Title :
An Information Transparency Evaluation Method Based on SVM
Author :
Hang, Wu ; Junfa, Dai
Author_Institution :
Hangzhou Inst. of Commerce, Zhejiang Gongshang Univ., Hangzhou, China
Abstract :
Nowadays, information disclosure is a noticeable topic to both practice and academy since it has significant effect on corporate governance and capital market operation. Open and transparent information disclosure can reduce the information asymmetry between insiders and outsiders. The main purpose of this study is to construct an information transparency evaluation model. In this paper, we used the information disclosure record obtained from the website of the Shenzhen Stock Exchange (SSE) as the level of listed companies information transparency and employed the support vector machine technique for building classification model. Experimental results demonstrate that the SVM has better performance than other methods and it is a considerable approach for information transparency research.
Keywords :
information management; pattern classification; stock markets; support vector machines; Shenzhen Stock Exchange; capital market operation; classification model; corporate governance; information asymmetry; information disclosure; information transparency evaluation; support vector machine; Buildings; Business; Educational institutions; Guidelines; Information technology; Machine learning; Manufacturing; Stock markets; Support vector machine classification; Support vector machines; SVM; classification; information disclosure; transparency;
Conference_Titel :
Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-0-7695-3859-4
DOI :
10.1109/IITA.2009.153