IDEAS home Printed from https://ideas.repec.org/a/mbr/jmonec/v10y2015i2p1-24.html
   My bibliography  Save this article

Modeling of Banks Bankruptcy in Iran (Multivariate Statistical Analysis)

Author

Listed:
  • Ahmadian , Azam

    (Monetary and Banking Research Institute (MBRI), Central Bank of the Islamic Republic of Iran (CBI))

  • Mahsa , Gorji

Abstract

In this paper we construct a modeling for detection of banks which are experiencing serious problems. Sample and variable set of the study contains 30 banks of Iran during 2006-2014 and their financial ratios. Well known multivariate statistical technique (principal component analysis) was used to explore the basic financial characteristics of the banks, and discriminant Logit and Probit models were estimated based on these characteristics. Results suggest that the model can be used as an analytical decision support tool in both on-site and off-site bank monitoring system to detect the banks which are experiencing serious problems.

Suggested Citation

  • Ahmadian , Azam & Mahsa , Gorji, 2015. "Modeling of Banks Bankruptcy in Iran (Multivariate Statistical Analysis)," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 10(2), pages 1-24, January.
    • Handle: RePEc:mbr:jmonec:v:10:y:2015:i:2:p:1-24
    as

    Download full text from publisher

    File URL: http://jme.mbri.ac.ir/article-1-192-en.pdf
    Download Restriction: no
    File URL: http://jme.mbri.ac.ir/article-1-192-en.html
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
    2. Lam, Kim Fung & Moy, Jane W., 2002. "Combining discriminant methods in solving classification problems in two-group discriminant analysis," European Journal of Operational Research, Elsevier, vol. 138(2), pages 294-301, April.
    3. Taha Zaghdoudi, 2013. "Bank Failure Prediction with Logistic Regression," International Journal of Economics and Financial Issues, Econjournals, vol. 3(2), pages 537-543.
    4. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    5. Coleen C. Pantalone & Marjorie B. Platt, 1987. "Predicting commercial bank failure since deregulation," New England Economic Review, Federal Reserve Bank of Boston, issue Jul, pages 37-47.
    6. Canbas, Serpil & Cabuk, Altan & Kilic, Suleyman Bilgin, 2005. "Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case," European Journal of Operational Research, Elsevier, vol. 166(2), pages 528-546, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    2. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    3. Canbas, Serpil & Cabuk, Altan & Kilic, Suleyman Bilgin, 2005. "Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case," European Journal of Operational Research, Elsevier, vol. 166(2), pages 528-546, October.
    4. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Post-Print halshs-01281948, HAL.
    5. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    6. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Documents de travail du Centre d'Economie de la Sorbonne 16016, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    7. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    8. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    9. Gutiérrez López, Cristina & Abad González, Julio, 2014. "¿Permitían los estados financieros predecir los resultados de los tests de estrés de la banca española? Una aplicación del modelo logit," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 17(1), pages 58-70.
    10. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Post-Print halshs-01314553, HAL.
    11. Halil Erdal & Aykut Ekinci, 2013. "A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 199-215, August.
    12. repec:zbw:bofrdp:2009_035 is not listed on IDEAS
    13. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    14. Jos� Eduardo Gómez-González, 2012. "Failing and Merging as Competing Alternatives during Times of Financial Distress: Evidence from the Colombian Financial Crisis," International Economic Journal, Taylor & Francis Journals, vol. 26(4), pages 655-671, October.
    15. Parnes, Dror & Gormus, Alper, 2024. "Prescreening bank failures with K-means clustering: Pros and cons," International Review of Financial Analysis, Elsevier, vol. 93(C).
    16. Tatom, John, 2011. "Predicting failure in the commercial banking industry," MPRA Paper 34608, University Library of Munich, Germany.
    17. Papanikolaou, Nikolaos I., 2018. "To be bailed out or to be left to fail? A dynamic competing risks hazard analysis," Journal of Financial Stability, Elsevier, vol. 34(C), pages 61-85.
    18. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    19. Karina ALVES & Aquiles KALATZIS & Alberto BORGES MATIAS, 2009. "Survival Analysis of Private Banks in Brazil," EcoMod2009 21500002, EcoMod.
    20. Jaizah Othman & Mehmet Asutay, 2018. "Integrated early warning prediction model for Islamic banks: the Malaysian case," Journal of Banking Regulation, Palgrave Macmillan, vol. 19(2), pages 118-130, April.
    21. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.

    More about this item

    Keywords

    Bank failure; Principal component analysis; Logit; Probit;
    All these keywords.

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mbr:jmonec:v:10:y:2015:i:2:p:1-24. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: M. E. (email available below). General contact details of provider: https://edirc.repec.org/data/mbcbiir.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.