Title :
Application of SVM in Financial Research
Author :
Cao, Bingyu ; Zhan, Deping ; Wu, Xianbin
Author_Institution :
Sch. of Econ. & Manage., Changsha Univ. of Sci. & Technol., Changsha, China
Abstract :
Based on the structural risk minimization, support vector machine is a new method of data mining. Since it has effectively solved complicated problems of classification and prediction, it has been widely used in many cross-disciplinary fields. This paper has reviewed and analyzed SVMpsilas application to the classification and prediction in the financial field. It has a promising future of applying to company´s credit rating, early warning, stock prices forecast and so on. However, we hold that the correct selection of kernel and different sub-assembly function, as well as parameters, is the key point to optimize the application of SVM.
Keywords :
data mining; finance; minimisation; pattern classification; support vector machines; company credit rating; cross-disciplinary field; data mining; financial field; stock price forecast; structural risk minimization; support vector machine; Conference management; Data mining; Economic forecasting; Finance; Financial management; Optimization methods; Risk management; Support vector machine classification; Support vector machines; Technology management;
Conference_Titel :
Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-0-7695-3605-7
DOI :
10.1109/CSO.2009.313
