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Activity Number: 504
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #320335
Title: Variable Selection for Corporate Bankruptcy Prediction: A Generalized Single-Index Approach
Author(s): Shaobo Li* and Yan Yu
Companies: University of Cincinnati and University of Cincinnati

Corporate bankruptcy prediction is of paramount interest in risk management. The response variable of interest, default vs. non-default is binary in nature. In this paper, we examine important predictor variables under a generalized single-index model framework. The model adopted is flexible and naturally provides interpretable index-coefficients. The univariate unknown function is estimated by polynomial splines. Least absolute shrinkage and selection operator (LASSO) penalty and SCAD penalties are used. We demonstrate through a comprehensive corporate bankruptcy database that contains both market and accounting variables, commonly used in the literature. The findings with generalized single-index approach shed new light in the bankruptcy prediction literature.

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

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