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Fuzzy Numbers in the Credit Rating of Enterprise Financial Condition

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  • Syau, Yu-Ru
  • Hsieh, Hai-Teh
  • Lee, E Stanley

Abstract

Most of the parameters used to describe the credit rating are in linguistic terms, which are vague and difficult to put into precise numerical values. Fuzzy set theory, which was developed to handle this kind of vagueness, is used to represent and to aggregate the various linguistic data usually used in commercial banks. To illustrate the approach, numerical examples are solved and compared with existing approaches. Copyright 2001 by Kluwer Academic Publishers

Suggested Citation

  • Syau, Yu-Ru & Hsieh, Hai-Teh & Lee, E Stanley, 2001. "Fuzzy Numbers in the Credit Rating of Enterprise Financial Condition," Review of Quantitative Finance and Accounting, Springer, vol. 17(4), pages 351-360, December.
    • Handle: RePEc:kap:rqfnac:v:17:y:2001:i:4:p:351-60
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    Cited by:

    1. Malcolm Beynon & Mark Clatworthy, 2013. "A fuzzy-based approach to residual income equity valuation," Review of Quantitative Finance and Accounting, Springer, vol. 40(4), pages 675-690, May.
    2. Rodrigo P. Dill & Newton Da Costa Jr. & André A. P. Santos, 2013. "Paraconsistent and fuzzy logic applied to company profitability analysis," Economics Bulletin, AccessEcon, vol. 33(2), pages 1348-1360.
    3. Hossein Rezayi Dolatabadi & Avaz Yari & Fatemeh Faghani & Ali Akbar Abedi Sharabiany & Mohammad Hossein Forghani & Mohammad Kazem Emadzadeh, 2013. "Prioritizing of Credit Ranking Criterions of Isfahan State banks' Costumers by Using AHP Fuzzy Method," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 3(1), pages 303-313, January.
    4. 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.
    5. Tomasz Korol & Anestis Fotiadis, 2016. "Applying Fuzzy Logic of Expert Knowledge for Accurate Predictive Algorithms of Customer Traffic Flows in Theme Parks," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1451-1468, November.
    6. Smimou, K. & Bector, C.R. & Jacoby, G., 2008. "Portfolio selection subject to experts' judgments," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 1036-1054, December.
    7. Srđan Jelinek & Pavle Milošević & Aleksandar Rakićević & Ana Poledica & Bratislav Petrović, 2022. "A Novel IBA-DE Hybrid Approach for Modeling Sovereign Credit Ratings," Mathematics, MDPI, vol. 10(15), pages 1-21, July.
    8. Deisy Cristina Corrêa Igarashi & Edson Pacheco Paladini, Dr & Wagner Igarashi, 2013. "Knowledge management: Postgraduate Alternative Evaluation Model (MAPA) in Brazil," International Journal of Business and Social Research, LAR Center Press, vol. 3(4), pages 14-26, April.

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