The Virtue of Complexity Everywhere

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 22-57, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We investigate the performance of non-linear return prediction models in the high complexity regime, i.e., when the number of model parameters exceeds the number of observations. We document a "virtue of complexity" in all asset classes that we study (US equities, international equities, bonds, commodities, currencies, and interest rates). Specifically, return prediction R2 and optimal portfolio Sharpe ratio generally increase with model parameterization for every asset class. The virtue of complexity is present even in extremely data-scarce environments, e.g., for predictive models with less than twenty observations and tens of thousands of predictors. The empirical association between model complexity and out-of-sample model performance exhibits a striking consistency with theoretical predictions.

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: 43 pages
Date: 2022-07
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-for
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2257

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