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Lookup NU author(s): Viktor Manahov, Professor Robert Hudson, Professor Bartosz GebkaORCiD
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In this paper we investigate how high frequency trading affects technical analysis and market efficiency in the foreign exchange (FX) market by using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We use this approach for real one-minute high frequency data of the most traded currency pairs worldwide: EUR/USD, USD/JPY, GBP/USD, AUD/USD, USD/CHF, and USD/CAD. The STGP performance is compared with that of parametric and non-parametric models and validated by two formal empirical tests. We perform in-sample and out-of-sample comparisons between all models on the basis of forecast performance and investment return. Furthermore, our paper shows the relative strength of these models with respect to the actual trading profit generated by their forecasts. Empirical experiments suggest that the STGP forecasting technique significantly outperforms the traditional econometric models. We find evidence that the excess returns are both statistically and economically significant, even when appropriate transaction costs are taken into account. We also find evidence that HFT has a beneficial role in the price discovery process.
Author(s): Manahov V, Hudson R, Gebka B
Publication type: Article
Publication status: Published
Journal: Journal of International Financial Markets, Institutions and Money
Year: 2014
Volume: 28
Pages: 131-157
Print publication date: 01/01/2014
Online publication date: 21/11/2013
Acceptance date: 01/01/1900
ISSN (print): 1042-4431
ISSN (electronic): 1873-0612
Publisher: Elsevier
URL: http://dx.doi.org/10.1016/j.intfin.2013.11.002
DOI: 10.1016/j.intfin.2013.11.002
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