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Forecasting Bank Credit Ratings

Author

Listed:
  • Periklis Gogas

    (Department of Economics, Democritus University of Thrace, Greece)

  • Theophilos Papadimitriou

    (Department of Economics, Democritus University of Thrace, Greece)

  • Anna Agrapetidou

    (Department of Economics, Democritus University of Thrace, Greece)

Abstract

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.

Suggested Citation

  • Periklis Gogas & Theophilos Papadimitriou & Anna Agrapetidou, 2013. "Forecasting Bank Credit Ratings," Working Paper series 60_13, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:60_13
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    1. repec:nea:journl:y:2017i:36:p:49-80 is not listed on IDEAS
    2. repec:eee:ecmode:v:67:y:2017:i:c:p:34-44 is not listed on IDEAS
    3. repec:blg:reveco:v:69:y:2017:i:3:p:42-56 is not listed on IDEAS

    More about this item

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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