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A new modular neural network approach for exchange rate prediction

A new modular neural network approach for exchange rate prediction A novel approach using modular neural networks to forecast exchange rates based on harmonic patterns in Forex market is introduced. The proposed approach employs three algorithms to predict price, validate its prediction and update the system. The model is trained by historical data using major currencies in Forex market. The proposed system's predictions were evaluated by comparing its results with a non-modular neural network. Results showed that the infrastructure market data consist of significant accurate relations that a single network cannot detect these relations and separate trained networks in specific tasks are needed. Comparison of modular and non-modular systems showed that modular neural network outperforms the other one. Keywords: ANNs; artificial neural networks; modular neural networks; exchange rate prediction; harmonic patterns. Reference to this paper should be made as follows: Zargany, E. and Ahmadi, A. (2015) `A new modular neural network approach for exchange rate prediction', Int. J. Electronic Finance, Vol. 8, Nos. 2/3/4, pp.97­123. Biographical notes: Ebtesam Zargany received the BSc in Applied Mathematics in 2004 at Sheikh Bahaei University of Isfahan, MSc in Industrial Engineering and Management Systems in 2012 at Amirkabir University of Technology in Tehran. She joined Payamnour University in Khorramshahr as a University http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Electronic Finance Inderscience Publishers

A new modular neural network approach for exchange rate prediction

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Publisher
Inderscience Publishers
Copyright
Copyright © 2015 Inderscience Enterprises Ltd.
ISSN
1746-0069
eISSN
1746-0077
DOI
10.1504/IJEF.2015.070515
Publisher site
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Abstract

A novel approach using modular neural networks to forecast exchange rates based on harmonic patterns in Forex market is introduced. The proposed approach employs three algorithms to predict price, validate its prediction and update the system. The model is trained by historical data using major currencies in Forex market. The proposed system's predictions were evaluated by comparing its results with a non-modular neural network. Results showed that the infrastructure market data consist of significant accurate relations that a single network cannot detect these relations and separate trained networks in specific tasks are needed. Comparison of modular and non-modular systems showed that modular neural network outperforms the other one. Keywords: ANNs; artificial neural networks; modular neural networks; exchange rate prediction; harmonic patterns. Reference to this paper should be made as follows: Zargany, E. and Ahmadi, A. (2015) `A new modular neural network approach for exchange rate prediction', Int. J. Electronic Finance, Vol. 8, Nos. 2/3/4, pp.97­123. Biographical notes: Ebtesam Zargany received the BSc in Applied Mathematics in 2004 at Sheikh Bahaei University of Isfahan, MSc in Industrial Engineering and Management Systems in 2012 at Amirkabir University of Technology in Tehran. She joined Payamnour University in Khorramshahr as a University

Journal

International Journal of Electronic Finance – Inderscience Publishers

Published: Jan 1, 2015

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