State-dependent Asset Allocation Using Neural Networks

TL;DR

This paper introduces a neural network-based method for conditional asset allocation that dynamically adjusts portfolios based on market states, capturing nonlinear relationships without assuming return distributions.

Contribution

It presents a novel machine learning approach that directly links market state variables to portfolio weights, improving adaptability over traditional models.

Findings

Outperforms traditional asset allocation methods in empirical tests.

Robust across different sample periods and objective functions.

Captures nonlinear relationships without assuming return distributions.

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.