Financial Time Series Forecasting by Developing a Hybrid Intelligent System
Abbas Ali Abounoori,
Nadiya Gandali Alikhani () and
MPRA Paper from University Library of Munich, Germany
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.
Keywords: Stock Return; Long Memory; NNAR; ARFIMA; Hybrid Models (search for similar items in EconPapers)
JEL-codes: C22 C45 C53 G10 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-ets, nep-for and nep-ore
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https://mpra.ub.uni-muenchen.de/45858/1/MPRA_paper_45615.pdf revised version (application/pdf)
Working Paper: Financial Time Series Forecasting by Developing a Hybrid Intelligent System (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:45615
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