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Explaining qualifications in audit reports using a support vector machine methodology

Pasiouras Fotios, Chrysovalantis Gaganis, Michael Doumpos

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URIhttp://purl.tuc.gr/dl/dias/B6596A66-239C-43BE-A49F-1DA410F91FBC-
Identifierhttps://doi.org/10.1002/isaf.v13:4-
Languageen-
TitleExplaining qualifications in audit reports using a support vector machine methodologyen
CreatorPasiouras Fotiosen
CreatorΠασιουρας Φωτιοςel
CreatorChrysovalantis Gaganisen
CreatorMichael Doumposen
PublisherAssociation for Computing Machineryen
Content SummaryThe verification of whether the financial statements of a firm represent its actual position is of major importance for auditors, who should provide a qualified report if they conclude that the financial statements fail to meet this requirement. This paper implements support vector machines (SVMs) to develop models that may support auditors in this task. Linear and non-linear models are developed and their performance is analysed using training samples of different size and out-of-sample/out-of-time data. The results show that all SVM models are capable of distinguishing between qualified and unqualified financial statements with satisfactory accuracy. The performance of the models over time is also explored. Copyright © 2005 John Wiley & Sons, Ltd.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-25-
Date of Publication2005-
Bibliographic CitationDoumpos M, Gaganis C, Pasiouras F., "Explaining qualifications in audit reports using a support vector machine methodology". Int. Syst. in Accounting, Finance and Management, Vol. 13, no. 4 , pp. 197-215, Dec. 2005.doi: 10.1002/isaf.268.en

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