Segmentation of stock trading customers according to potential value

Authors:
H. W. Shin;S. Y. Sohn
Affiliations:
Samsung Economy Research Institute, Kúkje Cener Building, 191, Hangangro 2-Ga, Seoul, South Korea;Department of Computer Science and Industrial Systems Engineering, Yonsei University, Seoul, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2004

Citing 0
Cited 26

An anticipation model of potential customers' purchasing behavior based on clustering analysis and association rules analysis

Expert Systems with Applications: An International Journal
Computer assisted customer churn management: State-of-the-art and future trends

Computers and Operations Research
Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand

Expert Systems with Applications: An International Journal
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting

Expert Systems with Applications: An International Journal
Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer

Expert Systems with Applications: An International Journal
Data-mining application for country segmentation based on the RFM model

International Journal of Data Analysis Techniques and Strategies
Outlier identification and market segmentation using kernel-based clustering techniques

Expert Systems with Applications: An International Journal
Simulation based sales forecasting on retail small stores

Proceedings of the 40th Conference on Winter Simulation
Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping

Expert Systems with Applications: An International Journal
Commodity recommendations of retail business based on decisiontree induction

Expert Systems with Applications: An International Journal
Modeling human behavior in user-adaptive systems: Recent advances using soft computing techniques

Expert Systems with Applications: An International Journal
An efficient greedy K-means algorithm for global gene trajectory clustering

Expert Systems with Applications: An International Journal
Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty

Expert Systems with Applications: An International Journal
A framework for discovering and analyzing changing customer segments

ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Clustering Indian stock market data for portfolio management

Expert Systems with Applications: An International Journal
Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques

Expert Systems with Applications: An International Journal
Trend-weighted fuzzy time-series model for TAIEX forecasting

ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Segmenting customers in online stores based on factors that affect the customer's intention to purchase

Expert Systems with Applications: An International Journal
Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Expert Systems with Applications: An International Journal
Using the Taguchi method for effective market segmentation

Expert Systems with Applications: An International Journal
Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems

AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
Using context to improve the effectiveness of segmentation and targeting in e-commerce

Expert Systems with Applications: An International Journal
A rule-based method for identifying the factor structure in customer satisfaction

Information Sciences: an International Journal
Extraction of Target User Group from Web Usage Data Using Evolutionary Biclustering Approach

International Journal of Applied Metaheuristic Computing
Review: Soft computing applications in customer segmentation: State-of-art review and critique

Expert Systems with Applications: An International Journal
A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.08

Visualization

Abstract

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.