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Machine Learning and the Implementable Efficient Frontier

Author

Listed:
  • 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))

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."

Suggested Citation

  • Theis Ingerslev Jensen & Bryan T. Kelly & Semyon Malamud & Lasse Heje Pedersen, 2022. "Machine Learning and the Implementable Efficient Frontier," Swiss Finance Institute Research Paper Series 22-63, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2263
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    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4187217
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    Citations

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    Cited by:

    1. Baba-Yara, Fahiz & Boons, Martijn & Tamoni, Andrea, 2024. "Persistent and transitory components of firm characteristics: Implications for asset pricing," Journal of Financial Economics, Elsevier, vol. 154(C).
    2. Trent Spears & Stefan Zohren & Stephen Roberts, 2023. "View fusion vis-\`a-vis a Bayesian interpretation of Black-Litterman for portfolio allocation," Papers 2301.13594, arXiv.org.
    3. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2023. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR).
    4. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).

    More about this item

    Keywords

    asset pricing; machine learning; transaction costs; economic significance; investments;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G00 - Financial Economics - - General - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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