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Forecasting Credit Ratings of EU Banks

Author

Listed:
  • Vasilios Plakandaras

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

  • Periklis Gogas

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

  • Theophilos Papadimitriou

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

  • Efterpi Doumpa

    (School of Economics, Business Administration and Legal Studies, International Hellenic University, 57001 Thessaloniki, Greece)

  • Maria Stefanidou

    (School of Economics, Business Administration and Legal Studies, International Hellenic University, 57001 Thessaloniki, Greece)

Abstract

The aim of this study is to forecast credit ratings of E.U. banking institutions, as dictated by Credit Rating Agencies (CRAs). To do so, we developed alternative forecasting models that determine the non-disclosed criteria used in rating. We compiled a sample of 112 E.U. banking institutions, including their Fitch assigned ratings for 2017 and the publicly available information from their corresponding financial statements spanning the period 2013 to 2016, that lead to the corresponding ratings. Our assessment is based on identifying the financial variables that are relevant to forecasting the ratings and the rating methodology used. In the empirical section, we employed a vigorous variable selection scheme prior to training both Probit and Support Vector Machines (SVM) models, given that the latter originates from the area of machine learning and is gaining popularity among economists and CRAs. Our results show that the most accurate, in terms of in-sample forecasting, is an SVM model coupled with the nonlinear RBF kernel that identifies correctly 91.07% of the banks’ ratings, using only 8 explanatory variables. Our findings suggest that a forecasting model based solely on publicly available financial information can adhere closely to the official ratings produced by Fitch. This provides evidence that the actual assessment procedures of the Credit Rating Agencies can be fairly accurately proxied by forecasting models based on freely available data and information on undisclosed information is of lower importance.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jijfss:v:8:y:2020:i:3:p:49-:d:395525
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    References listed on IDEAS

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    1. Ederington, Louis H, 1985. "Classification Models and Bond Ratings," The Financial Review, Eastern Finance Association, vol. 20(4), pages 237-262, November.
    2. Kraft, Pepa, 2015. "Do rating agencies cater? Evidence from rating-based contracts," Journal of Accounting and Economics, Elsevier, vol. 59(2), pages 264-283.
    3. Periklis Gogas & Theophilos Papadimitriou & Anna Agrapetidou, 2014. "Forecasting bank credit ratings," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 15(2), pages 195-209, March.
    4. Jie (Jack) He & Jun (Qj) Qian & Philip E. Strahan, 2012. "Are All Ratings Created Equal? The Impact of Issuer Size on the Pricing of Mortgage-Backed Securities," Journal of Finance, American Finance Association, vol. 67(6), pages 2097-2137, December.
    5. Periklis Gogas & Theophilos Papadimitriou & Anna Agrapetidou, 2014. "Forecasting bank credit ratings," Journal of Risk Finance, Emerald Group Publishing, vol. 15(2), pages 195-209, March.
    6. Dror Parnes & Sagi Akron, 2016. "Rating the credit rating agencies," Applied Economics, Taylor & Francis Journals, vol. 48(50), pages 4799-4812, October.
    7. Harald Hau & Sam Langfield & David Marques-Ibanez, 2013. "Bank ratings: what determines their quality? [Bank risk during the financial crisis: do business models matter?]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 28(74), pages 289-333.
    8. Pinches, George E & Mingo, Kent A, 1973. "A Multivariate Analysis of Industrial Bond Ratings," Journal of Finance, American Finance Association, vol. 28(1), pages 1-18, March.
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