Using fuzzy c-means clustering algorithm in financial health scoring

Pinar OKAN Gokten (), Furkan Baser () and Soner Gokten ()
Additional contact information
Pinar OKAN Gokten: Gazi University, Turkey
Furkan Baser: Ankara University, Turkey
Soner Gokten: Baskent University, Turkey

The Audit Financiar journal, 2017, vol. 15, issue 147, 385

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.

Keywords: Accounting numbers; financial analysis; financial classification; Fuzzy c-means (FCM) clustering algorithm (search for similar items in EconPapers)
JEL-codes: G30 M49 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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