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Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?

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  • Delavari, Majid
  • Gandali Alikhani, Nadiya
  • Naderi, Esmaeil

Abstract

The main purpose of the present study was to investigate the capabilities of two generations of models such as those based on dynamic neural network (e.g., Nonlinear Neural network Auto Regressive or NNAR model) and a regressive (Auto Regressive Fractionally Integrated Moving Average model which is based on Fractional Integration Approach) in forecasting daily data related to the return index of Tehran Stock Exchange (TSE). In order to compare these models under similar conditions, Mean Square Error (MSE) and also Root Mean Square Error (RMSE) were selected as criteria for the models’ simulated out-of-sample forecasting performance. Besides, fractal markets hypothesis was examined and according to the findings, fractal structure was confirmed to exist in the time series under investigation. Another finding of the study was that dynamic artificial neural network model had the best performance in out-of-sample forecasting based on the criteria introduced for calculating forecasting error in comparison with the ARFIMA model.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 45977.

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Date of creation: 11 Sep 2012
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Handle: RePEc:pra:mprapa:45977

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Keywords: Stock Return; Forecasting; Long Memory; NNAR; ARFIMA;

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  1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, Elsevier, vol. 53(1-3), pages 165-188.
  2. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
  3. Kuswanto, Heri & Sibbertsen, Philipp, 2008. "A Study on "Spurious Long Memory in Nonlinear Time Series Models"," Hannover Economic Papers (HEP), Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät dp-410, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  4. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, Elsevier, vol. 22(3), pages 443-473.
  5. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, 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.
  7. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, American Finance Association, vol. 47(5), pages 1731-64, December.
  8. Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, Elsevier, vol. 18(4), pages 154-163, September.
  9. Sirucek, Martin, 2012. "Macroeconomic variables and stock market: US review," MPRA Paper 39094, University Library of Munich, Germany.
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Cited by:
  1. Delavari, Majid & Gandali Alikhani, Nadiya, 2012. "The Effect of Crude Oil Price on the Methanol price," MPRA Paper 49727, University Library of Munich, Germany.
  2. Nazarian, Rafik & Gandali Alikhani, Nadiya & Naderi, Esmaeil & Amiri, Ashkan, 2013. "Forecasting Stock Market Volatility: A Forecast Combination Approach," MPRA Paper 46786, University Library of Munich, Germany.

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