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

  • Authors:

  • Ying Wang;Peng Zhang;Guangli Nie;Yong Shi

  • Affiliations:

  • Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing, China 100190;Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing, China 100190;Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing, China 100190;Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing, China 100190 and College of Information Science & Technology, University of Nebraska at Omaha, Omaha, US ...

  • Venue:
  • ICCS 2009 Proceedings of the 9th International Conference on Computational Science
  • Year:
  • 2009

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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.