Using a dataset consisting of complete bid information for 477 bookbuilt IPOs that took place during Nov 2010 to Oct 2012 in China, I examine whether the performance of institutional investors demonstrates persistence in the IPO market. Building on the adverse selection model as developed by Rock (1986) and a twoperiod analysis, I develop three hypotheses and obtain empirical results that are consistent with the hypotheses. Firstly, I find that the performance of institutional investors continues into the next period. Secondly, I find that the performance persistence exists only for the investors with good past performance but not for investors with bad past performance. Finally, an index capturing the past performance of institutional investors is shown to be informative about the IPO’s initial and medium-term post-market returns. Overall, the results are consistent with the existence of performance persistence among the institutional investors. I conduct additional tests to trace the roots of the observed performance persistence. Results support the hypothesis that institutional investors with good past performance are relatively more informed than those with bad past performance. Specifically, investors with good past performance are more likely to participate in issues with high underpricing, exhibit stronger bid shaving ability, provide more information in terms of high elasticity of demand curve, and show a weaker tendency of naïve reinforcement learning. The results are robust after controlling for the influence of underwriters and after ruling out different alternative explanations. Taking all the results together, my study provides the first systematic evidence on the performance persistence of institutional investors in the IPO market. The results provide important insights for understanding the role of institutional investors in the IPO process and have implications for the design of IPO methods.
|Date of Award||2014|
- Department of Finance and Insurance
|Supervisor||Man Lai Sonia WONG (Supervisor) & Jin GAO (Supervisor)|