Cost-Efficient Asset Allocation: Graph-Based Machine Learning for Dynamic Portfolio Rebalancing.

Cost-Efficient Asset Allocation: Graph-Based Machine Learning for Dynamic Portfolio Rebalancing. | 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 Cost-Efficient Asset Allocation: Graph-Based Machine Learning for Dynamic Portfolio Rebalancing. Diego Vallarino This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5925223/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 This research introduces a novel approach to portfolio rebalancing by integrating Graph Neural Networks (GNNs) with Dijkstra's algorithm to optimize transaction costs in financial markets. GNNs are trained on historical stock data from major technology companies to predict future transaction costs, capturing complex dependencies between assets and market conditions. These predicted costs are then embedded as edge weights in financial asset graphs, enabling a dynamic representation of transaction expenses within the portfolio structure. Using this enriched financial network, Dijkstra’s algorithm is applied to determine the most cost-efficient paths for asset capital reallocation. By leveraging this hybrid framework, portfolio managers can systematically identify low-cost trading routes, reducing slippage and improving execution efficiency, particularly in high-frequency trading environments. Empirical results demonstrate that this approach significantly minimizes transaction costs compared to traditional rebalancing strategies, highlighting the synergy between machine learning and graph-based optimization in financial decision-making. The study underscores the potential of AI-driven portfolio management techniques in enhancing capital efficiency and reducing execution risk. JEL: C61, G11, C63, G17, C45. Portfolio Optimization Transaction Costs Graph Neural Networks (GNN) Dijkstra's Algorithm Pathfinding Algorithms 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-5925223","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":409515948,"identity":"fcd9b4d4-fb98-4547-84ff-791d5b715676","order_by":0,"name":"Diego Vallarino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYDCCw1Cajx1IJDAwyIE4Bx4Qo4WNGaLFGKwlAZ+WA8hagCCxgQGiFyfgO858+MUHhsPybMzMzz483GGTPj/s8EOgLXZyug3YtUgeZkuznMFw2LCNmc14RuKZtNyNt9MMgFqSjc0OYNdicJjHzJiHIY2xjRnojcS2w7kbZyeAtBxI3IZTC/83kBb7Nmb2zyAt6Yaz0z8Q0MLD/JiHwSaxjZkHbEuCvHQOfluAfjFjnGFgkwzUUgzUkma4QTqn4ECCAW6/8J0//PjDhwoJ23729s2MP9ts5OVnp28GitjJ4dICBGwSDAbITgWrNMCuFgqYP6Bw5Rvwqh4Fo2AUjIIRCADD2VtEI8J/2wAAAABJRU5ErkJggg==","orcid":"","institution":"EGADE Business School","correspondingAuthor":true,"prefix":"","firstName":"Diego","middleName":"","lastName":"Vallarino","suffix":""}],"badges":[],"createdAt":"2025-01-29 15:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5925223/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5925223/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79351139,"identity":"db8edce1-c8d1-4616-be6a-696259a76e8f","added_by":"auto","created_at":"2025-03-27 10:23:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":390556,"visible":true,"origin":"","legend":"","description":"","filename":"DynamicPortfolioOptimizationwithGNN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5925223/v1_covered_f4038ec6-e5fd-4cbd-94d1-b97b80cf0aa2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost-Efficient Asset Allocation: Graph-Based Machine Learning for Dynamic Portfolio Rebalancing.","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":"Portfolio Optimization, Transaction Costs, Graph Neural Networks (GNN), Dijkstra's Algorithm, Pathfinding Algorithms","lastPublishedDoi":"10.21203/rs.3.rs-5925223/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5925223/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research introduces a novel approach to portfolio rebalancing by integrating Graph Neural Networks (GNNs) with Dijkstra's algorithm to optimize transaction costs in financial markets. 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