Forecasting Exchange Rates Using General Regression Neural Networks

Predicting currency movements has always been a problematic task as most conventional econometric models are not able to forecast exchange rates with significantly higher accuracy than a naive random walk model. For large multinational firms which conduct substantial currency transfers in the course of business, being able to accurately forecast the movements of exchange rates can result in considerable improvement in the overall profitability of the firm. In this study, we apply the General Regression Neural Network (GRNN) to predict the monthly exchange rates of three currencies, British pound, Canadian dollar, and Japanese yen. Our empirical experiment shows that the performance of GRNN is better than other neural network and econometric techniques included in this study. The results demonstrate the predictive strength of GRNN and its potential for solving financial forecasting problems

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Year of publication:	[2006]
Authors:	Leung, Mark T.
Other Persons:	Chen, An-Sing (contributor) ; Daouk, Hazem (contributor)
Publisher:	[2006]: [S.l.] : SSRN

Extent:	1 Online-Ressource (35 p)
Type of publication:	Book / Working Paper
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments April 1999 erstellt
Other identifiers:	10.2139/ssrn.200448 [DOI]
Classification:	G15 - International Financial Markets ; C45 - Neural Networks and Related Topics ; C53 - Forecasting and Other Model Applications
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10012735715