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The Effectiveness of Capital Adequacy Measures in Predicting Bank Distress

46 Pages Posted: 20 Dec 2012 Last revised: 13 Feb 2013

David G. Mayes

University of Auckland

Hanno Stremmel

WHU - Otto Beisheim School of Management

Date Written: December 20, 2012

Abstract

Our concern in this article is two-fold: first to see whether the determinants of bank distress and failure have been any different in the GFC from previous years: second to see whether simple measures of capital adequacy outperform their risk-weighted counterparts as predictors, despite the focus on the later in the Basel framework. This paper examines bank distress within a large quarterly data set of FDIC insured US banks from 1992 to 2012. We contrast two methods, the logit technique and discrete survival time analysis, to predict bank failures and draw inferences about the stability of contributing bank characteristics. The models incorporate CAMELS indicators that consider the bank-specific variables and macroeconomic conditions. We contrast risk-based and non-risked-weighted measures of capital adequacy. We find that the non-risk-weighted capital measure, the adjusted leverage ratio, explains bank distress and failures best. The logit model is able to distinguish failing from healthy banks with an accuracy of 80%. The corresponding survival time model achieves 98%. Further, we find evidence that the influence of the characteristics in the two methods differ only slightly. The characteristics of banks getting into bank distress do not change over time in this sample. That means that the familiar banking characteristics for identifying a distress-prone bank identified fragile banks effectively during the global crisis without new information and are likely to continue to work well in the future.

Keywords: Bank Failure, Basel III, CAMELS, Early Warning System, Leverage Ratio, Risk-Based Capital, Regulation

JEL Classification: G01, G21, G28, G33

Suggested Citation

Mayes, David G. and Stremmel, Hanno, The Effectiveness of Capital Adequacy Measures in Predicting Bank Distress (December 20, 2012). 2013 Financial Markets & Corporate Governance Conference. Available at SSRN: https://ssrn.com/abstract=2191861 or http://dx.doi.org/10.2139/ssrn.2191861

David G. Mayes (Contact Author)

University of Auckland ( email )

Private Bag 92019
Auckland Mail Centre
Auckland, 1142
New Zealand

Hanno Stremmel

WHU - Otto Beisheim School of Management ( email )

Burgplatz 2
Vallendar, 56179
Germany

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