Original Research

Predicting financial distress of companies listed on the JSE: A comparison of techniques

G. H. Muller, B. W. Steyn-Bruwer, W. D. Hamman
South African Journal of Business Management | Vol 40, No 1 | a532 | DOI: https://doi.org/10.4102/sajbm.v40i1.532 | © 2018 G. H. Muller, B. W. Steyn-Bruwer, W. D. Hamman | This work is licensed under CC Attribution 4.0
Submitted: 10 October 2018 | Published: 31 March 2009

About the author(s)

G. H. Muller, University of Stellenbosch Business School, South Africa
B. W. Steyn-Bruwer, Department of Accounting, University of Stellenbosch, South Africa
W. D. Hamman, University of Stellenbosch Business School, South Africa

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In 2006, Steyn-Bruwer and Hamman highlighted several deficiencies in previous research which investigated the prediction of corporate failure (or financial distress) of companies. In their research, Steyn-Bruwer and Hamman made use of the population of companies for the period under review and not only a sample of bankrupt versus successful companies. Here the sample of bankrupt versus successful companies is considered as two extremes on the continuum of financial condition, while the population is considered as the entire continuum of financial condition.
The main objective of this research, which was based on the above-mentioned authors’ work, was to test whether some modelling techniques would in fact provide better prediction accuracies than other modelling techniques. The different modelling techniques considered were: Multiple discriminant analysis (MDA), Recursive partitioning (RP), Logit analysis (LA) and Neural networks (NN).
From the literature survey it was evident that existing literature did not readily consider the number of Type I and Type II errors made. As such, this study 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 terms of the main research objective, the results show that 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; while the LA and NN techniques provide the best overall predictive accuracy.


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