Solving the Stock Option Forecast problem by a numerical method for the Black-Scholes Equation with Machine Learning Classification Model

Solving the Stock Option Forecast problem by a numerical method for the Black-Scholes Equation with Machine Learning Classification Model | 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 Solving the Stock Option Forecast problem by a numerical method for the Black-Scholes Equation with Machine Learning Classification Model Benjamin Jiang, Matthieu Durieux, Kirill V. golubnichiy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4583952/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 We proposed classification models that utilize the result from the Quasi-Reversibility Method, which solves the Black-Scholes equation to forecast the option prices one day in advance. Combining the minimizer from QRM with our machine learning classifications, we can classify the option as an increase or decrease in value. Based on the different classifications of the options, we can apply various trading strategies which we aim to figure out ways to improve the results from QRM's extrapolations. To further test the viability of our model, we collected 23548 options data from the real-world market for our model, and we will then feed in the data along with the minimizer from QRM to form decision trees and random forests, which we will later test for accuracy, precision, and recall. Computational Mathematics Quasi Reversibility Black-Scholes equation Random forest Machine Learning 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. 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-4583952","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314647859,"identity":"893b969f-2783-4ede-95be-f48d166f9ddf","order_by":0,"name":"Benjamin Jiang","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Jiang","suffix":""},{"id":314648048,"identity":"8350898f-7bf2-48ef-829e-c966fee64821","order_by":1,"name":"Matthieu Durieux","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Matthieu","middleName":"","lastName":"Durieux","suffix":""},{"id":314648051,"identity":"ea96c342-4655-4065-9b72-81a55e165ff7","order_by":2,"name":"Kirill V. golubnichiy","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-3067-7931","institution":"University of Calgary","correspondingAuthor":true,"prefix":"","firstName":"Kirill","middleName":"V.","lastName":"golubnichiy","suffix":""}],"badges":[],"createdAt":"2024-06-14 20:11:56","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4583952/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4583952/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58630553,"identity":"43add37d-a767-446e-9631-0caa6b4fc357","added_by":"auto","created_at":"2024-06-19 05:33:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1136228,"visible":true,"origin":"","legend":"","description":"","filename":"paperBMK.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4583952/v1_covered_672ab120-d970-45c3-9005-11461f229f40.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSolving the Stock Option Forecast problem by a numerical method for the Black-Scholes Equation with Machine Learning Classification Model\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Calgary","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":"Quasi Reversibility, Black-Scholes equation, Random forest, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-4583952/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4583952/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe proposed classification models that utilize the result from the Quasi-Reversibility Method, which solves the Black-Scholes equation to forecast the option prices one day in advance. 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