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Machine learning techniques have significantly improved the prediction of expected stock returns. By distinguishing between trading days with major macroeconomic announcements (A-days) and regular trading days (N-days), we find that machine learning effectively captures time-varying sources of predictability of returns on A-days and N-days. We construct an ensemble model that combines results from models trained separately on these distinct types of days. Notably, the ensemble method outperforms models using complete datasets or subsets in forecasting accuracy, which highlights the ¡°complexity in time-series variation¡±. Evidence from bond-related characteristics reinforces the presence of time variation in asset pricing models.

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