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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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    References listed on IDEAS

    as
    1. Gentry, James A & Whitford, David T & Newbold, Paul, 1988. "Predicting Industrial Bond Ratings with a Probit Model and Funds Flow Components," The Financial Review, Eastern Finance Association, vol. 23(3), pages 269-286, August.
    2. William H. Greene & David A. Hensher, 2008. "Modeling Ordered Choices: A Primer and Recent Developments," Working Papers 08-26, New York University, Leonard N. Stern School of Business, Department of Economics.
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    Cited by:

    1. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Efterpi Doumpa & Maria Stefanidou, 2020. "Forecasting Credit Ratings of EU Banks," IJFS, MDPI, vol. 8(3), pages 1-15, August.
    2. Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    3. Zhivaikina, A. & Peresetsky, A., 2017. "Russian Bank Credit Ratings and Bank License Withdrawal 2012-2016," Journal of the New Economic Association, New Economic Association, vol. 36(4), pages 49-80.
    4. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
    5. John A. Ruddy, 2021. "An Analysis of Bank Financial Strength Ratings and Credit Rating Data," Risks, MDPI, vol. 9(9), pages 1-16, August.
    6. Bojing Feng & Wenfang Xue & Bindang Xue & Zeyu Liu, 2020. "Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks," Papers 2012.03744, arXiv.org.
    7. Valdir Domeneghetti & Fabiano Guasti Lima, 2019. "Strategic direction re-evaluation of bank ratings in Brazil," Economics Bulletin, AccessEcon, vol. 39(2), pages 1336-1347.
    8. Pompella, Maurizio & Dicanio, Antonio, 2017. "Ratings based Inference and Credit Risk: Detecting likely-to-fail Banks with the PC-Mahalanobis Method," Economic Modelling, Elsevier, vol. 67(C), pages 34-44.
    9. GABAN Lucian & RUS IonuÈ› - Marius & FETITA Alin, 2017. "A Model Of Rating Of Eastern European Banks," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 69(3), pages 42-56, August.
    10. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    11. Oliver Takawira & John W. Muteba Mwamba, 2020. "Determinants of Sovereign Credit Ratings: An Application of the Naïve Bayes Classifier," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 8(4), pages 279-299.

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    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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