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Assessing bank soundness with classification techniques

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  • Ioannidis, Christos
  • Pasiouras, Fotios
  • Zopounidis, Constantin

Abstract

The recent crisis highlighted, once again, the importance of early warning models to assess the soundness of individual banks. In the present study, we use six quantitative techniques originating from various disciplines to classify banks in three groups. The first group includes very strong and strong banks; the second one includes adequate banks, while the third group includes banks with weaknesses or serious problems. We compare models developed with financial variables only, with models that incorporate additional information in relation to the regulatory environment, institutional development, and macroeconomic conditions. The accuracy of classification of the models that include only financial variables is rather poor. We observe a substantial improvement in accuracy when we consider the country-level variables, with five out of the six models achieving out-of-sample classification accuracy above 70% on average. The models developed with multi-criteria decision aid and artificial neural networks achieve the highest accuracies. We also explore the development of stacked models that combine the predictions of the individual models at a higher level. While the stacked models outperform the corresponding individual models in most cases, we found no evidence that the best stacked model can outperform the best individual model.

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  • Ioannidis, Christos & Pasiouras, Fotios & Zopounidis, Constantin, 2010. "Assessing bank soundness with classification techniques," Omega, Elsevier, vol. 38(5), pages 345-357, October.
  • Handle: RePEc:eee:jomega:v:38:y:2010:i:5:p:345-357
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    Cited by:

    1. Constantin Zopounidis & Michael Doumpos, 2013. "Multicriteria decision systems for financial problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 241-261, July.
    2. Calabrese, Raffaella & Degl’Innocenti, Marta & Osmetti, Silvia Angela, 2017. "The effectiveness of TARP-CPP on the US banking industry: A new copula-based approach," European Journal of Operational Research, Elsevier, vol. 256(3), pages 1029-1037.
    3. Andriosopoulos, Dimitrios & Gaganis, Chrysovalantis & Pasiouras, Fotios & Zopounidis, Constantin, 2012. "An application of multicriteria decision aid models in the prediction of open market share repurchases," Omega, Elsevier, vol. 40(6), pages 882-890.
    4. Mare, Davide Salvatore, 2015. "Contribution of macroeconomic factors to the prediction of small bank failures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 25-39.
    5. repec:bof:bofrdp:urn:nbn:fi:bof-201508181354 is not listed on IDEAS
    6. Carlos Serrano-Cinca & Yolanda Fuertes-Callén & Begoña Gutiérrez-Nieto & Beatriz Cuellar-Fernández, 2014. "Path modelling to bankruptcy: causes and symptoms of the banking crisis," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3798-3811, November.
    7. Gaganis, Chrysovalantis & Hasan, Iftekhar & Pasiouras, Fotios, 2016. "Regulations, institutions and income smoothing by managing technical reserves: International evidence from the insurance industry," Omega, Elsevier, vol. 59(PA), pages 113-129.
    8. Mare, Davide Salvatore & Moreira, Fernando & Rossi, Roberto, 2017. "Nonstationary Z-Score measures," European Journal of Operational Research, Elsevier, vol. 260(1), pages 348-358.
    9. Yi-Shu Wang & Xue Jiang & Zhen-Jia-Liu, 2016. "Bank Failure Prediction Models for the Developing and Developed Countries: Identifying the Economic Value Added for Predicting Failure," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(9), pages 522-533, September.
    10. Delis, Manthos & Hasan, Iftekhar & Tsionas, Efthymios, 2015. "Banks’ Risk Endogenous to Strategic Management Choices," MPRA Paper 64907, University Library of Munich, Germany.
    11. Delis, Manthos D. & Hasan, Iftekhar & Tsionas, Efthymios G., 2015. "Firms’ risk endogenous to strategic management choices," Research Discussion Papers 16/2015, Bank of Finland.
    12. Cáceres, Neila & Malone, Samuel W., 2013. "Forecasting leadership transitions around the world," International Journal of Forecasting, Elsevier, vol. 29(4), pages 575-591.
    13. Peng, Yi & Kou, Gang & Wang, Guoxun & Shi, Yong, 2011. "FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms," Omega, Elsevier, vol. 39(6), pages 677-689, December.
    14. repec:bof:bofrdp:urn:nbn:fi:bof-201508211363 is not listed on IDEAS

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