Forecasting Cryptocurrency Prices: An Inter-Exchange Feature Engineering Approach Using Ensemble Regression Models

Forecasting Cryptocurrency Prices: An Inter-Exchange Feature Engineering Approach Using Ensemble Regression Models | 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 Forecasting Cryptocurrency Prices: An Inter-Exchange Feature Engineering Approach Using Ensemble Regression Models Nor Khutaba, Mohammed Al-Hubaishi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8823840/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 In this paper, we tackle the challenge of short-term price prediction in the notoriously volatile cryptocurrency market. Our approach leverages inter-exchange signals within a machine-learning framework. Focusing on Ethereum (ETH), we analyze two complementary daily series: (i) high-granularity trading activity from Binance and (ii) a broad market index used as a benchmark for comparison. Our approach uses extensive feature engineering, including traditional technical indicators, volatility metrics, and new inter-exchange features that are specifically designed to capture possible lead-lag signals between markets, because these features are crucial for predicting the next day's closing price. We frame the task as supervised regression to predict the next day's closing price and compare three models - Linear Regression, Random Forest Regressor, and Gradient Boosting Regressor - with time-series split to mimic real-world deployment. Somewhat surprisingly, Linear Regression was the best-performing model, with an R 2 of 0.9894 and an RMSE of 55.49, indicating that the engineered features relate to the target in a largely linear fashion; therefore, a feature importance analysis was conducted, which further demonstrates that the general market price is the strongest predictor, confirming that inter-exchange information improves forecasting. Our results show that careful feature engineering, combined with simple linear and ensemble models, can produce highly accurate short-term cryptocurrency price predictions. Finance Artificial Intelligence and Machine Learning Cryptocurrency Forecasting Time Series Regression Ethereum Inter-Exchange Analysis Feature Engineering Machine Learning Full Text Additional Declarations The authors declare no competing interests. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8823840","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587804054,"identity":"f547109a-f83e-4bb4-b38f-6edf3c4a5952","order_by":0,"name":"Nor Khutaba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYHACZmYQaSDBwPgASPPwEanFAKSF2QCkhY0ULWwSIC5BLebsvY+NC9v+yJtLNz+r/JpjJ8PGwPzw0Q08Wix7jhsnz2wzMNw555jZbdltyUCHsRkb5+DRYnAjjfkwb5sB44YbCWa3JbcxA7XwsEkTo8V+w430b8WS2+qJ05IM1JK44UaOGePHbYeJ0HLmGLMxzznj5J1zzhRLM247zsPGTMgvx9uYpXnK5Gy3S7dv/PhzW7U9P3vzw8f4tKAAZh4wSaxyEGD8QYrqUTAKRsEoGDEAAEBHQoJC1xUPAAAAAElFTkSuQmCC","orcid":"","institution":"UG Scholar, Dept. of Software Engineering, Halic University, Istanbul, Turkiye","correspondingAuthor":true,"prefix":"","firstName":"Nor","middleName":"","lastName":"Khutaba","suffix":""},{"id":587804055,"identity":"baa2ffe9-d164-437a-a5e4-af512727266a","order_by":1,"name":"Mohammed Al-Hubaishi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFACHjDJuEGCsfEBiMtHipZmAxCXjQQtDGwSIBZBLbrtvQc/3cyxkd0u3dxW+TXHToaNgfnhoxt4tJidOZcsnbstzXjnnINtt2W3JQMdxmZsnINPy40cA6CWw4kbbiS23ZbcxgzUwsMmTUCL8e/cbf/BWoolt9UTpcUMaMsBsBbGj9sOE6HlzBkz69xtycYb7hxslmbcdpyHjZmQX473GN/O3WYnu+F2+8OPP7dV2/OzNz98jE8LCmAGxxEzscpBgPEHKapHwSgYBaNgxAAAfepNvQUHGuIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9940-3592","institution":"Assistant Professor, Dept. of Computer Engineering, Halic University, Istanbul, Turkiye","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Al-Hubaishi","suffix":""}],"badges":[],"createdAt":"2026-02-08 19:26:50","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8823840/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8823840/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102292196,"identity":"3368057f-0852-4419-aab8-43707cc0be95","added_by":"auto","created_at":"2026-02-10 09:26:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":573039,"visible":true,"origin":"","legend":"","description":"","filename":"EthPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8823840/v1_covered_fbec188e-ff9e-44a7-871f-a0b8f6b70a11.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eForecasting Cryptocurrency Prices: An Inter-Exchange Feature Engineering Approach Using Ensemble Regression Models\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Haliç University","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":"Cryptocurrency Forecasting, Time Series Regression, Ethereum, Inter-Exchange Analysis, Feature Engineering, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-8823840/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8823840/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, we tackle the challenge of short-term price prediction in the notoriously volatile cryptocurrency market. Our approach leverages inter-exchange signals within a machine-learning framework. Focusing on Ethereum (ETH), we analyze two complementary daily series: (i) high-granularity trading activity from Binance and (ii) a broad market index used as a benchmark for comparison. Our approach uses extensive feature engineering, including traditional technical indicators, volatility metrics, and new inter-exchange features that are specifically designed to capture possible lead-lag signals between markets, because these features are crucial for predicting the next day's closing price. We frame the task as supervised regression to predict the next day's closing price and compare three models - Linear Regression, Random Forest Regressor, and Gradient Boosting Regressor - with time-series split to mimic real-world deployment. Somewhat surprisingly, Linear Regression was the best-performing model, with an \u003cem\u003eR\u003c/em\u003e2 \u0026nbsp;of 0.9894 and an RMSE of 55.49, indicating that the engineered features relate to the target in a largely linear fashion; therefore, a feature importance analysis was conducted, which further demonstrates that the general market price is the strongest predictor, confirming that inter-exchange information improves forecasting. Our results show that careful feature engineering, combined with simple linear and ensemble models, can produce highly accurate short-term cryptocurrency price predictions.\u003c/p\u003e","manuscriptTitle":"Forecasting Cryptocurrency Prices: An Inter-Exchange Feature Engineering Approach Using Ensemble Regression Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 09:25:30","doi":"10.21203/rs.3.rs-8823840/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"f73b24c1-2b98-4f03-87f3-8815c2f6fc1f","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62535433,"name":"Finance"},{"id":62535434,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2026-02-10T09:25:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 09:25:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8823840","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8823840","identity":"rs-8823840","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}