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Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning

Cited 28 time in Web of Science Cited 39 time in Scopus
Authors
Kim, Yeonkook J.; Baik, Bok; Cho, Sungzoon
Issue Date
2016-11
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS Vol.62, pp. 32-43
Keywords
Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning자연과학
Abstract
We develop multi-class financial misstatement detection models to detect misstatements with fraud intention. Hennes, Leone and Miller (2008) conducted a post-event analysis of financial restatements and classified restatements as intentional or unintentional. Using their results (along with non-misstated firms) in the form of a three-class target variable, we develop three multi-class classifiers, multinomial logistic regression, support vector machine, and Bayesian networks, as predictive tools to detect and classify misstatements according to the presence of fraud intention. To deal with class imbalance and asymmetric misclassification costs, we undertake cost-sensitive learning using MetaCost. We evaluate features from previous studies of detecting fraudulent intention and material misstatements. Features such as the short interest ratio and the firm-efficiency measure show discriminatory potential. The yearly and quarterly context-based feature set created further improves the performance of the classifiers. (C) 2016 Elsevier Ltd. All rights reserved.
ISSN
09574174
Language
English
URI
https://hdl.handle.net/10371/116907
DOI
https://doi.org/10.1016/j.eswa.2016.06.016
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College of Engineering/Engineering Practice School (공과대학/대학원)Program in Technology, Management, Economics and Policy (협동과정-기술·경영·경제·정책전공)Journal Papers (저널논문_협동과정-기술·경영·경제·정책전공)
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