Using fuzzy c-means clustering algorithm in financial health scoring

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...

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Vydáno v:Audit financiar (Bucharest, Romania ) Ročník 15; číslo 147; s. 385 - 394
Hlavní autoři: GOKTEN, Pinar OKAN, BASER, Furkan, GOKTEN, Soner
Médium: Journal Article
Jazyk:angličtina
Vydáno: Chamber of Financial Auditors of Romania 01.08.2017
Témata:
Accounting numbers
financial analysis
financial classification
Fuzzy c-means (FCM) clustering algorithm
ISSN:1583-5812, 1844-8801, 1844-8801
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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.
AbstractList 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.
Author GOKTEN, Pinar OKAN
BASER, Furkan
GOKTEN, Soner
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CorporateAuthor Gazi University, Turkey
Ankara University, Turkey
Baskent University, Turkey
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SubjectTerms Accounting numbers
financial analysis
financial classification
Fuzzy c-means (FCM) clustering algorithm
Title Using fuzzy c-means clustering algorithm in financial health scoring
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