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Activity Number: 65 - Building a New and Essential Statistics Toolbox for Challenges in Finance and Business Analytics
Type: Topic Contributed
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #322627
Title: On Corporate Bankruptcy Prediction
Author(s): Yan Yu* and Aidong Ding and Shaonan Tian and Hui Guo
Companies: University of Cincinnati and Northeastern University and San Jose State University and University of Cincinnati
Keywords: Credit risk ; Proportional hazards ; Survival analysis ; LASSO ; Variable selection ; Logistic regression

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.

Authors who are presenting talks have a * after their name.

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