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Proceedings Paper

Foreign currency rate forecasting using neural networks
Author(s): Abhijit S. Pandya; Tadashi Kondo; Amit Talati; Suryaprasad Jayadevappa
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Paper Abstract

Neural networks are increasingly being used as a forecasting tool in many forecasting problems. This paper discusses the application of neural networks in predicting daily foreign exchange rates between the USD, GBP as well as DEM. We approach the problem from a time-series analysis framework - where future exchange rates are forecasted solely using past exchange rates. This relies on the belief that the past prices and future prices are very close related, and interdependent. We present the result of training a neural network with historical USD-GBP data. The methodology used in explained, as well as the training process. We discuss the selection of inputs to the network, and present a comparison of using the actual exchange rates and the exchange rate differences as inputs. Price and rate differences are the preferred way of training neural network in financial applications. Results of both approaches are present together for comparison. We show that the network is able to learn the trends in the exchange rate movements correctly, and present the results of the prediction over several periods of time.

Paper Details

Date Published: 30 March 2000
PDF: 9 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380592
Show Author Affiliations
Abhijit S. Pandya, Florida Atlantic Univ. (United States)
Tadashi Kondo, Univ. of Tokashima (Japan) (United States)
Amit Talati, Florida Atlantic Univ. (United States)
Suryaprasad Jayadevappa, Florida Atlantic Univ. (United States)

Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

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