Optimal Exit Strategy for CVC and IVC Backed startups
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Optimal Exit Strategy for CVC and IVC
Backed startups
Bing Guo, Yun Lou and David Pérez-Castrillo∗
April 14, 2011
Abstract
We theoretically and empirically study the differences in invest-
ment, duration and exit strategies between startups backed by two
types of venture capital funds: corporate venture capital (CVC) and
independent venture capital (IVC). According to our theoretical anal-
ysis, CVC backed startups stay longer in the market before exit and
they invest more than those financed by IVCs. Both properties imply
a higher rate of successful exits. Moreover, while longer duration leads
to a higher likelihood of an exit through acquisition, a larger invest-
ment increases the probability of an IPO exit. These predictions find
empirical support, using venture capital data from U.S.
JEL Classification: G32, G24.
Keywords: Startups, Duration Corporate Venture Capital, Independent Ven-
ture Capital, Investment Strategy, exit Strategy, IPO, M&A.
∗
We would like to thank Albert Banal-Estañol, Gary Dushnisky, Marı́a Gutiérrez, Inés
Macho-Stadler, Philipp Meyer, Pau Olivella-Cunill, and Pedro Rey-Biel for their helpful
suggestions. We are grateful to AGAUR, research projects ECO2009-07616 and 2009SGR-
169, Barcelona Graduate School of Economics, and ICREA Academia for the financial
support.
11 Introduction
There are two main exit routes for successful startups: the company can
go to an Initial Public Offering (IPO) or it can be sold to an existing firm
(Acquisition).1 Under an IPO, the venture achieves a stock market listing
so that it can receive additional financing for its projects and the insiders
(venture capitalists, entrepreneur and any other stockholders) can eventually
sell their shares to the public. If the startup is acquired, the insiders get im-
mediate cash in return from their shares.
The optimal exit route for startups depends on multiple factors, such
as expected profitability of the venture; level of uncertainty; asymmetry of
information between insiders and outsiders (potential acquirers, investors);
possible conflicts of interest among insiders; venture capital characteristics,
etc. Understanding the main trade-offs faced by startups at the exit stage
is crucial because it allows to see how venture capitalists and entrepreneurs
divest their companies, and also because of the impact of the (anticipated)
exit strategy on the decisions taken at the onset of the venture.
Another important element that influences the investment decisions is the
nature of the venture capital funds that finance the startup. Some startups
receive financing from Corporate Venture Capital funds (CVCs) while others
are financed only by Independent Venture Capital funds (IVCs). We argue
that an important difference between CVC and IVC funds is that IVC funds
care more about quick exits than CVC funds do; that is, IVC backed startups
have higher discount rate than those backed by CVCs. Indeed, IVC man-
agers’ payment is more based on financial returns and their ability to raise
additional funds depends on their reputation, which is influenced by their
history of successes (Gompers, 1996; Dushnitsky and Shapira, forthcoming).
Therefore, they have strong incentives to cash their return from profitable
projects early.
In this paper, we abstract from possible internal conflicts among insiders
and we propose a model of investment, duration, and exit taking into account
first, the high level of uncertainty regarding returns from the investment in
the startup, second, the more accurate information in the hands of insiders,
and finally, the startups’ discount rate depending on the nature of the ven-
ture capital fund.
1
Two other exit routes that are not so commonly used are Management Buy-out and
Refinancing (or secondary sale); see, for example Schwienbacher, 2009.
2In our model, the decision on the duration of the startup before exit affects
the investment level, as well as the market information about the successful
probability of a venture. Furthermore, the level of investment influences the
expected value of the startup: we assume that higher investment leads to a
more favorable distribution of the set of potential values.
The level of uncertainty concerning the actual value of a startup is very
high. Some of the uncertainty is resolved during the development stage
and the market has access to that information at the time of the exit. In
our model, the level of the potential value of the venture will be eventually
known by every market participants. Nevertheless, the insiders have more
precise information about the expected profitability of the startup because
they learn the probability of success. Whether the outsiders can be informed
of such probability depends on how long the startup stays in the market be-
fore exit.
We show that independently on the level of information received by the
potential acquirers, the ventures whose probability of success is higher are
more likely to try an IPO while those with lower probability prefer looking
for an acquirer. Moreover, the likelihood of going to IPO increases with the
potential value of the startup, if that value is high enough. Those startups
with low potential value are liquidated.
We relate the startup exit decision with the investment amount and with
the market level of information. First, a higher investment level brings about
both, a higher likelihood of a successful exit and a higher rate of IPO ex-
its among the successful ones. Second, the IPO exit rate is lower when the
outsiders receive more precise information, that is, when they are informed
about the success probability.
CVC backed startups have lower discount rate than IVC backed startups.
We show that this difference leads to higher duration of the venture, that is,
larger investment and more information transmission to the market. There-
fore, CVC backing leads to a higher rate of successful exists. On the rate of
IPO exits among successful ventures, the two forces that we identified go in
opposite directions: larger investment leads to more IPOs while more accu-
rate market information leads to more acquisitions.
In the previous literature, the main argument supporting the differences
between CVCs and IVCs is that the sole objective of IVCs is return on cap-
3ital while a very important objective of most CVC programs is strategic:
the development of new related, business (see for instance Sykes, 1990; Yost
and Devlin, 1993; Dushnitsky and Lenox, 2006; Hellmann, Lindsey and Puri,
2008). According to this strategic argument, CVC backed startups are more
likely to exit by acquisition when the potential acquirer is the affiliated com-
pany of the CVCs (Gompers and Lerner, 1999; Hellmann, 2002; Riyanto and
Schwienbacher, 2006; Cumming, 2008). And it is indeed the case empiri-
cally that the percentage of CVC backed startups that are acquired is higher
than that of IVC backed ventures (Siegel, Siegel and MacMillan, 1988; Sykes,
1990). However, it has also been shown that the number of startups acquired
by the company behind the CVC funds is small (Maula and Murrey, forth-
coming): and our own analysis using the VentureXpert database confirms
that the percentage of startups acquired by companies related to CVC in-
vestors is around 5%.
We claim that the difference in the discount rate between IVC and CVC
backed startups is also an important element that helps explaining the em-
pirically observed different behavior among the two types of venture. The
effect is large but indirect: A lower discount rate for CVC backed startups
implies longer duration and a higher investment level. Both imply a higher
success rate, while the former induces more exits through acquisition and the
latter leads to more IPO exits.
We empirically test the previous claim using data on 4801 US startups
from the period 1969 to 2008. According to our empirical results, one per-
cent increase in the level of investment significantly increases the probability
of IPO exit by 0.065%. Also, one percent increase in the duration of the
venture significantly decreases the likelihood of IPO exit by 0.017%. Finally,
we show that, after controlling for the duration effect and for the level of
investment, there is no significant difference in the rate of IPO exits between
IVC and CVC backed startups. In fact, we observe that the presence of CVC
investors has a positive, although not significant, effect on the IPO exit rate.
The rest of the paper is organized as follows. In Section 2, we introduce
the model. In Section 3, we develop the analysis of the optimal exit strategy.
In Section 4, we derive the main implications in terms of the optimal duration
decision and we apply our conclusions to the discussion about the differences
between CVC and IVC backed startups. In section 5, we empirically confirm
our theoretical predictions. Section 6 concludes and discusses some exten-
sions for future study. All the proofs are included in the Appendix.
42 The Model
We analyze the optimal investment and duration decisions and exit strategy
of startups (S). In our model, startups’ decisions at any stage are aimed
to maximize the expected discounted profits. We assume that they behave
as a unit and we abstract from the internal conflicts that may arise within
them (mainly the conflicts between entrepreneurs of the startups and venture
capitalists). However, we will allow later on startups receiving CVC funding
and those receiving only IVC funding to have different objectives. For our
purposes, the main difference between CVC and IVC funds is that they have
different discount rate (r).
The main characteristics of the model are the following. The first decision
taken by the startup is the duration of the venture d. The decision is made
at the beginning of the life of the startup and we do not take into account
its dynamic aspect, which is not relevant for our purpose. The duration of
the venture has two effects in the model: it determines the total level of
investment I, which in turn has a positive impact on the expected quality of
the venture, and it also influences the amount of information that flows to
the market.
When the startup makes its first decision, there is a high degree of un-
certainty with respect to both, the potential value of the venture V and the
probability p of being able to realize this value. Part of the uncertainty is
resolved as the startup develops. All the market participants will be able to
observe some of the information, but the insiders will acquire more precise
information on the expected quality of the project. In our model, we reflect
this asymmetry in the information between insiders and outsiders in a sim-
ple way. When it is revealed, everybody can observe the potential value V .
Moreover, the insiders always learn the probability p. However, the precision
of the information received by the outsiders about p depends on the dura-
tion of the venture: the longer d, the more precise the outsiders’ information.
The venture requires additional financing C to possibly achieve the value
V . Hence, if the potential value V is low, the startup will be liquidated (this
is the first exit option). If, on the contrary, continuing the venture is prof-
itable, then the startup will either look for a firm (an acquirer) interested in
adding the venture into its business, or it will go to an initial public offering
(IPO). In the first case, the acquirer will offer a deal to the startup that will
reflect the expected value of the business and the bargaining power of the
parties. Then, the acquirer will integrate the venture into its organization
5and, when it confirms that it is worthwhile doing it, it will make the addi-
tional financing to obtain V .
In case the startup tries an IPO, then the market investors will go through
a thorough analysis concerning all possible aspects of the startup. The mar-
ket investors will make a careful auditing of the corporate valuation, market
prospection and so on. The outcome of the analysis will be a new signal on
the profitability of the startup that we model also in a simple way: either the
market makers are convinced that the startup will be successful with proba-
bility 1 (High signal), or they will still not be able to assess it with certainty
(Low signal). All these processes are costly and the startup needs to cover
the cost. The market investors will make an offer to the startup owners in
case of a High signal.
More precisely, the model is the following:
At t = 1, the startup decides the duration of the venture d.
• We assume that the startup receives a fixed investment stream of i.
Hence, the total investment amount is I = di if the duration is d. In
turn, the level of I determines the distribution of the potential value
of the startup V : the value V follows a distribution function Γ(V ; I),
with density function γ(V ; I). It can only be cashed if at later stages a
fixed new funding C is made. After the investment and before all the
other decisions are taken, the value of V is realized and it is observable
by everybody.
• The level of d determines the information learned by the potential ac-
quirers about a signal p on the likelihood of success, i.e., the probability
of realizing V . For simplicity, we assume that ex ante p is uniformly
distributed over the interval [0, 1]. The startup always learn p. The
potential acquirers will learn p with probability h(d), increasing in the
duration d.
At t = 2, the startup takes the exit decision. It has three possibilities:
liquidation (Liq), looking for an acquirer (Acq), or going to an IPO.
• The liquidation value of the startup is always 0.
6• In case it decides to look for an acquirer, then a deal price is negotiated,
depending on the bargaining power of the two parties and on the ac-
quirer’s information. The bargaining power for the startup is denoted
by m. In case of acquisition, the acquirer will invest C to realize V if
it confirms that the project is successful.
• Going to an IPO is the most complex and costly exit route for the
startup. We denote by F all the fixed costs due to the IPO pro-
cess. It leads to one public signal βe on the profitability of the venture,
βe ∈ {H, L}. We assume that β is the probability for the market to
be able to verify a successful project after receiving the public signal.
Therefore, the probability of observing βe = H is equal to βp. In case
the signal is H, the competitive market will set a price Z for IPO. If
the startup accepts the price, then a successful IPO is carried out. In
order to realize V , in addition to Z, the market needs to raise C to
cover the remaining investment needs. In case the signal is L, then no
offer is issued.2
The time line is captured by Figure 1.
Given the fixed costs involved in IPOs, this exit route is an option only
if the screening is informative enough. In our stylized model, IPOs may be
chosen only if β > m, which we will assume from now on.
We solve the model by backward induction, taking into account that
there may exist asymmetric information among the participants. Therefore,
we use sequential equilibrium as the solution concept, since it combines sub-
game perfection ideas with Bayesian updating.
2
We assume that a startup that receives a low signal does not get any offer and quits
the market. We make this assumption for simplicity. First, for those startups that receive
a signal L, the situation is often similar to the lemon’s market in Akerlof (1970)’s model:
there is no price under which market profits are non negative (taking into account the
startups that accept that offer). Therefore, the assumption that IPOs do not make offers
to startups that receive low signals can be sustained as a result of a more general model.
Second, the startups may go to the acquisition market (at t = 3) once they fail at IPO,
where the acquirers will take into account the new information produced at IPO. This
adds some (small) additional profits to those ventures that choose the IPO exit. However,
the qualitative results of our analysis do not change if we add this possibility. (For an
analysis of the previous extensions, see Guo, 2010).
7Figure 1: The Time Line
3 The Analysis of the Optimal Exit Strategy
In this section, we start at t = 2, where the duration is decided and the
investment made at t = 1 is already sunk. The potential value for the venture
V is realized and observed by all the participants. Moreover, the startup has
already received the private signal concerning the probability of success p.
The potential acquirer may also know p (this happens with probability h(d))
or not. We study the optimal exit strategy in both situations.
3.1 Optimal Exit Strategy with informed outsiders
As mentioned in the previous section, the value of the startup in case of
Liq is 0. Also, the deal price of acquisition corresponds to a share m of
the expected value of the venture. Remember that the potential acquirer
needs to invest C to realize V , which will only be made when the venture
is believed to be successful. Taking into account that the acquirer knows p,
the expected value of the venture is p [V − C]. Therefore, if the startup goes
to the acquisition market at t = 2, the deal price is mp [V − C], whenever
V − C > 0.
Consider now a startup characterized by (V, p) that goes through an IPO,
with V − C > 0 (otherwise, profits are always negative). After the startup
pays F , the market receives the signal β.
e If the realization is βe = L, which
happens with probability (1 − βp), it will not receive any offer. If the realiza-
tion is βe = H, then the competitive market of investors will offer Z = V − C,
8which the startup will accept.
The startup obtains higher expected profits going to an IPO than looking
for an acquirer if and only if3
βp [V − C] − F ≥ mp [V − C] . (1)
The following proposition describes the optimal exit strategy of a startup
characterized by (V, p) when outsiders are informed about p, where we denote
1 F
po ≡ min ,1 . (2)
[β − m] [V − C]
Proposition 1. Consider the case of a startup characterized by (V, p) where
potential acquirers have learned p. The startup’s optimal exit strategy is as
follows:
1. If V − C 6 0, the startup is liquidated and gets the payoff Uo (V, p) = 0.
2. If V − C > 0 and p < po , the startup goes to the acquisition market
and gets a deal value Uo (V, p) = mp [V − C].
3. If V − C > 0 and p > po , the startup invests F and goes to the IPO
market. Moreover,
(a) if it gets public signal H, then it receives an offer Z = V − C from
the IPO market and it accepts it;
(b) if it gets public signal L, then it does not receive any offer from
the IPO market.
Therefore, in this case, Uo (V, p) = βp [V − C] − F .
The basic trade-off between IPO and acquisition is that while the IPO
process is very costly, it also allows the startup owners to get a larger share
of the value of profitable ventures. Startups with high enough probability
of success are ready to pay the cost of the process. To analyze the effect of
the different parameters on this trade off, we conclude the analysis of the
optimal exit strategy by doing the comparative statics of po with respect to
3
We take the convention that a startup indifferent between going to an IPO and looking
for an aquirer at t = 2 goes to an IPO. Similarly, a startup indifferent between being
liquidated and not chooses liquidation.
91 F
all the parameters, for the interior case where [β−m] [V −C]
< 1. This analysis
highlights the characteristics of the startups and the market that make it
more likely to observe exits through IPO or through acquisition. Indeed, a
higher po implies a lower likelihood of exit through IPO.
Proposition 2. Consider the situations where potential acquirers learn p.
Then, the likelihood of IPO increases with V and β and it decreases with F ,
C, and m.
According to Proposition ??, the higher the potential value of a startup
V (similarly, the lower the additional funding C), the more willing it is to go
to the IPO market. Given the costly IPO process, only those startups that
really benefit from the more competitive IPO market are willing to follow this
path. As expected, a higher bargaining power m in the acquisition market
leads to less IPO exits. Finally, an efficient IPO process, reflected by a low
cost F and powerful screening capability β, makes IPO an appealing exit.
3.2 Optimal Exit Strategy with uninformed outsiders
The analysis of the optimal strategy of a startup that looks for an exit when
the potential acquirers do not know the value of p has some similarities with
the one developed previously. First, if the startup’s potential value V is lower
than C , it is liquidated. Second, the IPO offer is Z = V − C if it receives a
signal βe = H which, in particular, implies that the startup will accept the
offer. Finally, potential profits from IPO versus Acquisition increase with
the value of p; therefore, there will be a cut-off value poo (that depends on V
and that can possibly be equal to 0 or 1) above which the startup goes to IPO.
The main new aspect when the value of p is unknown by the potential
acquirers is that the price that they may offer does not depend on the real
value of p but on the expected value of p from the point of view of the ac-
quirer, which is a function of the startup equilibrium behavior. The deal that
a potential acquirer will make to a startup that approaches it at t = 2 will
be based on the expected value of p at this time, which is p2oo . Therefore, the
deal price at t = 2 will be m p2oo [V − C].
Similar to the informed outsiders’ case, a startup whose probability of
success is equal to poo must be indifferent (if poo ∈ (0, 1)) between going to
IPO and looking for an acquirer. Therefore, an interior poo is characterized
by
poo
βpoo [V − C] − F = m [V − C] . (3)
2
10Equation ?? implies that the cut-off value is
( )
1 F
poo ≡ min m
,1 . (4)
β − 2 [V − C]
For completeness, we state in Proposition ?? the equilibrium behavior of
startups when outsiders are uninformed as to the value of p.
Proposition 3. Consider the case of a startup characterized by (V, p) where
potential acquirers have not learned p. The startup’ equilibrium exit strategy
is as follows:
1. If V −C 6 0, the startup is liquidated and gets the payoff Uoo (V, p) = 0.
2. If V − C > 0 and p < poo , the startup goes to the acquiring market and
gets a deal value Uoo (V, p) = m p2oo [V − C].
3. If V − C > 0 and p > poo , the startup invests F and goes to the IPO
market. Moreover,
(a) if it gets public signal H, then it receives an offer Z = V − C from
the IPO market and it accepts it;
(b) if it gets public signal L, then it does not receive any offer from
the IPO market.
Therefore, in this case, Uoo (V, p) = βp [V − C] − F .
Moreover, at equilibrium, the likelihood of IPO increases with V and β
and it decreases with F , C, and m.
The intuitions behind Proposition ?? are the same as those explained
after Propositions ?? and ??
4 Analysis of the Optimal Duration Decision
We address now the optimal duration decision by the startup at t = 1.
The analysis of the previous section allows computing the expected income
Uo (V, p) or Uoo (V, p) of a startup whose potential, publicly known value is V
and whose probability of success is p, depending on the level of information
by the outsiders. We now calculate the expected profits for a given duration
d, denoted as U (d), which requires taking the expectation of the expected
11income over the possible values of V (whose distribution function Γ(V ; I)
depends on I = di) and p:
Z Z
−rd i
U (d) = e [h(d)Uo (V, p) + [1 − h(d)] Uoo (V, p)] dpdΓ(V ; di) − (1 − e−rd )
V p r
i
= e−rd [h(d)EUo (di) + (1 − h(d))EUoo (di)] − (1 − e−rd )
r
(5)
where ri (1 − e−rd ) is the discounted cost of the stream of investment i for a
duration d and we denote
Z Z
EUo (di) = Uo (V, p)dpdΓ(V ; di), (6)
V p
Z Z
EUoo (di) = Uoo (V, p)dpdΓ(V ; di). (7)
V p
We can interpret EUo (di) as the expected profits at the time of exit of a
startup that has invested I = di at t = 0 and whose realized probability p is
known by potential acquirers. Similarly, EUoo (di) is the startup’s expected
profits if the realization of p is unknown by outsiders. The probability that
the information is known by the potential acquirers, h(d) increases with the
duration. Moreover, the longer d, the lower the expected profits at t = 0 due
to the discounting.
Next proposition shows an important result concerning the role of infor-
mation on the expected profits by the startup: they are higher when the
potential acquirers learn p than when they do not:
Proposition 4. EUo (I) ≥ EUoo (I) for every I > 0, and the inequality is
strict whenever poo < 1.
Assuming an interior solution for the duration decision, the optimal du-
ration d∗ is characterized by U 0 (d∗ ) = 0 and the implied optimal investment
level is I ∗ = d∗ i.
How does the optimal duration and the implied investment level change
with the discount rate r? The next proposition states the intuitive result
that more patient startups stay longer in the market before exit and there-
fore, they invest more resources.
12Proposition 5. The optimal duration d∗ and investment I ∗ decrease with
the discount rate r.
Our main interest is the analysis of the implications of a lower discount
rate (r) on the number of successful exits and on the percentage of IPO
among the total successful exits. The main consequence of a lower discount
rate is a longer duration, stated in Proposition ??. We now see how this
property affects the likelihood of success and IPO exit.
A longer duration means higher investment, which should imply that the
distribution of V s is more concentrated in higher values. This, in turn, should
lead to higher likelihood of successful exit. Proposition ?? states this result,
together with the simple hypothesis that guarranties it: all that is required
is that the probability that the value V lies above C is decreasing with the
investment level I.
Proposition 6. If the value Γ(C; I) is decreasing in I, then the rate of
successful exits decreases with r.
To discuss the effect of r on the likelihood of IPO exit, we identify two
effects. First, the longer duration implied by a lower r means that the mar-
ket has more precise information which, in our model, is reflected through a
higher probability for the public to know the successful probability p. Sec-
ond, the longer duration leads to higher investment level. We now analyze
the implications of these two facts on the likelihood of an IPO exit.
The first question is, which informational setting (informed or uninformed
outsiders) makes IPO exits more likely? The next proposition, whose proof
is straightforward from equations (??) and (??), answers this question.
Proposition 7. The probability of going to IPO is higher when the potential
acquirers have not learned p than when they have learned p. More precisely,
poo < po whenever poo ∈ (0, 1), and po = 1 whenever poo = 1.
Figure 2 depicts the optimal exit strategy highlighted in propositions ??
and ??. For high values of p and V , IPO is the optimal exit route inde-
pendently on the potential acquirers’ information. Similarly, going to the
acquisition market is always the optimal startups’ strategy for low values of
p and V (provided V > C). In the intermediate (shadow) region of Figure 2,
startups go to IPO when the outsiders have not learned p, while they prefer
going to the acquisition market if outsiders have observed p.
13Figure 2: Optimal Exit Strategy
The intuition of Proposition ?? is the following: When the outside ac-
quirers observe the true value of p, they offer a deal according to p. However,
when they do not observe the true successful probability, they can only offer
an deal according to the expected probability, which is independent of the
true value p. Consider a startup whose realized probability is po , that is, it
is indifferent between IPO and acquisition if information about p is public.
What happens if information is not public? The deal it will obtain in the
acquisition market is lower, as it is based on the expected probability. There-
fore, it would rather go to IPO than look for an acquirer. As a consequence,
uninformed markets are more likely to lead to IPO exits.
Longer duration leads to more information about p for outsiders that,
ceteris paribus, implies a reduction in the likelihood of IPO exit, according
to Proposition ??.
The second effect of a longer duration is a higher investment level that,
as mentioned before, should imply a shift in the distribution of V towards
higher values. As shown in propositions ?? and ?? (see also Figure 2), the
higher the value V , the more likely it is that the exit happens through an
14IPO rather than through acquisition, independently on whether potential
acquirers have learned p. Therefore, a lower discount rate r should imply a
higher IPO rate among successful stories. Proposition ?? states this result,
together with a sufficient condition on the behavior of the distribution func-
tion Γ(V ; I) with respect to I.
γ(V ;I)
Proposition 8. Assume that the function 1−Γ(C;I)
is non-decreasing in I
from C on, i.e.,
∂ γ(V ; I)
( ) > 0, f or V > C.
∂I 1 − Γ(C; I)
Then, the likelihood of IPO among the successful exits increases with I both,
when the potential acquirers have learned p and when they have not learned
p.
We notice that a simple distribution function that satisfies that sufficient
conditions on propositions ?? and ?? is a uniform distribution function whose
lower bound is increasing in I(t), for example, γ(V ; I) = V 1−I for V ∈ [I, V ],
where V is some high value.
Longer duration leads to a better distribution of values V that, ceteris
paribus, implies an increase in the likelihood of IPO exit, according to Propo-
sition ??. Therefore, we have identified two effects that go on different direc-
tions: on the one hand, a longer duration implies a better informed potential
acquirer, which should lead to more acquisitions; on the other hand, a larger
investment should lead to more exits through IPO.
5 CVC vs IVC Backed startups
The analysis of the previous sections allows us to contribute to the discussion
of the differences between startups that receive funds from CVC and those
that only receive IVC funding. As mentioned in the Introduction, several
authors have addressed the differences in the exit strategy between the two
types of startups, taking into account that CVCs (in addition to financial
profits) aim at the potential strategic benefits from their investment in the
startups (Gompers and Lerner, 1999; Hellmann, 2002; Riyanto and Schwien-
bacher, 2006; Cumming, 2008). According to this literature, the strategic
15motive behind CVC investment leads to more acquisitions.
One important difference between CVCs and IVCs that has not received
attention in the previous studies is the fact that CVCs are typically less
compelled to recover the investment earlier (see Schwienbacher, 2009). We
associate this difference with a lower discount rate for startups that receive
CVC funding.
According to our analysis, the difference in the discount rate between
CVC and IVC backed startups has testable implications on their strategy.
In this section, we report the results of an empirical study of our theoretical
implications: CVC and IVC backed startups have different durations before
exit, investment amount and exit strategies.
CVC backed startups stay longer in the market before exit. The
first theoretical prediction of our model shows that startups with lower dis-
count rate choose to stay longer before exit. Since CVC funds are more
patient than IVC funds, we expect that CVC invested startups have a longer
duration before exit than those backed by IVC funds.
CVC backed startups have a higher investment level than those
financed by IVC funds. According to our theoretical implications, we
should observe higher investments in CVC backed startups than in IVC
backed startups. With less cash constraint and having strategic aim, CVC
funds are less hurry to exit their startup projects than IVCs do, i.e. their
startups have a longer duration before exit. Therefore, those startups have
a higher investment amount.
Longer duration implies more Acquisition exits and higher in-
vestment level leads to more IPO exits. Our theoretical model indicates
an indirect impact of VC funds’ characteristics on startups’ exit strategies.
As we mentioned before, startups with CVC banking invest more in their
projects than those with IVCs, which is triggered by a lower discount rate
and longer duration in the market. The theoretical model predicts that a
higher investment level would in turn increase the probability of an IPO
exit. However, longer duration will increase the information related to the
value of the startups in the market. More information is predicted to re-
duce the probability of IPO exits. We are supposed to observe higher IPO
frequency for larger investment amount (CVC funds) and more Acquisition
exits for longer duration (CVC funds). Therefore, the effect of CVC funding
on startups’ exit strategy is not clear.
165.1 Data Description
We obtain the relevant data, i.e., investment amount, IPO date, acquisition
(Acquisition) date, investment rounds, number of investors, investors’ type,
IPO price, Acquisition deal value, VC fund size, etc, from the VentureXpert
database. Our dataset contains 4801 startups in the US market from 1969
to 2008.
Our final sample (4801 startups) covers 63 industries in US. Table 1 pro-
vides an overview of the industry composition of our sample (by two-digit
SIC code). In the table, we just include industries with more than 20 obser-
vations. We observe concentration of industries with SIC code 28, 35, 36, 38
and 73. Those coes correspond to Chemical, Electronic and Business Service
related industries, where venture capital investments are more common.
5.1.1 Dependent Variables
We are testing the effect of VC funds’ characteristics (CVC or IVC) on star-
tups’ investment and exit strategies. Therefore, two dependent variables are
needed: startups’ investment amount and the exit rule (IPO or Acquisition).
To measure the investment amount of a startup, we obtain the investment
for startups in U.S. at each investment round. Then we sum up the round
level investments to get the total investment amount for a startup.
The exit strategy of a startup is indicated by a dummy variable. It is
equal to 1 if a startup exits through an IPO and 0 if it exits through an
Acquisition.
5.1.2 Independen Variables
The theoretical model predicts the influence of different VC funds’ character-
istics on startups’ investment and exit behavior. Therefore, the independent
variable is whether a startup is financed with CVC funds or IVC funds.
There are two definitions of CVC backed startups in the literature (Toldra,
2010). Under the first definition, a startup is defined as CVC financed if
all the investments are from CVC funds. The second defines a startup as
CVC backed if at least one of the investors are corporate venture capitalists.
In this paper, we use the second definition because it gives us a larger dataset.
We use two variables to measure the VC fund characteristics. Firstly,
we create a dummy variable that is equal to 1 if a startup receives at least
17one investment from CVCs. If all the investments are from IVC funds, the
startup is IVC financed and the dummy variable is equal to 0. Secondly,
we calculate the percentage of investment made by CVC funds out of total
investment in each startup as another measure of the funds’ characteristics.
As is discussed, we attribute the main difference between CVC and IVC
funds to the fact that CVC funds are less compelled to recover the investment
earlier. Therefore, duration of a startup is another relevant variable. It is
calculated as the difference between the exit date (IPO date or Acquisition
date) and the date at which a startup receives the first investment from
venture capital firms.
5.1.3 Controlling Variables
Based on previous literature, we use a set of controlling variables to estimate
the effect of VC funds characteristics and the duration of startup projects,
on their investment and exit strategies. It includes the average VC fund size,
total number of investment round, VC syndication size, industry market-to-
book value, the relationship between CVC funds and their invested startups
(competitive or complimentary), 3 months and 6 months MSCI return4 be-
fore the exit date, the industry fixed effect and the year fixed effect.
Table 2 provides the definitions of all the variables and Table 3 summa-
rizes the basic statistics of those variables.
5.2 Analysis and Results
Two empirical questions are studied in this section: the influence of VC fund
characteristics on startups’ investment strategy and exit strategy. Since VC
fund characteristics is the main explanatory variable in our empirical study,
we first look at whether startups backed by CVC funds and those financed
by IVC funds have different behavior. We provide some basic statistic dif-
ferences between CVC and IVC financing in Panel A of Table 4.
We observe significant differences between startups with CVC funds and
with IVC funds in most variables. In fact, CVC backing implies a significantly
higher investment than IVC backing. The average investment per venture for
4
The Morgan Stanley Capital International (MSCI) constructs a free float-adjusted
market capitalization weighted index that measures the equity market performance of
developed and emerging markets. We use the MSCI ACWI (All Country World Index)
Index of the United States in the paper.
18both exit strategies is around 50 million USD for CVC backed startups while
it is only around 21 million USD for those only backed by IVCs. This fact has
already been highlighted by previous literature (Gompers and Lerner, 2000;
Hellmann, 2002). Moreover, there is a large gap between the mean duration
of the two types of venture. The mean duration for CVC backed startups is
1929 days, compared with 1649 days for IVC backed startups. We also find
that compared with IVC financing, CVC financing leads to more investment
rounds. CVC backed startups exit at later investment stages (i.e. more exits
at expansion or later stages than exits at seeds or early stages). However, we
have not found any significant difference in IPO exit rate and VC fund size
between the two types of VC funds.
We now have a deeper look at how the VC fund’s characteristics influence
on the startups’ investment amount and exit strategy.
5.2.1 Fund’s Characteristics and Investment Strategy
The difference in the investment amount might come either from differences
in the type of projects in which the funds invest (selection bias), or from the
intrinsic characteristics of the type of fund, such as the discount rate. We use
the VentureXpert database to confirm that the selection bias does not seem
important: it is only when CVCs enter the startups that there is a change
in the investment amount. This result is included in Panel B and Panel C of
Table 4.
Table 4 (Panel B) depicts the number of startups that receive funds from
the two types of the venture capitalists. The columns stand for different
investment rounds and the lines are groups of startups, differentiated by
the round in which CVC investors enter. We provide results until group
8, in which CVCs enter the project at the eighth investment round.5 The
highlighted numbers represent the number of survival startups when CVC
investors join in the venture. For example, the first line (Gr.0) describes the
group of startups that only receive IVC funds. There are 2778 of them, out
of which 2117 also receive second round financing, 1492 receive third round
financing, and so on. The third line (Gr.2) includes the group of startups
that start receiving CVC funds at the second investment round. There are
415 of them, out of which 291 also receive third round financing, and so on.6
5
There are CVCs that enter after the eighth round. Since the number of these CVCs
is small, we don’t show the details of those cases.
6
The number just before 415 should have been 415. However, it is only 401 due to
missing data. A similar problem appears in other lines.
19It is worth highlighting that most CVC backed startups start receiving CVC
financing at very early stages. One third of them (548 out of 1792) receive
CVC funds at the first round, and almost 55% of them get CVC financing
at the first two rounds. This is somehow at odds with previous findings sug-
gesting that CVCs often enter at later investment rounds (Hellmann, Lindsey
and Puri, 2008; Dushnitsky and Shapira, forthcoming; Masulis and Nahata,
2009).
More interestingly, Table 4 (Panel C) shows the average investment per
round and per group. Before CVCs enter the ventures, the investment
amount is similar for all groups. For example, startups in Group 0 (that
never receive CVC funds) invest almost 5.4 million USD in round 1, com-
parable with the 5.47 million USD of those that will receive CVC backing
in round 2. However, these numbers are quite lower than 8.90 million USD
received by startups backed by CVCs at round 1. A similar effect appears for
all the rounds. Hence, before CVC investors join in the ventures, IVCs invest
in similar projects, suggesting no selection bias among the projects. The in-
vestment levels are significantly increased when CVCs enter into the startups.
To see whether the intrinsic characteristics of the type of fund has an
effect on the investment decision, we also empirically test the different in-
vestment amount between CVC and IVC backed startups. The main idea is
summarized in the following hypothesis:
Hypothesis 1.
H0 : CVC backed startups have the same investment amount as IVC backed
startups do.
H1 : CVC backed startups have a higher investment amount than IVC backed
startups do.
The principal model applied for the estimation is:
6
X
0
ln Investi = α0 + α1 F und sCharacteristics + α2 ln Durationi + αk Zk + i
k=1
(8)
In equation(??), Investi is the total investment amount at the startup
level. F und0 sCharacteristics measures whether the startup is financed by
CVC funds or IVC funds. Durationi is the duration time for startup i. Zk
is a set of controlling variables, including investment rounds, VC fund size,
VC syndicate size, industry market-to-book value, industry and year fixed
effect. To obtain a robust estimation of how venture capital funds influence
20startups’ investment amount, we have estimated three models.
Model 1. The first estimated model is described by the Equation (??).
For F und0 sCharacteristics, we use the dummy variable CV C as
an explanatory variable. It is equal to 1 if the startup receives
investments from CVC funds and 0 otherwise.
Model 2. In the second model, we still use the dummy variable CV C as
a measure of fund’s characteristics, while include the controlling
variable of CVC strategic relationship. The variable measures
whether the corporation behind CVC funds is a potential com-
petitor to the invested startups or not. It is a dummy variable
which is equal to 1 if the corporation is in the same industry
as the startup (competitors) and 0 if not. It is included accord-
ing to Masulis and Nahata (2009). They indicate that because
of the strategic aim of CVC funds, they can be competitors to
the startups in the future. Therefore, startups ask for a higher
investment from CVC funds than from IVC funds in order to
be compensated for potential market competition. We include
the variable in the model to control possible effects on startups’
investment strategy.
Model 3. We use the percentage of investment made by CVCs (CV C per)
as an indicator of CVC backed startups in the third model. It
is constructed by dividing investment from CVC funds over the
total investment amount of a startup. A higher value means that
the startup is more CVC oriented.
According to the theoretical prediction, α1 is expected to be positive for
all the models.
The results of an OLS regression on the three models are reported in
Table 5. We obtain the same estimated results using three different mod-
els. CVC funds have a significantly positive impact on the total investment
amount of startups. Startups financed by CVC funds invest 0.28% more than
those financed by IVC funds if VC fund’s characteristics is attributed by the
dummy variable. Similarly, if the CVC investment amount as a percentage
of the total investment for a startup increases 1%, the startup receives 0.34%
more investment. Moreover, longer duration also implies significantly higher
investment level. For all the three models, one percent increase in duration
means 0.13% higher investment. These results match with our theoretical
prediction. In addition, more investment rounds, larger fund size and larger
21syndicate size lead to more investment in startups. We do not find any sig-
nificant effect of the corporate venture capital funds’ relationship with the
startups (competitive or complementary) on startups’ investment amount.
5.2.2 Funds’ Characteristics and Successful Exit Rate
The second implication of our theoretical analysis is that the rate of liqui-
dation (failure) among CVC backed startups should be smaller than that
of IVC backed startups. We can not directly test this result because our
database only contains successful stories, that is, those startups that either
went to a successful IPO or were acquired. However, using indirect methods,
the result has been empirically confirmed by Gompers and Lerner (2000);
Chemmanur and Loutskina (2008); Masulis and Nahata (2009); Dushnitsky
and Shapira (forthcoming); and Ivanov and Xie (forthcoming). For example,
Dushnitsky and Shapira (forthcoming), show that CVC backed startups ex-
hibit significantly better performance as measured by the rate of successful
portfolio exits. The increase in the successful exit rate ranges from 9.7% to
20% depending on CVC managers’ incentives.
5.2.3 Funds’ Characteristics and Exit Strategy
The common view on the influence of CVC funds on startups’ exit strategy
in the empirical literature, is that there is a higher rate of acquisition exits
for CVC startups, because of CVCs’ strategic aim. However, our theoretical
model identifies two forces that go in opposite directions: CVC backed star-
tups stay longer before exit and they invest more. The duration effect has a
negative, while the investment has a positive effect on the likelihood of IPO
exits. According to our model, the identity of the fund does not matter. It
only influences the exit decision throught duration and investment. There-
fore, it is interesting to see what happens in the real market. The following
hypotheses summarize the idea:
Hypothesis 2.
H0 : CVC backed startups have the same probability of IPO exit as IVC backed
startups.
H1 : CVC backed startups have a higher probability of IPO exit than IVC
backed startups do.
Hypothesis 3.
H0 : Duration of a startup before exit has no effect on the probability of IPO
exit.
22H1 : Duration of a startup before exit has a negative effect on the probability
of IPO exit.
Hypothesis 4.
H0 : Investment amount has no effect on the probability of IPO exit.
H1 : Investment amount has a positive effect on the probability of IPO exit.
The following simple regression summarizes the estimation:
X5
0
Exiti = α0 +α1 F und sCharacteristics+α2 ln Durationi +α3 ln Investi + αk Zk +i
k=1
(9)
The dependent variable Exiti is a dummy variable, with value of 1 if it
is IPO exit and 0 for an Acquisition exit. Duration has the same definition
as in Equation (??). We also control the effect of investment amount on
the probability of an IPO exit. The set of controlling variables are similar
to the previous regression, except that we include one additional controlling
variable: later stage dummy variable. It is a proxy for the relative control
between entrepreneurs and VC funds.7 If startups exit at some later stages
(i.e. the expansion or the later stage), entrepreneurs have more controlling
power. Otherwise, VC funds have more power. Hence, it is a dummy variable
equal to 1 for expansion or later stage, and 0 for seed or early stage. To
check the robustness of our results, we also apply regression estimation on
four models based on the Equation(??).
Model 1. The first estimated model is the one described in Equation (??),
with a dummy variable CV C to measure the funds’ characteris-
tics. We use an OLS regression to do the estimation.
Model 2. In our second model, we include two more controlling variables:
3-month and 6-month MSCI index. Those variables are used to
control the stock market condition before the exit date. This is
because a strong stock market before the exit date may increase
the probability of an IPO exit.
Model 3. Similar to the estimation on investment amount, we consider the
percentage of CVC investment out of the total investment amount
at startup level as a measure of the funds’ characteristics.
7
Cumming (2008) points out that the relative controlling power between the en-
trepreneur of a startup and VC funds influences the exit strategy. Entrepreneurs prefer
IPO exit while VC funds could vote for Acquisition exit. Therefore, the relative controlling
power between the two parties affects the choice of the exit strategy.
23Model 4. Since the dependent variable in the estimation model is a dummy
variable, we use a Logistic estimation in Model 4 to check the
robustness of our OLS estimation.
Model 5. In model 5, we estimate the effect of funds’ characteristics on
startups’ exit strategy, without the control of duration effect or
investment effect.
For all the models, we expect to observe α2 to be negative and α3 to be
positive.
The results are described in Table 6. The effect of CVC fund’s charac-
teristics on the IPO exit is positive but not statistically significant, using
either the CVC dummy variable or the percentage investment of CVC as an
explanatory variable. However, this effect of CVC funds on the exit strategy
can be explained by startups’ duration and the investment amount. Longer
duration means a significantly lower probability of IPO. One percent increase
in the duration will decrease the probability of IPO by 0.017%. Differently,
investment amount has a significantly positive effect on the probability of
IPO. We observe that one percent increase in the investment amount in-
creases the probability of IPO exit by 0.065%. These results are robust
for all the first four models, with both OLS estimation and Logistic esti-
mation. The results of Model 5 confirm that the duration effect and the
investment effect explain the impact of VC funds’ characteristics on the exit
strategy. Specifically, the investment effect dominates the duration effect, be-
cause CVC funds increase the likelihood of IPO exits when neither duration
nor investment effect is controlled. Furthermore, the competitive relation-
ship between CVC funds and startups leads to more IPO exits. When the
stock market is strong for 3 months before the exit date, there incur more
IPO exits. On the contrary, if the stock market is strong for 6 months be-
fore the exit date, the impact on IPO exits is negative. Interestingly, as the
entrepreneurs have more controlling power over the startups than VC funds
do, more IPO exits are observed. This result is consistent with the findings
of Cumming (2008).8 All the results match with our theoretical predictions.
8
We acknowledge that the result of later exit stage associated with higher IPO exit rate
can be explained by other stories. For example, since the startup exits at the expansion
or the late stages, it is better developed. This leads to a higher probability of IPO exit.
However, we can not measure the relative controlling power between the entrepreneurs
and VC funds directly because of limited data. The later exit stage dummy variable is the
best proxy we can find.
245.2.4 Sensitivity Test
In this section, we estimate the previous regressions by removing the star-
tups in the Business Service industry (SIC2 = 73). The Business Service
industry contains almost 50% startups in our sample. Therefore, we exclude
the startups from that industry, to mitigate the concern that our results
are driven by certain industry. Table 7 provides the estimated results for the
subsample. Our results are qualitively robust, although the effect of duration
on the exit strategy is not statistically significant.
6 Conclusion
In this paper, we study the optimal initial and exit decisions by startups.
In particular, we focus on the difference in behavior between CVC backed
startups and IVC backed startups.
In our theoretical model, the difference between CVC and IVC financing
is attributed to different discount rates. We assume that (for example be-
cause of strategic objectives) CVC funds are less hurried to exit than IVC
funds. Therefore, startups backed by CVC funds have a lower discount rate
than those backed by IVCs. We find that CVC backed startups have longer
duration before exit and larger investment level than those financed by IVCs.
These properties, in turn, lead to higher successful exit rates and to two op-
posite impacts on the likelihood of an IPO exit. Longer duration, implying
more information in the acquisition market, increases the probability that
the startup looks for an acquirer. On the contrary, higher investment level,
increasing the value of the startups, encourages more IPO exits.
The theoretical results are then empirically tested with data from Ventur-
eXpert database. Our empirical study indicates that CVC financing do imply
longer duration and larger investment level than IVC funding. Moreover, the
effect of venture capital funds’ characteristics on startups’ exit strategy can
be explained through the investment and duration decisions. Shorter dura-
tion as well as larger investment level significantly lead to a higher likelihood
of IPO exit. Once these two effects are taken into account, whether the
venture capital fund is corporate or independent does not have a significant
influence on the startup exit decision.
25Appendix
Proof of Lemma 1.
Proof. Propositions ?? and ?? imply that
Z Z p o Z 1
EUo (I) = mp(V − C)dp + [βp(V − C) − F ] dp dΓ(V ; I)
V ≥C 0 po
Z
1 1 2
= β(V − C) − (β − m) po (V − C) + F (1 − po ) dΓ(V ; I)
V ≥C 2 2
and
Z Z poo Z 1
poo
EUoo (I) = m (V − C)dp + [βp(V − C) − F ] dp dΓ(V ; I)
V ≥C 0 2 poo
Z
1 1 2
= β(V − C) − (β − m) poo (V − C) + F (1 − poo ) dΓ(V ; I).
V ≥C 2 2
Therefore, EUo (I) ≥ EUoo (I) if
1 1
(β − m) p2o (V −C)+F (1 − po ) ≤ (β − m) p2oo (V −C)+F (1 − poo ) . (10)
2 2
Equation (??) holds if equality if poo = 1 (and then, po = 1 as well).
Otherwise, denote j(p) ≡ 12 (β − m) p2 (V − C) + F (1 − p). Then, j 0 (p) =
(β − m) p(V − C) − F < 0 for all p < min {po , 1}, given the definition of po .
Therefore, j(poo ) > j(po ) whenever poo < 1, that is, (??) holds with strict
inequality when poo < 1.
Proof of Proposition ??
Proof. The First Order Condition of Equation (??) w.r.t. d is:
EU 0 (d) = −re−rd [h(d)EUo (di) + (1 − h(d)EUoo (di)] + e−rd h0 (d)[EUo (di)
− EUoo (di)] + ie−rd [h(d)EUo0 (di) + (1 − h(d))EUoo
0
(di)] − ie−rd .
(11)
Then at d = d∗ ,
∂EU 0 (d)
< 0, (12)
∂d
and
∂EU 0 (d)
= −dEU 0 (d) − e−rd [h(d)EUo (di) + (1 − h(d))EUoo (di)]
∂r (13)
= −e−rd [h(d)EUo (di) + (1 − h(d))EUoo (di)] < 0.
26Using the Implicit Function Theorem, we obtain
∂d
< 0. (14)
∂r
∂I
Moreover, since I = di, ∂r
< 0 as well.
Proof of Proposition ??
Proof. We notice that the rate of IPO exits over the total successful exits is
either Z
γ(V ; I)
[1 − po (V )] dV (15)
F
V >C+ β−m [1 − Γ(C; I)]
or Z
γ(V ; I)
[1 − poo (V )] dV. (16)
V >C+ β−Fm [1 − Γ(C; I)]
2
Both expressions increase in I under the proposed assumption.
27Table 1. Industry Composition of the Sample
Two-Digit SIC Code Industry Name Number of Startups
13 Oil and gas extraction 27
20 Food and kindred products 21
27 Printing and publishing 24
28 Chemicals and allied products 360
35 Industrial machinery and equipment 274
36 Electronic and other electronic equipment 510
38 Instruments and related products 348
48 Communications 199
50 Wholesale trade - durable goods 57
51 Wholesale trade - nondurable goods 20
59 Miscellaneous retail 64
62 Security and commodity brokers 20
63 Insurance carriers 29
73 Business services 2, 199
80 Health services 127
87 Engineering and management services 195
28Table 2. Definitions of Variables
Variables Definitions
CVC Dummy variable equal to 1 for CVC backed startups
and 0 for IVC backed startups
CVC Per Percentage of investment by CVC in each startup
IPO Dummy variable equal to 1 for IPO exit and 0 for Acquisition exit
Investment amount Total investment amount at startup level, measured by
disclosed equity amount summed over investment rounds
Duration Difference in days between the exit date and the date at which a startup
receives the first investment from venture capital firms
Investment rounds Number of investment rounds for a startup
VC syndicate Number of venture capital firms that invest in a startup
VC fund size Average size of venture capital funds that finance the startup
CVC strategic relationship Measure of CVC strategic competitors,
dummy variable of 1 if a CVC has the same 4-digit
SIC code as its start-up, and 0 otherwise
Industry MB Industry market-to-book value at the year
that CVC firm makes the first investment
MSCI 3 mon MSCI return 0-3 months prior to the exit date
MSCI 6 mon MSCI return 3-6 months prior to the exit date
Later stage dummy Relative controlling power between entrepreneur and VC, a
dummy variable of 1 if a startup is at expansion
or later stage at the exit, and 0 otherwise
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