State-dependent Asset Allocation Using Neural Networks
Proposes a machine learning-based approach for state-dependent asset allocation, outperforming traditional methods in portfolio optimization.
What it examines
This paper proposes a novel approach to conditional asset allocation using Artificial Neural Networks (ANN) to directly determine portfolio weights based on state variables, aiming to improve portfolio efficiency by capturing non-linear relationships without assuming specific return distributions.
What it concludes
The research suggests that the ANN-based approach can significantly improve portfolio management by dynamically adjusting allocations based on market conditions. Potential applications include portfolio optimization for asset managers. Future research could extend this method to multi-period asset allocation.
Evidence objects
The research suggests that the ANN-based approach can significantly improve portfolio management by dynamically adjusting allocations based on market conditions. Potential applications include portfolio optimization for asset managers. Future research could extend this method to multi-period asset allocation.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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 allocat… ▽ More 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. △ Less
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