Utilizing Macroeconomic Factors for Sector Rotation based on Interpretable Machine Learning and Explainable AI

摘要

This paper focuses on the application of explainable AI in finance, introducing the use of machine learning models such as multiple linear regression, ridge regression, and random forest. We also compare their effects through empirical analysis on Chinese stock market. In addition, we propose three methods, which are feature selection, discretization of returns, and signal timing strategy, to improve the utility of our model. The empirical results show that our models can effectively select industries that will perform well in the future, further proving the importance and application feasibility of explainable AI in the financial field.