[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117411-en":3,"doc-seo-117411-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117411,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning and company failure prediction - Evidence from South Africa","Machine learning can mitigate limitations of traditional statistical models for predicting company failure, yet evidence in emerging markets remains limited. This study evaluates prediction accuracy in South Africa by applying eight fundamental machine learning techniques alongside the traditional logit analysis. Using 56 Johannesburg Stock Exchange listed companies from 2010–2021, with failure labels for 28 firms, performance is assessed up to three years ahead. Results show that two machine learning algorithms outperform logit in some years, while suitability varies across years.","Acta Commercii-Independent Research Journal in the Management Sciences  \nISSN: (Online) 1684-1999,(Print) 2413-1903  \nPage 1 of 11  \n Origina~~l Research ~~  \nMachine learning and company failure prediction:  \nEvidence from South Africa  \nAuthors:  \nNicolene Wesson1  Dewald Mienie1  Anthea Myatt1   \nAffiliations:  \n1Stellenbosch Business School, Faculty of Economic and Management Sciences, Stellenbosch University, Bellville, South Africa  \nCorresponding author:  \nNicolene Wesson, [nwesson@sun.ac.za](nwesson@sun.ac.za)  \nDates:  \nReceived: 26 Nov. 2024  \nAccepted: 11 Feb. 2025  \nPublished: 19 Mar. 2025  \nHow to cite this article:  \nWesson, N., Mienie, D. & Myatt, A., 2025,‘Machine learning and company failure prediction: Evidence from South Africa’, Acta Commercii 25(1), a1365 . [https://doi](https://doi). org/10.4102/ac.v25i1.1365  \nCopyright:  \n© 2025. The Authors. Licensee: AOSIS. This work is licensed under the Creative Commons Attribution License.  \nRead online:  \nScan this QR code with your smart phone or mobile device to read online.  \nOrientation: Machine learning has advanced substantially over the past two decades and exhibits the potential to overcome the limitations of traditional statistical methods for predicting company failure. While extensive research has been conducted globally to predict company failure using machine learning, these techniques are relatively unexplored in an emerging market context.  \nResearch purpose: The accuracy of company failure prediction was assessed when applying an array of fundamental machine learning algorithms in South Africa.  \nMotivation for the study: Given the significant social and economic impact of company failures, insights are provided into appropriate company failure prediction techniques in an emerging market context.  \nResearch design, approach and method: The study sample consisted of 56 companies (of which 28 were classified as failed) that were listed on the Johannesburg Stock Exchange during 2010–2021. Company failure prediction up to 3 years in advance was measured by applying eight fundamental machine learning techniques and the traditional logit analysis statistical method.  \nMain findings: Two machine learning algorithms outperformed the traditional method in some years. Furthermore, not all machine learning techniques were suited to predict company failure in all years.  \nPractical implications: Machine learning is not necessarily more accurate than traditional statistical methods. Applying the appropriate technique in company failure prediction models requires a clear understanding of the available methodologies for the task at hand.  \nContribution: This study provides a benchmark for predictive accuracy in the South African context and lays the ground for a more sophisticated ensemble of methods to assess the accuracy of machine learning.  \nKeywords: company failure; failure prediction; logit analysis; machine learning; South Africa.  \nIntroduction  \nCompany failures have a significant social and economic impact (Huang et al. 2008; Perboli & Arabnezhad 2021). The ability to predict company failure is therefore crucial to all stakeholders, both internal and external to a company. Although each stakeholder has their own role and agenda, all are interested in the best possible state of health of the company (Huang & Yen 2019; Huang et al. 2008; Kim, Cho & Ryu 2020; Naidoo & Du Toit 2007; Perboli & Arabnezhad 2021; Qu et al. 2019) .  \nHistorically, company failure prediction was mainly performed by means of statistical methods such as multiple discriminant analysis and logit analysis (Aziz & Humayon 2006; Sewpersadh 2020; Tsai 2014). The seminal works on company failure prediction of Beaver (1966) and Altman (1968) applied univariant and multiple discriminant analysis, respectively. About a decade later, Ohlson (1980) introduced the logit analysis method, which has emerged as the dominant statistical model in company bankruptcy literature (Ding et al. 2023; Jone","cbCaiddx1o2WWiMx","https://ap.wps.com/l/cbCaiddx1o2WWiMx","pdf",974396,1,11,"English","en",105,"# Orientation\n# Research purpose\n# Motivation for the study\n# Research design, approach and method\n# Main findings\n# Practical implications\n# Contribution\n# Introduction","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study assesses how accurately different machine learning algorithms can predict company failure in South Africa compared with traditional logit analysis.\"},{\"question\":\"How is company failure prediction measured in the research?\",\"answer\":\"Prediction accuracy is measured using eight fundamental machine learning techniques and the traditional logit analysis, for company failure up to three years in advance.\"},{\"question\":\"What do the main findings show about machine learning vs. traditional methods?\",\"answer\":\"Two machine learning algorithms outperform the traditional method in some years, but not all techniques are suitable for predicting failure across every year.\"}]","Machine learning and company failure prediction - 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