中邮因子周报：成长风格主导，流动性占优-20250825

Quantitative Models and Construction 1. Model Name: GRU Model - Model Construction Idea: The GRU model is used to predict stock returns based on historical data and incorporates various factors to optimize portfolio performance [3][4][5] - Model Construction Process: - The GRU model is trained on historical data to capture temporal dependencies in stock returns - It uses multiple input features, including technical and fundamental factors, to predict future returns - The model is applied to different stock pools (e.g., CSI 300, CSI 500, CSI 1000) to evaluate its performance [5][6][7] - Model Evaluation: The GRU model demonstrates strong performance in most stock pools, with positive long-short returns across various factors. However, certain sub-models (e.g., barra5d) show occasional underperformance [5][6][7] 2. Model Name: Open1d and Close1d Models - Model Construction Idea: These models focus on short-term price movements and are designed to capture daily return patterns [8][31] - Model Construction Process: - Open1d and Close1d models are trained on daily open and close price data, respectively - They are evaluated based on their ability to generate excess returns relative to the CSI 1000 index [8][31] - Model Evaluation: These models show mixed performance, with occasional drawdowns relative to the benchmark index [8][31] 3. Model Name: Barra1d and Barra5d Models - Model Construction Idea: These models are based on the Barra factor framework and aim to capture short-term and medium-term return patterns [8][31] - Model Construction Process: - Barra1d focuses on daily factor returns, while Barra5d aggregates returns over a 5-day horizon - Both models are tested for their ability to generate excess returns relative to the CSI 1000 index [8][31] - Model Evaluation: Barra5d demonstrates strong year-to-date performance, significantly outperforming the benchmark, while Barra1d shows consistent but less pronounced gains [8][31] --- Model Backtest Results 1. GRU Model - Excess Return: Positive across most stock pools, with occasional underperformance in specific sub-models like barra5d [5][6][7] 2. Open1d Model - Weekly Excess Return: -0.01% - Year-to-Date Excess Return: 5.23% [32] 3. Close1d Model - Weekly Excess Return: -0.38% - Year-to-Date Excess Return: 3.64% [32] 4. Barra1d Model - Weekly Excess Return: 0.65% - Year-to-Date Excess Return: 3.80% [32] 5. Barra5d Model - Weekly Excess Return: 0.02% - Year-to-Date Excess Return: 6.44% [32] --- Quantitative Factors and Construction 1. Factor Name: Beta - Factor Construction Idea: Measures historical beta to capture market sensitivity [15] - Factor Construction Process: Historical beta is calculated based on the covariance of stock returns with market returns [15] 2. Factor Name: Momentum - Factor Construction Idea: Captures historical excess return trends [15] - Factor Construction Process: - Momentum = 0.74 * Historical Excess Return Volatility + 0.16 * Cumulative Excess Return Deviation + 0.1 * Historical Residual Return Volatility [15] 3. Factor Name: Volatility - Factor Construction Idea: Measures stock price fluctuations to identify high-volatility stocks [15] - Factor Construction Process: - Volatility = Weighted combination of historical residual return volatility and other metrics [15] 4. Factor Name: Growth - Factor Construction Idea: Focuses on earnings and revenue growth rates [15] - Factor Construction Process: - Growth = 0.24 * Earnings Growth Rate + 0.47 * Revenue Growth Rate [15] 5. Factor Name: Liquidity - Factor Construction Idea: Measures stock turnover to identify liquid stocks [15] - Factor Construction Process: - Liquidity = 0.35 * Monthly Turnover + 0.35 * Quarterly Turnover + 0.3 * Annual Turnover [15] --- Factor Backtest Results 1. Beta Factor - Weekly Long-Short Return: Positive [16][18] 2. Momentum Factor - Weekly Long-Short Return: Negative [16][18] 3. Volatility Factor - Weekly Long-Short Return: Positive [16][18] 4. Growth Factor - Weekly Long-Short Return: Positive [16][18] 5. Liquidity Factor - Weekly Long-Short Return: Positive [16][18]