Estimating the Model for Seasoned Equity Offering Underpricing: Application to the Chinese Financial Market

ABSTRACT

We estimate the binomial probit model to examine the significance of important explanatory variables documented in seasoned equity offering (SEO) underpricing literature using two statistical approaches: maximum likelihood estimation and Bayesian estimation. In particular, our estimation relies on SEO-related data in the Chinese financial market, where the pricing mechanism is less transparent compared to that in the U.S. market. We find that the signs of coefficients for the explanatory variables in each model are not different, but their magnitudes appear to be different. Our finding also shows that estimation results are generally consistent with the results observed in the U.S. market.

KEY WORDS:

• Bayesian estimation
• maximum likelihood estimation
• probit model
• SEO underpricing

Acknowledgments

The authors thank Editor Ali M. Kutan and an anonymous referee for their highly constructive comments.

Notes

1. The probit maximum likelihood estimator is also labeled a quasi-maximum likelihood estimator (QMLE) “in view of the possibility that the normal probability model might be misspecified” (Greene 2008, p. 603).