Forecasting Bank Failures and Stress Testing: A Machine Learning Approach

Abstract

In this paper, we present a forecasting model of bank failures based on machine-learning. The proposed methodology defines a linear decision boundary separating the solvent from the failed banks. This setup generates a novel alternative stress testing tool. Our sample of 1443 U.S. banks includes all 481 failed banks during the 2007-2013 period. The set of explanatory variables is selected using a two-step feature selection procedure. The selected variables were then fed to a to Support Vector Machines forecasting model, through a training-testing learning process. The model exhibits 99.22% overall forecasting accuracy and outperforms the well-established Ohlson’s score.