Financial-distress prediction of Islamic banks using tree-based stochastic techniques

Date of this Version


Document Type

Journal Article

Publication Details

Submitted version

Halteh, K., Kumar, K., Gepp, A. (2017). Financial-distress prediction of Islamic banks using tree-based stochastic techniques. Managerial Finance, 1-20.

Access the journal


1758-7743 (online), 0307-4358 (print)


Financial distress is a socially and economically important problem that affects companies the world over. Having the power to better understand – and hence aid banks from failing, has the potential to save not only the bank, but potentially prevent economies from sustained downturn. Although Islamic banks constitute a fraction of total banking assets, their importance has been substantially increasing, as their asset growth rate has surpassed that of conventional banks in recent years. This paper uses a data-set comprising 101 international publicly-listed Islamic banks to work on advancing financial distress prediction by utilising cutting-edge stochastic models, namely: decision trees, stochastic gradient boosting, and random forests. The most important variables pertaining to forecasting corporate failure are determined from an initial set of 18 variables. Our results indicate that the “Working Capital/Total Assets” ratio is the most crucial variable relating to forecasting financial distress using both the traditional ‘Altman Z-Score’ and the ‘Altman Z-Score for Service Firms’ methods. However, using the ‘Standardised Profits’ method, the “Return on Revenue” ratio was found to be the most important variable. This provides empirical evidence to support the recommendations made by Basel Accords for assessing a bank’s capital risks, specifically in relation to the application to Islamic banking. These findings provide a valuable addition to the limited literature surrounding Islamic banking in general, and financial distress prediction pertaining to Islamic banking in particular, by showcasing the most pertinent variables in forecasting financial distress so that appropriate proactive actions can be taken.

This document is currently not available here.



This document has been peer reviewed.