- 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]

Quantitative Models and Construction 1. Model Name: GRU (Gated Recurrent Unit) Models - **Model Construction Idea**: GRU models are used to capture sequential patterns in stock price movements and combine fundamental and technical features for prediction[3][4] - **Model Construction Process**: - The GRU models are trained on historical stock data, incorporating both fundamental and technical indicators as input features - Different variations of GRU models are used, such as `open1d`, `close1d`, and `barra1d`, which focus on specific aspects of stock price movements (e.g., open prices, close prices, or Barra-style factor adjustments)[4][5][6] - **Model Evaluation**: GRU models show mixed performance, with some models like `barra1d` performing well, while others like `close1d` exhibit significant drawdowns[5][6][8] --- Backtesting Results of Models GRU Models - **open1d**: Weekly excess return: -0.23%, Monthly: 2.34%, YTD: 6.70%[31][32] - **close1d**: Weekly excess return: 0.06%, Monthly: 3.83%, YTD: 5.55%[31][32] - **barra1d**: Weekly excess return: 0.00%, Monthly: 0.34%, YTD: 3.33%[31][32] - **barra5d**: Weekly excess return: 0.10%, Monthly: 2.88%, YTD: 7.01%[31][32] --- 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**: Calculated as the historical beta of the stock relative to the market[15] 2. Factor Name: Momentum - **Factor Construction Idea**: Captures the average historical excess returns of a stock over a specific period[15] - **Factor Construction Process**: - Momentum = Mean of historical excess return series[15] 3. Factor Name: Volatility - **Factor Construction Idea**: Measures the variability of a stock's excess returns over time[15] - **Factor Construction Process**: - Volatility = 0.74 * Historical excess return volatility + 0.16 * Cumulative excess return deviation + 0.10 * Residual return volatility[15] 4. Factor Name: Valuation - **Factor Construction Idea**: Represents the inverse of the price-to-book ratio, indicating undervaluation[15] - **Factor Construction Process**: - Valuation = 1 / Price-to-Book Ratio[15] 5. Factor Name: Growth - **Factor Construction Idea**: Measures the growth potential of a stock based on earnings and revenue growth[15] - **Factor Construction Process**: - Growth = 0.24 * Earnings Growth Rate + 0.47 * Revenue Growth Rate[15] 6. Factor Name: Profitability - **Factor Construction Idea**: Combines various profitability metrics to assess a stock's financial health[15] - **Factor Construction Process**: - Profitability = 0.68 * Analyst Forecast Earnings Yield + 0.21 * Inverse of Price-to-Cash Flow Ratio + 0.11 * Inverse of Price-to-Earnings Ratio (TTM) + 0.18 * Analyst Forecast Long-Term Growth Rate + 0.11 * Analyst Forecast Short-Term Growth Rate[15] --- Backtesting Results of Factors Fundamental Factors - **Static Financial Factors**: Weekly excess return: Negative[4][6] - **Growth Factors**: Weekly excess return: Positive[4][6] - **Surprise Growth Factors**: Weekly excess return: Positive[4][6] Technical Factors - **Short-Term Momentum**: Weekly excess return: Negative[4][6] - **Long-Term Momentum**: Weekly excess return: Positive[4][6] - **Volatility**: Weekly excess return: Positive[4][6] GRU Factors - **open1d**: Weekly excess return: Positive[4][6] - **close1d**: Weekly excess return: Negative[5][6] - **barra1d**: Weekly excess return: Positive[5][6]

Investment Rating - The report does not explicitly state an investment rating for the industry or specific companies [37]. Core Insights - The report highlights that the market is currently favoring high volatility and high momentum stocks, while low momentum and low volatility stocks are also performing well [3][5][20]. - It notes that growth and unexpected growth financial factors are showing positive returns, indicating a preference for stocks with stable growth despite short-term performance challenges [17][22]. - The GRU factor's performance is mixed, with most models showing negative returns, except for the open1d model which has shown positive returns [18][29]. Summary by Sections Style Factor Tracking - The report indicates strong performance in volatility, valuation, and liquidity factors, while non-linear market capitalization, market capitalization, and growth factors are underperforming [15][1]. Overall Market Factor Performance - Basic financial factors show a divergence in returns, with static financial factors yielding negative returns, while growth and unexpected growth factors yield positive returns [17]. - Technical factors are performing positively overall, with high volatility and high momentum stocks leading the performance [17]. CSI 300 Component Stock Factor Performance - Basic financial factors within the CSI 300 show mostly positive returns, with valuation factors underperforming and growth factors performing strongly [20]. - Technical factors show a mixed performance, with momentum factors significantly underperforming while volatility factors are performing positively [20]. CSI 500 Component Stock Factor Performance - Basic financial factors show a divergence in returns, with unexpected growth factors performing well, while static financial factors yield mostly negative returns [22]. - Technical factors show a mixed performance, with momentum factors underperforming and volatility factors performing positively [22]. CSI 1000 Component Stock Factor Performance - Basic financial factors show a divergence in returns, with static financial factors yielding negative returns and unexpected growth factors yielding positive returns [24]. - Technical factors are performing negatively overall, with low momentum and low volatility stocks performing better [25]. Strategy Performance Tracking - The GRU long position strategy has shown strong performance, with excess returns relative to the CSI 1000 index ranging from 0.84% to 1.89% [29]. - The open1d model has shown a strong performance year-to-date, with an excess return of 6.08% relative to the CSI 1000 index [29].

Quantitative Models and Construction Methods 1. Model Name: GRU (Generalized Recurrent Unit) - **Model Construction Idea**: GRU models are used to capture temporal dependencies and patterns in financial data, leveraging recurrent neural network structures to predict stock performance or factor returns[3][4][5] - **Model Construction Process**: The GRU model is trained on historical stock data, incorporating features such as price movements, volume, and other technical indicators. Specific GRU-based models mentioned include: - **open1d**: Focuses on daily opening prices - **close1d**: Focuses on daily closing prices - **barra1d**: Integrates Barra-style risk factors for daily predictions - **barra5d**: Extends Barra-style risk factors to a 5-day horizon[5][6][25] - **Model Evaluation**: GRU models show mixed performance, with some models like open1d performing well, while others like barra1d and barra5d experience significant drawdowns in certain market conditions[5][6][25] --- Model Backtesting Results GRU Model Performance - **open1d**: - Weekly excess return: 1.22% - Monthly excess return: 2.58% - Year-to-date excess return: 6.08%[29][30] - **close1d**: - Weekly excess return: 1.89% - Monthly excess return: 2.91% - Year-to-date excess return: 4.14%[29][30] - **barra1d**: - Weekly excess return: 0.85% - Monthly excess return: 1.50% - Year-to-date excess return: 3.48%[29][30] - **barra5d**: - Weekly excess return: 0.84% - Monthly excess return: 2.25% - Year-to-date excess return: 5.59%[29][30] --- Quantitative Factors and Construction Methods 1. Factor Name: Barra Style Factors - **Factor Construction Idea**: Barra factors are designed to capture systematic risk exposures across various dimensions such as size, value, momentum, and volatility[13][14] - **Factor Construction Process**: - **Beta**: Historical beta of the stock - **Size**: Natural logarithm of total market capitalization - **Momentum**: Weighted average of historical excess returns, combining volatility, cumulative deviation, and residual volatility $ Momentum = 0.74 \cdot \text{Volatility} + 0.16 \cdot \text{Cumulative Deviation} + 0.1 \cdot \text{Residual Volatility} $ - **Volatility**: Weighted average of historical residual return volatilities - **Valuation**: Inverse of price-to-book ratio - **Liquidity**: Weighted average of turnover ratios (monthly, quarterly, yearly) - **Profitability**: Weighted average of analyst forecasted earnings yield, cash flow yield, and other profitability metrics - **Growth**: Weighted average of earnings and revenue growth rates - **Leverage**: Weighted average of market leverage, book leverage, and debt-to-asset ratio[14][15] - **Factor Evaluation**: Barra factors demonstrate varying performance across different market conditions, with some factors like volatility and liquidity showing strong returns, while others like size and growth exhibit weaker performance[15][16] 2. Factor Name: Technical Factors - **Factor Construction Idea**: Technical factors aim to capture price and volume-based patterns, focusing on momentum and volatility metrics[17][20][24] - **Factor Construction Process**: - **Momentum**: Calculated over different time horizons (e.g., 20-day, 60-day, 120-day) - **Volatility**: Measured as the standard deviation of returns over specific periods (e.g., 20-day, 60-day, 120-day) - **Median Deviation**: Captures the median absolute deviation of returns[27] - **Factor Evaluation**: High-momentum and high-volatility stocks generally outperform, but certain periods show negative returns for these factors, especially in the 120-day horizon[17][27] 3. Factor Name: Fundamental Factors - **Factor Construction Idea**: Fundamental factors are derived from financial statements, focusing on profitability, growth, and valuation metrics[17][20][24] - **Factor Construction Process**: - **Static Financial Metrics**: Return on equity (ROE), return on assets (ROA), and profit margins - **Growth Metrics**: Earnings growth, revenue growth, and cash flow growth - **Surprise Metrics**: Earnings and revenue surprises relative to analyst expectations[19][21][23] - **Factor Evaluation**: Growth and surprise factors perform well, while static financial metrics like ROA and ROE show weaker performance in certain periods[19][21][23] --- Factor Backtesting Results Barra Factors - **Volatility**: Weekly return: 0.75%, Monthly return: 2.73% - **Liquidity**: Weekly return: 0.68%, Monthly return: 1.37% - **Size**: Weekly return: -1.45%, Monthly return: -3.60%[15][16] Technical Factors - **20-day Momentum**: Weekly return: -1.81%, Monthly return: -6.16% - **60-day Volatility**: Weekly return: -1.79%, Monthly return: -0.74% - **120-day Momentum**: Weekly return: -1.68%, Monthly return: -0.80%[27] Fundamental Factors - **ROA Growth**: Weekly return: 0.23%, Monthly return: 1.31% - **Earnings Surprise**: Weekly return: 0.20%, Monthly return: 1.11% - **Revenue Growth**: Weekly return: 0.17%, Monthly return: 0.77%[19][21][23]

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]

Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model is a machine learning-based quantitative model designed to capture complex patterns in stock price movements and factor relationships[3][19][23] - **Model Construction Process**: The GRU (Gated Recurrent Unit) model is trained on historical stock data, incorporating various financial and technical indicators as input features. The model uses a recurrent neural network structure to process sequential data, enabling it to predict stock price trends and factor performance. Specific details on the training parameters or architecture were not provided in the report[3][19][23] - **Model Evaluation**: The GRU model demonstrated strong performance in multi-factor strategies and outperformed other models in certain market conditions, particularly in the small-cap stock universe[7][29][33] 2. Model Name: Barra1d Model - **Model Construction Idea**: The Barra1d model is a factor-based quantitative model that utilizes the Barra risk model framework to analyze and predict stock returns[3][19][23] - **Model Construction Process**: The Barra1d model incorporates style factors such as size, value, momentum, and volatility, along with industry and country factors. It uses historical data to estimate factor exposures and risk premiums, which are then applied to construct long-short portfolios[3][19][23] - **Model Evaluation**: The Barra1d model experienced significant drawdowns in certain market conditions, particularly in the CSI 500 and CSI 1000 stock universes, indicating sensitivity to market volatility[5][26][29] 3. Model Name: Open1d Model - **Model Construction Idea**: The Open1d model focuses on short-term price movements and factor dynamics, leveraging daily open prices as a key input[3][19][29] - **Model Construction Process**: The Open1d model uses daily open prices and other technical indicators to construct long-short portfolios. It emphasizes short-term momentum and volatility factors to capture rapid market movements[3][19][29] - **Model Evaluation**: The Open1d model achieved strong performance, with its excess returns reaching new highs for the year, particularly in the CSI 1000 stock universe[6][29][33] 4. Model Name: Close1d Model - **Model Construction Idea**: The Close1d model is similar to the Open1d model but focuses on daily closing prices to capture end-of-day market dynamics[3][19][29] - **Model Construction Process**: The Close1d model uses daily closing prices and technical indicators to construct long-short portfolios. It emphasizes factors such as closing momentum and volatility to predict stock movements[3][19][29] - **Model Evaluation**: The Close1d model demonstrated strong performance, particularly in the CSI 1000 stock universe, with consistent positive excess returns[6][29][33] --- Model Backtesting Results 1. GRU Model - Weekly excess return: 0.46%-1.43% relative to the CSI 1000 index[7][33][34] - Year-to-date excess return: Not explicitly provided 2. Barra1d Model - Weekly excess return: 0.46%[33][34] - Year-to-date excess return: 2.10%[34] 3. Open1d Model - Weekly excess return: 1.43%[33][34] - Year-to-date excess return: 3.90%[34] 4. Close1d Model - Weekly excess return: 1.38%[33][34] - Year-to-date excess return: 1.87%[34] 5. Multi-Factor Strategy - Weekly excess return: 1.01%[33][34] - Year-to-date excess return: 2.15%[34] --- Quantitative Factors and Construction 1. Factor Name: Momentum - **Factor Construction Idea**: Momentum factors are designed to capture the tendency of stocks with strong past performance to continue performing well in the short term[15][16][19] - **Factor Construction Process**: - Historical excess return mean: $0.74 \times \text{volatility of excess returns} + 0.16 \times \text{cumulative excess return deviation} + 0.1 \times \text{residual return volatility}$[15] - **Factor Evaluation**: Momentum factors showed strong performance in the current market, particularly in high-volatility environments[3][19][23] 2. Factor Name: Valuation - **Factor Construction Idea**: Valuation factors aim to identify undervalued stocks based on financial ratios such as price-to-book (P/B) and price-to-earnings (P/E)[15][16][19] - **Factor Construction Process**: - Valuation factor: $1 / \text{P/B ratio}$[15] - **Factor Evaluation**: Valuation factors demonstrated strong performance in the small-cap stock universe, particularly in the CSI 1000 index[6][29][33] 3. Factor Name: Growth - **Factor Construction Idea**: Growth factors focus on identifying stocks with high earnings and revenue growth potential[15][16][19] - **Factor Construction Process**: - Growth factor: $0.24 \times \text{earnings growth rate} + 0.47 \times \text{revenue growth rate}$[15] - **Factor Evaluation**: Growth factors performed well across multiple stock universes, particularly in high-growth environments[3][19][23] 4. Factor Name: Volatility - **Factor Construction Idea**: Volatility factors measure the risk associated with stock price fluctuations, often used to identify low-risk investment opportunities[15][16][19] - **Factor Construction Process**: - Volatility factor: $0.74 \times \text{historical return volatility} + 0.16 \times \text{cumulative return deviation} + 0.1 \times \text{residual return volatility}$[15] - **Factor Evaluation**: Volatility factors showed mixed performance, with low-volatility stocks underperforming in certain market conditions[5][26][29] --- Factor Backtesting Results 1. Momentum Factor - Weekly excess return: 0.89%[17][19][23] - Year-to-date excess return: 42%[17][19][23] 2. Valuation Factor - Weekly excess return: 1.68%[17][19][23] - Year-to-date excess return: 1.14%[17][19][23] 3. Growth Factor - Weekly excess return: 1.20%[17][19][23] - Year-to-date excess return: 4.03%[17][19][23] 4. Volatility Factor - Weekly excess return: 0.16%[17][19][23] - Year-to-date excess return: 8.10%[17][19][23]