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

Khaled Halteh, Kuldeep Kumar, Adrian Gepp

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Abstract

Purpose: Financial distress is a socially and economically important problem that affects companies the world over. Having the power to better understand – and hence aid businesses from failing, has the potential to save not only the company, but also potentially prevent economies from sustained downturn. Although Islamic banks constitute a fraction of total banking assets, their importance have been substantially increasing, as their asset growth rate has surpassed that of conventional banks in recent years. The paper aims to discuss these issues. Design/methodology/approach: This paper uses a data set comprising 101 international publicly listed Islamic banks to work on advancing financial distress prediction (FDP) 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. Findings: The 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. Originality/value: These findings provide a valuable addition to the limited literature surrounding Islamic banking in general, and FDP pertaining to Islamic banking in particular, by showcasing the most pertinent variables in forecasting financial distress so that appropriate proactive actions can be taken.

Original languageEnglish
Pages (from-to)759-773
Number of pages15
JournalManagerial Finance
Volume44
Issue number6
Early online dateApr 2018
DOIs
Publication statusPublished - 11 Jun 2018

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Prediction
Islamic financial institutions
Financial distress
Islamic banking
Assets
Z-score
Bank capital
Basel Accord
Decision tree
Boosting
Corporate failure
Stochastic model
Service firms
Banking
Gradient
Profit
Revenue
Design methodology
Empirical evidence
Working capital

Cite this

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Financial-distress prediction of Islamic banks using tree-based stochastic techniques. / Halteh, Khaled; Kumar, Kuldeep; Gepp, Adrian.

In: Managerial Finance, Vol. 44, No. 6, 11.06.2018, p. 759-773.

Research output: Contribution to journalArticleResearchpeer-review

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