Boyacioglu, Melek AcarKara, YakupBaykan, Oemer Kaan2020-03-262020-03-2620090957-4174https://dx.doi.org/10.1016/j.eswa.2008.01.003https://hdl.handle.net/20.500.12395/23776Bank 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.en10.1016/j.eswa.2008.01.003info:eu-repo/semantics/closedAccessBankruptcy predictionFinancial failureBankingSavings deposit insurance fundArtificial neural networksSupport vector machinesMultivariate statistical analysisArticle36233553366Q1WOS:000262178100080Q1