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dc.contributor.authorJiang, Xuewei
dc.contributor.otherBaylor University.en
dc.date.accessioned2015-06-01T15:09:18Z
dc.date.available2015-06-01T15:09:18Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/2104/9371
dc.description.abstractFinancial distress is a condition where a company has difficulty paying off its financial obligations to its creditors. Failing to relieve financial distress can lead to bankruptcy, which is very costly to the firm’s creditors and investors, as well as its employees and citizens of the community where it is located. As a result, corporate bankruptcy predication is of great interest to both academics and practitioners. While a number of bankruptcy prediction models have been published, Altman’s Z-score model is still commonly used by practitioners as a standard to evaluate a firm’s financial health after almost fifty years since it was published in 1968. In this thesis, I explore the use of artificial neural networks to improve upon existing predictive techniques. My model shows strong discriminating power in a Receiver Operating Characteristics analysis, and outperforms the hazard model and the distance-to-default model in detecting bankruptcies.en_US
dc.language.isoen_USen_US
dc.rightsBaylor University projects are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.en
dc.subjectCorporate Bankruptcyen_US
dc.subjectArtificial Neural Networken_US
dc.titleCorporate Bankruptcy Prediction Using Artificial Neural Networken_US
dc.typeThesisen_US
dc.rights.accessrightsWorldwide access.


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