280 play a more active role in the corporate governance management of their portfolio firms in the
post-IPO period, and this continues to positively foster the post-IPO performance of VC-backed firms.
Thus, these two models predict opposite portfolio firm performance in the IPO and post-IPO process. In the IPO time, the certification model proposes lower IPO cost and less underpricing
for VC-backed IPOs while the adverse selection model expects higher IPO costs and greater underpricing because VC-backed IPOs have higher risk. Regarding the IPO period, the
monitoring model assumes worse operating performance of VC-backed companies while the adverse selection model expects same or superior performance to ensure the IPO success. As to
post-IPO market and operating performance, the monitoring model predicts that VC-backed ones are superior to non-VC-backed counterparties though the difference will decline as firm ages and
venture capitalists exit. In contrast, the grandstanding model predicts that VC-backed firms perform worse and as firm age increase the potential risk would come into truth.
3. Data and methodology
The objection of this paper is to explore the degree that the IPO and post-performance of Jiangsu listed firms is affected by the participation of VC investors. We intend to compare a match-pair
sample of 89 non-VC-backed and 102 VC-backed listed firms in Jiangsu Province.
3.1 Sample selection
We extract raw information from the CV Sources Database to set up a dataset of 190 VC-backed firms. We exclude firms, from which VCs exit by merger and acquisition. In other words, we
focus on the Chinese VC-backed companies that succeeded in IPOs. Then according to each industry, we download a group of 2230 firms from the CSMAR database. After excluding the
VC-backed listed firms, the rest 2040 is the total sample of non-VC-funded listed firms. Also, we exclude the VC-funded firms which are listed in the NYSE, DASDAQ, Korea Stock Exchange
KSE, Singapore Exchange Limited SGX and Taiwan Stock Exchange Corporation TSEC. The rest are all listed on the Shanghai Securities Exchange SSE.
Then, companies with incomplete have deleted from the sample, as well as some abnormal variables. For example, firm stock code starting with ‘900’ and ‘200’ were deleted since related
closing price could not be found from the CSMAR database. After deducting those data mentioned above, the whole sample consists of 1681 observations from 1993 the year time
when the first VC-backed firm was listed to 2014 the most recent accessible data for this research.
3.1.1 Methodological solution to selection bias adjusted
Based on previous studies, it is expected that venture-backed enterprises may differ significantly from non-funded even in the same industry so we cannot simply set up treatment and control
group by industry. Two major reasons lead to a statistical bias. First of all, venture capitalists are interested in those superior firms only they have survived from pre-investment screening.
Secondly, firms that are not worthy to being invested may not finance by VC. As a result, they normally do not involve in the screening process Engel and Keilbach, 2007. As suggested by
Rosenbusch et al. 2013, it is found that VC investment is positively related to performance of backed companies but after control the industry selection bias the positive influence is
eliminated. This selection bias is not sufficient to be accounted for positive factor of venture fund to firm
281 earning performance. We need to control for the selection biases so that we construct a new
control group of non-VC-backed firms using the propensity score matching Croce et al., 2013; Rosenbaum and Rubin, 1983. The aim is to find the non-VC-backed firms that have the most
similar probability of receiving VC for each VC-backed firm. Following the notation in Lee and Wahal 2004, we define 1 as VC-backed and 0 as non-VC-
backed. Hence Y and Y separately denote the daily return of non-VC-backed IPO and VC- backed IPO. Our intention is the impact of VCs on IPOs, which is Y − Y . Since one firm is
either to be funded or not be funded, Y − Y is unobtainable. Thus we estimate the average treatment effect ATT of VCs, which is as follows.
E[Y − Y |VC = , X] = E[Y |VC = , X] − E[Y |VC = , X] X is denoted as industry features that are related to the selection procedure. E[Y |VC = , X]
defines the average daily return for VC-backed IPOs but the E[Y |VC = , X] is unobtainable. From the conditional independence assumption, the traditional way is to apply
[Y |VC = , X
] = [Y |VC = ，X
] The conditional independence assumption Rubin, 1977 is firms vary in target factor
�
regardless whether realizations of
�
is the same or not. If the above assumption stands, the ATT is as follows,
E[Y |VC = , X] − E[Y |VC = , X]. Unfortunately, venture fund is not invested randomly but is an endogenous choice. It means the
existence of bias, which is �
= E[Y |VC = , X] − E[Y |VC = , X] To eliminate the bias, propose the propensity score method PSM is proposed by Rosenbaum
and Rubin 1983 to match the treated and control groups. A probit model is estimated for the endogenous selection variable Y=1 for venture fund and Y=0 otherwise with a set of X
variables including firm age, education of founders, numbers of employees, total assets, gross sales and industry dummies. The firms’ conditional possibility to obtain VC fund is employed as
the propensity score and VC-funded IPO is matched with the non-VC-funded IPO with the nearest propensity score. As PSM we used in STATA is a one-to-one matching technique, the
limitation is it drops potentially valuable sample.
