Machine learning and the cross-section of emerging market stock returns

Matthias X. Hanauer and Tobias Kalsbach

Emerging Markets Review, 2023, vol. 55, issue C

Abstract: This paper compares various machine learning models to predict the cross-section of emerging market stock returns. We document that allowing for non-linearities and interactions leads to economically and statistically superior out-of-sample returns compared to traditional linear models. Although we find that both linear and machine learning models show higher predictability for stocks associated with higher limits to arbitrage, we also show that this effect is less pronounced for non-linear models. Furthermore, significant net returns can be achieved when accounting for transaction costs, short-selling constraints, and limiting our investment universe to big stocks only.

Keywords: Machine learning; Return prediction; Cross-section of stock returns; Emerging markets; Random forest; Gradient boosting; Neural networks (search for similar items in EconPapers)
JEL-codes: C14 C52 C58 G11 G12 G14 G15 G17 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ememar:v:55:y:2023:i:c:s1566014123000274

DOI: 10.1016/j.ememar.2023.101022

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