Machine Learning and the Implementable Efficient Frontier

Theis Ingerslev Jensen, Bryan T. Kelly, Semyon Malamud and Lasse Heje Pedersen
Additional contact information
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)

No 22-63, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 (search for similar items in EconPapers)
JEL-codes: C5 C61 G00 G11 G12 (search for similar items in EconPapers)
Pages: 68 pages
Date: 2022-08
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2263

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