Quantitative Models and Construction GRU Model - Model Name: GRU baseline model [2][3][14] - Model Construction Idea: The GRU model is designed to extract information from historical price and volume data to predict future returns. It serves as a baseline to evaluate the impact of adding financial data [14][15]. - Model Construction Process: - Data Range: All A-share stocks (excluding Beijing Stock Exchange) from 2013-01-01 to 2025-04-30 [16]. - Input Features: Past 240 trading days' price and volume data, including open price, high price, low price, close price, trading volume, turnover, and turnover rate. Each feature is standardized using z-score [16]. - Prediction Target: Next month's standardized return (from the opening price at the beginning of the month to the closing price at the end of the month) [16]. - Training: Rolling training with a 4:1 split between training and validation sets over the past six years. Early stopping is applied if the loss function does not decrease for 10 consecutive iterations [16]. - Portfolio Construction: Enhanced portfolio based on the CSI 1000 index, with constraints on stock weight deviation (1%), style deviation (within 0.1 standard deviation), and industry deviation (1%). Monthly rebalancing with a turnover rate of 50% per side [18]. - Model Evaluation: The GRU model demonstrates stable performance in extracting price-volume information, achieving consistent excess returns across years [19]. GRU Model with Financial Data - Model Name: GRU with financial data [4][24][25] - Model Construction Idea: Incorporates financial data into the GRU model to enhance its ability to predict future returns by combining price-volume and fundamental information [14][24]. - Model Construction Process: - Financial Data: Includes 20 fields from income statements, such as revenue, cost of goods sold, management expenses, R&D costs, and net profit. Data is converted to TTM (trailing twelve months) values [24][25]. - Integration: Financial data is appended to the price-volume matrix, standardized, and input into the GRU model [25]. - Adjustment: To address frequency mismatches, financial data is adjusted daily based on the assumption of stable TTM growth rates. The adjustment formula is: where is the trading day and is the financial reporting quarter [36][38]. - Model Evaluation: Adding financial data improves performance before 2023 but weakens it afterward. Adjusting financial data enhances overall performance, especially in earlier years [42][45]. Mixed-Frequency GRU Model - Model Name: Mixed-frequency GRU model (barra5d + daily GRU) [5][56][65] - Model Construction Idea: Combines long-term and short-term prediction capabilities by integrating daily and intraday GRU models [56][65]. - Model Construction Process: - Daily GRU: Trained on 240 trading days of daily data to predict monthly returns [16]. - Intraday GRU (barra5d): Trained on 240 minutes of intraday data to predict 5-day returns, neutralized for Barra style factors [56]. - Integration: The two models are combined to leverage their complementary strengths [65]. - Model Evaluation: The mixed-frequency model significantly improves stability and excess returns, addressing weaknesses in individual models [67][68]. Mixed-Frequency GRU with Financial Data - Model Name: Mixed-frequency GRU with financial data (barra5d + daily GRU + financial data) [5][73][74] - Model Construction Idea: Enhances the mixed-frequency model by incorporating selected financial data to improve stability and performance across years [73][74]. - Model Construction Process: - Financial Data Selection: Only key financial indicators, such as net profit TTM and market capitalization, are retained to avoid redundancy [45]. - Integration: Financial data is appended to the mixed-frequency model, following the same adjustment process as the GRU with financial data model [36][38]. - Model Evaluation: The addition of financial data further stabilizes annual excess returns and improves overall performance metrics [77][80]. --- Model Backtesting Results GRU Baseline Model - Excess Annualized Return: 8.75% [19][23] - IR: 2.25 [19][23] - Maximum Drawdown: 4.71% [19][23] GRU with Financial Data - Excess Annualized Return: 6.86% [32][33] - IR: 1.46 [32][34] - Maximum Drawdown: 6.14% [32][34] GRU with Adjusted Financial Data - Excess Annualized Return: 7.76% [41][44] - IR: 1.65 [41][44] - Maximum Drawdown: 5.40% [41][44] GRU with Selected Financial Data - Excess Annualized Return: 9.97% [51][52] - IR: 1.93 [51][52] - Maximum Drawdown: 5.70% [51][52] Mixed-Frequency GRU Model - Excess Annualized Return: 11.32% [68][69] - IR: 2.42 [68][69] - Maximum Drawdown: 8.19% [68][69] Mixed-Frequency GRU with Financial Data - Excess Annualized Return: 11.82% [77][78] - IR: 2.39 [77][78] - Maximum Drawdown: 5.70% [77][78]
金工专题报告：结合基本面和量价特征的GRU模型
China Post Securities·2025-06-05 06:23