A Machine Learning Efficient Frontier
Operations Research Letters, DOI: https://doi.org/10.1016/j.orl.2020.07.016
14 Pages Posted: 14 Mar 2020 Last revised: 7 Aug 2020
Date Written: May 21, 2020
We propose a simple approach to bridge between portfolio theory and machine learning. The outcome is an out-of-sample machine learning efficient frontier based on two assets, high risk and low risk. By rotating between the two assets, we show that the proposed frontier dominates the mean-variance efficient frontier out-of-sample. Our results, therefore, shed important light on the appeal of machine learning into portfolio selection under estimation risk.
Keywords: Portfolio Theory, Machine Learning, Tactical Asset Allocation, Estimation Risk
JEL Classification: C4, C6, C13, D8, G1
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