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Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorAlonso Monsalve, Saúl
dc.contributor.authorSuárez Cetrulo, Andrés L.
dc.contributor.authorCervantes Rovira, Alejandro
dc.contributor.authorQuintana, David
dc.date.accessioned2022-04-01T09:59:59Z
dc.date.available2022-04-01T09:59:59Z
dc.date.issued2020-07-01
dc.description.abstractThis study explores the suitability of neural networks with a convolutional component as an alternative to traditional multilayer perceptrons in the domain of trend classification of cryptocurrency exchange rates using technical analysis in high frequencies. The experimental work compares the performance of four different network architectures -convolutional neural network, hybrid CNN-LSTM network, multilayer perceptron and radial basis function neural network- to predict whether six popular cryptocurrencies -Bitcoin, Dash, Ether, Litecoin, Monero and Ripple- will increase their value vs. USD in the next minute. The results, based on 18 technical indicators derived from the exchange rates at a one-minute resolution over one year, suggest that all series were predictable to a certain extent using the technical indicators. Convolutional LSTM neural networks outperformed all the rest significantly, while CNN neural networks were also able to provide good results specially in the Bitcoin, Ether and Litecoin cryptocurrencies.es
dc.description.sponsorshipWe would also like to acknowledge the financial support of the Spanish Ministry of Science, Innovation and Universities under grant PGC2018-096849-B-I00 (MCFin)en
dc.identifier.bibliographicCitationAlonso-Monsalve, S., Suárez-Cetrulo, A.L., Cervantes, A., Quintana, D. (2020). Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Systems with Applications, 149, 113250.en
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2020.113250
dc.identifier.issn0957-4174
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage15
dc.identifier.publicationtitleEXPERT SYSTEMS WITH APPLICATIONSen
dc.identifier.publicationvolume149en
dc.identifier.urihttps://hdl.handle.net/10016/34505
dc.identifier.uxxiAR/0000025965
dc.language.isoeng
dc.publisherElsevier
dc.relation.projectIDGobierno de España. PGC2018-096849-B-I00es
dc.rights© 2020 Elsevier Ltden
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaInformáticaes
dc.subject.otherneural networksen
dc.subject.otherfinanceen
dc.subject.othertechnical analysisen
dc.subject.otherdeep learningen
dc.subject.othercryptocurrenciesen
dc.titleConvolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicatorsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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