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Machine learning portfolios with equal ...
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Document type :
Article dans une revue scientifique
DOI :
10.1016/j.ememar.2022.100891
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 ...
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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
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