A Cluster Analysis on the Default Determinants in the European Banking Sector

Moldovan, Darie; Mutu, Simona

doi:10.1007/978-3-319-26762-3_7

Cited by 1 publication

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results indicate that support vector machines are capable of extracting useful information from financial data and can be used as part of an early warning system. Moldovan and Mutu (2015) identified the relationship between banks' probability of default and risk-taking incentives. They applied cluster analysis to a large set of bank-level financial data from 203 European banks for the years 2005 to 2013.…”

Section: Literature Reviewmentioning

confidence: 99%

Revisiting bank failure in the United States: a fuzzy-set analysis

Momparler

Ibáñez

Diranzo

2020

Economic Research-Ekonomska Istraživanja

View full text Add to dashboard Cite

Past financial crises have illustrated the importance of recognising the combinations of factors that can cause financial distress in the banking industry. Accordingly, this study uses fuzzy-set qualitative comparative analysis (fsQCA) to identify the combinations of factors that lead to bank failure. The data consist of 30 annual financial ratio series for 156 U.S. banks over a 15-year period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). Identifying combinations of conditions that can produce bank failure is crucial to help regulators and bank managers. The fsQCA presented in this paper sheds light on the relationships between combinations of conditions and bank failure, providing a solution comprising two sufficient and disjunctive sets of conditions that lead to bank failure. This solution includes financial indicators of performance, asset quality, efficiency and capitalisation. The robustness of the two sets of conditions is assessed using several tests.

show abstract

Section: Literature Reviewmentioning

confidence: 99%