Fraudulent financial reporting detection and business failure prediction models: a comparison

Fen‐May Liou

Managerial Auditing Journal, 2008, vol. 23, issue 7, 650-662

Abstract: Purpose - The purpose is to explore the differences and similarities between fraudulent financial reporting detection and business failure prediction (BFP) models, especially in terms of which explanatory variables and methodologies are most effective. Design/methodology/approach - In total, 52 financial variables were identified from previous studies as potentially significant. A number of Taiwanese firms experienced financial distress or were accused of fraudulent reporting in 2005. Data on these firms and their contemporaries were obtained from theTaiwan Economic Journaldata bank and Taiwan Stock Exchange Corporation. Financial variables were calculated for the years 2003 and 2004. Three well‐known data mining algorithms were applied to build detection/prediction models for this sample: logistic regression, neural networks, and classification trees. Findings - Many of the variables are effective at both detecting fraudulent financial reporting and predicting business failures. In terms of overall accuracy, logistic regression outperforms the other two algorithms for detecting fraudulent financial reporting. Whether logistic regression or a decision tree is best for BFP depends on the relative opportunity cost of misclassifying failing and healthy firms. Originality/value - The financial factors used to detect fraudulent reporting are helpful for predicting business failure.

Keywords: Financial reporting; Business failures; Taiwan; Fraud; Cluster analysis (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

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: https://EconPapers.repec.org/RePEc:eme:majpps:02686900810890625

DOI: 10.1108/02686900810890625

Access Statistics for this article

Managerial Auditing Journal is currently edited by Professor Jie Zhou

More articles in Managerial Auditing Journal from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().