The Virtue of Complexity Everywhere
45 Pages Posted: 25 Jul 2022 Last revised: 2 Aug 2022
There are 2 versions of this paper
Date Written: July 18, 2022
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
JEL Classification: C3, C58, C61, G11, G12, G14
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