Quantitative Portfolio Optimization Framework with Market Regimes Classification, Probabilistic Time Series Forecasting, and Hidden Markov Models

Quantitative Portfolio Optimization Framework with Market Regimes Classification, Probabilistic Time Series Forecasting, and Hidden Markov Models | 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 Quantitative Portfolio Optimization Framework with Market Regimes Classification, Probabilistic Time Series Forecasting, and Hidden Markov Models Marcus Oliveira, Gilson Costa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6173427/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2025 Read the published version in Digital Finance → Version 1 posted 9 You are reading this latest preprint version Abstract This paper introduces a three-step methodology for optimizing an investment portfolio. The first step involves selecting the best performing Exchange-Traded Funds (ETFs) from a comprehensive list of assets for each phase of the market cycle. The second step builds on the first by promoting allocation through the maximization of risk-adjusted returns under uncertainty, using a probabilistic framework. The third step employs a Hidden Markov Model (HMM) approach to model the dynamics of asset returns and volatility, allowing the use of the Mean-Variance framework to optimize allocation. The objective is to propose a framework capable of outperforming the S\&P 500 benchmark by achieving higher risk-adjusted returns, as confirmed by experimental results, thereby contributing to efficient capital allocation. The third stage, which involves HMM-based allocation optimization, also proves to be very effective in redefining asset weights in stock indices, achieving good performance when applied to IBOVESPA, the main equity index in Brazil. In particular, all proposed steps individually contribute to improving portfolio performance and can be used together or separately. The framework is sufficiently generic to accommodate various time series forecasting methods with different levels of complexity, as well as enables integration with fundamentalist approaches. JEL Classification: C15 , G11 Portfolio Optimization Quantitative Methods Market Regimes Hidden Markov Models. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2025 Read the published version in Digital Finance → Version 1 posted Editorial decision: Revision requested 09 May, 2025 Reviews received at journal 09 May, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 05 Apr, 2025 Reviewers invited by journal 10 Mar, 2025 Editor assigned by journal 08 Mar, 2025 Submission checks completed at journal 06 Mar, 2025 First submitted to journal 06 Mar, 2025 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-6173427","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431430721,"identity":"1d29a473-c988-4969-a232-5fd85779e343","order_by":0,"name":"Marcus Oliveira","email":"data:image/png;base64,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","orcid":"","institution":"Petróleo Brasileiro S.A. - PETROBRAS","correspondingAuthor":true,"prefix":"","firstName":"Marcus","middleName":"","lastName":"Oliveira","suffix":""},{"id":431430722,"identity":"82f20c9e-4675-4191-a1c5-e125c8b46108","order_by":1,"name":"Gilson Costa","email":"","orcid":"","institution":"Rio de Janeiro State University - UERJ","correspondingAuthor":false,"prefix":"","firstName":"Gilson","middleName":"","lastName":"Costa","suffix":""}],"badges":[],"createdAt":"2025-03-06 21:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6173427/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6173427/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42521-025-00153-4","type":"published","date":"2025-08-13T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89310684,"identity":"f99d50b5-7652-4120-a3b5-c0f268383f0c","added_by":"auto","created_at":"2025-08-18 16:09:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2609258,"visible":true,"origin":"","legend":"","description":"","filename":"PaperOliveiraCostaPortOpt.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6173427/v1_covered_a88c8f5d-0e70-4aca-9e88-0b8d663908a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative Portfolio Optimization Framework with Market Regimes Classification, Probabilistic Time Series Forecasting, and Hidden Markov Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"digital-finance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dfin","sideBox":"Learn more about [Digital Finance](http://link.springer.com/journal/42521)","snPcode":"42521","submissionUrl":"https://submission.nature.com/new-submission/42521/3","title":"Digital Finance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Portfolio Optimization, Quantitative Methods, Market Regimes, Hidden Markov Models.","lastPublishedDoi":"10.21203/rs.3.rs-6173427/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6173427/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper introduces a three-step methodology for optimizing an investment portfolio. 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