A machine learning efficient frontier

被引：5

作者：

Clark, Brian ^{[1
]}

Feinstein, Zachary ^{[2
]}

Simaan, Majeed ^{[2
]}

机构：

[1] Rensselaer Polytech Inst, Lally Sch Management, 110 8th St,Pittsburgh Bldg, Troy, NY 12180 USA

[2] Stevens Inst Technol, Sch Business, Babbio Ctr, 1 Castle Point Terrace, Hoboken, NJ 07030 USA

来源：

OPERATIONS RESEARCH LETTERS | 2020年 / 48卷 / 05期

关键词：

Portfolio theory; Machine learning; Tactical asset allocation; Estimation risk; PORTFOLIO; REGULARIZATION; VOLATILITY; SELECTION;

D O I：

10.1016/j.orl.2020.07.016

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

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. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：630 / 634

页数：5

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