State-dependent asset allocation using neural networks

Reza Bradrania and Davood Pirayesh Neghab

MPRA Paper from University Library of Munich, Germany

Abstract: Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods.

Keywords: asset allocation; portfolio optimization; market state, machine learning; neural networks; performance ratio (search for similar items in EconPapers)
JEL-codes: C1 C10 C15 C18 C53 C55 C58 G0 G1 G11 G12 G17 (search for similar items in EconPapers)
Date: 2021-02-01
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (1)

Published in European Journal of Finance 11.28(2021): pp. 1130-1156

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https://mpra.ub.uni-muenchen.de/115264/1/MPRA_paper_115254.pdf revised version (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:115254

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