A Comparison Of K-Means And Fuzzy C-Means Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets

Al-Augby Salam (), Majewski Sebastian (), Majewska Agnieszka () and Nermend Kesra ()
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Al-Augby Salam: University of Kufa, Research and Information Qualifying Centre, Kufa, P.O. Box (21), Najaf Governorate, Iraq
Majewski Sebastian: University of Szczecin, Faculty of Economics and Management, Institute of Finance, Department of Insurance and Capital Markets, Mickiewicza 64, 71-101 Szczecin, Poland
Majewska Agnieszka: University of Szczecin, Faculty of Economics and Management, Institute of Finance, Department of Insurance and Capital Markets, Mickiewicza 64, 71-101 Szczecin, Poland
Nermend Kesra: University of Szczecin, Faculty of Economics and Management, Institute of IT in Management, Department of Computer Methods in Experimental Economics, Mickiewicza 64, 71-101 Szczecin, Poland

Folia Oeconomica Stetinensia, 2014, vol. 14, issue 2, 19-36

Abstract: The main goal of this article is to compare data-mining clustering methods (k-means and fuzzy c-means) based on a sample of banking and energy companies on the Gulf Cooperation Council (GCC) stock markets. We examined these companies for a pattern that reflected the effect of news on the bank sector’s stocks throughout October, November, and December 2012. Correlation coefficients and t-statistics for the good news indicator (GNI) and the bad news indicator (BNI) and financial factors, such as PER, PBV, DY and rate of return, were used as diagnostic variables for the clustering methods.

Keywords: news; k-means; GCC; stock market; fuzzy c-means (search for similar items in EconPapers)
JEL-codes: A12 A13 C02 C63 G11 (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:foeste:v:14:y:2014:i:2:p:19-36:n:1

DOI: 10.1515/foli-2015-0001

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