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dc.contributor.authorAkyıldırım, E.
dc.contributor.authorGoncu, A.
dc.contributor.authorŞensoy, Ahmet
dc.date.accessioned2021-03-01T12:22:16Z
dc.date.available2021-03-01T12:22:16Z
dc.date.issued2020-04
dc.identifier.issn0254-5330
dc.identifier.urihttp://hdl.handle.net/11693/75677
dc.description.abstractIn this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the daily and minute level frequencies using the machine learning classification algorithms including the support vector machines, logistic regression, artificial neural networks, and random forests with the past price information and technical indicators as model features. The average classification accuracy of four algorithms are consistently all above the 50% threshold for all cryptocurrencies and for all the timescales showing that there exists predictability of trends in prices to a certain degree in the cryptocurrency markets. Machine learning classification algorithms reach about 55–65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms.en_US
dc.language.isoEnglishen_US
dc.source.titleAnnals of Operations Researchen_US
dc.relation.isversionofhttps://dx.doi.org/10.1007/s10479-020-03575-yen_US
dc.subjectCryptocurrencyen_US
dc.subjectMachine learningen_US
dc.subjectArtificial neural networksen_US
dc.subjectSupport vector machineen_US
dc.subjectRandom foresten_US
dc.subjectLogistic regressionen_US
dc.titlePrediction of cryptocurrency returns using machine learningen_US
dc.typeArticleen_US
dc.departmentDepartment of Managementen_US
dc.citation.spage3en_US
dc.citation.epage36en_US
dc.citation.volumeNumber297en_US
dc.identifier.doi10.1007/s10479-020-03575-yen_US
dc.publisherSpringeren_US
dc.contributor.bilkentauthorŞensoy, Ahmet


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