Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model integrates fundamental and technical features to predict stock performance, leveraging historical data and recurrent neural network structures for time-series analysis [3][4][5]. - **Model Construction Process**: - Input features include fundamental indicators (e.g., financial ratios) and technical indicators (e.g., momentum, volatility) [3][4]. - The GRU (Gated Recurrent Unit) architecture processes sequential data to capture temporal dependencies [3]. - The model is trained on historical data, with optimization targeting the minimization of prediction errors [3]. - **Model Evaluation**: The GRU model shows mixed performance across different stock pools, with notable underperformance in certain scenarios [3][5][6]. 2. Model Name: Barra1d - **Model Construction Idea**: The Barra1d model is a factor-based model that emphasizes short-term price movements and volatility [3][4][5]. - **Model Construction Process**: - Factors include short-term momentum and volatility metrics [3][4]. - The model applies a linear regression framework to estimate factor exposures and returns [3]. - Portfolio construction involves long positions in stocks with high factor scores and short positions in stocks with low scores [3][4]. - **Model Evaluation**: Barra1d demonstrates strong performance in multiple stock pools, with consistent positive returns in backtests [4][5][6]. 3. Model Name: Barra5d - **Model Construction Idea**: Barra5d extends the Barra1d model by incorporating a longer time horizon for factor evaluation [3][4][5]. - **Model Construction Process**: - Factors include medium-term momentum and volatility metrics [3][4]. - The model uses a similar regression-based approach as Barra1d but adjusts for longer-term trends [3]. - Portfolio construction follows the same long-short strategy as Barra1d [3][4]. - **Model Evaluation**: Barra5d shows strong year-to-date performance, outperforming benchmarks in multiple scenarios [5][6][7]. --- Model Backtest Results GRU Model - **Close1d**: Weekly return -1.59%, YTD return 8.61% [31] - **Barra1d**: Weekly return 0.80%, YTD return 22.50% [31] - **Barra5d**: Weekly return 0.63%, YTD return 28.18% [31] Multi-Factor Portfolio - Weekly excess return: -0.19% - YTD excess return: 2.73% [34] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures historical sensitivity of stock returns to market movements [15]. - **Factor Construction Process**: - Calculated as the slope of the regression of stock returns against market returns over a specified period [15]. - **Factor Evaluation**: Beta factor showed strong long-side performance in recent weeks [16]. 2. Factor Name: Momentum - **Factor Construction Idea**: Captures the persistence of stock price trends [15]. - **Factor Construction Process**: - Calculated as the mean of historical excess returns over a defined period [15]. - **Factor Evaluation**: Long-term momentum factors demonstrated positive returns, while short-term momentum factors underperformed [18][20]. 3. Factor Name: Volatility - **Factor Construction Idea**: Measures the variability of stock returns [15]. - **Factor Construction Process**: - Weighted combination of historical return volatility, cumulative deviation, and residual volatility [15]. - **Factor Evaluation**: Volatility factors showed strong positive returns, particularly in long-term horizons [18][20]. 4. Factor Name: Growth - **Factor Construction Idea**: Reflects the growth potential of companies based on financial metrics [15]. - **Factor Construction Process**: - Weighted combination of earnings growth rate and revenue growth rate [15]. - **Factor Evaluation**: Growth factors exhibited strong positive returns across multiple stock pools [18][20][23]. --- Factor Backtest Results Beta Factor - Weekly return: Positive [16] Momentum Factor - Long-term momentum: Weekly return 2.87% [22] - Short-term momentum: Weekly return -3.48% [22] Volatility Factor - Long-term volatility: Weekly return 4.01% [22] - Short-term volatility: Weekly return 2.87% [22] Growth Factor - Weekly return: Positive [18][20][23]

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]

- The liquidity factor has shown a rapid rebound, with active trading by major funds. This week's recommended sectors for the liquidity factor include electronics, electrical equipment and new energy, pharmaceuticals, machinery, non-bank finance, and non-ferrous metals[14][16] - The long-term prosperity expectation factor, which serves as a proxy for prosperity investment, has started to show a slight upward trend in effectiveness. This week's long-term prosperity expectation factor includes non-bank finance, building materials, transportation, electric power and public utilities, and non-ferrous metals[18][20] - The short-term prosperity expectation factor continues to focus on domestic demand, with significant upward movement in long-short excess returns. This week's short-term prosperity expectation factor includes agriculture, forestry, animal husbandry, and fishery, consumer services, non-bank finance, machinery, and non-ferrous metals[22][24] - The momentum reversal factor is currently unable to describe the market trend, but it is expected that sector rotation may shift to momentum in one to two weeks. This week's momentum reversal factor includes automobiles, communications, electrical equipment and new energy, machinery, and home appliances[25][26] - The composite factor for this week includes consumer services, non-bank finance, machinery, electrical equipment and new energy, electronics, and non-ferrous metals[32][33] Factor Backtesting Results - Liquidity factor, excess return of long positions: 0.7% to 2.3% over various periods[16] - Long-term prosperity expectation factor, excess return of long positions: 0.6% to 2.4% over various periods[20] - Short-term prosperity expectation factor, excess return of long positions: 0.6% to 2.0% over various periods[24] - Momentum reversal factor, excess return of long positions: 0.4% to 2.4% over various periods[26]

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]