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

See all articles by Brian J. Clark

Brian J. Clark

Rensselaer Polytechnic Institute (RPI)

Zachary Feinstein

Stevens Institute of Technology - School of Business

Majeed Simaan

Stevens Institute of Technology - School of Business

Date Written: May 21, 2020

Abstract

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

Suggested Citation

Clark, Brian J. and Feinstein, Zachary and Simaan, Majeed, A Machine Learning Efficient Frontier (May 21, 2020). Operations Research Letters, DOI: https://doi.org/10.1016/j.orl.2020.07.016, Available at SSRN: https://ssrn.com/abstract=3541387 or http://dx.doi.org/10.2139/ssrn.3541387

Brian J. Clark

Rensselaer Polytechnic Institute (RPI) ( email )

Troy, NY 12180
United States

Zachary Feinstein

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Majeed Simaan (Contact Author)

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

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