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Firm Financial Analysis

  • Yong Shi
  • Yingjie Tian
  • Gang Kou
  • Yi Peng
  • Jianping Li
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

Financial institutions and banks are among those industries that have relatively complete and accurate data and have first employed advanced analytic techniques on their data. Typical cases include stock investment, loan payment prediction, credit approval, bankruptcy prediction, and fraud detection. Classification is one of most extensively used data mining methods in finance and banking. To test the applicability of preceding multiple-criteria mathematical models in finance and banking, we select one model, MCQP in Chap.  9 and apply it to three financial datasets. These datasets come from three countries and represent consumer credit card application data, credit approval data, and corporation bankruptcy data. These three datasets represent different problems in finance and banking. For comparison purpose, the result of MCQP is compared with four well-know classification methods: SPSS, linear discriminant analysis (LDA), Decision Tree based See5, SVMlight, and LibSVM.

Keywords

Linear Discriminant Analysis Credit Risk Control Firm Fraud Detection Bankruptcy Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Yong Shi
    • 1
    • 2
  • Yingjie Tian
    • 1
  • Gang Kou
    • 3
  • Yi Peng
    • 3
  • Jianping Li
    • 4
  1. 1.Research Center on Fictitious Economy and Data ScienceChinese Academy of SciencesBeijingChina
  2. 2.College of Information Science & TechnologyUniversity of Nebraska at OmahaOmahaUSA
  3. 3.School of Management and EconomicsUniversity of Electronic Science and Technology of ChinaChengduChina
  4. 4.Institute of Policy and ManagementChinese Academy of SciencesBeijingChina

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