Stock Price Predictability and the Business Cycle Via Machine Learning

Abstract

We investigate the impact of business cycles on machine learning (ML) predictions using the S&P 500 index. Our findings reveal that ML models generally under-perform during most recessions, and incorporating recession history, risk-free rate, velocity, momentum, and macroeconomic variables does not necessarily enhance their performance. Upon examining recessions where models exhibit strong performance, we observe that these periods exhibit lower market volatility compared to other recessions. This suggests that the improved performance is primarily influenced by factors such as effective monetary policies that stabilize the market, rather than the inherent merit of ML methods. Nonetheless, providing advance notice of the beginning of a recession could help mitigate the negative impacts on model performance. Therefore, we propose a neural network architecture aimed at detecting the onset of recessions. Additionally, we recommend that ML practitioners evaluate their models across both recessions and expansions to gain comprehensive insights into their performance. Preprint submitted to Decision Support Systems October 29, 2023

Keywords: Stock Price Predictability, business cycle, Machine Learning