Identifying distress among banks prior to a major crisis using non-oriented super-SBM

Avkiran, Necmi Kemal and Cai, Lin (2014) Identifying distress among banks prior to a major crisis using non-oriented super-SBM. Annals of Operations Research, 217 1: 31-53. doi:10.1007/s10479-014-1568-8

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Author Avkiran, Necmi Kemal
Cai, Lin
Title Identifying distress among banks prior to a major crisis using non-oriented super-SBM
Journal name Annals of Operations Research   Check publisher's open access policy
ISSN 1572-9338
Publication date 2014-01-01
Sub-type Article (original research)
DOI 10.1007/s10479-014-1568-8
Open Access Status Not Open Access
Volume 217
Issue 1
Start page 31
End page 53
Total pages 23
Place of publication New York, NY, United States
Publisher Springer Netherlands
Language eng
Abstract We illustrate how data envelopment analysis (DEA) can be used as a forward-looking method to flag bank holding companies (BHCs) likely to become distressed. Various financial performance models are tested in the period leading up to the recent global financial crisis. Results generally support DEA's discriminatory and predictive power, suggesting that it can identify distressed banks up to 2 years in advance. Robustness tests reveal that DEA has a stable efficient frontier and its discriminatory and predictive powers prevail even after data perturbations. DEA can be used as a preliminary off-site screening tool by regulators, by business managers to ascertain their standing among competitors, and by investors. Attention by regulators can be further directed at potentially distressed banks as some of them would be candidates for closer monitoring. In conclusion, DEA may be useful in making economic decisions because there is an identifiable link between inefficiency and financial distress. To the best of our knowledge, application of DEA to predict financial distress among BHCs prior to a major crisis has not been published.
Keyword Bank holding companies
Data envelopment analysis
Distress prediction
Financial crisis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
UQ Business School Publications
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