科研之友
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摘要
The study proposes an alternate method, a soft computing system that mixes an entropy-based discretization method, fuzzy-rule-similarity, and rough set theory, for solving the real-world problems of initial public offerings (IPO) returns faced by both academicians and practitioners. The proposed method is illustrated by examining two practiced datasets for publicly traded firms in Taiwan. The experimental results reveal that the proposed method outperforms the listing methods in terms of accuracy and number of rules. Furthermore, the proposed method generates a set of comprehensible decision rules that can be applied in a knowledge-based system for investment decision-making of investors.
- 出版日期2010
