Company failure in South Africa: Classification and prediction by means of recursive partitioning

Abstract

The deficiencies in previous research on failure prediction studies were identified from the international literature. This study’s purpose is to address these deficiencies while using a new method in developing failure prediction models, namely recursive partitioning (specifically the classification tree algorithm).
The deficiencies were addressed as follows:

• Brute empirism was avoided by focussing on cash flow ratios in combination with certain accrual ratios. 
• Failure was not only defined as bankruptcy, but as any condition where the company cannot exist in future in its current form, therefore including delistings as well as major structural changes. 
• By using the population of listed industrial companies between June 1997 and May 2002, the grey area in-between ‘successful’ and ‘bankrupt’ was included in developing the models. 
• Every model developed was tested with the help of an independent sample. 
• The different economic cycles were considered by developing different models for a growth and a recessionary period. A combined model was also developed, with the economic cycle as a independent dichotomous variable.

When the prediction accuracy for the different classes and in total, of the models developed, is compared with the ex ante probability that an observation will fall in a particular class of the majority (non-failed companies), the prediction accuracy is in every instance higher than the ex ante probability.