(A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS, VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS)
By Nguyen Viet Duc (VNP 21)
Academic Supervisor: Dr. Nguyen Thi Thuy Linh
This thesis presents the approach and results of an attempt at using logistic regression to develop a probability of default (PD) predicting model, a linear regression which is also supported by literatures of relevant factors by an ologit model for predicting the future loan group of any applicant. Both logistic and linear regression are applied to find out the fit models for commercial banks.
By choosing suitable models and deeply data analysis about applicant information from Vietnamese commercial banks, the paper address almost big concerns in credit risk management and client credit worthiness assessment: determine suitable models for Vietnamese SMEs market for both predicting probability of default (PD) and number of late payment days (ELG); specify factors that could cause a loan's potential downgrade (PDL), important information that contributes in creditworthiness of an individual SMEs, the role of cut-off points in implementing banks' risk appetite and suitable data treatment approaches.
Keywords: credit rating, logistic regression, binominal, multinomial, linear regression, prediction, risk assessment.
Full version is available at Library of Vietnam-Netherland Progamme: 1A Hoang Dieu, Phu Nhuan Dist, Ho Chi Minh city, Vietnam.