[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117113-en":3,"doc-seo-117113-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117113,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Cryptocurrencies’ Prices Discovery Through Machine Learning Algorithms - Bitcoin and Beyond","The master’s thesis investigates the complexity of cryptocurrency price discovery by applying machine learning methods to forecast market behavior. Four algorithms—Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine—are used to predict daily prices for Bitcoin, Ethereum, Cardano, and Solana, with additional analysis of hourly Bitcoin price prediction. Results show algorithm-specific strengths: Logistic Regression performs strongly for Bitcoin and Ethereum daily forecasts, while Support Vector Machine is most effective for Cardano and Solana, and Logistic Regression attains a standout accuracy for hourly Bitcoin prediction. The study connects cryptocurrency fundamentals with advanced modeling techniques and outlines opportunities for future research.","Åbo Akademi University  \nFaculty of Social Sciences, Business and Economics,  \nand Law  \nSeptember 2023  \nMASTER’S THESIS  \nCRYPTOCURRENCIES’ PRICES DISCOVERY THROUGH MACHINE LEARNING ALGORITHMS: BITCOIN AND  \nBEYOND.  \nMominul Islam / Student ID 1900642 Master’s Degree Program in  \nGovernance of Digitalization  \n̊  \nABO AKADEMI UNIVERSITY – Faculty of Social Sciences, Business and Economics, and Law  \nAbstract for Master’s Thesis  \n\n| Subject: Information Systems |  |\n| --- | --- |\n| Author: Mominul Islam |  |\n| Title: Cryptocurrencies’ Prices Discovery Through Machine Learning Algorithms: Bitcoin and Beyond |  |\n| Supervisor: Prof. Jozsef Mezei |  |\n| The evolution of cryptocurrencies has emerged as a fundamental shift in the financial landscape, with price discovery being an area of intense interest and complexity. The thesis titled “Cryptocurrencies’price discovery through machine learning algorithms: Bitcoin and beyond” aims to investigate and unravel this complexity through the lens of machine learning.\u003Cbr>In this comprehensive study, four major machine learning algorithms - Logistic Regression (LR), Decision Tree, Random Forest (RF), and Support Vector Machine (SVM) were applied to forecast the daily prices of four leading cryptocurrencies: Bitcoin, Ethereum, Cardano, and Solana, alongside an analysis of hourly Bitcoin price prediction.\u003Cbr>The findings reveal distinct performance characteristics for each algorithm. Logistic Regression exhibited high accuracies for Bitcoin and Ethereum daily predictions at 0.86 and 0.85, respectively. Support Vector Machine proved particularly effective for Cardano and Solana with accuracies of 0.90 and 0.97. Conversely, the Decision Tree and RF algorithms demonstrated more modest performance across the examined cryptocurrencies. Besides, a specialized investigation into Bitcoin’s hourly price prediction, employing the same set of algorithms, yielded varying results, with LR showing a standout accuracy of 0.98.\u003Cbr>This research encompasses a journey from the foundational principles of cryptocurrency to the advanced techniques of machine learning, highlighting both the opportunities and challenges inherent in this rapidly evolving field. It acts as a roadmap for future investigations, offering the potential to deepen our understanding of cryptocurrencies ’ impact on the global financial landscape and to extend the boundaries of knowledge in the area of price discovery through machine learning. |  |\n| Keywords: Bitcoin, Blockchain, cryptocurrency, fiat currency, machine learning, prediction, traditional financial systems |  |\n| Date: 09.09.2023 | Number of pages: 119 + III |\n\nTable of Contents  \nTable of Contents.................................................................................................... I  \nList of Figures ....................................................................................................... 1  \nList of Tables ......................................................................................................... 3  \nList of Acronyms ................................................................................................... 4  \n1. Introduction ................................................................................................................... 5  \n1.1 Background ........................................................................................................... 5  \n1.2 Motivation ............................................................................................................. 7  \n1.3 Outcome ................................................................................................................ 9  \n1.4 Research Questions ................................................................................................ 9  \n1.5 Organization of this paper .................................................................................... 10  \n2. State of the art .........................................","cbCaisgcBSISsy6E","https://ap.wps.com/l/cbCaisgcBSISsy6E","pdf",4462554,1,124,"English","en",105,"# 1. Introduction\n## 1.1 Background\n## 1.2 Motivation\n## 1.3 Outcome\n## 1.4 Research Questions\n## 1.5 Organization of this paper\n# 2. State of the art\n## 2.1 Fiat currency\n## 2.2 Cryptocurrency","[{\"question\":\"Which machine learning algorithms are used for cryptocurrency price forecasting in the thesis?\",\"answer\":\"The thesis applies Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine to forecast daily prices and to analyze hourly Bitcoin price prediction.\"},{\"question\":\"Which cryptocurrencies are included in the daily price prediction task?\",\"answer\":\"Daily price prediction covers Bitcoin, Ethereum, Cardano, and Solana.\"},{\"question\":\"How do the algorithms compare in performance for different cryptocurrencies?\",\"answer\":\"Logistic Regression shows high accuracy for Bitcoin and Ethereum daily predictions, Support Vector Machine performs particularly well for Cardano and Solana, while Decision Tree and Random Forest are comparatively more modest. For hourly Bitcoin prediction, Logistic Regression achieves a standout accuracy.\"}]","Cryptocurrencies’ Prices Discovery Through Machine Learning Algorithms - Bitcoin and Beyond | PDF",1785673916,312,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cryptocurrencies-prices-discovery-through-machine-learning-algorithms-bitcoin-and-beyond","",{"@graph":36,"@context":85},[37,54,68],{"@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/cryptocurrencies-prices-discovery-through-machine-learning-algorithms-bitcoin-and-beyond/117113/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning algorithms are used for cryptocurrency price forecasting in the thesis?","Question",{"text":75,"@type":76},"The thesis applies Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine to forecast daily prices and to analyze hourly Bitcoin price prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which cryptocurrencies are included in the daily price prediction task?",{"text":80,"@type":76},"Daily price prediction covers Bitcoin, Ethereum, Cardano, and Solana.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the algorithms compare in performance for different cryptocurrencies?",{"text":84,"@type":76},"Logistic Regression shows high accuracy for Bitcoin and Ethereum daily predictions, Support Vector Machine performs particularly well for Cardano and Solana, while Decision Tree and Random Forest are comparatively more modest. 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