Abstract
AbstractEmploying a statistical model‐building strategy, this study aims to analyse the United States' bank failures across different size categories (small, medium, and large). Our results suggest that factors associated with bank failures vary across respective size categories, and the average marginal effects (AMEs) of mutually significant covariates also exhibit significant variability across different size classes of banks. The results are robust to up‐to 3 years of lagged regression estimates, various control variables, interaction between bank size and bank charter, alternative bank size classifications, and macroeconomic crisis periods.
Highlights
IntroductionThe answer to this question would be helpful to policymakers and bank regulators seeking to improve their understanding of bank failures across different size categories, and thereby promoting enhanced stability of the financial system
Does size matter in predicting bank failures? The answer to this question would be helpful to policymakers and bank regulators seeking to improve their understanding of bank failures across different size categories, and thereby promoting enhanced stability of the financial system
The failure of small banks in the early 1990s and the failure of large banks during the recent financial crisis are associated with considerable loan problems, profit reductions, credit risk, ineffective board of directors and their management, high unemployment, and low economic performance
Summary
The answer to this question would be helpful to policymakers and bank regulators seeking to improve their understanding of bank failures across different size categories, and thereby promoting enhanced stability of the financial system. Others argue that such restrictive regulations may lead to the failure of many small banks deemed “too important to fail”, which may cause the recurrence of financial crises (De Haan & Poghosyan, 2012). This debate reveals the need for further investigation into the heterogeneity of bank failures across different size classes, to recognize the similarities and differences before taking appropriate measures
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