DISCOVERING STOCK TRADING PREFERENCES BY SELF-ORGANIZING MAPS AND DECISION TREES

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题名:	DISCOVERING STOCK TRADING PREFERENCES BY SELF-ORGANIZING MAPS AND DECISION TREES
作者:	Tsai,CF;Lin,YC;Wang,YT
贡献者:	資訊管理研究所
关键词:	INVESTORS;INFORMATION;BUSINESS;NETWORKS
日期:	2009
上传时间:	2010-06-29 20:37:31 (UTC+8)
出版者:	中央大學
摘要:	Stock trading activities are always very popular in many countries. Generally, investors with various backgrounds have different preferences over the stocks they trade. In literature, a number of studies examine the institutions' holding preferences for certain stock characteristics when choosing the security portfolio. However, very few studies investigate the stock trading preferences of individual investors. In this paper, we focus on two factors which affect the portfolio choices of investors, which are stock characteristics and investor features. In particular, a self-organizing map (SOM) is used to group a certain number of clusters based on a chosen dataset. Then, the decision tree model is used to extract useful rules from the clusters which contain the most trading records in the sample. We find that if the investors are females, less wealthy, and make stock trades with lower frequencies, they will be more careful and conservative. On the other hand, if the investors are males, having a high level of wealth, and make stock trades very often, they tend to choose stocks with high EPS, high market-to-book, and high prices.
關聯:	INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
显示于类别:	[資訊管理研究所] 期刊論文

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