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-0.59%，本年超额收益20.90%。 中证500指数增强组合本周超额收益-0.54%，本年超额收益5.45%。 中证1000指数增强组合本周超额收益-0.19%，本年超额收益15.64%。 中证A500指数增强组合本周超额收益-0.24%，本年超额收益10.26%。 二、本周选股因子表现跟踪 沪深300成分股中标准化预期外盈利、DELTAROA、DELTAROE等因子表现 较好。 中证500成分股中SPTTM、单季SP、单季营收同比增速等因子表现较好。 中证1000成分股中非流动性冲击、三个月机构覆盖、三个月反转等因子表现 较好。 中证A500指数成分股中特异度、SPTTM、标准化预期外盈利等因子表现较 好。 公募基金重仓股中一年动量、单季EP、股息率等因子表现较好。 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0.59%，最低-0.68%，中位 数-0.01%。 中证500指数增强产品本周超额收益最高0.28%，最低-0.84%，中位 数-0.39%。 中证1000指数增强产品本周超额收益最高0.52%，最低-1 ...

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of single factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, and turnover rate. This approach ensures that factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[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 \( f^{T}w \) is the weighted exposure of the portfolio to the factor. \( w \) is the stock weight vector to be optimized. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix for stocks, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style factor exposure[41]. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, where \( H_{ij} = 1 \) if stock \( i \) belongs to industry \( j \), otherwise \( H_{ij} = 0 \). \( h_l, h_h \) are the lower and upper bounds for industry deviation[41]. 3. **Stock Deviation**: \( w_l, w_h \) are the lower and upper bounds for individual stock deviations from the benchmark[41]. 4. **Constituent Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark constituent. \( b_l, b_h \) are the lower and upper bounds for constituent weights[41]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \)[41]. 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T}w = 1 \)[42]. - **Implementation**: At the end of each month, MFE portfolios are constructed for each factor under the defined constraints. Historical returns are calculated during the backtest period, accounting for a 0.3% transaction cost on both sides[44]. **Model Evaluation**: The MFE portfolio effectively tests factor performance under realistic constraints, ensuring that selected factors contribute to return prediction in practical applications[40][41]. Quantitative Factors and Construction Methods - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual quarterly net profit from expected profit, standardized by the standard deviation of expected profit. It captures earnings surprises[17]. **Factor Construction Process**: $ SUE = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Quarterly Net Profit}}{\text{Standard Deviation of Expected Quarterly Net Profit}} $ **Factor Evaluation**: SUE is a widely used factor for capturing earnings surprises and has shown effectiveness in predicting stock returns[17]. - **Factor Name**: DELTAROE **Factor Construction Idea**: Measures the change in return on equity (ROE) compared to the same quarter of the previous year, reflecting profitability improvement[17]. **Factor Construction Process**: $ DELTAROE = \text{Quarterly ROE} - \text{ROE of the Same Quarter Last Year} $ **Factor Evaluation**: DELTAROE is effective in identifying companies with improving profitability, which can lead to positive stock performance[17]. - **Factor Name**: Non-Liquidity Shock **Factor Construction Idea**: Measures the average absolute daily return over the past 20 trading days, divided by the average trading volume, capturing liquidity risk[17]. **Factor Construction Process**: $ \text{Non-Liquidity Shock} = \frac{\text{Average Absolute Daily Return (20 Days)}}{\text{Average Trading Volume (20 Days)}} $ **Factor Evaluation**: This factor is useful for identifying stocks with higher liquidity risks, which may impact their returns[17]. Factor Backtest Results - **Standardized Unexpected Earnings (SUE)**: - **CSI 300 Universe**: Weekly return: 0.43%, monthly return: 2.55%, YTD return: 12.65%, historical annualized return: 4.22%[19]. - **CSI 500 Universe**: Weekly return: 0.07%, monthly return: 1.02%, YTD return: 7.47%, historical annualized return: 5.50%[21]. - **CSI 1000 Universe**: Weekly return: -0.36%, monthly return: 1.55%, YTD return: 20.90%, historical annualized return: 6.47%[23]. - **CSI A500 Universe**: Weekly return: -0.07%, monthly return: 1.17%, YTD return: 11.28%, historical annualized return: 4.55%[25]. - **DELTAROE**: - **CSI 300 Universe**: Weekly return: 0.33%, monthly return: 2.78%, YTD return: 18.51%, historical annualized return: 4.52%[19]. - **CSI 500 Universe**: Weekly return: -0.58%, monthly return: -0.75%, YTD return: 8.13%, historical annualized return: 7.56%[21]. - **CSI 1000 Universe**: Weekly return: -0.56%, monthly return: 1.36%, YTD return: 12.58%, historical annualized return: 8.77%[23]. - **CSI A500 Universe**: Weekly return: 0.01%, monthly return: 2.94%, YTD return: 20.42%, historical annualized return: 4.48%[25]. - **Non-Liquidity Shock**: - **CSI 300 Universe**: Weekly return: -0.06%, monthly return: -0.29%, YTD return: -1.78%, historical annualized return: 0.40%[19]. - **CSI 500 Universe**: Weekly return: -0.35%, monthly return: 0.79%, YTD return: -2.82%, historical annualized return: 0.18%[21]. - **CSI 1000 Universe**: Weekly return: 0.47%, monthly return: -1.66%, YTD return: 5.34%, historical annualized return: 2.23%[23]. - **CSI A500 Universe**: Weekly return: 0.13%, monthly return: -0.34%, YTD return: -3.95%, historical annualized return: 1.50%[25].

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.51% this week and 21.85% year-to-date [7] - The CSI 500 index enhanced portfolio recorded an excess return of -0.73% this week and 6.17% year-to-date [7] - The CSI 1000 index enhanced portfolio had an excess return of -1.12% this week and 15.93% year-to-date [7] - The CSI A500 index enhanced portfolio saw an excess return of -0.28% this week and 10.62% year-to-date [7] Group 2: Factor Performance Tracking - In the CSI 300 component stocks, factors such as one-year momentum, standardized unexpected earnings, and expected net profit quarter-on-quarter performed well [10] - In the CSI 500 component stocks, factors like expected net profit quarter-on-quarter, standardized unexpected earnings, and DELTAROE showed strong performance [10] - For the CSI 1000 component stocks, factors including one-month reversal, single-quarter revenue year-on-year growth, and standardized unexpected income performed well [10] - In the CSI A500 index component stocks, factors such as expected net profit quarter-on-quarter, one-year momentum, and standardized unexpected earnings performed well [10] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.61%, a minimum of -0.73%, and a median of 0.01% this week [23] - The CSI 500 index enhanced products had a maximum excess return of 0.79%, a minimum of -2.23%, and a median of -0.49% this week [25] - The CSI 1000 index enhanced products had a maximum excess return of 1.74%, a minimum of -1.55%, and a median of -0.15% this week [24] - The CSI A500 index enhanced products had a maximum excess return of 0.97%, a minimum of -1.15%, and a median of -0.12% this week [28] Group 4: Public Fund Index Enhanced Product Scale - There are currently 79 CSI 300 index enhanced products with a total scale of 79.9 billion [20] - There are 76 CSI 500 index enhanced products with a total scale of 51.4 billion [20] - There are 46 CSI 1000 index enhanced products with a total scale of 21.4 billion [20] - There are 71 CSI A500 index enhanced products with a total scale of 26.3 billion [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[10]. - **Model Construction Process**: - **Return Prediction**: Predicting the returns of stocks within the benchmark index. - **Risk Control**: Implementing risk control measures to manage the portfolio's risk exposure. - **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to the risk constraints[11]. - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors and optimizing the portfolio accordingly[10][11]. Model Backtesting Results - **Guosen JinGong Index Enhanced Portfolio**: - **CSI 300 Index Enhanced Portfolio**: Weekly excess return 0.51%, annual excess return 21.85%[4][13]. - **CSI 500 Index Enhanced Portfolio**: Weekly excess return -0.73%, annual excess return 6.17%[4][13]. - **CSI 1000 Index Enhanced Portfolio**: Weekly excess return -1.12%, annual excess return 15.93%[4][13]. - **CSI A500 Index Enhanced Portfolio**: Weekly excess return -0.28%, annual excess return 10.62%[4][13]. Quantitative Factors and Construction Methods Factor Name: Momentum - **Factor Construction Idea**: The momentum factor captures the tendency of stocks that have performed well in the past to continue performing well in the future[15]. - **Factor Construction Process**: - **One-Year Momentum**: Calculated as the return of a stock over the past year, excluding the most recent month[15]. - **Formula**: $ \text{One-Year Momentum} = \text{Return}_{t-12} - \text{Return}_{t-1} $[15]. - **Factor Evaluation**: The momentum factor is effective in identifying stocks with strong past performance that are likely to continue performing well[15]. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: The SUE factor measures the difference between actual and expected earnings, standardized by the standard deviation of expected earnings[15]. - **Factor Construction Process**: - **SUE Calculation**: $ \text{SUE} = \frac{\text{Actual Earnings} - \text{Expected Earnings}}{\text{Standard Deviation of Expected Earnings}} $[15]. - **Factor Evaluation**: The SUE factor is useful in identifying stocks with earnings surprises, which can lead to significant price movements[15]. Factor Name: Expected Net Profit QoQ - **Factor Construction Idea**: This factor measures the quarter-over-quarter change in expected net profit[15]. - **Factor Construction Process**: - **Calculation**: $ \text{Expected Net Profit QoQ} = \frac{\text{Expected Net Profit}_{t} - \text{Expected Net Profit}_{t-1}}{\text{Expected Net Profit}_{t-1}} $[15]. - **Factor Evaluation**: The factor is effective in identifying stocks with improving earnings expectations, which can lead to positive price movements[15]. Factor Backtesting Results - **CSI 300 Index**: - **One-Year Momentum**: Weekly excess return 1.09%, monthly excess return 2.08%, annual excess return 3.27%, historical annualized return 2.75%[18]. - **Standardized Unexpected Earnings**: Weekly excess return 0.87%, monthly excess return 2.24%, annual excess return 12.16%, historical annualized return 4.18%[18]. - **Expected Net Profit QoQ**: Weekly excess return 0.86%, monthly excess return 1.50%, annual excess return 6.56%, historical annualized return 1.72%[18]. - **CSI 500 Index**: - **Expected Net Profit QoQ**: Weekly excess return 0.73%, monthly excess return 1.98%, annual excess return 15.82%, historical annualized return 4.80%[20]. - **Standardized Unexpected Earnings**: Weekly excess return 0.72%, monthly excess return 2.48%, annual excess return 18.17%, historical annualized return 4.51%[20]. - **DELTAROE**: Weekly excess return 0.57%, monthly excess return 2.27%, annual excess return 11.09%, historical annualized return 5.36%[20]. - **CSI 1000 Index**: - **One-Month Reversal**: Weekly excess return 1.23%, monthly excess return 0.00%, annual excess return -2.94%, historical annualized return -3.76%[22]. - **Single-Quarter Revenue YoY Growth**: Weekly excess return 1.08%, monthly excess return 3.27%, annual excess return 23.01%, historical annualized return 5.16%[22]. - **Standardized Unexpected Revenue**: Weekly excess return 0.66%, monthly excess return 2.21%, annual excess return 21.47%, historical annualized return 6.55%[22]. - **CSI A500 Index**: - **Expected Net Profit QoQ**: Weekly excess return 1.89%, monthly excess return 1.49%, annual excess return 8.61%, historical annualized return 3.73%[24]. - **One-Year Momentum**: Weekly excess return 1.39%, monthly excess return 2.42%, annual excess return 2.76%, historical annualized return 1.81%[24]. - **Standardized Unexpected Earnings**: Weekly excess return 1.28%, monthly excess return 3.29%, annual excess return 14.02%, historical annualized return 5.92%[24]. - **Public Fund Heavyweight Index**: - **One-Year Momentum**: Weekly excess return 1.61%, monthly excess return 2.90%, annual excess return 4.13%, historical annualized return 15.48%[26]. - **Expected Net Profit QoQ**: Weekly excess return 0.70%, monthly excess return 2.31%, annual excess return 3.36%, historical annualized return 11.90%[26]. - **Single-Quarter Net Profit YoY Growth**: Weekly excess return 0.58%, monthly excess return 2.13%, annual excess return 2.95%, historical annualized return 9.10%[26].

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 portfolio constraints, making it more applicable in practical investment scenarios [40][41]. - **Model Construction Process**: - The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T}w = 1 \end{array} $$ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( f^{T}w \) is the weighted exposure of the portfolio to the factor. \( w \) is the weight vector of stocks in the portfolio [40][41]. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix for stocks, \( w_b \) is the weight vector of the benchmark index, and \( s_l, s_h \) are the lower and upper bounds for style factor deviations [41]. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, where \( H_{ij} = 1 \) if stock \( i \) belongs to industry \( j \). \( h_l, h_h \) are the lower and upper bounds for industry deviations [41]. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for individual stock weight deviations relative to the benchmark [41]. 4. **Component Stock Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a component of the benchmark. \( b_l, b_h \) are the lower and upper bounds for component stock weights [41]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights to a maximum \( l \) [41]. 6. **Full Investment**: Ensures the portfolio is fully invested, with the sum of weights equal to 1 [42]. - The MFE portfolio is constructed monthly, and historical returns are calculated after accounting for transaction costs (0.3% on both sides) [44]. - **Model Evaluation**: The MFE portfolio approach is effective in testing factor performance under realistic constraints, making it a robust method for practical applications [40][41]. --- Quantitative Factors and Construction Methods Factor Name: BP (Book-to-Price Ratio) - **Factor Construction Idea**: Measures the valuation of a stock by comparing its book value to its market price [16]. - **Factor Construction Process**: - Formula: \( \text{BP} = \frac{\text{Net Asset}}{\text{Market Value}} \) [16]. Factor Name: DELTAROE (Change in Return on Equity) - **Factor Construction Idea**: Captures the change in a company's profitability by comparing its return on equity (ROE) to the same period in the previous year [16]. - **Factor Construction Process**: - Formula: \( \text{DELTAROE} = \text{ROE}_{\text{current}} - \text{ROE}_{\text{previous year}} \) [16]. Factor Name: SPTTM (Sales-to-Price Ratio, Trailing Twelve Months) - **Factor Construction Idea**: Evaluates a company's valuation by comparing its trailing twelve-month sales to its market price [16]. - **Factor Construction Process**: - Formula: \( \text{SPTTM} = \frac{\text{Sales (TTM)}}{\text{Market Value}} \) [16]. Factor Name: One-Month Reversal - **Factor Construction Idea**: Measures the short-term reversal effect by calculating the stock's return over the past 20 trading days [16]. - **Factor Construction Process**: - Formula: \( \text{One-Month Reversal} = \text{Return over the past 20 trading days} \) [16]. Factor Name: Three-Month Turnover - **Factor Construction Idea**: Reflects the liquidity of a stock by calculating its average turnover rate over the past 60 trading days [16]. - **Factor Construction Process**: - Formula: \( \text{Three-Month Turnover} = \text{Average Turnover Rate over the past 60 trading days} \) [16]. --- Factor Backtesting Results Performance in the CSI 300 Universe - **DELTAROE**: Weekly excess return 0.74%, monthly 2.05%, YTD 16.88% [18]. - **BP**: Weekly excess return 0.34%, monthly -0.03%, YTD -1.23% [18]. - **SPTTM**: Weekly excess return 0.51%, monthly 0.36%, YTD -0.54% [18]. Performance in the CSI 500 Universe - **BP**: Weekly excess return 1.18%, monthly 1.34%, YTD -0.80% [20]. - **DELTAROE**: Weekly excess return -0.81%, monthly -0.60%, YTD 7.77% [20]. - **SPTTM**: Weekly excess return 0.79%, monthly -0.52%, YTD 2.01% [20]. Performance in the CSI 1000 Universe - **DELTAROE**: Weekly excess return 0.78%, monthly 2.46%, YTD 12.26% [22]. - **BP**: Weekly excess return 0.86%, monthly -0.19%, YTD -0.35% [22]. - **SPTTM**: Weekly excess return 1.05%, monthly 0.23%, YTD -2.52% [22]. Performance in the CSI A500 Universe - **DELTAROE**: Weekly excess return 0.60%, monthly 1.65%, YTD 18.37% [24]. - **BP**: Weekly excess return 0.51%, monthly 0.01%, YTD -4.35% [24]. - **SPTTM**: Weekly excess return 0.19%, monthly -0.31%, YTD -5.36% [24]. Performance in the Public Fund Heavyweight Index Universe - **BP**: Weekly excess return 1.18%, monthly -0.78%, YTD -8.84% [26]. - **DELTAROE**: Weekly excess return -0.11%, monthly -0.12%, YTD 8.42% [26]. - **SPTTM**: Weekly excess return 1.07%, monthly -1.21%, YTD -8.15% [26].

- The report tracks the performance of Guosen JinGong's index enhancement portfolios and public fund index enhancement products, and monitors the performance of common stock selection factors in different stock selection spaces[10] - Guosen JinGong's index enhancement portfolios are constructed using multi-factor stock selection, with benchmarks including CSI 300, CSI 500, CSI 1000, and CSI A500 indices[10][11] - The construction process of Guosen JinGong's index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization[11] Factor Construction and Performance - The report monitors the performance of factors in different stock selection spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy positions index[14] - The factor library includes over 30 common factors from dimensions such as valuation, reversal, growth, profitability, liquidity, corporate governance, and analysts[15] - Factors are constructed using specific calculation methods, for example, BP (Book-to-Price) is calculated as net assets divided by total market value[16] Factor Performance in Different Stock Selection Spaces - **CSI 300 Index**: Factors like DELTAROE, dividend yield, and DELTAROA performed well recently, while factors like single-quarter revenue YoY growth, one-month reversal, and standardized unexpected income performed poorly[1][17] - **CSI 500 Index**: Factors like expected BP, BP, and three-month institutional coverage performed well recently, while factors like DELTAROA, single-quarter net profit YoY growth, and standardized unexpected earnings performed poorly[1][19] - **CSI 1000 Index**: Factors like expected PEG, single-quarter SP, and SPTTM performed well recently, while factors like one-year momentum, three-month reversal, and one-month reversal performed poorly[1][21] - **CSI A500 Index**: Factors like three-month turnover, dividend yield, and DELTAROE performed well recently, while factors like three-month reversal, single-quarter revenue YoY growth, and one-year momentum performed poorly[1][23] - **Public Fund Heavy Positions Index**: Factors like BP, SPTTM, and expected BP performed well recently, while factors like one-year momentum, single-quarter revenue YoY growth, and single-quarter net profit YoY growth performed poorly[1][25] Public Fund Index Enhancement Products Performance - **CSI 300 Index Enhancement Products**: Recently, the highest excess return was 1.38%, the lowest was -0.44%, and the median was 0.41%[2][31] - **CSI 500 Index Enhancement Products**: Recently, the highest excess return was 1.55%, the lowest was -0.51%, and the median was 0.46%[2][34] - **CSI 1000 Index Enhancement Products**: Recently, the highest excess return was 1.57%, the lowest was -0.32%, and the median was 0.57%[2][37] - **CSI A500 Index Enhancement Products**: Recently, the highest excess return was 1.29%, the lowest was -0.35%, and the median was 0.43%[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 ratio, and individual stock weight limits[40][41] - The optimization model's objective function is to maximize single-factor exposure, with constraints including style factor relative exposure limits, industry deviation limits, individual stock deviation limits, component stock weight ratio limits, and individual stock weight limits[41][42] - The MFE portfolio construction process involves setting constraints, constructing the MFE portfolio at the end of each month, and calculating historical returns and risk statistics for the MFE portfolio during the backtest period[44]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.62% this week and 20.75% year-to-date [1][9] - The CSI 500 index enhanced portfolio recorded an excess return of -0.37% this week and 6.86% year-to-date [1][9] - The CSI 1000 index enhanced portfolio had an excess return of 0.97% this week and 16.87% year-to-date [1][9] - The CSI A500 index enhanced portfolio reported an excess return of 0.85% this week and 10.70% year-to-date [1][9] Group 2: Factor Performance Tracking - In the CSI 300 component stocks, factors such as DELTAROE, dividend yield, and DELTAROA performed well [1][10] - In the CSI 500 component stocks, factors like expected BP, BP, and three-month institutional coverage showed strong performance [1][12] - For the CSI 1000 component stocks, factors such as expected PEG, single-quarter SP, and SPTTM performed well [1][15] - In the CSI A500 index component stocks, factors like three-month turnover, dividend yield, and DELTAROE showed good performance [1][19] - In public fund heavy stocks, factors like BP, SPTTM, and expected BP performed well [1][23] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.38%, a minimum of -0.44%, and a median of 0.41% this week [1][28] - The CSI 500 index enhanced products achieved a maximum excess return of 1.55%, a minimum of -0.51%, and a median of 0.46% this week [1][30] - The CSI 1000 index enhanced products recorded a maximum excess return of 1.57%, a minimum of -0.32%, and a median of 0.57% this week [1][31] - The CSI A500 index enhanced products had a maximum excess return of 1.29%, a minimum of -0.35%, and a median of 0.43% this week [2][36]

金融工程丨深度报告 [Table_Title] 多因子选股（二十一）：日历效应下的因子投资 %% %% %% %% research.95579.com 1 丨证券研究报告丨 报告要点 [Table_Summary] 受市场交易影响，不同类别的因子在年内表现有所不同，寻找因子的日历效应规律，可以对因 子选股的收益进行增强，并降低尾部风险。 分析师及联系人 [Table_Author] 郑起 覃川桃 SAC：S0490520060001 SAC：S0490513030001 SFC：BUT353 请阅读最后评级说明和重要声明 2 / 29 %% %% %% %% 2 [Table_Title2] 多因子选股（二十一）：日历效应下的因子投资 [Table_Summary2] 因子的日历效应 本文对 12 个大类因子，共统计了季度、年末年初、春节、两会、国庆五种日历效应： 日历效应指数增强 根据因子的日历效应，在大类因子合成权重上进行调整，构建月度调仓频率的增强策略： 短期展望 进入十二月后，年末年初效应规律为以低波、拥挤度、质量、价值为代表的低风险偏好因子更 为有效，考虑到当前高 Beta 的成长风格仍有一定概率 ...

Quantitative Models and Construction Methods - **Model Name**: Comprehensive Volume-Price Factor **Model Construction Idea**: This factor integrates 11 sub-factors derived from high-frequency data to capture volume and price dynamics in the market. The aim is to smooth high-frequency data into monthly frequency to reduce turnover while maintaining strong stock selection capabilities[6][41] **Model Construction Process**: 1. The 11 sub-factors include "Moderate Adventure," "Complete Tide," "Climbing Peaks," "Team Coin," "Clouds Disperse," "Moth to Flame," "Grass in the Wind," "Sailing in Water," "Hidden in the Forest," "Wait and Rescue," and "Long-Short Game"[6][41] 2. Each sub-factor is calculated using high-frequency data (minute-level), except for "Team Coin," which uses daily data[41] 3. The high-frequency data is smoothed to monthly frequency to reduce turnover[41] 4. The 11 sub-factors are orthogonalized and equally weighted to form the comprehensive volume-price factor[45] **Model Evaluation**: The comprehensive factor significantly outperforms individual sub-factors, demonstrating enhanced performance metrics such as higher information ratios and lower maximum drawdowns[45] - **Model Name**: Expectation Inertia Factor **Model Construction Idea**: This factor analyzes the relationship between analyst expectations, momentum, and valuation, aiming to capture the persistence of expectations in the market[51] **Model Construction Process**: 1. The factor is derived from the analysis of analyst forecast revisions and their impact on stock prices[51] 2. It incorporates momentum and valuation metrics to identify stocks with consistent upward or downward revisions in expectations[51] **Model Evaluation**: The factor maintains stable upward trends in both long-short and long-only portfolios, with no significant drawdowns observed[51] Model Backtesting Results - **Comprehensive Volume-Price Factor**: - Rank IC: -12.64% - Rank ICIR: -5.48 - Annualized Return: 49.23% - Annualized Volatility: 10.66% - Information Ratio (IR): 4.62 - Monthly Win Rate: 91.94% - Maximum Drawdown: -4.84%[45] - **Expectation Inertia Factor**: - Long-Short Portfolio Net Value: Stable upward trend with no significant drawdowns[51][53] - Long-Only Portfolio Net Value: Stable upward trend with no significant drawdowns[51][55] Quantitative Factors and Construction Methods - **Factor Name**: Moderate Adventure **Factor Construction Idea**: Captures alpha information during moments of significant volume surges[41] **Factor Construction Process**: Derived from high-frequency volume data, smoothed to monthly frequency[41] **Factor Evaluation**: Demonstrates strong stock selection ability with a Rank ICIR of -4.87[43] - **Factor Name**: Complete Tide **Factor Construction Idea**: Analyzes tidal changes in individual stock trading volumes[41] **Factor Construction Process**: Derived from high-frequency volume data, smoothed to monthly frequency[41] **Factor Evaluation**: Rank ICIR of -4.15, indicating robust performance[43] - **Factor Name**: Climbing Peaks **Factor Construction Idea**: Focuses on changes in individual stock volatility[41] **Factor Construction Process**: Derived from high-frequency volatility data, smoothed to monthly frequency[41] **Factor Evaluation**: Rank ICIR of 4.89, showcasing strong predictive power[43] - **Factor Name**: Team Coin **Factor Construction Idea**: Identifies momentum effects in individual stocks[41] **Factor Construction Process**: Derived from daily momentum data, smoothed to monthly frequency[41] **Factor Evaluation**: Rank ICIR of -4.62, indicating effective stock selection[43] - **Factor Name**: Clouds Disperse **Factor Construction Idea**: Explores the volatility of volatility and investor ambiguity aversion[41] **Factor Construction Process**: Derived from high-frequency volatility data, smoothed to monthly frequency[41] **Factor Evaluation**: Rank ICIR of -4.72, demonstrating strong performance[43] - **Factor Name**: Moth to Flame **Factor Construction Idea**: Improves amplitude factors by analyzing stock price jumps[41] **Factor Construction Process**: Derived from high-frequency price jump data, smoothed to monthly frequency[41] **Factor Evaluation**: Rank ICIR of -4.69, indicating robust predictive ability[43] - **Factor Name**: Grass in the Wind **Factor Construction Idea**: Examines extreme return distortions and decision-making weights[41] **Factor Construction Process**: Derived from high-frequency return data, smoothed to monthly frequency[41] **Factor Evaluation**: Rank ICIR of -4.49, showcasing strong stock selection ability[43] - **Factor Name**: Sailing in Water **Factor Construction Idea**: Analyzes market-following behavior in individual stock turnover[41] **Factor Construction Process**: Derived from high-frequency turnover data, smoothed to monthly frequency[41] **Factor Evaluation**: Rank ICIR of -5.00, indicating strong performance[43] - **Factor Name**: Hidden in the Forest **Factor Construction Idea**: Decomposes factors driving individual stock price changes[41] **Factor Construction Process**: Derived from high-frequency price data, smoothed to monthly frequency[41] **Factor Evaluation**: Rank ICIR of -5.67, demonstrating robust predictive power[43] - **Factor Name**: Wait and Rescue **Factor Construction Idea**: Analyzes follow-up effects after large trades[41] **Factor Construction Process**: Derived from high-frequency trade data, smoothed to monthly frequency[41] **Factor Evaluation**: Rank ICIR of -4.23, showcasing effective stock selection[43] Factor Backtesting Results - **Moderate Adventure Factor**: - Rank IC: -9.42% - Rank ICIR: -4.87 - Annualized Return: 39.04% - Annualized Volatility: 9.21% - IR: 4.24 - Monthly Win Rate: 90.32% - Maximum Drawdown: -5.58%[43] - **Complete Tide Factor**: - Rank IC: -7.70% - Rank ICIR: -4.15 - Annualized Return: 25.63% - Annualized Volatility: 8.74% - IR: 2.93 - Monthly Win Rate: 81.45% - Maximum Drawdown: -8.19%[43] - **Climbing Peaks Factor**: - Rank IC: 6.07% - Rank ICIR: 4.89 - Annualized Return: 21.03% - Annualized Volatility: 5.75% - IR: 3.65 - Monthly Win Rate: 87.90% - Maximum Drawdown: -2.55%[43] - **Team Coin Factor**: - Rank IC: -9.73% - Rank ICIR: -4.62 - Annualized Return: 39.63% - Annualized Volatility: 10.93% - IR: 3.63 - Monthly Win Rate: 82.26% - Maximum Drawdown: -8.63%[43] - **Clouds Disperse Factor**: - Rank IC: -10.27% - Rank ICIR: -4.72 - Annualized Return: 30.76% - Annualized Volatility: 9.17% - IR: 3.35 - Monthly Win Rate: 83.87% - Maximum Drawdown: -6.86%[43] - **Moth to Flame Factor**: - Rank IC: -9.36% - Rank ICIR: -4.69 - Annualized Return: 38.15% - Annualized Volatility: 10.10% - IR: 3.78 - Monthly Win Rate: 90.32% - Maximum Drawdown: -6.19%[43] - **Grass in the Wind Factor**: - Rank IC: -8.92% - Rank ICIR: -4.49 - Annualized Return: 32.37% - Annualized Volatility: 8.21% - IR: 3.94 - Monthly Win Rate: 85.48% - Maximum Drawdown: -4.05%[43] - **Sailing in Water Factor**: - Rank IC: -9.13% - Rank ICIR: -5.00 - Annualized Return: 34.76% - Annualized Volatility

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.73% this week and 19.95% year-to-date [1][7] - The CSI 500 index enhanced portfolio recorded an excess return of -0.02% this week and 7.36% year-to-date [1][7] - The CSI 1000 index enhanced portfolio had an excess return of -0.31% this week and 15.60% year-to-date [1][7] - The CSI A500 index enhanced portfolio saw an excess return of 0.09% this week and 9.62% year-to-date [1][7] Group 2: Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month earnings adjustments, standardized unexpected earnings, and quarterly net profit year-on-year growth performed well [1][8] - In the CSI 500 component stocks, factors like quarterly ROA, quarterly ROE, and non-liquidity shocks showed strong performance [1][8] - For the CSI 1000 component stocks, factors including quarterly ROA, quarterly revenue year-on-year growth, and quarterly ROE performed well [1][8] - In the CSI A500 index component stocks, factors such as three-month earnings adjustments, one-year momentum, and standardized unexpected earnings performed well [1][8] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.75%, a minimum of -0.80%, and a median of 0.21% this week [1][21] - The CSI 500 index enhanced products recorded a maximum excess return of 0.44%, a minimum of -1.50%, and a median of -0.29% this week [1][23] - The CSI 1000 index enhanced products had a maximum excess return of 0.83%, a minimum of -1.22%, and a median of -0.27% this week [1][27] - The CSI A500 index enhanced products achieved a maximum excess return of 1.02%, a minimum of -0.67%, and a median of 0.01% this week [1][28]