Forecasting Short-Term Exchange Rates: A Recurrent Neural Network Approach

Forecasting Short-Term Exchange Rates: A Recurrent Neural Network Approach

Leong-Kwan Li (Hong Kong Polytechnic University, Hong Kong), Wan-Kai Pang (The Hong Kong Polytechnic University, Hong Kong), Wing-Tong Yu (The Hong Kong Polytechnic University, Hong Kong) and Marvin D. Troutt (Kent State University, USA)
Copyright: © 2004 |Pages: 18
DOI: 10.4018/978-1-59140-176-6.ch010
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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.

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