Predicting Bank Failures: A Comparison of On- and Off-Site Monitoring Systems

By Rebel A. Cole, Jeffery W. Gunther. 2007

307 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/8c1ec7883725ac6cd8bed11d9a0e2b2db93ffb74

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Citations in the corpus

Predicting Distress in European Banks (2014, methodology): “Moreover, Cole and Wu (2010) show that a simple and parsimonious probit model estimated using US data from the 1980s is highly accurate in predicting US bank failures occurring during 2009-10. This result provides strong support for use of simple static binary choice models in early-warning exercise”
Subordinated Debt and Bank Capital Reform (2000, unknown): “Cole and Gunther (1998) attempted to predict bank failure with models using publicly available financial data, and then augmented their model with CAMEL ratings to test if the additional information improved the predictive power of the model.”
Vicarious Learning from the Failures and Near-Failures of Others: Evidence from the U.S. Commercial Banking Industry (2007, methodology): “On the basis of the banking literature (e.g., Cole & Gunther, 1998; Gunther & Moore, 2000) and our qualitative evidence, we defined a near-failure as occurring when a bank received a below-average CAMEL rating for at least two consecutive quarters and then recovered from its low ratings.”
Incorporating Equity Market Information into Supervisory Monitoring Models (2001, background): “Cole, Rebel A., and Jeffery W. Gunther (1998).”
Detect & Describe: Deep Learning of Bank Stress in the News (2015, background): “Many efforts are concerned with identifying the build-up of risk at early stages, oftentimes relying upon aggregated accounting data to measure imbalances (e.g., [3], [8], [2]).”