[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118834-en":3,"doc-seo-118834-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118834,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Predicting Bitcoin Prices Using Machine Learning - Article","This paper predicts Bitcoin movements using a machine-learning framework built from a dataset of 24 explanatory variables commonly used in finance research. Daily data spanning December 2, 2014 to July 8, 2019 supports forecasting models that incorporate past Bitcoin values, other cryptocurrencies, exchange rates, and macroeconomic indicators. Empirical results show that logistic regression outperforms linear support vector machines and random forests, achieving 66% accuracy. The findings also provide evidence rejecting weak-form market efficiency in the Bitcoin market.","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 currencies that have emerged, such as Ethereum, XRP and Litecoin, among others.  \nPrior research has compared Bitcoin to gold due to its low correlation with other financial assets [1] . In a similar vein to gold, Bitcoin can be used to hedge against inflation or economic uncertainty, using futures contracts (Bakkt) and unregulated cryptocurrency derivatives exchanges, such as BitMex, Huobi and OKex [2,3] . The motivation behind this is that Bitcoin has a fixed supply, so it does not suffer from the devaluation problem of paper money that occurs through quantitative easing.  \nAlthough there are also some studies that focus on predicting stock market price movements, it is important to consider the cryptocurrency market, which, according to Ferreira et al. [4], is characterized by high volatility, no closed trading periods, relatively smaller capitalization, and high market data availability. However, in an efficient market [5], prices of securities in financial markets fully reflect all variable information. Given that the future is unknown, prices should follow a random walk; that is, future changes in stock (security) prices should, for all practical purposes, be unpredictable. In the weak-form efficiency case, future returns cannot be","cbCaioT18D6V6on5","https://ap.wps.com/l/cbCaioT18D6V6on5","pdf",1342960,1,10,"English","en",105,"# Abstract\n# Introduction\n## Bitcoin, shocks, and market efficiency\n## Prior research and modeling approaches\n## News, sentiment, and macroeconomic announcements","[{\"question\":\"What data and variables does the study use to predict Bitcoin movements?\",\"answer\":\"The study compiles 24 explanatory variables commonly used in finance and uses daily data from December 2, 2014 to July 8, 2019. It incorporates past Bitcoin values, other cryptocurrencies, exchange rates, and macroeconomic variables.\"},{\"question\":\"Which forecasting model performs best in the study?\",\"answer\":\"The traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching 66% accuracy.\"},{\"question\":\"What conclusion does the paper draw about the Bitcoin market’s weak-form efficiency?\",\"answer\":\"Based on the results, the paper provides evidence that rejects weak-form efficiency in the Bitcoin market.\"}]","Predicting Bitcoin Prices Using Machine Learning - Article | PDF",1785720517,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predicting-bitcoin-prices-using-machine-learning-article","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-bitcoin-prices-using-machine-learning-article/118834/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What data and variables does the study use to predict Bitcoin movements?","Question",{"text":76,"@type":77},"The study compiles 24 explanatory variables commonly used in finance and uses daily data from December 2, 2014 to July 8, 2019. It incorporates past Bitcoin values, other cryptocurrencies, exchange rates, and macroeconomic variables.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which forecasting model performs best in the study?",{"text":81,"@type":77},"The traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching 66% accuracy.",{"name":83,"@type":74,"acceptedAnswer":84},"What conclusion does the paper draw about the Bitcoin market’s weak-form efficiency?",{"text":85,"@type":77},"Based on the results, the paper provides evidence that rejects weak-form efficiency in the Bitcoin market.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]