Economics at your fingertips  


Efstathios Kirkos

Journal of Applied Economic Sciences, 2012, vol. 7, issue 3(21)/ Fall 2012, 246-261

Abstract: Auditor dismissals are considered to be a threat to audit quality. Several studies have examined auditor switches by applying typical statistical analysis. In the present study we deal with the auditor switching problem by applying data mining methodologies. Publicly available financial statement and auditing data are used as predictors. The optimum vector of significant input variables is defined by employing feature selection. A number of data mining classification methods are used to develop models capable of predicting the auditor change cases. The methods are compared against the widely used Logistic Regression. According to the results, all the data mining methods outperform Logistic Regression. Significant factors associated with auditor changes are revealed. The results can be useful to auditing firms, managers, investors, creditors and corporate regulators.

Keywords: Auditor switching; auditing; data mining (search for similar items in EconPapers)
JEL-codes: C38 C45 M42 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Journal of Applied Economic Sciences is currently edited by Laura Stefanescu

More articles in Journal of Applied Economic Sciences from Spiru Haret University, Faculty of Financial Management and Accounting Craiova Contact information at EDIRC.
Bibliographic data for series maintained by Laura Stefanescu ().

Page updated 2018-04-28
Handle: RePEc:ush:jaessh:v:7:y:2012:i:3(21)_fall2012:p:246