Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks

Authors:
Georgios Sermpinis;Jason Laws;Andreas Karathanasopoulos;Christian L. Dunis
Affiliations:
University of Glasgow Business School, University of Glasgow, Gilbert Scott Building, Glasgow G12 8QQ, United Kingdom;University of Liverpool Management School, The University of Liverpool, Chatham Street, Liverpool L69 7ZH, United Kingdom;London Metropolitan Business School, London Metropolitan University, London NE7 8DB, United Kingdom;Liverpool Business School, JMU, John Foster Building, 98 Mount Pleasant, Liverpool L3 5UZ, United Kingdom
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

The motivation for this paper is to investigate the use of two promising classes of artificial intelligence models, the Psi Sigma Neural Network (PSI) and the Gene Expression algorithm (GEP), when applied to the task of forecasting and trading the EUR/USD exchange rate. This is done by benchmarking their results with a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN), a genetic programming algorithm (GP), an autoregressive moving average model (ARMA) plus a naive strategy. We also examine if the introduction of a time-varying leverage strategy can improve the trading performance of our models.