Development of a model to predict financial distress of companies listed on the JSE

ENGLISH ABSTRACT: To date, there has been significant research completed on the topic of corporate financial distress. Two pioneering researchers in the field of predicting financial distress was Beaver in 1966 and Altman in 1968. More recent research, based on companies listed on the JSE has been that of Steyn-Bruwer and Hamman (2006).This project, based on the latter authors’ work, has been formulated with one main research objective and two subordinate research objectives. The main research objective is to prove that different modelling techniques provide better prediction accuracies than others. The two subordinate research objectives are firstly to prove that there is a difference in the overall predictive accuracy if the data (provided by Steyn-Bruwer and Hamman) is subdivided according to “year before failure” and not according to economic period and secondly to prove that more optimised, independent variables would provide a better overall predictive accuracy. This research report summarises several significant papers on the topic; and draws the conclusion that research on financial distress is fragmented with very little consensus on any of the major definitions, assumptions and findings. In order to contextualise these differences; this research report defines and discusses corporate financial distress and considers the major issues associated with the field of research. An interesting observation from the literature survey was the fact that existing literature does not readily take consideration of the number of Type I and Type II errors made. As such, this research report introduces a novel concept (not seen in other research) called the “Normalised Cost of Failure” (NCF) which takes cognisance of the fact that a Type I error typically costs 20 to 38 times that of a Type II error.In order to satisfy the main research objective several different modelling techniques were selected based on their popularity in the literature surveyed. They are: Multiple Discriminant Analysis (MDA), Recursive Partitioning (RP), Logit Analysis (LA) and Neural Networks (NN). A summary of each of the different techniques is provided in Chapter 4 of this research report.The research by Steyn-Bruwer and Hamman forms the departure point for this research and their work is summarised in Chapter 5 of this report.Chapters 6, 7 and 8 use the data from Steyn-Bruwer and Hamman along with the above mentioned modelling techniques to verify the main and subordinate objectives. In terms of the main research objective, the results of these chapters show that the different analysis techniques definitely produce different predictive accuracies. Here, the MDA and RP techniques correctly predict the most “failed” companies; and consequently have the lowest NCF. This research report also shows that LA and NN provide the best overall predictive accuracy.In terms of the first subordinate research objective; this research shows that using the year before failure rather than the economic period as a subdivision provides superior predictive accuracy.With regard to the second subordinate research objective: there is no difference in the predictive accuracies if the independent variables are further optimised. These results were disappointing and consequently disprove the second subordinate objective that widening the number of input variables actually improves the predictive accuracy. In fact, the results indicate that the information contained in the independent variables seems to saturate after the most important (key predictor) independent variables have been included in the model.It is important to take cognisance of the fact that each predictive technique has its own strength and weakness. It is proposed by the author that the strengths and weaknesses of these predictive techniques be combined to provide a better overall predictive methodology.