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Journal of Finance Vol. 79 No. 1 2024

The Virtue of Complexity in Return Prediction

Bryan Kelly; Semyon Malamud; KANGYING ZHOU1,2

1 Center for Economic and Policy Research · 2 University of Kang Ning

open access

Abstract

Much of the extant literature predicts market returns with “simple” models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.

DOI
10.1111/jofi.13298
Volume
79
Issue
1
Pages
459-503
Language
en
Sources
bibtex:phds-export.bib crossref openalex

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