Forecasting foreign exchange rates with artificial neural networks: A review

Institute of Systems Science, Academy of Mathematics and Systems Sciences, School of Knowledge Science, Chinese Academy of Sciences, 100080, Beijing, People's Republic of China; Japan Advanced Institute of Science and Technology, 923-1292, Asahidai, Ishikawa, Japan; Department of Management Sciences, City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong; School of Knowledge Science, Japan Advanced Institute of Science and Technology, 923-1292, Asahidai, Ishikawa, Japan; Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, 100080, Beijing, People's Republic of China
International Journal of Information Technology and Decision Making (Impact Factor: 1.31). 01/2004; 2(3):145-165. DOI: 10.1142/S0219622004000969

ABSTRACT Forecasting exchange rates is an important financial problem that is receiving increas-ing 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, prepar-ing 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.

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