Application of discriminant analysis, factor analysis, logistic regression, and KMV-merton model in credit risk analysis

Cheng Few Lee, Hai Chin Yu

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations


The main purposes of this paper are to review and integrate the applications of discriminant analysis, factor analysis, and logistic regression in credit risk management. First, we discuss how the discriminant analysis can be used for credit rating such as calculating financial z-score to determine the chance of bankruptcy of the firm. In addition, we also discuss how discriminant analysis can be used to classify banks into problem banks and non-problem banks. Secondly, we discuss how factor analysis can be combined with discriminant analysis to perform bond rating forecasting. Thirdly, we show how logistic and generalized regression techniques can be used to calculate the default risk probability. Fourthly, we will discuss the KMV-Merton model and Merton distance model for calculating default probability. Finally, we compare all techniques discussed in previous sections and draw conclusions and give suggestions for future research. We propose using CEV option model to improve the original Merton DD model. In addition, we also propose a modified naïve model to improve Bharath and Shumway’s (2008) naïve model.

Original languageEnglish (US)
Title of host publicationHandbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (In 4 Volumes)
PublisherWorld Scientific Publishing Co.
Number of pages36
ISBN (Electronic)9789811202391
ISBN (Print)9789811202384
StatePublished - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)


  • Default probability
  • Discriminant analysis
  • Factor analysis
  • Financial z-score
  • Hazard model
  • KMV-merton model
  • Logistic regression
  • Merton distance model
  • MIDAS logit model
  • Probit model


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