The use of recursive partitioning to build a financial distress prediction for JSE listed companies

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dc.contributor.advisorVan Rensburg, Paulen_ZA
dc.contributor.authorSmit, Candiceen_ZA
dc.date.accessioned2016-07-22T13:22:17Z
dc.date.available2016-07-22T13:22:17Z
dc.date.issued2016en_ZA
dc.description.abstractThe financial crises of 2008 increased the focus around financial distress and even more so on predicting financially distressed companies prior to the fact. This research paper investigates using recursive partitioning to predict financially distressed companies on the Johannesburg Stock Exchange, taking different business cycle periods into account over the time period 1997-2014. The updated as well as longer time period over which the analysis is conducted distinguishes this research paper from prior research. This paper employs both the CART and CHAID algorithm and obtains financially distressed prediction models which have a higher correct classification rate than chance alone and prior literature in South Africa. This paper also makes use of a matched data sample approach and the manner in which missing data is addressed makes a valuable contribution to financial distress prediction research. Furthermore, support is found for prior literature in that financial variables are statistically significant in predicting financial distress.en_ZA
dc.identifier.apacitationSmit, C. (2016). <i>The use of recursive partitioning to build a financial distress prediction for JSE listed companies</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax. Retrieved from http://hdl.handle.net/11427/20633en_ZA
dc.identifier.chicagocitationSmit, Candice. <i>"The use of recursive partitioning to build a financial distress prediction for JSE listed companies."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax, 2016. http://hdl.handle.net/11427/20633en_ZA
dc.identifier.citationSmit, C. 2016. The use of recursive partitioning to build a financial distress prediction for JSE listed companies. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Smit, Candice AB - The financial crises of 2008 increased the focus around financial distress and even more so on predicting financially distressed companies prior to the fact. This research paper investigates using recursive partitioning to predict financially distressed companies on the Johannesburg Stock Exchange, taking different business cycle periods into account over the time period 1997-2014. The updated as well as longer time period over which the analysis is conducted distinguishes this research paper from prior research. This paper employs both the CART and CHAID algorithm and obtains financially distressed prediction models which have a higher correct classification rate than chance alone and prior literature in South Africa. This paper also makes use of a matched data sample approach and the manner in which missing data is addressed makes a valuable contribution to financial distress prediction research. Furthermore, support is found for prior literature in that financial variables are statistically significant in predicting financial distress. DA - 2016 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2016 T1 - The use of recursive partitioning to build a financial distress prediction for JSE listed companies TI - The use of recursive partitioning to build a financial distress prediction for JSE listed companies UR - http://hdl.handle.net/11427/20633 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/20633
dc.identifier.vancouvercitationSmit C. The use of recursive partitioning to build a financial distress prediction for JSE listed companies. [Thesis]. University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax, 2016 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/20633en_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 and Risk Managementen_ZA
dc.titleThe use of recursive partitioning to build a financial distress prediction for JSE listed companiesen_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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