Bankruptcy prediction has been of great interest to academics in the fields of accounting and finance for decades. Prior literatures focus mostly on investigating the covariates that lead to bankruptcy. In this thesis, however, we extend the issue of interest to what are the possible covariates that cause significant jumps in bankruptcy probability for a company.
We consider the BSM-probability measure examined by Hillegeist, Keating, Cram, and Lundsedt (2004) to help us calculate the variation in bankruptcy probabilities for companies. In addition, recurrent event data analysis is applied to explore these jumps in bankruptcy intensity.
By investigating the S&P500 constituents with sample consists of 343 S&P500-listed companies and 17,836 quarter observations starting from 1994 to 2007, we find that, in three of our models, all of these six covariates are negatively related to the recurrences of event that a company will suffer significant jumps in its bankruptcy probability during the next quarter. Additionally, macroeconomic covariates have greater explanatory power as factors affecting the probability of these jumps, while company-specific covariates contribute less to these recurrences of events. In comparison, we conduct another estimation based on the observation of slight increases in bankruptcy probability for companies. Contrary to what we find on the prior dataset, our empirical results suggest the factors that evoke these events are less prominent and their influences on the event recurrence are mixed.

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