Footnote:
In: Operations Research Letters, DOI: https://doi.org/10.1016/j.orl.2020.07.016
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments May 21, 2020 erstellt
Description:
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