FORECASTING FOREIGN EXCHANGE RATES WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW

Wei Huang, K. K. Lai, Y. Nakamori and Shouyang Wang ()
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Wei Huang: Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, People's Republic of China;
K. K. Lai: Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
Y. Nakamori: School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1, Asahidai, Ishikawa 923-1292, Japan
Shouyang Wang: Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, People's Republic of China

International Journal of Information Technology & Decision Making (IJITDM), 2004, vol. 03, issue 01, 145-165

Abstract: Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. In this paper, we attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, preparing data, and network architecture. There is no consensus about the factors. In different cases, various decisions have their own effectiveness. We also describe the integration of ANNs with other methods and report the comparison between performances of ANNs and those of other forecasting methods, and finding mixed results. Finally, the future research directions in this area are discussed.

Keywords: Artificial neural networks; exchange rate; forecasting (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (15)

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DOI: 10.1142/S0219622004000969

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