Quantitative Models and Construction Methods Model Name: GRU(10,2)+NN(10) - Model Construction Idea: This model combines Gated Recurrent Units (GRU) with a neural network (NN) to capture temporal dependencies in high-frequency data[4] - Model Construction Process: The model uses a GRU with 10 units and 2 layers, followed by a neural network with 10 units. The GRU processes sequential data, and the NN captures non-linear relationships[4] - Model Evaluation: The model shows strong performance in capturing temporal patterns and generating significant returns[4] Model Name: GRU(50,2)+NN(10) - Model Construction Idea: Similar to the GRU(10,2)+NN(10) model but with more GRU units to capture more complex temporal dependencies[4] - Model Construction Process: The model uses a GRU with 50 units and 2 layers, followed by a neural network with 10 units. This setup allows for deeper temporal feature extraction[4] - Model Evaluation: The model is effective in capturing complex temporal patterns and generating significant returns[4] Model Name: Multi-Granularity Model (5-day label) - Model Construction Idea: This model uses multiple granularities of data to improve prediction accuracy[4] - Model Construction Process: The model labels data with a 5-day horizon and uses a combination of features from different time scales to enhance prediction[4] - Model Evaluation: The model shows strong performance in capturing multi-scale patterns and generating significant returns[4] Model Name: Multi-Granularity Model (10-day label) - Model Construction Idea: Similar to the 5-day label model but with a 10-day horizon to capture longer-term dependencies[4] - Model Construction Process: The model labels data with a 10-day horizon and combines features from different time scales to enhance prediction[4] - Model Evaluation: The model is effective in capturing longer-term patterns and generating significant returns[4] Model Backtesting Results - GRU(10,2)+NN(10): - Multi-Period Return: -1.32% (last week), -0.71% (November), 44.83% (2025)[4] - Excess Return: -0.77% (last week), -1.01% (November), 7.21% (2025)[4] - GRU(50,2)+NN(10): - Multi-Period Return: -1.5% (last week), -1.23% (November), 44.56% (2025)[4] - Excess Return: -0.83% (last week), -0.92% (November), 7.9% (2025)[4] - Multi-Granularity Model (5-day label): - Multi-Period Return: 0.75% (last week), 2.56% (November), 63.15% (2025)[4] - Excess Return: 1.07% (last week), 2.36% (November), 24.44% (2025)[4] - Multi-Granularity Model (10-day label): - Multi-Period Return: 0.91% (last week), 2.55% (November), 57.7% (2025)[4] - Excess Return: 0.98% (last week), 2.27% (November), 24.14% (2025)[4] Quantitative Factors and Construction Methods Factor Name: Intraday Skewness Factor - Factor Construction Idea: This factor captures the skewness of intraday returns to identify asymmetric return distributions[4] - Factor Construction Process: The factor is calculated using the skewness of intraday returns over a specified period[4] - Factor Evaluation: The factor is effective in identifying stocks with asymmetric return distributions[4] Factor Name: Downside Volatility Proportion Factor - Factor Construction Idea: This factor measures the proportion of downside volatility to capture risk characteristics[4] - Factor Construction Process: The factor is calculated as the proportion of downside volatility relative to total volatility over a specified period[4] - Factor Evaluation: The factor is effective in identifying stocks with higher downside risk[4] Factor Name: Post-Open Buy Intention Proportion Factor - Factor Construction Idea: This factor measures the proportion of buy intentions after market open to capture investor sentiment[4] - Factor Construction Process: The factor is calculated as the proportion of buy orders relative to total orders after market open[4] - Factor Evaluation: The factor is effective in capturing investor sentiment and predicting stock movements[4] Factor Name: Post-Open Buy Intensity Factor - Factor Construction Idea: This factor measures the intensity of buy intentions after market open to capture investor sentiment strength[4] - Factor Construction Process: The factor is calculated as the intensity of buy orders relative to total orders after market open[4] - Factor Evaluation: The factor is effective in capturing the strength of investor sentiment and predicting stock movements[4] Factor Backtesting Results - Intraday Skewness Factor: - Multi-Period Return: -0.26% (last week), 0.49% (November), 22.76% (2025)[4] - Excess Return: 0.42% (last week), 1.46% (November), 6.14% (2025)[4] - Downside Volatility Proportion Factor: - Multi-Period Return: 0.38% (last week), 1.35% (November), 20.32% (2025)[4] - Excess Return: 0.41% (last week), 1.08% (November), 3.54% (2025)[4] - Post-Open Buy Intention Proportion Factor: - Multi-Period Return: 0.28% (last week), -0.01% (November), 19.33% (2025)[4] - Excess Return: 0.47% (last week), 0.28% (November), 8.78% (2025)[4] - Post-Open Buy Intensity Factor: - Multi-Period Return: 0.27% (last week), 0.57% (November), 26.36% (2025)[4] - Excess Return: -0.22% (last week), -0.55% (November), 10.06% (2025)[4]
高频选股因子周报（20251110- 20251114）：高频因子走势分化，多粒度因子持续战胜市场。AI 增强组合继续表现亮眼，多数组合创年内新高。-20251116
GUOTAI HAITONG SECURITIES·2025-11-16 11:40