Moving Forward from Predictive Regressions: Boosting Asset Allocation Decisions

72 Pages Posted: 2 Jul 2020

See all articles by Lauri Nevasalmi

Lauri Nevasalmi

University of Turku

Henri Nyberg

University of Turku; Tampere University

Date Written: June 10, 2020

Abstract

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

Suggested Citation

Nevasalmi, Lauri and Nyberg, Henri, Moving Forward from Predictive Regressions: Boosting Asset Allocation Decisions (June 10, 2020). Available at SSRN: https://ssrn.com/abstract=3623956 or http://dx.doi.org/10.2139/ssrn.3623956

Lauri Nevasalmi (Contact Author)

University of Turku ( email )

20014 Turku
Finland

Henri Nyberg

University of Turku ( email )

Turku, 20014
Finland

Tampere University ( email )

Tampere, FIN-33101
Finland

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