Man versus Machine Learning Revisited

Yingguang Zhang, Yandi Zhu and Juhani T Linnainmaa

The Review of Financial Studies, 2025, vol. 38, issue 12, 3768-3790

Abstract: Binsbergen, Han, and Lopez-Lira (2023) predict analysts’ forecast errors using a random forest model. A strategy that trades against this model’s predictions earns a monthly alpha of 1.54% (-value = 5.84). This estimate represents a large improvement over studies using classical statistical methods. We attribute the difference to a look-ahead bias. Removing the bias erases the alpha. Linear models yield as accurate forecasts and superior trading profits. Neither alternative machine learning models nor combinations thereof resurrect the predictability. We discuss the state of research into the term structure of analysts’ forecasts and its causal relationship with returns.

Keywords: G12; G14 (search for similar items in EconPapers)
Date: 2025
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