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

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1297-1309
Number of pages13
JournalJournal of the American Statistical Association
Volume105
Issue number492
DOIs
StatePublished - Dec 1 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Bayesian inference
  • Decision model
  • Hazard model
  • IPO withdrawal
  • Self-selectivity

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