TY - JOUR
T1 - Economic policy uncertainty and bankruptcy filings
AU - Fedorova, Elena
AU - Ledyaeva, Svetlana
AU - Drogovoz, Pavel
AU - Nevredinov, Alexandr
N1 - 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
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
KW - 511 Economics
KW - economic policy uncertainty
KW - bankruptcy
KW - firm
KW - machine learning
KW - stacking
KW - bagging
UR - http://www.scopus.com/inward/record.url?scp=85129422250&partnerID=8YFLogxK
U2 - 10.1016/j.irfa.2022.102174
DO - 10.1016/j.irfa.2022.102174
M3 - Article
AN - SCOPUS:85129422250
SN - 1057-5219
VL - 82
JO - International Review of Financial Analysis
JF - International Review of Financial Analysis
M1 - 102174
ER -