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

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > journal & Dissertation > Item 987654321/29657

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29657

Title:	DISCOVERING STOCK TRADING PREFERENCES BY SELF-ORGANIZING MAPS AND DECISION TREES
Authors:	Tsai,CF;Lin,YC;Wang,YT
Contributors:	資訊管理研究所
Keywords:	INVESTORS;INFORMATION;BUSINESS;NETWORKS
Date:	2009
Issue Date:	2010-06-29 20:37:31 (UTC+8)
Publisher:	中央大學
Abstract:	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.
Relation:	INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
Appears in Collections:	[Graduate Institute of Information Management] journal & Dissertation

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