Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/297952 
Year of Publication: 
2022
Citation: 
[Journal:] Central Bank Review (CBR) [ISSN:] 1303-0701 [Volume:] 22 [Issue:] 3 [Year:] 2022 [Pages:] 119-127
Publisher: 
Elsevier, Amsterdam
Abstract: 
This paper examines prediction of U.S. bank failure with a probit model that uses bias-corrected technical efficiency estimated using bootstrap data envelopment analysis as the measure of management quality. The model is tested on a sample of failed and non-failed banks during the sub-prime mortgage meltdown, 2008-2009. Results demonstrate this measure of management efficiency, together with other CAMEL factors (i.e., capital adequacy, asset quality, earnings quality, and liquidity), is significant for predicting bank failure. This measure of managerial quality allows more accurate prediction of failure than other measures. The model successfully predicts bank failure one and two years prior to failure.
Subjects: 
Bank failure
Bootstrap data envelopment analysis
Early prediction
Management efficiency
Probit
JEL: 
G20
G21
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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