Machine Learning and the Implementable Efficient Frontier

70 Pages Posted: 18 Aug 2022 Last revised: 19 Aug 2022

See all articles by Theis Ingerslev Jensen

Theis Ingerslev Jensen

Copenhagen Business School

Bryan T. Kelly

Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)

Semyon Malamud

Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute

Lasse Heje Pedersen

AQR Capital Management, LLC; Copenhagen Business School - Department of Finance; New York University (NYU); Centre for Economic Policy Research (CEPR)

Date Written: August 10, 2022

Abstract

We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of "economic feature importance."

Keywords: asset pricing, machine learning, transaction costs, economic significance, investments

JEL Classification: C5, C61, G00, G11, G12

Suggested Citation

Jensen, Theis Ingerslev and Kelly, Bryan T. and Malamud, Semyon and Pedersen, Lasse Heje, Machine Learning and the Implementable Efficient Frontier (August 10, 2022). Swiss Finance Institute Research Paper No. 22-63, Available at SSRN: https://ssrn.com/abstract=4187217 or http://dx.doi.org/10.2139/ssrn.4187217

Theis Ingerslev Jensen

Copenhagen Business School ( email )

Solbjerg Plads 3, SOL/A4.17
Copenhagen, Frederiksberg 2000

Bryan T. Kelly

Yale SOM ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

AQR Capital Management, LLC ( email )

Greenwich, CT
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Semyon Malamud

Ecole Polytechnique Federale de Lausanne ( email )

Lausanne, 1015
Switzerland

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

Swiss Finance Institute

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

Lasse Heje Pedersen (Contact Author)

AQR Capital Management, LLC ( email )

Greenwich, CT
United States

Copenhagen Business School - Department of Finance ( email )

Solbjerg Plads 3
Frederiksberg, DK-2000
Denmark

New York University (NYU) ( email )

Stern School of Business
44 West 4th Street
New York, NY 10012-1126
United States

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

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