Do Venture Capitalists Add Value to Small ... - BYU Marriott School

Journal of Small Business Management 2004 42(1), pp. 78–92

Do Venture Capitalists Add Value to Small Manufacturing Firms? An Empirical Analysis of Venture and Nonventure Capital-Backed Initial Public Offerings by James C. Brau, Richard A. Brown, and Jerome S. Osteryoung

We examine a set of small, venture capital (VC)-backed manufacturing firms and compare it to a control sample of nonVC-backed manufacturing firms going public between 1990 and 1996. We use the degree of underpricing, three-year sales growth, three-year cumulative stock return, and three-year survivability as measures of success. First, we test if the presence of VC backing results in significant differences in success between the two samples. Next, we test if certain VC and deal characteristics are discriminators within the VC-backed sample of firms. Despite previous literature, which argues for either inferior or superior VC post-initial public offering (IPO) performance, these tests indicate no significant differences between VC- and nonVC-backed firms. Additionally, it is found that VC and deal characteristics are not discriminating factors within the VC sample.

Introduction In his 1994 survey of the venture capital literature, Barry (1994) suggests a potential future research question: “Do

venture capitalists add value within the portfolio firm?” Since the asking of this question, several articles have examined the performance of venture capital (VC)-

The authors express thanks to participants of the 2001 Babson College–Kauffman Foundation Entrepreneurship Research Conference. Jim Brau is assistant professor of finance and Goldman Sachs Fellow at the Marriott School at Brigham Young University. His current research interests include initial public offerings and entrepreneurial finance. Richard Brown worked on this paper as an undergraduate as part of his scholarship from the Office of Research and Creative Activities (ORCA) at Brigham Young University, 2000–2001. He currently works in New York City as a financial analyst. Jerry Osteryoung is Jim Moran Professor of Entrepreneurship, is executive director of the Jim Moran Institute of Global Entrepreneurship, and has served as a professor of finance at Florida State University for over 30 years. His current research interests include entrepreneurial finance, business growth cycles, and firm valuation.

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backed firms versus the performance on nonventure-backed firms (for example, see Brav and Gompers (1997)). In this paper, we extend the literature by further examining Barry’s question. Specifically, the authors’ extension is on three fronts. First, efforts are concentrated on small businesses that conduct an initial public offering (IPO) between 1990 and 1996. A small business is defined in accordance with the Small Business Administration standard—a firm with less than 500 employees. Examination of small businesses provides an extreme setting of potential asymmetric information and should represent a setting in which differences between VC-backed and nonVCbacked firms are detectible (if they exist). Megginson and Weiss (1991) argue that VCs provide a certification role to mitigate information asymmetries. By examining only small firms, firms with large degrees of asymmetric information, VC certification should be even more valuable. The analysis of small firms is not trivial, as the preponderance of the empirical evidence used to test VCrelated hypotheses is based upon samples that consist mainly of large firms. Ang (1991) argues that small firms are inherently different than larger firms. As such, whether the empirical findings of the large firm empirical tests are robust to small firms is a matter worthy of study. Second, the focus here is only on manufacturing firms that conduct an IPO. Creating a fairly homogeneous data sample permits the further refinement of comparison between VC- and nonVCbacked firms. Similar to the work of Lerner (1994), who chooses to analyze a particular industry (biotechnology), the authors argue that focusing on a certain subset of firms allows for a cleaner comparison of VC performance. Third, four dimensions are used to measure success: initial underpricing, stock performance, sales growth, and

survivability. Many of the existing studies consider only one of the measures of success in isolation. Using an array of success metrics allows for the addition of completeness to this study of small, manufacturing firms. The remainder of the paper flows as follows. Next, theories are presented that speak to the expected impact of VC presence in firms going public. One stream of literature suggests that VC-backed firms should have superior performance relative to a control sample, whereas a second line of literature suggests the opposite. The literature that supports both views is discussed. Following the discussion, the data sample and empirical tests are presented. The final section includes a discussion of the empirical results and interpretations of the findings.

Literature Review and Theoretical Camps Tyebjee and Bruno (1984) outline five specific functions of venture capitalists. In this paper the focus is mainly on their fifth point—the post-investment activities. Tyebjee and Bruno (1984) argue that VCs also serve to originate, to screen, to evaluate, and to structure the deal prior to the post-investment function. The measures of success that are considered all occur after an IPO. These measures may capture the first four functions listed above as well as the post-investment function. This study begins at the point of the IPO, so any benefits offered by VCs prior to the IPO are not addressed. For example, without the assistance of VC funding, mentoring, and monitoring, some of the VC-backed firms never may have made it to the IPO stage. If conducting an IPO is another measure of success, then this study omits this possible measure as a discriminator. Two lines of literature suggest that predictions pertaining to the post-IPO performance of a firm are possible based upon VC versus no VC backing.

BRAU, BROWN, AND OSTERYOUNG

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The first school of thought claims that VCs do not provide a certification function. Relying on an agency conflict explanation, Admati and Pfleiderer (1994) argue that original insiders may perpetuate an overinvestment problem when dealing with VC investors. Specifically, insiders may continue to invest in projects with significant probabilities of destroying value, if the insiders are using other people’s money. Relying on asymmetric information, the original insiders can attract VC capital (that is, other people’s money). By the time VCs learn of the problem, they now have become insiders themselves. As new insiders, VCs now have incentives to spend other people’s money and hence may look toward an IPO for new funds. Initial public offering investors, due to asymmetric information, may on average invest in inferior IPOs when they are VC backed. The empirical prediction from this information/agency framework is that VC-backed IPOs should underperform nonVC-backed IPOs. Amit, Glosten, and Muller (1990) provide another argument that VCbacked firms should fair worse in aftermarket performance measures. They reason that VCs attract lemons on average because strong entrepreneurial firms will be able to come to market without being forced to sell an equity interest to VCs. VC-backed IPOs are suspected to be inferior to nonVC-backed IPOs based on this logic. Gompers (1996) argues that a certain subset of VCs—young VCs trying to establish reputations—have perverse incentives to bring IPOs to market prematurely. He provides empirical evidence of this hypothesis. The evidence suggests that young VCs act in their own selfinterest relative to old VCs and do not optimize the value of the IPO through such functions as monitoring and timing the IPO market. The second school of thought claims that VCs do provide a certification func-

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tion for firms going public. Megginson and Weiss (1991) and Barry et al. (1990) are two of the early studies that analyze VC effects on IPO firms and find that VC-backed firms underprice to a lower degree than nonVC-backed IPOs. Both studies conclude that VCs serve a certification function and mitigate information asymmetries between the issuing firm and IPO investors. Brav and Gompers (1997) test the long-run performance of VC- and nonVCbacked IPOs. Using equally weighted returns, they find that VC-backed firms significantly outperform nonVC-backed firms. However, using value weighting and the Fama and French (1993) threefactor model, they find that only small, nonVC-backed IPOs underperform. Because small VC-backed IPOs do not underperform based upon Brav and Gomper’s results, one can infer that for small firms (defined using market capitalization), VC-backed IPOs should outperform nonVC-backed IPOs in the aftermarket. Thus, these two streams of research are at odds. One claims that VC-backed firms should underperform in the aftermarket; the other asserts that VC-backed firms should overperform in the aftermarket. In the next section, these two predictions are tested empirically. A VCbacked IPO sample and a control sample of nonVC-backed IPOs are constructed. The testable hypotheses are as follows: H1: Relative to nonVC-backed IPOs, VCbacked IPOs will have greater underpricing, lower three-year cumulative stock returns, lower three-year growth in sales, and a lower survivability rate. H2: Relative to nonVC-backed IPOs, VCbacked IPOs will have lower underpricing, greater three-year cumulative stock returns, greater three-year growth in sales, and a greater survivability rate.


Data Sample: Selection Criteria and Summary Statistics The VC-backed IPO sample is drawn from Security Data Company’s (SDC) new issues database. The screening criteria and the number of SDC firms meeting each successive criterion are as follows: (1) The firm conducted an IPO between January 1, 1990, and December 31, 1996 (n = 4,138); (2) The firm must have been venture backed (n = 1,296); (3) The firm must have fewer than 500 employees reported in the offering prospectus (n = 284); and (4) The firm is classified as a manufacturing company by SDC (n = 142). From these 142 firms, it is required that a firm list on the Center for Research in Security Prices (CRSP) database in order to retrieve the stock return data and delisting codes if applicable. Fourteen firms that do not list on CRSP are lost, and two firms that have extreme stock returns are deleted, for a final sample of 126 VCbacked IPOs. The control sample also is drawn from SDC’s new issues database. The screening criteria for the control sample are that the firm (1) conducted an IPO between January 1, 1990, and December 31, 1996 (n = 4,138); (2) must not have been venture backed (n = 2,842); (3) must have fewer than 500 employees reported in the offering prospectus (n = 368); and (4) is classified as a manufacturing company by SDC (n = 137). From these 137 firms, 92 have available stock market data from CRSP. The sample selection criteria suggest that if listing on CRSP is a measure of success, VC-backed firms are successful 90 percent of the time (128/142), whereas nonVC-backed firms are successful only 67 percent of the time (92/137). In an attempt to create a control sample with an equal number of observations as the VC sample, Bloomberg was searched for pricing data on the 45 firms lost due to the CRSP

screen. With the Bloomberg supplement, the control sample consists of 108 nonVC-backed IPOs with returns data. The summary statistics for both samples are presented in Table 1, with Panel A containing the VC-backed IPO data and Panel B containing the nonVCbacked IPO data. The offer price, number of primary shares, number of secondary shares, lead underwriter, number of employees at the offer date, total assets before the offer, book value per share prior to the offer, standard industrial code (SIC) code, and inside ownership prior to and after the offer from SDC are retrieved. To define the high-tech indicator variable, we use the SIC codes identified by Field and Hanka (2001), who also use SDC data. For manufacturing firms, these codes are 357, 367, 369, 382, and 384. Total shares outstanding, first day closing price, and the monthly returns used in the calculation of the three-year holding period return are from CRSP. Sales data are from Compustat. The Carter-Manaster underwriter metric is taken from Carter and Manaster (1990). Panel A indicates that the average VCbacked IPO issues 2,135,169 primary shares and 246,427 secondary shares at an offer price of $10.42, representing a float of 31 percent of the firm. The VC sample has an average of 164 employees and total assets prior to the offer of nearly $76 million per firm. Prior to the offer, managers own an average 50.5 percent of the firm. After the offer, managers own an average of 36 percent. VC-backed IPOs have an average underpricing of 10 percent, defined as the first day closing price minus the offer price, all divided by the offer price. The 10 percent underpricing may seem low based upon the size of the firms in the sample (that is, smaller firms have been shown to underprice to greater degrees). As a comparison, taking an equally weighted average of all IPOs that went public between


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Table 1 (Panel A) Summary Statistics for Venture Capital-Backed IPOs, 1990–1996 Panel A. Venture Capital-Backed IPOs Variable


Offering Characteristics Offer price ($ US) Number of primary shares Number of secondary shares Flotation of the issue (percent) Carter-Manaster underwriter measure Firm Characteristics Number of employees at offer date Total assets before the offer ($ mil US) Book value per share ($ US) Market equity to book equity ratio High-tech indicator variable Total shares outstanding Ownership characteristics Inside ownership prior to offer (percent) Insider ownership after offer (percent) Post-IPO Measures First day closing price ($ US) Initial return 1 year sales ($ mil US) 2 year cumulative sales ($ mil US) 3 year cumulative sales ($ mil US) 3 year sales growth Cumulative 3 year return

Standard n

Mean

Deviation

Minimum

Maximum

126 126 126 125 126

10.42 2,135,169 246,427 31.0 7.32

3.31 830,145 648,466 13.0 2.62

4 0 0 10.0 0

25 7,500,000 4,960,000 100.0 9

126 121 116 115 126 126

164 76.0 3.54 7.25 0.45 8,893,437

129 652.9 2.45 25.53 0.5 5,674,010

6 0.4 0.07 0 0 253,000

102 101

50.53 36.17

23.24 16.73

4.3 2.64

126 126 121 117 110 107 126

11.63 0.1 30.65 69.4 107.4 0.642 0.257

5.1 0.18 54.49 120.7 191.4 1.512 1.198

4.125 -0.16 0 0 0 -0.96 -0.99

500 7,190 14.67 259.36 1 47,803,000 96.5 75.7 45.13 0.97 420.13 934.9 1,450 9.36 5.16

Table 1 (Panel B) Summary Statistics for Nonventure Capital-Backed IPOs, 1990–1996 Panel B. Nonventure Capital-Backed IPOs Variable


Offering Characteristics Offer price ($ US) Number of primary shares Number of secondary shares Flotation of the issue (percent) Carter-Manaster underwriter measure Firm Characteristics Number of employees at offer date Total assets before the offer ($ mil US) Book value per share ($ US) Market equity to book equity ratio High-tech indicator variable Total shares outstanding Ownership Characteristics Inside ownership prior to offer (percent) Insider ownership after offer (percent) Post-IPO Measures First day closing price ($ US) Initial return 1 year sales ($ mil US) 2 year cumulative sales ($ mil US) 3 year cumulative sales ($ mil US) 3 year sales growth Cumulative 3 year return

Standard

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n

Mean

Deviation

Minimum

133 133 133 90 133

8.1 2,031,137 187,567 0.38 3.79

4.16 1,487,302 627,684 0.205 3.72

133 111 124 110 133 90

163 29.5 3.48 3.94 0.33 6,877,756

143 108.23 4.99 2.67 0.47 5,250,542

111 111

67.89 45.53

27.67 19.32

1.6 1.2

100 82.4

113 113 105 101 89 84 108

9.61 0.13 48.15 94.45 139.8 0.835 0.123

5.06 0.23 96.44 140.19 202.7 1.594 1.049

1.38 -0.34 0 0 0 -1 -1

32.125 1.18 785.15 720.75 1,081.7 9.365 4.25

1 300,000 0 0.151 0 1 0.3 -0.77 1.21 0 1,400,000

Maximum

21 8,850,000 5,916,055 1 9 500 882.4 43.5 19.58 1 30,862,000

1990 and 1996 from Jay Ritter’s website (http://bear.cba.ufl.edu/ritter/ index.html) results in a 15.7 percent initial return. Perhaps the firms in this sample, though small, have lower underpricing because they are all manufacturing firms. The average firm experienced a three-year cumulative return of 26.7 percent on a three-year sales growth of 64.2 percent. Panel B reports the same statistics for the control sample. A detailed examination of Panel B will be left to the reader.

Univariate Analysis: Venture Capital versus Nonventure Capital Table 2 presents the first series of tests. Panels A–C test the nature of the VC firms versus the control sample—Do the firms themselves vary in significant manners? Panel D tests three of the four success measures, initial underpricing, sales growth, and stock return. The t-statistic and p-value reported for each variable is for the parametric t-test of difference in means, with the null hypothesis of equal means between groups. Panel A indicates that VC-backed IPOs have larger offer prices by $2.32 ( p < 0.0001); float an average of seven percent less of the firm ( p = 0.0042); and use more prestigious underwriters ( p < 0.0001). Panel B indicates that 12 percent more of the VC-backed IPOs are in the high-technology industry ( p = 0.0455) and have an average of two million more shares outstanding after the offer. Panel C reports ownership characteristics. SDC defines the variable Inside Ownership Prior to [after] Offer as “percentage of shares held by insiders (management shares only) before [after] offer.” VC-backed IPO managers have significantly less ownership relative to the control sample both before and after the offer. Prior to the offer, VC-backed IPO managers have 17 percent less ownership (p < 0.0001) and after the offer, nine

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percent less ( p = 0.0001). Managers sell four percent more of their personal shares in VC-backed offerings ( p = 0.0029) and insiders in general sell three percent more (that is, secondary shares) in VC-backed offers ( p = 0.0950). Given the agency arguments of Jensen and Meckling (1976), ceteris paribus, the greater separation of managers and owners in the VC-backed IPOs would predict worse performance for VCbacked IPOs. However, the monitoring and consulting effects of VCs may counter the greater separation of ownership alignment. Panel D reports the univariate test results of H1 and H2. If either hypothesis holds, significant differences in means should be observed. Panel D indicates that none of the success measures (that is, initial underpricing, three-year sales growth, or three-year return) has significantly different means between samples. This finding of nonsignificance is surprising considering the previous section, in which prior literature was discussed that argues for either superiority or inferiority of VC-backed IPOs. The preliminary univariate tests indicate that there is no significance difference between VCand nonVC-backed IPOs and as such suggests the VCs do not add value in the post-IPO period.

Multivariate Analysis: Venture Capital versus Nonventure Capital The previous univariate results indicate no significant difference between the VC and control samples for underpricing, sales growth, or stock returns. In this section, three ordinary least-squares models are estimated using these three success measures as dependant variables. The general model estimated is as follows: Success variable i = a i + b VCi + g Ci + e i


(1)

Table 2 Difference Tests for Venture Capital versus Nonventure Capital-Backed IPOs Variable

VC-Control

Panel A. Offering Characteristics Offer price Number of primary shares Number of secondary shares Flotation of the issue Carter-Manaster underwriter measure Panel B. Firm Characteristics Number of employees at offer date Total assets (millions) before the offer Book value per share Market equity to book equity ratio Hi technology firms Total shares outstanding Panel C. Ownership Characteristics Inside ownership prior to offer Insider ownership after offer Change in insider ownership Percent of secondary to total shares offered Panel D. Post-IPO Measures First day closing price Initial return 1 year sales 2 year cumulative sales 3 year cumulative sales 3 years sales growth Cumulative 3 year return

t-statistic

p-value*

2.32 104,000 58,860 -0.07 3.53

4.98 0.7 0.74 -2.91 8.87