Forecasting Bank Credit Ratings

Periklis Gogas, Theophilos Papadimitriou and Anna Agrapetidou ()

No 9-2014, DUTH Research Papers in Economics from Democritus University of Thrace, Department of Economics

Abstract: Purpose - This study presents an empirical model designed to forecast bank credit ratings using only quantitative and publicly available information from their financial statements. 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 in annual frequency 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 are already known to the public.

Keywords: Banking; Forecasting; Credit Rating; Logit (search for similar items in EconPapers)
JEL-codes: G20 G24 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2014-02-14
New Economics Papers: this item is included in nep-ban, nep-for and nep-rmg
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Citations: View citations in EconPapers (11)

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Related works:
Journal Article: Forecasting bank credit ratings (2014)
Working Paper: Forecasting Bank Credit Ratings (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:ris:duthrp:2014_009

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