Combining Time-Scale Feature Extractions with SVMs for Stock Index Forecasting

  • Shian-Chang Huang
  • Hsing-Wen Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


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.


Support Vector Machine Wavelet Analysis Support Vector Machine Model Stock Index Reproduce Kernel Hilbert Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shian-Chang Huang
    • 1
  • Hsing-Wen Wang
    • 1
  1. 1.Department of Business AdministrationNational Changhua University of EducationChanghuaTaiwan

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