Hybrid machine learning and stochastic volatility models with blockchain data for high-frequency cryptocurrency trading

Hybrid machine learning and stochastic volatility models with blockchain data for high-frequency cryptocurrency trading | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hybrid machine learning and stochastic volatility models with blockchain data for high-frequency cryptocurrency trading Timothy King Avordeh, Samuel Arthur, Christopher Quaidoo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6352921/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract High-frequency cryptocurrency markets, such as Bitcoin, exhibit extreme volatility, posing significant challenges for traditional stochastic volatility models like the Heston framework, which often struggle to capture nonlinear patterns and sudden price jumps. This study proposes a hybrid framework that integrates the Heston stochastic volatility model with Long Short-Term Memory (LSTM) neural networks, leveraging real-time blockchain data feeds, including transaction counts, to enhance volatility forecasting. Using 1-minute Bitcoin data from January to March 2025, the hybrid model demonstrates superior forecasting accuracy, reducing the Mean Squared Error by 43% compared to the Heston model and by 20% compared to the standalone LSTM. In a high-frequency trading simulation over March 2025, the hybrid model achieves a cumulative return of 18.5%, a Sharpe ratio of 2.1, and a maximum drawdown of 4.2%, outperforming Heston (10.2%, 1.3, 6.8%) and LSTM (14.8%, 1.7, 5.5%). These findings highlight the model’s potential for algorithmic traders seeking robust volatility predictions and improved risk-adjusted returns in crypto markets. Additionally, the hybrid model’s interpretable stochastic base supports regulatory transparency, addressing compliance needs in a rapidly evolving financial landscape. Financial Mathematics High-frequency trading cryptocurrency stochastic volatility machine learning blockchain data financial engineering. Full Text Additional Declarations The authors declare potential competing interests as follows: No, I declare that authors have no interests, affiliations, or associations that might be perceived to influence the results and/or discussion reported in this preprint submission. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6352921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436874596,"identity":"6845c70f-248f-4720-990f-0d084294f4d8","order_by":0,"name":"Timothy King Avordeh","email":"data:image/png;base64,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","orcid":"","institution":"University of Professional Studies, Accra","correspondingAuthor":true,"prefix":"","firstName":"Timothy","middleName":"King","lastName":"Avordeh","suffix":""},{"id":436874597,"identity":"8fdad8ba-f750-44a8-8f90-04170c468659","order_by":1,"name":"Samuel Arthur","email":"","orcid":"","institution":"University of Professional Studies, Accra","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Arthur","suffix":""},{"id":436874598,"identity":"4964f532-ac73-4a4b-ba61-54e6edd05618","order_by":2,"name":"Christopher Quaidoo","email":"","orcid":"","institution":"University of Professional Studies, Accra","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Quaidoo","suffix":""}],"badges":[],"createdAt":"2025-04-01 12:06:40","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6352921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6352921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79778351,"identity":"60261d17-bd6b-4f77-872e-71391fd4f3d5","added_by":"auto","created_at":"2025-04-02 14:37:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":565020,"visible":true,"origin":"","legend":"","description":"","filename":"MFEJAvordehetal..pdf","url":"https://assets-eu.researchsquare.com/files/rs-6352921/v1_covered_75473080-f700-4eda-ab70-b2f44365ba16.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: No, I declare that authors have no interests, affiliations, or associations that might be perceived to influence the results and/or discussion reported in this preprint submission.","formattedTitle":"\u003cp\u003eHybrid machine learning and stochastic volatility models with blockchain data for high-frequency cryptocurrency trading\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Professional Studies","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"High-frequency trading, cryptocurrency, stochastic volatility, machine learning, blockchain data, financial engineering.","lastPublishedDoi":"10.21203/rs.3.rs-6352921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6352921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-frequency cryptocurrency markets, such as Bitcoin, exhibit extreme volatility, posing significant challenges for traditional stochastic volatility models like the Heston framework, which often struggle to capture nonlinear patterns and sudden price jumps. This study proposes a hybrid framework that integrates the Heston stochastic volatility model with Long Short-Term Memory (LSTM) neural networks, leveraging real-time blockchain data feeds, including transaction counts, to enhance volatility forecasting. Using 1-minute Bitcoin data from January to March 2025, the hybrid model demonstrates superior forecasting accuracy, reducing the Mean Squared Error by 43% compared to the Heston model and by 20% compared to the standalone LSTM. In a high-frequency trading simulation over March 2025, the hybrid model achieves a cumulative return of 18.5%, a Sharpe ratio of 2.1, and a maximum drawdown of 4.2%, outperforming Heston (10.2%, 1.3, 6.8%) and LSTM (14.8%, 1.7, 5.5%). These findings highlight the model\u0026rsquo;s potential for algorithmic traders seeking robust volatility predictions and improved risk-adjusted returns in crypto markets. 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