Predicting corporate turnaround of listed companies in South Africa

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dc.contributor.advisorWest, Darronen_ZA
dc.contributor.authorChin, Chu-Kuoen_ZA
dc.date.accessioned2017-01-23T07:55:32Z
dc.date.available2017-01-23T07:55:32Z
dc.date.issued2016en_ZA
dc.description.abstractCorporate turnaround, in comparison to financial distress, is not substantially researched either internationally or locally in South Africa. This study attempts to explore this area of research by developing models that identify financially distressed companies with a potential for turnaround. This analysis examines listed companies on both the JSE Securities Exchange ('JSE') and Alternative Exchange ('AltX') for the period 2007 to 2014 by using available data from iNet BFA. The financial distress model, Taffler's Z-score, is used to identify companies that fall within the sample. Multiple linear discriminant models with interaction variables are used as part of the process to derive the turnaround models. The first model shows that efficiency is a key driver for a successful turnaround. The second model reveals that JSE-listed companies are more likely to survive than AltX companies. This study contributes to the existing research by identifying significant factors for corporate turnaround and summarizing its findings in a practical manner.en_ZA
dc.identifier.apacitationChin, C. (2016). <i>Predicting corporate turnaround of listed companies in South Africa</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax. Retrieved from http://hdl.handle.net/11427/22915en_ZA
dc.identifier.chicagocitationChin, Chu-Kuo. <i>"Predicting corporate turnaround of listed companies in South Africa."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax, 2016. http://hdl.handle.net/11427/22915en_ZA
dc.identifier.citationChin, C. 2016. Predicting corporate turnaround of listed companies in South Africa. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Chin, Chu-Kuo AB - Corporate turnaround, in comparison to financial distress, is not substantially researched either internationally or locally in South Africa. This study attempts to explore this area of research by developing models that identify financially distressed companies with a potential for turnaround. This analysis examines listed companies on both the JSE Securities Exchange ('JSE') and Alternative Exchange ('AltX') for the period 2007 to 2014 by using available data from iNet BFA. The financial distress model, Taffler's Z-score, is used to identify companies that fall within the sample. Multiple linear discriminant models with interaction variables are used as part of the process to derive the turnaround models. The first model shows that efficiency is a key driver for a successful turnaround. The second model reveals that JSE-listed companies are more likely to survive than AltX companies. This study contributes to the existing research by identifying significant factors for corporate turnaround and summarizing its findings in a practical manner. DA - 2016 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2016 T1 - Predicting corporate turnaround of listed companies in South Africa TI - Predicting corporate turnaround of listed companies in South Africa UR - http://hdl.handle.net/11427/22915 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/22915
dc.identifier.vancouvercitationChin C. Predicting corporate turnaround of listed companies in South Africa. [Thesis]. University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax, 2016 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/22915en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Finance and Taxen_ZA
dc.publisher.facultyFaculty of Commerceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherFinancial Managementen_ZA
dc.subject.otherRisk Managementen_ZA
dc.titlePredicting corporate turnaround of listed companies in South Africaen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMComen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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