Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey

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dc.contributor.authorBoyacioglu, Melek Acar
dc.contributor.authorKara, Yakup
dc.contributor.authorBaykan, Oemer Kaan
dc.date.accessioned2020-03-26T17:39:43Z
dc.date.available2020-03-26T17:39:43Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractBank failures threaten the economic system as a whole. Therefore, predicting bank financial failures is crucial to prevent and/or lessen the incoming negative effects oil the economic system. This is originally a classification problem to categorize banks as healthy or non-healthy ones. This study aims to apply various neural network techniques, support vector machines and multivariate statistical methods to the bank failure prediction problem in a Turkish case, and to present a comprehensive computational comparison of the classification performances of the techniques tested. Twenty financial ratios with six feature groups including capital adequacy, asset quality, management quality, earnings, liquidity and sensitivity to market risk (CAMELS) are selected as predictor variables in the study. Four different data sets with different characteristics are developed using official financial data to improve the prediction performance. Each data set is also divided into training and validation sets. In the category of neural networks, four different architectures namely multi-layer perceptron, competitive learning, self-organizing map and learning vector quantization are employed. The multivariate statistical methods; multivariate discriminant analysis, k-means cluster analysis and logistic regression analysis are tested. Experimental results are evaluated with respect to the correct accuracy performance of techniques. Results show that multi-layer perceptron and learning vector quatization can be considered as the most successful models in predicting the financial failure of banks. (C) 2008 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2008.01.003en_US
dc.identifier.endpage3366en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3355en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2008.01.003
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23776
dc.identifier.volume36en_US
dc.identifier.wosWOS:000262178100080en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBankruptcy predictionen_US
dc.subjectFinancial failureen_US
dc.subjectBankingen_US
dc.subjectSavings deposit insurance funden_US
dc.subjectArtificial neural networksen_US
dc.subjectSupport vector machinesen_US
dc.subjectMultivariate statistical analysisen_US
dc.titlePredicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkeyen_US
dc.typeArticleen_US

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