Segmentation of stock trading customers according to potential value

In this article, we use three clustering methods (K-means, self-organizing map, and fuzzy K-means) to find properly graded stock market brokerage commission rates based on the 3-month long total trades of two different transaction modes (representative assisted and online trading system). Stock trad...

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Bibliographic Details
Published in:Expert systems with applications 2004-07, Vol.27 (1), p.27-33
Main Authors: Shin, H.W., Sohn, S.Y.
Format: Article
Language:English
Subjects:
Customer relationship management
Customer segmentation
Fuzzy K-means
K-means clustering
Self-organizing map
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Summary:In this article, we use three clustering methods (K-means, self-organizing map, and fuzzy K-means) to find properly graded stock market brokerage commission rates based on the 3-month long total trades of two different transaction modes (representative assisted and online trading system). Stock traders for both modes are classified in terms of the amount of the total trade as well as the amount of trade of each transaction mode, respectively. Results of our empirical analysis indicate that fuzzy K-means cluster analysis is the most robust approach for segmentation of customers of both transaction modes. We then propose a decision tree based rule to classify three groups of customers and suggest different brokerage commission rates of 0.4, 0.45, and 0.5% for representative assisted mode and 0.06, 0.1, and 0.18% for online trading system, respectively.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2003.12.002