2002 | OriginalPaper | Buchkapitel
Support Vector Machine Regression for Volatile Stock Market Prediction
verfasst von : Haiqin Yang, Laiwan Chan, Irwin King
Erschienen in: Intelligent Data Engineering and Automated Learning — IDEAL 2002
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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Recently, Support Vector Regression (SVR) has been introduced to solve regression and prediction problems. In this paper, we apply SVR to financial prediction tasks. In particular, the financial data are usually noisy and the associated risk is time-varying. Therefore, our SVR model is an extension of the standard SVR which incorporates margins adaptation. By varying the margins of the SVR, we could reflect the change in volatility of the financial data. Furthermore, we have analyzed the effect of asymmetrical margins so as to allow for the reduction of the downside risk. Our experimental results show that the use of standard deviation to calculate a variable margin gives a good predictive result in the prediction of Hang Seng Index.