Reducing asymmetric information in venture capital backed IPOs

Diego Escobari, Alejandro Serrano

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Purpose – The purpose of this paper is to model asymmetric information and study the profitability of venture capital (VC) backed initial public offerings (IPOs). The mixtures approach endogenously separates IPOs into differentiated groups based on their returns’ determinants. The authors also analyze the factors that affect the probability that IPOs belong to a specific group. Design/methodology/approach – The authors propose a new method to model asymmetric information between investors and firms in VC backed IPOs. The approach allows the authors to identify differentiated companies under incomplete information. The authors use a sample of 2,404 US firms from 1980 through 2012 to estimate the mixture model via maximum likelihood. Findings – The authors 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 the authors find that profitability is mainly affected by the reputation of the seed VC and capital expenditures. Tangible assets and age help explain group affiliation. The authors also motivate the 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 – The 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.

Original languageEnglish (US)
Pages (from-to)553-568
Number of pages16
JournalManagerial Finance
Issue number6
StatePublished - Jun 13 2016

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting (miscellaneous)
  • Finance


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