Titre :

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

Auteur(s) :

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

Titre de la revue :

Emerging Markets Review

Pagination :

100891

Éditeur :

Elsevier

Date de publication :

2022-06

ISSN :

1566-0141

Mot(s)-clé(s) en anglais :

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

Discipline(s) HAL :

Sciences de l'Homme et Société/Gestion et management

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

• Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL