Financial Leading Indicators of Banking Distress: A Micro Prudential Approach - Evidence from Europe

Abstract

Sovereign and Subprime crises have corrosive effects on European banking system. In this study, we aim to explain and predict the state of distress for 618 European banks for a five year period (2007-2011). For this purpose, we applied early warning systems using traditional and developed methods: discriminant analysis, logistic regression and the artificial intelligence. Those methods aim to predict bank distress up to a year (two years) before it actually happened. Our study seeks to compare between these three methods and to choose the most appropriate for prediction. The key finding of this study demonstrates that the neural network method outperforms the other models. This result is too much useful for banks and help policy makers to do a better job in terms of regulatory reforms.