Forecasting bank credit ratings
Abstract
Purpose
This study aims to present an empirical model designed to forecast bank credit ratings using only quantitative and publicly available information from their financial statements. For this reason, the authors use the long-term ratings provided by Fitch in 2012. The sample consists of 92 US banks and publicly available information in annual frequency from their financial statements from 2008 to 2011.
Design/methodology/approach
First, in the effort to select the most informative regressors from a long list of financial variables and ratios, the authors use stepwise least squares and select several alternative sets of variables. Then, these sets of variables are used in an ordered probit regression setting to forecast the long-term credit ratings.
Findings
Under this scheme, the forecasting accuracy of the best model reaches 83.70 percent when nine explanatory variables are used.
Originality/value
The results indicate that bank credit ratings largely rely on historical data making them respond sluggishly and after any financial problems are already known to the public.
Keywords
Acknowledgements
This research has been co-financed by the European Union (European Social Fund (ESF)) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) – Research Funding Program: THALES. Investing in knowledge society through the European Social Fund. The excellent comments and suggestions provided by the Editor-in-Chief of the journal and the reports of two anonymous referees greatly improved the paper. All errors of course are the authors' responsibility.
Citation
Gogas, P., Papadimitriou, T. and Agrapetidou, A. (2014), "Forecasting bank credit ratings", Journal of Risk Finance, Vol. 15 No. 2, pp. 195-209. https://doi.org/10.1108/JRF-11-2013-0076
Publisher
:Emerald Group Publishing Limited
Copyright © 2014, Emerald Group Publishing Limited