Predicting Global Banking Stability: A Machine Learning Approach Using the Camels Framework

59 Pages Posted: 4 Mar 2024

See all articles by Stefanos Theofilis

Stefanos Theofilis

affiliation not provided to SSRN

Ilias Kampouris

Abu Dhabi University

Aristeidis Samitas

National and Kapodistrian University of Athens

Abstract

This study assesses the performance of the 33 largest banks in the top 13 GDP countries using the CAMELS framework. Beyond traditional CAMELS ratings, the research explores predictability through 25 machine learning algorithms. Analyzing data on key CAMELS variables—capital adequacy, asset quality, management efficiency, earnings quality, liquidity, and market risk sensitivity—the study aims to predict future bank performance. Machine learning algorithms are employed to build predictive models, evaluating accuracy, precision, and generalization capabilities. The findings offer insights crucial for risk management, regulatory decisions, and proactive measures to prevent banking crises.

Keywords: Banking performance, CAMELS framework, machine learning, Early Warning Systems, Classification

Suggested Citation

Theofilis, Stefanos and Kampouris, Ilias and Samitas, Aristeidis, Predicting Global Banking Stability: A Machine Learning Approach Using the Camels Framework. Available at SSRN: https://ssrn.com/abstract=4747568 or http://dx.doi.org/10.2139/ssrn.4747568

Stefanos Theofilis

affiliation not provided to SSRN ( email )

No Address Available

Ilias Kampouris

Abu Dhabi University ( email )

Abu Dhabi
United Arab Emirates

Aristeidis Samitas (Contact Author)

National and Kapodistrian University of Athens ( email )

5 Stadiou Strt
Athens, 12131
Greece

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
102
Abstract Views
264
Rank
500,090
PlumX Metrics