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Comparative study of static and dynamic neural network models for nonlinear time series forecasting

Author

Listed:
  • Abounoori, Abbas Ali
  • Mohammadali, Hanieh
  • Gandali Alikhani, Nadiya
  • Naderi, Esmaeil

Abstract

During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis different types of these models have been used in forecasting. Now, there is this question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison between static and dynamic neural network models in forecasting the return of Tehran Stock Exchange (TSE) index in order to find the best model to be used for forecasting this series (as a nonlinear financial time series). The data were collected daily from 25/3/2009 to 22/10/2011. The models examined in this study included two static models (Adaptive Neuro-Fuzzy Inference Systems or ANFIS and Multi-layer Feed-forward Neural Network or MFNN) and a dynamic model (nonlinear neural network autoregressive model or NNAR). The findings showed that based on the Mean Square Error and Root Mean Square Error criteria, ANFIS model had a much higher forecasting ability compared to other models.

Suggested Citation

  • Abounoori, Abbas Ali & Mohammadali, Hanieh & Gandali Alikhani, Nadiya & Naderi, Esmaeil, 2012. "Comparative study of static and dynamic neural network models for nonlinear time series forecasting," MPRA Paper 46466, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:46466
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    File URL: https://mpra.ub.uni-muenchen.de/46466/1/MPRA_paper_46466.pdf
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    References listed on IDEAS

    as
    1. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," The Journal of Business, University of Chicago Press, vol. 62(3), pages 311-337, July.
    2. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    3. Lo, Andrew W. & MacKinlay, A. Craig, 1989. "The size and power of the variance ratio test in finite samples : A Monte Carlo investigation," Journal of Econometrics, Elsevier, vol. 40(2), pages 203-238, February.
    4. Cox, James Jr. & Loomis, David G., 2006. "Improving forecasting through textbooks -- A 25 year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 617-624.
    5. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    6. Matkovskyy, Roman, 2012. "Forecasting the Index of Financial Safety (IFS) of South Africa using neural networks," MPRA Paper 42153, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Forecasting; Stock Market; dynamic Neural Network; Static Neural Network.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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