Study And Application Of Credit Scoring Models To Appraisal Of A Loan To Chinese Companies

The main purpose of this thesis is to find out how to quantify creditrisk which appeared in loan appraisal. Multi-Discriminate analysis,Probitand Logit models,Probabilistic Neural Networks and Data EnvelopmentAnalysis are all adopted to develop credit scoring models. To find thebest and practical method, a quantified analysis and experimental studyare emphasized.. First existing methods were introduced, namely specialist's scoremethod, credit score model, and modern credit risk models. Thenadvantages and disadvantages of each method were discussed and theadaptability in China is presented. Some popular techniques fordeveloping credit scoring models such as multiple-discriminate analysis,probit and logit models were also introduced. In addition, probabilisticneural networks and data envelopment analysis techniques for creditscoring are presented. Lastly four techniques were applied tomeasurement of credit risk for listed companies in China, in which mainfactor method was adopted to select input variables. Experimental resultsshow credit scoring models developed here have good forecasting ability.