Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7302
Title: Prediction of bank financial strength ratings: The case of Turkey
Authors: Ögüt, Hulisi
Doğanay, M. Mete
Ceylan, Nildağ Başak
Aktaş, Ramazan
Keywords: Rating agencies
Bank financial strength rating
Financial and operational ratios
Rating prediction
Multivariate statistical model
Data mining technique
Issue Date: 2012
Publisher: Elsevier Science Bv
Abstract: Bank financial strength ratings have gained widespread popularity especially after the recent financial turmoil. Rating agencies were criticized because of their ratings and failure to predict the bankruptcy of the banks. Based on this observation, we investigate whether the forecast of the rating of bank's financial strength using publicly available data is consistent with those of the credit rating agency. We use the data of Turkish banks for this investigation. We take a country-specific approach because previous studies found that proxies used for environmental factors (political, economic, and financial risk of the country) did not have any explanatory power and it is hard to find international data for other important factors such as franchise value, concentration, and efficiency. We use two popular multivariate statistical techniques (multiple discriminant analysis and ordered logistic regression) to estimate a suitable model and we compare their performances with those of two mostly used data mining techniques (Support Vector Machine and Artificial Neural Network). Our results suggest that our predictions are consistent with those of Moody's financial strength rating in general.. The important factors in rating are found to be profitability (measured by return on equity), efficient use of resources, and funding the businesses and the households instead of the government that shows efficient placement of the funds. (C) 2012 Elsevier B.V. All rights reserved.
URI: https://doi.org/10.1016/j.econmod.2012.01.010
https://hdl.handle.net/20.500.11851/7302
ISSN: 0264-9993
Appears in Collections:İşletme Bölümü / Department of Management
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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