Venturing Beyond the IPO:

Venture Capitalists’ Investments in Newly Public Firms

Peter Iliev Pennsylvania State University

Michelle Lowry* Drexel University

April 17, 2016

ABSTRACT

Contrary to generally-held notions regarding the private firm focus of venture capital firms, we find that many VCs take an active investing role in firms after the IPO. Over one-quarter of VC-backed firms receive VC financing within the first five years after the IPO, in many cases from a VC that also funded the firm prior to the IPO. VCs concentrate their investments in firms that are most likely to find it prohibitively costly to raise public equity. Consistent with the added capital enabling the firm to undertake positive NPV projects, these post-IPO investments are associated with positive abnormal returns. Moreover, results indicate that the option to raise capital from a VC firm has positive value: the tendency of a firm’s pre-IPO VC to back firms after the IPO is positively related to post-IPO returns and to firm survival.

JEL Classifications: G24, G32. Keywords: Venture Capitalists, Newly Public Firms, Equity Issuance, IPOs.

*Corresponding authors: Peter Iliev (pgi1@psu.edu) and Michelle Lowry (michelle.lowry@drexel.edu).

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1. Introduction

A wide body of literature emphasizes that venture capitalists focus on young private

companies, generally in high-tech industries. Metrick and Yasuda (2011) put it succinctly: “A

VC invests only in private companies. … A VC’s primary goal is to maximize its financial

return by exiting investments through a sale or an initial public offering (IPO).” However,

contrary to this notion, in a sample of IPOs between 1988 and 2010, we find that 29% of the

firms that were backed by VCs prior to the IPO received additional VC funding within the first

five years after the IPO.

We conjecture that the documented tendency of venture capitalists to fund companies

after the IPO is related to frictions in the market, which VCs have an advantage overcoming.

Newly public firms are generally characterized by high information asymmetry and considerable

growth opportunities. They have substantial demands for capital, but Akerlof (1970) and Myers

and Majluf (1984) suggest that this type of firm can find it challenging to raise sufficient capital

at a viable price. Firms find it costly to issue equity because investors rationally conclude that

any firm attempting to raise outside equity is overvalued; for firms with high information

asymmetry, the extent of possible overvaluation is greater, meaning that the price at which they

can potentially raise outside equity is lower. Absent an intermediary who can differentiate the

high intrinsic quality firms from the lemons, many firms are better off by not raising outside

equity, even if the lack of capital forces them to forego positive NPV projects.

We conjecture that venture capitalists are in a unique position to serve as an intermediary

to these high information asymmetry firms. A VC has an advantage in both identifying the

extent to which a company is over- or undervalued, and in estimating the value of a company’s

future growth opportunities. Moreover, because the VC has an in-depth understanding of the

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industry, the requisite management time to explain the business model and the projected uses of

capital are lower. Finally, VCs have relatively long time horizons, suggesting they may have

less liquidity pressures than other providers of capital. In sum, the venture capitalist is well-

positioned to prevent a market break-down of the type discussed by Akerlof. Venture capitalists’

abilities to assess true firm value enable them to differentiate between firms and invest in the

high quality ones. In addition, VCs’ skill in screening companies suggests that an investment by

a VC should represent a positive signal to other investors.

Alternatively, although VCs have information-related advantages, other factors may

cause their investments in newly public firms to represent negative signals regarding firm value.

First, VCs with prior investments in the firm may have incentives that are not perfectly aligned

with outside shareholders. Their convex compensation schemes potentially give them incentives

to invest additional money in investments that are under-water, in the hopes of realizing the

small probability of a large payoff. Career-type concerns, in which past successes influence both

access to the highest potential private companies and ease of raising follow-on funds, further

contribute to such lottery-type gambles. Second, VCs with prior investments often hold debt-

type instruments in the firm, raising the possibility of a debt overhang problem in which the new

equity investment contributes to the value of debt and existing shareholders realize little benefit

(Myers, 1977). Third, the VC’s equity investment may signal negative information about the

company, for example if it is a source of capital of last resort analogous to what Chaplinsky and

Haushalter (2010) and Brophy et al (2009) document in the case of PIPEs.

We conduct an array of empirical tests to understand the factors that motivate VCs to

invest in companies after the IPO, and also to estimate the extent to which such investments

provide benefits to the companies. Across our sample of 2,459 venture-capital backed IPOs, 712

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companies received venture capital financing within the first five years after the IPO.1 The

characteristics of firms that receive post-IPO VC financing are consistent with our conjecture

that they would be very sensitive to Myers and Majluf-type information asymmetry problems. 2

They tend to be smaller and younger, and to have lower ROA, more negative cash flows, and

higher R&D. In 52% of the post-IPO VC investments, the VC that funded the company after the

IPO also funded the company prior to the IPO. The high rate of repeat investments is consistent

with both the information advantage and the incentives of VCs to invest being particularly strong

in cases where they have a previous investing relationship with the company.

Providing further evidence on the importance of information advantages, in 43% of the

companies that receive post-IPO VC financing, the VC has at least one director sitting on the

company’s board at the end of the fiscal year preceding the investment. In addition, we find that

the VC owns shares prior to the investment in 40% of these companies, and in multiple

specifications prior ownership is one of the most important determinants of a post-IPO VC

investment. This strong relation is consistent with the benefits of an investment being greater if

positive returns are earned across both newly purchased shares and previously owned shares.

Average abnormal returns around the time of VC investments are between two and four

percentage points. In addition, long-run abnormal returns over windows of 24 to 60 months

following the financing are also significantly positive. Finally, we find no evidence that these

firms underperformed the market or other similar firms prior to the post-IPO VC financing. In

aggregate, these results provide strong support for the conjecture that venture capitalists’

investments are good for the underlying companies, likely by enabling them to pursue positive

                                                            1 We eliminate firms in which a VC provided financing within the first 7 days after the IPO. In many of these cases, the prospectus states that the VC intends to buy shares shortly after the IPO. 2 Hertzel and Smith (1993) find results consistent with private placements being a solution to the Myers and Majlouf underinvestment problem.

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NPV projects; they provide no support for the conjecture that venture capitalists’ investments

are motivated by convex compensation contracts or influenced by debt overhang problems.

Further results provide evidence on the benefits to firms of post-IPO VC financing. If a

newly public firm demands capital, the ability to raise funding from its venture capitalist should

have positive value, particularly if it would be otherwise unable to raise capital at a viable price.

However, a selected subsample of firms receive VC funding after the IPO, and market reactions

at the time of these investments potentially reflect multiple pieces of information. We

circumvent this issue by examining ex ante the benefits related to the option to raise funding

from a VC. To achieve this, we take advantage of the fact that one of the strongest predictors of

whether a VC invests after the IPO is whether it invested prior to the IPO. We consider the

following strategy: we go long in companies that were backed prior to their IPO by a VC with a

high tendency to fund newly public firms after the IPO, and we short all other VC-backed IPOs.

Consistent with the benefits of post-IPO VC financing, we find that this long-short portfolio

earns significant abnormal returns. Similarly, we also find differences in rates of delisting:

companies are significantly less likely to delist for poor performance if they were funded prior to

the IPO by VCs with an above-median tendency to fund companies after the IPO.3 Our results

are consistent with these companies being better able to obtain external capital in adverse states

of the world, specifically in cases where they have positive NPV growth opportunities but also

high information asymmetries that make it difficult to raise capital at a viable price.

Finally, our last set of empirical tests considers the potential signaling role that VCs play.

In addition to providing funding, a possible role of the VC after the IPO is to credibly certify

firm value to other potential providers of capital. To examine this, we collect data on other

                                                            3 Finally, we confirm that we are not picking up the benefits of VC reputation: both of these results are robust to excluding companies backed by top 10 venture capitalists. 

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sources of financing and examine the frequency with which they occur in the same month as the

post-IPO financing. The finding that post-IPO VC financings frequently occur in the same

month as other capital raising events provides support for this signaling role.

Our paper contributes to the literature on financial constraints, and the factors that cause

intermediaries to specialize in different types of financing. While there is disagreement on how

to best identify financially constrained firms (see, e.g., Kaplan and Zingales 1997; Whited and

Wu 2006; Hadlock and Pierce 2010; Farre-Mensa and Ljungqvist 2016), there is broad

agreement that access to capital among such firms is critical. Diamond (1984), Ramakrishnan

and Thakor (1984), and Fama (1985) argue that a single private lender can mitigate information

asymmetries, and Boot (2000), Bharath et al (2011), and Hadlock and James (2002) show that

the advantages are greater among firms with higher information asymmetry and among cases in

which firms establish long-term relationships with banks. Our findings suggest that venture

capitalists’ industry expertise enables them to play a similar role in newly public firms. Our

findings also highlight the extent to which many newly public firms are financially constrained.

Despite frequently having substantial cash on hand, their high growth trajectories combined with

high information asymmetry causes them to have insufficient access to SEOs, public bonds, or

syndicated loans to fulfill their capital demands.

Our paper also contributes to the literature on the role of venture capitalists. Sahlman

(1990), Lerner (1995), and Gompers and Lerner (1996), among others, highlight the role of VCs

in young private firms, where VCs’ overarching objective is to exit the investment through an

IPO or via an acquisition. A small number of papers have examined VCs’ investments in public

firms. For example, Chaplinsky and Haushalter (2012) examine VCs’ investments across a

broad spectrum of public firms, focusing on the extent to which VCs profit from these

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investments, and Celikyurt et al (2012) and Dai (2007) both focus on much later stages in firms’

life cycles.4 In contrast to these prior papers, we focus on VCs’ investments in firms within the

first few years after the IPO, a unique point in firms’ life cycles. To the best of our knowledge,

we are the first to examine the ways in which VCs work with newly public firms to alleviate

problems related to financial constraints, with a focus on the associated benefits to the

underlying companies.

2. Data and Descriptive Statistics

We obtain our sample of IPO firms from Thomson Financial SDC Platinum. Following

prior literature, we omit REITs, ADRs, closed-end funds, unit offerings, and IPOs with an offer

price of less than five dollars. In addition to information regarding the IPO, we also obtain

information on venture capital financing rounds from the SDC New Issues Database. We limit

our sample to IPOs in which there was at least one pre-IPO venture capital financing round.5 We

require all firms to have CRSP data, in particular a CRSP price within the first ten trading days

after the IPO, and Compustat data. This leaves us with a sample of 2,459 VC-backed IPOs

between 1988 and 2010. The beginning year of 1988 is motivated by both available details in

the VentureExpert database and Dealscan data. We stop the sample in 2010 to enable to us to

collect five years of data on the post-IPO sources of financing for these firms. We obtain the

year of founding from Jay Ritter’s website, and use this to calculate firm age.6

                                                            4 Celikyurt et al find that VCs frequently sit on Boards of mature companies, but they emphasize that many of these companies have been public for many years and were never VC-backed. Dai compares the investments of VCs versus hedge funds in PIPEs, but similar to Celikyurt et al he focuses on older firms.  5 The VC indicator in the New Issues database underrepresents the number of VC-backed IPOs, starting in approximately 2001. Thus, we rely on the detailed information on pre-IPO VC rounds, rather than the VC dummy. 6 We thank Jay Ritter for making these data publicly available: https://site.warrington.ufl.edu/ritter/files/2015/08/FoundingDates.pdf . See Field and Karpoff (2002) and Loughran and Ritter (2004) for further details.

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Our data on VC financing rounds, which comes from the New Issues Database of

Thomson Financial SDC Platinum, includes information on rounds after the IPO. We focus on

VC financings that occur a maximum of five years after the IPO. Our choice of a five-year

cutoff follows prior literature that defines newly public firms as firms that have been public for

no more than five years. We also omit cases where firms received VC financing within the first

seven days after the IPO, because in many of these cases the VC had stated its intention to invest

within the prospectus.

Table 1 shows the sample breakdown. Focusing on financings that occur between 8 days

and 5 years post-IPO, we identify 712 firms that have post-IPO VC financing, which represents

29% of all VC-backed IPOs. Of the 712 firms with post-IPO VC financing, 370 (52%) receive

funding from a venture capitalist that also funded the firm prior to the IPO.7 We also collect

round amounts from Venture Expert. Of the 712 firms with post-IPO VC financing, 601 (84%)

have non-missing data on round amounts.

Finally, for each of the 462 firms that received post-IPO financing in 1997 or later, we

are able to search on EDGAR for a proxy statement following the IPO but prior to the post-IPO

financing event. For each of these firms we collect data on ownership by any venture capitalist,

including that which provided the post-IPO funding as well as any other VC firms.8 We also

record whether any venture capitalist had a seat on the Board, again differentiating between VCs

that provided post-IPO VC funding versus those that did not. In total, we are able to obtain

                                                            7 We are able to identify the VC fund that participated in each round in approximately 72% of the cases with both pre-IPO and post-IPO funding. Among this subset, we find that the same fund that provided financing prior to the IPO also provided financing after the IPO in 58% of the cases. 8 Many of the firms for which we cannot find proxies obtained financing within the first year after the IPO, meaning there is unlikely to be a proxy statement prior to the post-IPO VC funding. In these cases, we employ data from the IPO prospectus.

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information on the ownership and directorships of VCs in the year prior to the post-IPO VC

financing for 346 firms.

Figure 1 depicts the fraction of IPO firms each year that obtain post-IPO financing within

the first five years after the IPO, as well as the fraction that obtain post-IPO financing from a

venture capitalist that also funded the company prior to the IPO. Looking at those venture-

backed firms that went public in 2000, approximately 29% received further funding from a

venture capitalist within the first five years after the IPO, and approximately 15% received

funding from a VC that had also provided funding prior to the IPO. While the percent of

companies receiving post-IPO financing from a VC varies over time, we do not observe a strong

time trend.

Figure 2 provides evidence on the typical time between the IPO and the first post-IPO

VC financing. While our main empirical tests are all based on VC funding within the first five

years after the IPO, in this figure we follow firms up to a maximum of 20 years (though this

maximum will be smaller for IPOs in more recent years). Approximately 10% of venture-

backed firms receive venture financing within the first year after the IPO, an additional 8%

within the second year for a cumulative percentage of 18%, and an additional 5% during the

third year for a cumulative percentage of 23%. The percent of firms receiving post-IPO VC

financing continues to decline as the number of years since the IPO lengthens.

Panel A of Table 2 provides descriptive statistics on firm-years with versus without post-

IPO VC financing. Firms are included for up to the lesser of five years or the first year in which

they receive post-IPO VC financing. (We report at this observational level to make descriptive

statistics consistent with subsequent regressions). The sample in column 1, which includes firms

that received a post-IPO VC found, consists of 1,595 firm years. Column 2, which shows

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statistics for firms without post-IPO VC rounds, consists of 7,444 firm-years. Results suggest

that post-IPO VC financing is more likely for firms that are characterized by high information

asymmetry and high growth. For example, firm-years with a post-IPO VC round are smaller,

with an average $195 million in sales, compared to $361 million for all other firm-years. Firms

with post-IPO VC financing also have weaker operating performance. For example they have

mean ROA of –33.7%, compared to -18.5% for other firm-years. Fifty-nine percent of firm-

years with a post-IPO VC round have negative cash flow from operations (CFO), compared to

43.2% of other firm years. Looking at proxies for growth opportunities, R&D/assets,

CapEx/assets, and Tobin’s Q, are all higher in firm-years with post-IPO VC financing. To the

extent that growth opportunities tend to decrease over the life-cycle, the finding that firm-years

with post-IPO financing represent younger firms is also consistent with growth opportunities

being related to post-IPO VC funding.

Finally, we find that firm-years with post-IPO VC rounds represent firms that are

substantially more likely to have been backed by a top-10 VC prior to the IPO: 28% of these

firms were backed by a top-10 VC, compared to only 20% of other firms.

Panel B of Table 2 provides descriptive statistics on VC round amounts. As shown in

Table 1, we have data on round amounts for 84% of our sample, and thus Panel B of Table 2

focuses on this subsample. We focus on the rounds closest to the IPO, i.e., the last round prior to

the IPO and the first round after the IPO. We find that average round amounts are somewhat

larger prior to the IPO: an average of 45.6 million pre-IPO, versus 35.7 million post-IPO.

Looking in more detail at the size of the pre-IPO rounds, we find that the average round prior to

the IPO is significantly smaller for firms that subsequently received post-IPO financing: $32.1

million versus $51.3 million. Additionally, among firms that obtain post-IPO VC financing,

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round amounts are larger in cases where the post-IPO funding was provided by at least one VC

that also funded the company prior to the IPO: 38 million versus 33 million (though the

difference is not significant at conventional levels).

Finally, Panel C provides evidence on venture capitalists’ involvement with the company,

among the subset of firms that obtained post-IPO VC funding. Looking first at the top portion of

the table, we see that the venture capitalist that provides post-IPO funding sits on the company’s

Board prior to this funding in 42.4% of companies, with an average of 1.7 directors on the

Board. Somewhat surprisingly, another VC, i.e., a VC other than that which provided the post-

IPO funding, sits on the Board in 67.5% of companies, with an average 2.0 directors on the

Board. In the vast majority of cases, 86.9%, at least one VC sits on the Board at the time of post-

IPO funding. This statistic is notable, given that these funding rounds occur up to five years

after the IPO. This finding is consistent with Field, Lowry, and Mkrtchyan (2013) who argue

that VCs’ experience is useful for directors in newly public firms.

The middle portion of Panel C focuses on equity ownership, where statistics are based on

ownership positions greater than 5% that are reported in proxy statements. We find that in

39.8% of the companies, the venture capitalists that provide post-IPO funding own shares prior

to this funding, and average ownership among these VCs is 26.7%. In contrast, other VCs only

own shares in 19.6% of companies, and average ownership is a much smaller 11.1%. In total, at

least one VC owns shares prior to the post-IPO funding in 51.8% of companies. Finally, the last

portion of the table highlights the extent to which Board membership and share ownership

overlap. In 35.1% of the companies, the VC that provides the post-IPO funding both has a seat

on the Board and owns shares. In these cases, this VC has an average 1.8 directors on the Board

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and average ownership equals 28.0%. On the flip side, in 52.5% the VC has neither a Board seat

nor an ownership position greater than 5%.

3. Factors that influence post-IPO Venture Investments

3.1 Determinants of Post-IPO VC financing

So far we have documented that VCs invest in firms with higher growth options. Next,

we use a multivariate framework to test if this relationship holds once we control for firm

characteristics and allow for time trends. Table 3 examines in a multiple regression framework

the determinants of post-IPO VC investments, with Panel A focusing on whether or not there is a

post-IPO investment, and Panel B focusing on the amount of the investment. Looking first at

Panel A, much of the evidence is consistent with that gleaned from the univariate analyses in

Table 2. In columns one to three of Panel A, we focus on whether any VC invests over the five

years subsequent to the IPO. Columns four to six examine whether one of the pre-IPO VCs

invests subsequent to the IPO. Column one uses the broadest subset of data: all venture-backed

IPOs from 1988 to 2010, with the requisite CRSP and Compustat data as described earlier. In

columns two and three we limit the sample to those post-1996 cases where we have data on VC

directorships and VC ownership at the time of the IPO. Consistent with the dependent variable

being an indicator variable, columns one and two represent logistic regressions. However,

column three includes an interaction term, and thus we employ OLS.9 Columns four to six have a

format similar to that of columns one to three.

Looking first at the left-hand columns of Panel A, we find that the characteristics of the

pre-IPO VC, the operating characteristics of the firm, firm age, and the firm’s financing structure

                                                            9 As described by Ali and Norton (2003) and Greene (2010), the use of interaction terms in nonlinear regressions is problematic, potentially resulting in biased estimates of the interaction coefficient and its standard error. 

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all have significant effects, and there is some evidence that the firm’s growth opportunities are

also relevant. Regarding VC characteristics, firms that had a top ten ranked pre-IPO VC10, firms

that received more pre-IPO VC funding, and firms whose pre-IPO VC had greater ownership at

the time of the IPO are all significantly more likely to receive post-IPO financing. The finding

that firms receiving post-IPO VC financing were more likely backed by a highly ranked VC

prior to the IPO and received more funding at this point suggests that they may be higher quality

firms.

Regarding operating characteristics, firms with negative cash flows from operations and

firms with lower sales are significantly more likely to receive post-IPO VC financing. We find

some evidence that growth opportunities, as measured by R&D/assets, are positively related to

the incidence of post-IPO VC financing, but this effect is not consistent across all specifications.

We also find some evidence of a negative relation with firm age, measured as either years since

incorporation or years since IPO. Together, these findings suggest that the firms most likely to

receive post-IPO VC financing are firms with strong potential, who are at an earlier stage of their

lifecycle and are not generating positive cash flows.

Consistent with our predictions, these are the types of firms that tend to have high

demands for capital and are sensitive to Myers and Majluf-type information asymmetry

problems. In addition, the significance of both leverage and the presence of a syndicated loan

indicates that, on average, these firms are obtaining some capital in the form of debt. Among the

firms with syndicated loans, those with low cash flows from operations are particularly likely to

                                                            10 For these tests we define top 10 VC relying on Nahata (2008). Specifically, we use JPMorgan Chase, Kleiner Perkins Caufield & Byers, New Enterprise Associates, Sequoia Capital, Integral Capital Partners, Oak Investment Partners, Accel Partners, Sprout Group, Goldman Sachs, and Alta Partners, as top 10 VCs.

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obtain post-IPO financing from a VC, potentially because their low cash flows prevent them

from taking on more leverage.

Columns four to six indicate that the determinants of post-IPO financing from the VC

that provided VC pre-IPO funding (as opposed to any VC as in columns one to three) are largely

similar. Evidence regarding the effects of firm age is stronger within this subsample, with the

pre-IPO VCs being particularly focused on firms that have gone public more recently. This is

consistent with these cases being more likely motivated by previous ownership in the company,

i.e., cases in which the VC already owns shares and thus will realize any gain across more

shares.

Panel B of Table 3 shows regressions of the log of the post-IPO round amount, on the

same set of explanatory variables. The sample is limited to those cases in which the firm

obtained VC financing and round amounts are available. In column two the sample is further

limited to the post-1996 subsample in which we have data on VC ownership and VC

directorships at the time of the IPO. We find some evidence that round amounts tend to be

greater for firms that have been public longer. In addition, the size of round amounts are

positively related to leverage, which may reflect a greater need for equity capital as the firm is

closer to its debt capacity. Finally, results also suggest that round amounts tend to be higher

among firms that were backed by a top-10 VC prior to the IPO, suggesting a positive relation

with firm quality.

3.2 Alternative potential sources of financing

In a Modigliani and Miller (1958) setting firms are indifferent between alternative forms

of financing; deviations from this indifference must be related to a violation of at least one of the

Modigliani and Miller assumptions. Both high transactions costs and a lack of perfect

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information limit firms’ access to many forms of financing: firms without a steady stream of

positive cash flows generally have limited access to debt markets, and firms with high

information asymmetry find it difficult to credibly convey their value to a disperse set of

potential investors. Consistent with this, our findings in the prior subsection show that such

firms are particularly likely to rely on VCs for financing after the IPO.

The characteristics of these firms indicate that they are high growth firms, suggesting that

their demands for capital are likely to be substantial. It is unlikely that a venture capitalist will

find it feasible to supply the total capital that these firms need. Figure 3 provides evidence

consistent with this conjecture, showing that our sample firms (i.e., VC-backed IPO firms) rely

on multiple forms of financing over the five years following the IPO. Specifically, the figure

shows the percent of firms (Panels A and B) and the amount of money firms raise (Panel C)

through SEOs, post-IPO venture financing, syndicated loans, and PIPEs. PIPEs represent private

investments in public equity, and as discussed by Dai (2007) and Brophy, Ouimet, and Sialm

(2009), hedge funds represent frequent investors in PIPEs.11

Looking first at Panel A, which includes all VC-backed IPO firms, we see that

approximately 45% of VC-backed IPO firms have either a syndicated loan or an SEO, slightly

less than 30% receive additional VC financing after the IPO, and about 10% raise equity through

a PIPE. While the IPO is widely perceived as a critical event in the lifecycle of the firm, this

figure suggests that a firm’s ability to successfully raise capital in the following several years is

equally critical.

As shown in Panel B, among the subsample firms that receive post-IPO financing,

approximately 45% of firms rely on SEOs and/or syndicated loans in addition to the VC funded

                                                            11 By definition, PIPEs and post-IPO VC financing are defined as mutually exclusive. In other words, a PIPE in which VentureExpert indicates that a VC is involved is not included in our PIPE sample.

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private rounds. Interestingly, a much lower 18% raise capital through PIPEs. Finally, Panel C

similarly restricts the sample to firms with post-IPO VC financing, but the figure illustrates

proceeds raised through each form of financing, scaled by IPO proceeds. This figure provides

insight on the reasons why so many firms employ multiple sources of capital. While a large

percentage of firms obtain financing from a VC after the IPO, even among these firms the capital

provided by the VC does not come close to satisfying their total capital demands. On average,

over the five years after the IPO, the firms that obtain capital from a VC raise on average 50% of

what they obtained in IPO proceeds. In comparison, money raised through SEOs represents 1.3

times IPO proceeds, and money raised through syndicated loans represents 2.4 times IPO

proceeds. These results suggest that an important feature of post-IPO VC financing may be as a

signal to other intermediaries, as certification of the value of the firm to increase the willingness

of others to invest.

To the extent that VC funding provides such a signal, we would expect other

intermediaries to invest at the same time. We examine this conjecture in Figure 4. We order all

firms with post-IPO VC financing in event time, where month zero equals the month of the first

post-IPO VC investment. For each month, from month -12 to +12 relative to the post-IPO VC

financing, we determine the percent of firms that raised capital through an SEO, a PIPE, or a

syndicated loan. We observe a notable spike in both PIPEs and syndicated loans in the month of

the VC financing (month zero). Specifically, 4% of firms with post-IPO VC financing in month

zero also had a PIPE in the same month. This is substantial when compared to the Figure 3

result that only 10% of these firms have a PIPE at any point during the five year post-IPO period,

i.e., nearly half of firms that have a PIPE do so in the same month as the VC financing.

Moreover, 10% of firms issue a syndicated loan in the same month as the post-IPO financing.

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When compared to the fact that approximately 45% of these firms have a syndicated loan during

this five year period, this indicates that nearly one-quarter of firms choosing to obtain loans are

doing so in exactly the same month as the VC financing. We do not find similar issuance

increase for SEOs.

In sum, results suggest that the post-IPO financing is important both as a source of

financing and as a signal that increases access to capital from other sources. Importantly, Table

4 shows that while many firms obtain multiple forms of financing, there exist strong correlations

between firm type and each of these financing types. Each column shows an OLS regression,

where the dependent variable equals one if the firm obtained the designated form of financing

within the year, and zero otherwise.12 Firms are included in the regression until they obtain the

designated form of financing, or in the event that they do not obtain this type of financing until

the earlier of delisting or five years after the IPO. Column one focuses on whether a firm

obtained VC financing in one of the five years after the IPO, column two on PIPEs, column three

on SEOs, and column four on syndicated loans. Findings indicate that lower growth firms with

higher sales and more tangible assets are significantly more likely to have bank financing. In

contrast, firms with smaller sales, negative cash flows from operations, and higher growth are

more likely to raise capital through PIPEs. Finally, firms that are larger but also high growth are

more likely to have SEOs.

Evidence from this subsection highlights several findings. First, firms’ choices regarding

sources of financing after the IPO are consistent with implications of Myers and Majluf. For

example, while newly public firms in general are characterized by high information asymmetry,

those that are most sensitive to these issues rely on an intermediary with whom they are best able

                                                            12 We estimate OLS regressions rather than logistic regression to allow the inclusion of the interaction term between syndicated loan and CFO. 

17  

to credibly convey their value. In contrast, firms that are larger and/or have more positive cash

flows from operations are better able to issue an SEO or obtain more financing through

syndicated loans. Second, newly public firms are characterized by high growth and the IPO is

only the first stage in a series of financing events. Over the first several years after the IPO, most

firms rely on multiple sources of financing to satisfy their capital demands. Third, the VC

provides a relatively small portion of the total capital demanded by these firms, but appears to

serve as a signal to other providers of capital regarding the value of the firm, as evidenced that

firms frequently raise other forms of financing contemporaneous with the venture financing.

3.3 Venture Capitalist Type

To the extent that the post-IPO VC has a unique ability to both provide capital and play a

certification role, it follows that the characteristics of this post-IPO VC are important. In some

sense, one can think of this as a matching problem: the characteristics of the firm demanding the

financing must be matched with the VC that can best overcome the frictions that impede this

firm’s access to capital. Table 5 examines in more detail the types of venture capitalists that are

most likely to provide post-IPO financing to each firm. We base our predictions on factors likely

related to a VCs’ information advantage and a VC’s reputation concerns.

First, we conjecture that the information advantage of a VC is greater in some firms than

others. In particular, a VC that either invested in the firm prior to the IPO or has more

experience in the firm’s industry should have a greater information advantage, and thus be more

likely to make a post-IPO investment. To further identify the relative information advantage of

a VC, we compare across the multiple VCs that invested in a firm prior to the IPO. We predict

that a VC who made its last pre-IPO investment closer to the IPO, who was involved in more

18  

pre-IPO rounds, and who invested more dollars pre-IPO would be more involved in the firm and

thus have a greater information advantage.

Second, we conjecture that VCs differ in the extent of reputation concerns. Ex ante the

effects of reputation, which we proxy for using VC rank, on the tendency of a VC to invest in a

firm are not clear. Higher ranked VCs may have more experience and be better able to

determine when a post-IPO investment will be most valuable. Moreover, they may be motivated

to protect their strong reputation by providing post-IPO capital if this enhances their ability to

exit the pre-IPO investment at the highest possible value. Alternatively, less highly ranked VCs

may be more motivated to build their reputation, for example by supporting their pre-IPO

investments after the IPO.

We test these effects through a series of regressions in Table 5. Panel A examines the

tendency of any venture capital firm to invest in a firm after its IPO, while Panel B focuses on

the tendency of each of the pre-IPO VCs to provide financing after the IPO. Each column shows

a logit regression, where the dependent variable equals one if a particular VC invests in a

specific firm, zero otherwise. Looking first at Panel A, the sample in column one includes all

2,459 VC-backed firms in our sample, interacted with each of the VCs that has at least one round

within the five years beginning on the firm’s IPO date, yielding a total of about 2.7 million

observations. The sample in column 2 is limited to the set of firms that received at least one

round of post-IPO VC financing, which reduces the sample to just under 800,000 observations.

Results across both columns provide strong support for the conjecture that VCs with a stronger

information advantage are significantly more likely to make a post-IPO investment. VCs that

funded the firm prior to the IPO and VCs with more industry experience are significantly more

19  

likely to provide post-IPO financing. In addition, we also find that more highly ranked VCs are

significantly more likely to provide post-IPO financing.

Panel B provides further evidence on the extent to which the information advantage of a

VC in a particular firm is related to the tendency to invest. In this panel we restrict the set of VCs

to the ones that participated in a round with the firm before the IPO. We find that VCs that made

a pre-IPO investment closer to the IPO, that invested in a greater number of pre-IPO rounds, and

that invested in a greater percent of pre-IPO rounds are significantly more likely to invest after

the IPO.

In sum, evidence in Table 5 highlights the importance of the VC’s information

advantage. While over one-quarter of newly public firms receive VC financing within the first

five years after the IPO, the VC that provides the financing is far from random. Rather, this

financing is significantly more likely to be provided by a VC that has the strongest information

advantage with respect to this particular firm. This evidence supports the notion that VCs play

the role of informed investors that can overcome the adverse selection problems highlighted by

Akerlof and Myers-Majluf.

4. Market Performance around post-IPO VC investments

Results in the prior section provided strong evidence that firms receiving post-IPO

financing tend to be characterized by high information asymmetry, and that the VCs that invest

tend to be those with the greatest information advantage. To the extent that the presence of

informed investors that are willing to invest in the firm enables the firm to take positive NPV

projects that it otherwise would not be able to do, announcements of these investments should be

positive news. Absent any countervailing factors, we would expect firms to benefit from these

20  

infusions of capital, and thus predict positive abnormal returns. We test these ideas by

examining both CARs in the days immediately surrounding these investments and longer-run

returns over the subsequent one, three, and five year periods.

As noted previously, even if VCs have an information advantage, frictions and conflicts

of interest may limit the benefits of these investments to either the firm or to outside

shareholders. First, VCs may be motivated by convex compensation schemes or career concerns,

which cause VCs to pursue projects with a small probability of a large payoff; these scenarios

are particularly likely in cases where the VC had prior ownership in the firm and the investment

is currently under water. Second, even if the VC’s investment goes toward positive NPV

projects, a debt overhang problem may cause shareholders to not receive much of this value; this

is similarly particularly likely in cases where the VC funded the firm prior to the IPO (and likely

owns some debt-type instruments in addition to equity) and the investment is currently under

water. Third, the high information asymmetry of these firms raises the possibility that the firm

was overvalued, and the VC’s investment reveals information about the extent of overvaluation;

in this case, market returns may be negative even if the investment was good news for all parties.

Finally, it is also possible that venture capitalists represent a financing source of last resort, i.e., a

last available source of financing to distressed firms, similar to what Chaplinsky and Haushalter

(2010) and Brophy et al (2009) find for PIPEs.

Tables 6, 7, and 8 examine these issues, by looking at abnormal returns prior to the post-

IPO VC investment, in the days surrounding the investment (or announcement thereof), and in

the years following the investment, respectively.

Looking first at Table 6, we seek to determine if post-IPO financing is concentrated

among poorly performing firms, a finding that would increase the probability that VCs’

21  

investments were influenced by factors such as convex compensation schemes or debt overhang

problems. If companies have underperformed since the IPO, they may be at risk of bankruptcy

without additional capital infusions. If the company goes bankrupt or delists for poor

performance, the VC risks losing its entire investment. The VC might thus rationally choose to

invest money in the company, even if the investment represents a low probability of a high

payoff, analogous to an out-of-the money option.

Table 6 shows five regressions, to determine the extent to which companies receiving

post-IPO VC financing have underperformed since the IPO, relative to other IPO firms. Looking

first at Column one, for each IPO firm receiving funding in the first year after the IPO, we

choose a matched firm from amongst the sample of firms that received venture backing prior to

the IPO and went public during the same year. For each sample firm, the matched firm

represents the firm with the closest global proceeds, among those firms that went public within

one year. We choose the matched sample with replacement.13 For each firm (sample firms and

matched firms), we calculate the return from the IPO date until the VC financing date. We

repeat the procedure for firms receiving VC financing in the second, third, fourth, and fifth years

after the IPO, results of which are reported in columns 2 - 5.

We estimate a cross-sectional regression of industry-adjusted returns (measured between

the IPO date and the VC financing date) on a constant and a dummy equal to one for those firms

that received post-IPO VC financing. Industry-adjusted returns are calculated as raw returns

minus the average return on firms within the same Fama-French 49 industry group. In effect, the

coefficient on the constant term gives the equally weighted BHAR of the group of matched

                                                            13 Due to the small sample of possible matches in some cases, our main results are based on a matching procedure that does not include an industry parameter. However, univariate comparisons show no significant differences in industry composition between the sample and matched firms.

22  

firms, while the Post-IPO VC Financing Dummy gives the average BHAR of the portfolio that

longs the firms supported by VCs and shorts the matched firms. Looking first at Column 1, the

number of observations equals 352, which represents 176 sample firms and 176 matched firms.

If VCs are disproportionately injecting additional capital into investments that are under water,

i.e., investing in firms that have performed especially poorly, then we would expect a

significantly negative coefficient on the Post-IPO VC financing dummy. Table 6 provides no

evidence to support this conjecture. The dummy is not significant in any of the five regressions,

and it has a positive coefficient in four of the specifications.14

In sum, we find no evidence that VCs are injecting capital into the more poorly

performing IPOs. This casts doubt on the possibility that VCs’ investments are motivated by an

effort to recover the value of a previous investment that is underwater, cases in which there

exists a small probability of a large payoff but a negative NPV overall.

Table 7 examines the abnormal return at the time of the post-IPO VC funding. One

challenge with measuring abnormal returns pertains to the fact that we do not know the exact

date on which the VC funding was announced. In some cases, we are able to find exact

announcement dates, for example when the VC files an 8-K or a Form D. However, these filings

frequently do not coincide with the date reported in SDC. Moreover, in some cases, we are only

able to find mentions of these financings in subsequent 10-Ks. These issues lead us to place

more weight on the longer window CARs, for example day (-1, +5) or day (-1, +10), rather than

the shorter (-1,+1) window. CARs are measured as the raw firm return over the designated

window, minus the return on the matched size quartile over the same period. In addition, we

                                                            14 The substantially larger coefficient in the five-year regression, compared to other regressions, is driven by the small number of observations and the fact that BHARs at five years are extremely varied, ranging from -98% to 3,385%, with a mean of 70% and a median of -35%.

23  

report CARs for three different samples: the complete sample of VC-backed IPOs over the 1988

– 2010 period for which fundings potentially occur over 1988 – 2014, fundings over the 1996 –

2014 sample period that coincides with EDGAR filings and thus potentially represents greater

data availability, and a sample of fundings during 2003 – 2014 for which 8-Ks were filed and

include exact announcement dates.15

Looking at the (-1, +5) event window, we see that abnormal announcement returns equal

6.80% for the 8-K sample where we can most precisely estimate announcement returns.

Abnormal returns are slightly smaller using the broader 1996 – 2014 period, consistent with the

fact that not all of these are announced around the time of funding (i.e., some fundings are only

mentioned in subsequent 10-Ks). Finally, CARs across the entire 1988 – 2014 period are

similarly positive and highly significant (2.39%, with a t-statistic of 3.21), but their smaller

magnitude is consistent with the lower likelihood that these financings were publicly announced

around the time of the funding. In sum, there is strong evidence that the market interprets these

post-IPO VC financings very strongly. This evidence is consistent with the positive abnormal

returns reported by Dai (2007) for a sample of firms in which VCs provided financing.16 This

positive valuation impact is likely a combination of the signaling effect and the increased

financial flexibility, which presumably increases companies’ abilities to realize their growth

opportunities. Figure 5 depicts these CARs graphically. The figure shows abnormal returns

measured using three alternative algorithms: market-adjusted, size-adjusted, and industry-

adjusted. Results are qualitatively similar across all three.

                                                            15 Announcement dates were required to be reported in 8-K filings starting in 2003. 16 As noted earlier, the sample examined by Dai differs substantially from ours. While we focus on IPO firms, he examines a broader set of mature firms. 

24  

Unfortunately it is difficult to evaluate the precise present value of these investments for

the venture capitalists, as we lack information on the precise price the VC paid for the shares.

However, in a small sample of cases where we have detailed data (specifically cases where the

VC filed an 8-K), we know that the VC frequently buys the shares at a discount to the public

share price, a finding that is consistent with Chaplinsky and Haushalter (2012).

Panel B of Table 7 examines whether the magnitude of these abnormal returns are related

to VCs’ information advantage, as proxied by whether they held a seat on the Board, and by their

incentives to enhance the value of prior investments, as proxied by their prior ownership. In

cases where the VC firm holds one or more seats on the Board, they have better information

about the workings of the firm. In such cases, the VC firm is more likely to have the necessary

information to evaluate the NPV of an investment in the firm, and thus less likely to invest if the

NPV is not positive. Given limits to capital, they are also more likely to successfully restrict

their investments to the most positive NPV cases.

Predictions regarding the effects of prior ownership are less clear. It is likely that VCs

have superior information in cases where they have prior ownership, either because their

ownership position gives them increased access to management or because they have greater

incentives to expend resources collecting information. As outlined above, this greater

information should enable them to more precisely estimate the NPV of an investment and to

avoid negative NPV investments. However, their prior ownership gives them increased

incentives to take a position in all positive NPV cases, rather than just the highest NPV cases.

The fact that any gain will be earned across both newly purchased shares and previously owned

shares means that an investment that had a relatively low NPV on a stand-alone basis might have

a much higher NPV when evaluated across this wider ownership base.

25  

Our analysis is limited to the subsample of cases for which we have data on VCs’

directorships and ownership. As noted previously, our information on ownership is restricted to

that reported in proxy statements, meaning in most cases we will only capture ownership

positions greater than 5%. As shown in the top two rows, we have these data for 346 out of the

total 462 firms that received post-IPO VC financing over the 1997 – 2014 period. Most of the

missing observations are cases in which the financing occurred in the earlier portion of the

sample or when the financing occurred within one year of the IPO. Abnormal returns over the

subsample with data are somewhat larger than those for the overall sample, a pattern that is

potentially driven by the fact that we are more likely to have data on larger firms, and the market

is more likely to learn about these investments in a timely manner for larger firms.

Results provide some support for the conjecture that returns are positively related to VCs’

information advantage. Looking at rows 3 and 4 of Panel B, abnormal returns over the (-1,+5)

and (-1,+10) intervals are both greater among the subset of cases in which the VC firm holds one

or more board seats in the firm, prior to the post-IPO financing. This is consistent with these

VCs having superior information and with limits to capital restricting them to investing only in

the most positive NPV cases. Results are similar when we contrast cases where the VC does

versus does not have prior ownership in the firm as reported in the proxy, or when we contrast

the subsample with both board seats and ownership with the subsample that has neither. Across

all comparisons, we find that cases in which the VC likely has superior information have greater

abnormal returns over both the (-1, +5) and (-1, +10) windows. Finally, Panel B also shows

short window (-1, +1) abnormal returns for each of these subsamples. Although we put less

weight on the short window results due to the likelihood that relevant information has not

26  

become publicly available, it is somewhat puzzling that returns go in the opposite direction

versus those over longer windows.

To the extent that investors do not have precise information on many VC fundings, one

would expect some of the information to be incorporated into long-run returns. Thus, we

estimate calendar time portfolio regressions. We form a rolling portfolio, consisting of all firms

that have received post-IPO VC financing within the past twelve months. For each calendar

month we calculate net returns, equal to average returns across all firms in the portfolio minus

the risk-free rate. We regress these portfolio returns on the three Fama-French factors and the

Carhart momentum factor (see Fama and French (1993) and Carhart (1997)). The constant,

commonly referred to as the alpha, represents a measure of abnormal performance. Analogous

regressions are estimated for the 24 month, 36 month, and 60 month intervals.

As shown in Panel A of Table 8, we observe strong evidence that these firms

significantly outperform the market after receiving post-IPO VC financing. Alphas are positive

in every specification, and they are significant in three of the four, specifically for the 24, 36, and

60 month periods. In economic terms, these firms earn an abnormal return of approximately 70

basis points, which is equivalent to 8.4% per year.

Our finding of positive long-run abnormal returns following post-IPO VC investments is

contrary to Hertzel et al’s (2002) finding of significantly negative long-term abnormal returns

following private placements of equity (PIPEs). Given that the Hertzel et al (2002) results are

based on a very different sample, we examine the returns within our sample of newly public

firms following PIPEs (restricted to cases in which VCs are not a primary investor). Panel B of

Table 8 shows four-factor regressions similar to those shown in Panel A, with the exception that

portfolios are formed subsequent to PIPEs. In strong contrast to results following post-IPO VC

27  

investments, we find no evidence of significant abnormal returns following PIPEs. This contrast

in results is consistent with VCs representing a unique source of capital for these newly public

firms.

The evidence of positive abnormal returns both in the days around the announcement and

over the longer term suggests that these post-IPO VC investments benefit firms. This is

consistent with VCs being able to identify cases where a capital infusion will enable a firm to

pursue positive NPV projects, which it would otherwise not be able to do. Other intermediaries

would have charged a higher rate due to a lower ability to overcome the information asymmetry.

In contrast, an alternative possibility is that the VC is simply better able than other market

participants at identifying positive NPV opportunities, and that the firm would have performed

equally well without the VC’s investment. The next section attempts to differentiate between

these possibilities.

5. Do firms benefit from post-IPO VC financing?

The abnormal return analysis in the prior section implies that firms benefit from VC

financing. However, it is not clear whether VCs’ investments contribute to the firms’ higher

returns, for example by relaxing financial constraints and enabling the firm to pursue its positive

NPV projects, or whether VCs are simply investing in firms that would have outperformed the

market even in the absence of the investments from the venture capitalists.

If post-IPO VC financing truly helps firms, then firms with an above-average probability

of receiving such financing (if the state of the world where they demand such financing is

realized) should outperform those with a lower probability. This is precisely the relation that we

estimate. We take advantage of the fact that venture capitalists vary widely in their propensity to

28  

fund companies after the IPO. As shown in Appendix I, New Enterprise Associates and

Warburg Pincus, both invest in over 20% of their deals both prior to and following the IPO. In

contrast, TA Associates and Bessemer Venture Partners have analogous rates of 5% or less. To

avoid a look-ahead bias, for each IPO firm in our sample, we identify the VCs that provided

financing at the time of the IPO, and we calculate the number of post-IPO financings of these

VCs in the period ending one month prior to this firm’s IPO. We split each VC in our sample

into VCs that have been involved in above median amount of post-IPO firms. All companies

backed by a venture capitalist with above-median post-IPO experience are placed into the high

likelihood post-IPO funding portfolio, and all other companies are placed in the low likelihood

post-IPO funding portfolio. Table 9 shows calendar time portfolio regressions, similar to those

reported in Table 8. Panel A represents a long position in the high likelihood post-IPO funding

portfolio, Panel B represents a long position in the low likelihood post-IPO funding portfolio,

and Panel C shows a portfolio that is long in the former and short the latter.

Results provide strong evidence that companies benefit from funding by a venture

capitalist that is more likely to fund companies after the IPO. These companies significantly

outperform the market, particularly at longer windows when cash shortfalls might otherwise

prevent them from realizing positive NPV growth opportunities. Companies receiving funding

by other VCs obtain no similar benefits. Finally, the long-short portfolio is significant at the 24-,

36-, and 60-month intervals.

Among VC-backed IPOs, Krishnan et al (2011) show that companies backed by higher

quality IPOs tend to outperform those backed by lower quality IPOs. To the extent that this

effect of VC quality is not explained by the risk factors in the four-factor model, it might

contribute to the observed abnormal performance in Table 9. To examine this possibility, Table

29  

10 constructs long-short portfolios analogous to those in Table 9, but restricted to companies that

were backed prior to the IPO by either a Top 10 VC (Panel A) or a non-Top 10 VC (Panel B).

Interestingly, our results suggest that the advantages of post-IPO VC financing are concentrated

within the subsample that was not backed by a Top 10 VC prior to the IPO. We conjecture that

the lack of a Top 10 VC prior to the IPO contributes further to the level of information

asymmetry surrounding the firm, making it more difficult for them to obtain external capital at a

viable price in their first few years as a public firm. The option to raise financing from a VC is

particularly valuable in these cases.

In a similar vein, Table 11 examines whether companies backed by high post-IPO

funding VCs are less likely to delist, and in particular whether they are less likely to delist for

poor performance. We estimate logistic models, where the dependent variable is a dummy equal

to one if the firm delists (column 1), one if the firm merges (column 2), or one if the firm delists

for poor performance (column 3), zero otherwise. The independent variable of interest is a

dummy, equal to one if the firm was backed by a VC with an above-median tendency to fund its

pre-IPO firms after the IPO. Consistent with expectations, we find that firms backed by such

VCs are significantly less likely to delist. Moreover, this effect is driven by a lower likelihood of

delisting for poor performance, rather than a decreased likelihood of being acquired.

As discussed above, these results are consistent with the special role for venture

capitalists that are more likely to inject capital into a company following the IPO. For example,

such VCs can create value by financing a firm with positive NPV growth opportunities,

especially when it is financially constrained with a high level of information asymmetry that

makes it difficult to credibly convey its value and thus difficult to raise external equity from

outside investors.

30  

6. Conclusion

Our results suggest that VCs’ post-IPO investments are motivated by companies having a

combination of high information asymmetry and cash shortfalls. As characterized by Myers and

Majluf (1984), outside investors will interpret equity issues by such companies very negatively,

as they will rationally assess an increased probability that the company is overvalued. In such a

situation, an informed investor such as a venture capitalist can better differentiate between high

and low quality companies. The negative cash flow from operations and positive growth

opportunities of companies receiving post-IPO venture financing is consistent with this scenario.

Our results suggest that companies benefit from this infusion of capital from VCs after the IPO.

Abnormal returns are positive both around the time of the investment and over the several years

afterward.

If a newly public firm demands capital, the option to raise funding from its venture

capitalist should have positive value, particularly if high information asymmetry would

otherwise prevent it from raising equity. Consistent we this, we find that firms backed by

venture capitalists that are more likely to invest in firms following the IPO outperform other

firms, and they are less likely to delist for poor performance.

31  

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Appendix I

This table shows the names of the 30 most active venture capitalist firms, ranked based on the number of IPO firms that they have funded, over the 1988 – 2010 period. Column 2 shows the percentage of these deals where the VC was involved only prior to the IPO. Column 3 shows the percent of these IPO firms that the VC funded prior to the IPO only. Column 4 shows the percent of these IPO firms that the VC funded both prior to the IPO and subsequent to the IPO (where subsequent is defined as funding over the day 8 – year 5 period).

VC firm name Number of

deals by VC firm

% deals with VC involved only pre

% deals with VC involved pre and post

IPO

% deals with VC involved only post IPO

Pliant Corp 240 91.3 2.5 6.3Kleiner Perkins Caufield & Byers LLC 132 93.2 6.8 0New Enterprise Associates, Inc. 130 71.5 22.3 6.2Sequoia Capital 105 92.4 5.7 1.9TA Associates Management, L.P. 97 92.8 5.2 2.1Oak Investment Partners 88 84.1 12.5 3.4Venrock, Inc. 84 89.3 8.3 2.4Integral Capital Partners 81 64.2 3.7 32.1Institutional Venture Partners 80 77.5 8.8 13.8Accel Partners & Co Inc 77 79.2 14.3 6.5Mayfield Fund 77 93.5 1.3 5.2Warburg Pincus LLC 77 63.6 22.1 14.3Goldman Sachs & Co 76 89.5 2.6 7.9Summit Partners LP 75 90.7 9.3 0Sprout Group 74 74.3 14.9 10.8Advent International Corporation 69 89.9 7.2 2.9aPriori Capital Partners 68 82.4 4.4 13.2Greylock Partners 66 97 3 0Bessemer Venture Partners, L.P. 64 95.3 1.6 3.1RS Investment Trust 63 88.9 3.2 7.9HarbourVest Partners LLC 63 98.4 0 1.6Domain Associates LLC 60 63.3 21.7 15Intel Capital Corporation 57 94.7 5.3 0U.S. Venture Partners 57 87.7 12.3 0Norwest Venture Partners 56 96.4 0 3.6Apax Partners LLP 56 80.4 8.9 10.7InterWest Partners LLC 55 87.3 12.7 0Sutter Hill Ventures 54 94.4 3.7 1.9Menlo Ventures 53 94.3 3.8 1.9Opus Capital 52 71.2 3.8 25

35  

Appendix II: Variable Descriptions

VC Characteristics: these variables are from VentureExpert

Pre-IPO Funding – millions of dollars obtained in the last VC round prior to the IPO (in 2014 $)

Top-10 pre-IPO VC – a dummy equal to one if the firm was backed by one of the top-10 venture capitalists prior to the IPO, where the identity of these VCs is listed in Appendix 1

Total VC ownership at IPO – the number of shares owned by all VCs at the time of the IPO, divided by shares outstanding after the IPO

% VC directors at IPO – the number of Board seats held by any VC, divided by the number of directors

VC industry experience – the number of IPO firms that the VC has backed within the previous three years

VC Financed Firm pre-IPO – a dummy equal to one if the VC financed the firm prior to the IPO

Years between first VC involvement and IPO – the number of years between the first pre-IPO financing round in which the VC provided funding and the IPO

Total # VCs involved pre-IPO – the number of VC firms that provided funding at any point prior to the IPO

Number of Rounds VC involved in – the number of pre-IPO financing rounds in which the VC provided funding

Percent of Rounds VC involved in – the percent of pre-IPO financing rounds in which the VC provided funding

Post-IPO VC Round Amount – the total dollar amount of the round in which the VC participated

Compustat Variables: all defined as of the fiscal year end prior to the post-IPO VC financing

Total assets – millions of assets in 2014 $

Sales– millions of sales in 2014 $

ROA– Net Income / Total Assets

CFO – Net Cash Flow From Operating Activities/ Total assets

Negative CFO – a dummy equal to one if cash flow from operations was negative

Cash / Assets – holdings of cash plus marketable securities / assets

Tangibility – Property plant & equipment / assets

R&D / Sales – R&D expenditures / sales; set equal to 0 if R&D is missing

Capx / Assets – capital expenditures / assets

Tobin’s Q – Market Value of Equity plus Book Value of Assets minus Book Value of Equity and Deferred Taxes divided by Book Value of Assets

Debt / Assets – the sum of short-term plus long-term debt divided by assets

Life Cycle Variables

Firm Age – the number of years since incorporation, defined as the calendar year minus the year of incorporation (from Jay Ritter’s website)

Years Since IPO – the number of years between the IPO and the post-IPO VC funding

36  

Alternative Forms of Financing

Syndicated Loan – a dummy equal to one if the firm had a syndicated loan recorded during the year, as obtained from DealScan

Prior Syndicated Loan – a dummy equal to one if the firm has a syndicated loan recorded in DealScan either prior to the IPO or at any point after the IPO up until the post-IPO VC financing

SEO – a dummy equal to one if the firm had a seasoned equity offering during the year, as obtained from Thomson Financial

PIPE – a dummy equal to one if the firm had a private investment in public equity during the year (excluding cases where the PIPE observation represents a post-IPO VC financing), as recorded in Thomson Financial, during the year

Other Controls

Initial Return – the percent difference between the offer price and the first after-market closing price

37  

Table 1: Sample of firms that receive post-IPO financing 

Our sample consists of VC-backed IPOs between 1988 and 2010, excluding REITs, ADRs, closed-end funds, unit offerings, and IPOs with an offer price of less than $5. We require all firms to have CRSP and Compustat Data. We exclude firms that receive VC financing in the seven days immediately following the IPO. The % post-IPO VC financing sample is defined as firms that receive funding by a VC between 8 days post-IPO and five years post-IPO.

# Firms

% of total sample

% post-IPO VC financing

sample

VC-backed IPOs 2,459 100%

With post-IPO VC financing, day 8 – year 5 712 29% 100%

With non-missing round amounts 601 24% 84%

With post-IPO VC financing by pre-IPO VC 370 15% 52%

With non-missing round amounts 328 13% 46%

   

38  

Table 2: Descriptive Statistics 

Our sample consists of VC-backed IPOs between 1988 and 2010, as described in Table 1. The sample in Panel A consists of up to five firm years of post-IPO data, where firms that receive post-IPO VC financing are only included up to the year of this financing. Column 1 shows means for firm years with a post-IPO round (defined as post-IPO financing between day 8 and year 5), and column 2 shows means for firm years without a post-IPO round. Panel B summarizes the size of VC round amounts, including both the last round prior to the IPO and the first round after the IPO (defined as during the day 8 – year 5 period). Of the 2,459 VC-backed IPOs in our sample, 2,117 have data on the round amount for the last round prior to the IPO. As shown in the middle of the table, 631 of these received post-IPO VC financing and 1,486 did not. ‘Difference’ denotes whether the mean round amounts of these two groups are significantly different. Of the 601 firms with post-IPO financing and available data on post-IPO VC round amounts, the bottom of the table shows that 328 of these received this post-IPO funding from at least one VC that also funded the firm prior to the IPO and 273 did not. Similarly, ‘Difference’ denotes whether the mean round amounts of these two groups are significantly different. Panel C consists of firms that received VC financing following the IPO (defined as during the day 8 – year 5 period). For a subsample of 382 cases in which a company received post-IPO VC financing, we have data on the firm’s Board composition and ownership structure as of the last proxy prior to the post-IPO VC financing. Variable descriptions are provided in Appendix II.

Panel A: Descriptive Statistics in firm years after IPO

Firms with post-IPO round

(1,595 firm-years)

Firms without

post-IPO round (7,444 firm years)

Difference

Total Assets (mil) 352 465 -113***

Sales (mil) 195 361 -166***

ROA -0.337 -0.185 -0.151***

% w neg’ve CFO 0.593 0.432 0.161***

Cash / Assets 0.418 0.358 0.06***

Tobin's Q 4.49 3.6 0.895***

R&D / Assets 0.223 0.129 0.094***

CAPX / Assets 0.0733 0.0669 0.006***

Debt / Assets 0.211 0.188 0.022***

Tangibility 0.173 0.184 -0.011**

Syndicated Loan 0.42 0.462 -0.042***

Firm Age 10.2 15.6 -5.345***

Pre-IPO VC Funding 73.7 77.1 -3.378

Top-10 pre-IPO VC 0.281 0.198 0.083***

 

39  

Panel B: Descriptive Statistics on VC financing

Mean Q1 Median Q3 Obs

Pre- IPO VC financing (last round prior to IPO) 45.6 3.61 12.2 30.4 2,117

Post-IPO VC financing (first round after IPO) 35.7 1.56 5.2 26.5 601

Pre- IPO VC financing (last round prior to IPO)

Received post-IPO VC financing 32.1 3.37 12.0 28.3 631

No post-IPO VC financing 51.3 3.74 12.4 31.2 1,486

Difference -19.2**

Post-IPO VC financing (first round after IPO)

Post-IPO VC also funded firm prior to IPO 38.0 1.49 6.6 26.6 328

Post-IPO VC did not fund firm prior to IPO 33.0 1.58 4.3 26.5 273

Difference 5.0

40  

Panel C: Descriptive Statistics on companies with a post-IPO VC round and data on the firm’s Board composition and ownership structure (sample of 382 deals)

Percent of Companies

Avg # directors (among firms

with VC directors)

Avg ownership (among firms

with VC ownership)

Post-IPO VC has directors sitting on Board 42.4% 1.7

Another VC has directors sitting on Board 67.5% 2.0

At least one VC has directors sitting on Board 86.9% 2.4

Post-IPO VCs own shares 39.8% 26.7%

Other VCs own shares 19.6% 11.1%

At least one VC owns shares 51.8% 24.7%

Post-IPO VC has directors on Board and owns shares 35.1% 1.8 28.0%

Post-IPO VC has no directors on Board and doesn’t own shares

52.9%

41  

Table 3: Which firms receive post-IPO VC financing?

Panel A: Determinants of a post-IPO round

Dep’t Var =1 if Any VC funded post-IPO

Dep’t Var =1 if a Pre-IPO VC funded post-IPO

VC Characteristics

Total VC ownership at IPO

0.0006***(2.94)

0.0007***(2.99)

0.0005*** (4.39)

0.0008***(4.44)

% VC directors at IPO

0.0000 (0.04)

0.0001 (0.19)

-0.0000 (-0.00)

0.0000 (0.08)

Log(Pre-IPO VC Funds+1) 0.0082*** (4.06)

0.0056 (1.36)

0.0052 (1.31)

0.0053***(4.00)

0.0033 (1.39)

0.0030 (1.14)

Top 10 pre-IPO VC 0.0218*** (3.20)

0.0254***(2.70)

0.0306***(2.75)

0.0120***(2.77)

0.0162*** (2.95)

0.0248***(2.98)

Operating Char’s

Negative CFO 0.0205*** (2.94)

0.0239** (2.25)

0.0182 (1.49)

0.0172***(3.94)

0.0151** (2.57)

0.0119 (1.37)

CFO -0.0184 (-1.37)

-0.0059 (-0.32)

0.0023 (0.10)

-0.0169** (-2.19)

-0.0193** (-2.00)

-0.0248 (-1.55)

Log(Sales) -0.0028 (-1.26)

-0.0084**(-2.53)

-0.0110** (-2.54)

-0.0009 (-0.64)

-0.0031* (-1.78)

-0.0062**(-1.98)

ROA 0.0118 (1.21)

0.0059 (0.47)

0.0096 (0.73)

0.0075 (1.32)

0.0107 (1.59)

0.0148** (2.27)

Growth Opp’s

R&D/Assets 0.0325** (2.42)

0.0281 (1.55)

0.0561* (1.82)

0.0023 (0.28)

0.0048 (0.49)

0.0225 (0.95)

CAPX/Assets 0.0438 (1.09)

-0.0028 (-0.04)

-0.0004 (-0.00)

-0.0054 (-0.23)

-0.0364 (-1.08)

-0.0621 (-1.10)

Tobin's Q -0.0003 (-0.63)

-0.0003 (-0.34)

-0.0004 (-0.45)

-0.0002 (-0.45)

-0.0001 (-0.13)

-0.0003 (-0.42)

Firm Age

Firm Age -0.0005** (-2.49)

-0.0009 (-1.01)

-0.0007 (-1.18)

-0.0003** (-2.39)

-0.0010** (-2.34)

-0.0007***(-3.08)

Years Since IPO -0.0076*** (-3.54)

-0.0009 (-0.23)

-0.0017 (-0.43)

-0.0071*** (-4.89)

-0.0015 (-0.67)

-0.0034 (-1.15)

Financing Structure

Debt/Asset Ratio 0.0253** (2.21)

0.0214 (1.10)

0.0325 (1.23)

0.0114* (1.65)

-0.0026 (-0.23)

-0.0011 (-0.06)

Prior Syndicated Loan 0.0198*** (3.28)

0.0291***(3.13)

0.0231** (2.26)

0.0139***(3.56)

0.0152*** (2.74)

0.0144* (1.92)

Prior Syndicated Loan*CFO

-0.0670* (-1.85)

-0.0508* (-1.74)

Tangibility -0.0249 (-1.24)

-0.0136 (-0.34)

-0.0054 (-0.12)

-0.0013 (-0.12)

0.0091 (0.46)

0.0249 (0.85)

Other Controls

Cash/Assets 0.0131 (1.02)

-0.0138 (-0.69)

-0.0155 (-0.66)

0.0038 (0.47)

-0.0097 (-0.87)

-0.0137 (-0.83)

Initial Return -0.0061 (-1.12)

-0.0063 (-0.92)

-0.0062 (-0.92)

-0.0081** (-2.00)

-0.0076* (-1.73)

-0.0084* (-1.80)

Pseudo R-squared 0.059 0.064 0.084 0.116 Adj. R-Squared 0.028 0.031 Observations 9,039 3,577 3,583 9,039 3,539 3,583

42  

Panel B: Determinants of round amounts  

Dep’t Variable = log(post-IPO VC Round Amount)

VC Characteristics

Total VC ownership at IPO

0.0041 (0.78)

% VC directors at IPO

0.0081 (1.02)

Log(Pre-IPO VC Funding+1) 0.0616 (1.06)

0.0581 (0.49)

Top 10 pre-IPO VC 0.0647 (0.56)

0.3800** (2.01)

Operating Char’s

Negative CFO -0.0027 (-0.02)

0.1612 (0.50)

CFO -0.3054 (-1.13)

-0.0169 (-0.04)

Log(Sales) 0.0118 (0.20)

-0.0222 (-0.21)

ROA 0.1519 (0.76)

-0.1720 (-0.75)

Growth Opp’s

R&D/Assets -0.4065 (-1.61)

-0.5432 (-1.46)

CAPX/Assets 0.9099 (0.85)

1.5941 (0.86)

Tobin's Q 0.0061 (0.43)

0.0065 (0.27)

Firm Age

Firm Age -0.0013 (-0.23)

-0.0090 (-1.41)

Years Since IPO 0.1559*** (2.99)

0.1909** (2.00)

Financing Structure

Debt/Asset Ratio 0.8804*** (2.87)

0.6832 (1.29)

Prior Syndicated Loan 0.1457 (1.20)

0.4277* (1.73)

Prior Syndicated Loan*CFO 0.3317 (1.36)

0.7025* (1.83)

Tangibility 0.2061 (0.41)

-0.2411 (-0.25)

Other Controls

Cash/Assets 0.2535 (0.80)

0.0386 (0.07)

Initial Return 0.0624 (0.46)

-0.0867 (-0.49)

Adj. R-squared 0.326 0.119 Observations 601 243

43  

In Panel A, the sample consists of VC-backed IPOs between 1988 and 2010. We require all firms to have CRSP and Compustat data. Columns 1, 2, 4, and 5 represent logistic regressions, while columns 3 and 6 include interaction terms and represent OLS regressions. In columns 1 – 3 (4 – 6), the dependent variable equals one if the firm received VC financing (VC financing from a VC that also funded the firm prior to the IPO) between day 8 and year 5 after the IPO. Firms that receive VC financing between day 1 and day 7 are excluded. All specifications include year fixed effects, and standard errors are clustered by firm (z-statistics are in parenthesis). The reported coefficients are the marginal effects - the change in the probability to receive a financing for an infinitesimal change in each independent, continuous variable and, the discrete change in the probability for dummy variables. Asterisks denote statistical significance at the 1% (***), 5% (**), or 10% (*) levels. All variables are defined in Appendix II.

In Panel B, the sample consists of VC-backed IPOs between 1988 and 2010, which received post-IPO financing between day 8 and year 5 after the IPO and for which data on round amounts are available. This table shows OLS regressions, where the dependent variable equals the log of the post-IPO round amount. Firms that received multiple rounds of post-IPO VC financing are only included for the first round. In column 2, the sample is limited to IPOs after 1996 for which we can obtain data on VC ownership and prospectuses at the time of the IPO from IPO. Regressions include year fixed effects, and standard errors are clustered by firm. Asterisks denote statistical significance at the 1% (***), 5% (**), or 10% (*) levels. Robust t-statistics are in parentheses.

44  

Table 4: Comparing sources of post-IPO Financing

Each column shows an OLS regression, where the dependent variable equals one if the firm obtained the designated form of financing within the year: post-IPO VC financing, PIPE, SEO, and syndicated loan in columns 1 – 4, respectively. Firms are included in the regression until they obtain the designated type of financing, or in the event they do not obtain this type of financing until earlier of delisting or five years after the IPO. Year fixed effects are included in each regression, and standard errors are clustered by firm. Asterisks denote statistical significance at the 1% (***), 5% (**), or 10% (*) levels. All variables are defined in Appendix II. Robust t-statistics are in parentheses.

Post-IPO VC inv’t

PIPE SEO Syndicated

Loan VC Characteristics Total VC ownership at IPO 0.0007***

(2.99) -0.0000 (-0.30)

0.0004 (1.21)

-0.0001 (-0.38)

% VC directors at IPO 0.0001 (0.19)

-0.0001 (-0.28)

0.0009** (2.12)

-0.0000 (-0.09)

Log(Pre-IPO VC Funding+1) 0.0052 (1.31)

0.0031 (1.50)

0.0145*** (2.70)

-0.0009 (-0.15)

Top 10 pre-IPO VC 0.0306*** (2.75)

0.0009 (0.14)

-0.0119 (-0.94)

0.0123 (1.16)

Operating Char’s Negative CFO 0.0182

(1.49) 0.0286***

(4.36) 0.0197 (1.28)

0.0002 (0.02)

CFO 0.0023 (0.10)

0.0154 (0.79)

0.0743*** (2.59)

-0.0689***(-2.99)

Log(Sales) -0.0110** (-2.54)

-0.0093***(-3.19)

-0.0042 (-0.76)

0.0127*** (2.66)

ROA 0.0096 (0.73)

0.0023 (0.21)

-0.0131 (-0.90)

0.0262** (2.08)

Growth Opp’s R&D/Assets 0.0561*

(1.82) 0.0947***

(4.28) 0.0406 (1.30)

-0.1207***(-5.53)

CAPX/Assets -0.0004 (-0.00)

-0.0338 (-0.64)

0.2820** (2.33)

-0.0556 (-0.45)

Tobin's Q -0.0004 (-0.45)

-0.0003 (-0.91)

0.0030** (2.45)

0.0002 (0.25)

Firm Age Firm Age -0.0007

(-1.18) -0.0004**

(-2.52) -0.0000 (-0.01)

-0.0003 (-0.44)

Years Since IPO -0.0017 (-0.43)

0.0124*** (4.41)

-0.0346*** (-6.87)

-0.0110** (-2.44)

Financing Structure Debt/Asset Ratio 0.0325

(1.23) -0.0034 (-0.23)

0.0301 (0.95)

0.0968*** (2.67)

Prior Syndicated Loan 0.0231** (2.26)

0.0043 (0.74)

0.0045 (0.32)

0.0783*** (4.96)

Prior Syndicated Loan*CFO -0.0670* (-1.85)

0.0075 (0.48)

0.0250 (0.95)

0.1054*** (4.04)

Tangibility -0.0054 (-0.12)

0.0343 (1.43)

-0.0653 (-1.11)

0.1865** (2.51)

Other Controls Cash/Assets -0.0155

(-0.66) -0.0149 (-1.01)

-0.0212 (-0.75)

-0.0856***(-3.36)

Initial Return -0.0062 (-0.92)

-0.0049 (-1.34)

-0.0003 (-0.04)

-0.0085 (-1.18)

Adj. R-squared 0.028 0.041 0.063 0.103 Observations 3,583 4,034 3,197 3,399

45  

Table 5: Which VC provides post-IPO funding?

Panel A examines the tendency of any VC to provide post-IPO financing to a firm. The sample in column 1 consists of each VC-backed IPO firm matched with each venture-capitalist that participated in a VC round within the five years after the firm IPO, meaning the total number of observations equals the product # VC-backed IPOs * # VCs. The dependent variable equals one if the VC funded the firm within the first five years after the IPO, zero otherwise. The regressions in column 2 is defined similarly, but the sample is restricted to those firms that received post-IPO VC financing. In Panel B, we limit the sample to VC firms that financed the firm prior to the IPO, and similar to Panel A the sample is defined as #Firms * #VCs in column 1 and as #Firms that received post-IPO VC financing * #VCs in column 2. Logistic regressions include year fixed effects, and standard errors are clustered by firm. The reported coefficients are the marginal effects - the change in the probability to receive a financing for an infinitesimal change in each independent, continuous variable and, the discrete change in the probability for dummy variables. Robust z-statistics are in parentheses. Asterisks denote statistical significance at the 1% (***), 5% (**), or 10% (*) levels. Variables are defined in Appendix II. Panel A: Which VC firm provided financing after the IPO?

All Firms

Firms that received post-IPO financing

Top 10 VC 0.0010*** (6.70)

0.0037*** (6.33)

VC Industry Experience 0.0004*** (11.84)

0.0012*** (12.54)

VC Financed Firm pre-IPO 0.0277*** (11.83)

0.0866*** (13.34)

Pseudo R-squared 0.247 0.274 Observations 2,679,624 798,716

Panel B: Which pre-IPO VC firm provided financing?

All Firms

Firms that received post-IPO financing

Top 10 pre-IPO VC 0.0554*** (4.91)

0.1386*** (4.79)

VC Industry Experience 0.0234*** (3.35)

0.0496** (2.46)

Years b/w first VC involvement and IPO -0.0088*** (-6.62)

-0.0177*** (-4.52)

Total # of VCs involved pre-IPO -0.0026*** (-3.71)

-0.0126*** (-6.21)

Number of Rounds VC involved in 0.0111*** (7.11)

0.0215*** (4.22)

Percent of Rounds VC involved in 0.0275*** (2.75)

0.1100*** (3.86)

Log(Total Pre-IPO Funding) 0.0020 (0.77)

0.0045 (0.50)

Pseudo R-squared 0.091 0.128 Observations 8,954 3,322

46  

Table 6: Relation between post-IPO returns and tendency to receive post-IPO VC financing

The post-IPO VC financing (treatment) sample consists of VC-backed IPOs between 1988 and 2010, which received post-IPO financing at least 8 days after the IPO. For the one-year regression, treatment firms consist of IPO firms that received financing within the first year after the IPO, and the control sample consists of firms that didn’t receive post-IPO financing within this period, defined as follows: we choose a firm that went public within one year, with the closest global proceeds and the closest IPO date. Control firms are chosen with replacement. The two-year sample is defined analogously, as well as the three-year, four-year and five-year samples. This table shows OLS regressions, where the dependent variable equals the industry adjusted return from the IPO date to the funding date. Post-IPO financing dummy equals one if the firm received post-IPO financing from a VC within the year, zero otherwise. The number of observations equals the number of treatment firms plus the number of control firms. Robust t-statistics are in parentheses.

(1) (2) (3) (4) (5) 1 YR 2 YRs 3 YRs 4 YRs 5 YRs Constant 0.5816

(0.12) -11.7721 (-1.56)

11.8755 (0.49)

-10.0339 (-0.93)

5.7655 (0.25)

Post-IPO VC Financing Dummy

-5.1173 (-0.79)

11.1421 (0.84)

-29.5885 (-1.16)

-3.9710 (-0.24)

52.9152 (0.87)

Adj. R-squared -0.003 -0.001 -0.003 0.001 -0.001 Observations (Treated + Controls)

352 372 228 174 98

 

   

47  

Table 7: CARs around post-IPO VC financings

The sample consists of VC-backed IPOs that received VC financing during the day 8 – year 5 period following the IPO. Cumulative abnormal returns (CARs) are defined as the raw firm return over the designated window, minus the return on the matched size decile over the same period. The top of Panel A shows CARs over the complete sample of VC-backed IPOs between 1988-2010 (which obtained post-IPO VC funding between 1988 – 2014), based on SDC VC funding dates. The middle of Panel A shows CARs of fundings over the 1996 – 2014 period, based on SDC VC funding dates. The bottom of Panel A shows CARs based on announcement dates reported in 8-K filings, which are available for the 2003 – 2014 period. Panel B focuses on CARs for fundings over the 1996 – 2014 period, based on SDC VC funding dates. CARs are shown for the whole sample, conditional on whether the VC (who is providing the post-IPO financing) has a director on the Board, and conditional on the VC’s level of ownership. Robust t-statistics are in parentheses.

Panel A: CARs at time of post-IPO VC financings, over sample periods

CAR (T-Test)

(-1, +1) (-1, +5) (-1, +10) SDC VC funding dates, 1988 - 2014 1.66% 2.39% 2.66% (3.24) (3.21) (3.06)

SDC VC funding dates, 1996 - 2014 2.47% 3.47% 3.83% (3.55) (3.54) (3.32)

Ann’t dates from 8-Ks, 2003 - 2014 1.58% 6.80% 9.35% (1.03) (2.52) (3.04)

48  

Panel B: CARs at time of post-IPO VC financings, conditional on involvement of VC at time of financing

CAR (T-Test)

Obs

(-1, +1) (-1, +5) (-1, +10) Whole Sample, [SDC VC funding dates,

1997 – 2014] 2.47% 3.47% 3.83% 462 (3.55) (3.54) (3.32)

Sample with VC director and VC

ownership information, 1997 – 2014 2.51% 4.29% 5.28% 346 (3.03) (3.72) (3.90)

VC has director on Board 1.45% 5.23% 7.28% 151 (1.17) (2.53) (3.00) VC has no director on Board 3.32% 3.57% 3.72% 195 (2.99) (2.79) (2.51) VC owns shares 1.49% 5.28% 7.58% 143 (1.15) (2.43) (2.95) VC does not own shares 3.23% 3.60% 3.65% 203 (2.99) (2.91) (2.57) VC has a director and owns shares 1.45% 5.23% 7.28% 151

(1.17) (2.53) (3.00) VC has no director and does not own shares

3.77% 3.98% 4.04% 179 (3.16) (2.97) (2.65)

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Table 8: Long-run returns, following post-IPO VC investments and following PIPEs

The sample consists of VC-backed IPOs over the 1987 – 2010 period, which received VC financing during the day 8 – year 5 period following the IPO. This table shows calendar time portfolio regression, where a firm enters the portfolio in the month following the post-IPO VC financing. The firm remains in the portfolio for the first 12 months (col 1), 24 months (col 2), 36 months (col 3), or 60 months (col 4) following the post-IPO financing. Monthly common stock returns on this portfolio, net of the risk-free rate, are regressed on the three Fama-French factors and the Carhart momentum factor. Robust t-statistics are in parentheses. Asterisks denote statistical significance at the 1% (***), 5% (**), or 10% (*) levels.

Panel A: Long-run returns, following post-IPO VC investments

Portfolio formed at post-IPO Financing and continuing for:

12 months 24 months 36 months 60 months

Constant 0.118

(0.26) 0.723** (1.98)

0.759** (2.27)

0.852*** (2.78)

Market-Rf 1.253*** (12.27)

1.294*** (15.87)

1.238*** (15.92)

1.232*** (16.92)

SMB Factor 1.474*** (10.25)

1.447*** (12.88)

1.438*** (12.68)

1.387*** (13.20)

HML Factor -0.824*** (-5.58)

-0.722*** (-6.57)

-0.771*** (-7.78)

-0.702*** (-7.85)

Momentum Factor -0.317** (-2.15)

-0.355*** (-3.41)

-0.320*** (-3.72)

-0.328*** (-4.36)

Adj. R-squared 0.632 0.714 0.738 0.757 Observations 311 311 311 311

Panel B: Long-run returns, following post-IPO PIPEs

Portfolio formed at post-IPO Financing and continuing for:

12 months 24 months 36 months 60 months

Constant -0.160

(-0.23) 0.131

(0.27) 0.051

(0.11) 0.034

(0.08)

Market-Rf 1.408*** (7.02)

1.369*** (10.79)

1.283*** (9.92)

1.253*** (10.90)

SMB Factor 1.623*** (8.15)

1.433*** (8.98)

1.673*** (7.58)

1.565*** (8.00)

HML Factor -0.612** (-2.31)

-0.730*** (-4.16)

-0.772*** (-4.61)

-0.793*** (-5.57)

Momentum Factor -0.256 (-1.51)

-0.210 (-1.39)

-0.218 (-1.35)

-0.187 (-1.55)

Adj. R-squared 0.442 0.601 0.603 0.650 Observations 261 273 282 282

50  

Table 9: Performance of IPO firms that were funded (prior to IPO) by VCs with above- vs below-median propensity to provide post-IPO financing 

The sample consists of the 2,459 VC-backed IPOs over the 1988 – 2010 period. This table shows calendar time portfolio regressions, where a firm enters the portfolio in the month following the IPO. The firm remains in the portfolio for the first 12 months (col 1), 24 months (col 2), 36 months (col 3), or 60 months (col 4) following the IPO. Monthly returns on this portfolio, net of the risk-free rate, are regressed on the three Fama-French factors and the Carhart momentum factor. In Panel A (Panel B), the sample is further restricted to firms that were backed prior to the IPO by a VC that funded an above-median (below-median) percent of companies following the IPO (over the 1988 – 2010 sample period). Panel C shows returns on a long-short portfolio, going long the firms in Panel A and short those in Panel B. Asterisks denote statistical significance at the 1% (***), 5% (**), or 10% (*) levels. Robust t-statistics are in parentheses.

Panel A: Long in firms that were funded prior to IPO by a VC with above-median tendency to invest in firms following IPO

12 months post IPO

24 months post IPO

36 months post IPO

60 months post IPO

Constant 0.648* (1.73)

0.427 (1.55)

0.725*** (2.75)

0.766*** (3.19)

Market-Rf 1.327*** (14.36)

1.323*** (19.81)

1.312*** (21.44)

1.278*** (23.21)

SMB Factor 0.994*** (6.16)

1.096*** (8.65)

1.173*** (11.30)

1.228*** (15.37)

HML Factor -0.964*** (-6.60)

-0.955*** (-8.40)

-0.957*** (-10.18)

-0.885*** (-10.81)

Momentum Factor -0.097 (-0.93)

-0.214*** (-2.69)

-0.304*** (-3.75)

-0.330*** (-4.22)

Adj. R-squared 0.700 0.818 0.827 0.843 Observations 286 298 310 322

Panel B: Long in firms that were funded prior to IPO by a VC with below-median tendency to invest in firms following IPO

12 months post IPO

24 months post IPO

36 months post IPO

60 months post IPO

Constant -0.070 (-0.17)

-0.185 (-0.69)

0.149 (0.60)

0.118 (0.54)

Market-Rf 1.078*** (10.45)

1.115*** (16.54)

1.135*** (17.33)

1.175*** (21.37)

SMB Factor 1.120*** (5.62)

1.084*** (7.05)

1.168*** (9.23)

1.228*** (11.17)

HML Factor -0.704*** (-4.37)

-0.716*** (-7.14)

-0.578*** (-6.63)

-0.356*** (-4.69)

Momentum Factor -0.171 (-0.97)

-0.234*** (-2.65)

-0.362*** (-4.13)

-0.336*** (-4.57)

Adj. R-squared 0.617 0.766 0.805 0.834 Observations 286 298 310 322

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Panel C: Long – short portfolio, across all VC-backed IPOs

12 months post IPO

24 months post IPO

36 months post IPO

60 months post IPO

Constant 0.718 (1.61)

0.612** (2.37)

0.576** (2.32)

0.648*** (3.01)

Market-Rf 0.249** (2.12)

0.208*** (3.32)

0.177*** (2.85)

0.103** (2.02)

SMB Factor -0.126 (-0.98)

0.012 (0.12)

0.005 (0.05)

0.000 (0.00)

HML Factor -0.260* (-1.68)

-0.239*** (-2.83)

-0.379*** (-5.16)

-0.529*** (-8.03)

Momentum Factor 0.074 (0.53)

0.019 (0.41)

0.058 (1.29)

0.006 (0.13)

Adj. R-squared 0.040 0.091 0.130 0.194 Observations 286 298 310 322

52  

Table 10: Performance of IPO firms that were funded (prior to IPO) by VCs with above- vs below-median propensity to provide post-IPO financing, conditional on VC rank

The sample consists of the 2,459 VC-backed IPOs over the 1988 – 2010 period. Each panel shows calendar time portfolio regressions, where a firm enters the portfolio in the month following the IPO. The firm remains in the portfolio for the first 12 months (col 1), 24 months (col 2), 36 months (col 3), or 60 months (col 4) following the IPO. Monthly returns on a portfolio that is long (short) in firms that were backed prior to the IPO by a VC that funded an above-median (below-median) percent of companies following the IPO are regressed on the three Fama-French factors and the Carhart momentum factor. In Panel A (Panel B), the sample is further restricted to firms that were backed prior to the IPO by a top 10 (non-top 10) venture capitalist. Asterisks denote statistical significance at the 1% (***), 5% (**), or 10% (*) levels. Robust t-statistics are in parentheses.

Panel A: Long – short portfolio, across IPOs backed by a top-10 VC prior to the IPO

12 months post IPO

24 months post IPO

36 months post IPO

60 months post IPO

Constant -2.520* (-1.76)

-1.135 (-1.20)

-0.793 (-0.81)

0.648*** (3.01)

Market-Rf 0.168 (0.45)

0.125 (0.56)

0.092 (0.40)

0.103** (2.02)

SMB Factor 0.217 (0.51)

0.435 (1.40)

0.063 (0.23)

0.000 (0.00)

HML Factor 0.150 (0.29)

0.004 (0.01)

-0.506 (-1.32)

-0.529*** (-8.03)

Momentum Factor 0.526 (1.56)

0.247 (0.92)

0.404* (1.66)

0.006 (0.13)

Adj. R-squared 0.024 0.021 0.039 0.194 Observations 105 152 173 322

Panel B: Long – short portfolio, across IPOs backed by a NON top-10 VC prior to the IPO

12 months post IPO

24 months post IPO

36 months post IPO

60 months post IPO

Constant 1.103** (2.50)

0.833*** (3.05)

0.746*** (3.17)

0.648*** (3.01)

Market-Rf 0.166 (1.39)

0.134** (2.10)

0.119** (2.15)

0.103** (2.02)

SMB Factor -0.024 (-0.22)

0.048 (0.65)

0.030 (0.47)

0.000 (0.00)

HML Factor -0.139 (-0.86)

-0.162* (-1.80)

-0.313*** (-4.09)

-0.529*** (-8.03)

Momentum Factor 0.073 (0.57)

0.042 (0.72)

0.058 (1.36)

0.006 (0.13)

Adj. R-squared 0.032 0.063 0.106 0.194 Observations 284 296 308 322

53  

Table 11: Are firms funded (prior to IPO) by VCs that tend to invest in companies following the IPO less likely to delist following IPO?

Firm Delist

Firm Delists (Merger)

Firm Delist (Dropped from

Exchange) VC Characteristics

Pre IPO VC VIPE experience -0.031*** (-2.74)

-0.011 (-1.11)

-0.015*** (-2.75)

Log(Pre-IPO VC Funding+1) 0.021*** (2.68)

0.014** (2.19)

0.003 (0.85)

Top 10 pre-IPO VC 0.063* (1.92)

0.034 (1.21)

0.019 (1.26)

Operating Char’s Negative CFO 0.032

(1.18) -0.016 (-0.69)

0.039*** (3.13)

CFO 0.022 (0.40)

0.043 (1.05)

-0.014 (-0.67)

Log(Sales) -0.011 (-1.19)

0.003 (0.37)

-0.007* (-1.69)

ROA -0.062 (-1.53)

-0.056** (-1.97)

0.001 (0.08)

Growth Opp’s R&D/Assets -0.135***

(-2.78) -0.064 (-1.55)

-0.036* (-1.74)

CAPX/Assets 0.146 (1.02)

-0.104 (-0.84)

0.104* (1.79)

Tobin's Q -0.002 (-1.20)

-0.000 (-0.11)

-0.001 (-1.55)

Firm Age Firm Age -0.001*

(-1.74) 0.000 (0.06)

-0.002*** (-3.55)

Financing Structure

Debt/Asset Ratio 0.052 (1.35)

-0.043 (-1.33)

0.059*** (3.45)

Syndicated Loan 0.018 (0.81)

0.017 (0.85)

-0.001 (-0.11)

Tangibility -0.216*** (-2.84)

-0.107 (-1.59)

-0.060* (-1.78)

Cash/Assets -0.077 (-1.45)

-0.007 (-0.15)

-0.049** (-2.16)

Other Controls Initial Return -0.040**

(-2.12) -0.011 (-0.69)

-0.019* (-1.79)

Pseudo R-squared 0.064 0.040 0.120 Observations 2,366 2,366 2,324

54  

The sample consists of 2,366 VC-backed IPOs over the 1988 – 2010 period. This table shows conditional logistic regressions, where the dependent variable equals one if the firm delists within five years after the IPO, zero otherwise. Control variables are all defined as of the time of the IPO. Pre-IPO VC experience equals the number of other firms in which the VCs was involved as a post IPO investor, and is scaled by the variable standard deviation. All specifications include year fixed effects, and standard errors are clustered by firm (z-statistics are in parenthesis). The reported coefficients are the marginal effects - the change in the probability to receive a financing for an infinitesimal change in each independent, continuous variable and, the discrete change in the probability for dummy variables. Asterisks denote statistical significance at the 1% (***), 5% (**), or 10% (*) levels. All variables are defined in Appendix II.

55  

Figure 1 Our sample consists of VC-backed IPOs between 1988 and 2010. We require all firms to have CRSP data, initial return data, and Compustat data. The sample consists of 2,459 firms. The solid red line shows the percent of firms that receive VC financing between 8 days post-IPO and five years post-IPO. The green line shows the percent of firms that receive VC financing during this same interval, from a venture capitalist that also funded the company prior to the IPO.

 

 

   

56  

Figure 2 

The sample consists of VC-backed IPOs between 1988 and 2010. We require all firms to have CRSP data, initial return data, and Compustat Data. The solid red line shows the percent of firms that receive VC financing each year following the IPO. Year 1 is defined as the period between day 8 and day 365 (following the IPO). Each subsequent year is defined as the following 365-day period.

 

 

   

57  

Figure 3: Extent to which firms rely on multiple sources of financing

The sample consists of VC-backed IPOs between 1988 and 2010. We require all firms to have CRSP and Compustat Data, and we determine the percent of firms that raise capital during the first 60 months after the IPO through an SEO, via a PIPE, from a VC, and through a syndicated loan . Panel A shows the percent of firms that receive these types of financing across all VC-backed IPOs, and Panel B limits the sample to those that received post-IPO VC financing. Panel C graphs the amount of money raised as a fraction of proceeds through each of these forms of financing, for the subsample of firms that received post-IPO VC financing.

Panel A: All firms, with or without post-IPO VC financing

Panel B: Subsample of firms with post-IPO VC financing

58  

Panel C: Subsample of firms with post-IPO VC financing

59  

Figure 4: Do firms raise other financing simultaneous with VC financing after the IPO?

The sample in Panel A consists of 25 months of issuance data around 712 VC-backed IPOs between 1988 and 2010, which received post-IPO financing between day 8 and year 5 after the IPO. Because VC rounds potentially occur up to five years after the IPO, the post-IPO VC rounds extend through 2014. The sample in Panel B similarly consists of 25 months of issuance data around 1,119 SEOs between 1988 and 2014.

Panel A: Monthly issuance percentages around VC funding dates, 1988 - 2014

60  

Panel B: Monthly issuance percentages around SEO dates, 1988 - 2014

61  

Figure 5  The sample in Panel A consists of 712 VC-backed IPOs between 1988 and 2010, which received post-IPO financing between day 8 and year 5 after the IPO. Because VC rounds potentially occur up to five years after the IPO, the post-IPO VC rounds extend through 2014 The sample in Panel B consists of 468 VC-backed IPOs between 1996 and 2010. The sample in panel C consists of 36 VC-backed IPOs between, which received post-IPO financing between day 8 and year 5 after the IPO, and which were reported in 8-K filings.

Panel A: Daily returns around SDC round dates, 1988 - 2014

Panel B: Daily returns around SDC round dates, 1996– 2014

62  

Panel C: Daily returns around announcement dates reported in 8-Ks