An analysis of the correlation of stock volume and stock price in index funds using OLS regression

An analysis of the correlation of stock volume and stock price in index funds using OLS regression | 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 An analysis of the correlation of stock volume and stock price in index funds using OLS regression Melike Kaya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6786835/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 The relationship between stock trading volume and stock price has been widely debated, with prior research reporting both positive and negative correlations. Understanding this relationship is essential for identifying potential arbitrage opportunities and anticipating market movements. This study examines the correlation between trading volume and price by applying an Ordinary Least Squares (OLS) regression model specifically to index fund data. The analysis reveals a modest correlation of 3.25%, with an adjusted value of 3.22%, suggesting that while the relationship is weak, the variables are not statistically independent. Additionally, the results indicate that, for the given dataset, the Laplacian error metric offers a better fit than the Gaussian metric. These findings provide a nuanced perspective on volume–price dynamics and highlight the potential benefits of alternative error modeling approaches in financial time series analysis. Financial Mathematics Applied Mathematics Finance Trading Volume Stock Price OLS Regression Index Funds Gauss-Markov Theorem 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-6786835","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464314687,"identity":"15a714d4-a527-444a-86f3-0cea5db577ef","order_by":0,"name":"Melike Kaya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYFAC5oaDDQwJIMYBICUBEeTBq4URpoUtgXgtjBAtPAYIQXxadNsbGw/OqEiTNzh+5pvEgz8WcubsZx8wvKk4jFOL2ZmDDQc3nMkx3HAmd5tEYpuEsWVPugHjnDN4tNxIbDj4sK2CccOB3M0GiQ0SiRsOpDEw87al4dZy/yFQy78K+w3n3zw2SPgD1HL+GVDLPzxabgBDbGNDTuKGGzmMDxLYgFpugGxpsMHjF6DDZhxLS55545nhA5BfDG48Yzg45xgeLccPH/7YU5Ns23c++cHBH3/q5AzOpzE+eFMjgVMLHCgcQOIcwKEIFcg3EKVsFIyCUTAKRiIAAJPQZcZ7Q5jeAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-1784-1349","institution":"Ankara University","correspondingAuthor":true,"prefix":"","firstName":"Melike","middleName":"","lastName":"Kaya","suffix":""}],"badges":[],"createdAt":"2025-05-30 17:53:27","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-6786835/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6786835/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83751385,"identity":"56ff179b-5585-453f-95d5-788bd3bd672a","added_by":"auto","created_at":"2025-06-02 06:56:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":431244,"visible":true,"origin":"","legend":"","description":"","filename":"MKSP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6786835/v1_covered_7b83c70f-ec73-4b5f-a756-4990e3c01898.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAn analysis of the correlation of stock volume and stock price in index funds using OLS regression\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Ankara 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":"Trading Volume, Stock Price, OLS Regression, Index Funds, Gauss-Markov Theorem","lastPublishedDoi":"10.21203/rs.3.rs-6786835/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6786835/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe relationship between stock trading volume and stock price has been widely debated, with prior research reporting both positive and negative correlations. 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