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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Bibliographic Details
Published in:Audit financiar (Bucharest, Romania ) Vol. 15; no. 147; pp. 385 - 394
Main Authors: GOKTEN, Pinar OKAN, BASER, Furkan, GOKTEN, Soner
Format: Journal Article
Language:English
Published: Chamber of Financial Auditors of Romania 01.08.2017
Subjects:
Accounting numbers
financial analysis
financial classification
Fuzzy c-means (FCM) clustering algorithm
ISSN:1583-5812, 1844-8801, 1844-8801
Online Access:Get full text
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Summary: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.
ISSN:1583-5812
1844-8801
1844-8801
DOI:10.20869/AUDITF/2017/147/385