Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market - Archive ouverte HAL
Article Dans Une Revue Emerging Markets Review Année : 2022

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

Résumé

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.
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hal-03707365 , version 1 (22-07-2024)

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Alexandre Rubesam. Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market. Emerging Markets Review, 2022, 51 (Part B), pp.100891. ⟨10.1016/j.ememar.2022.100891⟩. ⟨hal-03707365⟩
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