The Ability of Altman’s Z”-score Model to Detect the Economic Distress of Kazakh Banks

This study contributes to the literature by evaluating the ability of Altman’s Z”-score model to predict the economic distress of 12 Kazakh banks over the period 2008–2014. The original Z”-score model with a cut-off point implied by Altman (2005) produced a prediction accuracy ratio of 44.05% and correctly classifies 76.19% of the observations as an economically distressed group. This study then re-estimates the model using three approaches, namely, the “leave-one-out”, Direct, and Wilks’ methods, and identifies new, optimal cut-off points for the re-estimated models. The re-estimated models, together with the new, optimal cut-off points, improved the prediction accuracy ratio to 70% and correctly classified over 90% of the observations originally assigned to the economically distressed group. The results imply that the Kazakh banking regulator and other market participants could use Altman’s Z”-score model to detect economically distressed banks.