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]
中邮因子周报:高波强势,基本面回撤-20250506
China Post Securities·2025-05-06 12:55