EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks

Kristóf, Tamás ORCID: and Virág, Miklós (2022) EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks. Research in International Business and Finance, 61 . DOI

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This article provides evidence that machine learning methods are suitable for reliably predicting the failure risk of European Union-27 banks from the experiences of the past decade. It demonstrates that earnings, capital adequacy, and management capability are the strongest predictors of bank failure. Critical and relevant field research is presented in the context of economic uncertainties arising from the COVID-19 pandemic. The results suggest that the developed models possess high predictive power, with the C5.0 decision tree model providing the best performance. The findings have policy implications for bank supervisory authorities, bank executives, risk management professionals, and policymakers working in finance. The models can be used to recognize bank weaknesses in time to take appropriate mitigating actions.

Item Type:Article
Uncontrolled Keywords:bank failure, classification, credit risk modeling, machine learning
JEL classification:C38 - Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
C45 - Neural Networks and Related Topics
C53 - Forecasting Models; Simulation Methods
G17 - Financial Forecasting and Simulation
G21 - Banks; Depository Institutions; Micro Finance Institutions; Mortgages
Subjects:Economic development
ID Code:7462
Deposited By: MTMT SWORD
Deposited On:27 Jun 2022 13:30
Last Modified:27 Jun 2022 13:30

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