Supervised portfolios

Guillaume Chevalier, Guillaume Coqueret and Thomas Raffinot

Quantitative Finance, 2022, vol. 22, issue 12, 2275-2295

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.

Date: 2022
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DOI: 10.1080/14697688.2022.2122543

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