Banka başarısızlıklarının yapay sinir ağlarıyla tahmini: Türk bankacılık sistemi üzerine karşılaştırmalı bir uygulama

In this study, neural network models are introduced and employed for the classification of failed non-failed banks prior to the failure of banks. To perform classification 36 financial and operational ratio of banks operating in Turkey were used as an input to the models. Two types of neural network structure were employed: Multilayer Perceptron (MLP) and Generalized Feed Forward Networks (GFW). Based on these network structures ten Artificial Neural Network (ANN) model were constructed having a varying number of hidden layers and perceptron in each layer. Rival models are evaluated and compared in terms of classification accuracy. Models are estimated for 1995-2000 period prior to bank failures. In addition to ANN models, to compare the classification performance, discriminant and logistic regression models are employed. Comparison of classification performance of ANN, discriminant and logistic regression models yield that ANN outperforms other models.