Stock Price Predictability and the Business Cycle via Machine Learning

2023/04/06 by Li Rong Wang, Wang, Li Rong, Hsuan Fu +3
Decision Sciences · Economics, Econometrics and Finance · #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Machine Learning (cs.LG) #Monetary Policy and Economic Impact #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2304.09937

openalex publication_date 2023/04/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We study the impacts of business cycles on machine learning (ML) predictions. Using the S&P 500 index, we find that ML models perform worse during most recessions, and the inclusion of recession history or the risk-free rate does not necessarily improve their performance. Investigating recessions where models perform well, we find that they exhibit lower market volatility than other recessions. This implies that the improved performance is not due to the merit of ML methods but rather factors such as effective monetary policies that stabilized the market. We recommend that ML practitioners evaluate their models during both recessions and expansions.

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