Résumé
We examine the failures of 132 U.S. banks over the 2002-2009 period using discriminant analysis and successfully distinguish between banks that failed and those that didn't 92% of the time using in-sample quarterly data. Our two most important variables are related to bank capital and loan quality, as one might expect; although bank profitability is also important. The resulting model is then used out-of-sample to examine the failure of 191 banks during 2010-11, with predictive accuracy in the 90-95% range. Our results demonstrate that our model can also easily be applied to a large number of firms (even those that don't fail) and does an excellent job of distinguishing healthy from distressed banks. Combining this effectiveness with its ease of implementation makes it very functional. Such a model should be of obvious interest to regulators, analysts, and all those with a direct interest in assessing bank financial health.
| Langue d'origine | English |
|---|---|
| Pages (de-à) | 101-111 |
| Nombre de pages | 11 |
| Journal | Journal of Banking and Finance |
| Volume | 64 |
| DOI | |
| Statut de publication | Published - mars 1 2016 |
Note bibliographique
Funding Information:The authors would like to acknowledge financial support from the Social Sciences Humanities and Research (SSHRC) Canada, and the Smith School of Business at Queen’s University. We are grateful for comments received from participants at the 2014 World Finance Conference, and at a finance seminar at the Schulich School of Business at York University. The paper was improved substantially by incorporating the suggestions of an anonymous referee and Associate Editor, and from the Managing Editor, Carol Alexander.
Publisher Copyright:
© 2015 Elsevier B.V.
ASJC Scopus Subject Areas
- Finance
- Economics and Econometrics