Quantitative Models and Factor Construction Factor Selection and Data Processing Pipeline - The MSCI Barra CNE5 model includes 10 primary factors and 21 secondary factors, covering classic academic factors such as beta, size, and book-to-price ratio, as well as fundamental and technical factors like value, growth, momentum, and residual volatility[22][23][24] - Secondary factors are standardized and weighted to synthesize primary factors, with weights optimized for explanatory power. However, later versions of MSCI Barra shifted to equal weighting for simplicity[23] - Data processing pipeline includes six steps: defining the base universe, outlier handling, missing value imputation, standardization, primary factor synthesis, and secondary outlier/standardization adjustments[31][32][35] Pure Factor Return Estimation - Pure factor returns are estimated using constrained weighted least squares (WLS). Constraints are introduced to address multicollinearity caused by the inclusion of intercepts (country factors)[44][45][49] - WLS weights are inversely proportional to the square root of market capitalization, ensuring smaller residual variance for larger stocks[45] - The solution for pure factor returns is derived using matrix transformations and Cholesky decomposition, ensuring variance homogeneity[46][57][59] Evaluation of Risk Factors and Factor Systems - MSCI Barra's six-dimensional evaluation criteria include statistical significance, stability, intuition, completeness, simplicity, and low multicollinearity[75][76][77] - Quantitative metrics such as average absolute t-values, variance inflation factors (VIF), and pure factor performance are used to assess factor quality. Factors like beta, liquidity, and size exhibit strong statistical significance but may overlap in information[83][84][85] Practical Applications of Risk Models - Risk models are applied for performance attribution in external products (e.g., public equity funds) and internal portfolios (e.g., brokerage "gold stock" portfolios). Attribution results include style/sector exposures and return/risk contributions[148][151][181] - For public equity funds, factor and idiosyncratic returns are decomposed to classify funds into "style advantage" or "stock-picking advantage" categories[152][153][155] - For brokerage gold stock portfolios, attribution reveals the superior performance of newly added stocks due to idiosyncratic returns, while recent underperformance is linked to systematic exposure to small-cap factors[157][169][170] --- Factor Backtesting Results Daily Frequency Results - **Beta**: Annual return 8.20%, annual volatility 4.87%, IR 1.69[86][111] - **Size**: Annual return -6.82%, annual volatility 4.57%, IR -1.49[86][105] - **Liquidity**: Annual return -9.46%, annual volatility 3.10%, IR -3.05[86][123] - **Value**: Annual return 4.32%, annual volatility 2.40%, IR 1.80[86][134] Monthly Frequency Results - **Beta**: Annual return 2.64%, annual volatility 3.95%, IR 0.15[95][111] - **Size**: Annual return -7.02%, annual volatility 5.99%, IR -0.26[95][105] - **Liquidity**: Annual return -5.74%, annual volatility 2.77%, IR -0.45[95][123] - **Value**: Annual return 2.94%, annual volatility 2.87%, IR 0.22[95][134] Gold Stock Portfolio Attribution - **All Gold Stocks**: Total return 61.86%, factor return -54.02%, idiosyncratic return 83.46%[171] - **Newly Added Gold Stocks**: Total return 83.50%, factor return -59.75%, idiosyncratic return 108.20%[174] - **Repeated Gold Stocks**: Total return 6.39%, factor return -44.66%, idiosyncratic return 19.60%[162] Factor Contribution Analysis - **Beta**: Positive contribution across all years, cumulative return 35.75% for all gold stocks, 44.47% for newly added gold stocks[175][176] - **Liquidity**: Negative contribution, cumulative return -48.67% for all gold stocks, -57.24% for newly added gold stocks[175][176] - **Size**: Mixed contribution, cumulative return 72.78% for all gold stocks, 97.27% for newly added gold stocks[175][176]

Group 1 - The core view of the report indicates that risk assets are expected to maintain relative advantages, with the profitability factor likely to recover [2][4][10] - The asset allocation model is an improved version of the Black-Litterman (BL) model, which combines market consensus with active views to optimize asset allocation and enhance the Sharpe ratio [3][5] - The model predicts that in the third quarter of 2025, the allocation ratio for domestic stocks will continue to increase while the bond allocation ratio will remain relatively high [10][11] Group 2 - In the A-share market, the profitability factor is expected to recover, and the advantage of small-cap stocks is likely to continue [2][17] - As of June 30, 2025, the market style performance for the second quarter showed strong results for small-cap and low-valuation factors, with weak profitability and weak reversal [13][16] - The report recommends focusing on indices such as the ChiNext Index, CSI A500, and CSI 2000, which exhibit high profitability and small-cap attributes [20][21]

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]

Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model integrates fundamental and technical features to predict stock performance[3][19] - **Model Construction Process**: The GRU model is a recurrent neural network (RNN) variant designed to handle sequential data. It uses gating mechanisms to control the flow of information, allowing it to capture temporal dependencies in financial data. Specific details on the input features or training process are not provided in the report[3][19] - **Model Evaluation**: The GRU model shows mixed performance, with significant drawdowns in certain market segments[3][19] 2. Model Name: Barra1d - **Model Construction Idea**: A short-term factor model based on the Barra framework, focusing on daily data[3][19] - **Model Evaluation**: Barra1d exhibits significant drawdowns in multiple market segments, indicating weaker performance[3][19] 3. Model Name: Barra5d - **Model Construction Idea**: A medium-term factor model based on the Barra framework, focusing on 5-day data[3][19] - **Model Evaluation**: Barra5d demonstrates strong performance, achieving positive returns in various market segments[3][19] 4. Model Name: Close1d - **Model Construction Idea**: A short-term model focusing on daily closing prices[3][19] - **Model Evaluation**: Close1d performs well in certain market segments, achieving positive returns[3][19] 5. Model Name: Open1d - **Model Construction Idea**: A short-term model focusing on daily opening prices[3][19] - **Model Evaluation**: Open1d shows weaker performance, with significant drawdowns in certain market segments[3][19] --- Model Backtesting Results 1. GRU Model - **Weekly Excess Return**: -0.08% to -0.54% relative to the CSI 1000 Index[7][30] 2. Barra1d - **Weekly Excess Return**: -0.54%[31] - **Year-to-Date Excess Return**: 3.75%[31] 3. Barra5d - **Weekly Excess Return**: -0.31%[31] - **Year-to-Date Excess Return**: 7.42%[31] 4. Close1d - **Weekly Excess Return**: -0.40%[31] - **Year-to-Date Excess Return**: 5.73%[31] 5. Open1d - **Weekly Excess Return**: -0.08%[31] - **Year-to-Date Excess Return**: 6.68%[31] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures historical beta to capture market sensitivity[15] 2. Factor Name: Market Capitalization - **Factor Construction Idea**: Logarithm of total market capitalization[15] 3. Factor Name: Momentum - **Factor Construction Idea**: Average historical excess returns[15] 4. Factor Name: Volatility - **Factor Construction Process**: $ Volatility = 0.74 * \text{Historical Excess Return Volatility} + 0.16 * \text{Cumulative Excess Return Deviation} + 0.1 * \text{Residual Return Volatility} $ - **Parameters**: - Historical Excess Return Volatility: Measures the standard deviation of excess returns - Cumulative Excess Return Deviation: Captures deviations in cumulative returns - Residual Return Volatility: Measures the volatility of residual returns[15] 5. Factor Name: Nonlinear Market Capitalization - **Factor Construction Idea**: Cubic transformation of market capitalization[15] 6. Factor Name: Valuation - **Factor Construction Idea**: Inverse of price-to-book ratio[15] 7. Factor Name: Liquidity - **Factor Construction Process**: $ Liquidity = 0.35 * \text{Monthly Turnover} + 0.35 * \text{Quarterly Turnover} + 0.3 * \text{Annual Turnover} $ - **Parameters**: - Monthly Turnover: Measures trading activity over a month - Quarterly Turnover: Measures trading activity over a quarter - Annual Turnover: Measures trading activity over a year[15] 8. Factor Name: Profitability - **Factor Construction Process**: $ Profitability = 0.68 * \text{Analyst Forecast Earnings Yield} + 0.21 * \text{Inverse Price-to-Cash Flow} + 0.11 * \text{Inverse Price-to-Earnings (TTM)} $ $ + 0.18 * \text{Analyst Long-Term Growth Forecast} + 0.11 * \text{Analyst Short-Term Growth Forecast} $ - **Parameters**: - Analyst Forecast Earnings Yield: Measures expected earnings relative to price - Inverse Price-to-Cash Flow: Captures cash flow efficiency - Analyst Growth Forecasts: Reflects expected growth rates[15] 9. Factor Name: Growth - **Factor Construction Process**: $ Growth = 0.24 * \text{Earnings Growth Rate} + 0.47 * \text{Revenue Growth Rate} $ - **Parameters**: - Earnings Growth Rate: Measures growth in earnings - Revenue Growth Rate: Measures growth in revenue[15] 10. Factor Name: Leverage - **Factor Construction Process**: $ Leverage = 0.38 * \text{Market Leverage} + 0.35 * \text{Book Leverage} + 0.27 * \text{Debt-to-Asset Ratio} $ - **Parameters**: - Market Leverage: Measures leverage based on market value - Book Leverage: Measures leverage based on book value - Debt-to-Asset Ratio: Captures the proportion of debt in total assets[15] --- Factor Backtesting Results 1. Momentum Factors - **120-Day Momentum**: Weekly return -2.37%[28] - **60-Day Momentum**: Weekly return -2.17%[28] - **20-Day Momentum**: Weekly return -1.69%[28] 2. Volatility Factors - **60-Day Volatility**: Weekly return -1.53%[28] - **20-Day Volatility**: Weekly return -0.96%[28] - **120-Day Volatility**: Weekly return 0.78%[28] 3. Median Deviation - **Weekly Return**: -0.40%[28]

- The report tracks style factors including profitability, volatility, and momentum, which showed strong long positions, while nonlinear market capitalization, valuation, and leverage factors demonstrated strong short positions[3][16] - Barra style factors include Beta (historical beta), market capitalization (logarithm of total market capitalization), momentum (mean of historical excess return series), volatility (weighted combination of historical excess return volatility, cumulative excess return deviation, and residual return volatility), nonlinear market capitalization (third power of market capitalization style), valuation (inverse of price-to-book ratio), liquidity (weighted turnover rates across monthly, quarterly, and yearly periods), profitability (weighted combination of analyst forecast earnings-price ratio, inverse cash flow ratio, and inverse trailing twelve-month PE ratio), growth (weighted combination of earnings growth rate and revenue growth rate), and leverage (weighted combination of market leverage, book leverage, and debt-to-asset ratio)[15] - GRU factors demonstrated strong multi-directional performance across various stock pools, with models like barra5d showing particularly strong positive returns[4][5][7] - GRU long-only portfolio outperformed the CSI 1000 index with excess returns ranging from 0.06% to 0.95% this week, while the barra5d model achieved a year-to-date excess return of 7.75%[8][30][31]

Core Insights - The report emphasizes the importance of style factors in A-share investment strategies, highlighting a quantitative framework for constructing style factor portfolios [3][7] - The manufacturing PMI shows a marginal recovery, indicating a need for continued policy support for domestic demand [9][10] Market Indices - The Shanghai Composite Index closed at 3385.36, with a slight increase of 0.04% [4] - The Shenzhen Component Index decreased by 0.19%, closing at 10183.70 [4] - The CSI 300 Index fell by 0.09%, ending at 3873.98 [4] Industry Performance - The non-ferrous metals sector saw an increase of 1.16%, while the beauty care sector declined by 1.70% [5] - The communication industry rose by 1.00%, whereas the textile and apparel sector decreased by 1.18% [5] - The petroleum and petrochemical sector increased by 0.88%, while the food and beverage sector fell by 0.92% [5] Style Factor Analysis - The report identifies four main dimensions for constructing style factors: market capitalization, valuation, profitability, and momentum [7] - Historical data indicates that different periods in the A-share market have been dominated by different style factors, with high valuation factors expected to strengthen from 2025 [7][8] - The report suggests that high profitability, high valuation, and small-cap stocks will dominate the A-share market in the current year [8] PMI Insights - The manufacturing PMI for May was reported at 49.5%, a 0.5 percentage point increase from the previous month, indicating a slight recovery [9] - New export orders increased by 2.8 percentage points to 47.5%, while new orders only rose by 0.6 percentage points, suggesting weaker domestic demand compared to external demand [9][10] - The report notes that the construction sector's PMI showed a slowdown in expansion, while the service sector's PMI slightly increased to 50.2% [10]

Core Insights - The report explores the construction and application of a style factor system for A-shares, focusing on four dimensions: market capitalization, valuation, profitability, and momentum [2][9][12] - A-shares have exhibited different dominant factors over various periods, with profitability leading from 2013 to 2014, small-cap factors from 2015 to 2016, valuation from 2016 to 2018, and a return to profitability dominance from 2019 to early 2021 [2][24][27] - The report predicts a resurgence of high valuation factors starting in 2025, driven by expectations of weak profit recovery and strong policy support [2][27] Style Factor Construction and Performance - The style factor system is constructed using a bottom-up approach, assigning style labels to each stock based on their factor indicators [9][12] - The performance of the style factors shows that small-cap stocks have generally outperformed large-cap stocks since 2010, with a notable fivefold return from small-cap strategies [12][17] - Valuation factors indicate that low valuation styles have been particularly strong, especially during specific periods such as 2017-2018 and 2022-2024 [14][15] Influencing Factors of Style Factors - Profitability factors are highly correlated with economic cycles, showing better performance during economic upturns [45][46] - Valuation factors are closely linked to market sentiment, with high valuation stocks performing better during periods of positive sentiment [49][50] - Market capitalization factors are significantly influenced by remaining liquidity, with small-cap factors performing strongly in liquidity-rich environments [53][54] Application of Style Factor System - The report establishes an A-share style investment system based on the identified style factors, suggesting that the current dominant styles are high profitability, high valuation, and small-cap [2][27] - The analysis indicates that the A-share market has not fully priced in the expected profit recovery, suggesting potential upside for high profitability and high valuation factors [2][27] - Different asset types exhibit varying dominant style factors, with emerging growth assets showing significant small-cap advantages and dividend assets reflecting low valuation strengths [29][33]

2025 年 05 月 18 日 主力资金有效性持续修复，景气预期超额收益开 始抬头 —行业和风格因子跟踪报告 投资要点 ▌ 行业因子最新变动情况 上周 3400 得而复失，回调后仍站上 3350。我们最新推荐 的非银行业收益亮眼。因子角度来看近期市场热点受事件 驱动影响较大，分析师短期预期表现更好，也能抓住非银 的反弹行情。此外市场资金驱动特征明显，主力资金因子 多头的非银、汽车领涨市场。动量反转层面处于反转因子 和短端动量的交接时刻，可以提前关注短期动量有效性的 回升。财报质量因子继续偏向成长口径，景气投资思路有 效性开始小幅回升。 整体看行业轮动因子指向内需成长板块：大盘成交金额持 续在 1.1 万亿以上，主题投资和景气投资均有反弹，主力 资金因子反弹延续，本期主力资金因子行业选择电子、电 力设备及新能源、医药、机械、有色金属、非银行金融。 长期分析师预期非银行金融、建材、交通运输、电力及公 用事业、有色金属，空头无意义。短期分析师预期指向农 林牧渔、消费者服务、非银行金融、机械、有色金属。本 期选择行业继续偏向内需中的成长，继续关注低位消费短 期反弹机会。 ▌ 定量行业推荐 基于权重分配，我们推荐内资 ...

Group 1: Small Cap and Value Style Rotation Strategy - The latest quantitative model signals indicate a shift towards small-cap style for May, with historical data suggesting small-cap style is likely to outperform in this month [1] - The current market capitalization factor valuation spread is at 1.05, which is relatively low compared to historical highs of 1.7 to 2.6, indicating potential for small-cap outperformance [1] - Year-to-date, the small-cap style rotation strategy has achieved an excess return of 2.94% relative to an equal-weighted benchmark (CSI 300 and CSI 2000) [1] Group 2: Value and Growth Style Rotation Strategy - The latest quantitative model signals continue to favor value style for May, with expectations for value style to maintain its advantage [2] - Year-to-date, the value-growth style rotation strategy has generated an excess return of 4.6% compared to an equal-weighted benchmark (National Growth and National Value) [2] Group 3: Factor Performance Tracking - Among eight major factors, momentum and growth factors showed high positive returns in April, while liquidity and volatility factors exhibited high negative returns [2] - Year-to-date, momentum, analyst sentiment, and earnings volatility factors have shown positive returns, whereas industry momentum, liquidity, and short-term reversal factors have shown negative returns [2]

Quantitative Models and Construction 1. Model Name: GRU - **Model Construction Idea**: The GRU model is used to predict future stock returns based on historical data and incorporates various technical and fundamental factors[3][4][5] - **Model Construction Process**: The GRU model is trained on historical data to predict future returns. It uses a recurrent neural network structure, specifically the Gated Recurrent Unit (GRU), to capture sequential dependencies in time-series data. The model is applied to different stock pools (e.g., CSI 300, CSI 500, CSI 1000) and is evaluated based on its long-short portfolio returns[3][4][5] - **Model Evaluation**: The GRU model demonstrates strong performance in predicting returns, with positive long-short portfolio returns in most cases. However, its performance varies across different stock pools and time horizons[3][4][5] 2. Model Name: Barra5d - **Model Construction Idea**: The Barra5d model predicts future returns by incorporating short-term technical factors and ensuring style neutrality[6][25] - **Model Construction Process**: The Barra5d model uses a combination of short-term technical indicators (e.g., 5-day momentum) and applies style-neutral constraints to ensure that the predictions are not biased by market-wide factors. The model is tested on various stock pools, including CSI 300, CSI 500, and CSI 1000[6][25] - **Model Evaluation**: The Barra5d model shows strong performance, particularly in the CSI 500 and CSI 1000 stock pools, with weekly long-short portfolio returns exceeding 3% in some cases[6][25] 3. Model Name: Open1d - **Model Construction Idea**: The Open1d model focuses on short-term price movements and is designed to capture immediate market reactions[19][21][23] - **Model Construction Process**: The Open1d model uses one-day price changes as its primary input and applies machine learning techniques to predict short-term returns. It is evaluated based on its ability to generate excess returns in long-short portfolios[19][21][23] - **Model Evaluation**: The Open1d model has shown strong performance year-to-date, with cumulative excess returns of 4.24% relative to the CSI 1000 index[19][21][23] --- Model Backtesting Results 1. GRU Model - Weekly long-short portfolio return: Positive in most cases, with variations across stock pools[3][4][5] - CSI 500 stock pool: Weekly long-short return > 3%[5] - CSI 1000 stock pool: Performance is mixed, with some models (e.g., Barra1d, Barra5d) performing well[6][25] 2. Barra5d Model - Weekly long-short portfolio return: > 3% in the CSI 500 stock pool[6][25] - Strong performance in the CSI 1000 stock pool, particularly in predicting style-neutral future returns[6][25] 3. Open1d Model - Year-to-date excess return: 4.24% relative to the CSI 1000 index[19][21][23] - Weekly long-short portfolio return: Mixed, with some weeks showing slight negative returns[19][21][23] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures the historical sensitivity of a stock's returns to market returns[15] - **Factor Construction Process**: Beta is calculated as the slope of the regression line between a stock's returns and market returns over a specified historical period[15] 2. Factor Name: Momentum - **Factor Construction Idea**: Captures the tendency of stocks with strong past performance to continue performing well[15] - **Factor Construction Process**: Momentum is calculated as the mean of historical excess returns over a specified period[15] 3. Factor Name: Volatility - **Factor Construction Idea**: Measures the variability of a stock's returns over time[15] - **Factor Construction Process**: $ \text{Volatility} = 0.74 \times \text{Historical Excess Return Volatility} + 0.16 \times \text{Cumulative Excess Return Deviation} + 0.1 \times \text{Residual Return Volatility} $ - Historical Excess Return Volatility: Standard deviation of excess returns - Cumulative Excess Return Deviation: Deviation of cumulative excess returns from the mean - Residual Return Volatility: Standard deviation of residual returns after removing market effects[15] 4. Factor Name: Liquidity - **Factor Construction Idea**: Measures the ease of trading a stock based on turnover rates[15] - **Factor Construction Process**: $ \text{Liquidity} = 0.35 \times \text{Monthly Turnover Rate} + 0.35 \times \text{Quarterly Turnover Rate} + 0.3 \times \text{Annual Turnover Rate} $ - Turnover Rate: Ratio of trading volume to total shares outstanding[15] --- Factor Backtesting Results 1. Beta Factor - Weekly long-short portfolio return: Strong performance in recent weeks[16] 2. Momentum Factor - Weekly long-short portfolio return: Positive for long-term momentum (e.g., 120-day), negative for short-term momentum (e.g., 20-day)[18][23][25] 3. Volatility Factor - Weekly long-short portfolio return: Positive for long-term volatility (e.g., 120-day), mixed for short-term volatility (e.g., 20-day)[18][23][25] 4. Liquidity Factor - Weekly long-short portfolio return: Strong performance, particularly in high-turnover stocks[16]