Moving Forward from Predictive Regressions: Boosting Asset Allocation Decisions
Nevasalmi Lauri; Nyberg Henri
https://urn.fi/URN:NBN:fi-fe2021042827204
Tiivistelmä
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 single-step 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
Kokoelmat
- Rinnakkaistallenteet [19207]