Combining time-scale feature extractions with SVMs for stock index forecasting

Authors:
Shian-Chang Huang;Hsing-Wen Wang
Affiliations:
Department of Business Administration, National Changhua University of Education, Changhua, Taiwan;Department of Business Administration, National Changhua University of Education, Changhua, Taiwan
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

Support vector machine (SVM) has appeared as a powerful tool for time series forecasting and demonstrated better performance over other methods. This paper proposes a novel hybrid model which combines time-scale feature extractions with SVM models for stock index forecasting. The time series of explanatory variables are decomposed by the wavelet basis, and the extracted time-scale features then serve as inputs of a SVM model which performs the nonparametric forecasting. Compared with pure SVM models or traditional GARCH models, the performance of the new method is the best. The root-mean-squared forecasting errors are significantly reduced. The results of this study can help investors for controlling and reducing their risks in international investments.