we assume that the processes of pricing in financial markets are governed by the noise. This would make it useless to study models of forecasting because the case is not predictable.
If the second hypothesis (h2) was invalid, however, it would assume that all relevant information are instantly incorporated in the pricing of financial products, making essentially unnecessary and economically inconvenient any efforts to develop models to predict the future based on present information.
This research aims to analyze the ability of mathe-matical models of nonlinear nature, such as artificial neural networks, to highlight non-random and therefore predictable behaviour in a highly liquid market and therefore characterized by high efficiency, such as the exchange rate Euro/US dollar. To this end, it was devel-oped and empirically tested a non-linear model for fore-casting exchange rates.