Do Anomalies Really Predict Market Returns? New Data and New Evidence

Nusret Cakici, Christian Fieberg, Daniel Metko and Adam Zaremba

Review of Finance, 2024, vol. 28, issue 1, 1-44

Abstract: Using new data from US and global markets, we revisit market risk premium predictability by equity anomalies. We apply a repertoire of machine-learning methods to forty-two countries to reach a simple conclusion: anomalies, as such, cannot predict aggregate market returns. Any ostensible evidence from the USA lacks external validity in two ways: it cannot be extended internationally and does not hold for alternative anomaly sets—regardless of the selection and design of factor strategies. The predictability—if any—originates from a handful of specific anomalies and depends heavily on seemingly minor methodological choices. Overall, our results challenge the view that anomalies as a group contain helpful information for forecasting market risk premia.

Keywords: Equity anomalies; Return predictability; Machine learning; International stock markets; Equity premium (search for similar items in EconPapers)
JEL-codes: G11 G12 G14 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (4)

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