A comparative study between Altman Z-Score and verifier

Abstract

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 company distress