Predicting failure in the U.S. banking sector: An extreme gradient boosting approach

Pedro Carmona, Francisco Climent and Alexandre Momparler

International Review of Economics & Finance, 2019, vol. 61, issue C, 304-323

Abstract: Banks play a central role in developed economies. Consequently, systemic banking crises destabilize financial markets and hamper global economic growth. In this study, extreme gradient boosting was used to predict bank failure in the U.S. banking sector. Key variables were identified to anticipate and prevent bank defaults. The data, which spanned the period 2001 to 2015, consisted of annual series of 30 financial ratios for 156 U.S. national commercial banks. Identifying leading indicators of bank failure is vital to help regulators and bank managers act swiftly before distressed financial institutions reach the point of no return. The findings indicate that lower values for retained earnings to average equity, pretax return on assets, and total risk-based capital ratio are associated with a higher risk of bank failure. In addition, an exceedingly high yield on earning assets increases the chance of bank financial distress.

Keywords: Bank failure prediction; Bank failure prevention; Bank financial distress; Machine learning; Extreme gradient boosting; XGBoost (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (56)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:61:y:2019:i:c:p:304-323

DOI: 10.1016/j.iref.2018.03.008

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