Quantitative Models and Construction Methods 1. Model Name: GRU(50,2)+NN(10) Factor - **Model Construction Idea**: This factor leverages a deep learning architecture combining Gated Recurrent Units (GRU) and Neural Networks (NN) to capture high-frequency trading patterns and predict stock returns[4][55] - **Model Construction Process**: - The GRU(50,2) component processes sequential high-frequency data with 50 units and 2 layers - The NN(10) component is a fully connected neural network with 10 neurons in the output layer - The model is trained on historical high-frequency data to predict stock returns, optimizing for multi-class classification or regression tasks[4][55] - **Model Evaluation**: Demonstrates robust performance in capturing high-frequency trading signals and generating stable returns[4][55] 2. Model Name: Multi-Granularity Model (5-Day Label) - **Model Construction Idea**: This model uses multi-granularity data to predict stock returns over a 5-day horizon, leveraging bidirectional AGRU (Attention-based GRU) for feature extraction[57][60] - **Model Construction Process**: - Input data is segmented into multiple granularities (e.g., daily, intraday) - Bidirectional AGRU is applied to extract temporal features from the data - A 5-day label is used as the prediction target, and the model is trained to optimize for this horizon[57][60] - **Model Evaluation**: Effective in capturing medium-term trading patterns and generating consistent returns[57][60] 3. Model Name: Multi-Granularity Model (10-Day Label) - **Model Construction Idea**: Similar to the 5-day label model, this version extends the prediction horizon to 10 days, using bidirectional AGRU for feature extraction[60][65] - **Model Construction Process**: - Multi-granularity data is processed with bidirectional AGRU - A 10-day label is used as the prediction target, and the model is trained to optimize for this extended horizon[60][65] - **Model Evaluation**: Provides a longer-term perspective on trading patterns, with slightly lower returns compared to the 5-day model but still effective[60][65] --- Model Backtesting Results GRU(50,2)+NN(10) Factor - **IC**: Historical: 0.066, 2025: 0.039[4][55] - **e^(-RankMAE)**: Historical: 0.336, 2025: 0.334[4][55] - **Long-Short Return**: Weekly: 0.70%, June: 3.58%, 2025 YTD: 19.78%[4][55] - **Long-Only Excess Return**: Weekly: -0.30%, June: 0.92%, 2025 YTD: -1.06%[4][55] Multi-Granularity Model (5-Day Label) - **IC**: Historical: 0.081, 2025: 0.070[57][60] - **e^(-RankMAE)**: Historical: 0.344, 2025: 0.343[57][60] - **Long-Short Return**: Weekly: 1.56%, June: 5.97%, 2025 YTD: 35.45%[57][60] - **Long-Only Excess Return**: Weekly: 0.40%, June: 2.16%, 2025 YTD: 11.87%[57][60] Multi-Granularity Model (10-Day Label) - **IC**: Historical: 0.074, 2025: 0.065[60][65] - **e^(-RankMAE)**: Historical: 0.342, 2025: 0.343[60][65] - **Long-Short Return**: Weekly: 1.66%, June: 5.76%, 2025 YTD: 33.44%[60][65] - **Long-Only Excess Return**: Weekly: 0.71%, June: 2.06%, 2025 YTD: 11.11%[60][65] --- Quantitative Factors and Construction Methods 1. Factor Name: Intraday Skewness Factor - **Factor Construction Idea**: Measures the skewness of intraday returns to capture asymmetry in price movements[4][10] - **Factor Construction Process**: - Calculate intraday returns for each stock - Compute the skewness of these returns using the formula: $ Skewness = \frac{E[(X - \mu)^3]}{\sigma^3} $ where $X$ is the return, $\mu$ is the mean, and $\sigma$ is the standard deviation[4][10] - **Factor Evaluation**: Effective in identifying stocks with asymmetric return distributions, though performance varies across periods[4][10] 2. Factor Name: Downside Volatility Ratio - **Factor Construction Idea**: Focuses on the proportion of downside volatility relative to total volatility to capture risk-averse behavior[4][14] - **Factor Construction Process**: - Calculate downside volatility as the standard deviation of negative returns - Compute the ratio of downside volatility to total volatility[4][14] - **Factor Evaluation**: Useful for identifying stocks with higher downside risk, though returns are sensitive to market conditions[4][14] 3. Factor Name: Opening Buy Intensity - **Factor Construction Idea**: Measures the intensity of buy orders during the opening period to capture early trading sentiment[4][17] - **Factor Construction Process**: - Aggregate buy orders in the first 30 minutes of trading - Normalize by total trading volume during the same period[4][17] - **Factor Evaluation**: Captures short-term sentiment effectively, though performance is volatile[4][17] --- Factor Backtesting Results Intraday Skewness Factor - **IC**: Historical: 0.027, 2025: 0.047[4][10] - **e^(-RankMAE)**: Historical: 0.324, 2025: 0.330[4][10] - **Long-Short Return**: Weekly: -0.51%, June: 1.48%, 2025 YTD: 14.73%[4][10] - **Long-Only Excess Return**: Weekly: -0.03%, June: 0.18%, 2025 YTD: 2.59%[4][10] Downside Volatility Ratio - **IC**: Historical: 0.025, 2025: 0.046[4][14] - **e^(-RankMAE)**: Historical: 0.324, 2025: 0.328[4][14] - **Long-Short Return**: Weekly: -0.04%, June: 1.86%, 2025 YTD: 12.84%[4][14] - **Long-Only Excess Return**: Weekly: 0.09%, June: 0.50%, 2025 YTD: 1.07%[4][14] Opening Buy Intensity - **IC**: Historical: 0.031, 2025: 0.028[4][17] - **e^(-RankMAE)**: Historical: 0.322, 2025: 0.322[4][17] - **Long-Short Return**: Weekly: 0.77%, June: 1.85%, 2025 YTD: 11.44%[4][17] - **Long-Only Excess Return**: Weekly: 0.04%, June: 0.61%, 2025 YTD: 5.91%[4][17]