Research Article

Forecasting Bank Failure with Machine Learning Models: A study on Turkish Banks

Authors

  • Safa SEN Ph.D. Student, The University of Miskolc, Miskolc, Hungary
  • Sara Almeida de Figueiredo Ph.D. Student, The University of Miskolc, Miskolc, Hungary

Abstract

Forecasting bank failures has been an essential study in the literature due to their significant impact on the economic prosperity of a country. Acting as an intermediary player, banks channel funds from those with surplus capital to those who require capital to carry out their economic activities. Therefore, it is essential to generate early warning systems that could warn banks and stakeholders in case of financial turbulence. In this paper, three machine learning models named as GLMBoost, XGBoost, and SMO were used to forecast bank failures. We used commercial bank failure data of Turkey between 1997 and 2001, where we have 17 failed and 20 healthy banks. Our results show that the Sequential Minimal Optimization and GLMBoost provide the same performance when classifying failed banks, while GLMBoost performs better in AUC and SMO when considering total classification success. Lastly, XGBoost, one of the most recent and robust classification models, surprisingly underperformed in all three metrics we used in research.

Article information

Journal

Journal of Economics, Finance and Accounting Studies

Volume (Issue)

3 (2)

Pages

51-59

Published

2021-09-11

How to Cite

SEN, S., & Figueiredo, S. A. de. (2021). Forecasting Bank Failure with Machine Learning Models: A study on Turkish Banks . Journal of Economics, Finance and Accounting Studies, 3(2), 51–59. https://doi.org/10.32996/jefas.2021.3.2.6

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Keywords:

Financial crisis, Bank failure, SMO, GLMBoost, XGBoost, Gradient Boosting