The Impact of Data Normalization on Stock Market Prediction: Using SVM and Technical Indicators

  • Jiaqi PanEmail author
  • Yan Zhuang
  • Simon Fong
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 652)


Predicting stock index and its movement has never been lack of attention among traders and professional analysts, because of the attractive financial gains. For the last two decades, extensive researches combined technical indicators with machine learning techniques to construct effective prediction models. This study is to investigate the impact of various data normalization methods on using support vector machine (SVM) and technical indicators to predict the price movement of stock index. The experimental results suggested that, the prediction system based on SVM and technical indicators, should carefully choose an appropriate data normalization method so as to avoid its negative influence on prediction accuracy and the processing time on training.


Stock market prediction Technical indicator Support vector machine (SVM) Data normalization 



The authors of this paper would like to thank Research and Development Administrative Office of the University of Macau, for the funding support of this project which is called “Building Sustainable Knowledge Networks through Online Communities” with the project code MYRG2015-00024-FST.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.Department of Computer Information ScienceUniversity of MacauTaipaMacau SAR

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