Predicting financial distress in IT and services companies in South Africa
| dc.contributor.advisor | Smit, A. V. A. | |
| dc.contributor.author | Kidane, Habtom Woldemichael | |
| dc.date.accessioned | 2015-09-01T12:02:11Z | |
| dc.date.available | 2015-09-01T12:02:11Z | |
| dc.date.copyright | 04-Nov | |
| dc.date.issued | 2004 | en_ZA |
| dc.date.submitted | 04-Nov | |
| dc.description | Dissertation (M.Com.(Business Management))--University of the Free State, 2004 | en_ZA |
| dc.description.abstract | The study of bankruptcy is becoming more relevant and important as even large companies are failing that cause economic and social problems to the society. Using financial distress models to predict failure in advance is for most businesses absolutely essential in their decision making process. Hence, this study involves a critical investigation in the applicability of the Altman (1968) and Springate (1978) z-score models in predicting financial distress in IT and Services companies of South Africa. The Altman and Springate models were however developed in a different economic environment, time horizon, industry and country. Testing these models in the South African context is important to determine the practical applicability and relevance of the models. The main objective of the study is to test the Altman and Springate models in determining practical predictive ability of failure in selected South African IT and services companies listed on the Johannesburg Security Exchange and to comment on the models applicability according to the empirical results. The study is designed into three sections. The first section will discuss the theoretical aspects of the study. The second part will be the discussion of the research results, and finally the conclusion and recommendations of the study will be presented. The first sample companies was 24 failed and 46 nonfailed information technology and services companies listed on the Johannesburg Security Exchange from 1999 to 2003. The failed companies were matched to two nonfailed companies in the same sector according to their turnover size. Additional nonfailed real estate and information technology companies were added to evaluate the prediction ability of the models in these sectors using substantial samples, as the first sample results were inconsistent, especially on the nonfailed companies. Therefore, the final sample of the study is composed of 86 (24 failed and 62 nonfailed) services and information technology companies. The study employed an analysis of financial statements and derived the z-score of the sampled companies to test the predictive ability of the models in forecasting bankruptcy. The analysis utilized ratios, which are related to the models in the study. The results reported in the empirical study for total failed and nonfailed sample companies show these models fail to predict failure and non-failure amongst South African service and information technology sample companies. The study is also extended to predict failure and non-failure to investigate if the models are more applicable to predict failure in some sub-sectors of the sampled services and information technology companies. The results are not consistent as the models predicted failure but not nonfailure, or vice versa. Therefore, the models are not successful to predict any sub-sector correctly. It is generally assumed bankruptcy prediction models are useful regardless of industry, time horizon, and country. The findings reported in the study for each model indicated that the overall accuracy of the Altman and Springate z-scores models accuracy rate were reduced when used on the South African service and information technology sample, which is different from those companies used to develop the original models. Therefore, the study concluded that the Altman and Springate bankruptcy prediction models are not justifiable to be applied to predict bankruptcy in the South African service and information technology. Hence, it is not advisable to use these models in predicting failure in the non-manufacturing firms, especially in South African services and information technology companies. Important recommendations were outlined based on the results of the study. Some of the recommendations are the development of practically applicable bankruptcy prediction models in the IT and services companies of South Africa to elevate inappropriate financial decisions, incorporation of other important indicators of financial well-being during bankruptcy prediction process, checking the practical applicability of prediction models after some period of time. The future research work based on this study is also suggested as developing models to the database in the study, developing new bankruptcy prediction model to the services and information technology companies of South Africa, testing the Altman and/or Springate models on the South African manufacturing and retailing companies, and testing bankruptcy prediction models to the non-listed relatively smaller turnover sized firms where the incidence of business failure is greater than larger corporations. | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11660/1117 | |
| dc.language.iso | en | en_ZA |
| dc.publisher | University of the Free State | en_ZA |
| dc.rights.holder | University of the Free State | en_ZA |
| dc.subject | Business failures -- South Africa | en_US |
| dc.subject | Bankruptcy -- Forecasting | en_ZA |
| dc.subject | Bankruptcy -- South Africa | en_ZA |
| dc.title | Predicting financial distress in IT and services companies in South Africa | en_ZA |
| dc.type | Dissertation | en_ZA |