Publications
Detailed Information
Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Yeonkook J. | - |
dc.contributor.author | Baik, Bok | - |
dc.contributor.author | Cho, Sungzoon | - |
dc.date.accessioned | 2017-04-19T00:20:04Z | - |
dc.date.available | 2017-11-28T09:59:46Z | - |
dc.date.created | 2018-08-27 | - |
dc.date.created | 2018-08-27 | - |
dc.date.issued | 2016-11 | - |
dc.identifier.citation | Expert Systems with Applications, Vol.62, pp.32-43 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://hdl.handle.net/10371/116907 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Pergamon Press Ltd. | - |
dc.title | Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning | - |
dc.type | Article | - |
dc.contributor.AlternativeAuthor | 김연국 | - |
dc.contributor.AlternativeAuthor | 백복 | - |
dc.contributor.AlternativeAuthor | 조성준 | - |
dc.identifier.doi | 10.1016/j.eswa.2016.06.016 | - |
dc.citation.journaltitle | Expert Systems with Applications | - |
dc.identifier.wosid | 000380626000003 | - |
dc.identifier.scopusid | 2-s2.0-84975062598 | - |
dc.description.srnd | OAIID:RECH_ACHV_DSTSH_NO:T201621912 | - |
dc.description.srnd | RECH_ACHV_FG:RR00200001 | - |
dc.description.srnd | ADJUST_YN: | - |
dc.description.srnd | EMP_ID:A004522 | - |
dc.description.srnd | CITE_RATE:2.981 | - |
dc.description.srnd | DEPT_NM:산업공학과 | - |
dc.description.srnd | EMAIL:zoon@snu.ac.kr | - |
dc.description.srnd | SCOPUS_YN:Y | - |
dc.description.srnd | CONFIRM:Y | - |
dc.citation.endpage | 43 | - |
dc.citation.startpage | 32 | - |
dc.citation.volume | 62 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Baik, Bok | - |
dc.contributor.affiliatedAuthor | Cho, Sungzoon | - |
dc.identifier.srnd | T201621912 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | DATA MINING TECHNIQUES | - |
dc.subject.keywordPlus | EARNINGS MANAGEMENT | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
dc.subject.keywordPlus | STATEMENT FRAUD | - |
dc.subject.keywordPlus | CLASS IMBALANCE | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | RESTATEMENTS | - |
dc.subject.keywordPlus | DETERMINANTS | - |
dc.subject.keywordPlus | ERRORS | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Financial misstatement detection | - |
dc.subject.keywordAuthor | Financial restatements | - |
dc.subject.keywordAuthor | Fraud intention | - |
dc.subject.keywordAuthor | Multi-class cost sensitive learning | - |
- Appears in Collections:
- Files in This Item:
- There are no files associated with this item.
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.