Machine learning goes global: Cross-sectional return predictability in international stock markets

Nusret Cakici, Christian Fieberg, Daniel Metko and Adam Zaremba

Journal of Economic Dynamics and Control, 2023, vol. 155, issue C

Abstract: We examine return predictability with machine learning in 46 stock markets around the world. We calculate 148 firm characteristics and use them to feed a repertoire of different models. The algorithms extract predictability mainly from simple yet popular factor types—such as momentum, reversal, value, and size. All individual models generate substantial economic gains; however, combining them proves particularly effective. Despite the overall robustness, the machine learning performance depends heavily on firm size and availability of recent information. Furthermore, it varies internationally along two critical dimensions: the number of listed firms in the market and the average idiosyncratic risk limiting arbitrage.

Keywords: Machine learning; Return predictability; International stock markets; The cross-section of stock returns; Forecast combination; Asset pricing; Firm size (search for similar items in EconPapers)
JEL-codes: C52 G10 G12 G15 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:155:y:2023:i:c:s0165188923001318

DOI: 10.1016/j.jedc.2023.104725

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