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An Analysis of the Relative Performance of Auditors' Going-Concern Opinions and Statistical Failure Prediction Models for Listed Companies in Jordan

Friday, 17 June, 2011 - 11:00
Faculty: Social Sciences and Solvay Business School
Bahaaeddin Alareeni
phd defence

In his Ph.D. thesis, Bahaaeddin Alareeni first gives an overview of the most accurate statistical
failure prediction models (SFPMs) and of the factors affecting auditors’ going-concern opinions
(GCOs). Second, he tests the generalisability of existing ‘western’ SFPMs (the Altman models)
in a non-western setting (Jordan). He finds that the Altman models are generalisable for
industrial companies, but not for service companies. Third, he develops new SFPMs for the
Jordanian context, using logit analysis. Fourth, he compares the effectiveness of SFPMs and
auditors’ GCOs in failure prediction in Jordan. He finds that SFPMs, and especially the newly
developed logit models, perform much better than auditors’ GCOs in predicting failed
companies. Fifth, he attempts to predict the differences and agreements in the classification
between SFPMs and auditors’ GCOs. The developed logit models offer investors and other
stakeholders additional confidence about which methodology (SFPMs or auditor’s GCOs) to use
in a particular case.

PDF icon Alareeni_a.pdf