Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP

Abstract

This paper presents three hitherto unused neural network architectures for bankruptcy prediction in banks. These networks are Group Method of Data Handling (GMDH), Counter Propagation Neural Network (CPNN) and fuzzy Adaptive Resonance Theory Map (fuzzy ARTMAP). Efficacy of each of these techniques is tested by using four different datasets pertaining to Spanish banks, Turkish banks, UK banks and US banks. Further t-statistic, f-statistic and GMDH are used for feature selection purpose and the features so selected are fed as input to GMDH, CPNN and fuzzy ARTMAP for classification purpose. In each of these cases, top five features are selected in the case of Spanish dataset and top seven features are selected in the case of Turkish and UK datasets. It is observed that the features selected by t-statistic and f-statistic are identical in all datasets. Further, there is a good overlap in the features selected by t-statistic and GMDH. The performance of these hybrids is compared with that of GMDH, CPNN and fuzzy ARTMAP in their stand-alone mode without feature selection. Ten-fold cross validation is performed throughout the study. Results indicate that the GMDH outperformed all the techniques with or without feature selection. Furthermore, the results are much better than those reported in previous studies on the same datasets in terms of average accuracy, average sensitivity and average specificity.