Machine Learning Goes Global: Cross-Sectional Return Predictability in International Stock Markets

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Authors
Cakici, Nusret
Fieberg, Christian
Metko, Daniel
Zaremba, Adam
Date
2023-08-18
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
License
Creative Commons License
Creative Commons Attribution 4.0 International License
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 in-formation. Furthermore, it varies internationally along two critical dimensions: the number of listed firms in the market and the average idiosyncratic risk limiting arbitrage.
Description
Keywords
machine learning    return predictability    international stock markets    the cross-section of stock returns    forecast combination    asset pricing    firm size
Citation
Cakici, N., Fieberg, C., Metko, D., & Zaremba, A. (2023). Machine learning goes global: Cross-sectional return predictability in international stock markets. Journal of Economic Dynamics and Control, 155, 104725.
URI
https://open.icm.edu.pl/handle/123456789/24915
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Artykuły / Articles