Non-fixed and asymmetrical margin approach to stock market prediction using Support Vector Regression
Haiqin Yang
King, I.
Laiwan Chan
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, China;
This paper appears in: Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Publication Date: 18-22 Nov. 2002
Volume: 3,
On page(s): 1398- 1402 vol.3
ISSN:
ISBN: 981-04-7524-1
INSPEC Accession Number: 7929003
DOI: 10.1109/ICONIP.2002.1202850
Posted online: 2003-06-05 10:21:41.0
Abstract
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.
Index Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
