Moving Forward from Predictive Regressions: Boosting Asset Allocation Decisions
72 Pages Posted: 2 Jul 2020
Date Written: June 10, 2020
We introduce a flexible utility-based empirical approach to directly determine asset allocation decisions between risky and risk-free assets. This is in contrast to the commonly used two-step approach where least squares optimal statistical equity premium predictions are first constructed to form portfolio weights before economic criteria are used to evaluate resulting portfolio performance. Our singlestep customized gradient boosting method is specifically designed to find optimal portfolio weights in a direct utility maximization. Empirical results of the monthly U.S. data show the superiority of boosted portfolio weights over several benchmarks, generating interpretable results and profitable asset allocation decisions.
Keywords: utility maximization, return predictability, machine learning, gradient boosting
JEL Classification: C22, C53, C58, G11, G17
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