[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290446-105":59,"doc-detail-290446-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","predicting-bitcoin-prices-using-machine-learning","Predicting Bitcoin Prices Using Machine Learning","","Predicting Bitcoin movements with a machine-learning framework, the study compiles a dataset of 24 explanatory variables commonly used in finance research. Using daily data from 2 December 2014 to 8 July 2019, it builds forecasting models based on past Bitcoin values, other cryptocurrencies, exchange rates, and macroeconomic variables. Results indicate that logistic regression outperforms linear support vector machines and random forests, achieving 66% accuracy, with evidence rejecting weak-form market efficiency.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-bitcoin-prices-using-machine-learning/290446/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-bitcoin-prices-using-machine-learning/290446.png","ImageObject",300,407,{"name":92,"@type":93},"Ivy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does the study predict Bitcoin movements?","Question",{"text":113,"@type":114},"It uses a machine-learning framework to forecast Bitcoin direction based on compiled explanatory variables and historical market data.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What data and forecasting inputs are used in the models?",{"text":118,"@type":114},"The models are trained on daily data from 2 December 2014 to 8 July 2019, using past Bitcoin values, other cryptocurrencies, exchange rates, and macroeconomic variables.",{"name":120,"@type":111,"acceptedAnswer":121},"Which model performs best and what accuracy is reported?",{"text":122,"@type":114},"Logistic regression performs best, outperforming linear support vector machine and random forest, reaching 66% accuracy.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290446,1789641703,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":105,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","Central Lancashire Online Knowledge (CLoK)  \n\n| Title | Predicting Bitcoin Prices Using Machine Learning |\n| --- | --- |\n| Type | Article |\n| URL | [https://clok.uclan.ac.uk/55346/](https://clok.uclan.ac.uk/55346/) |\n| DOI | [https://doi.org/10.3390/e25050777](https://doi.org/10.3390/e25050777) |\n| Date | 2023 |\n| Citation | Dimitriadou, Athanasia and Gregoriou, Andros (2023) Predicting Bitcoin Prices Using Machine Learning. Entropy, 25 (5) . p. 777. |\n| Creators | Dimitriadou, Athanasia and Gregoriou, Andros |\n\nIt is advisable to refer to the publisher’s version if you intend to cite from the work. [https://doi.org/10.3390/e25050777](https://doi.org/10.3390/e25050777)  \nFor information about Research at UCLan please go to [http://www.uclan.ac. uk/research/](http://www.uclan.ac. uk/research/)  \n[All outputs in CLoK are protected by Intellectual Property Rights law](All outputs in CLoK are protected by Intellectual Property Rights law), including Copyright law. Copyright, IPR and Moral Rights for the works on this site are retained by the individual authors and/or other copyright owners. Terms and conditions for use of this material are defined in the  \n[http://clok.uclan.ac.uk/policies/](http://clok.uclan.ac.uk/policies/)  \n entropy   \nArticle  \nPredicting Bitcoin Prices Using Machine Learning  \nAthanasia Dimitriadou 1 and Andros Gregoriou 2, *  \nCitation: Dimitriadou, A.; Gregoriou, A. Predicting Bitcoin Prices Using Machine Learning. Entropy 2023, 25, 777. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)e25050777  \nAcademic Editors: Andreia Dion½sio, Paulo Ferreira, Dora Almeida and Isabel Vieira  \nReceived: 12 April 2023  \nRevised: 6 May 2023  \nAccepted: 7 May 2023  \nPublished: 10 May 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 College of Business, Law and Social Sciences, University of Derby, Lonsdale House, Quaker Way, Derby DE1 3HD, UK  \n2 School of Business and Law, University of Brighton, Elm House, Lewes Road, Brighton BN2 4AT, UK  \n* Correspondence: [a.gregoriou@brighton.ac.uk](a.gregoriou@brighton.ac.uk)  \nAbstract: In this paper we predict Bitcoin movements by utilizing a machine-learning framework. We compile a dataset of 24 potential explanatory variables that are often employed in the finance literature. Using daily data from 2nd of December 2014 to July 8th 2019, we build forecasting models that utilize past Bitcoin values, other cryptocurrencies, exchange rates and other macroeconomic variables. Our empirical results suggest that the traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching an accuracy of 66% . Moreover, based on the results, we provide evidence that points to the rejection of weak form efficiency in the Bitcoin market.  \nKeywords: Bitcoin; machine learning; linear support vector machine; random forest  \n1. Introduction  \nDoes Bitcoin respond to financial, cryptocurrency, and macroeconomic shocks? Should Bitcoin follow the efficient market hypothesis? Do the other cryptocurrencies affect the volatility of Bitcoin prices? Bitcoin emerged in 2009 as the world's first cryptocurrency, attracting new investors due to high returns. This is reflected by the returns of Bitcoin, as quoted on Coinbase, increasing by more than 120% from 2016 to 2017, reaching USD20.000 from USD900 for the purchase of a single Bitcoin token. In early 2017, the market capitalization of Bitcoin grew significantly from around USD18 billion to nearly USD600 billion atthe end of that year. As an investment asset, Bitcoin was originally in the retail sector but has now become the benchmark for all other digital curre","cbCaiu4xn7FZ2Fkz","https://ap.wps.com/l/cbCaiu4xn7FZ2Fkz","pdf",1426404,"English","# Abstract\n# Introduction\n## Bitcoin and market efficiency","[{\"question\":\"How does the study predict Bitcoin movements?\",\"answer\":\"It uses a machine-learning framework to forecast Bitcoin direction based on compiled explanatory variables and historical market data.\"},{\"question\":\"What data and forecasting inputs are used in the models?\",\"answer\":\"The models are trained on daily data from 2 December 2014 to 8 July 2019, using past Bitcoin values, other cryptocurrencies, exchange rates, and macroeconomic variables.\"},{\"question\":\"Which model performs best and what accuracy is reported?\",\"answer\":\"Logistic regression performs best, outperforming linear support vector machine and random forest, reaching 66% accuracy.\"}]","Predicting Bitcoin Prices Using Machine Learning | PDF",28]