[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290453-105":59,"doc-detail-290453-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","bitcoin-replication-using-machine-learning","Bitcoin replication using machine learning","","Cryptocurrencies are characterized by high volatility and low correlations with traditional asset classes, yet they also bring substantial risks and regulatory uncertainty that hinder direct investment. This paper proposes a replication framework that uses machine learning to construct synthetic portfolios matching Bitcoin’s risk-adjusted return profile and diversification benefits. Results show synthetic portfolios outperform direct Bitcoin investment on risk-adjusted returns net of trading costs, while reducing risks, improving diversification, and lowering tail risk.",{"@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/bitcoin-replication-using-machine-learning/290453/",{"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/bitcoin-replication-using-machine-learning/290453.png","ImageObject",300,407,{"name":92,"@type":93},"Hazel","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",18,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is direct investment in cryptocurrencies challenging for many investors?","Question",{"text":113,"@type":114},"Cryptocurrencies face high volatility, non-market risks (operational, security, fraud), liquidity risks, and legal/regulatory uncertainties that can restrict ownership and trading.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What does the proposed paper framework do?",{"text":118,"@type":114},"It uses machine learning to generate synthetic portfolios designed to replicate Bitcoin’s risk-adjusted return profile and diversification benefits.",{"name":120,"@type":111,"acceptedAnswer":121},"What benefits do synthetic portfolios provide compared with holding Bitcoin directly?",{"text":122,"@type":114},"They deliver superior risk-adjusted returns net of trading costs, better diversification, and lower tail risk while mitigating risks associated with direct Bitcoin holdings.","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},290453,1789641726,{"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":52,"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},137441390410,"https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984","University of Dundee  \nBitcoin replication using machine learning  \nHarris, Richard D. F.; Mazibas, Murat; Rambaccussing, Dooruj  \nPublished in:  \nInternational Review of Financial Analysis  \nDOI:  \n10.1016/j.irfa.2024.103207  \nPublication date:  \n2024  \nLicence: CC BY  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in Discovery Research Portal  \nCitation for published version (APA):  \nHarris, R. D. F. , Mazibas, M. , & Rambaccussing, D. (2024) . Bitcoin replication using machine learning. International Review of Financial Analysis, 93, Article 103207. [https://doi.org/10.1016/j.irfa.2024.103207](https://doi.org/10.1016/j.irfa.2024.103207)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 11. May. 2024  \nInternational Review of Financial Analysis 93 (2024) 103207  \nContents lists available at ScienceDirect  \nInternational Review of Financial Analysis  \njournal [homepage:](homepage: www.elsevier.com/locate/irfa)[ www.elsevier.com/locate/irfa](homepage: www.elsevier.com/locate/irfa)  \n| Bitcoin replication using machine learning\u003Cbr>Richard D.F. Harris a, *, Murat Mazibasb, Dooruj Rambaccussing ba University of Bristol Business School, 15-19 Tyndalls Park Road, Bristol BS8 1PQ, UK\u003Cbr>b University of Dundee School of Business, 3 Perth Road, Nethergate, Dundee DD1 4HN, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| JEL codes:\u003Cbr>G10\u003Cbr>G11\u003Cbr>Keywords:\u003Cbr>Portfolio replication\u003Cbr>Cryptocurrencies\u003Cbr>Bitcoin\u003Cbr>Machine learning algorithms |  | Cryptocurrencies are characterized by high volatility and low correlations with traditional asset classes, and present an intriguing investment opportunity. However, their inherent risks and regulatory uncertainties make direct investment challenging for many investors. This paper addresses this challenge by proposing a replication framework that employs machine learning to create synthetic portfolios that replicate the risk-adjusted return profile and diversification benefits of Bitcoin, by far the largest cryptocurrency by market share. We show that the synthetic portfolios offer a compelling alternative to direct investment in Bitcoin, delivering superior riskadjusted returns net of trading costs while mitigating the risks that are associated with holding Bitcoin directly. Furthermore, the synthetic portfolios provide better diversification benefits and lower tail risk. |\n\n1. Introduction  \nThe last decade has witnessed very significant growth in the market capitalization of cryptocurrencies, from \u003C100 million USD in 2012 to about two trillion USD in 2022 (Statista, 2022). During that time, cryptocurrencies have become increasingly important as a medium of exchange, a store of value and, most notably, a speculative investment asset. Indeed, cryptocurrencies are now considered an asset class in their own right and are widely held by both individual and institutional investors (see, for example, Corbet, Lucey, Urquhart, & Yarovaya, 2019). Of the >9000 actively traded cryptocurrencies in existence, Bitcoin is by far the largest, representing over 50% of the total cryptocurrency market capitalization.  \nIn this paper, we investigate whether it is possible to replicate the return distribution of Bitcoin with a portfolio of conventional assets that can be traded at low cost in highly liquid and properly regulated markets, either directly or through instruments such as exchange traded funds, futures and options.1 The motivation for this is twofold. First, from an invest","cbCaihBgv9iOtv04","https://ap.wps.com/l/cbCaihBgv9iOtv04","pdf",652716,"English","# Introduction\n## Background and motivation\n## Replication problem and approaches","[{\"question\":\"Why is direct investment in cryptocurrencies challenging for many investors?\",\"answer\":\"Cryptocurrencies face high volatility, non-market risks (operational, security, fraud), liquidity risks, and legal/regulatory uncertainties that can restrict ownership and trading.\"},{\"question\":\"What does the proposed paper framework do?\",\"answer\":\"It uses machine learning to generate synthetic portfolios designed to replicate Bitcoin’s risk-adjusted return profile and diversification benefits.\"},{\"question\":\"What benefits do synthetic portfolios provide compared with holding Bitcoin directly?\",\"answer\":\"They deliver superior risk-adjusted returns net of trading costs, better diversification, and lower tail risk while mitigating risks associated with direct Bitcoin holdings.\"}]","Bitcoin replication using machine learning | PDF",25]