TY - JOUR
T1 - A machine learning efficient frontier
AU - Clark, Brian
AU - Feinstein, Zachary
AU - Simaan, Majeed
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Estimation risk
KW - Machine learning
KW - Portfolio theory
KW - Tactical asset allocation
UR - http://www.scopus.com/inward/record.url?scp=85089197941&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089197941&partnerID=8YFLogxK
U2 - 10.1016/j.orl.2020.07.016
DO - 10.1016/j.orl.2020.07.016
M3 - Article
AN - SCOPUS:85089197941
SN - 0167-6377
VL - 48
SP - 630
EP - 634
JO - Operations Research Letters
JF - Operations Research Letters
IS - 5
ER -