报告题目：On Corporate Bankruptcy Prediction
报 告 人：Yan Yu，美国辛辛那提大学Lindner College of Business，终身教授
报告时间：2019年6月19日（星期三），15:00—16:30
报告地点：阳明麻豆视频av303
报告摘要：Corporate bankruptcy prediction plays a central role in academic finance research, business practice, and government regulation. Consequently, accurate default probability prediction is extremely important. I will present a discrete transformation family of survival models to corporate default risk predictions. A class of Box-Cox transformations and logarithmic transformations is naturally adopted. The proposed transformation model family is shown to include the popular Shumway model and the grouped relative risk model. We show that a transformation parameter different from those two models is needed for default prediction using a bankruptcy dataset. Due to some distinct features of the bankruptcy application, the proposed class of discrete transformation survival models with time-varying covariates is different from the continuous survival models in the survival analysis literature. Their similarities and differences are discussed. In addition, I will present some recent results on dynamic variable selection to investigate the relative importance of various bankruptcy predictors commonly used in the existing literature.
