Quantitative Models and Construction Methods - **Model Name**: GRU **Model Construction Idea**: GRU is used for industry rotation and stock selection based on historical data and market trends[3][5][7] **Model Construction Process**: GRU utilizes gated recurrent units to process sequential data, capturing temporal dependencies in stock price movements and industry performance. It incorporates multiple factors such as momentum, volatility, and valuation metrics to predict future trends[3][5][7] **Model Evaluation**: GRU demonstrates strong performance in multi-factor combinations and industry rotation strategies, with notable differentiation across different stock pools[3][5][7] - **Model Name**: Barra **Model Construction Idea**: Barra focuses on style factors to explain stock returns and risks[14][15][16] **Model Construction Process**: Barra includes multiple style factors such as Beta, Size, Momentum, Volatility, Non-linear Size, Valuation, Liquidity, Profitability, Growth, and Leverage. Each factor is calculated using specific formulas: - **Beta**: Historical beta - **Size**: Natural logarithm of total market capitalization - **Momentum**: Mean of historical excess return series - **Volatility**: $0.74 \times \text{historical excess return volatility} + 0.16 \times \text{cumulative excess return deviation} + 0.1 \times \text{historical residual return volatility}$ - **Non-linear Size**: Cubic transformation of market capitalization - **Valuation**: Reciprocal of price-to-book ratio - **Liquidity**: $0.35 \times \text{monthly turnover rate} + 0.35 \times \text{quarterly turnover rate} + 0.3 \times \text{annual turnover rate}$ - **Profitability**: Weighted combination of analyst forecast earnings-price ratio, reciprocal of cash flow ratio, reciprocal of trailing twelve-month P/E ratio, and forecasted growth rates - **Growth**: Weighted combination of earnings growth rate and revenue growth rate - **Leverage**: Weighted combination of market leverage, book leverage, and debt-to-asset ratio[15] **Model Evaluation**: Barra style factors provide a comprehensive framework for analyzing stock returns, with strong differentiation in multi-factor strategies[14][15][16] Model Backtesting Results - **GRU Model**: - **open1d**: Weekly excess return 0.61%, monthly 1.56%, yearly 7.78% - **close1d**: Weekly excess return 0.02%, monthly 1.45%, yearly 7.28% - **barra1d**: Weekly excess return -0.24%, monthly -0.07%, yearly 3.61% - **barra5d**: Weekly excess return 0.06%, monthly 1.35%, yearly 8.63% - **Multi-factor combination**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] Quantitative Factors and Construction Methods - **Factor Name**: Beta **Factor Construction Idea**: Measures historical sensitivity to market movements[15] **Factor Construction Process**: Calculated as historical beta using regression analysis of stock returns against market returns[15] - **Factor Name**: Size **Factor Construction Idea**: Captures the impact of market capitalization on stock returns[15] **Factor Construction Process**: Natural logarithm of total market capitalization[15] - **Factor Name**: Momentum **Factor Construction Idea**: Reflects the persistence of stock price trends[15] **Factor Construction Process**: Mean of historical excess return series[15] - **Factor Name**: Volatility **Factor Construction Idea**: Measures risk associated with stock price fluctuations[15] **Factor Construction Process**: $0.74 \times \text{historical excess return volatility} + 0.16 \times \text{cumulative excess return deviation} + 0.1 \times \text{historical residual return volatility}$[15] - **Factor Name**: Non-linear Size **Factor Construction Idea**: Captures non-linear effects of market capitalization on returns[15] **Factor Construction Process**: Cubic transformation of market capitalization[15] - **Factor Name**: Valuation **Factor Construction Idea**: Reflects the relative attractiveness of stock prices[15] **Factor Construction Process**: Reciprocal of price-to-book ratio[15] - **Factor Name**: Liquidity **Factor Construction Idea**: Measures ease of trading stocks[15] **Factor Construction Process**: $0.35 \times \text{monthly turnover rate} + 0.35 \times \text{quarterly turnover rate} + 0.3 \times \text{annual turnover rate}$[15] - **Factor Name**: Profitability **Factor Construction Idea**: Captures earnings quality and growth potential[15] **Factor Construction Process**: Weighted combination of analyst forecast earnings-price ratio, reciprocal of cash flow ratio, reciprocal of trailing twelve-month P/E ratio, and forecasted growth rates[15] - **Factor Name**: Growth **Factor Construction Idea**: Reflects revenue and earnings growth trends[15] **Factor Construction Process**: Weighted combination of earnings growth rate and revenue growth rate[15] - **Factor Name**: Leverage **Factor Construction Idea**: Measures financial risk associated with debt levels[15] **Factor Construction Process**: Weighted combination of market leverage, book leverage, and debt-to-asset ratio[15] Factor Backtesting Results - **Beta**: Weekly excess return -0.24%, monthly -0.07%, yearly 3.61%[31][32][33] - **Size**: Weekly excess return 0.02%, monthly 1.45%, yearly 7.28%[31][32][33] - **Momentum**: Weekly excess return 0.61%, monthly 1.56%, yearly 7.78%[31][32][33] - **Volatility**: Weekly excess return 0.06%, monthly 1.35%, yearly 8.63%[31][32][33] - **Non-linear Size**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Valuation**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Liquidity**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Profitability**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Growth**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Leverage**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33]

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]

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