Economic policy uncertainty and bankruptcy filings

Applying machine learning techniques to predict bankruptcy in the sample of French, Italian, Russian and Spanish firms, the study demonstrates that the inclusion of economic policy uncertainty (EPU) indicator into bankruptcy prediction models notably increases their accuracy. This effect is more pronounced when we use novel Twitter-based version of EPU index instead of original news-based index. We further compare the prediction accuracy of machine learning techniques and conclude that stacking ensemble method outperforms (though marginally) machine learning methods, which are more commonly used for bankruptcy prediction, such as single classifiers and bagging.

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Funding Information: We would like to thank Dmitriy Afanasyev, Igor Demin, Vera Kononova, Yuri Zelenkov and webinar participants for helpful comments on earlier drafts of this paper. We also thank Artiom Cozin for excellent research assistance. During part of the research for this publication Svetlana Ledyaeva was hosted by the Aleksanteri Institute, University of Helsinki. Publisher Copyright: © 2022 The Authors

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economic policy uncertainty, bankruptcy, firm, machine learning, stacking, bagging, bankruptcy, firm, machine learning, stacking, bagging

DOI

10.1016/j.irfa.2022.102174

Citation

Fedorova, E, Ledyaeva, S, Drogovoz, P & Nevredinov, A 2022, ' Economic policy uncertainty and bankruptcy filings ', International Review of Financial Analysis, vol. 82, 102174 . https://doi.org/10.1016/j.irfa.2022.102174

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https://urn.fi/URN:NBN:fi:aalto-202205173227

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[article-cris] Kauppakorkeakoulu / BIZ

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Economic policy uncertainty and bankruptcy filings

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