dc.contributor.author | Akyildirim, E. | |
dc.contributor.author | Goncu, A. | |
dc.contributor.author | Sensoy, Ahmet | |
dc.date.accessioned | 2022-02-04T11:58:56Z | |
dc.date.available | 2022-02-04T11:58:56Z | |
dc.date.issued | 2021-02 | |
dc.identifier.issn | 0254-5330 | |
dc.identifier.uri | http://hdl.handle.net/11693/77044 | |
dc.description.abstract | In 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.iso | English | en_US |
dc.source.title | Annals of Operations Research | en_US |
dc.relation.isversionof | https://doi.org/10.1007/s10479-020-03575-y | en_US |
dc.subject | Cryptocurrency | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Artificial neural networks | en_US |
dc.subject | Support vector machine | en_US |
dc.subject | Random forest | en_US |
dc.subject | Logistic regression | en_US |
dc.title | Prediction of cryptocurrency returns using machine learning | en_US |
dc.type | Article | en_US |
dc.department | Department of Management | en_US |
dc.citation.spage | 3 | en_US |
dc.citation.epage | 36 | en_US |
dc.citation.volumeNumber | 297 | en_US |
dc.identifier.doi | 10.1007/s10479-020-03575-y | en_US |
dc.publisher | Springer | en_US |
dc.contributor.bilkentauthor | Sensoy, Ahmet | |
dc.identifier.eissn | 1572-9338 | |
buir.contributor.orcid | Sensoy, Ahmet|0000-0001-7967-5171 | en_US |