Forecasting Exchange Rates Using General Regression Neural Networks

Posted: 1 Oct 2000

See all articles by Mark T. Leung

Mark T. Leung

University of Texas at San Antonio - Department of Management Science and Statistics

Hazem Daouk

Cornell University - School of Applied Economics and Management

Abstract

In this study, we examine the forecastability of a specific neural network architecture called General Regression Neural Network (GRNN) and compare its performance with a variety of forecasting techniques, including Multi-Layered Feedforward Network (MLFN), multivariate transfer function, and random walk models. The comparison with MLFN provides a measure of GRNN's performance relative to the more conventional type of neural networks while the comparison with transfer function models examines the difference in predictive strength between the non-parametric and parametric techniques. The random walk model is used for benchmark comparison. Our findings show that GRNN not only has a higher degree of forecasting accuracy but also performs statistically better than other evaluated models for different currencies.

Note: This is a description of the paper and is not the actual abstract.

Keywords: general regression neural networks, currency exchange rate, forecasting

JEL Classification: G15, C45, C53

Suggested Citation

Leung, Mark T. and Daouk, Hazem, Forecasting Exchange Rates Using General Regression Neural Networks. Computers & Operations Research. Available at SSRN: https://ssrn.com/abstract=237031

Mark T. Leung

University of Texas at San Antonio - Department of Management Science and Statistics ( email )

San Antonio, TX
United States

Hazem Daouk (Contact Author)

Cornell University - School of Applied Economics and Management ( email )

446 Warren Hall
Ithaca, NY 14853
United States
331-45-78-63-88 (Fax)

HOME PAGE: http://courses.cit.cornell.edu/hd35/

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