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

Majid Delavari, Nadiya Gandali Alikhani () and Esmaeil Naderi

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Stock Return; Forecasting; Long Memory; NNAR; ARFIMA (search for similar items in EconPapers)
JEL-codes: C14 C22 C45 C53 (search for similar items in EconPapers)
Date: 2012-09-11
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/45977/1/MPRA_paper_45977.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/46152/1/MPRA_paper_45977.pdf revised version (application/pdf)

Related works:
Journal Article: Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting? (2013)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:45977

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().