Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks |
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| Authors: | Georgios Sermpinis Jason Laws Andreas Karathanasopoulos Christian L. Dunis |
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| Affiliation: | 1. Curtin University, Australia;2. RMIT, Australia;1. School of Information, Renmin University of China, Beijing 100872, PR China;2. Smart City Research Center, Renmin University of China, Beijing 100872, PR China;1. Department of Computer Science and Engineering, Port City International University, Chattogram, Bangladesh;2. Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh |
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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 naïve strategy. We also examine if the introduction of a time-varying leverage strategy can improve the trading performance of our models. |
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