A Comparison OfK-Means And FuzzyC-Means Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets

• Authors: Salam Al-Augby , Sebastian Majewski , Agnieszka Majewska , Kesra Nermend
• Languages of publication: EN

Abstracts

EN

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

EN

news; k-means; GCC; stock market; fuzzy c-means; A12; A13; C02; C63; G11

• Publisher: Sciendo
• Journal: Folia Oeconomica Stetinensia
• Year: 2014
• Volume: 14
• Issue: 2
• Pages: 19-36

Dates

published

2014-12-01

received

2014-02-03

accepted

2014-10-24

online

2015-06-03

Contributors

author

Salam Al-Augby

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

author

Sebastian Majewski

• University of Szczecin, Faculty of Economics and Management, Institute of Finance, Department of Insurance and Capital Markets, Mickiewicza 64, 71-101 Szczecin, Poland

author

Agnieszka Majewska

• University of Szczecin, Faculty of Economics and Management, Institute of Finance, Department of Insurance and Capital Markets, Mickiewicza 64, 71-101 Szczecin, Poland

author

Kesra Nermend

• 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

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Identifiers

DOI

10.1515/foli-2015-0001