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Activity Number: 621 - Portfolio Choice, Stock Returns, Bankrupcty, and Default
Type: Contributed
Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #324849 View Presentation
Title: Additive Logistic Model with Stochastic Macro-Economic Covariates for Corporate Bankruptcy Prediction
Author(s): Xiaorui Zhu* and Yan Yu and Shaonan Tian
Companies: University of Cincinnati, Lindner College of Business and University of Cincinnati and San Jose State University
Keywords: corporate bankruptcy prediction ; Logistic Regression ; financial time series ; Additive model

Accurate corporate bankruptcy prediction is essential for risk management. We propose additive logistic spline model that incorporate stochastic macro-economic variables to predict the baseline of bankruptcy probabilities. We show that including the overall likelihood of bankruptcy under certain economic environment can significantly improve the prediction power of corporate bankruptcy. The time-series model of baseline probabilities shows that the baseline is correlated with the yield spread between AAA and BAA bonds, excess value stock return, and market volatility. Therefore, forecasting of the baseline may represent additional market information that cannot be explained by firm-specific covariates in the hazard model. The spline model sheds light on the nonlinear bankruptcy relationships, and its prediction performance is substantially improved compared with state-of-the-art models in the literature. Several goodness-of-fit tests are applied for the calibration performance.

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