Hybrid Evolutionary Techniques for FX Arbitrage Prediction

Posted: 6 Jan 2009

Date Written: August 31, 2007

Abstract

This paper discusses the need for a missing value technique to fill in gaps in time series representing foreign exchange (FX) prices and assist in the observation of potential arbitrage opportunities. It highlights the requirement for prediction methods to establish the persistence of these opportunities (latency). Naieve missing value and prediction techniques are investigated and then compared with Kalman Filtration, Ensemble Kalman Filtration, Regression and Neural Network techniques. A technique not known to be applied in this domain before, namely NeuroEvolution using Augmented Topologies (NEAT), is then examined in order to asses its ability in filling in missing values and the prediction of arbitrage opportunities in comparison to these other more established techniques. Hybrid functions, incorporating the most successful of the techniques, are constructed in order to ascertain whether combinations of techniques are more successful than their constituents. Data from for various data providers for three markets is used taken over periods representing different levels of market activity (liquidity).

Keywords: Genetic Algorithms, Neural Networks, Neuroevolution, Kalman Filter, FX Arbitrage

JEL Classification: C45, G11, C11, C44, C63

Suggested Citation

Fletcher, Tristan, Hybrid Evolutionary Techniques for FX Arbitrage Prediction (August 31, 2007). Available at SSRN: https://ssrn.com/abstract=1323607

Tristan Fletcher (Contact Author)

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

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