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dc.contributor.authorLin, Chiun-Sinen_US
dc.contributor.authorLin, Tzu-Yuen_US
dc.contributor.authorChiu, Sheng-Hsiungen_US
dc.date.accessioned2014-12-08T15:30:41Z-
dc.date.available2014-12-08T15:30:41Z-
dc.date.issued2013-05-01en_US
dc.identifier.issn0090-3973en_US
dc.identifier.urihttp://dx.doi.org/10.1520/JTE20120027en_US
dc.identifier.urihttp://hdl.handle.net/11536/21911-
dc.description.abstractThis paper proposed the hybrid model using rough set theory (RST), neural networks (NN), and data envelopment analysis (DEA) to predict the corporate performance directly. First, to evaluate corporate performance, the DEA was employed. Second, integrated RST with BPN techniques, which is one of the popular used models of NN, named RST+BPN, was used to build the corporate performance-prediction model and the corporate governance variables are used as predictive variables. This hybrid method enabled us to evaluate an individual firm and provided performance information without comparing it with other companies. The experimental result showed that the proposed model outperforms the NN model with nonextracted predictive variables and provides a promising alternative in corporate performance prediction.en_US
dc.language.isoen_USen_US
dc.subjectcorporate governanceen_US
dc.subjectrough set theoryen_US
dc.subjectneural networken_US
dc.subjectdata envelopment analysisen_US
dc.subjectbackpropagation networken_US
dc.titleCorporate Performance Forecasting Using Hybrid Rough Set Theory, Neural Networks, and DEAen_US
dc.typeArticleen_US
dc.identifier.doi10.1520/JTE20120027en_US
dc.identifier.journalJOURNAL OF TESTING AND EVALUATIONen_US
dc.citation.volume41en_US
dc.citation.issue3en_US
dc.citation.spage359en_US
dc.citation.epage365en_US
dc.contributor.department管理科學系zh_TW
dc.contributor.departmentDepartment of Management Scienceen_US
dc.identifier.wosnumberWOS:000318465000002-
dc.citation.woscount0-
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