The Virtue of Complexity in Machine Learning Portfolios

Bryan T. Kelly, Semyon Malamud and Kangying Zhou
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Bryan T. Kelly: Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)
Semyon Malamud: Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute
Kangying Zhou: Yale School of Management

No 21-90, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We theoretically characterize the behavior of machine learning portfolios in the high complexity regime, i.e. when the number of parameters exceeds the number of observations. We demonstrate a surprising \virtue of complexity:" Sharpe ratios of machine learning portfolios generally increase with model parameterization, even with minimal regularization. Empirically, we document the virtue of complexity in US equity market timing strategies. High complexity models deliver economically large and statistically significant out-of-sample portfolio gains relative to simpler models, due in large part to their remarkable ability to predict recessions.

Keywords: Portfolio choice; machine learning; random matrix theory; benign overfit; overparameterization (search for similar items in EconPapers)
JEL-codes: C3 C58 C61 G11 G12 G14 (search for similar items in EconPapers)
Pages: 100 pages
Date: 2021-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-ecm and nep-ore
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2190

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