Supervised Portfolios

33 Pages Posted: 2 Nov 2021 Last revised: 21 Jul 2022

Date Written: July 21, 2022

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.

Keywords: Supervised learning, portfolio choice, transaction costs

JEL Classification: G12, C62, D81

Suggested Citation

CHEVALIER, Guillaume and Coqueret, Guillaume and Raffinot, Thomas, Supervised Portfolios (July 21, 2022). Available at SSRN: https://ssrn.com/abstract=3954109 or http://dx.doi.org/10.2139/ssrn.3954109

Guillaume CHEVALIER

AXA Investment Managers ( email )

Tour Majunga
6 Place de la Pyramide
La Défense, Paris 92908
France

Guillaume Coqueret (Contact Author)

EMLYON Business School ( email )

23 Avenue Guy de Collongue
Ecully, 69132
France

Thomas Raffinot

AXA-IM ( email )

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