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.
CITATION STYLE
Yang, H., Chan, L., & King, I. (2002). Support vector machine regression for volatile stock market prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2412, pp. 391–396). Springer Verlag. https://doi.org/10.1007/3-540-45675-9_58
Mendeley helps you to discover research relevant for your work.