Hybrid Machine Learning Models for Predicting Relative Returns of Five Major Global Stock Indices

Hybrid Machine Learning Models for Predicting Relative Returns of Five Major Global Stock Indices | 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 Article Hybrid Machine Learning Models for Predicting Relative Returns of Five Major Global Stock Indices Liang Hu, Yinru Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4818027/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 Accurately predicting stock market indices' returns is crucial for making informed financial decisions. This study introduces a hybrid approach using machine learning models to forecast the relative returns of five major global stock indices: S&P 500 (US), FTSE 100 (UK), Nikkei 225 (Japan), DAX 30 (Germany), and CAC 40 (France). By employing Long Short-Term Memory (LSTM), Dual-Layer LSTM (DL-LSTM), and Transformer models with Multi-Head Self-Attention, we aim to capture both short-term and long-term trends for better prediction accuracy. The dataset includes stocks from these indices from January 1, 2019, to December 31, 2023, ensuring stability by excluding stocks with frequent index changes. The results show that the DL-LSTM model significantly outperforms both traditional and other machine learning models in predicting relative returns, improving accuracy, recall, precision, and root mean square error (RMSE). This research highlights the potential of advanced machine learning techniques in financial market analysis, offering valuable insights for investors and financial analysts. The study not only enhances predictive accuracy but also adds to the growing literature on hybrid models in financial forecasting. Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Physical sciences/Mathematics and computing/Statistics stock index prediction attention-based neural network Long Short-Term Memory (LSTM) machine learning Full Text Additional Declarations No competing interests reported. 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-4818027","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":345876551,"identity":"f974e7c9-119e-4a2f-be6f-ba8036787a58","order_by":0,"name":"Liang Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACPgn2A4d//rNhYGBnYGBmMACJJeDXwibBk/iYgS0NpJ5oLQzGxgxsh6FaGIjRIt2QJl3Ac16en5nHdHNBgR0DP3uOAX4tMgePSc+QuG04s5nH7PYMg2QGyZ43BLRIJKRJ8BjcTjA4DNTCYwD0zg1CtkgkmEnwJJyDaalnsCdCi7Exz4EDMC2HGQwkCGrJSXw4syEZ6Be2MqBfjvNInHlWgFcLv0T6gQMfG+zk+dmbt90u+FMtx9+evAGvFgzAQ5ryUTAKRsEoGAVYAQBJJz42CliDpAAAAABJRU5ErkJggg==","orcid":"","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Liang","middleName":"","lastName":"Hu","suffix":""},{"id":345876552,"identity":"effbde1b-6ecf-43b4-88ce-a7fa31732c25","order_by":1,"name":"Yinru Shen","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Yinru","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2024-07-28 20:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4818027/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4818027/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73283029,"identity":"9108a817-047e-43f8-80e3-e42824510475","added_by":"auto","created_at":"2025-01-08 12:53:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":800581,"visible":true,"origin":"","legend":"","description":"","filename":"FiveMajorGlobalStockIndices.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4818027/v1_covered_bdd90848-5d05-488a-b13c-54a3c50d661c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Machine Learning Models for Predicting Relative Returns of Five Major Global Stock Indices","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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 index prediction, attention-based neural network, Long Short-Term Memory (LSTM), machine learning","lastPublishedDoi":"10.21203/rs.3.rs-4818027/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4818027/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurately predicting stock market indices' returns is crucial for making informed financial decisions. 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