Using Multi-Transformer Architecture Hybrid Models to Predict Stock Market Volatility

Using Multi-Transformer Architecture Hybrid Models to Predict Stock Market Volatility | 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 Using Multi-Transformer Architecture Hybrid Models to Predict Stock Market Volatility ASWINI MISHRA, Aaryaman Gupta, Jayashree Renganathan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4321494/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 Forecasts of stock volatility are crucial for estimating equity risk and, therefore, for the management decisions made by financial institutions. Therefore, this work aims to provide new machine and deep learning techniques-based stock volatility models that are more accurate. This study presents the Multi-Transformer architecture based on neural networks that integrate Transformer and Multi-Transformer (M.T.) layers with the GARCH and the LSTM model and compares their performance with the traditional GARCH-type models. The empirical findings based on the daily returns of NIFTY-50 data suggest that compared to previous autoregressive algorithms, hybrid models based on Multi-Transformer and Transformer layers provide more accuracy and, therefore, are more appropriate for risk assessments. Moreover, even in highly variable conditions like the COVID-19 pandemic, the hybrid Neural-network models beat individual traditional GARCH-type models. The result shows that the bagging mechanism added to the Multi-Transformer architecture helped to reduce the error in the variance in the noisy data and, therefore, reduced the RMSE of hybrid Multi-Transformer models to almost a tenth of the RMSE observed in traditional GARCH-type models. deep learning neural networks risk management market volatility Transformer GARCH LSTM Full Text 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. 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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-4321494","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497640437,"identity":"6f65c5db-20ff-4d25-84d7-4a754c26cf7c","order_by":0,"name":"ASWINI MISHRA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYJCCAwwGDDwMEkBWQgWQYGZuIEHLgzMgLYyEtUAAUAvjwzYQi4AW/v4zhocLCrbJyM/uffghcV5tNH87UMuPim24zb6RY3B4hsFtHoM7x40lErcdz51xmLGBsefMbdzW3ODdcJgHpEUijQGo5VhuA1ALM2Mbbi3y589CtMjPSGP+kTjnWO58QloMDuRCtDDcSGOTSGyoAXIJaDG8kf8B4jCgFouEYwdyNwK1HMTnF7nzx5I/8/y5bQ9y2M0fNXW5884fPvjgRwUe76OBw2DyANHqgaCOFMWjYBSMglEwQgAAmxpfSLIMZoEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7754-0633","institution":"BITS KK Birla Goa Campus: Birla Institute of Technology \u0026 Science Pilani - K K Birla Goa Campus","correspondingAuthor":true,"prefix":"","firstName":"ASWINI","middleName":"","lastName":"MISHRA","suffix":""},{"id":497640438,"identity":"8ed57031-2cd3-43fc-a16b-eda68a13cad6","order_by":1,"name":"Aaryaman Gupta","email":"","orcid":"","institution":"BITS KK Birla Goa Campus: Birla Institute of Technology \u0026 Science Pilani - K K Birla Goa Campus","correspondingAuthor":false,"prefix":"","firstName":"Aaryaman","middleName":"","lastName":"Gupta","suffix":""},{"id":497640439,"identity":"819aec83-6fc9-41ae-b4bb-716c2a9fd6ca","order_by":2,"name":"Jayashree Renganathan","email":"","orcid":"","institution":"BITS KK Birla Goa Campus: Birla Institute of Technology \u0026 Science Pilani - K K Birla Goa Campus","correspondingAuthor":false,"prefix":"","firstName":"Jayashree","middleName":"","lastName":"Renganathan","suffix":""}],"badges":[],"createdAt":"2024-04-25 04:31:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4321494/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4321494/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89270426,"identity":"fddbd563-8c45-441e-8c68-24e1ef9f2627","added_by":"auto","created_at":"2025-08-18 08:47:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":516047,"visible":true,"origin":"","legend":"","description":"","filename":"MainDocumentwithAuthorDetailsFinalVersion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4321494/v1_covered_2a067573-a45f-4eef-b84e-ab229441135f.pdf"}],"financialInterests":"","formattedTitle":"Using Multi-Transformer Architecture Hybrid Models to Predict Stock Market Volatility","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":"deep learning, neural networks, risk management, market volatility, Transformer, GARCH, LSTM","lastPublishedDoi":"10.21203/rs.3.rs-4321494/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4321494/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForecasts of stock volatility are crucial for estimating equity risk and, therefore, for the management decisions made by financial institutions. 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