Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
A corporate failure prediction model for non-financial South African corporates incorporating best practices used by the credit industry
In the context of the current macroeconomic environment there is an expectation of an increase in South African non-financial corporate failure, where advance prediction thereof will become even more important. A number of South African non-financial corporate failures have occurred following the fi...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Finance and Tax
2016
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613359211282432 |
|---|---|
| access_status_str | Open Access |
| author | Rowlings, Douglas |
| author2 | Correia, Carlos |
| author_browse | Correia, Carlos Rowlings, Douglas |
| author_facet | Correia, Carlos Rowlings, Douglas |
| author_sort | Rowlings, Douglas |
| collection | Thesis |
| description | In the context of the current macroeconomic environment there is an expectation of an increase in South African non-financial corporate failure, where advance prediction thereof will become even more important. A number of South African non-financial corporate failures have occurred following the financial crisis. In addition, South Africa experienced a watershed moment with the first default on a non-financial corporate bond in 2013. At the same time, with the adoption of the International Financial Reporting Standards (IFRS) framework there have been significant advances in the quality of financial information which should improve its usage in predicting corporate failure. This study used the latest sample to date of listed South African non-financial corporates that met the definition of failure but limited the universe of financial information to that which was prepared under IFRS. At the same time, adjustments were made to the financial data based upon pre-selection of independent credit statistic variables most commonly used in ranking relative credit risk for non-financial corporates. Additionally, equity market price data was introduced into the model to add a forward-looking information consideration. This resulted in an eleven variable model where differentiation of corporate failure was facilitated through the use of multiple discriminant analysis. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/20439 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:34:53.657Z |
| 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:11427/20439 A corporate failure prediction model for non-financial South African corporates incorporating best practices used by the credit industry Rowlings, Douglas Correia, Carlos Finance and Tax In the context of the current macroeconomic environment there is an expectation of an increase in South African non-financial corporate failure, where advance prediction thereof will become even more important. A number of South African non-financial corporate failures have occurred following the financial crisis. In addition, South Africa experienced a watershed moment with the first default on a non-financial corporate bond in 2013. At the same time, with the adoption of the International Financial Reporting Standards (IFRS) framework there have been significant advances in the quality of financial information which should improve its usage in predicting corporate failure. This study used the latest sample to date of listed South African non-financial corporates that met the definition of failure but limited the universe of financial information to that which was prepared under IFRS. At the same time, adjustments were made to the financial data based upon pre-selection of independent credit statistic variables most commonly used in ranking relative credit risk for non-financial corporates. Additionally, equity market price data was introduced into the model to add a forward-looking information consideration. This resulted in an eleven variable model where differentiation of corporate failure was facilitated through the use of multiple discriminant analysis. 2016-07-18T12:55:43Z 2016-07-18T12:55:43Z 2016 Master Thesis Masters MCom http://hdl.handle.net/11427/20439 eng application/pdf Department of Finance and Tax Faculty of Commerce University of Cape Town |
| spellingShingle | Finance and Tax Rowlings, Douglas A corporate failure prediction model for non-financial South African corporates incorporating best practices used by the credit industry |
| thesis_degree_str | Master's |
| title | A corporate failure prediction model for non-financial South African corporates incorporating best practices used by the credit industry |
| title_full | A corporate failure prediction model for non-financial South African corporates incorporating best practices used by the credit industry |
| title_fullStr | A corporate failure prediction model for non-financial South African corporates incorporating best practices used by the credit industry |
| title_full_unstemmed | A corporate failure prediction model for non-financial South African corporates incorporating best practices used by the credit industry |
| title_short | A corporate failure prediction model for non-financial South African corporates incorporating best practices used by the credit industry |
| title_sort | corporate failure prediction model for non financial south african corporates incorporating best practices used by the credit industry |
| topic | Finance and Tax |
| url | http://hdl.handle.net/11427/20439 |
| work_keys_str_mv | AT rowlingsdouglas acorporatefailurepredictionmodelfornonfinancialsouthafricancorporatesincorporatingbestpracticesusedbythecreditindustry AT rowlingsdouglas corporatefailurepredictionmodelfornonfinancialsouthafricancorporatesincorporatingbestpracticesusedbythecreditindustry |