Comparing Machine Learning and Traditional Statistical Methods for Gold Price Prediction

Comparing Machine Learning and Traditional Statistical Methods for Gold Price Prediction | 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 Comparing Machine Learning and Traditional Statistical Methods for Gold Price Prediction Richard Lesko This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6993888/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 Gold is as a chemical element with the symbol AU, which belongs to the metal chemical element group. However, this thesis examines gold from a financial rather than a chemical perspective. We can assume that gold was the first instrument used to store the value and for other exchanging purposes. Ever since, gold is an instrument that is still widely used to store value, its demand shifts when economic recessions or instability is expected in the future, which affects the price. It is still considered financial instrument that relatively stable and not volatile. Countries might use this instrument to prepare for upcoming recession or to simply store its cash resources in gold, hence its price plays important role when it comes to countries’ political or economic decisions. Knowing gold’s exact future price therefore plays crucial role for investment decisions. Knowing the exact future price is of course impossible, but having a reliable estimate may be very useful for policymakers or investors. Machine Learning and Traditional Statistical methods are well-suited for estimating future gold price. Traditional Statistical methods are grounded by statistical and mathematical theory, machine learning models can identify non-linear patterns. By conducting predictive analysis using both type of models, this thesis comprehensively explains selected predictive models, identifies their pros and cons and compares performance of selected models 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-6993888","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481111227,"identity":"558422cd-6fdc-4eac-b477-08f84c1d70b4","order_by":0,"name":"Richard Lesko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACZhBRwMAgwcB8AMiSkCFSiwFIC1sCSAsPkVaBtfAYgJiEtZi3sz988MHAxl6yvefzqxs1FjwM7IePbsCnReYwj7HhDIO0xNk8Z7dZ5xwDOownLe0GPi0SzDxs0jwGhxPkJHK3GeewAbVI8JgR0ML+DKjlv72cRM4z45x/RGlhMANqOcA4WyKH+XFuG1FawH5JTpzZc8yMObdPgoeNoF/4jwNDrMLOXuJ48+PPOd/q5PjZDx/DqwUZsEmASWKVgwDzB1JUj4JRMApGwcgBAHY3PGQM5AirAAAAAElFTkSuQmCC","orcid":"","institution":"Aarhus University","correspondingAuthor":true,"prefix":"","firstName":"Richard","middleName":"","lastName":"Lesko","suffix":""}],"badges":[],"createdAt":"2025-06-27 18:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6993888/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6993888/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86203960,"identity":"964e2344-2ee0-4d6d-9360-ba49b7e658df","added_by":"auto","created_at":"2025-07-08 02:20:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2587167,"visible":true,"origin":"","legend":"","description":"","filename":"Publicationpaperrevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6993888/v1_covered_6fdb2dc2-e083-494c-91a3-fc5ab437e002.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparing Machine Learning and Traditional Statistical Methods for Gold Price Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-6993888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6993888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGold is as a chemical element with the symbol AU, which belongs to the metal chemical element group. However, this thesis examines gold from a financial rather than a chemical perspective. We can assume that gold was the first instrument used to store the value and for other exchanging purposes. Ever since, gold is an instrument that is still widely used to store value, its demand shifts when economic recessions or instability is expected in the future, which affects the price. It is still considered financial instrument that relatively stable and not volatile. Countries might use this instrument to prepare for upcoming recession or to simply store its cash resources in gold, hence its price plays important role when it comes to countries\u0026rsquo; political or economic decisions. Knowing gold\u0026rsquo;s exact future price therefore plays crucial role for investment decisions. Knowing the exact future price is of course impossible, but having a reliable estimate may be very useful for policymakers or investors. 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