一、本周指数增强组合表现 沪深300指数增强组合本周超额收益-0.87%，本年超额收益11.58%。 中证500指数增强组合本周超额收益-0.22%，本年超额收益11.11%。 中证1000指数增强组合本周超额收益0.02%，本年超额收益14.85%。 中证A500指数增强组合本周超额收益-1.49%，本年超额收益10.27%。 二、本周选股因子表现跟踪 沪深300成分股中标准化预期外收入、一年动量、单季营收同比增速等因子 表现较好。 中证500成分股中EPTTM一年分位点、高管薪酬、DELTAROA等因子表现较 好。 中证1000成分股中标准化预期外收入、三个月反转、单季营收同比增速等因 子表现较好。 中证A500指数成分股中单季营收同比增速、三个月反转、一年动量等因子表 现较好。 公募基金重仓股中一年动量、单季营利同比增速、单季营收同比增速等因子 表现较好。 三、本周公募基金指数增强产品表现跟踪 沪深300指数增强产品本周超额收益最高0.69%，最低-1.53%，中位 数-0.57%。 中证500指数增强产品本周超额收益最高0.78%，最低-1.40%，中位 数-0.31%。 中证1000指数增强产品本周 ...

多因子选股周报 成长因子表现出色，四大指增组合年内超额均超 10% 证券研究报告 | 2025年08月23日 核心观点 金融工程周报 国信金工指数增强组合表现跟踪 因子表现监控 以沪深 300 指数为选股空间。最近一周，标准化预期外收入、一年动量、单 季营收同比增速等因子表现较好，而 EPTTM、单季 EP、一个月换手等因子 表现较差。 以中证 500 指数为选股空间。最近一周，EPTTM 一年分位点、高管薪酬、 DELTAROA 等因子表现较好，而预期 EPTTM、EPTTM、单季 EP 等因子 表现较差。 以中证 1000 指数为选股空间。最近一周，标准化预期外收入、三个月反转、 单季营收同比增速等因子表现较好，而一个月反转、预期 EPTTM、股息率 等因子表现较差。 以中证 A500 指数为选股空间。最近一周，单季营收同比增速、三个月反转、 一年动量等因子表现较好，而单季 EP、EPTTM、预期 EPTTM 等因子表现 较差。 以公募重仓指数为选股空间。最近一周，一年动量、单季营利同比增速、单 季营收同比增速等因子表现较好，而单季 EP、预期 EPTTM、EPTTM 等因 子表现较差。 公募基金指数增强 ...

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.93% this week and 12.11% year-to-date [1][6] - The CSI 500 index enhanced portfolio recorded an excess return of -0.58% this week and 10.97% year-to-date [1][6] - The CSI 1000 index enhanced portfolio had an excess return of -1.56% this week and 14.33% year-to-date [1][6] - The CSI A500 index enhanced portfolio saw an excess return of -0.15% this week and 11.56% year-to-date [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as single-season ROA, standardized expected external income, and standardized expected external profit performed well [1][9] - In the CSI 500 component stocks, factors like one-year momentum, single-season surprise magnitude, and standardized expected external profit showed strong performance [1][9] - For the CSI 1000 component stocks, one-year momentum, EPTTM one-year percentile, and standardized expected external profit were notable [1][9] - In the CSI A500 index component stocks, DELTAROA, standardized expected external income, and DELTAROE performed well [1][9] - Among public fund heavy stocks, one-year momentum, DELTAROA, and single-season revenue year-on-year growth were strong [1][9] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.91%, a minimum of -1.41%, and a median of -0.09% this week [1][20] - The CSI 500 index enhanced products recorded a maximum excess return of 0.52%, a minimum of -2.05%, and a median of -0.51% this week [1][21] - The CSI 1000 index enhanced products achieved a maximum excess return of 0.94%, a minimum of -1.70%, and a median of -0.53% this week [1][22] - The CSI A500 index enhanced products had a maximum excess return of 0.71%, a minimum of -1.10%, and a median of -0.25% this week [1][25]

- The report tracks the performance of Guosen JinGong's index enhancement portfolios and public fund index enhancement products, as well as monitors the performance of common stock selection factors across different sample spaces[11][12][15] - Guosen JinGong's index enhancement portfolios are constructed based on three main components: return prediction, risk control, and portfolio optimization. These portfolios are benchmarked against indices such as CSI 300, CSI 500, CSI 1000, and CSI A500[12][14] - The report introduces the concept of Maximized Factor Exposure (MFE) portfolios to test the effectiveness of single factors under real-world constraints. The optimization model maximizes single-factor exposure while controlling for style, industry, stock weight deviations, and other constraints[41][42][43] - The optimization model for MFE portfolios is expressed as: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style exposure, industry exposure, stock weight deviations, and component stock weight limits[41][42] - The report tracks the performance of single-factor MFE portfolios across different sample spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy positions index. Factors are evaluated based on their excess returns relative to benchmarks[15][18][26] - Common stock selection factors are categorized into valuation, reversal, growth, profitability, liquidity, company governance, and analyst dimensions. Examples include BP (Book-to-Price), ROA (Return on Assets), and one-year momentum[16][17] - In the CSI 300 sample space, factors such as single-season ROA, standardized unexpected income, and standardized unexpected earnings performed well recently, while factors like one-month volatility and three-month volatility performed poorly[19] - In the CSI 500 sample space, factors such as one-year momentum and standardized unexpected earnings showed strong performance recently, while factors like one-month turnover and three-month volatility underperformed[21] - In the CSI 1000 sample space, factors such as one-year momentum and standardized unexpected earnings performed well recently, while factors like BP and single-season SP (Sales-to-Price) performed poorly[23] - In the CSI A500 sample space, factors such as DELTAROA (Change in ROA) and standardized unexpected income performed well recently, while factors like three-month volatility and one-month turnover performed poorly[25] - In the public fund heavy positions index sample space, factors such as one-year momentum and DELTAROA performed well recently, while factors like one-month turnover and three-month turnover underperformed[27] - Public fund index enhancement products are tracked for their excess returns relative to benchmarks. For CSI 300 products, recent weekly excess returns ranged from -1.41% to 1.91%, with a median of -0.09%[32] - For CSI 500 products, recent weekly excess returns ranged from -2.05% to 0.52%, with a median of -0.51%[34] - For CSI 1000 products, recent weekly excess returns ranged from -1.70% to 0.94%, with a median of -0.53%[37] - For CSI A500 products, recent weekly excess returns ranged from -1.10% to 0.71%, with a median of -0.25%[40]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.86% this week and 10.78% year-to-date [1][6] - The CSI 500 index enhanced portfolio recorded an excess return of 0.16% this week and 11.24% year-to-date [1][6] - The CSI 1000 index enhanced portfolio experienced an excess return of -0.29% this week but has a year-to-date excess return of 15.73% [1][6] - The CSI A500 index enhanced portfolio had an excess return of 0.29% this week and 11.42% year-to-date [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as DELTAROE, expected PEG, and expected EPTTM performed well [1][7] - In the CSI 500 component stocks, factors like one-year momentum, expected net profit month-on-month, and one-month reversal showed strong performance [1][7] - For the CSI 1000 component stocks, factors such as DELTAROA, single-quarter net profit year-on-year growth rate, and single-quarter surprise magnitude performed well [1][7] - In the CSI A500 index component stocks, factors like expected PEG, DELTAROE, and expected EPTTM showed good performance [1][7] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 0.82%, a minimum of -0.24%, and a median of 0.26% this week [1][18] - The CSI 500 index enhanced products achieved a maximum excess return of 0.95%, a minimum of -0.73%, and a median of 0.14% this week [1][22] - The CSI 1000 index enhanced products recorded a maximum excess return of 0.69%, a minimum of -0.64%, and a median of -0.02% this week [1][25] - The CSI A500 index enhanced products had a maximum excess return of 0.85%, a minimum of -0.33%, and a median of 0.34% this week [1][24]

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

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

Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure (MFE) Portfolio - **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of single factors under realistic constraints, such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures that the factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[38][39]. - **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, and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure[39]. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation[39]. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation[39]. 4. **Constituent Weight Control**: \( B_b \) is a 0-1 vector indicating benchmark constituents, and \( b_l, b_h \) are the lower and upper bounds for constituent weights[39]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights[39]. 6. **Full Investment**: Ensures the portfolio is fully invested (\( \mathbf{1}^{T}w = 1 \))[40]. - **Implementation**: - Constraints are set monthly, and the MFE portfolio is rebalanced accordingly. - Historical returns are calculated, and transaction costs of 0.3% (double-sided) are deducted to evaluate the portfolio's performance relative to the benchmark[42]. - **Model Evaluation**: The MFE portfolio effectively identifies factors that can predict returns under realistic constraints, making it a robust tool for factor validation[38][39]. --- Quantitative Factors and Construction Methods 1. 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[15]. - **Factor Construction Process**: $ SUE = \frac{\text{Actual Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}} $ - **Parameters**: - Actual Net Profit: Reported quarterly net profit. - Expected Net Profit: Consensus analyst forecast for the quarter. - Standard Deviation: Variability in analyst forecasts[15]. 2. Factor Name: Delta ROA (DELTAROA) - **Factor Construction Idea**: Tracks the change in return on assets (ROA) compared to the same quarter in the previous year to capture profitability trends[15]. - **Factor Construction Process**: $ \Delta ROA = \text{ROA}_{\text{current quarter}} - \text{ROA}_{\text{same quarter last year}} $ - **Parameters**: - ROA: \( \frac{\text{Net Income} \times 2}{\text{Average Total Assets}} \)[15]. 3. Factor Name: Standardized Unexpected Revenue (SUR) - **Factor Construction Idea**: Measures the deviation of actual revenue from expected revenue, standardized by the standard deviation of expected revenue, to capture revenue surprises[15]. - **Factor Construction Process**: $ SUR = \frac{\text{Actual Revenue} - \text{Expected Revenue}}{\text{Standard Deviation of Expected Revenue}} $ - **Parameters**: - Actual Revenue: Reported quarterly revenue. - Expected Revenue: Consensus analyst forecast for the quarter. - Standard Deviation: Variability in analyst forecasts[15]. --- Factor Backtesting Results 1. **Performance in CSI 300 Universe** - **Top-Performing Factors (1 Week)**: Single-quarter ROA (1.09%), Standardized Unexpected Revenue (0.73%), Single-quarter Revenue Growth (0.71%)[17]. - **Underperforming Factors (1 Week)**: Specificity (-0.93%), 3-Month Reversal (-0.53%), 1-Month Volatility (-0.46%)[17]. 2. **Performance in CSI 500 Universe** - **Top-Performing Factors (1 Week)**: Standardized Unexpected Revenue (1.07%), Single-quarter Net Profit Growth (1.00%), Standardized Unexpected Earnings (0.99%)[19]. - **Underperforming Factors (1 Week)**: 3-Month Volatility (-1.08%), BP (-0.28%), 1-Month Volatility (-1.14%)[19]. 3. **Performance in CSI 1000 Universe** - **Top-Performing Factors (1 Week)**: Standardized Unexpected Revenue (1.07%), Standardized Unexpected Earnings (1.00%), Single-quarter Revenue Growth (0.90%)[21]. - **Underperforming Factors (1 Week)**: 1-Month Volatility (-1.14%), 3-Month Volatility (-1.08%), 3-Month Reversal (-1.02%)[21]. 4. **Performance in CSI A500 Universe** - **Top-Performing Factors (1 Week)**: Single-quarter ROA (1.14%), Delta ROA (1.12%), Delta ROE (1.02%)[23]. - **Underperforming Factors (1 Week)**: Specificity (-0.65%), Non-Liquidity Shock (-0.64%), 1-Month Volatility (-0.62%)[23]. 5. **Performance in Public Fund Heavyweight Index** - **Top-Performing Factors (1 Week)**: Delta ROA (1.12%), Expected PEG (0.94%), Standardized Unexpected Earnings (0.99%)[25]. - **Underperforming Factors (1 Week)**: 3-Month Volatility (-0.60%), 1-Month Volatility (-0.62%), 1-Month Reversal (-0.37%)[25].

Group 1 - The core viewpoint of the article is to track the performance of various index enhancement portfolios and the factors influencing stock selection across different indices, highlighting the excess returns achieved by these portfolios [1][2][3]. Group 2 - The performance of the HuShen 300 index enhancement portfolio this week showed an excess return of 0.78%, with a year-to-date excess return of 9.31% [5]. - The performance of the Zhongzheng 500 index enhancement portfolio this week showed an excess return of -0.52%, with a year-to-date excess return of 9.90% [5]. - The Zhongzheng 1000 index enhancement portfolio had an excess return of 0.07% this week, with a year-to-date excess return of 15.69% [5]. - The Zhongzheng A500 index enhancement portfolio reported an excess return of 0.26% this week, with a year-to-date excess return of 9.96% [5]. Group 3 - In the HuShen 300 component stocks, factors such as specificity, EPTTM one-year quantile, and quarterly net profit year-on-year growth performed well [8]. - In the Zhongzheng 500 component stocks, factors like three-month volatility, EPTTM one-year quantile, and expected BP showed good performance [8]. - For Zhongzheng 1000 component stocks, factors such as three-month institutional coverage, three-month reversal, and expected BP performed well [8]. - In the Zhongzheng A500 index component stocks, factors like specificity, three-month reversal, and expected net profit month-on-month growth performed well [8]. Group 4 - The HuShen 300 index enhancement products had a maximum excess return of 1.28%, a minimum of -0.98%, and a median of 0.12% this week [21]. - The Zhongzheng 500 index enhancement products had a maximum excess return of 1.41%, a minimum of -1.31%, and a median of 0.04% this week [21]. - The Zhongzheng 1000 index enhancement products had a maximum excess return of 0.82%, a minimum of -0.47%, and a median of 0.15% this week [21]. - The Zhongzheng A500 index enhancement products had a maximum excess return of 1.16%, a minimum of -0.57%, and a median of -0.04% this week [21].

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