Forecasting bank failures and stress testing: A machine learning approach |
| |
Authors: | Periklis Gogas Theophilos Papadimitriou Anna Agrapetidou |
| |
Affiliation: | Democritus University of Thrace, Department of Economics, Komotini 69100, Greece |
| |
Abstract: | This paper presents a forecasting model of bank failures based on machine-learning. The proposed methodology defines a linear decision boundary that separates the solvent banks from those that failed. This setup generates a novel alternative stress-testing tool. Our sample of 1443 U.S. banks includes all 481 banks that failed during the period 2007–2013. The set of explanatory variables is selected using a two-step feature selection procedure. The selected variables were then fed to a support vector machines forecasting model, through a training–testing learning process. The model exhibits a 99.22% overall forecasting accuracy and outperforms the well-established Ohlson’s score. |
| |
Keywords: | Machine learning Bank failures Stress testing Forecasting |
本文献已被 ScienceDirect 等数据库收录! |