Assessing Bank Soundness with Classification Techniques

The recent crisis highlighted, once again, the importance of early warning models to assess the soundness of individual banks. In the present study, we use six quantitative techniques originating from various disciplines to classify banks in three groups. The first group includes very strong and strong banks; the second one includes adequate banks, while the third group includes banks with weaknesses or serious problems. We compare models developed with financial variables only, with models that incorporate additional information in relation to the regulatory environment, institutional development, and macroeconomic conditions. The accuracy of classification of the models that include only financial variables is rather poor. We observe a substantial improvement in accuracy when we consider the country-level variables, with five out of the six models achieving out-of-sample classification accuracy above 70% on average. The models developed with multi-criteria decision aid and artificial neural networks achieve the highest accuracies. We also explore the development of stacked models that combine the predictions of the individual models at a higher level. While the stacked models outperform the corresponding individual models in most cases, we found no evidence that the best stacked model can outperform the best individual model