Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

An ordinal classification framework for bank failure prediction: methodology and empirical evidence for US banks

Manthoulis Georgios, Doumpos Michail, Zopounidis Konstantinos, Galariotis, Emilios

Simple record


URIhttp://purl.tuc.gr/dl/dias/1277B779-6451-411C-BAB7-2E9604C80D7D-
Identifierhttps://doi.org/10.1016/j.ejor.2019.09.040-
Identifierhttps://www.sciencedirect.com/science/article/pii/S0377221719307970-
Languageen-
Extent16 pagesen
TitleAn ordinal classification framework for bank failure prediction: methodology and empirical evidence for US banksen
CreatorManthoulis Georgiosen
CreatorΜανθουλης Γεωργιοςel
CreatorDoumpos Michailen
CreatorΔουμπος Μιχαηλel
CreatorZopounidis Konstantinosen
CreatorΖοπουνιδης Κωνσταντινοςel
CreatorGalariotis, Emiliosen
PublisherElsevieren
Content SummaryBank failure prediction models usually combine financial attributes through binary classification approaches. In this study we extend this standard framework in three main directions. First, we explore the predictive power of attributes that describe the diversification of banking operations. Second, we consider the prediction of failure in a multi-period context. Finally, an enhanced ordinal classification framework is introduced, which considers multiple instances of failed banks prior to failure (up to three years prior to bankruptcy). Various ordinal models are developed using techniques from multiple criteria decision analysis, statistics, and machine learning. Moreover, ensemble models based on error-correcting output codes are examined. The analysis is based on a sample consisting of approximately 60,000 observations for banks in the United States over the period 2006–2015. The results show that diversification attributes improve the predictive power of bank failure prediction models, mainly for mid to long-term prediction horizons. Moreover, ordinal classification models provide a better description of the state of the banks prior to failure and are competitive to standard binary classification models.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2022-04-20-
Date of Publication2020-
SubjectOR in bankingen
SubjectBank failure predictionen
SubjectOrdinal classificationen
SubjectMultiple criteria analysisen
Bibliographic CitationG. Manthoulis, M. Doumpos, C. Zopounidis, and E. Galariotis, “An ordinal classification framework for bank failure prediction: methodology and empirical evidence for US banks,” Eur. J. Oper. Res., vol. 282, no. 2, pp. 786–801, Apr. 2020, doi: 10.1016/j.ejor.2019.09.040.en

Services

Statistics