Leading edge forecasting techniques for exchange rate prediction

Ian T. Nabney, Christian Dunis, Richard Dallaway, Swee Leong, Wendy Redshaw

Research output: Contribution to journalArticlepeer-review


This paper describes how modern machine learning techniques can be used in conjunction with statistical methods to forecast short term movements in exchange rates, producing models suitable for use in trading. It compares the results achieved by two different techniques, and shows how they can be used in a complementary fashion. The paper draws on experience of both inter- and intra-day forecasting taken from earlier studies conducted by Logica and Chemical Bank Quantitative Research and Trading (QRT) group's experience in developing trading models.
Original languageEnglish
Pages (from-to)311-323
Number of pages13
JournalEuropean Journal of Finance
Issue number4
Publication statusPublished - 4 Dec 1995


  • machine learning technique
  • leading-edge forecasting
  • rule induction
  • neural networks


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