Self-Selectivity in Firm's Decision to Withdraw IPO: Bayesian Inference for Hazard Models of Bankruptcy With Feedback

• Published in: Journal of the American Statistical Association Vol. 105; no. 492; pp. 1297 - 1309
• Main Authors: Chen, Rong, Guo, Re-Jin, Lin, Ming
• Format: Journal Article
• Language: English
• Published: Alexandria, VA American Statistical Association 01.12.2010; Taylor & Francis Ltd
• Subjects: Applications; Applications and Case Studies; Bankruptcy; Bayesian analysis; Bayesian method; Business structures; Decision making models; Decision theory; Econometric models; Economic models; Estimation; Exact sciences and technology; General topics; Initial public offerings; Insurance, economics, finance; Mathematics; Modeling; Nonparametric models; Parametric inference; Parametric models; Probabilities; Probability; Probability and statistics; Sciences and techniques of general use; Statistical models; Statistics; Time dependence; Bayes estimation; Self-selectivity; Numerical linear algebra; Determinant; Bias; Finance; Econometric model; Statistical estimation; Statistical decision; Hazard model; Decision model; Statistical method; Numerical analysis; Feedback; Decision theory; Statistical model; Model specification; Survival probability; IPO withdrawal; Bayesian inference; Bayes decision; Biased estimation
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1198/jasa.2010.ap08663

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 postwithdrawal 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 the subsequent performance of a firm, and that the "feedback" is an important determinant in managerial decisions. The econometric and statistical model specification and the accompanying estimation procedure we used can be widely applicable to study self-selective corporate transactions.