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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 mostrecessions, and the inclusion of recession history or the risk-free rate doesnot necessarily improve their performance. Investigating recessions wheremodels perform well, we find that they exhibit lower market volatility thanother recessions. This implies that the improved performance is not due to themerit of ML methods but rather factors such as effective monetary policies thatstabilized the market. We recommend that ML practitioners evaluate their modelsduring both recessions and expansions.

Stock Price Predictability and the Business Cycle via Machine Learning