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Predicting stock index movement using twin support vector machine as an integral part of enterprise system

Author

Listed:
  • Borong Zou
  • Hong Wang
  • Hui Li
  • Ling Li
  • Yuhan Zhao

Abstract

In order to improve the predicting performance of stock index movement, this study proposes a new predicting model called Twin Support Vector Machines (TWSVM), which will be used to predict the trend of Shanghai Securities Composite Index (SSCI) and Standard and Poor's 500 Index (S&P500 Index), respectively. Thirteen indicators constructed by stock index historical data are selected as input features of the predicting model. The predicting target is the stock index daily movement, up or down. The decision tree (DT), Naive‐Bayes (NB), random forests (RF), probabilistic neural network (PNN) and support vector machine (SVM) are set as contrast experiments. The experiment results indicate that the TWSVM predicting model has a better predicting performance on both stock price and index daily movement.

Suggested Citation

  • Borong Zou & Hong Wang & Hui Li & Ling Li & Yuhan Zhao, 2022. "Predicting stock index movement using twin support vector machine as an integral part of enterprise system," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 428-439, May.
    • Handle: RePEc:bla:srbeha:v:39:y:2022:i:3:p:428-439
    DOI: 10.1002/sres.2862
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    References listed on IDEAS

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    Cited by:

    1. Li Da Xu, 2022. "Systems research on artificial intelligence," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 359-360, May.

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