Forecasting Bank Credit Ratings
Purpose-This study presents an empirical model designed to forecast bank credit ratings. For this reason we use the long term ratings provided by Fitch in 2012. Our sample consists of 92 U.S. banks and publicly available information from their financial statements from 2008 to 2011. Methodology -First, in the effort to select the most informative regressors from a long list of financial variables and ratios we 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 our best model reaches 83.70% when 9 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 were already known to the public.
|Date of creation:||Nov 2013|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.rcfea.orgEmail:
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:rim:rimwps:60_13. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marco Savioli)
If references are entirely missing, you can add them using this form.