Online from: 1986
Subject Area: Accounting and Finance
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|Title:||Fraudulent financial reporting detection and business failure prediction models: a comparison|
|Author(s):||Fen-May Liou, (Graduate Institute of Business and Management, Yuanpei University, Hsinchu, Taiwan, Republic of China)|
|Citation:||Fen-May Liou, (2008) "Fraudulent financial reporting detection and business failure prediction models: a comparison", Managerial Auditing Journal, Vol. 23 Iss: 7, pp.650 - 662|
|Keywords:||Business failures, Cluster analysis, Financial reporting, Fraud, Taiwan|
|Article type:||Research paper|
|DOI:||10.1108/02686900810890625 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
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 the
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.
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