IDEAS home Printed from https://ideas.repec.org/a/bdd/journl/v7y2013i2p13-36.html
   My bibliography  Save this article

Classification of Turkish Commercial Banks Under Fuzzy c-Means Clustering

Author

Listed:
  • Ismail Hakki GOKGOZ
  • Fatih ALTINEL
  • F.Pinar Yetkin GOKGOZ
  • Ilker KOC

Abstract

As the major actors of credit system, banks have a great importance not just for financial system but also for the whole of economy. Thus, financial soundness o f banks, affected by many financial risks, should be monitored closely. This study focuses on classification of the deposit and participation banks of Turkey regarding their soundness. Financial Stability Indicators (FSIs) are used to attain this goal. Research method is mainly based on f uzzy c - means clustering method which relies on fuzzy logic. The results show that the participation banks are grouped together in the same cluster. Also, Denizbank A.Þ., Finansbank A.Þ., Yapý ve Kredi Bankasý A.Þ. and Türk Ekonomi Bankasý A.Þ., having similar characteristics regarding ownership and scope of financial services, are found to be grouped together in all periods under consideration. Moreover, it has been seen that size is not the most decisive factor for classification purposes.

Suggested Citation

  • Ismail Hakki GOKGOZ & Fatih ALTINEL & F.Pinar Yetkin GOKGOZ & Ilker KOC, 2013. "Classification of Turkish Commercial Banks Under Fuzzy c-Means Clustering," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 7(2), pages 13-36.
    • Handle: RePEc:bdd:journl:v:7:y:2013:i:2:p:13-36
    as

    Download full text from publisher

    File URL: http://www.bddk.org.tr/Content/docs/bddkDergiTr/dergi_0014_03.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Liang-Hsuan & Chiou, Tai-Wei, 1999. "A fuzzy credit-rating approach for commercial loans: a Taiwan case," Omega, Elsevier, vol. 27(4), pages 407-419, August.
    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. Haider A. Khan, 2004. "General Conclusions: From Crisis to a Global Political Economy of Freedom," Palgrave Macmillan Books, in: Global Markets and Financial Crises in Asia, chapter 9, pages 193-211, Palgrave Macmillan.
    2. David Opresnik & Maurizio Fiasché & Marco Taisch & Manuel Hirsch, 0. "An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy," Information Technology and Management, Springer, vol. 0, pages 1-17.
    3. Carlo Alberto Magni & Stefano Malagoli & Andrea Marchioni & Giovanni Mastroleo, 2020. "Rating firms and sensitivity analysis," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(12), pages 1940-1958, December.
    4. David Opresnik & Maurizio Fiasché & Marco Taisch & Manuel Hirsch, 2017. "An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy," Information Technology and Management, Springer, vol. 18(2), pages 131-147, June.
    5. Pranith K. Roy & Krishnendu Shaw, 2023. "A credit scoring model for SMEs using AHP and TOPSIS," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 372-391, January.
    6. Zhixin Liu & Ping He & Bo Chen, 2019. "A Markov decision model for consumer term-loan collections," Review of Quantitative Finance and Accounting, Springer, vol. 52(4), pages 1043-1064, May.
    7. Oralhan Burcu & Oralhan Zeki & Kirdök Nur, 2022. "Evaluation of Ski Centers’ Performance Using Multiple-Criteria Decision-Making Methods," Polish Journal of Sport and Tourism, Sciendo, vol. 29(3), pages 29-35, September.
    8. Doumpos, Michalis & Figueira, José Rui, 2019. "A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method," Omega, Elsevier, vol. 82(C), pages 166-180.
    9. Haider A. Khan, 2002. "Can Banks Learn to Be Rational?," CIRJE F-Series CIRJE-F-151, CIRJE, Faculty of Economics, University of Tokyo.
    10. Feng, Cheng-Min & Wu, Pei-Ju & Chia, Kai-Chieh, 2010. "A hybrid fuzzy integral decision-making model for locating manufacturing centers in China: A case study," European Journal of Operational Research, Elsevier, vol. 200(1), pages 63-73, January.
    11. Kao, Chiang & Liu, Shiang-Tai, 2003. "A mathematical programming approach to fuzzy efficiency ranking," International Journal of Production Economics, Elsevier, vol. 86(2), pages 145-154, November.
    12. Jan-Henning Trustorff & Paul Konrad & Jens Leker, 2011. "Credit risk prediction using support vector machines," Review of Quantitative Finance and Accounting, Springer, vol. 36(4), pages 565-581, May.
    13. Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    14. Xinping Wang & Cheng Zhang & Jun Deng & Chang Su & Zhenzhe Gao, 2022. "Analysis of Factors Influencing Miners’ Unsafe Behaviors in Intelligent Mines using a Novel Hybrid MCDM Model," IJERPH, MDPI, vol. 19(12), pages 1-30, June.
    15. Beynon, Malcolm J. & Peel, Michael J. & Tang, Yu-Cheng, 2004. "The application of fuzzy decision tree analysis in an exposition of the antecedents of audit fees," Omega, Elsevier, vol. 32(3), pages 231-244, June.
    16. Ozer, Muammer, 2001. "User segmentation of online music services using fuzzy clustering," Omega, Elsevier, vol. 29(2), pages 193-206, April.
    17. Dursun, Mehtap & Karsak, E. Ertugrul & Karadayi, Melis Almula, 2011. "Assessment of health-care waste treatment alternatives using fuzzy multi-criteria decision making approaches," Resources, Conservation & Recycling, Elsevier, vol. 57(C), pages 98-107.

    More about this item

    Keywords

    Financial Risk; Financial Soundness Indicators; Turkish Commercial Banks; Data Clustering; Fuzzy c-Means Clustering.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    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:bdd:journl:v:7:y:2013:i:2:p:13-36. 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: Sumeyye Azize CENGIZ (email available below). General contact details of provider: https://edirc.repec.org/data/bddgvtr.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.