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Predicting financial distress of JSE-Listed companies using Bayesian networks
This study aims to test the suitability of using Bayesian probabilistic models to predict bankruptcy of JSE-listed companies. A sample of 132 companies is considered with fourteen years of financial statement information and macroeconomic indicators used as predictor variables. Various permutations...
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| Format: | Thesis |
| Language: | English |
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Division of Actuarial Science
2016
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| _version_ | 1867613222258868224 |
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| access_status_str | Open Access |
| author | Cassim, Ziyad |
| author2 | Kruger, Ryan |
| author_browse | Cassim, Ziyad Kruger, Ryan |
| author_facet | Kruger, Ryan Cassim, Ziyad |
| author_sort | Cassim, Ziyad |
| collection | Thesis |
| description | This study aims to test the suitability of using Bayesian probabilistic models to predict bankruptcy of JSE-listed companies. A sample of 132 companies is considered with fourteen years of financial statement information and macroeconomic indicators used as predictor variables. Various permutations of Bayesian models are tested relating to different learning algorithms, intervals of discretisation and scoring metrics. In contrast to previous research, we explore a variety of evaluation measures and it is found that predictive accuracy for bankrupt firms does not exceed 70% in any model augmentation. On comparison to other popular models such as the Altman Z-score and the logit model, it is found that Bayesian networks produce marginally better predictive accuracy. Furthermore, a comparison to previous research on the same subject is carried and reasons for significantly different results are considered. Finally, the reasons for low predictive accuracies is considered with issues relating specifically to South Africa being discussed. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/20484 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:42.829Z |
| 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 | Division of Actuarial Science |
| publisherStr | Division of Actuarial Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/20484 Predicting financial distress of JSE-Listed companies using Bayesian networks Cassim, Ziyad Kruger, Ryan Actuarial Science This study aims to test the suitability of using Bayesian probabilistic models to predict bankruptcy of JSE-listed companies. A sample of 132 companies is considered with fourteen years of financial statement information and macroeconomic indicators used as predictor variables. Various permutations of Bayesian models are tested relating to different learning algorithms, intervals of discretisation and scoring metrics. In contrast to previous research, we explore a variety of evaluation measures and it is found that predictive accuracy for bankrupt firms does not exceed 70% in any model augmentation. On comparison to other popular models such as the Altman Z-score and the logit model, it is found that Bayesian networks produce marginally better predictive accuracy. Furthermore, a comparison to previous research on the same subject is carried and reasons for significantly different results are considered. Finally, the reasons for low predictive accuracies is considered with issues relating specifically to South Africa being discussed. 2016-07-20T06:56:47Z 2016-07-20T06:56:47Z 2016 Master Thesis Masters MPhil http://hdl.handle.net/11427/20484 eng application/pdf Division of Actuarial Science Faculty of Commerce University of Cape Town |
| spellingShingle | Actuarial Science Cassim, Ziyad Predicting financial distress of JSE-Listed companies using Bayesian networks |
| thesis_degree_str | Master's |
| title | Predicting financial distress of JSE-Listed companies using Bayesian networks |
| title_full | Predicting financial distress of JSE-Listed companies using Bayesian networks |
| title_fullStr | Predicting financial distress of JSE-Listed companies using Bayesian networks |
| title_full_unstemmed | Predicting financial distress of JSE-Listed companies using Bayesian networks |
| title_short | Predicting financial distress of JSE-Listed companies using Bayesian networks |
| title_sort | predicting financial distress of jse listed companies using bayesian networks |
| topic | Actuarial Science |
| url | http://hdl.handle.net/11427/20484 |
| work_keys_str_mv | AT cassimziyad predictingfinancialdistressofjselistedcompaniesusingbayesiannetworks |