A study of applying data mining approach to the information disclosure for Taiwan's stock market investors

The 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 asymmetric information and the agency cost between the insider and the 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 the 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 the 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 that 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 the prediction or the classification of good/poor information disclosure potential and like expert systems.