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Comparing Neural Networks and ARMA Models in Artificial Stock Market

Author

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  • Jiri Krtek

    ()

  • Miloslav Vošvrda

    ()

Abstract

Neural networks - feed-forward neural networks and Elman's simple recurrent neural networks - are compared with vector ARMA models - VAR and VARMA - in this paper. They are compared in an artificial stock market. One risk free and one risky asset are traded in the market. There are only trend followers in this model, which use the mentioned models for forecasting change of a price of the risky asset and the dividend.

Suggested Citation

  • Jiri Krtek & Miloslav Vošvrda, 2011. "Comparing Neural Networks and ARMA Models in Artificial Stock Market," Bulletin of the Czech Econometric Society, The Czech Econometric Society, vol. 18(28).
  • Handle: RePEc:czx:journl:v:18:y:2011:i:28:id:174
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    File URL: http://ces.utia.cas.cz/bulletin/index.php/bulletin/article/view/174
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    More about this item

    Keywords

    neural networks; vector ARMA; artificial market;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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