Segmentation of stock trading customers according to potential value

H. W. Shin, S. Y. Sohn

Research output: Contribution to journalArticlepeer-review

79 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)27-33
Number of pages7
JournalExpert Systems with Applications
Issue number1
Publication statusPublished - 2004 Jul

Bibliographical note

Funding Information:
This work was supported by grant No. R04-2002-000-20003-0 from Korea Science & Engineering Foundation.

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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