<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The main goal of this article is to compare data-mining clustering methods (<italic>k</italic>-means and fuzzy <bold><italic>c</italic></bold>-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 <bold><italic>t</italic></bold>-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.</p></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.

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

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

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

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

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

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...

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

Published Online: Jun 03, 2015

Page range: 19 - 36

Received: Feb 03, 2014

Accepted: Oct 24, 2014

DOI: https://doi.org/10.1515/foli-2015-0001

Keywords
news, k-means, GCC, stock market, fuzzy c-means

© University of Szczecin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Jun 03, 2015

Page range: 19 - 36

Received: Feb 03, 2014

Accepted: Oct 24, 2014

DOI: https://doi.org/10.1515/foli-2015-0001

Keywordsnews, k-means, GCC, stock market, fuzzy c-means

© University of Szczecin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
news, k-means, GCC, stock market, fuzzy c-means