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Financial Time Series Forecasting by Developing a Hybrid Intelligent System

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  • Abounoori, Abbas Ali
  • Naderi, Esmaeil
  • Gandali Alikhani, Nadiya
  • Amiri, Ashkan

Abstract

The design of models for time series forecasting has found a solid foundation on statistics and mathematics. On this basis, in recent years, using intelligence-based techniques for forecasting has proved to be extremely successful and also is an appropriate choice as approximators to model and forecast time series, but designing a neural network model which provides a desirable forecasting is the main concern of researchers. For this purpose, the present study tries to examine the capabilities of two sets of models, i.e., those based on artificial intelligence and regressive models. In addition, fractal markets hypothesis investigates in daily data of the Tehran Stock Exchange (TSE) index. Finally, in order to introduce a complete design of a neural network for modeling and forecasting of stock return series, the long memory feature and dynamic neural network model were combined. Our results showed that fractal markets hypothesis was confirmed in TSE; therefore, it can be concluded that the fractal structure exists in the return of the TSE series. The results further indicate that although dynamic artificial neural network model have a stronger performance compared to ARFIMA model, taking into consideration the inherent features of a market and combining it with neural network models can yield much better results.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 45615.

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Date of creation: 17 Jan 2013
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Handle: RePEc:pra:mprapa:45615

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Keywords: Stock Return; Long Memory; NNAR; ARFIMA; Hybrid Models;

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  1. Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, vol. 18(4), pages 154-163, September.
  2. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
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  8. Matkovskyy, Roman, 2012. "Forecasting the Index of Financial Safety (IFS) of South Africa using neural networks," MPRA Paper 42153, University Library of Munich, Germany.
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Cited by:
  1. 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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