Predicting financial statement manipulation in South Africa: A comparison of the Beneish and Dechow models

Abstract

Recently, South Africa has suffered from several large financial statement frauds. To assist stakeholders in identifying fraud, this study investigated the ability of the Beneish M-score and the Dechow et al. F-score to identify fraud in South Africa. The study also explored similarities in earnings management characteristics between false positives and fraudulent companies. Finally, the study re-estimated the models’ coefficients based on current South African data to determine if this improved their predictive capabilities. The study used a sample of 23 manipulated and 2 320 non-manipulated observations from 2006 to 2018 and found that both scores showed low sensitivity and precision. The false positives share similar, or higher, earnings management characteristics to the manipulators. Re-estimating the coefficients reduced the M-scores’ sensitivity by, on average, 6.52% but improved precision by, on average, 4.21%. Conversely, re-estimation increased the F-scores’ sensitivity by, on average, 58.70% but increased the type II error by, on average, 48.09%. These findings suggested that either the M- and F-scores are unsuitable in the South African context or that regulators have failed to identify manipulators adequately. Therefore, investors and other stakeholders should use caution when applying these models in South Africa.