Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The MFE portfolio is designed to maximize the exposure of a single factor while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures that the factor's predictive power is tested under realistic constraints, making it more applicable in actual portfolio construction [40][41]. **Model Construction Process**: The MFE portfolio is constructed using the following optimization model: $ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style factor exposure. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. 4. **Constituent Stock Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark constituent, and \( b_l, b_h \) are the lower and upper bounds for constituent stock weights. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights. 6. **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [40][41][42]. **Model Evaluation**: The MFE portfolio effectively tests factor validity under realistic constraints, making it a robust tool for practical portfolio construction [40][41]. --- Quantitative Factors and Construction Methods - **Factor Name**: BP (Book-to-Price Ratio) **Factor Construction Idea**: Measures valuation by comparing book value to market value [18]. **Factor Construction Process**: $ BP = \frac{\text{Net Asset}}{\text{Market Value}} $ **Factor Evaluation**: BP is a widely used valuation factor and has shown consistent performance in various market conditions [18]. - **Factor Name**: SPTTM (Sales-to-Price Ratio, Trailing Twelve Months) **Factor Construction Idea**: Evaluates valuation by comparing sales to market value over the trailing twelve months [18]. **Factor Construction Process**: $ SPTTM = \frac{\text{TTM Sales}}{\text{Market Value}} $ **Factor Evaluation**: SPTTM is effective in identifying undervalued stocks with strong sales performance [18]. - **Factor Name**: DELTAROE (Change in Return on Equity) **Factor Construction Idea**: Captures growth by measuring the change in ROE compared to the same period last year [18]. **Factor Construction Process**: $ DELTAROE = \text{ROE}_{\text{current quarter}} - \text{ROE}_{\text{same quarter last year}} $ **Factor Evaluation**: DELTAROE is a strong indicator of improving profitability and growth potential [18]. - **Factor Name**: One-Month Reversal **Factor Construction Idea**: Measures short-term price reversal by calculating the return over the past 20 trading days [18]. **Factor Construction Process**: $ \text{One-Month Reversal} = \text{Return over the past 20 trading days} $ **Factor Evaluation**: This factor is effective in capturing mean-reversion effects in the short term [18]. --- Backtesting Results of Models - **MFE Portfolio (HS300)**: - Weekly excess return: -0.04% - Year-to-date excess return: 3.07% [6][15] - **MFE Portfolio (CSI500)**: - Weekly excess return: -1.72% - Year-to-date excess return: -2.50% [6][15] - **MFE Portfolio (CSI1000)**: - Weekly excess return: -1.58% - Year-to-date excess return: 1.63% [6][15] - **MFE Portfolio (CSI A500)**: - Weekly excess return: 0.26% - Year-to-date excess return: 3.78% [6][15] --- Backtesting Results of Factors - **BP Factor (CSI500)**: - Weekly excess return: 1.39% - Monthly excess return: 3.39% - Year-to-date excess return: 1.81% [21][22] - **SPTTM Factor (CSI1000)**: - Weekly excess return: 0.85% - Monthly excess return: 0.15% - Year-to-date excess return: -1.81% [23][24] - **DELTAROE Factor (CSI A500)**: - Weekly excess return: 0.10% - Monthly excess return: 0.72% - Year-to-date excess return: 1.20% [25][26] - **One-Month Reversal Factor (HS300)**: - Weekly excess return: -0.72% - Monthly excess return: -1.41% - Year-to-date excess return: -2.52% [19][20]

Quantitative Models and Factor Analysis Quantitative Models and Construction Methods Model Name: Guosen JinGong Index Enhanced Portfolio - **Model Construction Idea**: The model aims to outperform its respective benchmarks by constructing enhanced portfolios based on multiple factors[11][12]. - **Model Construction Process**: 1. **Return Prediction**: Predicting the returns of stocks within the benchmark index. 2. **Risk Control**: Implementing risk control measures to manage the portfolio's risk exposure. 3. **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to the risk constraints[12]. - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors[11]. Model Backtesting Results - **Guosen JinGong Index Enhanced Portfolio**: - **CSI 300 Index Enhanced Portfolio**: Weekly excess return 0.24%, annual excess return 3.21%[5][14]. - **CSI 500 Index Enhanced Portfolio**: Weekly excess return 0.53%, annual excess return -0.27%[5][14]. - **CSI 1000 Index Enhanced Portfolio**: Weekly excess return 1.63%, annual excess return 3.92%[5][14]. - **CSI A500 Index Enhanced Portfolio**: Weekly excess return 0.40%, annual excess return 3.28%[5][14]. Quantitative Factors and Construction Methods Factor Name: Single-Season SP - **Factor Construction Idea**: This factor measures the ratio of single-quarter operating revenue to total market value[17]. - **Factor Construction Process**: - Formula: $ \text{Single-Season SP} = \frac{\text{Single-Quarter Operating Revenue}}{\text{Total Market Value}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 300 and public fund heavy index selection spaces[1][26]. Factor Name: Single-Season EP - **Factor Construction Idea**: This factor measures the ratio of single-quarter net profit attributable to the parent company to total market value[17]. - **Factor Construction Process**: - Formula: $ \text{Single-Season EP} = \frac{\text{Single-Quarter Net Profit Attributable to Parent}}{\text{Total Market Value}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 300 and CSI A500 index selection spaces[1][24]. Factor Name: SPTTM - **Factor Construction Idea**: This factor measures the ratio of trailing twelve months (TTM) operating revenue to total market value[17]. - **Factor Construction Process**: - Formula: $ \text{SPTTM} = \frac{\text{TTM Operating Revenue}}{\text{Total Market Value}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 300 and public fund heavy index selection spaces[1][26]. Factor Name: One-Month Reversal - **Factor Construction Idea**: This factor measures the price change over the past 20 trading days[17]. - **Factor Construction Process**: - Formula: $ \text{One-Month Reversal} = \text{Price Change over Past 20 Trading Days} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 500 and CSI 1000 index selection spaces[1][20][22]. Factor Name: Three-Month Reversal - **Factor Construction Idea**: This factor measures the price change over the past 60 trading days[17]. - **Factor Construction Process**: - Formula: $ \text{Three-Month Reversal} = \text{Price Change over Past 60 Trading Days} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 500 and CSI 1000 index selection spaces[1][20][22]. Factor Name: Non-Liquidity Shock - **Factor Construction Idea**: This factor measures the average absolute value of daily price changes over the past 20 trading days divided by the average trading volume[17]. - **Factor Construction Process**: - Formula: $ \text{Non-Liquidity Shock} = \frac{\text{Average Absolute Daily Price Change}}{\text{Average Trading Volume}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 1000 index selection space[1][22]. Factor Backtesting Results - **Single-Season SP**: - **CSI 300**: Weekly excess return 1.33%, monthly excess return 0.89%, annualized historical return 2.80%[19]. - **Public Fund Heavy Index**: Weekly excess return 1.45%, monthly excess return 1.56%, annualized historical return 1.98%[26]. - **Single-Season EP**: - **CSI 300**: Weekly excess return 0.99%, monthly excess return 1.48%, annualized historical return 5.37%[19]. - **CSI A500**: Weekly excess return 1.29%, monthly excess return 1.59%, annualized historical return 5.16%[24]. - **SPTTM**: - **CSI 300**: Weekly excess return 1.11%, monthly excess return 0.81%, annualized historical return 2.03%[19]. - **Public Fund Heavy Index**: Weekly excess return 1.44%, monthly excess return 1.09%, annualized historical return 0.76%[26]. - **One-Month Reversal**: - **CSI 500**: Weekly excess return 1.19%, monthly excess return -0.32%, annualized historical return -1.60%[20]. - **CSI 1000**: Weekly excess return 1.77%, monthly excess return -0.37%, annualized historical return -4.29%[22]. - **Three-Month Reversal**: - **CSI 500**: Weekly excess return 1.42%, monthly excess return -3.14%, annualized historical return -2.38%[20]. - **CSI 1000**: Weekly excess return 1.56%, monthly excess return 0.76%, annualized historical return -1.95%[22]. - **Non-Liquidity Shock**: - **CSI 1000**: Weekly excess return 1.52%, monthly excess return 2.73%, annualized historical return 2.48%[22].

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

Quantitative Models and Factor Analysis Summary Quantitative Models and Construction Methods Model Name: Guosen JinGong Index Enhanced Portfolio - **Model Construction Idea**: The model aims to outperform its respective benchmarks by constructing enhanced portfolios based on multiple factors[11] - **Model Construction Process**: - **Return Prediction**: Predicting the returns of stocks within the benchmark index - **Risk Control**: Implementing risk control measures to manage portfolio risk - **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate[12] - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors[11] Model Backtesting Results Guosen JinGong Index Enhanced Portfolio - **CSI 300 Index Enhanced Portfolio**: - Weekly Excess Return: 0.00% - Year-to-Date Excess Return: 3.00%[5][14] - **CSI 500 Index Enhanced Portfolio**: - Weekly Excess Return: 0.01% - Year-to-Date Excess Return: -0.88%[5][14] - **CSI 1000 Index Enhanced Portfolio**: - Weekly Excess Return: 0.90% - Year-to-Date Excess Return: 2.17%[5][14] - **CSI A500 Index Enhanced Portfolio**: - Weekly Excess Return: -0.53% - Year-to-Date Excess Return: 2.90%[5][14] Quantitative Factors and Construction Methods Factor Name: Dividend Yield - **Factor Construction Idea**: Measures the dividend income relative to the stock price, indicating the return on investment from dividends[17] - **Factor Construction Process**: - Formula: $\text{Dividend Yield} = \frac{\text{Dividend per Share}}{\text{Stock Price}}$ - The factor is calculated as the sum of the dividends declared over the last four quarters divided by the current market capitalization[17] - **Factor Evaluation**: The dividend yield factor is effective in identifying stocks with high dividend returns, which can be attractive to income-focused investors[17] Factor Name: EPTTM (Earnings to Price TTM) - **Factor Construction Idea**: Measures the earnings relative to the stock price over the trailing twelve months, indicating the profitability of the company[17] - **Factor Construction Process**: - Formula: $\text{EPTTM} = \frac{\text{Earnings TTM}}{\text{Market Capitalization}}$ - The factor is calculated as the total earnings over the trailing twelve months divided by the current market capitalization[17] - **Factor Evaluation**: The EPTTM factor is useful for identifying undervalued stocks with strong earnings performance[17] Factor Name: BP (Book to Price) - **Factor Construction Idea**: Measures the book value relative to the stock price, indicating the intrinsic value of the company[17] - **Factor Construction Process**: - Formula: $\text{BP} = \frac{\text{Book Value}}{\text{Market Capitalization}}$ - The factor is calculated as the book value of equity divided by the current market capitalization[17] - **Factor Evaluation**: The BP factor helps in identifying stocks that are potentially undervalued based on their book value[17] Factor Backtesting Results CSI 300 Index Sample Space - **Best Performing Factors (Recent Week)**: Expected PEG, Single Quarter ROA, EPTTM[1][19] - **Worst Performing Factors (Recent Week)**: Three-Month Reversal, Expected Net Profit QoQ, One-Month Turnover[1][19] CSI 500 Index Sample Space - **Best Performing Factors (Recent Week)**: Dividend Yield, EPTTM, BP[1][20] - **Worst Performing Factors (Recent Week)**: Expected Net Profit QoQ, Single Quarter Operating Profit YoY Growth, Three-Month Institutional Coverage[1][20] CSI 1000 Index Sample Space - **Best Performing Factors (Recent Week)**: Single Quarter ROA, Single Quarter ROE, Standardized Unexpected Revenue[1][22] - **Worst Performing Factors (Recent Week)**: One-Month Reversal, Single Quarter Net Profit YoY Growth, Three-Month Reversal[1][22] CSI A500 Index Sample Space - **Best Performing Factors (Recent Week)**: Dividend Yield, Single Quarter Revenue YoY Growth, Single Quarter ROA[1][24] - **Worst Performing Factors (Recent Week)**: Expected Net Profit QoQ, Single Quarter Net Profit YoY Growth, Single Quarter Operating Profit YoY Growth[1][24] Public Fund Heavy Index Sample Space - **Best Performing Factors (Recent Week)**: Dividend Yield, Single Quarter ROA, DELTAROA[1][27] - **Worst Performing Factors (Recent Week)**: Three-Month Reversal, Three-Month Institutional Coverage, Single Quarter Net Profit YoY Growth[1][27]

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed using multi-factor stock selection models targeting benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices[11][12][14] - The construction process of the index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization[12] - Single-factor Maximized Factor Exposure (MFE) portfolios are constructed for each factor within different stock selection spaces (e.g., CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy positions index) to evaluate the effectiveness of individual factors under real-world constraints[15][40][41] - The optimization model for MFE portfolios aims to maximize single-factor exposure while controlling for constraints such as style exposure, industry exposure, individual stock weight deviation, component stock weight proportion, and individual stock weight limits[40][41][42] - The formula for the optimization model is: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style, industry, and stock-specific limits[40][41] - Factors are categorized into valuation, reversal, growth, profitability, liquidity, volatility, corporate governance, and analyst dimensions, with over 30 factors tracked, such as BP, EPTTM, ROA, ROE, and dividend yield[16][17] - Factor performance varies across different stock selection spaces. For example, in the CSI 300 space, factors like expected PEG, single-quarter ROA, and EPTTM performed well recently, while factors like three-month reversal and one-month turnover performed poorly[19] - In the CSI 500 space, factors such as dividend yield, EPTTM, and BP showed strong performance recently, while factors like three-month reversal and single-quarter profit growth rate underperformed[20][21] - In the CSI 1000 space, factors like single-quarter ROA, single-quarter ROE, and standardized unexpected income performed well, while factors like one-month reversal and three-month reversal lagged[22][23] - In the CSI A500 space, factors such as dividend yield, single-quarter revenue growth rate, and single-quarter ROA performed well, while factors like expected net profit quarter-on-quarter and single-quarter profit growth rate underperformed[24][25] - In the public fund heavy positions index space, factors like dividend yield, single-quarter ROA, and DELTAROA performed well recently, while factors like three-month reversal and single-quarter profit growth rate lagged[26][27] - The report also tracks the performance of public fund index enhancement products, including CSI 300, CSI 500, CSI 1000, and CSI A500 enhancement products, with metrics such as excess returns over different periods[28][32][35][37][39]

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

证券研究报告 | 2026年01月24日 多因子选股周报 超额全线回暖，中证 A500 增强组合年内超额 3.50% 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，三个月波动、非流动性冲击、BP 等因子表现较好，而标准化预期外盈利、单季超预期幅度、一年动量等因子 表现较差。 以中证 500 指数为选股空间。最近一周，SPTTM、单季 SP、单季超预期幅 度等因子表现较好，而三个月反转、高管薪酬、非流动性冲击等因子表现较 差。 以中证 1000 指数为选股空间。最近一周，标准化预期外收入、单季 EP、预 期 BP 等因子表现较好，而一个月波动、高管薪酬、三个月波动等因子表现 较差。 以中证 A500 指数为选股空间。最近一周，三个月换手、三个月波动、一个 月换手等因子表现较好，而一年动量、一个月反转、标准化预期外盈利等因 子表现较差。 以公募重仓指数为选股空间。最近一周，BP、预期 BP、单季 SP 等因子表 现较好，而一年动量、高管薪酬、单季 ROE 等因子表现较差。 公募基金指数增强产品表现跟踪 目前，公募基金沪深 300 指数增强产品共有 7 ...

Group 1 - The core viewpoint of the article is to track the performance of various index-enhanced portfolios and the factors influencing stock selection across different indices [2][3][19] Group 2 - The performance of the CSI 300 index-enhanced portfolio showed an excess return of 1.60% for the week and 2.09% year-to-date [7][19] - The CSI 500 index-enhanced portfolio had an excess return of 0.23% for the week but a negative return of -1.59% year-to-date [7][19] - The CSI 1000 index-enhanced portfolio achieved an excess return of 1.77% for the week and -0.36% year-to-date [7][19] - The CSI A500 index-enhanced portfolio reported an excess return of 0.97% for the week and 1.63% year-to-date [7][19] Group 3 - In the CSI 300 component stocks, factors such as standardized unexpected earnings, quarterly earnings surprises, and DELTAROE performed well [8][10] - In the CSI 500 component stocks, factors like year-on-year revenue growth, specificity, and expected net profit quarter-on-quarter showed strong performance [10][12] - For the CSI 1000 component stocks, factors such as illiquidity shock, one-month turnover, and three-month turnover performed well [10][14] - In the CSI A500 index component stocks, factors like three-month earnings adjustments, standardized unexpected revenue, and specificity showed good performance [10][16] Group 4 - The public fund index-enhanced products for the CSI 300 had a maximum excess return of 2.12% and a minimum of -0.45% for the week, with a median of 0.44% [21][23] - The CSI 500 index-enhanced products had a maximum excess return of 0.42% and a minimum of -2.18% for the week, with a median of -0.14% [25] - The CSI 1000 index-enhanced products reported a maximum excess return of 1.18% and a minimum of -0.52% for the week, with a median of 0.49% [24][25] - The CSI A500 index-enhanced products had a maximum excess return of 2.00% and a minimum of -0.52% for the week, with a median of 0.37% [26]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.44% this week, with the same excess return for the year [8] - The CSI 500 index enhanced portfolio recorded an excess return of -1.80% this week, matching the year-to-date performance [8] - The CSI 1000 index enhanced portfolio saw an excess return of -2.20% this week, consistent with the year-to-date performance [8] - The CSI A500 index enhanced portfolio achieved an excess return of 0.61% this week, with the same year-to-date performance [8] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month institutional coverage, DELTAROA, and DELTAROE performed well [9] - In the CSI 500 component stocks, factors like quarterly net profit year-on-year growth, expected net profit quarter-on-quarter, and specificity showed strong performance [11] - In the CSI 1000 component stocks, factors such as one-year momentum, quarterly revenue year-on-year growth, and standardized expected external income performed well [14] - In the CSI A500 index component stocks, factors like quarterly net profit year-on-year growth, DELTAROE, and quarterly profit year-on-year growth showed strong performance [17] - In public fund heavy stocks, factors like quarterly net profit year-on-year growth, expected net profit quarter-on-quarter, and three-month reversal performed well [20] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.48%, a minimum of -1.42%, and a median of 0.09% this week [28] - The CSI 500 index enhanced products had a maximum excess return of 0.06%, a minimum of -3.87%, and a median of -1.38% this week [30] - The CSI 1000 index enhanced products had a maximum excess return of 1.11%, a minimum of -1.84%, and a median of -0.38% this week [29] - The CSI A500 index enhanced products had a maximum excess return of 2.21%, a minimum of -1.13%, and a median of -0.26% this week [28]

Quantitative Models and Factor Analysis Quantitative Models and Construction Methods Model Name: Guosen JinGong Index Enhanced Portfolio - **Model Construction Idea**: The model aims to outperform its respective benchmarks by constructing enhanced portfolios based on multiple factors[11][12] - **Model Construction Process**: 1. **Return Prediction**: Predicting the returns of stocks within the benchmark index 2. **Risk Control**: Implementing risk control measures to manage the portfolio's risk exposure 3. **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to risk constraints[12] - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors[11][12] Model Backtesting Results - **Guosen JinGong Index Enhanced Portfolio**: - **CSI 300 Index Enhanced Portfolio**: Weekly excess return 0.44%, annual excess return 0.44%[5][14] - **CSI 500 Index Enhanced Portfolio**: Weekly excess return -1.80%, annual excess return -1.80%[5][14] - **CSI 1000 Index Enhanced Portfolio**: Weekly excess return -2.20%, annual excess return -2.20%[5][14] - **CSI A500 Index Enhanced Portfolio**: Weekly excess return 0.61%, annual excess return 0.61%[5][14] Quantitative Factors and Construction Methods Factor Name: Single Factor MFE (Maximized Factor Exposure) Portfolio - **Factor Construction Idea**: The factor aims to maximize the exposure to a single factor while controlling for various constraints such as industry exposure, style exposure, and stock weight deviations[40][41] - **Factor Construction Process**: 1. **Optimization Model**: The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T} w \\ \text{s.t.} & s_{l} \leq X(w - w_{b}) \leq s_{h} \\ & h_{l} \leq H(w - w_{b}) \leq h_{h} \\ & w_{l} \leq w - w_{b} \leq w_{h} \\ & b_{l} \leq B_{b} w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $$ where \( f \) represents the factor values, \( w \) is the stock weight vector, and the constraints include style exposure, industry exposure, stock weight deviations, and component stock weight limits[40][41] 2. **Constraints**: The constraints include: - **Style Exposure**: \( X \) is the factor exposure matrix, \( w_{b} \) is the benchmark weight vector, \( s_{l} \) and \( s_{h} \) are the lower and upper bounds for style exposure[41] - **Industry Exposure**: \( H \) is the industry exposure matrix, \( h_{l} \) and \( h_{h} \) are the lower and upper bounds for industry exposure[41] - **Stock Weight Deviations**: \( w_{l} \) and \( w_{h} \) are the lower and upper bounds for stock weight deviations[41] - **Component Stock Weight Limits**: \( B_{b} \) is the 0-1 vector indicating whether a stock is a benchmark component, \( b_{l} \) and \( b_{h} \) are the lower and upper bounds for component stock weights[41] - **No Short Selling**: The weights are non-negative and sum to 1[41] 3. **Portfolio Construction**: The MFE portfolio is constructed by maximizing the factor exposure while adhering to the constraints[42][44] - **Factor Evaluation**: The MFE portfolio is used to test the effectiveness of single factors under realistic constraints, making it more likely to reflect the true predictive power of the factors in the final portfolio[40][41] Factor Backtesting Results - **CSI 300 Index**: - **Best Performing Factors (Weekly)**: Three-month institutional coverage (0.86%), DELTAROA (0.61%), DELTAROE (0.52%)[19] - **Worst Performing Factors (Weekly)**: Expected net profit QoQ (-0.78%), one-year momentum (-0.45%), idiosyncratic volatility (-0.42%)[19] - **CSI 500 Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (0.06%), expected net profit QoQ (0.33%), idiosyncratic volatility (0.22%)[21] - **Worst Performing Factors (Weekly)**: One-month volatility (-2.47%), EPTTM (-3.56%), single-quarter ROE (-0.67%)[21] - **CSI 1000 Index**: - **Best Performing Factors (Weekly)**: One-year momentum (1.94%), single-quarter revenue YoY growth (1.31%), standardized unexpected income (0.92%)[23] - **Worst Performing Factors (Weekly)**: EPTTM (-3.56%), dividend yield (-3.27%), expected EPTTM (-3.22%)[23] - **CSI A500 Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (1.14%), DELTAROE (0.88%), single-quarter operating profit YoY growth (0.70%)[25] - **Worst Performing Factors (Weekly)**: EPTTM (-1.29%), one-month volatility (-1.22%), three-month volatility (-1.09%)[25] - **Public Fund Heavy Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (1.14%), expected net profit QoQ (0.88%), three-month reversal (0.29%)[27] - **Worst Performing Factors (Weekly)**: Expected EPTTM (-0.74%), EPTTM (-1.29%), one-month volatility (-1.22%)[27]