国泰海通｜金工：基于GRU、TCN模型的深度学习因子选股效果研究

Core Viewpoint - The report demonstrates the effectiveness of deep learning models, specifically GRU and TCN, in stock selection, with GRU showing slightly better performance than TCN+GRU and TCN. The 10-day return prediction model outperforms the 5-day model. The deep learning factors are highly correlated with low volatility and low liquidity factors, indicating potential investment strategies [1][2]. Group 1: Model Performance - The GRU model is confirmed to be effective, with advantages in prediction accuracy and training speed, making it widely used in the industry [1]. - The TCN model, based on CNN architecture, effectively captures long-term dependencies in time series data through causal convolution and residual connections [1]. - The annualized excess returns since 2017 for various indices are as follows: - CSI 300: 11.8% - CSI 500: 13.6% - CSI 1000: 21.7% - CSI 2000: 27.1% The current year's excess returns are -0.4%, 2.7%, 9.9%, and 9.3% respectively [1][3]. Group 2: Single Factor Stock Selection - The single-factor stock selection shows better performance in small and mid-cap stock pools (CSI 1000, CSI 2000), with minimal impact from market capitalization and industry neutrality [2]. - The original factor values in CSI 300 outperform the market capitalization and industry-neutralized factor values, indicating that deep learning factors capture style and industry rotation patterns [2]. Group 3: Composite Factor Stock Selection - Composite factors, when equally weighted, outperform single factors, and the report outlines the construction of index-enhanced strategies with specific constraints on stock turnover and market exposure [3]. - The maximum drawdown for the CSI 300 index-enhanced strategy since January 2017 is -6.0%, with a current year excess return of -0.4% [3]. - Allowing for slight market and industry exposure results in annualized excess returns of 8.8% for CSI 300 and 14.6% for CSI 500, with current year excess returns of -1.7% and 5.2% respectively [3].