Forecasting exchange rates with linear and nonlinear models

Rakesh Bissoondeeal, Jane Binner, Muddun Bhuruth, Alicia M. Gazely, Veemadevi P. Mootanah

Research output: Preprint or Working paperWorking paper


In this paper the exchange rate forecasting performance of neural network models are evaluated against random walk and a range of time series models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore the parameters are chosen according to what the researcher considers to be the best. Such an approach, however, implies that the risk of making bad decisions is extremely high which could explain why in many studies neural network models do not consistently perform better than their time series counterparts. In this paper through extensive experimentation the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of performing well. Our results show that in general neural network models perform better than traditionally used time series models in forecasting exchange rates.
Original languageEnglish
Place of PublicationBirmingham (UK)
PublisherAston University
ISBN (Print)978-1-85449-664-5
Publication statusPublished - Jan 2007

Publication series

NameAston Business School research paper
PublisherAston University


  • exchange rates
  • forecasting
  • time series models
  • neural networks


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