Abstract
Abstract 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
