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

Abstract

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.

Examining small bank failures in the United States: an application of the random effects parametric survival model

“…Finding a bank that matches the troubled bank in all relevant ways to perform a perfect peer comparison is challenging, which is the problem with this strategy. The fsQCA technique is used by Momparler et al (2020) to compare financially challenged banks with those that continue to be sound and healthy. In contrast to the conventional method, their analysis does not isolate financial factors to identify any important ones.…”

“…As a result, the conclusions reached using the logit model could be inaccurate. Small bank failures have Examining small bank failures been studied using more sophisticated models, such as fuzzy-set Qualitative Comparative Analysis (fsQCA) or propensity score matching (PSM) (Kandrac, 2014;Momparler et al, 2020). These methods could aid in contrasting unhealthy banks with those that have failed, but they could not typically identify the dynamic shifts taking place inside a bank as it approaches the point of failure.…”

Predicting Bank Failures with Machine Learning Algorithms: A Comparison of Boosting and Cost-Sensitive Models

“…Furthermore, they indicated that ordinal classification models provide a better description of the state of the banks before failure and are competitive to standard binary classification models. Momparler et al (2020) used the dataset of 157 US national commercial banks from 2001 to 2015, considering 30 financial ratios in their model. They implemented the fuzzy-set qualitative comparative analysis to identify the factors that lead a bank to failure.…”

Revisiting the interconnection between governance mechanisms and firm performance: evidence from Vietnamese listed firms

“…In QCA, contradictory cases could be sufficient for a particular outcome (Momparler et al, 2020). For example, Configuration 3 (Table 7) consisted of low ownership concentration, large board size and non-duality in combination with both high and low gender diversity (indicated by "don't care").…”

“…For example, Configuration 3 (Table 7) consisted of low ownership concentration, large board size and non-duality in combination with both high and low gender diversity (indicated by "don't care"). However, it is seemingly paradoxical for a configuration to be sufficient for a given outcome and its negation, as well as a configuration and its negation, to be consistent with the same outcome (Dusa and Alrik, 2013;Momparler et al, 2020). Hence, a robustness analysis for sufficiency was performed in the last step of the fsQCA.…”