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

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  • 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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 46466.

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Date of creation: 12 Oct 2012
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Handle: RePEc:pra:mprapa:46466

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

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  1. Andrew W. Lo & Craig A. MacKinlay, . "The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation," Rodney L. White Center for Financial Research Working Papers 28-87, Wharton School Rodney L. White Center for Financial Research.
  2. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," The Journal of Business, University of Chicago Press, vol. 62(3), pages 311-37, July.
  3. Matkovskyy, Roman, 2012. "Forecasting the Index of Financial Safety (IFS) of South Africa using neural networks," MPRA Paper 42153, University Library of Munich, Germany.
  4. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
  5. 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.
  6. 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.
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