Multiple Criteria Quadratic Programming for Financial Distress Prediction of the Listed Manufacturing Companies

Wang, Y; Zhang, P; Nie, G; Shi, Y

Multiple Criteria Quadratic Programming for Financial Distress Prediction of the Listed Manufacturing Companies

Wang, Y Zhang, P

Nie, G Shi, Y

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science, 2009, 5545 (1), pp. 616 - 624
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Nie, G	en_US
dc.contributor.author	Shi, Y	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science, 2009, 5545 (1), pp. 616 - 624	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28906
dc.description.abstract	Nowadays, how to effectively predict financial distress has become an important issue for companies, investors and many other user groups. The purpose of this paper is to apply the Multiple Criteria Quadratic Programming (MCQP) model to predict financial distress of the listed manufacturing companies. Firstly, we introduce the formulation of MCQP model. Then we use ten-folder cross validation to test the stability and accuracy of MCQP model on a real-life listed companies financial ratios dataset. At last, we compare MCQP model with other two well-known models: Logistic Regression and SVM models. The experimental results show that MCQP is accurate and stable for predicting the financial distress of the listed manufacturing companies. Consequently, we can safely say that MCQP is capable of providing stable and credible results in predicting financial distress.	en_US
dc.publisher	Elsevier	en_US
dc.relation.ispartof	Lecture Notes in Computer Science	en_US
dc.relation.isbasedon	10.1007/978-3-642-01973-9_69	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Multiple Criteria Quadratic Programming for Financial Distress Prediction of the Listed Manufacturing Companies	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	5545	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.volume	5545	en_US

Abstract:

Nowadays, how to effectively predict financial distress has become an important issue for companies, investors and many other user groups. The purpose of this paper is to apply the Multiple Criteria Quadratic Programming (MCQP) model to predict financial distress of the listed manufacturing companies. Firstly, we introduce the formulation of MCQP model. Then we use ten-folder cross validation to test the stability and accuracy of MCQP model on a real-life listed companies financial ratios dataset. At last, we compare MCQP model with other two well-known models: Logistic Regression and SVM models. The experimental results show that MCQP is accurate and stable for predicting the financial distress of the listed manufacturing companies. Consequently, we can safely say that MCQP is capable of providing stable and credible results in predicting financial distress.

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http://hdl.handle.net/10453/28906