Abstract
This paper applies an integrated artificial neural network approach to forecast foreign exchange rates between the US dollar and Chinese Renminbi. In order to obtain a better forecasting performance in foreign exchange rates, this study develops an integrated forecasting model, which applies the SPSS as data preprocess method and an artificial neural network application named Alyuda Neuro Intelligence as forecasting tool. The results of this study provide evidence on the effectiveness and efficiency of the integrated artificial neural network model. The findings of this study should contribute positively to the development of theory, methodology, and practice of using artificial neural network to develop a forecasting model with enhanced forecasting accuracy.
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© 2009 Springer-Verlag Berlin Heidelberg
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Chen, PC., Lo, CY., Chang, HT. (2009). An Empirical Study of the Artificial Neural Network for Currency Exchange Rate Time Series Prediction. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_57
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DOI: https://doi.org/10.1007/978-3-642-01216-7_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01215-0
Online ISBN: 978-3-642-01216-7
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