Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data

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dc.contributor.authorKim, Hyeongjunko
dc.contributor.authorCho, Hoonko
dc.contributor.authorRyu, Doojinko
dc.date.accessioned2022-04-14T06:52:05Z-
dc.date.available2022-04-14T06:52:05Z-
dc.date.created2021-06-07-
dc.date.created2021-06-07-
dc.date.created2021-06-07-
dc.date.issued2022-03-
dc.identifier.citationCOMPUTATIONAL ECONOMICS, v.59, no.3, pp.1231 - 1249-
dc.identifier.issn0927-7099-
dc.identifier.urihttp://hdl.handle.net/10203/292817-
dc.description.abstractWe examine whether corporate bankruptcy predictions can be improved by utilizing the recurrent neural network (RNN) and long short-term memory (LSTM) algorithms, which can process sequential data. Employing the RNN and LSTM methodologies improves bankruptcy prediction performance relative to using other classification techniques, such as logistic regression, support vector machine, and random forest methods. Because performance indicators, such as sensitivity and specificity, differ depending on the methodology, selecting a model that suits the purpose of the bankruptcy predictions is necessary. Our ensemble model, a synthesis of all methodologies, exhibits the best forecasting performance. In the test sample for the ensemble model, none of the observations with a default probability of less than 10% defaults within one year.-
dc.languageEnglish-
dc.publisherSPRINGER-
dc.titleCorporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data-
dc.typeArticle-
dc.identifier.wosid000655037600001-
dc.identifier.scopusid2-s2.0-85106528005-
dc.type.rimsART-
dc.citation.volume59-
dc.citation.issue3-
dc.citation.beginningpage1231-
dc.citation.endingpage1249-
dc.citation.publicationnameCOMPUTATIONAL ECONOMICS-
dc.identifier.doi10.1007/s10614-021-10126-5-
dc.contributor.localauthorCho, Hoon-
dc.contributor.nonIdAuthorKim, Hyeongjun-
dc.contributor.nonIdAuthorRyu, Doojin-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorBankruptcy prediction-
dc.subject.keywordAuthorClassification-
dc.subject.keywordAuthorLong short-term memory-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorRecurrent neural network-
dc.subject.keywordPlusSUPPORT VECTOR MACHINES-
dc.subject.keywordPlusFINANCIAL RATIOS-
dc.subject.keywordPlusNEURAL-NETWORK-
dc.subject.keywordPlusVARIABLE SELECTION-
dc.subject.keywordPlusDEFAULT PREDICTION-
dc.subject.keywordPlusMODELS-
dc.subject.keywordPlusRISK-
dc.subject.keywordPlusINFORMATION-
dc.subject.keywordPlusALGORITHM-
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