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

Author

Listed:
  • Al-Augby Salam

    (University of Kufa, Research and Information Qualifying Centre, Kufa, P.O. Box (21), Najaf Governorate, Iraq)

  • Majewski Sebastian
  • 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)

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.

Suggested Citation

  • Al-Augby Salam & Majewski Sebastian & Majewska Agnieszka & Nermend Kesra, 2014. "A Comparison Of K-Means And Fuzzy C-Means Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets," Folia Oeconomica Stetinensia, Sciendo, vol. 14(2), pages 19-36, December.
    • Handle: RePEc:vrs:foeste:v:14:y:2014:i:2:p:19-36:n:1
    DOI: 10.1515/foli-2015-0001
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    References listed on IDEAS

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