A STUDY OF APPLYING DATA MINING APPROACH TO THE INFORMATION DISCLOSURE FOR TAIWAN’S STOCK MARKET INVESTORS
Financial literature and practices have shown the importance of corporate governance for decades, not only for firm’s management but also for investor protection. Information disclosure plays a key role in all of the governance mechanisms. With good information disclosure, the information asymmetric and the agency cost between insider and outsider of firms can be reduced effectively. However, the information disclosure status of listed companies is hard to be evaluated or judged by investors before the annual official announcement is reported in next year. The main purpose of this study is to explore the hidden knowledge of information disclosure status among the listed companies in Taiwan’s stock market. In this paper we employed decision tree based mining techniques to explore the classification rules of information transparency levels of listed firms in Taiwan’s stock market. Moreover, the multi-learner model constructed by boosting ensemble approach with decision tree algorithm has been applied. The numerical results show the classification accuracy has been improved by using multi-leaner model in terms of less Type I and Type II errors. In particular, the extracted rules from the data mining approach can be developed as a computer model for prediction or classification of good/poor information disclosure potential and like expert systems.
information disclosure data mining classification rule.
C. -L. Lu T.-C. Chen
Department of Business Administration, National Formosa University, Taiwan Department of Information Management, National Formosa University, Taiwan
国际会议
北京
英文
2007-11-01（万方平台首次上网日期，不代表论文的发表时间）