点击注册小程序 沪深300股票池中，本周表现较好的因子有日内波动率与成交金额的相关性(1.23%)、ROE稳定性 (1.14%)、下行波动率占比 (1.13%)。表现较差的因子有早盘收益因子 (-2.46%)、动量弹簧因子 (-2.21%)、 净利润断层(-1.72%)。 中证500股票池中，本周表现较好的因子有单季度总资产毛利率(1.82%)、动量调整大单(1.66%)、总资产毛 利率TTM (1.63%)。表现较差的因子有单季度ROA同比(-0.66%)、单季度ROE同比(-0.55%)、ROIC增强因 子(-0.53%)。 查看完整报告 特别申明： 本订阅号中所涉及的证券研究信息由光大证券研究所编写，仅面向光大证券专业投资者客户，用作新媒体形势下研究 信息和研究观点的沟通交流。非光大证券专业投资者客户，请勿订阅、接收或使用本订阅号中的任何信息。本订阅号 难以设置访问权限，若给您造成不便，敬请谅解。光大证券研究所不会因关注、收到或阅读本订阅号推送内容而视相 关人员为光大证券的客户。 报告摘要 量化市场跟踪 大类因子表现： 本周全市场股票池中，市值因子获取正收益0.99%，市场大市值风格显著；杠杆因子、流动 ...

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Combination - **Model Construction Idea**: The PB-ROE-50 combination is constructed based on the principle of selecting stocks with low price-to-book (PB) ratios and high return on equity (ROE), aiming to capture value and profitability factors[25] - **Model Construction Process**: - Stocks are selected based on their PB and ROE metrics - The portfolio is rebalanced periodically to maintain the desired exposure to these factors - The construction details are referenced in earlier reports[25][26] - **Model Evaluation**: The model experienced a drawdown in excess returns across all stock pools during the week, indicating potential short-term underperformance[25] --- Model Backtesting Results 1. PB-ROE-50 Combination - **Excess Return**: - CSI 500: -0.23% this week, 2.92% year-to-date - CSI 800: -0.98% this week, 15.82% year-to-date - Full Market: -1.39% this week, 18.21% year-to-date[26] - **Absolute Return**: - CSI 500: -1.49% this week, 30.06% year-to-date - CSI 800: -2.10% this week, 38.80% year-to-date - Full Market: -1.91% this week, 46.11% year-to-date[26] --- Quantitative Factors and Construction Methods 1. Factor Name: Residual Volatility Factor - **Factor Construction Idea**: Captures the residual volatility of stocks after controlling for market and sector effects, aiming to identify stocks with stable performance[20] - **Factor Construction Process**: - Calculate the residual volatility of stock returns after regressing against market and sector returns - Rank stocks based on their residual volatility and construct a portfolio with the desired exposure[20] - **Factor Evaluation**: The factor delivered positive returns this week, indicating its effectiveness in capturing stable stocks during the period[20] 2. Factor Name: Leverage Factor - **Factor Construction Idea**: Measures the financial leverage of companies, aiming to capture the risk-return tradeoff associated with leverage[20] - **Factor Construction Process**: - Calculate the leverage ratio of companies (e.g., debt-to-equity ratio) - Rank stocks based on their leverage and construct a portfolio with the desired exposure[20] - **Factor Evaluation**: The factor delivered positive returns this week, suggesting its relevance in the current market environment[20] 3. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market returns, aiming to capture systematic risk[20] - **Factor Construction Process**: - Calculate the beta of stocks using historical return data - Rank stocks based on their beta and construct a portfolio with the desired exposure[20] - **Factor Evaluation**: The factor delivered negative returns this week, indicating underperformance in the current market environment[20] 4. Factor Name: Size Factor - **Factor Construction Idea**: Captures the size effect by focusing on small-cap stocks, which tend to outperform large-cap stocks over time[20] - **Factor Construction Process**: - Rank stocks based on their market capitalization - Construct a portfolio with a tilt towards smaller-cap stocks[20] - **Factor Evaluation**: The factor delivered negative returns this week, despite the general preference for small-cap stocks in the market[20] 5. Factor Name: Momentum Factor - **Factor Construction Idea**: Captures the momentum effect by focusing on stocks with strong recent performance[20] - **Factor Construction Process**: - Calculate the past returns of stocks over a specific period (e.g., 6 months or 12 months) - Rank stocks based on their momentum and construct a portfolio with the desired exposure[20] - **Factor Evaluation**: The factor delivered negative returns this week, indicating a reversal effect in the market[20] --- Factor Backtesting Results 1. Residual Volatility Factor - Weekly Return: 0.50%[20] 2. Leverage Factor - Weekly Return: 0.36%[20] 3. Beta Factor - Weekly Return: -1.10%[20] 4. Size Factor - Weekly Return: -0.92%[20] 5. Momentum Factor - Weekly Return: -0.70%[20]

Report Overview - The report analyzes the convertible bond holdings of public funds in Q3 2025, including scale, industry and style preferences, and factor performance [1][9] 1. Report Industry Investment Rating - Not provided in the report 2. Report's Core View - As of Q3 2025, public funds held convertible bonds worth 303.8 billion yuan, with the market value ratio increasing to 38.94%. Funds prefer convertible bonds with low BS pricing premium, high conversion value, large balance, and low conversion premium ratio. Factors such as maturity, implied volatility, and pure bond value have performed well this year [1][9] 3. Summary by Relevant Catalog 3.1 Publicly - Held Convertible Bond Scale - As of Q3 2025, 1581 public funds held convertible bonds, with a total scale of 303.8 billion yuan, accounting for 38.94% of the total convertible bond market value [9][13] 3.2 Convertible Bond Funds and Heavy - Held Convertible Bonds - Funds with large convertible bond holdings in Q3 2025 include Boshi CSI Convertible and Exchangeable Bond ETF, Haifutong Shanghai Stock Exchange Investment - Grade Convertible and Exchangeable Bond ETF, etc., all with holdings over 7 billion yuan. Funds with a high proportion of convertible bonds include Huashang Convertible Bond Selection A, Rongtong Convertible Bond A, etc., all with a proportion over 105% [15] 3.3 Convertible Bond Holding Industry Distribution - In terms of market value, the banking, power equipment and new energy, basic chemicals, and electronics industries have the largest holdings, all over 20 billion yuan. The banking industry accounts for 19%. Power equipment and new energy, banking, and basic chemicals are over - allocated, while power and utilities, non - banking, and construction are under - allocated [9][20] 3.4 Convertible Bond Holding Style Distribution - 21 style factors are constructed from four aspects: convertible bond valuation, underlying stock, trading, and terms. The market's funds prefer convertible bonds with low BS pricing premium, high conversion value, large scale, and low conversion premium ratio [22][27] 3.5 Convertible Bond Holding Factor Performance - From December 31, 2024, to October 29, 2025, factors such as maturity, implied volatility, implied volatility premium for 1 year, pure bond value, and peak factor have performed relatively well, with information ratios above 1.7 [29][30]

Quantitative Models and Construction - **Model Name**: PB-ROE-50 **Model Construction Idea**: The model combines Price-to-Book ratio (PB) and Return on Equity (ROE) to construct a stock selection strategy[25] **Model Construction Process**: The PB-ROE-50 model selects stocks based on their PB and ROE metrics. Stocks with favorable PB and ROE values are included in the portfolio. The model uses a monthly rebalancing approach to optimize the portfolio[25][26] **Model Evaluation**: The model demonstrates positive excess returns in most stock pools, indicating its effectiveness in capturing value and profitability factors[25][26] - **Model Name**: Institutional Research Tracking Strategy **Model Construction Idea**: This strategy leverages institutional research activities (public and private) to identify stocks with potential excess returns[27] **Model Construction Process**: The strategy tracks stocks that are frequently researched by public and private institutions. Stocks with higher research frequency are included in the portfolio. The portfolio is rebalanced periodically to reflect updated research trends[27][28] **Model Evaluation**: The strategy shows consistent positive excess returns, suggesting that institutional research activities can be a reliable indicator for stock selection[27][28] - **Model Name**: Block Trade Strategy **Model Construction Idea**: The strategy identifies stocks with high block trade activity and low volatility to construct a portfolio[31] **Model Construction Process**: Stocks are selected based on two criteria: high block trade transaction ratios and low 6-day transaction volatility. The portfolio is rebalanced monthly to maintain these characteristics[31][32] **Model Evaluation**: The strategy has mixed results, with negative excess returns in the recent 2-week period, but positive performance over the year[31][32] - **Model Name**: Directed Issuance Strategy **Model Construction Idea**: The strategy focuses on stocks involved in directed issuance events to capture potential investment opportunities[36] **Model Construction Process**: Stocks are selected based on the announcement date of directed issuance events. The strategy considers market capitalization, rebalancing frequency, and position control to construct the portfolio[36][37] **Model Evaluation**: The strategy shows negative excess returns in the recent 2-week period, raising questions about its effectiveness under current market conditions[36][37] Model Backtesting Results - **PB-ROE-50 Model**: - Excess return in CSI 500: -0.82% - Excess return in CSI 800: 1.45% - Excess return in the entire market: 0.75%[25][26] - **Institutional Research Tracking Strategy**: - Public research excess return: 1.03% - Private research excess return: 1.89%[27][28] - **Block Trade Strategy**: - Excess return relative to CSI All Index: -0.57%[31][32] - **Directed Issuance Strategy**: - Excess return relative to CSI All Index: -1.13%[36][37] Quantitative Factors and Construction - **Factor Name**: Liquidity Factor **Factor Construction Idea**: Measures the liquidity of stocks to identify those with higher trading activity[20] **Factor Construction Process**: The liquidity factor is calculated using metrics such as turnover rate and trading volume. Stocks with higher liquidity scores are assigned positive weights[20] **Factor Evaluation**: The factor shows positive returns in the recent 2-week period, indicating its effectiveness in capturing market liquidity trends[20] - **Factor Name**: Leverage Factor **Factor Construction Idea**: Evaluates the financial leverage of companies to identify those with higher risk-adjusted returns[20] **Factor Construction Process**: The leverage factor is derived from financial ratios such as debt-to-equity and interest coverage. Companies with optimal leverage levels are favored[20] **Factor Evaluation**: The factor demonstrates positive returns, suggesting its utility in identifying financially stable companies[20] - **Factor Name**: Profitability Factor **Factor Construction Idea**: Captures the profitability of companies to identify those with strong earnings performance[20] **Factor Construction Process**: The profitability factor is calculated using metrics such as ROE, ROA, and net profit margin. Stocks with higher profitability metrics are given positive weights[20] **Factor Evaluation**: The factor shows positive returns, indicating its effectiveness in identifying profitable companies[20] - **Factor Name**: Valuation Factor **Factor Construction Idea**: Measures the relative valuation of stocks to identify undervalued opportunities[20] **Factor Construction Process**: The valuation factor is derived from metrics such as Price-to-Earnings (P/E) and Price-to-Book (P/B) ratios. Stocks with lower valuation scores are assigned positive weights[20] **Factor Evaluation**: The factor demonstrates positive returns, supporting its use in identifying undervalued stocks[20] - **Factor Name**: Non-linear Market Capitalization Factor **Factor Construction Idea**: Captures the non-linear relationship between market capitalization and stock returns[20] **Factor Construction Process**: The factor is constructed using a non-linear transformation of market capitalization data. Stocks with optimal market capitalization are assigned positive weights[20] **Factor Evaluation**: The factor shows positive returns, indicating its ability to capture market capitalization trends effectively[20] - **Factor Name**: Beta Factor **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market movements[20] **Factor Construction Process**: The beta factor is calculated using historical return data and market indices. Stocks with lower beta values are assigned positive weights[20] **Factor Evaluation**: The factor shows negative returns, suggesting its limited effectiveness in the current market environment[20] - **Factor Name**: Residual Volatility Factor **Factor Construction Idea**: Evaluates the idiosyncratic risk of stocks to identify those with stable performance[20] **Factor Construction Process**: The residual volatility factor is derived from the standard deviation of residuals in a regression model of stock returns against market returns[20] **Factor Evaluation**: The factor shows negative returns, indicating its limited utility in the recent market conditions[20] - **Factor Name**: Growth Factor **Factor Construction Idea**: Captures the growth potential of companies based on their financial performance[20] **Factor Construction Process**: The growth factor is calculated using metrics such as revenue growth and earnings growth. Stocks with higher growth rates are assigned positive weights[20] **Factor Evaluation**: The factor shows negative returns, suggesting its limited effectiveness in the current market environment[20] Factor Backtesting Results - **Liquidity Factor**: Return: 0.36%[20] - **Leverage Factor**: Return: 0.34%[20] - **Profitability Factor**: Return: 0.27%[20] - **Valuation Factor**: Return: 0.18%[20] - **Non-linear Market Capitalization Factor**: Return: 0.18%[20] - **Market Capitalization Factor**: Return: 0.11%[20] - **Beta Factor**: Return: -0.65%[20] - **Residual Volatility Factor**: Return: -0.55%[20] - **Growth Factor**: Return: -0.21%[20]

Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model is used to capture temporal dependencies in financial data, leveraging its recurrent structure to predict stock movements and generate long-short signals[4][5][6] - **Model Construction Process**: - Input data includes historical stock prices, technical indicators, and fundamental factors - The GRU network processes sequential data to learn patterns over time - Outputs are used to construct long-short portfolios based on predicted returns[4][5][6] - **Model Evaluation**: The GRU model demonstrates strong performance in certain market conditions, though its results vary across different stock pools[4][5][6] 2. Model Name: Barra Models (barra1d, barra5d) - **Model Construction Idea**: Barra models are factor-based models designed to decompose stock returns into systematic and idiosyncratic components, enabling factor-based portfolio construction[4][5][6] - **Model Construction Process**: - Factors such as size, value, momentum, and volatility are calculated for each stock - Stocks are ranked based on factor scores, and portfolios are constructed by going long the top 10% and short the bottom 10% of stocks based on factor rankings - barra1d uses daily data, while barra5d aggregates data over a 5-day window[4][5][6] - **Model Evaluation**: barra1d shows consistent strong performance, while barra5d experiences significant drawdowns in certain periods[4][5][6] --- Backtesting Results of Models GRU Model - **Open1d**: Weekly excess return: -1.80%, Monthly: -1.96%, YTD: 5.24%[33] - **Close1d**: Weekly excess return: -2.40%, Monthly: -3.10%, YTD: 4.04%[33] Barra Models - **Barra1d**: Weekly excess return: -0.63%, Monthly: -0.34%, YTD: 3.13%[33] - **Barra5d**: Weekly excess return: -1.80%, Monthly: -2.08%, YTD: 6.42%[33] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market movements[15] - **Factor Construction Process**: Calculated as the historical beta of the stock relative to the market index[15] 2. Factor Name: Size - **Factor Construction Idea**: Captures the size effect, where smaller firms tend to outperform larger firms[15] - **Factor Construction Process**: Natural logarithm of total market capitalization[15] 3. Factor Name: Momentum - **Factor Construction Idea**: Stocks with strong past performance tend to continue performing well in the short term[15] - **Factor Construction Process**: - Weighted combination of historical excess return volatility (0.74), cumulative excess return deviation (0.16), and residual return volatility (0.10)[15] 4. Factor Name: Volatility - **Factor Construction Idea**: Measures the risk or variability in stock returns[15] - **Factor Construction Process**: Weighted combination of historical residual return volatility and other metrics[15] 5. Factor Name: Valuation - **Factor Construction Idea**: Identifies undervalued stocks based on fundamental metrics[15] - **Factor Construction Process**: Inverse of price-to-book ratio[15] 6. Factor Name: Liquidity - **Factor Construction Idea**: Measures the ease of trading a stock[15] - **Factor Construction Process**: Weighted combination of monthly turnover (0.35), quarterly turnover (0.35), and annual turnover (0.30)[15] 7. Factor Name: Profitability - **Factor Construction Idea**: Captures the financial health and earnings quality of a firm[15] - **Factor Construction Process**: Weighted combination of analyst-predicted earnings yield, cash flow yield, and other profitability metrics[15] 8. Factor Name: Growth - **Factor Construction Idea**: Identifies firms with strong earnings and revenue growth[15] - **Factor Construction Process**: Weighted combination of earnings growth rate (0.24) and revenue growth rate (0.47)[15] 9. Factor Name: Leverage - **Factor Construction Idea**: Measures the financial risk associated with a firm's debt levels[15] - **Factor Construction Process**: Weighted combination of market leverage (0.38), book leverage (0.35), and debt-to-asset ratio (0.27)[15] --- Backtesting Results of Factors Fundamental Factors - **Growth**: Weekly excess return: 2.41%, Monthly: -2.18%, YTD: 3.20%[28] - **Profitability**: Weekly excess return: 0.22%, Monthly: 40.98%, YTD: 6.12%[28] Technical Factors - **Momentum (20-day)**: Weekly excess return: 1.72%, Monthly: 4.23%, YTD: -5.29%[30] - **Volatility (120-day)**: Weekly excess return: 4.85%, Monthly: 8.64%, YTD: -14.60%[30]

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Combination - **Model Construction Idea**: This model aims to capture excess returns by selecting stocks based on their Price-to-Book (PB) ratio and Return on Equity (ROE), focusing on stocks with favorable valuation and profitability metrics[24][25] - **Model Construction Process**: - Stocks are filtered based on their PB and ROE metrics - The portfolio is rebalanced periodically to maintain alignment with the PB-ROE strategy - The model is applied across different stock pools, including CSI 500, CSI 800, and the entire market[24][25] - **Model Evaluation**: The model demonstrates significant excess returns in the CSI 800 and full-market stock pools, indicating its effectiveness in capturing valuation and profitability-driven opportunities[24][25] 2. Model Name: Block Trade Combination - **Model Construction Idea**: This model leverages the "high transaction, low volatility" principle to identify stocks with favorable post-trade performance based on block trade characteristics[31] - **Model Construction Process**: - Stocks are selected based on two key metrics: "block trade transaction amount ratio" and "6-day transaction amount volatility" - Stocks with higher transaction ratios and lower volatility are included in the portfolio - The portfolio is rebalanced monthly to reflect updated metrics[31] - **Model Evaluation**: The model effectively captures the information embedded in block trades, delivering consistent excess returns relative to the benchmark[31] 3. Model Name: Private Placement Combination - **Model Construction Idea**: This model focuses on the event-driven opportunities surrounding private placements, considering factors such as market value, rebalancing cycles, and position control[37] - **Model Construction Process**: - Stocks involved in private placements are identified using the shareholder meeting announcement date as the event trigger - The portfolio is constructed by integrating market value considerations and rebalancing strategies - Position control mechanisms are applied to manage risk exposure[37] - **Model Evaluation**: The model's performance is sensitive to market conditions, with occasional drawdowns observed during adverse market phases[37] --- Model Backtesting Results 1. PB-ROE-50 Combination - CSI 500: Weekly excess return of -0.44%, absolute return of 3.42%[25] - CSI 800: Weekly excess return of 1.12%, absolute return of 3.92%[25] - Full Market: Weekly excess return of 1.23%, absolute return of 4.18%[25] 2. Block Trade Combination - Weekly excess return of 1.69%, absolute return of 4.65%[32] 3. Private Placement Combination - Weekly excess return of -3.21%, absolute return of -0.39%[38] --- Quantitative Factors and Construction Methods 1. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market movements, capturing systematic risk exposure[20] - **Factor Construction Process**: - Beta is calculated using regression analysis of stock returns against market returns over a specified period - Stocks with higher beta values are expected to exhibit greater volatility relative to the market[20] - **Factor Evaluation**: The beta factor delivered a weekly return of 1.35%, indicating a positive contribution to portfolio performance during the observed period[20] 2. Factor Name: Scale Factor - **Factor Construction Idea**: Focuses on the size effect, where smaller-cap stocks tend to outperform larger-cap stocks over time[20] - **Factor Construction Process**: - Stocks are ranked based on their market capitalization - Smaller-cap stocks are given higher weights in the portfolio[20] - **Factor Evaluation**: The scale factor achieved a weekly return of 1.34%, reflecting the market's preference for larger-cap stocks during the observed period[20] 3. Factor Name: BP Factor (Book-to-Price) - **Factor Construction Idea**: Captures valuation opportunities by focusing on stocks with high book-to-price ratios[20] - **Factor Construction Process**: - The book-to-price ratio is calculated as the book value per share divided by the stock price - Stocks with higher BP ratios are included in the portfolio[20] - **Factor Evaluation**: The BP factor recorded a weekly return of -0.16%, indicating underperformance during the observed period[20] 4. Factor Name: Leverage Factor - **Factor Construction Idea**: Measures the financial leverage of a company, with higher leverage potentially indicating higher risk and return[20] - **Factor Construction Process**: - Leverage is calculated as the ratio of total debt to equity - Stocks with higher leverage ratios are included in the portfolio[20] - **Factor Evaluation**: The leverage factor delivered a weekly return of -0.34%, reflecting its sensitivity to market conditions[20] --- Factor Backtesting Results 1. Beta Factor - Weekly return: 1.35%[20] 2. Scale Factor - Weekly return: 1.34%[20] 3. BP Factor - Weekly return: -0.16%[20] 4. Leverage Factor - Weekly return: -0.34%[20]

- The report tracks the performance of style factors, including momentum, beta, and liquidity factors, which showed strong long positions, while leverage, market capitalization, and valuation factors exhibited strong short positions[3][16] - The report includes the performance of fundamental factors across different stock pools, such as the CSI 300, CSI 500, and CSI 1000, highlighting that low valuation and high growth stocks were generally strong[5][6][7][20][22][25] - Technical factors' performance was mostly positive, with high volatility and long-term momentum stocks performing well, except for the 20-day momentum factor which showed negative performance[4][18][23][26] - The GRU factors' performance was weak overall, with the close1d model showing strong performance, while other models like open1d and barra1d experienced drawdowns[4][5][6][7][18][20][23][26] - The report details the construction and recent performance of the GRU long-only portfolios, noting that the barra1d model outperformed the CSI 1000 index by 0.38%, while the open1d and close1d models underperformed by 0.40%-0.53%[8][31][32] Factor Construction and Performance - **Barra Style Factors**: The report lists several style factors such as Beta, Market Cap, Momentum, Volatility, Non-linear Size, Valuation, Liquidity, Profitability, Growth, and Leverage, with detailed formulas for each[14][15] - **Fundamental Factors**: The report tracks various fundamental factors, including unexpected growth and growth-related financial factors, with mixed performance across different stock pools[4][5][6][7][18][20][22][25] - **Technical Factors**: The report includes several technical factors, such as 20-day momentum, 60-day momentum, 120-day momentum, and various volatility measures, with detailed performance metrics[4][18][23][26] Factor Performance Metrics - **Fundamental Factors**: - Operating Turnover: -1.14% (1 week), 4.19% (1 month), -11.23% (6 months), -11.52% (YTD), -1.86% (3-year annualized), 3.31% (5-year annualized)[19] - ROC: -0.68% (1 week), 0.89% (1 month), -10.51% (6 months), -10.59% (YTD), -13.06% (3-year annualized), -11.85% (5-year annualized)[19] - ROE Growth: 0.36% (1 week), 2.01% (1 month), 10.43% (6 months), 2.27% (YTD), 0.38% (3-year annualized), 2.61% (5-year annualized)[19] - **Technical Factors**: - 20-day Momentum: -0.73% (1 week), 0.66% (1 month), -8.17% (6 months), -12.18% (YTD), -13.19% (3-year annualized), -13.77% (5-year annualized)[19] - Median Deviation: -0.38% (1 week), -3.25% (1 month), -5.83% (6 months), -4.72% (YTD), -15.12% (3-year annualized), -15.62% (5-year annualized)[19] - 60-day Momentum: 0.35% (1 week), -3.31% (1 month), 2.64% (6 months), 5.08% (YTD), -12.82% (3-year annualized), -16.17% (5-year annualized)[19] GRU Model Performance - **GRU Long-Only Portfolios**: - open1d: -0.40% (1 week), -0.20% (1 month), 2.37% (3 months), 6.32% (6 months), 7.16% (YTD)[32] - close1d: -0.53% (1 week), -0.83% (1 month), 4.38% (3 months), 6.80% (6 months), 6.59% (YTD)[32] - barra1d: 0.38% (1 week), -0.25% (1 month), 0.85% (3 months), 2.85% (6 months), 3.78% (YTD)[32] - barra5d: 0.00% (1 week), -0.36% (1 month), 3.59% (3 months), 7.41% (6 months), 8.37% (YTD)[32] - Multi-Factor: -0.38% (1 week), -0.30% (1 month), 1.62% (3 months), 2.54% (6 months), 2.54% (YTD)[32]

Core Viewpoint - The report provides a comprehensive analysis of market performance, highlighting the positive and negative returns of various factors across different stock pools, indicating a mixed market sentiment and potential investment opportunities in specific sectors [3][4][5][6]. Group 1: Market Factor Performance - The overall market showed a positive return of 0.49% for the Beta factor, while momentum and liquidity factors experienced negative returns of -0.60% and -0.49% respectively, suggesting a reversal effect in the market [3]. - In the CSI 300 stock pool, the best-performing factors included quarterly operating profit growth rate (2.40%), price-to-book ratio (2.30%), and turnover rate relative volatility (2.19%), while the worst performers were operating profit margin TTM (-0.95%), total asset gross margin TTM (-0.76%), and net profit margin TTM (-0.71%) [4]. - The CSI 500 stock pool saw strong performance from the downside volatility ratio (3.85%), intraday volatility and trading volume correlation (3.44%), and inverse price-to-earnings ratio TTM (2.31%), with poor performance from quarterly ROE (-1.66%), post-opening return factor (-1.42%), and ROIC enhancement factor (-1.31%) [4]. Group 2: Liquidity and Industry Performance - In the liquidity 1500 stock pool, the best-performing factors were price-to-book ratio (1.67%), inverse price-to-earnings ratio TTM (1.20%), and price-to-earnings ratio (0.97%), while the worst performers included 5-day reversal (-2.11%), post-opening return factor (-1.69%), and logarithmic market value factor (-1.69%) [5]. - Fundamental factors showed varied performance across industries, with net asset growth rate, net profit growth rate, earnings per share, and operating profit TTM factors yielding consistent positive returns in the non-ferrous metals, beauty care, and diversified industries [6]. - Valuation factors, particularly the BP factor, performed well in the coal and diversified industries, while residual volatility and liquidity factors showed significant positive returns in agriculture, forestry, animal husbandry, and beauty care sectors [6]. Group 3: Strategy Performance Tracking - The PB-ROE-50 combination achieved positive excess returns in the overall market stock pool, with excess returns of -0.57% in the CSI 500 stock pool and -0.45% in the CSI 800 stock pool, while the overall market stock pool saw an excess return of 0.06% [7]. - Public and private fund research selection strategies yielded positive excess returns, with public research selection strategy outperforming the CSI 800 by 1.02% and private research tracking strategy outperforming by 2.72% [8]. - The block trading combination achieved positive excess returns relative to the CSI All Index, with an excess return of 0.83% [9]. - The targeted issuance combination, however, recorded negative excess returns relative to the CSI All Index, with an excess return of -0.46% [10].

Quantitative Models and Construction Methods Model: PB-ROE-50 Combination - **Construction Idea**: The PB-ROE-50 combination aims to capture excess returns by selecting stocks with favorable Price-to-Book (PB) and Return on Equity (ROE) metrics. - **Construction Process**: The combination is constructed by selecting the top 50 stocks based on their PB and ROE metrics from the entire market stock pool, the CSI 500 stock pool, and the CSI 800 stock pool. The selection is updated periodically to maintain the combination's effectiveness.[23][24] - **Evaluation**: The PB-ROE-50 combination has shown the ability to generate positive excess returns in the overall market stock pool, although it has underperformed in the CSI 500 and CSI 800 stock pools this week.[23][24] Model: Institutional Research Combination - **Construction Idea**: This model leverages the insights from public and private institutional research to select stocks that are expected to outperform. - **Construction Process**: The combination is constructed by tracking the stocks that have been researched by public and private institutions. The performance of these stocks is then compared to the CSI 800 index to measure excess returns.[25][26] - **Evaluation**: Both the public and private institutional research strategies have generated positive excess returns this week, indicating the effectiveness of institutional insights in stock selection.[25][26] Model: Block Trade Combination - **Construction Idea**: This model aims to capture the information embedded in block trades, which are large transactions that can indicate significant investor interest. - **Construction Process**: The combination is constructed by selecting stocks with high block trade transaction amounts and low 6-day transaction amount volatility. The combination is rebalanced monthly to maintain its effectiveness.[29][30] - **Evaluation**: The block trade combination has generated positive excess returns this week, suggesting that the "high transaction, low volatility" principle is effective in identifying outperforming stocks.[29][30] Model: Private Placement Combination - **Construction Idea**: This model focuses on the event-driven opportunities presented by private placements, which can indicate significant corporate actions and investor interest. - **Construction Process**: The combination is constructed by selecting stocks involved in private placements, considering factors such as market capitalization, rebalancing cycle, and position control. The combination is updated based on the announcement date of the shareholders' meeting.[35][36] - **Evaluation**: The private placement combination has underperformed this week, generating negative excess returns, which raises questions about the current effectiveness of private placement event-driven strategies.[35][36] Model Backtesting Results PB-ROE-50 Combination - **CSI 500**: Excess return this week: -0.57%, Year-to-date excess return: 2.97%, Absolute return this week: 2.69%, Year-to-date absolute return: 13.29%[24] - **CSI 800**: Excess return this week: -0.45%, Year-to-date excess return: 7.47%, Absolute return this week: 1.64%, Year-to-date absolute return: 14.12%[24] - **Overall Market**: Excess return this week: 0.06%, Year-to-date excess return: 9.34%, Absolute return this week: 2.22%, Year-to-date absolute return: 20.17%[24] Institutional Research Combination - **Public Research**: Excess return this week: 1.02%, Year-to-date excess return: 7.37%, Absolute return this week: 3.15%, Year-to-date absolute return: 14.02%[26] - **Private Research**: Excess return this week: 2.72%, Year-to-date excess return: 18.45%, Absolute return this week: 4.88%, Year-to-date absolute return: 25.78%[26] Block Trade Combination - **Excess return this week**: 0.83%, Year-to-date excess return: 27.95%, Absolute return this week: 3.01%, Year-to-date absolute return: 40.62%[30] Private Placement Combination - **Excess return this week**: -0.46%, Year-to-date excess return: 7.55%, Absolute return this week: 1.69%, Year-to-date absolute return: 18.19%[36] Quantitative Factors and Construction Methods Single Factors - **Top Performing Factors in CSI 300**: Single-quarter operating profit YoY growth rate (2.40%), Price-to-Book ratio (2.30%), Turnover rate relative volatility (2.19%)[12][13] - **Top Performing Factors in CSI 500**: Downside volatility proportion (3.85%), Intraday volatility and transaction amount correlation (3.44%), Price-to-Earnings TTM inverse (2.31%)[14][15] - **Top Performing Factors in Liquidity 1500**: Price-to-Book ratio (1.67%), Price-to-Earnings TTM inverse (1.20%), Price-to-Earnings ratio (0.97%)[16][17] Factor Backtesting Results CSI 300 - **Single-quarter operating profit YoY growth rate**: 2.40%[12][13] - **Price-to-Book ratio**: 2.30%[12][13] - **Turnover rate relative volatility**: 2.19%[12][13] CSI 500 - **Downside volatility proportion**: 3.85%[14][15] - **Intraday volatility and transaction amount correlation**: 3.44%[14][15] - **Price-to-Earnings TTM inverse**: 2.31%[14][15] Liquidity 1500 - **Price-to-Book ratio**: 1.67%[16][17] - **Price-to-Earnings TTM inverse**: 1.20%[16][17] - **Price-to-Earnings ratio**: 0.97%[16][17]

Quantitative Models and Construction - **Model Name**: barra1d **Model Construction Idea**: Focuses on short-term factor performance using daily data **Model Construction Process**: Utilizes historical data to calculate factor exposures and applies industry-neutral adjustments. Stocks are ranked based on factor scores, with the top 10% selected for long positions and the bottom 10% for short positions. Adjustments include equal weighting and monthly rebalancing[19][21][30] **Model Evaluation**: Demonstrates strong performance in short-term factor analysis[19][21][30] - **Model Name**: barra5d **Model Construction Idea**: Focuses on medium-term factor performance using five-day data **Model Construction Process**: Similar to barra1d, but uses a five-day rolling window for factor calculations. Stocks are ranked and selected based on factor scores, with monthly rebalancing and equal weighting applied[19][21][30] **Model Evaluation**: Exhibits robust medium-term factor performance, outperforming other models in cumulative returns[19][21][30] - **Model Name**: open1d **Model Construction Idea**: Focuses on factor performance using daily open prices **Model Construction Process**: Factors are calculated using daily open price data, with industry-neutral adjustments applied. Stocks are ranked based on factor scores, and the top 10% are selected for long positions, while the bottom 10% are shorted. Monthly rebalancing is implemented[19][21][30] **Model Evaluation**: Performs well in certain market conditions but shows higher volatility compared to other models[19][21][30] - **Model Name**: close1d **Model Construction Idea**: Focuses on factor performance using daily close prices **Model Construction Process**: Factors are calculated using daily close price data, with industry-neutral adjustments applied. Stocks are ranked based on factor scores, and the top 10% are selected for long positions, while the bottom 10% are shorted. Monthly rebalancing is implemented[19][21][30] **Model Evaluation**: Demonstrates weaker performance compared to other models, with significant drawdowns observed[19][21][30] Model Backtesting Results - **barra1d**: Weekly excess return 0.17%, monthly excess return 0.32%, six-month excess return 4.09%, year-to-date excess return 3.93%[32] - **barra5d**: Weekly excess return 0.13%, monthly excess return 0.39%, six-month excess return 7.59%, year-to-date excess return 7.56%[32] - **open1d**: Weekly excess return -0.35%, monthly excess return -0.71%, six-month excess return 5.85%, year-to-date excess return 6.30%[32] - **close1d**: Weekly excess return 0.55%, monthly excess return 0.40%, six-month excess return 6.40%, year-to-date excess return 6.31%[32] - **Multi-factor model**: Weekly excess return -0.38%, monthly excess return -0.04%, six-month excess return 3.56%, year-to-date excess return 2.82%[32] Quantitative Factors and Construction - **Factor Name**: Beta **Factor Construction Idea**: Measures historical beta to assess market sensitivity **Factor Construction Process**: Calculated using historical beta values derived from regression analysis of stock returns against market returns[15][16] **Factor Evaluation**: Demonstrates strong performance in high-volatility environments[15][16] - **Factor Name**: Momentum **Factor Construction Idea**: Captures historical excess return trends **Factor Construction Process**: Combines weighted averages of historical excess return volatility, cumulative excess return deviation, and residual return volatility using the formula: $ Momentum = 0.74 * Historical Excess Return Volatility + 0.16 * Cumulative Excess Return Deviation + 0.1 * Residual Return Volatility $[15][16] **Factor Evaluation**: Performs well in trending markets but struggles in reversal scenarios[15][16] - **Factor Name**: Volatility **Factor Construction Idea**: Measures stock price fluctuation intensity **Factor Construction Process**: Combines weighted averages of monthly, quarterly, and annual turnover rates using the formula: $ Volatility = 0.35 * Monthly Turnover Rate + 0.35 * Quarterly Turnover Rate + 0.3 * Annual Turnover Rate $[15][16] **Factor Evaluation**: Strong performance in high-volatility stocks[15][16] - **Factor Name**: Valuation **Factor Construction Idea**: Assesses stock valuation using price-to-book ratio **Factor Construction Process**: Calculated as the inverse of the price-to-book ratio[15][16] **Factor Evaluation**: Performs well in identifying undervalued stocks[15][16] Factor Backtesting Results - **Beta**: Weekly excess return 0.17%, monthly excess return 0.32%, six-month excess return 4.09%, year-to-date excess return 3.93%[32] - **Momentum**: Weekly excess return -0.38%, monthly excess return -0.04%, six-month excess return 3.56%, year-to-date excess return 2.82%[32] - **Volatility**: Weekly excess return 0.55%, monthly excess return 0.40%, six-month excess return 6.40%, year-to-date excess return 6.31%[32] - **Valuation**: Weekly excess return 0.13%, monthly excess return 0.39%, six-month excess return 7.59%, year-to-date excess return 7.56%[32]