ELSEVIER Journal of Economic Dynamics and Control 21 (1997) 1543-157s A learning-to-forecast experiment on the foreign exchange market with a classifier system Luca Beltramett?, Riccardo Fiorentinib, Luigi Marengo”, Roberto TamborinibT* a Institute of Economics, University of Genoa, Ita1.v b Department of Economics, University of Padua, via de1 Santa 28, 35100 Paduva, Italy ‘Department of Economics, University of Trento, Italy Abstract This paper reports on an experiment of learning and forecasting on the foreign exchange market by means of an Artificial Intelligence methodology (a ‘Classifier System’) which simulates learning and adaptation in complex and changing environ- ments. The experiment has been run for two different exchange rates, the US dollar-D mark rate and the US dollar-yen rate, representative of two possibly different market environments. A fictitious “artificial agent” is first trained on a monthly data base from 1973 to 1990, and then tested out-of-sample from 1990 to 1992. Its forecasting perfor- mance is then compared with the performance of decision rules which follow the prescription of various economic theories on exchange rate behaviour, and the perfor- mance of forecasts given by VAR estimations of the exchange-rate’s determinants. Keywords: Learning; Artificial Intelligence; Foreign exchange market JEL classification: F31; C53 1. Introduction The search for a rational basis for forecasting economic variables is still open. As is well known, the advent of the rational-expectations hypothesis (REH) *Corresponding author. The authors would like to thank two anonymous Referees, Prof. Stavros A. Zenios, Prof. Massimo Egidi, Prof. Giovanni Dosi and Dott. Diego Lubian for their useful comments and suggestions on previous version of the paper. 0165-1889/97/$17.00 0 1997 Elsevier Science B.V. All rights reserved PII SO165-1889(97)00035-3