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Using fuzzy c-means clustering algorithm in financial health scoring

Author

Listed:
  • Pinar OKAN GOKTEN

    (Gazi University, Turkey)

  • Furkan BASER

    (Ankara University, Turkey)

  • Soner GOKTEN

    (Baskent University, Turkey)

Abstract

Classification of firms according to their financial health is currently one of the major problems in the literature. To our knowledge, as a first attempt, we suggest using fuzzy c-means clustering algorithm to produce single and sensitive financial health scores especially for shortterm investment decisions by using recently announced accounting numbers. Accordingly, we show the calculation of fuzzy financial health scores step by step by benefit from Piotroski’s criteria of liquidity/solvency, operating efficiency and profitability for the firms taken as a sample. The results of correlation analysis indicate that calculated scores are coherent with short-term price formations in terms of investors’ behavior and so fuzzy c-means clustering algorithm could be used to sort firm in a more sensitive perspective.

Suggested Citation

  • Pinar OKAN GOKTEN & Furkan BASER & Soner GOKTEN, 2017. "Using fuzzy c-means clustering algorithm in financial health scoring," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 15(147), pages 385-385.
    • Handle: RePEc:aud:audfin:v:15:y:2017:i:147:p:385
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    More about this item

    Keywords

    Accounting numbers; financial analysis; financial classification; Fuzzy c-means (FCM) clustering algorithm;
    All these keywords.

    JEL classification:

    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • M49 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Other

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