Stock price prediction with SCA-LSTM network and Statistical model ARIMA-GARCH

Stock price prediction with SCA-LSTM network and Statistical model ARIMA-GARCH | 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 Stock price prediction with SCA-LSTM network and Statistical model ARIMA-GARCH Homa Mehtarizadeh, Najme Mansouri, Behnam Mohammad Hasani zade, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4458517/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 Forecasting the stock market is one of the most challenging things for investors to do to increase their profits. The stock market is predicted using statistical strategies and learning tools. The objective of this study is to predict the closing price of the stock using Long Short-Term Memory (LSTM) network modified by Sin-Cosine Algorithm (SCA), Autoregressive Integrated Moving Average (ARIMA) and Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) statistical models which is called LSTM-SCA-ARIMA-GARCH model. An evaluation of the proposed method was performed using time series that are of varying stability. In this work, the data of 8 stocks including State Bank of India Network (SBIN), Oracle Corporation (ORCL), Microsoft Corporation (MSFT), Halliburton Company (HAL), Goldman Sachs Group Inc (GS), Cognizant Technology Solution Corporation (CTSH), Bank of America Corp (BAC) and Amazon (AMZN), which included closing stock price have been predicted on a daily and weekly basis, and the daily prediction was more accurate than the weekly prediction. In general, for daily prediction the SCA-LSTM-ARIMA-GARCH model 83.37%, 84.05% and 55.8% better than LSTM, Combination of LSTM and Particle Swarm Algorithm (LSTM-PSO) and LSTM-ARIMA, respectively. Stock market Sin-Cosine algorithm Long Short-Term Memory Prediction 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. 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-4458517","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308632704,"identity":"8f6a443b-a9df-4289-a564-540aad116ab6","order_by":0,"name":"Homa Mehtarizadeh","email":"","orcid":"","institution":"Shahid Bahonar University of Kerman","correspondingAuthor":false,"prefix":"","firstName":"Homa","middleName":"","lastName":"Mehtarizadeh","suffix":""},{"id":308632705,"identity":"5a8b78e7-ef2e-49a9-8f22-cac4f8f850de","order_by":1,"name":"Najme Mansouri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3PIWvDQBTA8RdVE5guFfsKmblSSJMPMnMh8GY6qKyouKmZfoB+icGpwtyNg6qwsxfOpGK+sqKiLzVtxSXUFXZ/ceTg/XgXgFDoMeNX30lKR/Sh7iHYEtFHbtLns5OMB+pP7mHzPH7S29FibrKvT01blumrj0xWHOs1uJfvNeKwSly5qQoiW3wXHpIoji4GF0kbs6EgwhSRSGg/MQ26I7hcmqolvyUzux5iaQuAK6SatURlzPZtsQ3WK3qPtFhORFJyZmkL7/oXM0N7WLipNPqnFscsZ+Zt1+yXqZcAxJzg5VqcJ7l3vG2gbq5553AoFAr9y06NmWonQn1MwAAAAABJRU5ErkJggg==","orcid":"","institution":"Shahid Bahonar University of Kerman","correspondingAuthor":true,"prefix":"","firstName":"Najme","middleName":"","lastName":"Mansouri","suffix":""},{"id":308632706,"identity":"b9fa0202-5059-4f03-9937-dd811301b941","order_by":2,"name":"Behnam Mohammad Hasani zade","email":"","orcid":"","institution":"Shahid Bahonar University of Kerman","correspondingAuthor":false,"prefix":"","firstName":"Behnam","middleName":"Mohammad Hasani","lastName":"zade","suffix":""},{"id":308632707,"identity":"e582809c-c9be-453e-b3f6-72f8251bfeab","order_by":3,"name":"Mohammad Mehdi Hosseini","email":"","orcid":"","institution":"Shahid Bahonar University of Kerman","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Mehdi","lastName":"Hosseini","suffix":""}],"badges":[],"createdAt":"2024-05-22 06:06:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4458517/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4458517/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58074950,"identity":"14eeb75d-90aa-48d9-bc38-11ff2bb082a1","added_by":"auto","created_at":"2024-06-10 21:03:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":873286,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4458517/v1_covered_708099e0-0730-4c50-9657-e2b8bff385e6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stock price prediction with SCA-LSTM network and Statistical model ARIMA-GARCH","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 market, Sin-Cosine algorithm, Long Short-Term Memory, Prediction","lastPublishedDoi":"10.21203/rs.3.rs-4458517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4458517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForecasting the stock market is one of the most challenging things for investors to do to increase their profits. 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