## Abstract

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 language | English (US) |
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Title of host publication | Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (In 4 Volumes) |

Publisher | World Scientific Publishing Co. |

Pages | 4313-4348 |

Number of pages | 36 |

ISBN (Electronic) | 9789811202391 |

ISBN (Print) | 9789811202384 |

DOIs | |

State | Published - Jan 1 2020 |

## All Science Journal Classification (ASJC) codes

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

## Keywords

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