Abstract
AbstractArtificial neural networks were used to search for non‐linear relations in high‐ frequency foreign exchange time series. Three years (1985‐7) tick‐by‐tick bid prices for the Swiss franc to the US dollar exchange rate were used in this study as training data to specify predictive models for intra‐day trading, which was then tested on the same exchange rate time series in the following year (1988). A simple trading rule was adopted to evaluate the models, which showed statistically significant trading profit under moderate transaction costs. In contrast, a standard linear model did not produce profit with the same training and test data and under the same trading rule and transaction cost assumption. This provides evidence for the non‐linear nature of the foreign exchange time series under study.
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