Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques
Abstract
The present paper aims in investigating the performance of state-of-the-art machine learning techniques in trading with the EUR/USD exchange rate at the ECB fixing. For this purpose, five supervised learning classification techniques (K-Nearest Neighbors algorithm, Naïve Bayesian Classifier, Artificial Neural Networks, Support Vector Machines and Random Forests) were applied in the problem of the one day ahead movement prediction of the EUR/USD exchange rate with only autoregressive terms as inputs. For comparison reasons, the performance of all machine learning techniques was benchmarked by two traditional techniques (Naïve Strategy and moving average convergence/divergence model). Trading strategies produced by the machine learning techniques of Support Vector Machines and Random Forests clearly outperformed all other strategies in terms of annualized return and sharp ratio. To the best of our knowledge, this is the first application of Random Forests in the problem of trading with the EUR/USD exchange rate providing extremely satisfactory results.
Keywords:
EUR/USD Exchange Rate, future direction prediction, naive strategy, MACD strategy, Naive Bayesian Classifier, K-nearest neighbors classifier, SVM, Random Forests, leverage, transaction costsDownloads
References
C. Dunis, M. Williams, “Modeling and trading the Euro/Us Dollar exchange rate: do neural networks perform better?”, Derivatives Use, Trading and Regulation, Vol. 8, No. 3, pp. 211-240, 2002
C. Ullrich, D. Seese, S. Chalup, “Foreign exchange trading with support vector machines”, Advances in Data Analysis: Studies in Classification, Data Analysis and Knowledge Organization, Part VII, pp. 539-546, 2007 DOI: https://doi.org/10.1007/978-3-540-70981-7_62
C. Dunis, J. Laws, G. Sermpinis, “Modelling and trading the EUR/USD exchange rate at the ECB fixing”, The European Journal of Finance, Vol. 16, No. 6, pp. 641-561, 2010 DOI: https://doi.org/10.1080/13518470903037771
Thomson Reuters Datastream: http://online.thomsonreuters.com/ datastream/
J. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, Cambridge: Mass, MIT Press, 1995
T. Cover, P. Hart, “Nearest neighbor pattern classification”, IEEE Trans. Inform. Theory, Vol. 13, No. 1, pp. 21-27, 1967 DOI: https://doi.org/10.1109/TIT.1967.1053964
C. Howson, P. Urbach, Scientific Reasoning: The Bayesian Approach, Third Edition, Open Course Publishing Company, 1993
S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998
V. Vapnik, The Nature of Statistical Learning Theory, Springer 2000 DOI: https://doi.org/10.1007/978-1-4757-3264-1
L. Breiman, “Random Forests”, Machine Learning, Vol. 45, No. 1, pp. 5-32 2001 DOI: https://doi.org/10.1023/A:1010933404324
K. Manish, M. Thenmozhi, “Forecasting stock index movement: A comparison of support vector machines and random forest”. In Proceedings of ninth Indian institute of capital markets conference, Mumbai, India, 2005
Downloads
How to Cite
License
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
