S-Space 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)
Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning
- Kim, Yeonkook J.; Baik, Bok; Cho, Sungzoon
- Issue Date
- PERGAMON-ELSEVIER SCIENCE LTD
- EXPERT SYSTEMS WITH APPLICATIONS Vol.62, pp. 32-43
- Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning; 자연과학
- 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.
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