An analysis of the relative performance of auditors’ going concern opinions and statistical failure prediction models for listed companies in Jordan.

Student thesis: Doctoral Thesis


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.
Date of Award17 Jun 2011
Original languageEnglish
SupervisorJoël Branson (Promotor), Diane Breesch (Jury), Veronique Weets (Jury), Patrick Uyttendaele (Jury), Roland Paemeleire (Jury) & Jean-Paul Loozen (Jury)


  • Going-Concern Opinion
  • Failure Prediction

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