Title: A new modular neural network approach for exchange rate prediction

Authors: Ebtesam Zargany; Abbas Ahmadi

Addresses: Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran ' Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran

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; exchange rates; forecasting.

DOI: 10.1504/IJEF.2015.070515

International Journal of Electronic Finance, 2015 Vol.8 No.2/3/4, pp.97 - 123

Received: 08 May 2014
Accepted: 27 Oct 2014
Published online: 09 Jul 2015 *

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