Hypothesis Testing Data and methodology
283
H2 predicts the following IPO characteristics and firm specific variables significantly influence
the underpricing level of the IPOs backed by VCs. H2 explores the value-added effect of VC. To test the features of VC-funded IPOs and the alternations in the IPO underpricing, OLS
estimations would be used and the basic model is as follows: IR
i,t
= + X
,i,t
+ X
,i,t
+ ε
i,t
Where: IR
i,t
=performance of the � th firm at time period‘t’ = the overall constant in the model
X
,i,t
= firm specific variables of � th firm X
,i,t
= IPO characteristics of � th firm ε
i,t
= error term Based on existing literature, the initial return is the dependent variable and we divide the
independent variables into IPO features and firm particular variables. IPO features contain the number of days between offering and listing, log of the number of shares being issued, log of
gross issue proceeds capital raised during the IPO, the proportion of the non-negotiable shares, a dummy variable of VC funded, issue price and VC age. Firm specific variables are log of total
assets, log of gross sales. The VC age is defined as the time interval between the year of the establishment and the IPO Wang et al., 2003.
Based on Chen et al. 2004, we use DAY, LNUM and PERCENT to explain underpricing that is caused by section difference. DAY denotes the days form offering to listing. Different from
developed markets where only a short time exists between the offering and the listing, the lasting days normally takes more two months in China. As a result of the information asymmetry, longer
dates between the offering and the listing reflect higher risk for investors to obtain the payback, so that a larger underpricing is expected by investors Su and Fleisher, 1999. Thus, a positive
relationship between DAY and underpricing is proposed. This paper defines LNUM as the number of issued shares at the IPO. Shleifer 1986 states that
the shares’ demand curve in an individual company is not perfectly elastic. When there are more shares being issued, investors do not need to buy the shares urgently on the first day of trading so
that initial returns will be lower. Thus, a negative relationship between LNUM and underpricing is predicted.
PERCENT represents the percentage of non-negotiable shares. When the legal or state institutions hold a large percent of shares, this may be regarded as a signal of operational
inefficiency and bureaucratic control. Hence, most investors may be unwilling to pay a higher price. Mok and Hui 1998 indicate that the IPO return is negatively associated with the
proportion of institution-owned shares. However, Su and Fleisher 1999 claim that the IPO underpricing is positively related to the proportion of government state. We still conjecture that
PERCENT is negatively related to the underpricing.
H3 estimates the influence of VC’s participant on the market performance of the IPO. By
verifying whether PE ratio of VC-funded IPO is greater than or equal to non-VC-funded ones, test is performed:
H : PE VC = PE NON − VC
H4 assumed that excess return of VC-treated IPOs is the same as excess return of non-VC-
treated IPOs. It tests if VCs will affect the market performance of the VC-treated listed firms. The aftermarket return is defined as buy-and-hold market return for a particular time interval,
which is calculated as the closing price after the IPO minus the closing price of the first day of
284 trading divided by the closing price of the first day of trading Wang et al., 2003. We adjust the
raw market returns to illustrate firm abnormal return beyond the average market level represented by the Shanghai Composite Index Stock code: 000001.
Thus, the fourth hypothesis is that performance of VC-treated firms is not different from that of non-VC-treated counterparties regarding one-month aftermarket returns, 3-month aftermarket
and 1-year aftermarket: H : ARonemonth VC = ARonemonth NON − VC
H : ARthreemonth VC = ARthreemonth NON − VC H : ARoneyear VC = ARoneyear NON − VC
H5 is VC-backed firms and non-VC-backed firms have the same EPS, ROA, ROE and operating
profit margin. Venture capitalists could certify the IPO process and add value to the post-IPO performance of portfolio companies from the certificationmonitoring model. To compare
operating and market behavior after the IPO between VC-funded IPOs and non-VC-funded ones, we postulate that:
H : EPS VC = EPS NON − VC H : ROA VC = ROA NON − VC
H : ROE VC = ROE NON − VC H : Margin VC = Margin NON − VC
3.3 Sample data analysis 3.3.1 Results of Propensity Score Matching
Ta le : I dustr Distri utio of No -VC- a ked fir s a d VC- a ked fir s:
Sector VC-backed
Matched Group Total Group
No. Percent No.
Percent No.
Percent Graziery
1 0.98
0.00 9
0.54 Wine, drinks and refined tea
2 1.96
3 3.37
29 1.73
Textile 1
0.98 0.00
33 1.96
Chemical Products 21
20.59 17 19.10
367 21.83
Non-metallic Mineral Products
7 6.86
12 13.48
92 5.47
Metal Products 3
2.94 0.00
59 3.51
Manufacturing 49
48.04 43 48.31
713 42.42
Construction 3
2.94 2
2.25 52
3.09 Wholesale, Retail Trade and
Warehousing 4
3.92 4
4.49 187
11.12 Computer and related services 2
1.96 2
2.25 89
5.29 Financial service
5 4.90
4 4.49
32 1.90
Ecological preservation and environmental treatment
3 2.94
1 1.12
10 0.59
Conglomerates 1
0.98 1
1.12 9
0.54 Total
102 100.00 89
100.00 1681 100.00
The whole sample, the treated and matched group classified by industry code is shown in Table 2. Firm’s industry affiliation is the firm specific variable that is expected to affect the venture
fund decision. We use a whole sample set of 102 VC-backed firms and 1579 non-VC-backed firms to do the PSM. Based on the results in Table 2, we compute the propensity score for each
firm, whose simple description is in Table 3. We based on 200 replications bootstrapped standard
285 errors to do statistical inference. 1551 observations are left to after calculating the 95
confidence intervals. Then, we employ the nearest neighbor matching procedure to get the treated and control groups. It can be seen from Table 4 this step shrank the sample to 102 treated
and 89 untreated firms.
Ta le : Des riptio of Esti ated prope sit s ore
Obs Mean
Std. Dev. Skewness
Kurtosis 1551
.0662039 .0601257
8.11689 116.3705
Ta le : Des riptio of A erage Treat e t Effe t
No. treat. No. contr.
ATT Std. Err.
t-statistics 103
89 -
226.845 204.836
- 1.107