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dc.contributor.authorEvans, Cain
dc.contributor.authorPappas, Konstantinos
dc.contributor.authorXhafa Xhafa, Fatos
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2019-02-01T09:48:44Z
dc.date.available2019-02-01T09:48:44Z
dc.date.issued2013-09
dc.identifier.citationEvant, C.; Pappas, K.; Xhafa, F. Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation. "Mathematical and computer modelling", Setembre 2013, vol. 58, núm. 5-6, p. 1249-1266.
dc.identifier.issn0895-7177
dc.identifier.urihttp://hdl.handle.net/2117/128089
dc.description.abstractThe Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP\USD, EUR\GBP, and EUR\USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return.
dc.format.extent18 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshGenetic algorithms
dc.subject.lcshDecision making
dc.subject.otherForeign exchange
dc.subject.otherTechnical analysis
dc.subject.otherTrading strategies
dc.titleUtilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation
dc.typeArticle
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacAlgorismes genètics
dc.subject.lemacDecisió, Presa de
dc.contributor.groupUniversitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
dc.identifier.doi10.1016/j.mcm.2013.02.002
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0895717713000290
dc.rights.accessOpen Access
local.identifier.drac12439779
dc.description.versionPostprint (author's final draft)
local.citation.authorEvant, C.; Pappas, K.; Xhafa, F.
local.citation.publicationNameMathematical and computer modelling
local.citation.volume58
local.citation.number5-6
local.citation.startingPage1249
local.citation.endingPage1266


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