A machine learning efficient frontier

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4 Scopus citations

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.

Original languageEnglish
Pages (from-to)630-634
Number of pages5
JournalOperations Research Letters
Volume48
Issue number5
DOIs
StatePublished - Sep 2020

Keywords

  • Estimation risk
  • Machine learning
  • Portfolio theory
  • Tactical asset allocation

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