Title :

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

Author(s) :

Rubesam, Alexandre [Auteur]
Lille économie management - UMR 9221 [LEM]

Journal title :

Emerging Markets Review

Pages :

100891

Publisher :

Elsevier

Publication date :

2022-06

ISSN :

1566-0141

English keyword(s) :

Emerging markets
Machine learning
Stock market prediction
Portfolio optimization
Equal risk contribution
Risk parity

HAL domain(s) :

Sciences de l'Homme et Société/Gestion et management

English abstract : [en]

We investigate the use of machine learning (ML) to forecast stock returns in the Brazilian market using a rich proprietary dataset. While ML portfolios can easily outperform the local market, the performance of long-short ...
Show more >We investigate the use of machine learning (ML) to forecast stock returns in the Brazilian market using a rich proprietary dataset. While ML portfolios can easily outperform the local market, the performance of long-short strategies using ML is hampered by the high volatility of the short portfolios. We show that an Equal Risk Contribution (ERC) approach significantly improves risk-adjusted returns. We further develop an ERC approach that combines multiple long-short strategies obtained with ML models, equalizing risk contributions across ML models, which outperforms, on a risk-adjusted basis, all individual ML long-short strategies, as well as alternative combinations of ML strategies.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL