Self-Selectivity in Firm’s Decision to Withdraw IPO: Bayesian Inference for Hazard Models of Bankruptcy with Feedback
Rong Chen, Re-Jin Guo, Ming Lin
Journal of the American Statistical Association
#002122 20131014 (published)
Examination on firm performance subsequent to a chosen event is widely used in finance studies to analyze the motivation behind managerial decisions. However, results are often subject to bias when the self-selectivity behind managerial decisions is ignored and unspecified. This study investigates a unique corporate event of initial public offering (IPO) withdrawal, where a firm's subsequent likelihood of bankruptcy is specified in a system of switching hazard models, and the expected difference in post-IPO and post-withdrawal survival probabilities serves as a "feedback" on a firm's decision to cancel its offering. Our Bayesian inference procedure generates strong evidence that incidence of withdrawal unfavorably affects subsequent performance of a firm, and that the "feedback" is an important determinant in managerial decision. The econometric and statistical model specification and the accompanying estimation procedure we used can be widely applicable to study self-selective corporate transactions.

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