Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/42F1E2C7-53D4-476C-8651-CB5BF500932E | ||
| Year | 2007 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | F. Pasiouras, C. Gaganis, and C. Zopounidis, "Multicriteria decision support methodologies for auditing decisions: the case of qualified audit reports in the UK", Europ. J. Operat. Res., vol. 180, no. 3, pp. 1317-1330, Aug. 2007. doi:10.1016/j.ejor.2006.04.039 https://doi.org/10.1016/j.ejor.2006.04.039 | ||
| Appears in Collections |
All UK companies are required by company law to prepare financial statements that must comply with law and accounting standards. With the exception of very small companies, financial accounts must then be audited by UK registered auditors who must express an opinion on whether these statements are free from material misstatements, and have been prepared in accordance with legislation and relevant accounting standards (unqualified opinion) or not (qualified opinion). The objective of the present study is to explore the potentials of developing multicriteria decision aid models for reproducing, as accurately as possible, the auditors’ opinion on the financial statements of the firms. A sample of 625 company audited years with qualified statements and 625 ones with unqualified financial statements over the period 1998–2003 from 823 manufacturing private and public companies is being used in contrast to most of the previous works in the UK that have mainly focused on very small or very large public companies. Furthermore, the models are being developed and tested using the walk-forward approach as opposed to previous studies that employ simple holdout tests or resampling techniques. Discriminant analysis and logit analysis are also used for comparison purposes. The out-of-time and out-of-sample testing results indicate that the two multicriteria decision aid techniques achieve almost equal classification accuracies and are both more efficient than discriminant and logit analysis.
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