Machine Learning and the Implementable Efficient Frontier

Theis Ingerslev Jensen, Bryan T. Kelly, Semyon Malamud, Lasse Heje Pedersen

Research output: Working paperResearch

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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."
Original languageEnglish
Place of PublicationZürich
PublisherSwiss Finance Institute
Number of pages70
Publication statusPublished - 2022
SeriesSwiss Finance Institute Research Paper Series
Number22-63

Keywords

  • Asset pricing
  • Machine learning
  • Transaction costs
  • Economic significance
  • Investments

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