Machine learning portfolios with equal risk contributions: evidence from the Brazilian market

56 Pages Posted: 8 Aug 2019 Last revised: 21 Oct 2021

See all articles by Alexandre Rubesam

Alexandre Rubesam

IESEG School of Management; French National Center for Scientific Research (CNRS) - Lille Economie & Management (LEM) UMR 9221

Date Written: October 10, 2021

Abstract

We investigate the use of machine learning (ML) to forecast stock returns in the Brazilian market usinga rich proprietary dataset. While ML portfolios can easily outperform the local market, the performanceof long-short strategies using ML is hampered by the high volatility of the short portfolios. We showthat an Equal Risk Contribution (ERC) approach significantly improves risk-adjusted returns. We furtherdevelop an ERC approach that combines multiple long-short strategies obtained with ML models, equal-izing risk contributions across ML models, which outperforms, on a risk-adjusted basis, all individualML long-short strategies, as well as alternative combinations of ML strategies.

Keywords: emerging markets, machine learning, stock market prediction, portfolio optimization, equalrisk contribution, risk parity

JEL Classification: C53, G11, G15

Suggested Citation

Rubesam, Alexandre, Machine learning portfolios with equal risk contributions: evidence from the Brazilian market (October 10, 2021). Available at SSRN: https://ssrn.com/abstract=3432760 or http://dx.doi.org/10.2139/ssrn.3432760

Alexandre Rubesam (Contact Author)

IESEG School of Management ( email )

Socle de la Grande Arche
1 Parvis de la Defense
Puteaux, Paris 92800
France

French National Center for Scientific Research (CNRS) - Lille Economie & Management (LEM) UMR 9221 ( email )

Lille
France

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