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

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dc.contributor.authorCakici, Nusret
dc.contributor.authorFieberg, Christian
dc.contributor.authorMetko, Daniel
dc.contributor.authorZaremba, Adam
dc.contributor.organizationFordham University
dc.contributor.organizationCity University of Applied Sciences
dc.contributor.organizationUniversity of Luxembourg
dc.contributor.organizationConcordia University
dc.contributor.organizationUniversity of Bremen
dc.contributor.organizationMontpellier Business School
dc.contributor.organizationPoznan University of Economics and Business
dc.date.accessioned2024-10-08T13:08:45Z
dc.date.available2024-10-08T13:08:45Z
dc.date.issued2023-08-18
dc.description.abstractWe 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.en
dc.identifier.citationCakici, 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.
dc.identifier.doi10.1016/j.jedc.2023.104725
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/24915
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofseries104725
dc.rightsUznanie autorstwa 4.0 Międzynarodoween
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceJournal of Economic Dynamics and Control
dc.subjectmachine learningen
dc.subjectreturn predictabilityen
dc.subjectinternational stock marketsen
dc.subjectthe cross-section of stock returnsen
dc.subjectforecast combinationen
dc.subjectasset pricingen
dc.subjectfirm sizeen
dc.titleMachine Learning Goes Global: Cross-Sectional Return Predictability in International Stock Marketsen
dc.typearticle
dc.type.versionacceptedVersion
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