Abstract
The crises of 2002 and 2008 have raised the issue of banking sector’s ranking system efficiency. The credit rating agencies (CRAs) are crucial for the modern financial market. The impact that the ratings have is indisputable. This paper is seeking to find the factors that affect the ratings and to create a prediction mechanism. The originality of the paper is based on the fact that it utilizes a number of different variables (ownership status, corporate governance, etc.), and the hypothesis is innovative because it addresses the issue of divergence–convergence of the European banking sector and its respective ratings. In order to do that, all ranking evaluations have been matched with a numerical value (i.e., AAA has value 24, A- has value 18, and B- has value 9). A good grade is considered to be above A- (value 18) and a bad grade below that number. Seven groups of indicators are examined: performance, size, ownership, corporate governance, capital adequacy or capital structure, sovereign–country ranking, and loan growth. Three econometric methods (probit, logit, and OLS) have been used to create a system that predicts the rating grade. The independent variable is binary (good grade = 1/bad grade = 0). Inactive banks have been excluded from the sample. This paper provides a model for predicting the factors that affect the grade of the bank rating. Many of the factors, which other studies have pointed out, are found to be statistically important in our models, too. This paper contributes to the literature by establishing a unique framework for the European banks and identifying the factors that affect the rating grade by giving an answer to the issue of an isomorphic banking sector in Europe. It shows that there are significant differences among the various geographical regions [variables cgsys, ns, European countries with major economic problems or those under stress (Portugal, Ireland, Spain, and Greece)].
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Notes
Bankscope does not provide historical data for ownership. The only data given is for the last year of entry and can only be used to classify the sample and to make panels.
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Lazarides, T., Drimpetas, E. Defining the factors of Fitch rankings in the European banking sector. Eurasian Econ Rev 6, 315–339 (2016). https://doi.org/10.1007/s40822-016-0046-9
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DOI: https://doi.org/10.1007/s40822-016-0046-9