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.
- machine learning technique
- leading-edge forecasting
- rule induction
- neural networks
Nabney, I. T., Dunis, C., Dallaway, R., Leong, S., & Redshaw, W. (1995). Leading edge forecasting techniques for exchange rate prediction. European Journal of Finance, 1(4), 311-323. https://doi.org/10.1080/13518479500000022