Title :
An Improved Model of Executive Stock Option Based on Rough Set and Support Vector Machines
Author :
Jia, Zhengyuan ; Han, Jia
Author_Institution :
Bus. & Manage. Dept., North China Electr. Power Univ., Baoding
Abstract :
This paper shows that share price could be confirmed accurately by the assessment model of stock price based on rough set (RS) and support vector machines (SVM). This model can remove the impact of "bull" and "bear" market and avoid controlling share price from executives effectively at the exercising date. According to the case analysis, the model is proved to be more exercisable, and it paves the way for actualizing executive stock option (ESO) in listed company.
Keywords :
pricing; rough set theory; share prices; support vector machines; executive stock option; rough set; share price; stock price; support vector machines; Computational intelligence; Computer industry; Conferences; Decision making; Energy management; Large-scale systems; Neural networks; Share prices; Support vector machine classification; Support vector machines; Executive Stock Option; Rough set; SVM;
Conference_Titel :
Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3490-9
DOI :
10.1109/PACIIA.2008.160
