Core Viewpoint - The article discusses the integration of return factors into an orthogonal layer within deep learning models to enhance stock selection effectiveness while maintaining low correlation with existing return factors [1][2]. Group 1: Deep Learning Model Enhancements - By incorporating return factors into the orthogonal layer, deep learning factors can maintain good stock selection performance while ensuring low correlation with these return factors [1]. - The deep learning model's black-box nature makes it challenging to manually adjust factor weights during significant market style shifts; thus, the orthogonal layer allows for easier manual adjustments without compromising stock selection effectiveness [1]. Group 2: Performance Metrics - After adding return factors to the orthogonal layer, deep learning factors still exhibit strong stock selection capabilities, achieving an Information Coefficient (IC) above 0.02 and an IC Information Ratio (IR) exceeding 6 [2]. - The combination of deep learning factors with manually constructed return factors leads to significant improvements in overall market long positions compared to using deep learning factors alone, although the enhancement varies across different index-enhanced portfolios [2]. Group 3: Correlation and Performance - The correlation between deep learning factors and multi-granularity factors remains low after integrating return factors into the orthogonal layer, with high-frequency data inputs showing a correlation of no more than 0.01 [2]. - Utilizing deep learning factors alongside multi-granularity factors can significantly enhance the performance of overall market long positions, although the deep learning factors show limited predictive capability for mid to large-cap stock returns, resulting in less noticeable improvements for index-enhanced portfolios [2].