Short-term prediction of the Romanian stock market benchmark index using genetic programming

Short-term prediction of the Romanian stock market benchmark index using genetic programming | 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 Short-term prediction of the Romanian stock market benchmark index using genetic programming Florin Sebastian Duma, Rodica Ioana Lung This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6370687/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2026 Read the published version in Digital Finance → Version 1 posted 10 You are reading this latest preprint version Abstract The evolution of stock market indices is one of the most intensely studied subjects, given that in this particular field successful insights can offer both academic and financial benefits. Understandably, most focus lies on developed markets that drive economies, with less interest in frontier or emerging markets such as the Romanian stock market. In this paper, we offer an analysis of the connections between the Romanian stock market index BET and major indices from the USA (NASDAQ, S&P500, DIJA), Western Europe (FTSE100, CAC40, DAX) and Central and Eastern Europe (ATX,BUX, WIG20). The analysis explores the use of genetic programming models for the prediction of index values using short-term intervals and compares them with standard regression models. We find that most methods perform in an almost similar manner and that the best numerical results are obtained when considering the Central and Eastern European indices. stock market indices genetic programming regression prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Mar, 2026 Read the published version in Digital Finance → Version 1 posted Editorial decision: Revision requested 01 Jan, 2026 Reviews received at journal 01 Jan, 2026 Reviews received at journal 31 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers invited by journal 03 Dec, 2025 Editor assigned by journal 05 Apr, 2025 Submission checks completed at journal 04 Apr, 2025 First submitted to journal 03 Apr, 2025 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-6370687","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554542131,"identity":"d17ec739-ff30-4098-ac56-9753efd861a8","order_by":0,"name":"Florin Sebastian Duma","email":"","orcid":"","institution":"Babeș-Bolyai University","correspondingAuthor":false,"prefix":"","firstName":"Florin","middleName":"Sebastian","lastName":"Duma","suffix":""},{"id":554542132,"identity":"edcd170f-0cd4-44f5-a716-d182f525792e","order_by":1,"name":"Rodica Ioana Lung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYHACgwNAQgaIGR9ABHjYGBgKGJgJaeEBYmYDhBYD/FoYoFrYJJC14ATm7c0bD/yoYeAxuN1+reJDjU0eg0TusQc/DA6zM0gfPoBNi8yZYwUHe44Btdw5U3ZzxrG0YgaJvHTDHoPDzAx8aQnYtEhI5BgcZmADarmRk3abt+FwYoNEjpk0A0gLDw9W50G0/INoKf5LtBbGNpCW9GPMjERp4QH6pbdPgkfyRg6zZM+xtMQ2nncgv6Qzs/GwYfcLe/PmDz++2cjx3Uh/+OFHjU1iPzsoxCqsk/l5mLGGGEwnEEOdwQYVSmbDqRoO2B+gcO0I6xgFo2AUjIIRAgCCUFeWxcwv0gAAAABJRU5ErkJggg==","orcid":"","institution":"Babeș-Bolyai University","correspondingAuthor":true,"prefix":"","firstName":"Rodica","middleName":"Ioana","lastName":"Lung","suffix":""}],"badges":[],"createdAt":"2025-04-03 15:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6370687/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6370687/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42521-026-00188-1","type":"published","date":"2026-03-26T16:09:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":105754902,"identity":"e4b80c5c-be0c-41ed-b997-4195929895c0","added_by":"auto","created_at":"2026-03-30 16:22:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3976081,"visible":true,"origin":"","legend":"","description":"","filename":"paulklee3digitalfinance.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6370687/v1_covered_ad9193ad-7975-44d5-9c64-c2fc326157c7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-term prediction of the Romanian stock market benchmark index using genetic programming","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"digital-finance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dfin","sideBox":"Learn more about [Digital Finance](http://link.springer.com/journal/42521)","snPcode":"42521","submissionUrl":"https://submission.nature.com/new-submission/42521/3","title":"Digital Finance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"stock market indices, genetic programming, regression, prediction","lastPublishedDoi":"10.21203/rs.3.rs-6370687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6370687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The evolution of stock market indices is one of the most intensely studied subjects, given that in this particular field successful insights can offer both academic and financial benefits. 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