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Supervised portfolios

Author

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  • Guillaume Chevalier
  • Guillaume Coqueret
  • Thomas Raffinot

Abstract

We propose an asset allocation strategy that engineers optimal weights before feeding them to a supervised learning algorithm. In contrast to the traditional approaches, the machine is able to learn risk measures, preferences, and constraints beyond simple expected returns, within a flexible, forward-looking, and non-linear framework. Our empirical analysis illustrates that predicting the optimal weights directly instead of the traditional two-step approach leads to more stable portfolios with statistically better risk-adjusted performance measures.

Suggested Citation

  • Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Quantitative Finance, Taylor & Francis Journals, vol. 22(12), pages 2275-2295, December.
    • Handle: RePEc:taf:quantf:v:22:y:2022:i:12:p:2275-2295
    DOI: 10.1080/14697688.2022.2122543
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    Cited by:

    1. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2023. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR).
    2. Caldeira, João F. & Santos, André A.P. & Torrent, Hudson S., 2023. "Semiparametric portfolios: Improving portfolio performance by exploiting non-linearities in firm characteristics," Economic Modelling, Elsevier, vol. 122(C).

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