent Month Performance**: Strong [27][28] - **Year-to-Date Performance**: Weak [27][28] 4. **Total Market Capitalization Factor** - **Recent Month Performance**: Weak [27][28] - **Year-to-Date Performance**: Strong [27][28] 5. **SUE Factor** - **Recent Month Performance**: Weak [27][28] - **Year-to-Date Performance**: Not specified [27][28] 6. **Single-Quarter Earnings Surprise Factor** - **Recent Month Performance**: Weak [27][28] - **Year-to-Date Performance**: Strong [27][28] 7. **Single-Quarter Revenue Growth Factor** - **Recent Month Performance**: Not specified [27][28] - **Year-to-Date Performance**: Strong [27][28] 8. **Analyst Net Upward Revision Factor** - **Recent Month Performance**: Not specified [27][28] - **Year-to-Date Performance**: Strong [27][28] 9. **Expected Dividend Yield Factor** - **Recent Month Performance**: Not specified [27][28] - **Year-to-Date Performance**: Weak [27][28]

========= - The "券商金股业绩增强组合" (Broker Gold Stock Performance Enhanced Portfolio) aims to outperform the median of public funds by optimizing the broker gold stock pool[39] - The portfolio uses the偏股混合型基金指数 (Equity-biased Hybrid Fund Index) as its benchmark, with a 90% position last month[39] - The absolute return of the portfolio for the month (2050901-20250930) was -0.55%, with an excess return of -3.50% relative to the偏股混合型基金指数[42] - The absolute return for the year (20250102-20250930) was 33.26%, with an excess return of 1.19% relative to the偏股混合型基金指数[42] - The portfolio ranked in the 43.07% percentile among active equity funds this year (1494/3469)[42] - The annualized return of the portfolio from 2018.1.2 to 2025.6.30 was 19.34%, with an annualized excess return of 14.38% relative to the偏股混合型基金指数[44] - The portfolio consistently ranked in the top 30% of active equity funds each year from 2018 to 2025[44] - The portfolio's performance statistics for each year from 2018 to 2025 are detailed in Table 6[47] - The portfolio's historical performance is illustrated in Figure 12[46] =========

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio recorded an excess return of -0.17% for the week and 16.49% year-to-date [1][6] - The CSI 500 index enhanced portfolio achieved an excess return of 0.26% for the week and 8.94% year-to-date [1][6] - The CSI 1000 index enhanced portfolio had an excess return of 0.91% for the week and 17.72% year-to-date [1][6] - The CSI A500 index enhanced portfolio reported an excess return of -0.21% for the week and 9.06% year-to-date [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as quarterly earnings surprises, year-on-year revenue growth, and quarterly ROE performed well [1][9] - In the CSI 500 component stocks, factors like three-month turnover, quarterly revenue year-on-year growth, and EPTTM one-year percentile showed strong performance [1][9] - For the CSI 1000 component stocks, factors including three-month institutional coverage, quarterly ROE, and executive compensation performed well [1][9] - In the CSI A500 index component stocks, factors such as quarterly revenue year-on-year growth, EPTTM one-year percentile, and quarterly ROE were notable [1][9] - Among publicly offered fund heavy stocks, factors like executive compensation, quarterly ROE, and three-month institutional coverage performed well [1][9] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 0.91%, a minimum of -1.54%, and a median of -0.17% for the week [1][22] - The CSI 500 index enhanced products recorded a maximum excess return of 1.63%, a minimum of -1.35%, and a median of -0.01% for the week [1][23] - The CSI 1000 index enhanced products achieved a maximum excess return of 1.66%, a minimum of -0.37%, and a median of 0.44% for the week [1][24] - The CSI A500 index enhanced products had a maximum excess return of 0.53%, a minimum of -0.76%, and a median of -0.11% for the week [1][26]

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed based on benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices. The construction process includes three main components: return prediction, risk control, and portfolio optimization[12][14][42] - The MFE (Maximized Factor Exposure) portfolio is used to test the effectiveness of single factors under real-world constraints. The optimization model maximizes single-factor exposure while controlling for industry exposure, style exposure, stock weight deviation, and turnover rate. The objective function is defined as: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ Here, `f` represents factor values, `w` is the stock weight vector, and constraints include style factor exposure (`X`), industry exposure (`H`), stock weight deviation (`w`), and component stock weight control (`B_b`). The weights are normalized to sum to 1[42][43][44] - The MFE portfolio construction process involves setting constraints, optimizing the portfolio at the end of each month, and calculating historical returns during the backtesting period. Transaction costs of 0.3% are deducted on both sides to compute risk-return statistics relative to the benchmark[46] - The report monitors the performance of 30+ factors across different sample spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices. Factors are categorized into valuation, reversal, growth, profitability, liquidity, governance, and analyst-related dimensions. Examples include BP (Book-to-Price), ROE (Return on Equity), and momentum factors[15][16][17] - Factor performance varies across sample spaces. For example, in the CSI 300 space, factors such as single-quarter ROE, single-quarter revenue growth, and single-quarter surprise magnitude performed well recently, while factors like BP and expected net profit growth performed poorly[18][19] - In the CSI 500 space, factors such as three-month turnover, single-quarter revenue growth, and EPTTM percentile performed well recently, while factors like one-year momentum and standardized unexpected income performed poorly[20][21] - In the CSI 1000 space, factors such as three-month institutional coverage, single-quarter ROE, and executive compensation performed well recently, while factors like one-year momentum and DELTAROA performed poorly[22][23] - In the CSI A500 space, factors such as single-quarter revenue growth, EPTTM percentile, and single-quarter ROE performed well recently, while factors like one-year momentum and DELTAROE performed poorly[24][25] - In the public fund heavy-holding index space, factors such as executive compensation, single-quarter ROE, and three-month institutional coverage performed well recently, while factors like one-year momentum and expected EPTTM performed poorly[26][27] - The report tracks the performance of public fund index enhancement products for CSI 300, CSI 500, CSI 1000, and CSI A500 indices. For example, CSI 300 index enhancement products had a maximum excess return of 0.91% and a minimum of -1.54% in the past week, with a median of -0.17%[28][32] - CSI 500 index enhancement products had a maximum excess return of 1.63% and a minimum of -1.35% in the past week, with a median of -0.01%[35] - CSI 1000 index enhancement products had a maximum excess return of 1.66% and a minimum of -0.37% in the past week, with a median of 0.44%[38] - CSI A500 index enhancement products had a maximum excess return of 0.53% and a minimum of -0.76% in the past week, with a median of -0.11%[41]

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 [42][43][44] **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, \( f^{T}w \) is the weighted exposure of the portfolio to the factor, 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 to \( l \) 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T}w = 1 \) - **Implementation**: 1. Set constraints for style, industry, and stock weights 2. Construct MFE portfolios for each factor at the end of each month 3. Backtest the MFE portfolios, calculate historical returns, and adjust for transaction costs (0.3% on both sides) [42][43][46] **Model Evaluation**: The MFE portfolio approach is effective in testing factor validity under realistic constraints, ensuring that factors deemed "effective" can contribute to actual portfolio performance [42][43] Quantitative Factors and Construction Methods - **Factor Name**: Single-Quarter ROE **Factor Construction Idea**: Measures the return on equity for a single quarter to capture profitability trends [17] **Factor Construction Process**: $ \text{Single-Quarter ROE} = \frac{\text{Net Income (Quarterly)} \times 2}{\text{Average Shareholders' Equity}} $ - **Net Income (Quarterly)**: Quarterly net income attributable to shareholders - **Average Shareholders' Equity**: Average of beginning and ending equity for the quarter [17] - **Factor Name**: Single-Quarter Revenue Growth (YoY) **Factor Construction Idea**: Tracks the year-over-year growth in quarterly revenue to identify growth trends [17] **Factor Construction Process**: $ \text{Single-Quarter Revenue Growth (YoY)} = \frac{\text{Revenue (Current Quarter)} - \text{Revenue (Same Quarter Last Year)}}{\text{Revenue (Same Quarter Last Year)}} $ [17] - **Factor Name**: Analyst Coverage (3-Month) **Factor Construction Idea**: Measures the number of analysts covering a stock over the past three months to gauge market attention [17] **Factor Construction Process**: $ \text{3-Month Analyst Coverage} = \text{Number of Analysts Covering the Stock in the Last 3 Months} $ [17] Factor Backtesting Results - **Single-Quarter ROE**: - **CSI 300**: Weekly excess return: 0.42%, monthly: 2.94%, YTD: 15.41%, historical annualized: 4.92% [19] - **CSI 500**: Weekly excess return: 0.47%, monthly: 0.89%, YTD: 4.43%, historical annualized: 5.85% [21] - **CSI 1000**: Weekly excess return: 1.20%, monthly: 1.70%, YTD: -0.61%, historical annualized: 7.62% [23] - **CSI A500**: Weekly excess return: 0.30%, monthly: 1.68%, YTD: 13.78%, historical annualized: 3.35% [25] - **Single-Quarter Revenue Growth (YoY)**: - **CSI 300**: Weekly excess return: 0.48%, monthly: 2.34%, YTD: 17.35%, historical annualized: 4.94% [19] - **CSI 500**: Weekly excess return: 1.28%, monthly: 2.58%, YTD: 15.18%, historical annualized: 3.81% [21] - **CSI 1000**: Weekly excess return: 0.69%, monthly: 2.73%, YTD: 15.73%, historical annualized: 5.17% [23] - **CSI A500**: Weekly excess return: 0.47%, monthly: 1.15%, YTD: 15.65%, historical annualized: 2.96% [25] - **3-Month Analyst Coverage**: - **CSI 300**: Weekly excess return: 0.17%, monthly: 0.90%, YTD: 10.33%, historical annualized: 3.07% [19] - **CSI 500**: Weekly excess return: 0.29%, monthly: 0.07%, YTD: 4.10%, historical annualized: 5.56% [21] - **CSI 1000**: Weekly excess return: 1.30%, monthly: 0.52%, YTD: 5.98%, historical annualized: 7.22% [23] - **CSI A500**: Weekly excess return: -0.21%, monthly: 0.97%, YTD: 8.12%, historical annualized: 3.93% [25]