2012
DOI: 10.2139/ssrn.2191861
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The Effectiveness of Capital Adequacy Measures in Predicting Bank Distress

Abstract: Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in… Show more

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Cited by 52 publications
(112 citation statements)
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References 62 publications
(70 reference statements)
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“…Moreover, the literature improved the toolkits with methods such as non-parametric clustering methods like the binary recursive tree method (Barrel et al 2009). A recent extensive literature review of both old and new publications is presented for example by Kauko (2014) and Mayes and Stremmel (2014).…”
Section: Related Literaturementioning
confidence: 99%
“…Moreover, the literature improved the toolkits with methods such as non-parametric clustering methods like the binary recursive tree method (Barrel et al 2009). A recent extensive literature review of both old and new publications is presented for example by Kauko (2014) and Mayes and Stremmel (2014).…”
Section: Related Literaturementioning
confidence: 99%
“…Equity to Net Loans ratio is considered as a buffer to absorb potential losses that can be faced by banks. A high percentage of capital implies lower risk of distress and helps to absorb potential losses (Mayes & Stremmel, 2012). This ratio is significant at a level of 5%.…”
Section: Logit Methodsmentioning
confidence: 99%
“…He determined six relevant variables inherent to the CAEL rating system presented as follows: capital adequacy, asset quality, earnings ability and liquidity. Mayes and Stremmel (2012) used also logit method and survival time analysis in order to predict banking failure. The models incorporate CAMELS indicators.…”
Section: Related Studiesmentioning
confidence: 99%
“…Mayes and Stremmel (2012) used the logit technique and discrete time analysis in USA to determine the influence of GDP growth rate in predicting bank failures using US banks data from 1992-2012 and research results revealed negative influence of GDP growth rate on bank failures. Negative correlation implied that when economic conditions are good, real GDP will be high and banks are less likely to fail.Researchers such as Lanine and Vennet (2006) neglected macroeconomic variables basing on the fact that all banks will be facing similar conditions.…”
Section: Literature Reviewmentioning
confidence: 99%