Forecasting Model of Microsoft’s Daily Return and Its Volatility

Forecasting Model of Microsoft’s Daily Return and Its 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Article Forecasting Model of Microsoft’s Daily Return and Its Volatility Runsheng Rong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4818203/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 Stock returns tend to float sharply. It is vital for financial analysts to build appropriate model to forecast the returns of stocks and their volatility. This paper builds an initial AR model to predict the daily return of Microsoft, then checks both the ARCH effect in the residual terms by ARCH-LM test method and the autocorrelation relationship between the square of the residual terms by drawing the ACF and PACF plots, finally builds the full model, which consists of an AR model for forecasting the daily return of Microsoft and a GARCH model to predict its volatility. Econometrics forecast the return of stock and its volatility AR model ARCH-LM test ACF and PACF plots GARCH model 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. 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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-4818203","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332861314,"identity":"2723e1da-597e-46e5-911e-3d063b658ee5","order_by":0,"name":"Runsheng Rong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACfobDhx//4WHjYWxvPkCcFsnGY2kGPDJ8csw9xxKI02Jw+IyBBI+NnDH7DB8DIl127IyBgUSOWWLvDJ6PN94w2MnpNhDQwdhzrOCBwZm0xJmzezdbzmFINjY7QEALs8ThDQaJPccSN845u02ah+FA4jZCWtjkHxhIHPz3P3H/jZxnxGnhYThiINnAw2bMOCOHjTgtEgzH0owZeNjkgJ4ytpxjQIRf7A8Ao5IBEpUPb7ypsJMjqAXVSh5iowZJC6k6RsEoGAWjYEQAAKSLRp+gbeifAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-7953-2752","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":true,"prefix":"","firstName":"Runsheng","middleName":"","lastName":"Rong","suffix":""}],"badges":[],"createdAt":"2024-07-28 22:42:23","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-4818203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4818203/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61390624,"identity":"8f3e3b74-d258-4e14-8f60-ed8eb014dde1","added_by":"auto","created_at":"2024-07-30 07:59:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":512587,"visible":true,"origin":"","legend":"","description":"","filename":"ForecastingModelofMicrosoftsDailyReturnandItsVolatility.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4818203/v1_covered_ff0a8fdc-6116-44ed-80fc-caa6957bf272.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eForecasting Model of Microsoft’s Daily Return and Its Volatility\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Xi'an Jiaotong-Liverpool 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":"forecast the return of stock and its volatility, AR model, ARCH-LM test, ACF and PACF plots, GARCH model","lastPublishedDoi":"10.21203/rs.3.rs-4818203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4818203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStock returns tend to float sharply. 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