Supervised portfolios

Guillaume Chevalier (), Guillaume Coqueret () and Thomas Raffinot ()
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Guillaume Chevalier: AXA Investment Managers, Multi Asset Client Solutions, Quantitative Research - AXA
Guillaume Coqueret: EM - EMLyon Business School
Thomas Raffinot: AXA Investment Managers, Multi Asset Client Solutions, Quantitative Research - AXA

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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. To foster reproducibility and future comparisons, our code is publicly available on Google Colab.

Date: 2022-12-02
New Economics Papers: this item is included in nep-cmp and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-04144588v1
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Published in Quantitative Finance, 2022, 22 (12), pp.2275-2295. ⟨10.1080/14697688.2022.2122543⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04144588

DOI: 10.1080/14697688.2022.2122543

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