机器学习因子选股月报（2026年1月）-20251231

Quantitative Models and Construction Methods 1. Model Name: GAN_GRU - Model Construction Idea: The GAN_GRU model combines Generative Adversarial Networks (GAN) for feature generation and Gated Recurrent Unit (GRU) for time-series feature encoding to construct a stock selection factor[4][13][14] - Model Construction Process: 1. GAN Component: - The generator (G) learns the real data distribution and generates realistic samples from random noise $z$ (Gaussian or uniform distribution). The generator's loss function is: $L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))]$ where $D(G(z))$ represents the discriminator's probability of classifying generated data as real[24][25][26] - The discriminator (D) distinguishes real data from generated data. Its loss function is: $L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))]$ where $D(x)$ is the probability of real data being classified as real, and $D(G(z))$ is the probability of generated data being classified as real[27][29][30] - GAN training alternates between optimizing $G$ and $D$ until convergence[30] 2. GRU Component: - Two GRU layers (GRU(128, 128)) are used to encode time-series features, followed by a Multi-Layer Perceptron (MLP) with layers (256, 64, 64) to predict returns. The final output $pRet$ is used as the stock selection factor[22] 3. Feature Input and Processing: - Input features include 18 price-volume characteristics (e.g., closing price, turnover, etc.) sampled over the past 400 days, with a shape of $40 \times 18$ (40 days of features)[18][19][37] - Features undergo outlier removal, standardization, and cross-sectional normalization[18] 4. Training Details: - Training-validation split: 80%-20% - Semi-annual rolling training (June 30 and December 31 each year) - Hyperparameters: batch size equals the number of stocks, Adam optimizer, learning rate $1e-4$, IC loss function, early stopping (10 rounds), max training rounds (50)[18] 5. Stock Selection: - Stocks are filtered to exclude ST stocks and those listed for less than six months[18] - Model Evaluation: The GAN_GRU model effectively captures price-volume time-series features and demonstrates strong predictive power for stock returns[4][13][22] --- Model Backtesting Results 1. GAN_GRU Model - IC Mean: 0.1119*** (2019-2025)[4][41] - ICIR (non-annualized): 0.89[42] - Turnover Rate: 0.83X[42] - Recent IC: 0.0331*** (December 2025)[4][41] - 1-Year IC Mean: 0.0669***[4][41] - Annualized Return: 37.40%[42] - Annualized Volatility: 23.39%[42] - IR: 1.60[42] - Maximum Drawdown: 27.29%[42] - Annualized Excess Return: 22.42%[4][42] --- Quantitative Factors and Construction Methods 1. Factor Name: GAN_GRU Factor - Factor Construction Idea: The GAN_GRU factor is derived from the GAN_GRU model, leveraging GAN for price-volume feature generation and GRU for time-series encoding[4][13][14] - Factor Construction Process: - The GAN generator processes raw price-volume time-series features ($Input\_Shape = 40 \times 18$) and outputs transformed features with the same shape ($Input\_Shape = 40 \times 18$)[37] - The GRU component encodes these features into a predictive factor for stock selection[22] - The factor undergoes industry and market capitalization neutralization and standardization[22] - Factor Evaluation: The GAN_GRU factor demonstrates robust performance across various industries and time periods, with significant IC values and excess returns[4][41] --- Factor Backtesting Results 1. GAN_GRU Factor - IC Mean: 0.1119*** (2019-2025)[4][41] - ICIR (non-annualized): 0.89[42] - Turnover Rate: 0.83X[42] - Recent IC: 0.0331*** (December 2025)[4][41] - 1-Year IC Mean: 0.0669***[4][41] - Annualized Return: 37.40%[42] - Annualized Volatility: 23.39%[42] - IR: 1.60[42] - Maximum Drawdown: 27.29%[42] - Annualized Excess Return: 22.42%[4][42] 2. Industry-Specific Performance - Top 5 Industries by Recent IC (October 2025): - Social Services: 0.4243*** - Coal: 0.2643*** - Environmental Protection: 0.2262*** - Retail: 0.1888*** - Steel: 0.1812***[4][41][42] - Top 5 Industries by 1-Year IC Mean: - Social Services: 0.1303*** - Steel: 0.1154*** - Non-Bank Financials: 0.1157*** - Retail: 0.1067*** - Building Materials: 0.1017***[4][41][42] 3. Industry-Specific Excess Returns - Top 5 Industries by December 2025 Excess Returns: - Banking: 4.30% - Real Estate: 3.51% - Environmental Protection: 2.18% - Retail: 1.76% - Machinery: 1.71%[2][45] - Top 5 Industries by 1-Year Average Excess Returns: - Banking: 2.12% - Real Estate: 1.93% - Environmental Protection: 1.50% - Retail: 1.46% - Machinery: 1.23%[2][46]