Quantitative Models and Construction Methods GRU Model - Model Name: GRU - Model Construction Idea: The GRU model is used to mine volume and price information, and this report explores its ability to incorporate financial information[2][14]. - Model Construction Process: - Data Range: 20130101-20250430, all market stocks (excluding Beijing Stock Exchange)[16] - Input: Each stock has one sample at the end of each month, containing volume and price information for the past 240 trading days, including 7 fields: opening price, highest price, lowest price, closing price, trading volume, trading amount, and turnover rate. Each field is standardized using z-score for 240 values[16]. - Prediction Target: Next month's return rate standardized by cross-section (opening price at the beginning of the month to closing price at the end of the month)[16]. - Training Set: Samples from the past 6 years, divided into training and validation sets in a 4:1 ratio according to time sequence[16]. - Training Method: Rolling training every month, early stopping if the loss function does not decrease for 10 consecutive rounds[16]. - Model Evaluation: The GRU model can simultaneously mine volume and price information and financial information. The high-frequency processing of financial information improves the model results to some extent[2][18]. - Model Testing Results: - Annualized Excess Return: 8.75% - IR: 2.25 - Maximum Drawdown: 4.71%[3][19][23] GRU Model with Financial Information - Model Name: GRU with Financial Information - Model Construction Idea: Incorporating financial information into the GRU model to improve its performance[4][24]. - Model Construction Process: - Simple Splicing of Financial Information: Financial data is calculated as TTM value according to the latest available quarterly report for each trading day, then spliced into new columns. The matrix containing volume and price information and fundamental information is standardized and input into the GRU network[25]. - Adjusted Financial Information: Assuming the TTM value of financial indicators grows steadily at the quarterly growth rate, the daily adjustment formula for TTM values is: where t is the trading day, q is the financial report period (March 31, June 30, September 30, December 31)[36][38]. - Model Evaluation: Incorporating financial information improves the overall performance of the baseline model, especially before 2022. However, after 2023, the improvement is weaker or even negative[4][35][42]. - Model Testing Results: - Annualized Excess Return: 7.76% - IR: 1.65 - Maximum Drawdown: 5.40%[41][44] GRU Model with Simplified Financial Information - Model Name: GRU with Simplified Financial Information - Model Construction Idea: Simplifying the financial indicators to only include important ones like net profit TTM and market value[45]. - Model Construction Process: - Simplified Financial Information: Only retaining important indicators like net profit TTM and market value, and incorporating them into the GRU model[45]. - Model Evaluation: Simplifying the financial indicators improves the overall performance of the model, especially before 2022. After 2023, the improvement is weaker but still positive[45][55]. - Model Testing Results: - Annualized Excess Return: 9.97% - IR: 1.93 - Maximum Drawdown: 5.70%[51][52] Mixed Frequency Model - Model Name: Mixed Frequency Model (barra5d + daily GRU) - Model Construction Idea: Combining long-term and short-term prediction capabilities by integrating barra5d and daily GRU models[56][65]. - Model Construction Process: - Input: Combining the daily GRU model with the barra5d model, which is trained on 240-minute intraday data to predict the next 1-5 days' returns[56][65]. - Model Evaluation: The mixed frequency model significantly improves the performance of the barra5d model, especially after October 2024. Adding fundamental information further stabilizes the annual excess performance[65][72][80]. - Model Testing Results: - Annualized Excess Return: 11.82% - IR: 2.39 - Maximum Drawdown: 5.70%[77][78] Model Backtesting Results GRU Model - Annualized Excess Return: 8.75% - IR: 2.25 - Maximum Drawdown: 4.71%[3][19][23] GRU Model with Financial Information - Annualized Excess Return: 7.76% - IR: 1.65 - Maximum Drawdown: 5.40%[41][44] GRU Model with Simplified Financial Information - Annualized Excess Return: 9.97% - IR: 1.93 - Maximum Drawdown: 5.70%[51][52] Mixed Frequency Model (barra5d + daily GRU) - Annualized Excess Return: 11.82% - IR: 2.39 - Maximum Drawdown: 5.70%[77][78]
结合基本面和量价特征的GRU模型
China Post Securities·2025-06-05 07:20