NDLI: Non-fixed and asymmetrical margin approach to stock market prediction using Support Vector Regression

Content Provider	IEEE Xplore Digital Library
Author	Haiqin Yang King, I. Laiwan Chan
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, China (Haiqin Yang; King, I.; Laiwan Chan)
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.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	1398
Ending Page	1402
File Size	295976
Page Count	5
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1202850
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	Stock markets Computer science Loss measurement Risk management Testing Accuracy
Content Type	Text
Resource Type	Article

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