The identification of reliable early warnings signs which encompass qualitative and quantitate
inputs to business distress and failure prediction could reduce the incidence of business failure
if companies take corrective action early enough as the signals of distress emerge.
The concept of verifier determinants as early warning signs of business failure and distress as
introduced by Holtzhauzen & Pretorius (2013) has largely been theoretical and unexamined
in terms of the methodology's ability to identify business distress. The performance of the
model is tested against the well-established Altman Z-Score model of prediction.
This study tests the consistency of the classification of companies as falling, grey and nonfailing
by applying the Altman Z-Score model and the verifier determinants theory to a sample
38 JSE listed companies. 19 Suspended companies were selected and matched with another
19 companies of similar size and operating in the same industries.
The consistency of the classifications was tested via a simple measure of percentage
agreement using a cross tabulation, then a Cohen Kappa coefficient was applied to test for
agreement over and above agreement by chance. The study further applied a Spearman
correlation coefficient to determine the level of association between the results produced by
the two models.
The findings of the study indicate a statistically significant association between the Altman ZScore
and the aggregate score of default as calculated through the application of verifier
determinants theory. The study further identifies two verifier determinants (i) Late submission
of financial information and (ii) Underutilisation of assets which have the strongest association
with the Altman model and overall aggregate score of default. We argue that these individual
verifier determinants could be used as a proxy for the overall model to monitor the risk of
Mini Dissertation (MBA)--University of Pretoria, 2017.