Investment, Duration, and Exit Strategies forCorporate and Independent Venture Capital

Backed Startups∗

Bing Guo†, Yun Lou‡, and David Perez-Castrillo§

September 30, 2011

Abstract

We propose a model of investment, duration, and exit strategiesfor startups that takes into account first, the high level of uncertaintyregarding returns from the investment in the startup, second, the moreaccurate information in the hands of insiders, and finally, the discountrate of the partners in the startups. According to our theoreticalanalysis, CVC backed startups stay longer in the market before exitand they invest more than those financed by IVCs. While longerduration leads to a higher likelihood of an exit through acquisition,a larger investment increases the probability of an IPO exit. Thesepredictions find strong empirical support, using venture capital datafrom U.S.

JEL Classification: G32, G24.

Keywords: Startups, Corporate Venture Capital, Independent Venture Cap-ital, Investment Amount, Duration, Exit Strategy, IPO, Acquisition.

∗We thank Albert Banal-Estanol, Gary Dushnisky, Marıa Gutierrez, Ines Macho-Stadler, Philipp Meyer, Pau Olivella-Cunill, Pedro Rey-Biel, Jo Seldeslachts and partici-pants at the MOVE workshop on venture capital, the 2011 LSE Alternative InvestmentsResearch Conference and the Symposium of Industrial Organization and ManagementStrategy in Chengdu 2011 for their helpful suggestions. We are grateful to AGAUR, re-search projects ECO2009-07616, 2009SGR-169, and INFOINNOVA (03513A), BarcelonaGSE, and ICREA Academia for the financial support. Perez-Castrillo is MOVE fellow.

†Universidad Carlos III de Madrid, Business and Administration Department,bing.guo@uc3m.es.

‡London Business School, Accounting Department, ylou.phd2007@london.edu.§Universitat Autonoma de Barcelona, Economics Department, david.perez@uab.es.

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1 Introduction

Entrepreneurs and venture capitalists make investment decisions and choosethe length of their involvement in a startup to maximize the chances of suc-cess and the value of their venture. They also look ahead and plan for astrategy of cashing in on their company allowing, in particular the venturecapitalists, to liquidate their shares. Planning an exit strategy is as impor-tant as figuring out how to start the enterprise.

There are two main exit routes for a successful startup: the company cango 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 listingso that it can receive additional financing for its projects and the insiderscan eventually sell their shares to the public. If the startup is acquired, theinsiders get immediate cash in return from their shares.

The optimal exit route for startups depends on multiple factors, such asexpected profitability of the venture; level of uncertainty; asymmetry of in-formation between insiders and outsiders (potential buyers, new investors);2

possible conflicts of interest among insiders;3 venture capital characteristics,etc. Some of these factors are affected by the partners’ investment and du-ration decisions. Understanding the main trade-offs faced by startups at theexit stage is crucial because it allows to see how venture capitalists and en-trepreneurs divest their companies, and also because of the impact of the(anticipated) exit strategy on the decisions taken at the onset of the venture.

In this paper, we abstract from possible internal conflicts among insidersand we propose a model of investment, duration, and exit taking into accountfirst, the high level of uncertainty regarding returns from the investment inthe startup, second, the more accurate information in the hands of insiders,and finally, the discount rate of the partners in the startups.

In our model, the level of investment of a startup influences its expectedvalue. We assume that a higher investment leads to a more favorable dis-tribution of the set of potential values. Furthermore, the decision on the

1Two other exit routes that are not so commonly used are Management Buy-out andRefinancing (or secondary sale); see, for example Schwienbacher (2009).

2See Cumming and MacIntosh (2003) for a discussion about the information asymme-tries between sellers and potential purchasers of startups.

3For a recent review of papers that analyze internal conflicts, see for example Macho-Stadler and Perez-Castillo (2010)).

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duration of the startup before exit, that is, the length of the relationship, af-fects the market information about the successful probability of the venture.Indeed, the level of uncertainty concerning the actual value of a startup isvery high. Some of the uncertainty is resolved during the development stageand the market has access to that information. We assume that the level ofthe potential value of the venture at the time of the exit will be known byevery market participants. Nevertheless, the insiders have more precise in-formation about the expected profitability of the startup because they learnthe probability of success. Whether the outsiders can be informed of suchprobability depends on how long the startup stays in the market before exit.

We show that independently on the level of information received by thepotential acquirers, the ventures whose probability of success is higher aremore likely to try an IPO while those with lower probability prefer lookingfor an acquirer. Moreover, the likelihood of going to IPO increases with thepotential value of the startup, if that value is high enough. Startups withlow potential value are liquidated.

We link the startup exit strategy with the investment decision and withthe market level of information. First, a higher investment level brings aboutboth, a higher likelihood of successful exit and a higher rate of IPO exitsamong the successful ones. Second, the IPO exit rate is lower when theoutsiders receive more precise information, that is, when they are informedabout the success probability. Finally, we analyze the optimal investmentand duration decision of the startup. We show, in particular, that both thelevel of investment and the duration of the venture decrease with the dis-count rate of the startup.

Our analysis allows to shed light on the discussion concerning the differ-ences in behavior between the startups that receive financing from CorporateVenture Capital funds (CVCs) and those that are financed only by Indepen-dent Venture Capital funds (IVCs).

Unlike the traditional IVCs, CVCs are private equity funds invested bylarge corporations. Therefore, while the sole objective of IVCs is financialreturn on capital, a very important goal of most CVC programs is strate-gic: the development of new, related business (see for instance Sykes, 1990;Yost and Devlin, 1993; Dushnitsky and Lenox, 2006; Hellmann, Lindsey andPuri, 2008). According to this strategic argument, CVC backed startupsare more likely to exit by acquisition because the affiliated company of theCVCs may be particularly interested in the venture. This argument has

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been confirmed by both theoretical studies (Hellmann, 2002; Riyanto andSchwienbacher, 2006) and empirical studies using European dataset (Cum-ming, 2008). Based on some questionnaires, Siegel, Siegel and MacMillan(1988) and Sykes (1990) find that the percentage of CVC backed startupsthat are acquired is higher than that of IVC backed ventures. However, it hasbeen shown that the number of startups acquired by the company behind theCVC funds is small (Maula and Murrey, forthcoming) and our own analysisusing the VentureXpert database confirms that the percentage of startupsacquired by companies related to CVC investors is around 5%. Moreover,Gompers and Lerner (2000) and Chemmanur and Loutskina (2008) find thatstartups with CVC investments exit more often through IPO rather than byacquisition.

We argue that another important difference between CVC and IVC fundsis that IVC funds care more about quick exits than CVCs; that is, IVC backedstartups have higher discount rate than those backed by CVCs. Indeed, com-pared to IVC funds, CVCs have more unused resources such as technologyand marketing resources (Sahaym, Steensma and Barden, 2010; Basu, Phelpsand Kotha, 2011; Da Gbadji, Gailly and Schwienbacher, 2011). Moreover,IVC managers’ payment is more based on financial returns and their abilityto raise additional funds depends on their reputation, which is influenced bytheir history of successes (Gompers, 1996; Dushnitsky and Shapira, forth-coming). Therefore, they have strong incentives to cash their return fromprofitable projects early.

According to our model, the difference in the discount rate between IVCand CVC backed startups results in different behavior between the two typesof venture. First, a lower discount rate for CVC backed startups implies ahigher investment level and longer duration. Second, although the identityof the VC fund does not have a direct effect on the choice of exit, it does havetwo strong indirect effects: on the one hand, the larger investment decided byCVCs leads to more IPO exits; on the other hand, the longer duration inducesmore exits through acquisition. Therefore, the two forces that we identify goin opposite directions. Finally, a larger investment implies a higher successrate for CVC backed startups.

We empirically test our main results using data on 4801 US startups fromthe period 1969 to 2008. First, we find that startups financed by CVC fundsinvest around 25% more than those financed by IVC funds. Moreover, theeffect is doubled if the syndicate leader of a startup is a CVC fund. Sec-ond, CVC backed startups do stay longer before the exit than IVC backed

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startups. Third, one percent increase in the level of investment significantlyincreases the probability of IPO exit by 0.065%. Fourth, one percent increasein the duration of the venture significantly decreases the likelihood of IPOexit by 0.017%. Fifth, we show that, after controlling for the duration effectand for the level of investment, there is no significant difference in the rate ofIPO exits between IVC and CVC backed startups. In fact, we observe thatthe presence of CVC investors has a positive, although not significant, effecton the IPO exit rate. All the previous empirical results strongly supportthe predictions of the theoretical model. Finally, we use an enlarged datasetto test the theoretical results concerning the influence of the level of invest-ment, the duration and the fund’s characteristics on the successful rate. Assuggested by our model, duration and fund’s characteristics do not have asignificant influence in the rate of successful exists. However, the effect ofinvestment, while positive, is also not significant.

The rest of the paper is organized as follows. In Section 2, we introducethe model. In Section 3, we develop the analysis of the optimal exit strategy.In Section 4, we analyze the impact of investment and duration decisionson the likelihood of IPO and on the success rate. In section 5, we studythe optimal investment and duration decisions. In Section 6, we derive theempirical hypotheses concerning the differences in behavior between CVCand IVC backed startups suggested by our theoretical results. In Section 7,we describe the dataset, which we use to empirically test the hypotheses inSection 8. Finally, Section 9 concludes. All the proofs are included in theAppendix.

2 The Model

We analyze the optimal investment and duration decisions and exit strategyof startups (S). In our model, startups’ decisions at any stage aim to maxi-mize the expected discounted profits.

The main characteristics of the model are the following. The first deci-sions taken by a startup concern the level of investment I and the duration ofthe venture d. We consider that these decisions are made at the beginning ofthe life of the startup and we do not take into account their dynamic aspect,which is not relevant for our purpose. The level of investment has a positiveimpact on the expected quality of the venture while the duration influencesthe amount of information that will flow to the market.

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The optimal investment and duration decisions depend on the discountrate r of the startups. The parameter r will play an important role in ouranalysis because, in the empirical sections of the paper, we will differentiatebetween startups receiving CVC funding and those receiving only IVC fund-ing. As we argued in the Introduction, CVC funds tend to be more patientthan IVC funds.

When the startup makes its first decisions, there is a high degree of un-certainty with respect to both, the potential value of the venture V and theprobability p of being able to realize this value. Part of the uncertainty isresolved as the startup develops. All the market participants will be able toobserve some of the information, but the insiders will acquire more preciseinformation on the expected quality of the project. In our model, we reflectthis 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 precisionof 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 valueV . Hence, if the potential value V is low, the startup will be liquidated (thisis 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 inadding 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 willreflect the expected value of the business and the bargaining power of theparties. Then, the acquirer will integrate the venture into its organizationand, 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 througha thorough analysis concerning all possible aspects of the startup. The mar-ket investors will make a careful auditing of the corporate valuation, marketprospection and so on. The outcome of the analysis will be a new signal onthe profitability of the startup that we model also in a simple way: either themarket 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 coverthe cost. The market investors will make an offer to the startup owners incase of a High signal.

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More precisely, the model is the following:

At t = 1, the startup decides the level of investment I and the durationd.

• The level of I determines the distribution of the potential value of thestartup V : the value V follows a distribution function Γ(V ; I), withdensity function γ(V ; I). For simplicity, we assume that ex ante V isuniformly distributed over the interval

�f(I), V + f(I)

�, where f(I) is

an increasing function of I. The potential value V can only be cashedif at later stages a fixed new funding C is made. After the investmentand before all the other decisions are taken, the value of V is realizedand it is observable by everybody.

• The level of d determines the information learnt by the potential acquir-ers about a signal p on the likelihood of success, i.e., the probability ofrealizing V . Also for simplicity, we assume that ex ante p is uniformlydistributed over the interval [0, 1]. The startup always learns p. Thepotential acquirers will learn p with probability h(d), increasing in theduration d.

At t = 2, the startup takes the exit decision. It has three possibilities:liquidation, looking for an acquirer, or going to an IPO.

• The liquidation value of the startup is always 0.

• 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 denotedby m. In case of acquisition, the acquirer will invest C to realize V ifit confirms that the project is successful.

• Going to an IPO is the most complex and costly exit route for thestartup. We denote by F all the fixed costs due to the IPO process.It leads to one public signal �β on the profitability of the venture, �β ∈{H,L}. We assume that β is the probability for the market to be able toverify a successful project after receiving the public signal. Therefore,the probability of observing �β = H is equal to βp. In case the signalis H, the competitive market will set a price Z for IPO. If the startupaccepts the price, then a successful IPO is carried out. In order torealize V , in addition to Z, the market needs to raise C to cover the

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Figure 1: The Time Line

remaining investment needs. In case the signal is L, then no offer isissued.4

The time line is captured by Figure 1.

We make assumptions on the functions f(I) and h(d) that make surethat the three exit routes are possible. We assume that V + f(0) > C + F

β−m

and that f(I) is concave enough, in particular limI−→∞ f(I) ≤ C. Also, thescreening is informative enough: β > m. Finally, the venture makes interiorchoices of I and d if limI−→0 f

�(0) = limd−→0 h�(0) = ∞ and h(d) is concave

enough.5

4We assume that a startup that receives a low signal does not get any offer and quitsthe market. We make this assumption for simplicity. First, for those startups that receivea 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 thestartups that accept that offer). Therefore, the assumption that IPOs do not make offersto 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. Thisadds 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 ananalysis of the previous extensions, see Guo, 2010).

5If β < m, then the IPO is never chosen. If V + f(0) < C + Fβ−m , then some exit

routes are never taken for low investment levels while liquidation never happens for highinvestment levels if limI−→∞ f(I) > C. However, our qualitative results would remainwith changes in the hypotheses. Similarly, if limI−→0 f �(0) = limd−→0 h�(0) < ∞, then

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We solve the model by backward induction, taking into account thatthere 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.

3 The Analysis of the Optimal Exit Strategy

In this section, we start at t = 2, where the duration has already beendecided and the investment made at t = 1 is sunk. The potential value forthe venture V is realized and observed by all the participants. Moreover, thestartup has received the private signal concerning the probability of successp. The potential acquirer may also know p (this happens with probabilityh(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 ofliquidation is 0. Also, the deal price in an acquisition corresponds to a sharem of the expected value of the venture. Taking into account that the ac-quirer knows p and that he needs to invest C to realize V when the ventureis believed to be successful, the expected value of the venture is p [V − C].Therefore, if the startup goes to the acquisition market at t = 2, the dealprice 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 non-positive).6 After thestartup pays F , the market receives the signal �β. If the realization is �β = L,which happens with probability (1 − βp), it will not receive any offer. Ifthe realization is �β = H, then the competitive market of investors will offerZ = V − C, which the startup will accept.

The startup obtains higher expected profits going to an IPO than lookingfor an acquirer if and only if

βp [V − C]− F ≥ mp [V − C] . (1)

the optimal decision on I and/or d may be the corner solution I = 0 and/or d = 0, whichwould complicate the analysis without adding new insights.

6We take the convention that a startup indifferent between being liquidated and notchooses liquidation. Similarly, a startup indifferent between going to an IPO and lookingfor an aquirer at t = 2 goes to an IPO.

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Proposition 1, whose proof follows the previous discussion, describes theoptimal exit strategy for a startup characterized by (V, p) when outsiders areinformed about p, where we denote

po(V ) ≡ min

�1

[β −m]

F

[V − C], 1

�. (2)

Proposition 1. Consider a startup characterized by (V, p) in a situationwhere potential acquirers have learned p. The startup’s optimal exit strategyis as follows:

1. If V −C � 0, the startup is liquidated and gets the payoff Uo(V, p) = 0.

2. If V −C > 0 and p < po(V ), the startup goes to the acquisition marketand gets a deal value Uo(V, p) = mp [V − C].

3. If V −C > 0 and p � po(V ), the startup invests F and goes to the IPOmarket. Moreover,

(a) if it gets the public signal H, then it receives an offer Z = V − C

from the IPO market and it accepts it;

(b) if it gets the public signal L, then it does not receive any offer fromthe IPO market.

Therefore, in this case, the startup’s expected payoff is Uo(V, p) =βp [V − C]− F .

The basic trade-off between IPO and acquisition is that while the IPOprocess is very costly, it also allows the startup owners to get a larger shareof the value of profitable ventures. Startups with high enough probability ofsuccess are ready to pay the cost of the process. To analyze the effect of thedifferent parameters on this trade off, we conclude the analysis of the optimalexit strategy by doing the comparative statics of po(V ) with respect to allthe parameters, for the interior case where 1

[β−m]F

[V−C] < 1. This analysishighlights the characteristics of the startups and the market that make itmore likely to observe exits through IPO or through acquisition. Indeed, ahigher po(V ) implies a lower likelihood of exit through IPO.

Corollary 1. Consider situations where potential acquirers learn p. Then,the likelihood of IPO increases with V and β and it decreases with F , C, andm.

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According to Corollary 1, the higher the potential value V (similarly, thelower the additional funding C), the more willing is the startup to go to theIPO market. Given the costly IPO process, only those startups that reallybenefit from the more competitive IPO market are willing to follow this path.As expected, a higher bargaining power m in the acquisition market leads toless 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 whenthe potential acquirers do not know the value of p has some similarities withthe one developed previously. First, if the startup’s potential value V is lowerthan C it is liquidated. Second, the IPO offer is Z = V − C if it receives asignal �β = H which, in particular, implies that the startup will accept theoffer. Finally, potential profits from IPO versus Acquisition increase with thevalue of p; therefore, there will be a cut-off equilibrium value poo(V ) (thatcan 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 potentialacquirers is that the price that they may offer does not depend on the realvalue of p but on its expected value from the point of view of the acquirer,which is a function of the startup equilibrium behavior. The deal that apotential acquirer will make to a startup that approaches it at t = 2 willnot be based on the true probability but on the expected (equilibrium) valueof p, which is poo(V )

2 . Therefore, the deal price at t = 2 will bempoo(V )2 [V − C].

Similar to the informed outsiders’ case, a startup whose probability ofsuccess is equal to poo(V ) must be indifferent (if poo(V ) ∈ (0, 1)) betweengoing to IPO and looking for an acquirer. Therefore, an interior poo(V ) ischaracterized by

βpoo(V ) [V − C]− F = mpoo(V )

2[V − C] . (3)

Equation (3) implies that the cut-off value is

poo(V ) ≡ min

�1�

β − m2

� F

[V − C], 1

�. (4)

For completeness, we state the equilibrium behavior of startups whenoutsiders are uninformed as to the value of p in Proposition 2.

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Proposition 2. Consider a startup characterized by (V, p) in a situationwhere potential acquirers have not learned p. The startup’ equilibrium exitstrategy is as follows:

1. If V −C � 0, the startup is liquidated and gets the payoff Uoo(V, p) = 0.

2. If V − C > 0 and p < poo(V ), the startup goes to the acquiring marketand gets a deal value Uoo(V, p) = m

poo(V )2 [V − C].

3. If V − C > 0 and p � poo(V ), the startup invests F and goes to theIPO market. Moreover,

(a) if it gets public signal H, then it receives an offer Z = V −C fromthe IPO market and it accepts it;

(b) if it gets public signal L, then it does not receive any offer fromthe 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 andit decreases with F , C, and m.

The intuitions behind Proposition 2 are the same as those explained afterProposition 1 and Corollary 1.

4 The Impact of Investment and Duration on

the Likelihood of IPO and on the Rate of

Success

We now analyze how the optimal exit strategy of a startup is influenced byits investment and duration strategies.

4.1 The Investment Effect

The level of investment affects the likelihood of an IPO exit. A higher invest-ment level implies a shift in the distribution of V towards higher values. Asshown in propositions 1 and 2 (see also Figure 2), the higher the value V , themore likely it is that the exit happens through an IPO rather than throughacquisition, independently on whether potential acquirers have learned p.Therefore, a higher investment level should imply a higher IPO rate among

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successful stories. Proposition 3 states this result.

Proposition 3. The likelihood of IPO among the successful exits increaseswith I both, when the potential acquirers have learned p and when they havenot learned p.

Moreover, the shift in the distribution of V towards higher values impliedby a larger investment should also lead to higher likelihood of successful exit.Proposition 4 states this result, whose proof is immediate.

Proposition 4. The rate of successful exits increases with I.

4.2 The Duration Effect

Longer duration means that the market has more precise information which,in our model, is reflected by a higher probability for the public to know thesuccessful probability p. The next proposition investigates whether an IPOexit is more likely with informed or with uninformed outsiders. The proof ofthe proposition is straightforward from equations (2) and (4).

Proposition 5. The probability of going to IPO is higher when the potentialacquirers have not learned p than when they have learned p. More precisely,poo(V ) < po(V ) whenever poo(V ) ∈ (0, 1), and po(V ) = 1 whenever poo(V ) =1.

Figure 2 helps to explain Proposition 5. It depicts the optimal exit strat-egy highlighted in propositions 1 and 2. For high values of p and V , IPO isthe optimal exit route independently 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 notlearned p, while they prefer going to the acquisition market if outsiders haveobserved p.

The intuition for the existence of the intermediate region in Figure 2 isthe following. When the outside acquirers observe the true value of p, theyoffer a deal according to p. However, when they do not observe the truesuccessful probability, they can only offer a deal according to the expectedprobability, which is independent of the true value p. Consider a startupwhose realized probability is po(V ), that is, it is indifferent between IPO andacquisition if information about p is public. The deal it will obtain in the

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Figure 2: Optimal Exit Strategy

acquisition market if information is not public is lower, as it is based on theexpected probability. Therefore, it would rather go to IPO than look for anacquirer. As a consequence, uninformed markets are more likely to lead toIPO exits.

Longer duration leads to more information about p for outsiders. Ac-cording to Proposition 5, this implies, ceteras paribus, a reduction in thelikelihood of IPO exit. We state this result in the following corollary.

Corollary 2. The likelihood of IPO among the successful exits decreases withd.

We note that, according to our model, the duration has no effect on thelevel of V . Therefore, the successful rate is not influenced by the duration d.

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5 Analysis of the Optimal Investment and

Duration Decisions

We address now the optimal initial decisions by the startup at t = 1. Theanalysis of Section 3 allows computing the expected income Uo(V, p) orUoo(V, p) of a startup whose potential, publicly known value is V and whoseprobability of success is p, depending on the level of information by the out-siders. We now calculate the expected profits for a given duration d and agiven investment I, denoted as U(d, I), which requires taking the expecta-tion of the expected income over the possible values of V (whose distributionfunction Γ(V ; I) depends on I) and p:

U(d, I) = e−rd

�

V

�

p

[h(d)Uo(V, p) + [1− h(d)]Uoo(V, p)] dpdΓ(V ; I)− I

= e−rd [h(d)EUo(I) + (1− h(d))EUoo(I)]− I

(5)

where we denote

EUo(I) =

�

V

�

p

Uo(V, p)dpdΓ(V ; I), (6)

EUoo(I) =

�

V

�

p

Uoo(V, p)dpdΓ(V ; I). (7)

We can interpret EUo(I) as the expected profits at the time of exit ofa startup that has invested I at t = 0 and whose realized probability p isknown by potential acquirers. Similarly, EUoo(I) is the startup’s expectedprofits if the realization of p is unknown by outsiders. The probability thatthe information is known by the potential acquirers, h(d) increases with theduration. Moreover, the longer d, the lower the expected profits at t = 0 dueto the discounting.

Lemma 1 shows that EUo(I) is always higher than EUoo(I), that is, thestartup’s expected profits are higher when the potential acquirers learn p

than when they do not. This result, interesting by itself, will be useful in theanalysis of the optimal duration and investment decisions.

Lemma 1. EUo(I) ≥ EUoo(I) for every I > 0, and the inequality is strictwhenever poo(V + f(I)) < 1.

The intuition behind Lemma 1 is the following. The asymmetry of infor-mation between startups and potential acquirers makes it more profitable

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for some ventures (those whose successful probability lies in the interval(poo(V ), po(V ))) to go to the IPO market although they would obtain a betterdeal in the acquisition market if the information were symmetric. Therefore,in expectation, startup’s profits are higher when information about p reachesthe potential acquirers, that is, EUo(I) ≥ EUoo(I).

The optimal duration and investment decisions (d∗, I∗) are interior. There-fore, they satisfy the first-order conditions ∂U

∂d (d∗, I

∗) = ∂U∂I (d

∗, I

∗) = 0. InProposition 6, we study the effect of the discount rate r on (d∗, I∗).

Proposition 6. The optimal duration d∗ and investment I

∗ decrease withthe discount rate r.

The result in Proposition 6 is quite intuitive. If the participants in thestartups care less about the future, they invest less and stay a shorter periodof time in the ventures.

Proposition 6 allows us to discuss the effect of r on the likelihood of IPOexit. The discount rate r indirectly influences the likelihood of IPO exitthrough the investment effect and the duration effect. A lower discount rater means both higher investment level and longer duration which, accordingto Proposition 3 and Corollary 2, have opposite impacts on the likelihood ofIPO exit. On the one hand, a larger investment leads to more exits throughIPO. On the other hand, a longer duration implies a better informed poten-tial acquirer, which leads to more acquisitions.

6 CVC vs IVC Backed Startups: Empirical

Implications from the Theoretical Model

The analysis of the previous sections allows us to contribute to the discussionof the differences between startups that receive funds from CVC and thosethat only receive IVC funding. CVCs are typically less compelled to recoverthe investment earlier. We associate this difference with a lower discount ratefor startups that receive CVC funding. According to our analysis, the differ-ence in the discount rate between CVC and IVC backed startups has testableimplications on their strategies. We use our theoretical results to propose em-pirical hypotheses concerning the differences in investment amount, durationbefore exit, exit strategy, and successful rate between CVC and IVC backed

16

startups.

First, one implication of our model (see Proposition 6) is that star-tups with lower discount rate choose higher investment levels. Therefore,we should observe higher investments in CVC backed startups than in IVCbacked startups. With more unused resources and having strategic aim, CVCfunds invest more in the startup projects. We state this empirical implicationin the following hypothesis.

Hypothesis 1: CVC backed startups receive a higher investment amountthan IVC backed startups.

Second, Proposition 6 also implies that CVC backed startups (havinglower discount rate) choose to stay longer before exit. Since CVC funds aremore patient than IVC funds, we expect that CVC invested startups have alonger duration before exit than those backed by IVC funds. This is reflectedin Hypothesis 2.

Hypothesis 2: CVC backed startups have a longer duration than IVCbacked startups.

Third, our theoretical model indicates an indirect impact of VC funds’characteristics on startups’ exit strategy. As we mentioned before, startupswith CVC backing invest more in their projects than those with IVCs. Thetheoretical model predicts that a higher investment level increases the prob-ability of an IPO exit (see Proposition 3). Furthermore, a startup with CVCbacking also stays longer and, after Corollary 2, longer duration reduces theprobability of IPO exits. According to our model, once we take into accountthe effect of investment and duration, the characteristics of the VC funds donot play a role in the choice of the exit strategy. We will test the theoreticalresults through Hypotheses 3, 4 and 5.

Hypothesis 3: Investment amount has a positive effect on the probabilityof IPO exit.

Hypothesis 4: Duration of a startup before exit has a negative effect onthe probability of IPO exit.

Hypothesis 5: CVC backed startups have the same probability of IPO exitas IVC backed startups.

17

Fourth, higher investments imply higher successful exit rates (see Propo-sition 4) while the duration and the characteristics of the fund do not haveany direct effect on the successful rate. Hypotheses 6, 7 and 8 state theseimplications from our theoretical model.

Hypothesis 6: Investment amount has a positive effect on the probabilityof successful exit.

Hypothesis 7: Duration of a startup before exit has no effect on the prob-ability of successful exit.

Hypothesis 8: CVC backed startups have the same probability of successfulexit as IVC backed startups.

7 Data Description

We obtain the relevant data, i.e., investment amount, IPO date, acquisitiondate, investment rounds, number of investors, investors’ type, IPO price, Ac-quisition deal value, VC fund size, etc, from the VentureXpert database.

To test the first five hypotheses, we use a dataset that contains 4801successful startups in the US market from 1969 to 2008. We describe thisdataset in detail. In Subsection 7.5, we will comment on the enlarged datasetthat includes unsuccessful startups and successful startups with other typesof exit besides IPO and Acquisition, which we use to test the last three hy-potheses.

Our sample of successful startups covers 63 industries in the US. Table1 provides an overview of the industry composition of the sample (by two-digit SIC code). In the table, we just include industries with more than20 observations. We observe concentration of industries with SIC code 28,35, 36, 38 and 73. These codes correspond to Chemical, Electronic andBusiness Service related industries, where venture capital investments aremore common.

7.1 Dependent Variables

We use the dataset with successful startups to test the effect of VC funds’characteristics (CVC or IVC) on investment amount, startups’ duration and

18

exit strategies. Therefore, we need three dependent variables: the total in-vestment amount, startups’ duration before exit, and the exit rule (IPO orAcquisition).

To measure the investment amount of a startup, we sum up the roundlevel investments to get the total investment amount. The duration beforeexit is measured by the number of days. It is calculated as the differencebetween the exit date (IPO date or Acquisition date) and the date at whicha startup receives the first investment from venture capital firms. Finally,the exit strategy of a startup is indicated by a dummy variable. It is equal to1 if a startup exits through an IPO and 0 if it exits through an Acquisition.

7.2 Independent Variables

The theoretical model predicts the influence of different VC funds’ character-istics on startups’ decisions and exit behavior. The most important indepen-dent 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 allthe investments are from CVC funds. The second defines a startup as CVCbacked if at least one of the investors is a corporate venture fund.

We use two variables to measure the VC fund characteristics. Firstly,we use the second definition in Toldra (2010) and create a dummy variablethat is equal to 1 if a startup receives at least one investment from CVCs.If all the investments are from IVC funds, the startup is IVC financed andthe dummy variable is equal to 0.7 Secondly, we use a continuous variable tomeasure the funds’ characteristics: we calculate the percentage of investmentmade by CVC funds out of total investment in each startup.

7.3 Controlling Variables

Based on the previous literature, we use a set of controlling variables to es-timate the effect of VC funds characteristics on the investment amount ofstartup projects, their duration before exit, and exit strategies. It includesthe average VC fund size, average VC fund age, total number of investmentrounds, VC syndicate size, syndicate leader characteristics (i.e., whether theleader is a CVC fund or an IVC fund), industry market-to-book value, the

7We can not use the first definition of CVC in Toldra (2010) because the number ofstartups that are fully funded by CVCs is very small.

19

relationship between CVC funds and their invested startups, 3 months and6 months MSCI return8 before the exit date, the industry fixed effect, andthe year fixed effect. We also take into account the relative controlling powerbetween the entrepreneur and the VC funds through the Later Stage Dummyvariable that takes value of 1 if the startup is at expansion or later stagesat the exit. As stated by Smith (2005) and Schwienbacher (2009), VC fundsguarantee themselves more controlling rights at the beginning of their involve-ment and for seed financing than at late-stage financing. Hence, exiting atexpansion or later stages proxies for higher entrepreneurs’ controlling power.9

Table 2 provides the definitions of all the variables and Table 3 summa-rizes the basic statistics of these variables.

7.4 Correlations across Variables

Table 4 reports the correlation matrix. It represents the correlations acrossthe main variables. The variables in our estimation are not highly correlated,except the 3 months and 6 months MSCI returns. In particular, the correla-tion between Duration and Investment Amount is 0.07. This low correlationsuggests low multicollinearity of the independent variables.

7.5 Enlarged Dataset with Unsuccessful Startups

The last three hypotheses estimate the influence of the level of investment,the duration and the fund’s characteristics on the successful rate. We testthese hypotheses with an enlarged dataset which includes both successfuland unsuccessful startups in the U.S. before the year 2009. We define astartup as a “Failure” if the company status is “Defunction” in the databaseof VentureXpert. On the contrary, a startup is a “Success” if the status is“Acquisition”, “Merger”, “Went Public”, “LBO”, etc. The enlarged datasethas a similar industry distribution to the one which only contains startupssuccessfully exited through IPO or Acquisition. Around 65% of the startupshave a successful exit and 35% are defined as “Defunction” in the dataset.10

8The Morgan Stanley Capital International (MSCI) constructs a free float-adjustedmarket capitalization weighted index that measures the equity market performance ofdeveloped and emerging markets. We use the MSCI ACWI (All Country World Index)Index of the United States in the paper.

9We use this dummy variable because we can not measure directly the relative control-ling power between the entrepreneurs and VC funds due to limited data.

10The successful rate of the startups in our dataset is very high. This might due to therestrictions of the database. VentureXpert database does not have the information of allthe failure cases in the U.S. market.

20

We create a new dependent variable Failurei for the empirical estimation.It is a dummy variable equal to 1 if the startup is a “Failure” as was definedbefore and 0 if it is a “Success”. The independent variables are the same asthose described in Section 7.2. We exclude some controlling variables, such asthe industry market-to-book value, fund age, due to data insufficiency. FromVentureXpert, we can not observe the date that startups are considered as“Defunction”. Hence, all the controlling variables that need the exact dateare excluded in the regressions of the last three hypotheses. The duration ofthe startups in this case is defined as the number of days between the firstand the last investment dates.

8 Empirical Analysis and Results

We first look at whether startups backed by CVC funds and those financedby IVC funds have different behavior. We provide some basic statistic dif-ferences between CVC and IVC financing in Panel A of Table 5.

We observe significant differences between startups with CVC funds andwith IVC funds in most variables. CVC backing implies a significantly higherinvestment than IVC backing. The average investment per venture for bothexit strategies is around 50 million USD for CVC backed startups while it isonly around 21 million USD for those only backed by IVCs. This fact hasalready been highlighted by previous literature (Gompers and Lerner, 2000).Moreover, there is a large gap between the mean duration of the two types ofventure. The mean duration for CVC backed startups is 1929 days, comparedto 1649 days for IVC backed startups. We also find that CVC financing leadsto more investment rounds. CVC backed startups exit at later investmentstages (i.e. more exits at expansion or later stages than exits at seeds orearly stages). Compared to IVC funds, CVC funds are older. However, wehave not found any significant difference in IPO exit rate and VC fund sizebetween the two types of VC funds.

8.1 Fund’s Characteristics and Investment Strategy

The difference in the investment amount might come either from differencesin the type of projects in which the funds invest (selection bias), or from theintrinsic characteristics of the type of fund, such as the discount rate. We usethe dataset to confirm that the selection bias does not seem important: it isonly when CVCs enter the startups that there is a change in the investment

21

amount. This analysis is included in Panel B and Panel C of Table 5.

Table 5 (Panel B) depicts the number of startups that receive funds fromthe two types of VCs. The columns stand for different investment roundsand the lines are groups of startups, differentiated by the round in whichCVC investors enter. We provide results until group 8, in which CVCs en-ter the project at the eighth investment round.11 The highlighted numbersrepresent the number of survival startups when CVC investors join in theventure. For example, the first line (Gr.0) describes the group of startupsthat only receive IVC funds. There are 2778 of them, out of which 2117 alsoreceive second round financing, 1492 receive third round financing, and soon. The third line (Gr.2) includes the group of startups that start receivingCVC funds at the second investment round. There are 415 of them, out ofwhich 291 also receive third round financing, and so on.12 It is worth high-lighting that most CVC backed startups start receiving CVC financing atvery early stages. One third of them (548 out of 1792) receive CVC fundsat the first round, and almost 55% of them get CVC financing at the firsttwo rounds. This is somehow at odds with previous findings suggesting thatCVCs often enter at later investment rounds (Hellmann, Lindsey and Puri,2008; Dushnitsky and Shapira, forthcoming; Masulis and Nahata, 2009).

More interestingly, Table 5 (Panel C) shows the average investment perround and per group. Before CVCs enter the ventures, the investmentamount is similar for all groups. For example, startups in Group 0 (thatnever 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 backingin round 2. However, these numbers are quite lower than 8.90 million USDreceived by startups backed by CVCs at round 1. A similar effect appears forall the rounds. Hence, before CVC investors join in the ventures, IVCs investin similar projects, suggesting no obvious selection bias among the projects.The investment levels are significantly increased when CVCs enter into thestartups.

To see whether the intrinsic characteristics of the type of fund has aneffect on the investment decision, we test Hypothesis 1 using the following

11There are startups where CVCs only enter after the eighth round. Since the numberof these ventures is small, we don’t show the details of those cases.

12The number just before 415 should have been 415. However, it is only 401 due tomissing data. A similar problem appears in other lines.

22

model:

ln Investi = αI0 + α

I1Fund

�sCharacteristics+

7�

k=1

αIkZk + �i (8)

In equation (8), Investi is the total investment amount at the startuplevel. Fund

�sCharacteristics measures whether the startup is financed by

CVC funds or IVC funds. Zk is a set of controlling variables, includingnumber of investment rounds, VC fund size, VC syndicate size, syndicateleader characteristics, industry market-to-book value, industry and year fixedeffect. To obtain a robust estimation of how venture capital funds influencestartups’ investment amount, we have estimated four models.

Model 1 We use the dummy variable CV C for the funds’ characteristics.

Model 2 It still uses the dummy variable CV C and it also includes the con-trolling variable of CVC strategic relationship. The variable measureswhether the corporation behind CVC funds is a potential competitorto the invested startups or not. It is included according to Masulis andNahata (2009). They indicate that because of the strategic aim of CVCfunds, they can be competitors to the startups in the future. Therefore,startups ask for a higher investment from CVC funds than from IVCfunds in order to be compensated for potential market competition.

Model 3 It includes an additional controlling variable: the average fund ageacross all the investing funds in a startup. More mature VC funds havemore experience, better reputation and richer resources. Therefore,they can invest more into startups.

Model 4 It uses the percentage of investment made by CVCs (CV C per) asan indicator of CVC backed startups. A higher value means that thestartup is more CVC oriented.

The results of an OLS regression on the models are reported in Table6. The four models provide similar estimated results and they give strongsupport to Hypothesis 1. CVC funds have a significantly positive impact onthe total investment amount of startups. Startups financed by CVC fundsinvest 25% more than those financed by IVC funds if VC fund’s character-istic is attributed by the dummy variable. Moreover, if the syndicate leaderof a startup is a CVC fund, the startup receives an additional 25% increasein its level of investment. Similarly, if the CVC investment amount as apercentage of the total investment for a startup increases by 1%, the startup

23

receives 0.28% additional investment. Moreover, more investment rounds,larger fund size, more mature VC funds, larger syndicate size and higherindustrial market-to-book value lead to more investment in startups. Wedo not find any significant effect of the corporate venture capital funds’ re-lationship with the startups (competitive or not) on startups’ investmentamount.

8.2 Funds’ Characteristics and Duration

Our theoretical model predicts that startups financed by CVC funds staylonger in the market before exit than those financed by IVC funds. Thefollowing survival model describes the estimation for Hypothesis 2.

Duration = αD0 + α

D1 Fund

�sCharacteristics+

6�

k=1

αkZk + �i (9)

The dependent variable in Equation (9) is the duration of the startupsbefore exit, which is measured by the number of days between the exit dateand the first investment date. The independent variable indicates whetherthe startup is invested by only IVC funds or by CVC funds. To estimate theduration model, we have the same controlling variables as in the Equation(8) and the industry fixed effect. As before, we have estimated four regres-sion models. We use the dummy variable CVC to indicate that a startupis financed by CVC funds for Models 1 to 3. In Models 2 and 3, we addthe controlling variables that measure the competition relation between thestartups and the parent company of CVC funds and the VC fund age respec-tively in the regression. We then use the CVC percentage investment as thedependent variable in Model 4.

We estimate the survival model in a parametric way and assuming aWeibull distribution of the residual values.13 The hazard rates of the regres-sion are shown in Table 7. As is suggested by Hypothesis 2, CVC backedstartups do stay longer before the exit than IVC backed startups. Moreover,startups receiving finance from larger and more mature VC funds have alonger duration. Interestingly, more investment rounds reduce the durationfor startups.

13The assumption of a Weibull distribution is based on the Akaike Information Criterion(AIC) and the Bayesian Information Criterion (BIC). We also tried the semi-parametricestimation (COX estimation) of the survival model. However, the post estimation PH testrejected the proportional hazards assumption of the COX estimation.

24

Finally, the impact of the relationship between CVCs and startups onduration is not significant. This, together with the similar finding concerningthe level of investment, suggests that the behavior of CVC funds in thestartups in the same industry as the parent corporation is the same as theirbehavior in those startups in other industries.

8.3 Funds’ Characteristics and Exit Strategy

The indirect impact of the characteristics of venture capital funds (via in-vestment and via duration before exit) on the exit strategy for startups isconveyed by Hypotheses 3 to 5. The following simple regression summarizesthe test of the hypotheses:

Exiti = α0 + αEx1 Fund

�sCharacteristics+ α

Ex2 lnDuration(Days)i

+ αEx3 ln Investi +

9�

k=1

αExk Zk + �i

(10)

The dependent variable Exiti is a dummy variable, with value of 1 if it isIPO exit and 0 for an Acquisition exit. Invest and Duration(Days) have thesame definition as in Equations (8) and (9). The set of controlling variablesis similar to the previous regression, except that we include three additionalcontrolling variables: 3-month and 6-month MSCI index, and later stagedummy variable. The first two variables are used to control the stock marketconditions before the exit date: a strong stock market before the exit datemay increase the probability of an IPO exit. The later stage dummy variableis a proxy for the relative control between entrepreneurs and VC funds.14

If startups exit at some later stages (i.e. the expansion or the later stage),entrepreneurs have more controlling power. Hence, it is a dummy variableequal to 1 for expansion or later stage, and 0 for seed or early stage. To checkthe robustness of our results, we estimate six models based on Equation (10).

Model 1 The first estimated model is an OLS regression that uses the dummyvariable CV C to measure the funds’ characteristics.

Model 2 It includes the average fund age as controlling variable.

Model 3 It considers the percentage of CVC investment out of the total invest-ment amount as the measure of the funds’ characteristics.

14Aghion and Bolton (1992) and Cumming (2008) point out that the relative controllingpower between the entrepreneur of a startup and VC funds influences the exit strategy.Entrepreneurs prefer IPO exit while VC funds could vote for Acquisition exit.

25

Model 4 Since the dependent variable in the estimation model is a dummy vari-able, we use a Logistic estimation in Model 4 to check the robustnessof our OLS estimation.

Model 5 We test the possibility of a quadratic effect of the duration on the IPOlikelihood for startups in model 5.15

Model 6 It estimates the effect of funds’ characteristics on startups’ exit strategy,without the control of duration effect or investment effect.

The results are described in Table 8. The effect of CVC fund’s char-acteristics on the IPO exit is not statistically significant, using either theCVC dummy variable or the percentage investment of CVC as an explana-tory variable. This conclusion provides support for Hypothesis 5. However,CVC funds have an influence on the exit strategy through the startups’ in-vestment amount and the duration. Investment amount has a significantlypositive effect on the probability of IPO. We observe that one percent in-crease in the investment amount increases the probability of IPO exit by0.068%. Moreover, longer duration means a significantly lower probabilityof IPO. One percent increase in the duration will decrease the probability ofIPO by 0.019%.16 These results are robust for all the first four models, withboth OLS estimation and Logistic estimation. They provide strong supportto Hypotheses 3 and 4. The results of Model 6 confirm that the durationeffect and the investment effect explain the impact of VC funds’ character-istics on the exit strategy. Specifically, the investment effect dominates theduration effect, because CVC funds increase the likelihood of IPO exits whenneither duration nor investment effect is controlled.

Table 8 also provides indirect support to the idea that the discount ratehas a strong influence on the startups’ decisions. We note that the effect ofVC fund size on the probability of IPO is not statistically significant whenboth the duration and investment effects are controlled. However, if the twoeffects are not controlled, we observe that larger VC funds significantly in-crease the probability of IPO exits. The effect of VC fund size is particularlyinteresting because it seems reasonable that the discount rate of a fund de-creases with its size. Therefore, in the framework of our theoretical model,the impact of an increase in the size of the fund should be similar to theimpact of receiving financing from CVC instead of IVC funds. This claim is

15To capture the quadratic effect of the duration on the IPO likelihood for start-ups,we use the square of duration at year level.

16Cumming and MacIntosh (2003) also find a negative (although not significant) impactof duration on the likelihood of IPO exit versus acquisition exits.

26

also empirically validated. Fund size has a positive effect on both investmentand duration decisions (see Tables 6 and 7). Moreover, similar to the CVCdummy, fund size does not have any direct effect in the probability of IPOexits; its effects are indirect through the investment and duration decisions.These empirical findings provide further support to our theoretical model.

We now comment on the other effects. First, if the syndicate is led byCVC funds, then IPO exit is more likely. Second, a stronger stock marketfor 3 months before the exit date also leads to more IPO exits. Third, thelikelihood of IPO exist decreases with the strength of the stock market for6 months before the exit date. Fourth, the Later Stage Dummy has a sig-nificantly positive effect, that is, as the entrepreneurs have more controllingpower over the startups than VC managers, more IPO exits are observed.This result is consistent with the findings of Cumming (2008). Finally, thestartups exit more often through IPO if they are in the same industry, de-fined by 4-digit SIC code, as the parent company behind CVC funds. Thissuggests that CVCs may push not to sell the startups to potential competi-tors of the parent company.

Based on Model 5, we also find that the duration has a quadratic effecton the probability of IPO exit. According to this result, the negative effectof duration on the probability of IPO exit is stronger when the duration isshort than when it is long. To better understand the quadratic effect, werun similar regressions for subsamples of our dataset. They suggest that theeffect of duration on IPO exit has two regions: it is linear (and significant)for startups whose duration is shorter than 7 years while it is not significant(neither linear nor quadratic) when duration is longer than 7 years. In ourdataset, the average duration for startups is around 4− 5 years; more than80% of the startups in our sample stay less than 8 years.

8.4 Funds’ Characteristics and Successful Exit Rate

Hypotheses 6 to 8 summarize the indirect effect of funds’ characteristics (viathe investment effect) on the successful exit rate of startups. We test themthrough the following equation:

Failurei = α0 + αS1Fund

�sCharacteristics+ α

S2 lnDuration(Days)i

+ αS3 ln Investi +

5�

k=1

αSkZk + �i

(11)

27

According to the hypotheses, αS1 and α

S2 are expected to be insignificant.

However, we expect a negative but significant coefficient αS3 .

The estimation results are shown in Table 9. Model 1 includes the resultswhen we use CVC dummy variable as a measure of the Funds’ Characteristics.The results with the independent variable of CVC percentage investment arein Model 2. We find that funds’ characteristics, duration, investment amountand whether CVC is the syndicate leader, are negatively but not significantlycorrelated with the liquidation probability. These results support Hypothe-ses 7 and 8. However, we do not find support for Hypothesis 6. The onlysignificant impact is from the company strategic relationship on the fail-ure rate. If the startup and the parent company of CVC funds are potentialcompetitors, the probability for the startups to be liquidated is lower. There-fore, CVC backed startups are at least as successful as those backed by IVCs.Those CVC-backed startups having no potential competition from the parentcompanies perform significantly better than those only receiving investmentfrom IVCs. Venture capital fund characteristics indirectly influences star-tups’ successful exit rate through their strategic relationship. These resultsare consistent with the findings of Gompers and Lerner (2000).

Our theoretical predictions are closer to the findings of Chemmanur andLoutskina (2008). Using a different subsample of the same database, Chem-manur and Loutskina have found that CVC investments in the startups leadto a higher but not significant successful exit rates. It is a higher investmentlevel that increases the successful rate, as is predicted by our theoreticalmodel. Other empirical studies also confirm that CVC backed startups per-form better than IVC backed startups. For example, Dushnitsky and Shapira(forthcoming), show that CVC backed startups exhibit significantly betterperformance as measured by the rate of successful portfolio exits. The in-crease in the successful exit rate ranges from 9.7% to 20% depending on CVCmanagers’ incentives.

8.5 Sensitivity Test

To check the sensitivity of our estimated results, we estimate the previ-ous regressions by removing the startups in the Business Service industry(SIC2 = 73), which contains almost 50% startups in our sample. Table 10provides the estimated results for the subsample. Our results are qualitivelyrobust, although the effect of CVC funds on the duration and the durationeffect on the exit strategy are not statistically significant.

28

8.6 Robustness Test for Possible Endogeneity

One concern regarding our empirical analysis is that the investment amount,duration period before exit, the exit strategy and the VC fund characteristicsmay all depend on the potential value of the startups (parameter V in thetheoretical model).17 This would mean that the choice of investment strat-egy, duration strategy and the exit strategy of startups can also be triggeredby the quality of the startups. Since we don’t have direct measure of thepotential value V of a startup, we use two methods to keep the effect of Vconstant in our estimation.

First, we split our dataset into two subsamples: startups that finally ex-ited through the IPO market and those that chose to go to the acquisitionmarket. According to our theoretical model, the startups with IPO exits havehigher value than those with Acquisition exits. Within each subsample, thestartups have similar value. We estimate again the investment strategy andthe duration before exit (see Equations (8) and (9)) for the set of startupsthat chose IPO-exit and for the set that went to Acquisition-exit separately.The estimated results are shown in Tables 11 and 12.

For the investment strategy, the results indicate that the previous mainconclusions still hold for both IPO-exit startups and Acquisition-exit star-tups. In the two subsamples, we find that CVC backed startups receive asignificantly higher investment amount. One interesting difference is that,for Acquisition-exit startups, the strategic relationship between the startupsand CVC funds does play a role. This suggests that, if the startups and theparent companies of the CVC funds are in the same industry, i.e. they arepotential competitors, CVCs invest more in the ventures that end up beingacquired.

For the duration strategy, CVC investment significantly increases the du-ration for startups in the acquisition market. Interestingly, we have foundthat the VC fund characteristics is not important in deciding the durationbefore exit for startups in the IPO market. This result matches with ourtheoretical predictions: according to our model, the decision on the durationbefore exit (that is, the level of information) only matters if the startups goto the acquisition market.

Second, we use a less stringent definition for the strategic relationship

17We recall that our analysis in Section 8.1 already suggested that no obvious selectionbias between CVC and IVC projects exit.

29

between startups and VC funds (potential competitors or not). If the startupand the parent company of the CVC fund are in the same industry, the latteris likely to have more information about the potential value of the venturethan if the partners operate in different industries. We have controlled thiseffect using a 4-digit SIC code (SIC4) in Sections 8.1 to 8.4. In Tables 6 to 9we have shown that, after controlling for the information effect, we still findthat CVC significantly influences investment and duration, while it does notinfluence the exit strategy. To make sure that these results are not due tothe fact that the measure we have used is too stringent, we now redefine thevariable of CVC Strategic Relation using the 2-digit SIC code. The dummyvariable is equal to 1 if the startup and the parent company of CVC fundhave the same 2-digit SIC code, and 0 otherwise. The regression results arein Tables 13 to 15. There is no significant difference between the results ofusing SIC4 code and those of using SIC2 code. This means that our resultsare robust for either definition of the strategic relationship between CVCsand ventures.

9 Conclusion

In this paper, we have studied the optimal initial and exit decisions by star-tups. In particular, we have focused on the difference in behavior betweenCVC backed startups and IVC backed startups.

In our theoretical model, the difference between CVC and IVC financingis attributed to different discount rates. We have assumed that (for examplebecause of strategic objectives) CVC funds are less hurried to exit than IVCfunds. Therefore, startups backed by CVC funds have a lower discount ratethan those backed by IVCs. We have found that CVC backed startups havelonger duration before exit and larger investment level than those financedby IVCs. These properties, in turn, lead to higher successful exit rates and totwo opposite impacts on the likelihood of an IPO exit. Longer duration, im-plying more information in the acquisition market, increases the probabilitythat the startup looks for an acquirer. On the contrary, higher investmentlevel, increasing the value of the startups, encourages more IPO exits.

The theoretical results have been then empirically tested with data fromVentureXpert database. Our empirical study indicates that CVC financ-ing 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 deci-

30

sions. Shorter duration as well as larger investment level significantly leadto a higher likelihood of IPO exit. Once these two effects are taken intoaccount, whether the venture capital fund is corporate or independent doesnot have a significant influence on the startup exit decision.

31

Appendix

Proof of Proposition 3

Proof. We notice that the rate of IPO exits over the total successful exits iseither

1

1− Γ(C; I)

�

V≥C+ Fβ−m

[1− po(V )] γ(V ; I)dV (12)

or1

1− Γ(C; I)

�

V≥C+ Fβ−m

2

[1− poo(V )] γ(V ; I)dV. (13)

Given that V is uniformly distributed over the interval�f(I), V + f(I)

�,

γ(V ; I)

1− Γ(C; I)=

1

V + f(I)− C.

Therefore,

∂

∂I

�1

1− Γ(C; I)

�

V≥C+ Fβ−m

[1− po(V )] γ(V ; I)dV

�=

∂

∂I

1�V + f(I)− C

�� V+f(I)

C+ Fβ−m

[1− po(V )] dV

=

f�(I)

�V + f(I)− C

�2

��1− po(V + f(I))

� �V + f(I)− C

�−� V+f(I)

C+ Fβ−m

[1− po(V )] dV

�> 0,

where the inequality holds because f �(I) > 0 and [1− po(V )] is an increasingfunction of V . Therefore, the expression (12) is increasing in I. A similarargument allow to prove that (13) is also increasing in I.

Proof of Lemma 1.

Proof. Propositions 1 and 2 imply that

EUo(I) =

�

V≥C

�� po(V )

0

mp(V − C)dp+

� 1

po(V )

[βp(V − C)− F ] dp

�dΓ(V ; I)

=

�

V≥C

�1

2β(V − C)−

�1

2(β −m) po(V )2(V − C) + F (1− po(V ))

��dΓ(V ; I)

32

and

EUoo(I) =

�

V≥C

�� poo(V )

0

mpoo(V )

2(V − C)dp+

� 1

poo(V )

[βp(V − C)− F ] dp

�dΓ(V ; I)

=

�

V≥C

�1

2β(V − C)−

�1

2(β −m) poo(V )2(V − C) + F (1− poo(V ))

��dΓ(V ; I).

Therefore, EUo(I) ≥ EUoo(I) if

1

2(β −m) po(V )2(V−C)+F (1− po(V )) ≤ 1

2(β −m) poo(V )2(V−C)+F (1− poo(V )) .

(14)Equation (14) holds equality if poo(V ) = 1 (and then, po(V ) = 1 as

well). Otherwise, denote j(p) ≡ 12 (β −m) p2(V − C) + F (1− p). Then,

j�(p) = (β −m) p(V −C)−F < 0 for all p < min {po, 1}, given the definitionof po. Therefore, j(poo(V )) > j(po(V )) whenever poo(V ) < 1, that is, (14)holds with strict inequality when poo(V ) < 1 for some V in the support ofthe distribution Γ(d; I), that is, when poo(V + f(I)) < 1.

Proof of Proposition 6

Proof. Maximizing Equation (5), d∗ and I∗ are characterized by

∂U

∂d(d∗, I∗) = 0 (15)

∂U

∂I(d∗, I∗) = 0. (16)

We differentiate Equations (15) and (16) and solve the system to obtainthat, at (d∗, I∗),

∂I∗

∂r= −ΛI

∆(17)

∂d∗

∂r= −Λd

∆, (18)

where

ΛI =∂2U

∂d2

∂2U

∂I∂r− ∂

2U

∂I∂d

∂2U

∂d∂r

Λd =∂2U

∂I2

∂2U

∂d∂r− ∂

2U

∂I∂d

∂2U

∂I∂r

and

∆ =∂2U

∂I2

∂2U

∂d2−

�∂2U

∂I∂d

�2

.

33

Before the analysis of the second derivatives of the function U(d, I) =e−rd [h(d)EUo(I) + (1− h(d))EUoo(I)]− I, we analyze the functions EUo(I)and EUoo(I).

EUo(I) =

�

V≥C

1

2β(V−C)dΓ(V ; I)−

�

V≥C

�1

2(β −m)po(V )2(V − C) + F (1− po(V ))

�dΓ(V ; I).

(19)Taking into account that dΓ(V ; I) = 1

V, the first term of the right-hand side

of (19) is� V+f(I)

C

1

2β(V − C)dΓ(V ; I) =

β

4V

�V + f(I)− C

�2

and, given that po(V ) = min�

1(β−m)

F(V−C) , 1

�, we split the right-hand side

of (19) in two parts:� C+ F

β−m

C

1

2(β −m)(V − C)

1

VdV =

F2

4(β −m)V

and� V+f(I)

C+ Fβ−m

�F

2

2(β −m)(V − C)+ F

�1− F

(β −m)(V − C)

��1

VdV =

− F2

2(β −m)V

�log

�V + f(I)− C

�− log

�F

β −m

��+F

V

�V + f(I)− C − F

β −m

�.

Therefore,

EUo(I) =β

4V

�V + f(I)− C

�2− F

V

�V + f(I)− C − 3F

4 (β −m)

�+

F2

2(β −m)V

�log

�V + f(I)− C

�− log

�F

β −m

��. (20)

Similarly, taking into account that poo(V ) = min

�1

(β−m2 )

F(V−C) , 1

�, we

obtain:

EUoo(I) =β

4V

�V + f(I)− C

�2− (β −m)F 2

4�β − m

2

�2V

− F

V

�V + f(I)− C − F�

β − m2

��−

(β −m)F 2

2�β − m

2

�2V

�log

�V + f(I)− C

�− log

�F

β − m2

��+

F2

�β − m

2

�V

�log

�V + f(I)− C

�− log

�F

β − m2

��. (21)

34

From (20) and (21),

EU�o(I) =

f�(I)

V

β

2

�V + f(I)− C

�− F +

F2

2(β −m)�V + f(I)− C

�

(22)

EU�oo(I) =

f�(I)

V

�β

2[V + f(I)− C]− F +

βF2

2(β − m2 )

2(V + f(I)− C)

�.

(23)

As it is intuitive and easy to check, EU�o(I) > 0 and EU

�oo(I) > 0 always.

We now analyze the sign of the second derivatives of the function U(., .).

∂2U

∂d2(d∗, I∗) = e

−rd[h��(d)− rh�(d)][EUo(I)− EUoo(I)]. (24)

In Equation (24), h�(d) > 0 and h��(d) < 0. Moreover, Proposition 1 implies

that EUo(I) > EUoo(I). Therefore,∂2U∂d2 (d∗, I∗) < 0.

∂2U

∂I∂r(d∗, I∗) = −d < 0. (25)

∂2U

∂I2(d∗, I∗) = e

−rd[h(d)EU��o (I) + (1− h(d))EU

��oo(I)] < 0 (26)

because

EU��o (I) =

f��(I)

V

β

2(V + f(I)− C)− F +

F2

2(β −m)�V + f(I)− C

�

−

f�(I)2

2V

β − F2

(β −m)�V + f(I)− C

�2

EU��oo(I) =

f��(I)

V[β

2[V + f(I)− C]− F +

βF2

2(β − m2 )

2(V + f(I)− C)]+

f�(I)2

2V[β − βF

2

(β − m2 )

2(V + f(I)− C)2.

and both EU��o (I) < 0 and EU

��oo(I) < 0 if f(I) is concave enough.

35

∂2U

∂d∂r(d∗, I∗) = −e

−rd[h(d)EUo(I) + (1− h(d))EUoo(I)] < 0. (27)

We notice that ∂2U∂I∂d (d

∗, I

∗) = −r + e−rd

h�(d)[EU

�o(I) − EU

�oo(I)], with

EU�o(I) − EU

�oo(I) = f �(I)F 2

V (V+f(I)−C)

m2

4

2(β−m)(β−m2 )

2 > 0. Therefore, investment

and duration may be complement or substitute, depending on the compar-ison of the two terms. If they are complements, that is, ∂2U

∂I∂d (d∗, I

∗) ≥ 0,then ΛI > 0 and Λd > 0. If they are substitutes, the same inequalities holdas long as the functions h(d) and f(I) are sufficiently concave, which alsoimplies that ∆ > 0 (it is always positive in any strict maximum).

Therefore, ∂I∗

∂r < 0 and ∂d∗

∂r < 0, as we wanted to prove.

36

Table 1. Industry Composition of the Sample

Two-Digit SIC Code Industry Name Number of Startups13 Oil and gas extraction 2720 Food and kindred products 2127 Printing and publishing 2428 Chemicals and allied products 36035 Industrial machinery and equipment 27436 Electronic and other electronic equipment 51038 Instruments and related products 34848 Communications 19950 Wholesale trade - durable goods 5751 Wholesale trade - nondurable goods 2059 Miscellaneous retail 6462 Security and commodity brokers 2063 Insurance carriers 2973 Business services 2, 19980 Health services 12787 Engineering and management services 195

37

Table 2. Definitions of Variables

Variables DefinitionsCVC 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 bydisclosed equity amount (USD Million) summed over investment rounds

Duration (Days) Difference in days between the exit date and the date at whicha startup receives the first investment from venture capital firms

Duration (Years) Duration (Days) divided by 365

Investment rounds Number of investment rounds for a startup

VC syndicate Number of venture capital firms that invest in a startup

Syndicate CVC Dummy variable equal to 1 if the syndicate leader is CVC fundand 0 for IVC fund as the leader

VC fund size Average size (USD Million) 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-digitSIC code as its start-up, and 0 otherwise

VC fund age (Years) Average fund age across all funds invested in a startup

Industry MB Industry market-to-book value at the yearthat 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 Dummy variable equal to 1 if a startup is at expansionor later stage at the exit and 0 otherwise

38

Table 3. Summary Statistics

No. of Obs. Mean Std. Dev. Min MaxCVC 4801 0.37 0.48 0 1CVC Per 4801 0.23 0.34 0 1IPO 4801 0.35 0.48 0 1Investment amount 4801 31.47 78.04 0.02 4653.06Duration (Days) 4801 1753.26 1197.6 10 12056Duration (Years) 4801 4.8 3.28 0.03 33.03Investment rounds 4801 4.26 2.87 1 27VC syndicate 4801 5.81 4.37 1 35Syndicate CVC 4801 0.05 0.22 0 1VC fund size 4801 211.75 383.62 0.09 6011.62VC fund age (Years) 4737 7.98 5.46 0 45.33CVC strategic relationship 4801 0.01 0.12 0 1Industry MB 4801 13.51 22.53 0.74 432.1MSCI 3 mon 4801 2861.97 1391.96 113.88 4881.96MSCI 6 mon 4801 2821.1 1395.53 108.83 4881.96Later stage dummy 4801 0.81 0.39 0 1

39

Table 4. Correlation Matrix

VC Fund Synd. CVC Invest Invest Duration VC VC Fund

Age CVC Amount Rounds Synd. Size

VC Fund Age 1.00Syndicate CVC −0.01 1.00

CVC 0.06∗∗∗ 0.29∗∗∗ 1.00Investment Amount 0.03∗∗ 0.04∗∗∗ 0.18∗∗∗ 1.00Investment Rounds 0.05∗∗∗ −0.04∗∗∗ 0.25∗∗∗ 0.19∗∗∗ 1.00

Duration −0.02 −0.01 0.12∗∗∗ 0.07∗∗∗ 0.46∗∗∗ 1.00VC Syndicate 0.05∗∗∗ 0.01 0.46∗∗∗ 0.25∗∗∗ 0.58∗∗∗ 0.29∗∗∗ 1.00VC Fund Size 0.03∗∗ −0.02 −0.02 0.14∗∗∗ −0.03 −0.04∗∗∗ −0.06∗∗∗ 1.00

CVC Strategic Re. 0.02 0.13∗∗∗ 0.15∗∗∗ 0.04∗∗∗ 0.04∗∗∗ 0.02 0.06∗∗∗ −0.01IPO −0.10∗∗∗ 0.05∗∗∗ 0.01 0.04∗∗∗ 0.07∗∗∗ −0.05∗∗∗ 0.14∗∗∗ −0.12∗∗∗

Industry MB 0.07∗∗∗ 0.01 0.06∗∗∗ 0.03∗∗ −0.02 −0.01 −0.02 0.07∗∗∗

CVC per 0.04∗∗ 0.39∗∗∗ 0.85∗∗∗ 0.16∗∗∗ 0.10∗∗∗ 0.04∗∗∗ 0.36∗∗∗ −0.02MSCI mon. 3 0.21∗∗∗ 0.02 0.14∗∗∗ 0.16∗∗∗ 0.01 0.08∗∗∗ −0.01 0.22∗∗∗

MSCI mon. 6 0.21∗∗∗ 0.01 0.14∗∗∗ 0.16∗∗∗ 0.01 0.08∗∗∗ −0.01 0.22∗∗∗

Later Stage Dummy 0.00 −0.01 −0.03∗∗ 0.01 −0.02 0.01 −0.02 0.02

CVC Strat. IPO Industry CVC per MSCI MSCI Later Stage

Re. MB mon. 3 mon. 6 Dummy

VC Fund Age

Syndicate CVC

CVC

Investment Amount

Investment Rounds

Duration

VC Syndicate

VC Fund Size

CVC Strategic Re. 1.00IPO 0.03 1.00

Industry MB 0.00 −0.23∗∗∗ 1.00CVC per 0.12∗∗∗ −0.01 0.07∗∗∗ 1.00

MSCI mon. 3 0.05∗∗∗ −0.49∗∗∗ 0.24∗∗∗ 0.12∗∗∗ 1.00MSCI mon. 6 0.05∗∗∗ −0.50∗∗∗ 0.26∗∗∗ 0.12∗∗∗ 0.99∗∗∗ 1.00

Later Stage Dummy −0.04∗∗∗ −0.01 0.02 −0.03∗∗ −0.01 −0.01 1.00∗∗ and ∗∗∗ denote statistical significance at 5% and 1% level.

40

Table 5. CVCs vs IVCs

Panel A. CVC VS. IVC: Summary Statistics

IVC CVC Difference t-statisticsIPO 0.35 0.36 −0.01 −0.64

Investment amount 20.5 49.88 −29.38 −12.83∗∗∗

Duration 1648.86 1928.57 −279.71 −7.88∗∗∗

Investment rounds 3.71 5.19 −1.48 −17.9∗∗∗

VC syndicate 4.25 8.42 −4.16 −35.93∗∗∗

VC fund size 216.7 203.43 13.27 1.16VC fund age 7.73 8.39 −0.66 −4.03∗∗∗

Later stage dummy 0.77 0.88 −0.11 −9.46∗∗∗

∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

Panel B. CVC VS. IVC: No. of startups per Round

No. R1 R2 R3 R4 R5 R6 R7 R8Gr. 0 2778 2117 1492 977 617 408 238 161Gr. 1 548 422 322 233 164 95 63 44Gr. 2 401 415 291 188 114 68 39 24Gr. 3 344 334 352 254 156 95 54 35Gr. 4 183 178 179 187 131 83 41 25Gr. 5 97 99 96 95 100 54 30 19Gr. 6 50 51 51 49 49 55 28 20Gr. 7 25 23 20 24 24 22 26 13Gr. 8 11 10 9 9 9 10 11 11

Panel C. CVC VS. IVC: Investment Amount per startup per Round

Invest. R1 R2 R 3 R4 R5 R6 R7 R8Gr. 0 5.39 6.05 7.11 6.71 6.41 5.64 4.24 4.57Gr. 1 8.90 11.34 10.91 10.95 9.83 6.76 4.56 4.49Gr. 2 5.47 16.42 11.74 13.18 11.37 15.23 7.13 9.37Gr. 3 3.91 9.06 18.16 14.15 12.33 8.90 10.14 7.09Gr. 4 2.97 6.28 9.53 18.26 14.66 11.21 14.81 8.17Gr. 5 2.89 5.12 7.48 8.34 15.54 11.41 9.19 6.89Gr. 6 2.75 5.46 5.20 3.84 6.46 17.56 12.97 12.07Gr. 7 2.45 4.41 3.60 6.51 5.09 10.05 14.03 17.30Gr. 8 2.18 3.38 3.57 6.27 7.26 6.94 12.31 18.43

41

Table 6: Investment Strategy

Dependent Variable: ln(Investment Amount)Model 1 Model 2 Model 3 Model 4

CVC0.2525∗∗∗ 0.249∗∗∗ 0.2484∗∗∗

(8.08) (7.91) (7.85)

CVC per0.2852∗∗∗

(6.44)

Investment Rounds0.1105∗∗∗ 0.1104∗∗∗ 0.1081∗∗∗ 0.1131∗∗∗

(20.15) (20.13) (19.58) (20.33)

VC Syndicate0.1289∗∗∗ 0.129∗∗∗ 0.1287∗∗∗ 0.1311∗∗∗

(32.81) (32.83) (32.63) (33.3)

Syndicate CVC0.2545∗∗∗ 0.2491∗∗∗ 0.2638∗∗∗ 0.2458∗∗∗

(4.2) (4.09) (4.29) (3.83)

ln(VC Fund Size)0.35∗∗∗ 0.35∗∗∗ 0.356∗∗∗ 0.356∗∗∗

(32.2) (32.21) (32.47) (32.39)

VC Fund Age0.0118∗∗∗ 0.012∗∗∗

(4.96) (5.05)

CVC Strategic Re.0.1137 0.1083 0.1504(1.05) (0.99) (1.38)

ln(Industry MB)0.0516∗∗ 0.051∗∗ 0.0502∗∗ 0.0497∗∗

(2.55) (2.52) (2.47) (2.44)Year Fixed Effect Yes Yes Yes YesIndustry Fixed Effect Yes Yes Yes Yes

The t statistic is in the parentheses.∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

42

Table 7: Duration Strategy

Dependent Variable: Duration (Days)Model 1 Model 2 Model 3 Model 4

CVC1.06∗ 1.06 1.07∗

(1.64) (1.60) (1.78)

CVC per1.16∗∗∗

(2.78)

Investment Rounds0.86∗∗∗ 0.86∗∗∗ 0.86∗∗∗ 0.86∗∗∗

(−21.39) (−21.38) (−21.23) (−20.88)

VC Syndicate0.99 0.99 0.99∗ 0.99∗∗

(−1.35) (−1.35) (−1.80) (−2.17)

Syndicate CVC0.899 0.898 0.90 0.86∗∗

(−1.48) (−1.5) (−1.43) (−2.02)

ln(VC Fund Size)1.06∗∗∗ 1.06∗∗∗ 1.06∗∗∗ 1.06∗∗∗

(4.77) (4.77) (4.57) (4.54)

VC Fund Age1.01∗∗∗ 1.01∗∗∗

(3.82) (3.89)

CVC Strategic Re.1.04 1.01 1.02(0.28) (0.1) (0.15)

ln(Industry MB)0.99 0.99 0.98 0.98

(−0.38) (−0.39) (−1.05) (−1.16)Year Fixed Effect No No No NoIndustry Fixed Effect Yes Yes Yes Yes

The Z value is in the parentheses.∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

43

Table 8: Exit Strategy

Dependent Variable: Dummy Variable of IPO ExitModel 1 Model 2 Model 3 Model 4 Model 5 Model 6

CVC−0.003 −0.004 0.034 −0.0045 0.052∗∗∗

(−0.24) (−0.33) (0.36) (−0.35) (4.34)

CVC per−0.013(−0.72)

ln(Duration)−0.018∗∗ −0.019∗∗ −0.019∗∗ −0.405∗∗∗

(−2.39) (−2.46) (−2.49) (−7.44)

Duration Year−0.013∗∗∗

(−3.19)

(Duration Year)20.0008∗∗∗

(3.39)

ln(Invest)0.067∗∗∗ 0.068∗∗∗ 0.068∗∗∗ 0.602∗∗∗ 0.069∗∗∗

(13.08) (12.96) (13.33) (14.45) (13.09)

ln(VC Fund Size)−0.005 −0.004 −0.004 −0.163∗∗∗ −0.003 0.025∗∗∗

(−0.9) (−0.74) (−0.77) (−4.44) (−0.64) (5.32)

VC Fund Age0.0005 0.0005 −0.002 0.0006 0.002∗

(0.48) (0.47) (−0.31) (0.63) (1.68)

Syndicate CVC0.05∗∗ 0.054∗∗ 0.059∗∗ 0.3236∗ 0.052∗∗ 0.046∗

(1.97) (2.08) (2.19) (1.84) (2.01) (1.75)

CVC Strategic Re.0.076∗ 0.076∗ 0.076∗ 0.741∗∗∗ 0.077∗ 0.076(1.67) (1.64) (1.65) (2.74) (1.67) (1.63)

ln(MSCI mon. 3)0.44∗∗∗ 0.45∗∗∗ 0.45∗∗∗ 5.53∗∗∗ 0.437∗∗∗ 0.429∗∗∗

(3.56) (3.58) (3.58) (7.29) (3.48) (3.35)

ln(MSCI mon. 6)−0.278∗∗ −0.296∗∗ −0.296∗∗ −7.23∗∗∗ −0.294∗∗ −0.26∗∗

(−2.33) (−2.44) (−2.44) (−9.55) (−2.43) (−2.11)

ln(Industry MB)0.009 0.01 0.01 −0.394∗∗∗ 0.009 0.013(0.85) (0.87) (0.88) (−8.26) (0.84) (1.11)

Later stage dummy0.058∗∗∗ 0.055∗∗∗ 0.055∗∗∗ 0.48∗∗∗ 0.053∗∗∗ 0.103∗∗∗

(4.00) (3.81) (3.79) (4.2) (3.66) (7.39)Year Fixed Effect Yes Yes Yes No Yes YesIndustry Fixed Effect Yes Yes Yes No Yes Yes

The t statistic is in the parentheses for Model 1, 2, 3, 5 and 6.The Z-value is in the parentheses for Model 4.∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

44

Table 9: Successful Exit Rate

Dependent Variable: Failure rateModel 1 Model 2

CVC0.0014(0.12)

CVC per0.0073(0.31)

ln(Duration)−0.005 −0.0049(−0.88) (−0.87)

ln(Invest)−0.0005 −0.0005(−0.10) (−0.11)

Syndicate CVC−0.0261 −0.0267(−1) (−1.05)

ln(VC Fund Size)0.0046 0.0046(1.04) (1.06)

CVC Strategic Re.−0.3149∗∗∗ −0.3148∗∗∗

(−5.43) (−5.43)

Later stage dummy−0.0161 −0.0162(−1.19) (−1.19)

Year Fixed Effect No NoIndustry Fixed Effect Yes Yes

The t statistic is in the parentheses.∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

45

Table 10: Sensitivity Test

Dependent variable ln(Investment Amount) Duration(Days) Dummy Variable of IPO Exit

CVC0.26∗∗∗ 1.01 −0.0056(5.53) (0.24) (−0.29)

ln(Duration)−0.008(−0.75)

ln(Invest)0.074∗∗∗

(10.25)

Investment Rounds0.107∗∗∗ 0.87∗∗∗

(14.14) (−15.04)

VC Syndicate0.126∗∗∗ 1.002(23.81) (0.34)

Syndicate CVC0.367∗∗∗ 0.913 0.076∗

(3.88) (−0.85) (1.92)

ln(VC Fund Size)0.39∗∗∗ 1.06∗∗∗ −0.01(24.2) (3.56) (−1.25)

VC Fund Age0.01∗∗∗ 1.01∗∗∗ 0.001(2.73) (3.28) (0.67)

CVC Strategic Re.0.09 1.15 0.08(0.7) (0.91) (1.43)

ln(MSCI mon. 3)0.7∗∗∗

(3.47)

ln(MSCI mon. 6)−0.407∗∗

(−2.07)

ln(Industry MB)0.04 0.95∗ 0.02(1.52) (−1.82) (1.44)

Later stage dummy0.05∗∗

(2.18)Year Fixed Effect Yes No YesIndustry Fixed Effect Yes Yes Yes

The t statistic and the Z value are in the parentheses.∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

46

Table 11: Investment Strategy for IPO Startups and for Acqui-

sition Startups

Dependent Variable: ln(Investment Amount)IPO Exits Acquisition Exits

Model 3 Model 4 Model 3 Model 4

CVC0.2387∗∗∗ 0.2287∗∗∗

(4.17) (4.53)

CVC per0.3188∗∗∗ 0.2852∗∗∗

(3.84) (6.44)

Investment Rounds0.1492∗∗∗ 0.1539∗∗∗ 0.0906∗∗∗ 0.0950∗∗∗

(14.95) (15.38) (14.10) (14.64)

VC Syndicate0.1011∗∗∗ 0.1020∗∗∗ 0.1452∗∗∗ 0.1485∗∗∗

(16.53) (16.72) (27.98) (28.69)

Syndicate CVC0.3785∗∗∗ 0.3333∗∗∗ 0.0768 0.0723(3.74) (3.11) (1.02) (0.93)

ln(VC Fund Size)0.2970∗∗∗ 0.2970∗∗∗ 1.08∗∗∗ 1.08∗∗∗

(14.62) (14.25) (29.10) (29.03)

VC Fund Age0.0083 0.0086∗ 0.0130∗∗∗ 0.0130∗∗∗

(1.62) (1.69) (5.07) (5.09)

CVC Strategic Re.−0.1495 −0.1352 0.2318∗ 0.2823∗∗

(−0.87) (−0.78) (1.70) (2.07)

ln(Industry MB)0.0826∗ 0.0822∗ 0.08∗∗∗ 0.0792∗∗∗

(1.72) (1.70) (3.58) (3.54)Year Fixed Effect Yes Yes Yes YesIndustry Fixed Effect Yes Yes Yes Yes

The t statistic is in the parentheses.∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

47

Table 12: Duration Strategy for IPO Startups and for Acquisi-

tion Startups

Dependent Variable: Duration (Days)IPO Exits Acquisition Exits

Model 3 Model 4 Model 3 Model 4

CVC1.01 1.17∗∗∗

(0.14) (3.38)

CVC per1.14 1.27∗∗∗

(1.38) (3.75)

Investment Rounds0.89∗∗∗ 0.89∗∗∗ 0.84∗∗∗ 0.84∗∗∗

(−10.02) (−9.94) (−19.43) (−18.92)

VC Syndicate1.01 1.00 0.97∗∗∗ 0.97∗∗∗

(0.92) (0.92) (−4.55) (−4.69)

Syndicate CVC0.99 0.91 0.91 0.87∗∗

(−0.10) (−0.76) (−1.02) (−1.47)

ln(VC Fund Size)1.08∗∗∗ 1.08∗∗∗ 1.06∗∗∗ 1.06∗∗∗

(3.41) (3.42) (3.77) (3.82)

VC Fund Age1.01 1.01 1.01∗∗∗ 1.02∗∗∗

(1.45) (1.48) (4.69) (4.78)

CVC Strategic Re.0.95 0.94 0.99 1.04

(−0.24) (−0.32) (−0.04) (0.22)

ln(Industry MB)0.94 0.94 1.04∗ 1.04∗

(−1.51) (−1.63) (1.71) (1.66)Year Fixed Effect No No No NoIndustry Fixed Effect Yes Yes Yes Yes

The Z value is in the parentheses.∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

48

Table 13: Investment and Duration Strategies - SIC2

ln(Investment Amount) Duration (Days)Model 3 Model 4 Model 3 Model 4

CVC0.247∗∗∗ 1.07∗

(7.74) (1.87)

CVC per0.2815∗∗∗ 1.16∗∗∗

(6.32) (2.84)

Investment Rounds0.1081∗∗∗ 0.1130∗∗∗ 0.86∗∗∗ 0.86∗∗∗

(19.56) (20.29) (−21.23) (−20.86)

VC Syndicate0.1287∗∗∗ 0.1311∗∗∗ 0.99∗ 0.99∗∗

(32.63) (33.3) (−1.81) (−2.16)

Syndicate CVC0.2621∗∗∗ 0.2427∗∗∗ 0.90 0.86∗∗

(4.24) (3.77) (−1.37) (−1.96)

ln(VC Fund Size)0.3561∗∗∗ 0.3561∗∗∗ 1.06∗∗∗ 1.06∗∗∗

(32.47) (32.4) (4.57) (4.54)

VC Fund Age0.0118∗∗∗ 0.012∗∗∗ 1.01∗∗∗ 1.01∗∗∗

(4.97) (5.06) (3.83) (3.90)

CVC Strategic Re.0.0644 0.1 0.95 0.96(0.89) (1.39) (−0.55) (−0.54)

ln(Industry MB)0.0504∗∗ 0.05∗∗ 0.98 0.98(2.48) (2.46) (−1.04) (−1.14)

Year Fixed Effect Yes Yes Yes YesIndustry Fixed Effect Yes Yes Yes Yes

The t statistic is in the parentheses.∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

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Table 14: Exit Strategy - SIC2

Dependent Variable: Dummy Variable of IPO ExitModel 1 Model 2 Model 3 Model 4 Model 5 Model 6

CVC−0.0058 −0.0074 −0.0023 −0.0076 0.0493∗∗∗

(−0.45) (−0.57) (−0.02) (−0.59) (4.04)

CVC per−0.0165(−0.92)

ln(Duration)−0.0183∗∗ −0.0191∗∗ −0.0194∗∗ −0.405∗∗∗

(−2.41) (−2.48) (−2.52) (−7.43)

Duration Year−0.0133∗∗∗

(−3.22)

(Duration Year)20.0008∗∗∗

(3.42)

ln(Invest)0.0675∗∗∗ 0.0676∗∗∗ 0.0679∗∗∗ 0.6017∗∗∗ 0.0685∗∗∗

(13.08) (12.96) (13.3) (14.44) (13.1)

ln(VC Fund Size)−0.0045 −0.0038 −0.0039 −0.1642∗∗∗ −0.0033 0.0248∗∗∗

(−0.91) (−0.74) (−0.76) (−4.45) (−0.64) (5.33)

VC Fund Age0.0005 0.0005 −0.0021 0.0007 0.0017∗

(0.51) (0.50) (−0.28) (0.66) (1.72)

Syndicate CVC0.0464∗ 0.0493∗ 0.0544∗∗ 0.2843 0.0475∗ 0.0414(1.81) (1.90) (2.02) (1.61) (1.83) (1.57)

CVC Strategic Re.0.069∗∗ 0.0746∗∗ 0.0752∗∗ 0.7086∗∗∗ 0.0759∗∗ 0.0754∗∗

(2.29) (2.44) (2.47) (3.68) (2.48) (2.43)

ln(MSCI mon. 3)0.4374∗∗∗ 0.4451∗∗∗ 0.4452∗∗∗ 5.4843∗∗∗ 0.433∗∗∗ 0.4246∗∗∗

(3.52) (3.54) (3.55) (7.23) (3.45) (3.32)

ln(MSCI mon. 6)−0.2742∗∗ −0.293∗∗ −0.294∗∗ −7.1869∗∗∗ −0.2915∗∗ −0.2574∗∗

(−2.3) (−2.42) (−2.42) (−9.49) (−2.41) (−2.09)

ln(Industry MB)0.0094 0.0097 0.0098 −0.3961∗∗∗ 0.0093 0.0126(0.85) (0.86) (0.88) (−8.29) (0.83) (1.11)

Later stage dummy0.0575∗∗∗ 0.055∗∗∗ 0.055∗∗∗ 0.4816∗∗∗ 0.0529∗∗∗ 0.1023∗∗∗

(3.99) (3.79) (3.76) (4.19) (3.65) (7.37)Year Fixed Effect Yes Yes Yes No Yes YesIndustry Fixed Effect Yes Yes Yes No Yes Yes

The t statistic is in the parentheses for Model 1, 2, 3, 5 and 6.The Z-value is in the parentheses for Model 4.∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

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Table 15: Successful Exit Rate - SIC2

Dependent Variable: Failure rateModel 1 Model 2

CVC−0.0007(−0.06)

CVC per0.0002(0.01)

ln(Duration)−0.0052 −0.0052(−0.92) (−0.92)

ln(Invest)−0.0004 −0.0005(−0.09) (−0.12)

Syndicate CVC−0.03 −0.03(−1.13) (−1.18)

ln(VC Fund Size)0.0045 0.0045(1.02) (1.03)

CVC Strategic Re.0.151∗∗ 0.15∗∗

(2.09) (2.07)

Later stage dummy−0.017 −0.017(−1.23) (−1.23)

Year Fixed Effect No NoIndustry Fixed Effect Yes Yes

The t statistic is in the parentheses.∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1% level.

51

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