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Evaluating the Application of a Financial Early Warning System in the Iranian Banking System

Author

Listed:
  • Rezaei , Pooria

    (Bank Ayandeh)

  • Ebrahimi , Seyed Babak

    (Khaje Nasir University of Technology)

  • Azin , Pejman

    (Bank Ayandeh)

Abstract

One of the significant problems of banks and investors in Iran is the lack of precise awareness about the financial performance of each bank and the roadmap for improving the conditions. Besides, the undesirable status of the financial performance of banks becomes evident only when the improvement of conditions is complicated. In this paper, a data mining-based early warning system (EWS) model has been presented to capture the financial performance of banks. To design this model, the CHAID decision tree has been used. Using this model, the banks have been classified as poor, medium, and good regarding financial performance, and the roadmap to achieving the desirable status has been determined. For this purpose, 13 Iranian banks have been investigated within the years 2003-2017. Eventually, the results obtained from the decision tree have been compared with the findings achieved from the CAMELS model. Based on the designed decision tree, 8 profiles have been extracted; 2 representing good, 3 medium, and 3 poor financial performance. Based on these profiles, according to the latest reports published by the studied banks, eight banks have a mediocre financial performance while five banks suffer poor financial performance. According to these profiles, four variables of the asset to shareholders’ equity ratio, the shareholders’ equity to loans ratio, the long-term debt to equity ratio, and liquidity coverage ratio were identified as the most relevant variables associated with the financial performance of banks.

Suggested Citation

  • Rezaei , Pooria & Ebrahimi , Seyed Babak & Azin , Pejman, 2019. "Evaluating the Application of a Financial Early Warning System in the Iranian Banking System," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 14(2), pages 177-204, April.
    • Handle: RePEc:mbr:jmonec:v:14:y:2019:i:2:p:177-204
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    References listed on IDEAS

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    1. Lestano & Jacobs, Jan & Kuper, Gerard H., 2003. "Indicators of financial crises do work! : an early-warning system for six Asian countries," CCSO Working Papers 200313, University of Groningen, CCSO Centre for Economic Research.
    2. Aykut Kibritçioğlu, 2004. "An Analysis of Early Warning Signals of Currency Crises in Turkey, 1986-2004," International Finance kibritcioglu_aykut.62178-, Socionet.
    3. O'Brien, Martin & Wosser, Michael, 2018. "An Early Warning System for Systemic Banking Crises: A Robust Model Specification," Research Technical Papers 9/RT/18, Central Bank of Ireland.
    4. Hali J. Edison, 2003. "Do indicators of financial crises work? An evaluation of an early warning system," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 8(1), pages 11-53.
    5. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    6. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
    7. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, December.
    8. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    9. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
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    More about this item

    Keywords

    Abasgholipour; M. (2010). Factors Affecting the Improvement of the Performance of Banks. Banking and Economy Quarterly; 106; 24-35. Abounouri; A.; Erfani; A. (2008). Markov Switching Algorithm and Predicting the Probability of Incidence of Liquidity Rati;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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