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

Alexandre Rubesam

Emerging Markets Review, 2022, vol. 51, issue PB

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)
JEL-codes: C53 G11 G15 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Working Paper: Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ememar:v:51:y:2022:i:pb:s1566014122000085

DOI: 10.1016/j.ememar.2022.100891

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