Foreign exchange market prediction with multiple classifiers

Bo Qian and Khaled Rasheed
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Bo Qian: Boyd Graduate Studies Research Center, Department of Computer Science, University of Georgia, Athens, Georgia, USA, Postal: Boyd Graduate Studies Research Center, Department of Computer Science, University of Georgia, Athens, Georgia, USA
Khaled Rasheed: Boyd Graduate Studies Research Center, Department of Computer Science, University of Georgia, Athens, Georgia, USA, Postal: Boyd Graduate Studies Research Center, Department of Computer Science, University of Georgia, Athens, Georgia, USA

Journal of Forecasting, 2010, vol. 29, issue 3, 271-284

Abstract: Foreign exchange market prediction is attractive and challenging. According to the efficient market and random walk hypotheses, market prices should follow a random walk pattern and thus should not be predictable with more than about 50% accuracy. In this article, we investigate the predictability of foreign exchange spot rates of the US dollar against the British pound to show that not all periods are equally random. We used the Hurst exponent to select a period with great predictability. Parameters for generating training patterns were determined heuristically by auto-mutual information and false nearest-neighbor methods. Some inductive machine-learning classifiers-artificial neural network, decision tree, k -nearest neighbor, and naïve Bayesian classifier-were then trained with these generated patterns. Through appropriate collaboration of these models, we achieved a prediction accuracy of up to 67%. Copyright © 2009 John Wiley & Sons, Ltd.

Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:29:y:2010:i:3:p:271-284

DOI: 10.1002/for.1124

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