An Explainable Modeling Method for Analyzing ESG–Finance Interactions in Strategic Decision-Making

An Explainable Modeling Method for Analyzing ESG–Finance Interactions in Strategic Decision-Making | 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 An Explainable Modeling Method for Analyzing ESG–Finance Interactions in Strategic Decision-Making Thanh Lam Nguyen, Dinh Phung Tran, Chanda Rani Debi, Abdul Kadar Muhammad Masum, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8267526/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 Environmental, Social, and Governance (ESG) is a well-established criterion for corporate social performance, but its financial materiality remains elusive. This study aims to unpack the nuanced and often contentious relationship between ESG and corporate financial performance, focusing not just on if ESG matters but more critically, on how, when, and for whom it becomes a significant value driver. Using a Random Forest model and SHapley Additive exPlanations (SHAP), we analyze a large panel dataset of publicly listed firms across Asia, the United States, and Europe from 2017 to 2024. The results show that firms with high ESG ratings exhibited greater resilience during the COVID-19 crisis period, confirming ESG's role as a risk mitigation factor. Moreover, our findings refine stakeholder theory by showing that satisfying stakeholder interests is not uniformly rewarded; rather, its financial benefits are conditional on both industry context and existing financial strength. These findings provide a data-driven rationale for strategic ESG investments. Machine learning Explainable Artificial Intelligence Financial analytics Sustainable investing Performance assessment Strategic analysis 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-8267526","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569072076,"identity":"37f48468-f0b2-4df4-a50a-e61e3406e31e","order_by":0,"name":"Thanh Lam Nguyen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACPjBZYcPAwHyAgYGHGC1sIOLAmTQgK4EULQfbDpOihf102uMPbOcT+9kYGB+8bWNI3E5QC0/udoMDPLcTZ7YxMBvOBWrZ2UDQYbnbJA5I3E7ccL+BTZq3jcHY4AAhLfxvgVoMziXuP8bA/ps4LRIgWxIOJG5gY2BjBmqRI0IL0JYzB5KNZxxjbJacc06CsBZ+fqAtlf/sZPvbmA9+eFNmw0NQCxJgbAASEsSrHwWjYBSMglGAGwAAkgw9Uib81I0AAAAASUVORK5CYII=","orcid":"","institution":"Ho Chi Minh City University of Economics and Finance","correspondingAuthor":true,"prefix":"","firstName":"Thanh","middleName":"Lam","lastName":"Nguyen","suffix":""},{"id":569072078,"identity":"6c3c32bb-97e3-41ee-9e5f-fb807274a9aa","order_by":1,"name":"Dinh Phung Tran","email":"","orcid":"","institution":"Saigon University","correspondingAuthor":false,"prefix":"","firstName":"Dinh","middleName":"Phung","lastName":"Tran","suffix":""},{"id":569072080,"identity":"af3d5999-3cb1-4770-be44-21f8b2e72763","order_by":2,"name":"Chanda Rani Debi","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Chanda","middleName":"Rani","lastName":"Debi","suffix":""},{"id":569072081,"identity":"80c2fcb8-77fb-439d-8d80-9202c1d24c9c","order_by":3,"name":"Abdul Kadar Muhammad Masum","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"Kadar Muhammad","lastName":"Masum","suffix":""},{"id":569072083,"identity":"0ebda2bb-4136-42b7-938d-a6d57152e605","order_by":4,"name":"Md. 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Decision-Making","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":"Machine learning, Explainable Artificial Intelligence, Financial analytics, Sustainable investing, Performance assessment, Strategic analysis","lastPublishedDoi":"10.21203/rs.3.rs-8267526/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8267526/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Environmental, Social, and Governance (ESG) is a well-established criterion for corporate social performance, but its financial materiality remains elusive. This study aims to unpack the nuanced and often contentious relationship between ESG and corporate financial performance, focusing not just on if ESG matters but more critically, on how, when, and for whom it becomes a significant value driver. Using a Random Forest model and SHapley Additive exPlanations (SHAP), we analyze a large panel dataset of publicly listed firms across Asia, the United States, and Europe from 2017 to 2024. The results show that firms with high ESG ratings exhibited greater resilience during the COVID-19 crisis period, confirming ESG's role as a risk mitigation factor. Moreover, our findings refine stakeholder theory by showing that satisfying stakeholder interests is not uniformly rewarded; rather, its financial benefits are conditional on both industry context and existing financial strength. These findings provide a data-driven rationale for strategic ESG investments.","manuscriptTitle":"An Explainable Modeling Method for Analyzing ESG–Finance Interactions in Strategic Decision-Making","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-05 01:32:49","doi":"10.21203/rs.3.rs-8267526/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"968d4e88-ac12-437f-b795-5d40d32cdc8f","owner":[],"postedDate":"January 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-08T00:08:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-05 01:32:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8267526","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8267526","identity":"rs-8267526","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}