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A Hybrid Forecasting Model for Stock Market Prediction

Author

Listed:
  • Huseyin INCE

    (Gebze Technical University, Faculty of Business Administration Gebze/Kocaeli, TURKEY)

  • Theodore B. TRAFALİS

Abstract

Stock market predictions have been studied by academics and practitioners. In this paper, a hybrid model is proposed to predict the stock market movement. We have combined the independent component analysis (ICA) and kernel methods. ICA is used to select the important indicators. After determining the inputs, kernel methods are employed to predict the direction of the stock market. We have used the Dow-Jones, Nasdaq and S&P500 indices for experiments. Technical indicators of the indices are used as input variables for the proposed model. According to the analysis of the experimental results, kernel methods are capable of producing satisfactory forecasting accuracies and gain rates for Dow-Jones, Nasdaq and S&P 500 indices. The trading experiment shows that the kernel methods obtain higher rate of returns than the other investment strategies.

Suggested Citation

  • Huseyin INCE & Theodore B. TRAFALİS, 2017. "A Hybrid Forecasting Model for Stock Market Prediction," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(3), pages 263-280.
    • Handle: RePEc:cys:ecocyb:v:50:y:2017:i:3:p:263-280
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    References listed on IDEAS

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    1. Piotroski, JD, 2000. "Value investing: The use of historical financial statement information to separate winners from losers," Journal of Accounting Research, Wiley Blackwell, vol. 38, pages 1-41.
    2. Wang, Ju-Jie & Wang, Jian-Zhou & Zhang, Zhe-George & Guo, Shu-Po, 2012. "Stock index forecasting based on a hybrid model," Omega, Elsevier, vol. 40(6), pages 758-766.
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    Cited by:

    1. M. Mallikarjuna & R. Prabhakara Rao, 2019. "Evaluation of forecasting methods from selected stock market returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-16, December.

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    More about this item

    Keywords

    Hybrid Model; Kernel Methods; Stock Market Forecasting; Support Vector Machines; Minimax Probability Machines;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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