Equity Market Structure and Trading Diversification: Insights from Panel Data, Clustering, and Machine Learning

Equity Market Structure and Trading Diversification: Insights from Panel Data, Clustering, and Machine Learning | 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 Equity Market Structure and Trading Diversification: Insights from Panel Data, Clustering, and Machine Learning Angelo Leogrande, Fabio Anobile, Alberto Costantiello, Carlo Drago, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8947864/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 This article aims to contribute to a relatively understudied area of financial development, namely, the internal dispersion of trading activity. The focus is not on overall financial development measures such as total market capitalization and liquidity but rather on trading diversification, defined as the proportion of trading volume contributed by firms outside the VTX, representing the top ten most frequently traded firms. The article uses data from the World Bank’s Global Financial Development Database. The sample is constructed as a balanced panel of 23 countries over the period 2002-2021, starting with a sample of 38 countries. The article uses four key explanatory variables, namely, relative size of deposit-taking banks (DBS), remittance inflows (REM), market capitalization excluding the top ten firms (MCX), and outstanding international public debt (IPU). The article uses a combination of panel econometrics, hierarchical clustering, and machine learning methods. The econometric results show that a diversified financial system structure and remittance inflows are strongly, positively related to overall and less concentrated trading activity, while bank dominance and reliance on international public debt are related to more concentrated trading activity. The clustering results show significant cross-country heterogeneity and a core-periphery structure. The machine learning results show that, using all models, equity market structure is again found to be the most important explanatory variable, with external financial flows being important as well. The article concludes that equity market structure is key to understanding internal dispersion, with important policy implications. Stock market diversification Market structure Remittances Financial development 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-8947864","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595697229,"identity":"a72114a5-b703-41cf-a79c-dc8170ddfdc4","order_by":0,"name":"Angelo Leogrande","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACPijN2AAiH4AI9mYQWwKnFjYULQkJQILnIFgLTj1YtEgkgtm4tbA3H35dUXNHtp+B+ZlE4g+bfH7Jh21SNxgs6nBq4TmWZnnm2DPjmQ1sZhIJCWmWM2cntknn4HOYRI6ZYQPb4cQNB3jYgFoOGxjcJqRF/g1Qy7/DifthWuxvHiRkC4/xw8Y2oC0MMFskGAlo4UlLY2zsO2w84zCbsUVCWpqBxJnEZuscAwnJBhxa+NkPH/7Y8O2wbH9788MbH2xsDPjbDx+8nVNRx4/LFrDbwBQziqABHg1AtR/wSo+CUTAKRsEoAABg1U/Q4kxZZwAAAABJRU5ErkJggg==","orcid":"","institution":"LUM University Giuseppe Degennaro","correspondingAuthor":true,"prefix":"","firstName":"Angelo","middleName":"","lastName":"Leogrande","suffix":""},{"id":595697230,"identity":"341d5f30-8531-4c89-aa33-9fa401be3b18","order_by":1,"name":"Fabio Anobile","email":"","orcid":"","institution":"LUM University Giuseppe Degennaro","correspondingAuthor":false,"prefix":"","firstName":"Fabio","middleName":"","lastName":"Anobile","suffix":""},{"id":595697231,"identity":"c328864d-e1af-48f3-86b1-e285b32c56a2","order_by":2,"name":"Alberto Costantiello","email":"","orcid":"","institution":"LUM University Giuseppe Degennaro","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Costantiello","suffix":""},{"id":595697232,"identity":"07077864-24bb-4805-be22-47fb6e2a3695","order_by":3,"name":"Carlo Drago","email":"","orcid":"","institution":"Unicusano University","correspondingAuthor":false,"prefix":"","firstName":"Carlo","middleName":"","lastName":"Drago","suffix":""},{"id":595697233,"identity":"464376c4-dd11-48c4-8c26-093907aa519a","order_by":4,"name":"Massimo Arnone","email":"","orcid":"","institution":"University of Catania","correspondingAuthor":false,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Arnone","suffix":""}],"badges":[],"createdAt":"2026-02-23 13:54:20","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-8947864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8947864/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103837419,"identity":"dbf0d67a-b06b-4296-97e3-ee4864d29535","added_by":"auto","created_at":"2026-03-03 14:11:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":811035,"visible":true,"origin":"","legend":"","description":"","filename":"EquityMarketStructureandTradingDiversification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8947864/v1_covered_1a3a7953-554c-4ede-976a-800190e4e1f5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEquity Market Structure and Trading Diversification: Insights from Panel Data, Clustering, and Machine Learning\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Stock market diversification; Market structure; Remittances; Financial development; Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8947864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8947864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article aims to contribute to a relatively understudied area of financial development, namely, the internal dispersion of trading activity. The focus is not on overall financial development measures such as total market capitalization and liquidity but rather on trading diversification, defined as the proportion of trading volume contributed by firms outside the VTX, representing the top ten most frequently traded firms. The article uses data from the World Bank’s Global Financial Development Database. The sample is constructed as a balanced panel of 23 countries over the period 2002-2021, starting with a sample of 38 countries. The article uses four key explanatory variables, namely, relative size of deposit-taking banks (DBS), remittance inflows (REM), market capitalization excluding the top ten firms (MCX), and outstanding international public debt (IPU). The article uses a combination of panel econometrics, hierarchical clustering, and machine learning methods. The econometric results show that a diversified financial system structure and remittance inflows are strongly, positively related to overall and less concentrated trading activity, while bank dominance and reliance on international public debt are related to more concentrated trading activity. The clustering results show significant cross-country heterogeneity and a core-periphery structure. The machine learning results show that, using all models, equity market structure is again found to be the most important explanatory variable, with external financial flows being important as well. The article concludes that equity market structure is key to understanding internal dispersion, with important policy implications.\u003c/p\u003e","manuscriptTitle":"Equity Market Structure and Trading Diversification: Insights from Panel Data, Clustering, and Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 14:10:22","doi":"10.21203/rs.3.rs-8947864/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":"8bd783af-4ef0-42e0-823a-b1317eec47b0","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T14:10:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 14:10:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8947864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8947864","identity":"rs-8947864","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}