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

Alexandre Rubesam

Post-Print from HAL

Abstract: 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.

Keywords: Emerging markets; Machine learning; Stock market prediction; Portfolio optimization; Equal risk contribution; Risk parity (search for similar items in EconPapers)
Date: 2022-06
Note: View the original document on HAL open archive server: https://hal.science/hal-03707365
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in Emerging Markets Review, 2022, 51 (Part B), pp.100891. ⟨10.1016/j.ememar.2022.100891⟩

Downloads: (external link)
https://hal.science/hal-03707365/document (application/pdf)

Related works:
Journal Article: Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03707365

DOI: 10.1016/j.ememar.2022.100891

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().