Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/63366
Title: Forecasting short-term exchange rates : a recurrent neural network approach
Authors: Li, LK
Pang, WK
Yu, WT
Troutt, MD
Issue Date: 2004
Publisher: Idea Group Pub.
Source: In GP Zhang (Ed.), Neural networks in business forecasting, p. 195-212. Hershey, Penn.: Idea Group Pub., 2004 How to cite?
Abstract: Movements in foreign exchange rates are the results of collective human decisions, which are the results of the dynamics of their neurons. In this chapter, we demonstrate how to model these types of market behaviors by recurrent neural networks (RNN). The RNN approach can help us to forecast the short-term trend of foreign exchange rates. The application of forecasting techniques in the foreign exchange markets has become an important task in financial strategy. Our empirical results show that a discrete-time RNN performs better than the traditional methods in forecasting short-term foreign exchange rates.
URI: http://hdl.handle.net/10397/63366
ISBN: 1-59140-215-8
DOI: 10.4018/978-1-59140-176-6.ch010
Appears in Collections:Book Chapter

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