Predicting Financial Institution Failure with Financial and Ownership Variables

Abstract

Predicting failure of financial institutions can have a significant impact on the economy. Early warning systems developed from failure prediction models have proven to reduce as much business bankruptcy as possible. The recent East Asian economic crisis is a great lesson one should learn from lacking effective early warning systems. To serve as a sound early warning signal, the accuracy of a failure prediction model is as important as its robustness over time to failure. For an emerging market economy where ownership concentration is common, we show that only financial variables are not sufficient to produce models with a good predictive power. In our logit models that also incorporate ownership variables, 85.45%, 85.41%, and 91.49% of financial institutions are correctly classified in the models that used the data of one, two, and three years prior to the failure, respectively. In neural network model, the classification accuracy of a testing set is equal to 90.91% for one-year-ahead bankruptcy forcasting.