Purpose – The purpose of this paper is to model asymmetric information and study the profitability of venture capital (VC) backed initial public offerings (IPOs). Our mixtures approach endogenously separates IPOs into differentiated groups based on their returns’ determinants. We also analyze the factors that affect the probability that IPOs belong to a specific group. Design/methodology/approach – We propose a new method to model asymmetric information between investors and firms in VC backed IPOs. Our approach allows us to identify differentiated companies under incomplete information. We use a sample of 2,404 U.S. firms from 1980 through 2012 to estimate our mixture model via maximum likelihood. Findings – We find strong evidence that companies can be separated into two groups based on how IPO returns are determined. For companies in the first group the results are similar to previous studies. For companies in the second group we find that profitability is mainly affected by the reputation of the seed VC and capital expenditures. Tangible assets and age help explain group affiliation. We also motivate our findings for a continuum of heterogeneous IPO groups. Practical implications – The proposed mixture approach helps decrease asymmetric information for investors, regulators, and companies. Originality/value – Our mixture methods help decrease asymmetric information between investors and firms improving the probability of making profitable investments. Separating between groups of IPOs is crucial because different determinants of an IPO operating performance can potentially have opposite effects for different groups.
Venture Capital Mixture Model Initial Public Offerings Asymmetric Information