Economic policy uncertainty and bankruptcy filings

Elena Fedorova, Svetlana Ledyaeva*, Pavel Drogovoz, Alexandr Nevredinov

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number102174
Peer-reviewed scientific journalInternational Review of Financial Analysis
Volume82
ISSN1057-5219
DOIs
Publication statusPublished - 07.2022
MoE publication typeA1 Journal article - refereed

Keywords

  • 511 Economics
  • economic policy uncertainty
  • bankruptcy
  • firm
  • machine learning
  • stacking
  • bagging

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