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The use of recursive partitioning to build a financial distress prediction for JSE listed companies
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,...
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| Format: | Thesis |
| Language: | English |
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Department of Finance and Tax
2016
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| _version_ | 1867613157951799296 |
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| access_status_str | Open Access |
| author | Smit, Candice |
| author2 | Van Rensburg, Paul |
| author_browse | Smit, Candice Van Rensburg, Paul |
| author_facet | Van Rensburg, Paul Smit, Candice |
| author_sort | Smit, Candice |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/20633 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:41.113Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Department of Finance and Tax |
| publisherStr | Department of Finance and Tax |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/20633 The use of recursive partitioning to build a financial distress prediction for JSE listed companies Smit, Candice Van Rensburg, Paul Financial and Risk Management 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. 2016-07-22T13:22:17Z 2016-07-22T13:22:17Z 2016 Master Thesis Masters MCom http://hdl.handle.net/11427/20633 eng application/pdf Department of Finance and Tax Faculty of Commerce University of Cape Town |
| spellingShingle | Financial and Risk Management Smit, Candice The use of recursive partitioning to build a financial distress prediction for JSE listed companies |
| thesis_degree_str | Master's |
| title | The use of recursive partitioning to build a financial distress prediction for JSE listed companies |
| title_full | The use of recursive partitioning to build a financial distress prediction for JSE listed companies |
| title_fullStr | The use of recursive partitioning to build a financial distress prediction for JSE listed companies |
| title_full_unstemmed | The use of recursive partitioning to build a financial distress prediction for JSE listed companies |
| title_short | The use of recursive partitioning to build a financial distress prediction for JSE listed companies |
| title_sort | use of recursive partitioning to build a financial distress prediction for jse listed companies |
| topic | Financial and Risk Management |
| url | http://hdl.handle.net/11427/20633 |
| work_keys_str_mv | AT smitcandice theuseofrecursivepartitioningtobuildafinancialdistresspredictionforjselistedcompanies AT smitcandice useofrecursivepartitioningtobuildafinancialdistresspredictionforjselistedcompanies |