Classifying Initial Returns of Electronic Firm_s IPOs Using Entropy Based Rough Sets in Taiwan Trading Systems

This paper deals with forecasting initial returns in initial public offerings (IPOs) market in Taiwan's stock trading systems using rough set theory. It is very important for investors that correctly predict initial returns from trading systems. In this paper, we use a new approach, an entropy-based fuzzy discretization, for enhancing rough set classifier. The enhanced rough set theory involves two main procedures: (1) convert discretized continuous data into a unique corresponding linguistic value using a MEPA approach; and (2) utilize the linguistic value to extract decision rules by LEM2 algorithm. The actual IPOs dataset is employed in this empirical case study to illustrate the proposed approach. From the results, the proposed approach improves accuracy and generates fewer rules, and the performance is superior to the listing methods.