Training a multilayer neural network for the Euro-dollar (EUR/ USD) exchange rate

Abstract

A mathematical tool or model for predicting how an economic variable like the exchange rate (relative price between two currencies) will respond is a very important need for investors and policy-makers. Most current techniques are based on statistics, particularly linear time series theory. Artificial neural networks (ANNs) are mathematical models which try to emulate biological neural networks’ parallelism and nonlinearity; these models have been successfully applied in Economics and Engineering since the 1980s. ANNs appear to be an alternative for modelling the behavior of financial variables which resemble (as first approximation) a random walk. This paper reports the results of using ANNs for Euro/USD exchange rate trading and the usefulness of the algorithm tor chemotaxis leading to training networks thereby maximising an objective function re predicting a trader’s profits. JEL: F310, C450.