Non-fixed and asymmetrical margin approach to stock market prediction using Support Vector Regression
Haiqin Yang; King, I.; Laiwan Chan
Neural Information Processing, 2002. ICONIP apos;02. Proceedings of the 9th International Conference on
Volume 3, Issue , 18-22 Nov. 2002 Page(s): 1398 - 1402 vol.3
Digital Object Identifier   10.1109/ICONIP.2002.1202850
Summary: Recently, support vector regression (SVR) has been applied to financial time series prediction. Typical characteristics of financial time series are non-stationary and noisy in nature. The volatility, usually time-varying, of the time series is therefore some valuable information about the series. Previously, we had proposed to use the volatility to adaptively change the width of the margin of SVR. We have noticed that upside margin and downside margin do not necessary be the same, and we have observed that their choice would affect the upside risk, downside risk and as well as the overall prediction result. In this paper, we introduce a novel approach to adapt the asymmetrical margins using momentum. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average.

» View citation and abstract

Log in by entering your IEEE Web Account Username and Password.

IEEE Communications Society members: If you subscribe to the IEEE Electronic Periodicals Package or IEEE Electronic Periodicals Package Plus, you must access your subscription at www.comsoc.org.

Check with your librarian, information professional, or system manager to determine if you need to log in. Please complete the online Technical Support Form if you need assistance.

Select the Purchase History link to access the document. You will have 5 Days after purchase to access the Full Text PDF. Please complete the online Technical Support Form if you need assistance.

• Search and access Abstract records free of charge
• Register for table of contents alerts
• Purchase Full Text PDF documents

» Learn more about subscription options or how to become an IEEE Member.