Prediction of cryptocurrency returns using machine learning
| buir.contributor.author | Sensoy, Ahmet | |
| buir.contributor.orcid | Sensoy, Ahmet|0000-0001-7967-5171 | |
| dc.citation.epage | 36 | en_US |
| dc.citation.spage | 3 | en_US |
| dc.citation.volumeNumber | 297 | en_US |
| 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.department | Department of Management | en_US |
| 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.identifier.doi | 10.1007/s10479-020-03575-y | en_US |
| dc.identifier.eissn | 1572-9338 | |
| dc.identifier.issn | 0254-5330 | |
| dc.identifier.uri | http://hdl.handle.net/11693/77044 | |
| dc.language.iso | English | en_US |
| dc.publisher | Springer | en_US |
| dc.relation.isversionof | https://doi.org/10.1007/s10479-020-03575-y | en_US |
| dc.source.title | Annals of Operations Research | 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 |