Predicting Financial Market Crises using Multilayer Network Analysis and LSTM-based Forecasting of Spillover Effects

Predicting Financial Market Crises using Multilayer Network Analysis and LSTM-based Forecasting of Spillover Effects | 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 Predicting Financial Market Crises using Multilayer Network Analysis and LSTM-based Forecasting of Spillover Effects Mahdi Kohan Sefidi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6678634/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 Financial crises often occur without warning, yet markets leading up to these events display increasing volatility and complex interdependencies across multiple sectors. This study proposes a novel approach to predicting market crises by combining multilayer network analysis with Long Short-Term Memory (LSTM) models, using Granger causality to capture within-layer connections and Random Forest to model interlayer relationships. Specifically, we utilize Granger causality to model the temporal dependencies between market variables within individual layers, such as asset prices, trading values, and returns. To represent the interactions between different market variables across sectors, we apply Random Forest to model the interlayer connections, capturing the spillover effects between these features. The LSTM model is then trained to predict market instability and potential crises based on the dynamic features of the multilayer network. Our results demonstrate that this integrated approach, combining Granger causality, Random Forest, and LSTM, significantly enhances the accuracy of market crisis prediction, outperforming traditional forecasting models. This methodology provides a powerful tool for financial institutions and policymakers to better monitor systemic risks and take proactive measures to mitigate financial crises. Finance Financial crises Multilayer network analysis Spillover effects 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-6678634","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457488637,"identity":"c8648d01-68f2-4d7a-a780-d7d468ef4c0e","order_by":0,"name":"Mahdi Kohan Sefidi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie3Rv0vDQBTA8RcCL0varIGA/gWCUghOzb9yj0CnIIIgGTKkFG6SzhWk/Rfikjly8Lr4B5wgSCk6OwWn0vhjKJLUjg73Xd5wfLh3HIDJ9C9DsL+m/ZD7IhVi5+AvgtSQx4bg4USAb8kd0tVJjrS+zJ4jD+2xXs1HF95i8lpANoR+ULWSsEI1mPEb3Uprck5lcuUzhho4BuyLDuLIwEUlTl/G0qcypZyhIVgBuu2LfZONiiK2GnKX0oKdWsNmH0EOelJZBX6SPKGC3VBbcg9RGA96U0Uzbt4ieET3nFxrmsZuJ1nKs7Vbq8iTzurpI4tprpalfq+HR8c37eTnU34nADruMJlMJtMhbQHjLFycGsANQwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8492-0901","institution":"khatam university","correspondingAuthor":true,"prefix":"","firstName":"Mahdi","middleName":"Kohan","lastName":"Sefidi","suffix":""}],"badges":[],"createdAt":"2025-05-16 08:08:58","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6678634/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6678634/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83006074,"identity":"acf4899c-47a3-4daf-bc21-ed5e5033a296","added_by":"auto","created_at":"2025-05-19 03:15:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":532207,"visible":true,"origin":"","legend":"","description":"","filename":"netfineconomicspaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6678634/v1_covered_6747520a-c18c-4636-968d-4ec4415474f1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePredicting Financial Market Crises using Multilayer Network Analysis and LSTM-based Forecasting of Spillover Effects\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Khatam University","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":"Financial crises, Multilayer network analysis, Spillover effects","lastPublishedDoi":"10.21203/rs.3.rs-6678634/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6678634/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFinancial crises often occur without warning, yet markets leading up to these events display increasing volatility and complex interdependencies across multiple sectors. 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