Application of Explainable Artificial Intelligence to model the influence of firm-specific factors on stock performance in sub-Saharan Africa during COVID-19.

Application of Explainable Artificial Intelligence to model the influence of firm-specific factors on stock performance in sub-Saharan Africa during COVID-19. | 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 Application of Explainable Artificial Intelligence to model the influence of firm-specific factors on stock performance in sub-Saharan Africa during COVID-19. Mbongiseni Ncube, Mabutho Sibanda, Frank R Matenda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4308946/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 The COVID-19 pandemic has brought forth a novel and challenging investment environment for stock markets in both developed and emerging economies. This study delves into the under-researched field of factor investing, analysing the influence of firm-specific factors on stock performance in sub-Saharan Africa during the pandemic in four selected stock exchanges. Using explainable artificial intelligence (XAI), we analysed the cross-sectional data of firms listed on these exchanges in both the pre-pandemic and pandemic periods to identify the key factors driving stock returns. Our findings reveal that quality factors such as high profitability and low leverage emerged as the dominant drivers of resilience during the pandemic. This contrasts with the pre-pandemic scenario, where traditional factors, such as size, momentum, and value, were the primary determinants. We also find that during the pandemic, small-cap stocks generally outperformed large-cap stocks on larger stock exchanges, whereas large-cap stocks outperformed on smaller stock exchanges. The pandemic also ushered in a shift in the norm, as we found that highly volatile stocks outperformed low-volatility stocks and stocks classified as winners in the pre-pandemic period experienced a momentum crash. Further analysis explores the potential of factor-based portfolios. We demonstrate that portfolios constructed based on the identified key factors consistently outperform their market-cap weighted counterparts across all exchanges, both pre- and post-pandemic. This research highlights the importance of focusing on firm-specific factors to construct more resilient investment portfolios and potentially attain superior returns, even during times of economic turbulence. Finance Artificial Intelligence and Machine Learning Econometrics sub-Saharan Africa Stock markets COVID-19 pandemic Explainable Artificial Intelligence Factor investing 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. 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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-4308946","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294330270,"identity":"cdb97de8-1047-4b50-a35c-d014e62a9a9d","order_by":0,"name":"Mbongiseni Ncube","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYDACCRBhAMTMYO4BBn4GBjYStUg2EKUFAQ4wGBwgoEV3dvOxh18K6uTM2RnYHvxsuyNnfCP52YMPFQzy/GIHsGoxu3Ms3VjG4LCxZTMDu2Fv2zNjsxtp5oYzzjAYzpydgF3LjRwzaQmDA4kbDjOwSTO2HU7cdiPBTJq3jSHB4DZeLXX1MC31m2ekfyOoRfKDAXOCAVRLgoFEDgFb7hxLk2YwOGy44TBjm2TPuWdAb7wpk5xxRgK3X243H5P88adO3uD84WMSP8ruyPO3p2+T+FBhI88vjV0LCDDzgCnGBghXAKxSAodiCGD8gcLlP4BX9SgYBaNgFIw8AADXE13SLKFQ6wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Kwazulu- Nafal","correspondingAuthor":true,"prefix":"","firstName":"Mbongiseni","middleName":"","lastName":"Ncube","suffix":""},{"id":294330618,"identity":"25b1e38a-2abd-40e6-a9e6-155c8f8a40b2","order_by":1,"name":"Mabutho Sibanda","email":"","orcid":"","institution":"University of Kwazulu Natal","correspondingAuthor":false,"prefix":"","firstName":"Mabutho","middleName":"","lastName":"Sibanda","suffix":""},{"id":294330619,"identity":"93a6552d-1073-48c8-a376-1d917408464a","order_by":2,"name":"Frank R Matenda","email":"","orcid":"","institution":"University of Kwazulu -Natal","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"R","lastName":"Matenda","suffix":""}],"badges":[],"createdAt":"2024-04-23 03:14:06","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4308946/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4308946/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55198537,"identity":"d33de3d0-45a9-432b-a5cc-c662e70a4572","added_by":"auto","created_at":"2024-04-24 01:42:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2086208,"visible":true,"origin":"","legend":"","description":"","filename":"FactorInvestingpaper24.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4308946/v1_covered_1d30573e-28f5-404d-9db0-c7ccc96c9acd.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e Application of Explainable Artificial Intelligence to model the influence of firm-specific factors on stock performance in sub-Saharan Africa during COVID-19.\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Kwazulu Natal","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":"sub-Saharan Africa, Stock markets, COVID-19 pandemic, Explainable Artificial Intelligence, Factor investing","lastPublishedDoi":"10.21203/rs.3.rs-4308946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4308946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eThe COVID-19 pandemic has brought forth a novel and challenging investment environment for stock markets in both developed and emerging economies. 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