A data-driven machine learning framework for forecasting institutional dynamics and structural breaks: Evidence from ASEAN economies

A data-driven machine learning framework for forecasting institutional dynamics and structural breaks: Evidence from ASEAN economies | 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 Article A data-driven machine learning framework for forecasting institutional dynamics and structural breaks: Evidence from ASEAN economies Xiong luoqin, Han yue, Lu guangsheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8639367/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 Institutional conditions are central to investment risk and development outcomes in emerging economies, yet they are typically assessed through retrospective indicators that offer limited insight into how institutional environments evolve or when they become unstable. This study examines whether institutional dynamics display systematic patterns of short-horizon predictability, and where the boundaries of such predictability lie. Drawing on a panel dataset covering ASEAN economies from 2009 to 2023, we construct a composite Business Environment Index integrating governance quality, regulatory conditions, and policy uncertainty. Using a strict rolling out-of-sample design, we evaluate the one-year-ahead predictability of institutional conditions with several machine-learning models and benchmark econometric specifications. Three core findings emerge. First, under relatively stable conditions, institutional dynamics exhibit pronounced path dependence, allowing historical information to generate meaningful short-term forecasts with low absolute errors. Second, forecast performance deteriorates sharply during periods of major disruption, including political crises and the COVID-19 pandemic, indicating clear limits to data-driven predictability when institutional trajectories are subject to structural breaks. Third, investment-related variables, particularly foreign direct investment flows, contain forward-looking information that precedes changes in conventional governance indicators, suggesting that market behavior embeds early signals of institutional stress. Together, these results demonstrate that institutional environments are neither fully predictable nor entirely opaque. Instead, they operate across distinct regimes in which predictability is conditional on the stability of underlying political and economic structures. By clarifying both the scope and the limits of institutional forecasting, this study contributes to emerging work on institutional risk monitoring and offers a data-driven perspective on how structural breaks in governance environments may be anticipated. JEL Classification : C53; C45; F21; O17; P48 Business and commerce/Economics Social science/Economics Business and commerce/Finance Social science/Finance Physical sciences/Mathematics and computing Institutional dynamics Predictive analytics Structural breaks Machine learning Early warning systems ASEAN 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. 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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-8639367","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":600225526,"identity":"fd93bdfd-4c15-484e-a165-9daea532e231","order_by":0,"name":"Xiong luoqin","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Xiong","middleName":"","lastName":"luoqin","suffix":""},{"id":600225531,"identity":"d7f3829d-f729-4d65-8bd2-d89d56c91f96","order_by":1,"name":"Han yue","email":"","orcid":"","institution":"Yunnan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"yue","suffix":""},{"id":600225532,"identity":"65872a2c-b9d8-4214-a1ce-03e7476df3e6","order_by":2,"name":"Lu guangsheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBACPhDxAcI2IE4LGxAzziBZCzMPaVok0i9/tm27k9jA3rxNgqHmDjFacsqkc9ueJTbwHCuTYDj2jCgtacy5bYcTGyRyzCQYGw4TpSX5syVIi/wborWkH5BmBNvCQ6wWnjdskj3nDhu38aQVWyQcI0ILP3v64w8/yg7L9rMf3njjQw0RWhgYeAwYGNkgEcSQQIwGBgb2BwwMf4hTOgpGwSgYBSMUAACElzWUJHC8kAAAAABJRU5ErkJggg==","orcid":"","institution":"Yunnan University","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"guangsheng","suffix":""}],"badges":[],"createdAt":"2026-01-19 12:13:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8639367/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8639367/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108806948,"identity":"5dcde2ef-b994-4762-b075-bad2cfa94dc7","added_by":"auto","created_at":"2026-05-08 15:29:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":835265,"visible":true,"origin":"","legend":"","description":"","filename":"AdatadrivenmachinelearningframeworkforforecastinginstitutionaldynamicsandstructuralbreaksEvidencefromASEANeconomies.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8639367/v1_covered_cf1cf5a9-af40-433b-8d0c-5f0e297329b5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A data-driven machine learning framework for forecasting institutional dynamics and structural breaks: Evidence from ASEAN economies","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":"Institutional dynamics, Predictive analytics, Structural breaks, Machine learning, Early warning systems, ASEAN","lastPublishedDoi":"10.21203/rs.3.rs-8639367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8639367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInstitutional conditions are central to investment risk and development outcomes in emerging economies, yet they are typically assessed through retrospective indicators that offer limited insight into how institutional environments evolve or when they become unstable. This study examines whether institutional dynamics display systematic patterns of short-horizon predictability, and where the boundaries of such predictability lie.\u003c/p\u003e\n\u003cp\u003eDrawing on a panel dataset covering ASEAN economies from 2009 to 2023, we construct a composite Business Environment Index integrating governance quality, regulatory conditions, and policy uncertainty. Using a strict rolling out-of-sample design, we evaluate the one-year-ahead predictability of institutional conditions with several machine-learning models and benchmark econometric specifications.\u003c/p\u003e\n\u003cp\u003eThree core findings emerge. First, under relatively stable conditions, institutional dynamics exhibit pronounced path dependence, allowing historical information to generate meaningful short-term forecasts with low absolute errors. Second, forecast performance deteriorates sharply during periods of major disruption, including political crises and the COVID-19 pandemic, indicating clear limits to data-driven predictability when institutional trajectories are subject to structural breaks. Third, investment-related variables, particularly foreign direct investment flows, contain forward-looking information that precedes changes in conventional governance indicators, suggesting that market behavior embeds early signals of institutional stress.\u003c/p\u003e\n\u003cp\u003eTogether, these results demonstrate that institutional environments are neither fully predictable nor entirely opaque. Instead, they operate across distinct regimes in which predictability is conditional on the stability of underlying political and economic structures. 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