1

Are staging and syndication good predictors of performance for

venture-backed companies?

Empirical evidence from the U.S. context.

DUBOCAGE Emmanuelle1

University of Paris Sud/Paris Saclay

SANNAJUST Aurélie2

University of Saint-Etienne

ABSTRACT

We model the impact of staging and syndication on the performance of venture-backed

companies. Using robust probit estimates, we test our hypotheses on 884 American venture-

backed companies created from 2005 to 2010. The econometric estimates fully support our

contentions that syndication has a positive impact on success: The size of the syndication at the

first round and during the lifespan in the portfolio are positive predictors of success exit by

IPOs or trade sale. Moreover, we find a positive impact of the renewal of VCs in syndication

in the case of trade sale, which attests to the key role of new competencies in syndicates. Our

empirical results are enlightened by a new perspective: VCs in syndicates improve the dynamic

capabilities of venture-backed companies and then enhance performance. In addition, we

provide evidence that staged investment slows down the entrepreneur with respect to the rate

of innovation and reinforces the “liability of lateness,” which is detrimental to performance.

KEY-WORDS: Venture capital, performance, exit route, IPO, trade sale, write-off,

syndication, staging

JEL CLASSIFICATION: G24 G33 L25 M13

1 Associate Professor in the University of Paris-Sud/Paris Saclay, France, RITM lab., emmanuelle.dubocage@u-psud.fr 2 Associate Professor in the University of Saint-Etienne, France, aurelie.sannajust@univ-st-etienne.fr

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1. Introduction

Venture capital (VC) has a growing impact in the context of the digital revolution. As a

result, a better understanding of the mechanisms of its performance and, more precisely, the

identification of the key drivers of success of venture-backed companies is more important than

ever. Indeed, this performance issue is decisive for venture capitalists (VCists), their investors

and the CEOs of venture-backed companies but also for government as venture-backed

companies contribute to employment (Peneder, 2010) and innovation (Wahwad et al., 2016).

Many academic studies analyse the exit choice of VCists and venture-backed companies. Bayar

and Chemmanur (2012) analyse the situation in which private firms choose to be acquired rather

than to go public at higher valuations. They find that companies more viable against product

market competition are more likely to go public than to be acquired. In contrast, more capital-

intensive firms, those operating in industries characterized by greater private benefits of control,

and those that are more difficult to value by IPO market investors are more likely to be acquired.

Based on an empirical analysis of VC-backed UK start-ups, Clarysse et al. (2013) find that both

the trade sale experience of the VCists and learning from syndicate partners with trade sale

experience significantly increase the trade sale hazard. According to the authors, the routines

and procedures learned from experienced syndicate partners complement experience

accumulated through trial and error. Giot and Schwienbacher (2007) use competing risks

models to analyse jointly exit type and exit timing. They find that the hazard rates for IPOs are

clearly non-monotonic with respect to time: VC-backed firms first exhibit an increased

likelihood of exiting to an IPO, but after having reached a plateau, non-exited investments have

fewer possibilities of IPO exits over time. The hazard rate is less time-varying for trade sale

exits. Schwienbacher (2008) analyses how venture-backed companies choose their innovation

strategy based on the investor's exit preferences. The entrepreneur distorts the innovation

strategy to induce the VCist to bring the company public to remain independent. Hege and

Palomino (2009) compare the success of venture-capital investments in the United States and

in Europe by analysing the value generated within the stage financing process. Felix et al.

(2014) uses a competing risks model to analyse the impact of VCist type and their investments

on the exit decision.

However, to the best of our knowledge, the links between staging, syndication and performance

in terms of the exit of venture-backed companies are under-researched, as underlined by

scholars (Burchardt et al., 2016) and practitioners (Cannice et al., 2016).

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The objective of this paper is to analyse the impact of staged investment and syndication

on performance. This performance is measured by the exit route of venture-backed companies,

initial public offering (IPO), trade sale and write-off. The two first exit routes are regarded as

successes, and the third is regarded as failure. As Burchardt et al. (2016) underline, “More work

is needed to analyse the linkage between staging, syndication of VC investments and pre-

planned exits (e.g., Cumming and Johan (2008))” (p.42). According to VCists themselves, more

research about exit strategies and performance is needed (Cannice et al., 2016). This paper aims

to fill these gaps and therefore differs from many articles studying the exit choice of venture-

backed companies. Our research question is not “Should I stay or should I go?”3 but “Do

syndication and staged-investment practices have an impact on performance measured by exit

of venture-backed company?” Moreover our theoretical positioning is original as it combines

different analytical tools: Beyond agency theory (Jensen and Meckling, 1976; Gompers and

Lerner, 2004) and the resource-based view (Penrose, 1959; Barney, 1991; Wernerfelt, 1984)

used in the academic literature, our arguments stem from innovation economic theory

(innovation race, economies of scale…) and the dynamic capabilities approach (Teece, 2007;

Teece et al., 1997). To the best of our knowledge, there is little empirical research based on

these different areas. Based on this original framework of analysis, we gain insight into the

impact of staged investment and syndication on performance and provide a more

comprehensive understanding of the drivers of this performance.

We create a sample from Thomson Private Equity, and we collect data from 2005 to 2015

for 2535 American companies that were created between 2005 and 2010 and that received VC

from American VC firms. For each company, we collect complete data about staging and

syndication. After fully verifying all information for each variable, we obtain a final sample of

884 venture-backed companies having exited from the portfolio of VCs in the US.

Our results do not provide evidence that staged investment has a positive impact on

success exit. These empirical results can be interpreted as a result of the dark side described by

Krohmer et al. (2009). In addition, the negative impact of staged investment can be explained,

according to us, by the fact that providing capital on a piecemeal basis slows down the

entrepreneur with respect to the rate of innovation. The econometric estimates fully support our

contentions that syndication has a positive impact on performance. First, the positive effect of

the syndication at the first round on performance validates the view according to which

syndication allows for a better selection of portfolios companies (Desbrieres, 2015; Manigart

3 As stated by Bock and Schmidt (2015) in the title of their article.

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et al., 2006; Casamatta and Haritchabalet, 2007; Hopp, 2010). Second, our empirical results

provide evidence that the size of syndication throughout the entire lifespan in the portfolio

increases the probability of success by trade sale and by IPO and validates the managerial value-

added hypothesis (Fried and Hisrich, 1995; Sapienza et al., 1996; Brander et al., 2002; Obrimah

and Prakash, 2010). Finally, the positive impact of the renewal of VCs in syndication in the

case of trade sale attests to the key role of new competencies in syndicates. These empirical

results are enlightened by a new perspective: VCs in syndicates improve the dynamic

capabilities (in the sense of Teece et al. (1997)) of venture-backed companies and then enhance

their performance.

The remainder of the paper proceeds as follows. The next sections present the theoretical

background and develop our hypotheses. We then present our methodology and empirical

analysis. The final section details our main contribution, discusses the limitations of our study

and outlines promising avenues for further research.

2. Literature and Hypothesis

2.1 Staged investment and performance

The positive impact of staged investment on performance is analysed by referring to two

theoretical frameworks: agency theory and the real option approach.

First, in the VC literature, the main assumption is that venture-backed companies evolve in an

asymmetric information context and that investors-investee relationships are characterized by

agency conflicts. In this framework, investors provide capital on a piecemeal basis, and each

capital instalment depends on the achievement of strategic milestones. This well-known

practice - the staged-investment - has been deeply investigated by scholars as a way for

investors to minimize agency conflicts (Gompers 1995; Bergemann and Hege 1998; Lerner

1998; Casamatta, 2003; Cornelli and Yosha, 2003; Wang and Zhou, 2004; Landström and

Mason, 2012) and thereby enhance the performance of their portfolio companies. Stage

financing can be used to monitor either top management or the project (Bergemann and Hege,

1998; Lerner, 1998; Cornelli and Yosha, 2003; Wang and Zhou, 2004; Li 2008; Bergemann et

al., 2009). Gompers (1995) assumed that investors monitor companies subject to higher

performance and higher agency costs between the entrepreneur and the VC firm more closely

(Gompers, 1995). Staged investment value versus upfront investment is equivalent to the value

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of agency cost (Neher, 1999). To summarize, staged investment is a governance mechanism

used by VCists as a way to mitigate agency conflicts between VCists and CEOs arising from

the delegation of power (Jensen et Meckling, 1976), moral hazard (Arrow, 1968) and

engagement issues (Hart et Moore, 1994). For all these reasons, staged investment is supposed

to have a positive impact on performance.

Second, according to the real option approach, staged investment is a tool used by VCists in a

context of radical uncertainty with irreversible investment and sunk costs (Dixit and Pindyck,

1994). When there is no asymmetry of information, VCs and entrepreneurs are supposed to

observe the outcomes at the end of each financial round, and if the milestone is achieved, the

company receives additional equity. In other cases, two alternatives are possible: The ‘wait-

and-see’ alternative allows the company to stay in the portfolio, and the ‘stop’ alternative

involves the write-off of the company and thereby prior investments become sunk costs. When

entrepreneurs hold a private information and divergences between the actors are considered,

providing capital on a piecemeal basis is a way to reduce informational asymmetries and to

overcome agency conflicts (Neher, 1999; Hsu, 2010). Real options are viewed as learning

options: To learn about a project is a way to stop investment in poor performant projects. In

this respect, stage-investment has a positive impact on performance as it allows for better

investment allocation. Li (2008) and Wang and Zhou (2004) mix the two perspectives (agency

theory and option real approach).

Hypothesis 1a: Staged investment has a positive impact on venture-backed companies’

performance

The academic literature does not mention the alternative view addressing the inefficacy of

staged investment. To the best of our knowledge, there is only one article that refers to the dark

side of staged investment, that of Krohmer et al. (2009). On the dark side of staging, negative

performance is what invites closer scrutiny, and VCs monitor poor performant companies more

closely as a way to avoid complete failure and the disclosure of bad results. We propose adding

other arguments relating to the inefficacy of staged investment to improve the performance of

venture-backed companies. This original view is based on two arguments stemming from

innovation economic theory. The first is linked to economies of scale (Guellec, 1999). Indeed,

staged-investment reduces the first round amount in a context of increasing return to scale and

thereby reduces the success of companies and, in turn, performance. The second argument is

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linked to the “liability of lateness”4 : Staged-investment reduces the probability to benefit from

monopoly rent related to the exploitation of a radical innovation. In other words, stage

investment slows down the entrepreneur with respect to the rate of innovation. High-growth

ventures need to rely on timely execution to take advantage of early-mover advantages; delayed

execution can have a significantly negative impact on their success (Alhers et al. 2015).

Hypothesis 1b: Staged investment has a negative impact on venture-backed companies’

performance.

2.2 Syndication and performance

Numerous articles identify motives driving VCists to syndicate (Desbrieres, 2015; Manigart et

al., 2006). There are mainly two competing views regarding why VCists syndicate investments.

First, VCists may provide important productive resources to firms. Second, syndication can be

viewed as a means of risk-sharing.

The first hypothesis (H2a) can be divided into two sub-hypothesis: the selection hypothesis and

the managerial value-added hypothesis. According to the proponents of the selection

hypothesis, syndication allows for a better selection of portfolios companies (Manigart et al.,

2006; Casamatta and Haritchabalet, 2007; Brander and De Bettignies, 2009; Hopp, 2010;

Dimov and Milanov, 2010), and the accumulation of resources increases performance (Brander

et al., 2002). According to the proponents of the managerial value-added hypothesis, VCists

provide value-added services to their portfolio companies, networks, moral support and

business knowledge (Fried and Hisrich, 1995; Sapienza et al., 1996), and the impact of added

value should increase with syndication (Das et al., 2011). Xuan (2012) provides evidence that

syndication creates product value for companies, nurtures the innovation of portfolio firms and

therefore helps firms achieve better post-IPO operating performance. VC syndicate-backed

firms are more likely to have a successful exit, enjoy lower IPO underpricing, and receive a

higher IPO market valuation. According to Giot and Schwienbacher (2007), at the time of exit,

a larger number of VC firms increase the pool of contacts that are required for trade sales and

IPOs. According to Lerner (1994), the probability of success increases with syndication.

Brander et al. (2002) find that syndicated investments have higher returns than standalone

investments. The theoretical background of the selection hypothesis and the managerial value-

4 This expression draws a parallel with the “liability of newness” (Stinchcombe, 1965).

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added hypothesis is the resource-based approach (Penrose, 1959; Barney, 1991; Wernerfelt,

1984).

The dynamic capabilities approach, an extension of the resource-based view, studies a

company’s needs for sustaining competitive advantages in fast-moving business environments

(Teece, 2007; Teece et al., 1997). According to Teece (2011), these dynamic capabilities refer

to three classes of activities and adjustments: (1) the identification and assessment of

opportunities (sensing); (2) the mobilization of resources to address opportunities and capture

value (seizing); and (3) continued renewal (transforming). The challenge of the company

consists in shaping its environment and adapting to it. This theoretical framework focuses on

the strategic role of top management as an orchestra conductor combining these three

dimensions. In the highly specific case of a venture-backed company, VCs play an important

role in corporate governance. Thus, with the accumulation of competencies within the

syndication, VCs may have a crucial role to play in developing the dynamic capabilities of their

portfolio companies.

Hypothesis 2a: Syndication has a positive impact on venture-backed companies’ performance.

The second hypothesis (H2b) stems from financial theory: VCists averse to risk use syndication

to diversify their investments and reduce the variance of portfolio return (Lehmann, 2006). The

impact is expected to be neutral on venture-backed companies’ performance (H2b). According

to Manigart et al. (2006), the motives of syndication in European countries are driven far more

by portfolio management considerations than by the desire to exchange firm-specific resources

for selecting and managing specific deals. Moreover, according to Hopp (2010), in the German

context, syndication is more widespread when VCs face higher risks and capital burdens are

larger. The results of these empirical studies support portfolio diversification as the main

rationale for syndication. At a theoretical level, Huang and Xu (2003) provide a model

analysing the capacity of syndication for risk diversification to refinance short-run inefficient

projects.

Hypothesis 2b: Syndication has no impact on venture-backed companies’ performance.

We propose adding a third hypothesis (H2c): Syndication can have a negative effect on

performance. Recall that for some scholars, syndication has a negative effect as a mechanism

through which VCs reduce informational uncertainty or exploit it to overstate their performance

(Admati and Pfleiderer, 1994; Lerner, 1994). Nevertheless, to the best of our knowledge, the

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academic literature does not explore the following viewpoint: This inefficiency may be related

to a loosening of control of VCists and to conflict between syndication’ partners. Through this

lens, greater syndication may increase coordination problems and ensuing transaction costs and

therefore may have a negative impact on venture-backed companies’ performance (H2c).

Hypothesis 2c: Syndication has a negative impact on venture-backed companies’ performance.

3. Methodology

3.1 Sample

The survey of the academic literature on the performance of venture-backed companies shows

that most empirical studies are based on a comparison between two samples of quoted

companies regarded as similar, the only difference being that the first sample contains venture-

backed companies and the second sample contains non-venture-backed companies. This

methodology is adapted from medical research, and its accuracy is linked to the degree of

similarity between the two samples. In the highly specific case of venture-backed companies,

we consider that similarity is not obvious. Therefore, we choose to not follow this methodology.

Empirical studies related to unquoted venture-backed companies are rare, are mostly based on

surveys and suffer from a small response rate and a bias related to declarative answers. Our

database avoids these pitfalls by excluding surveys and by analysing performance in terms of

exit success (IPO or trade sale) and exit failure (write-off) of venture-backed companies.

The sample used in this work is extracted from the Thomson Private Equity database. We

collect data from 2005 to 2015 for 2535 American Companies created between 2005 and 2010

and that received VC from American VC firms. By VC we mean the professional asset

management activity through which funds raised from institutional investors, or wealthy

individuals, are invested into promising new ventures with high growth potential. We therefore

exclude other forms of investments in these companies by non-professional investors such as

business angels and other forms of financial intermediation that are targeted at different types

of private companies, such as buyouts, turnarounds, or mezzanine financing (Da Rin et al.,

2013). The criterion for selecting our sample companies is defined to produce a critical size of

sample companies having exited from VCists’ portfolios. As the average time before exit is

approximately 5 years (according to the National Venture Capital Association (NVCA)

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Yearbook), we select venture-backed companies created from 2005 to 2010 and collect data

from 2005 to 2015. For each company in the database, we collect complete data about staging

and syndication. After fully verifying all information for each variable, we obtain a final sample

of 884 venture-backed companies having exited from the portfolio of VCs.

[Insert Tables 1-2-3]

Table 1 illustrates the distribution of the sample companies by main sector. Three main sectors

are represented: software (43%), information retrieval services (24%), and medical (17%). In

Table 2, we report the distribution of the sample companies by exit route. In our sample, 197

companies exit by IPOs and 446 exit by trade sale. This distribution of exit route is in line with

the observations of Espenlaub et al. (2015), who find that trade sales are the most frequently

used exit route for all investments. In the specific case of biotechnology, Benk and

Hülenschmidt (2007) provide evidence that biotech start-ups increasingly choose trade sales to

large pharma or biotech players to move their drug discoveries into the marketplace. In our

sample, the median time for an IPO is equal to 7 years and that for trade sales is equal to 5.7

years. This result is in line with that of Felix et al. (2014), who provide evidence that IPO

candidates take longer to be selected than trade sales. The main statistics of our variables are

presented in Table 3.

3.2 Model and variables

We model the performance of venture-backed companies by staged-investment and syndication

mechanisms using robust probit estimates. The first model is a binomial probit with two

outcomes: IPO and trade sale (643 companies) and write-off (241 companies). The second

model is a trinomial probit with three outcomes: IPO (197 companies), trade sale (446

companies) and write-off (241 companies). The dependent variable has more than two outcome

categories, and the outcomes have no natural order. Using an ordered probit model is not

suitable. The order would be an exit with IPO, an exit with a trade sale and an exit with a write-

off. Although the first exit mode and the last one are opposed and choices between both can be

ordered, a company with trade sale in the portfolio is not a second best choice and cannot be

considered an intermediate category. A multinomial probit model is used rather than a logit one

because the former requires only a normal distribution for the error term.

In a multinomial probit model, the utility Uij of option j to individual i (which corresponds to

the relative attractiveness of the option) is treated as a random variable consisting of the sum

of an observable part Vij plus an error term εij that follows the normal distribution Uij = Vij + εij.

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The error term represents the modeller’s inability to observe all of the variables that influence

the choice made. The ith alternative is selected if, and only if, Uij ≥ Uik for all j≠k. Thus, the

probability that the jth alternative is chosen is

𝑃(𝑦𝑖 = 𝑗) = 𝑃𝑖𝑗 = 𝑃 [𝜀𝑖1 − 𝜀𝑖𝑗 < (𝑥𝑖𝑗 − 𝑥𝑖1)′𝛽, … , 𝜀𝑖𝐽 − 𝜀𝑖𝑗 < (𝑥𝑖𝑗 − 𝑥𝑖𝐽)

′𝛽],

where yi is a random variable that indicates the choice made and J is the number of alternatives.

The multinomial probit model relaxes the multinomial logit model restrictions, as it allows the

random components of the utility of the different alternatives to be non-independent and non-

identical. Thus, it does not impose the independence of irrelevant alternatives (IIA) property.

In practice, because the model uses maximum likelihood estimators, convergence often requires

restrictions on the elements of the error covariance matrix. We make the error terms

independent given the sample size and the absence of alternative-specific attributes in our

database. Therefore, a multinomial logit model provides relatively similar results. In a

multinomial model, a positive coefficient does not mean that an increase in the regressor k (

βk1) leads to an increase in the probability of outcome i being selected; rather, it means that as

the regressor k increases, we are more likely to choose alternative i than the base alternative.

[Insert Table 4]

The variables of our model are described in Table 4. Explanatory variables are the following:

round #1/(round#1 + round #2), round number, duration between two rounds, duration between

creation and first round table, number of VCists at the first round, number of new VCists/old

VCists and syndication size.

We add control variables that can influence the staged investment and syndication. First, the

sector may have an impact. Each company has been affected in a sector category (computer,

information retrieval services, commercial, industry, software and medical). We control for age

and include year dummies as well.

4. Findings

4.1 The binomial probit model

[Insert Table 5]

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The results from the binomial probit model estimates are displayed in Table 5. The dependent

variable has two outcome categories, a success exit mode (IPO and trade sale) and a failure exit

mode (write-off). We provide two sub-models (column I and II) because of correlation issues

between our two variables of duration (duration between creation and the first round and

duration between two subsequent rounds).

[Insert Table 6]

We observe that the duration between two rounds has a negative and significant impact on the

likelihood of success by IPO or trade sale. In parallel, the duration between the creation of the

company and the first round decreases the likelihood of success and increases the likelihood of

failure. The syndication size during the lifespan in the portfolio is also an important factor for

the success of IPO and trade sale: As the importance of the size of the syndicate increases, the

importance of the probability of success with an IPO or with a trade sale increases as well. The

correlation is negative with the write-off exit. Moreover, the number of VCists at the first round

has a positive (negative) and significant impact on the likelihood of success (failure). The

number of new VCists relative to the number of old VCists shows a positive and significant

impact on the likelihood of success by IPO and trade sale. However, the impact of this variable

is not significant for the write-off exit. Companies belonging to the computer, information,

software and medical sectors are more likely to succeed. The older (younger) a company is, the

more likely it is to exit by success (failure).

4.2 The multinomial probit model

[Insert Table 7]

To perform a more in-depth analysis of the impact of staged-investment and syndication, we

conduct a trinomial probit model. This model allows for the analysis of the impact on the three

exit routes separately. For the binomial model, we propose two sub-models (column I and

column II) because of correlation issues between the two variables of duration. The results of

the multinomial probit model are consistent with those of the binomial probit model. What we

learn with the multinomial model is that not all exit modes are equally affected by syndication

or staged investment: The results are more robust for trade sale than for IPO. Indeed, for the

duration between two subsequent rounds and for the number of VCists at the first round, the

significance threshold is at 1% for trade sale and 5% for IPO. Moreover, it should be noted that

with the multinomial probit model, the impact of the new VCists on the old VCists is no more

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significant for IPO. Regarding the control variables, the results are similar to those of the

binomial model.

5. Discussion and conclusion

The link between staging, syndication and exits is under-investigated according to scholars

(Burchardt et al., 2016), and more research on exit strategies and performance according to

practitioners is needed (Cannice et al., 2016). The most important contribution of this paper is

the filling of these gaps. Our hypotheses stem from an original theoretical positioning that goes

beyond agency theory and the resource-based view. Our empirical results provide new insight

into the impact of staged-investment and syndication on performance.

5.1 The role of staged investment on venture-backed companies’ performance

Contrary to our hypothesis H1a, our results do not provide evidence that staged investment has

a positive impact on success exit. Thus, our empirical analysis does not validate predictions of

agency theory or of the real options approach. According to agency theory, staged investment

is assumed to improve the probability of success and thereby performance by mitigating agency

conflicts in a context of asymmetric information (Bergemann and Hege 1998; Lerner 1998;

Casamatta, 2003; Cornelli and Yosha, 2003; Landström and Mason, 2012). According to the

real options approach, learning options enable investors to stop investing in poorly performing

projects and to focus on high-performing companies and therefore to enhance performance. Our

econometrical analysis provides evidence that number of rounds has no impact on performance

and does not support hypothesis H1a. These empirical findings can be interpreted as the result

of the opposite sides of staged investment, the bright side (described above) and the dark side

described by Krohmer et al. (2009). On the dark side of staging, negative performance is what

invites closer scrutiny, and VCs monitor poorly performing companies more closely as a way

to avoid complete failure and the disclosure of bad results. Moreover, the amount of equity at

round #1 divided by the sum of the amount of equity at rounds #1 and #2 has no impact on

performance and does not support the view that staged investment reduces the probability of

success in a context of increasing returns to scale (Guellec, 1999). Finally, we find a negative

relation between the duration between the creation and the first round and the duration between

two subsequent rounds and the probability of success. Furthermore, the negative impact of the

duration between two subsequent rounds is more important in the case of trade sale. This result

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supports our hypothesis H1b: Our empirical analysis provides evidence that stage investment

slows down the entrepreneur with respect to the rate of innovation. As underlined by Alhers et

al. (2015), venture-backed companies need to rely on timely execution to take advantage of

early-mover advantages (monopoly rent); delayed execution can have a significantly negative

impact on their success. In other words, to provide capital on a piecemeal basis reinforces the

“liability of lateness,” which is detrimental to performance.

5.2 The role of syndication on venture-backed companies’ performance

The econometric estimates fully support our contentions that syndication has a positive impact

on performance (H2a): The syndication size at the first round and during the lifespan in the

portfolio and the number of new VCists relative to the number of old VCists in the syndication

are positively associated with the probability of success (IPO or trade sale) and negatively with

the probability of failure. These results are in line with the literature (Lerner, 1994; Das et al.,

2011). The positive effect of the syndication at the first round on performance validates the

view according to which syndication allows for a better selection of portfolio companies

(Desbrieres, 2015; Manigart et al., 2006; Casamatta and Haritchabalet, 2007; Hopp, 2010). Our

empirical results provide evidence that this selection effect is more important for trade sale than

for IPOs. Moreover, scholarly opinion suggests that the accumulation of resources within the

syndication increases performance. Indeed, VCs provide value-added services to their portfolio

companies, networks, moral support and business knowledge, and the impact of added value

increases with syndication (Fried and Hisrich, 1995; Sapienza et al., 1996). Our empirical

results provide evidence that the size of syndication increases the probability of success by trade

sale and by IPO and validates this managerial value-added hypothesis. In addition, we find a

positive impact of the renewal of VCs in syndication in the case of trade sale, thereby attesting

to the key role of new competencies in syndicates. We also find, somewhat surprisingly, that

the renewal of VCs has no impact for IPOs. To conclude, the empirical analysis of Brander et

al. (2002) favours the managerial value-added hypothesis over the selection hypothesis as a

rationale for syndication. In contrast, Obrimah and Prakash (2010) provide evidence that VCs’

deal-screening skills are more important for success than advisory or monitoring skills. Our

empirical results validate these two (compatible) hypotheses.

5.3 Theoretical Implications

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Most scholars analyse staging and syndication based on agency theory and the resource-based

view. In this paper, we adopt an original theoretical positioning by mobilizing innovation

economic theory and the dynamic capabilities approach. To the best of our knowledge, there is

little empirical research on these different areas.

Except for Krohmer et al. (2009), the academic literature does not mention an alternative view

addressing the inefficacy of staged investment. We propose the addition of new arguments

stemming from innovation economic theory. First, staged investment may reduce the first round

amount in a context of increasing returns to scale and thereby reduce performance. Second,

staging increases what we refer to as the “liability of lateness” by slowing down the

entrepreneur with respect to the rate of innovation. Indeed, staged investment reduces the

probability of benefitting from monopoly rent related to the exploitation of a radical innovation

and thus affects performance. Our empirical results regarding staged investment are enlightened

by this original concept of “liability of lateness”.

Academic articles studying syndication focus on financial theory (VCs averse to risk use

syndication to diversify their investments) and resource-based theory (Penrose, 1959; Barney,

1991; Wernerfelt, 1984). We propose extending the theoretical framework regarding

syndication to the dynamic capabilities approach that studies a company’s needs for sustaining

competitive advantages in fast-moving business environments (Teece, 2007; Teece, Pisano, &

Shuen, 1997). Indeed, venture-backed companies evolve in this highly specific context, and

time is a key factor for performance. According to this theoretical positioning, the challenge for

a company consists in shaping its environment and adapting to it. By considering insights

gathered from the dynamic capabilities approach for the first time, we propose the following:

VCs play a key role in developing the dynamic capabilities of their portfolio companies. They

act as orchestra conductors by identifying and assessing opportunities (sensing), by mobilizing

resources to address opportunities and capture value, by continued renewal. All these activities

take place (at least partly) thanks to exchange within the syndication. Our empirical results are

enlightened by this new perspective: The size of the syndication at the first round and during

the lifespan in the portfolio and the renewal of VCs within this syndication are positive

predictors of success exit because VCs in syndicates improve the dynamic capabilities of

venture-backed companies and thereby enhance performance.

6. Limitations and Further Research

15

This research presents some limitations and opens many new perspectives for future research.

We analyse performance in terms of the exit success of venture-backed companies, a choice

that is limiting in some ways. In our paper, exit by IPO or trade sale is used as a proxy for

success and write-off is a proxy for failure. The definition of success and failure may be more

complex as one may consider that a return on investment less than the expected one regarding

risk is a failure. Moreover, IPO does not systematically imply capital gains for VCs who can

sell their shares only after the lock-up period. By studying performance in terms of exit success,

we focus on performance at a very specific time, and we do not provide evidence of

performance after VCs exit and over time. Following Meles et al. (2014), we consider further

investigating the effect of staging and syndication on performance over time.

Our empirical context is the American one. This choice brings forth the issue of the

generalisation of our results to other contexts. Cumming et al. (2006), Groh et al. (2010) and

Espenlaub et al. (2015) provide evidence that the institutional environment (the legal systems

protecting investors, market liquidity) affect the mode of exit. In this respect, it will be

interesting to analyse to what extent this institutional environment influences the impact of

staging and syndication on performance.

Other fertile areas for research include a deeper analysis of the composition of syndication. Our

research analyses the impact on new VCs in syndicates. Bertoni and Groh (2014) examine the

manner in which the exit mode is influenced by the additional exit opportunities introduced by

cross-border VC investors in European countries. Moreover, Li and Li (2014) examine how

institutional and cultural distances between VCists and venture-backed companies affect

performance in terms of exit success. In this respect, it will be interesting to investigate to what

extent international syndication affects performance in the U.S context. In addition, following

Guo et al. (2015), it could also be relevant to study to what extent the impact of staging and

syndication varies between corporate VCists nd independent VCists.

Finally, our paper focuses on the impact of staging and syndication. Another possible direction

for future research could be the analysis of other determinants of performance such as the

characteristics of the founding teams of companies (Streletzki and Schulte, 2013), VCs’ human

capital (Ewens and Rhodes-Kropf (2015), the reputation of VCs (Rajarishi, 2008; Espenlaub et

al., 2015) and founder-CEO replacement (Gerasymenko and Arthurs, 2014).

16

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21

Table 1: Distribution of sample firms by sector

Number %

Computer 69 7,81

Information retrieval services 210 23,76

Commercial 50 5,66

Industry 25 2,83

Prepackaged software 380 42,99

Medical 150 16,97

Total 884 100,00

Table 2: Distribution of investment by exit route

IPO Trade Sale Write-Off

Computer 14 45 10

Information retrieval services 47 96 67

Commercial 15 10 25

Industry 8 5 12

Prepackaged software 53 248 79

Medical 60 42 48

IPO Trade Sale Write-Off

Computer 7,11% 10,09% 4,15%

Information retrieval services 23,86% 21,52% 27,80%

Commercial 7,61% 2,24% 10,37%

Industry 4,06% 1,12% 4,98%

Prepackaged software 26,90% 55,61% 32,78%

Medical 30,46% 9,42% 19,92%

22

Table 3: Summary statistics

mean median St. Dv. min max observations

round1/(round1+round2) 0,42 0,37 0,26 0,00 0,98 884

Round number 4,20 4,00 2,43 1 16 884

duration between 2 rounds 1,13 0,70 1,22 0,1 3,60 884

duration between creation and 1st round 7,30 7,00 3,5 0,1 10 884

number of VC at the first round 14,04 10,00 12,07 1 90 884

number of new VC/olds VC 2,26 2,00 1,03 0 7,5 884

23

Table 4: Definition of variables

Variable name Description

Main variables

Round1/(round1/round2) Equity amount at the first round / (Equity amount at the first Round + Equity amount at the

second round)

Round number Number of rounds

Duration between two rounds Duration between two subsequent rounds in number of years

Duration between creation and the 1st

round

Duration between the creation of the firm and the first round in number of years

Number of VC at the 1st round Number of VCs at the 1st round

Number of new VCists/old VCists Number of new VCists / old VCists

Syndication Size Number of VCists at time t

Control Variables

Sector Computer, Information retrieval services, commercial, industry, software, medical

Age Number of years existence of venture-backed companies

Bubble Dummy variable equals 1 when the first round has been received in the year 2000

Year Dummies Dummy variable for the year

24

Table 5: Binomial Probit (IPO+Trade Sale/Write-Off)

Variables IPO/Trade Sale Write-off

I II I II

Round1/(round1+round2) 0,324 0,421 -0,189 -0,902

(1,453) (1,590) (1,732) (1,924)

Round number -0,205 -0,342 0,219 0,285

(1,462) (1,502) (1,529) (1,612)

Duration between two rounds -0,646 0,702

(2,791) *** (2,718) ***

Duration between creation and 1st round -0,197 0,187

(2,304) ** (2,338)**

25

Number of VC at the 1st round 0,378 0,428 -0,489 -0,504

(2,821)*** (2,893)*** (2,763) *** (2,694)***

Number of new VC/olds VC 0,242 0,229 -0,278 -0,325

(1,992) ** (1,983) ** (1,875) (1,891)

Syndication Size 0,876 0,743 -0,231 -0,339

(3,543) *** (3,821) *** (3,421) *** (3,396)***

Computer 0,593 0,621 -0,452 -0,493

(2,984)*** (2,923)*** (3,002) *** (3,105) ***

Information retrieval services 0,293 0,314 -0,492 -0,503

(2,971) *** (2,992) *** (2,989) *** (2,981) ***

Commercial -0,378 -0,401 -0,394 -0,298

(1,245) (1,356) (1,152) (1,208)

Industry 0,187 0,203 -0,352 -0,415

(1,539) (1,632) (1,719) (1,674)

26

Software 0,782 0,821 -0,912 -0,892

(2,629) *** (2,651) *** (2,712) *** (2,769) ***

Medical 0,345 0,358 -0,542 -0,621

(2,456)** (2,572) ** (2,523)** (2,582)**

Age of firm 0,513 0,456 -0,239 -0,312

(2,031) ** (2,134) ** (2,315) ** (2,219) **

Bubble 0,386 0,402 0,449 0,408

(2,487)** (2,476)** (2,343)** (2,439)**

Year Dummies Yes Yes Yes Yes

Adjusted R2 43,5% 44,6% 41,4% 42,3%

Observations 884 884 884 884

27

Table 6: Correlation matrix

(1) (2) (3) (4) (5) (6) (7)

(1) Round1/(round1+round2) 1,00

(2) Round number 0,11 1,00

(3) Duration between two rounds 0,10 0,04 1,00

(4) Duration between creation and 1st round 0,09 0,11 0,49 1,00

(5) Number of VC at the 1st round 0,07 0,05 0,08 0,03 1,00

(6) Number of new VC/olds VC 0,02 0,06 0,08 0,04 0,12 1,00

(7) Syndication Size 0,08 0,04 0,06 0,05 0,09 0,07 1,00

28

Table 7: Multinomial Probit (IPO/Trade Sale/Write off)

____________________________________________________________________________________________________________________

IPO Trade Sale Write off

I II I II I II

Round1/(round1+round2) 0,342 0,435 0,241 0,176 -0,097 -1,654

(1,231) (1,229) (1,336) (1,347) (1,134) (1,125)

Round number -0,189 -0,204 -0,245 -0,197 0,561 0,502

(1,543) (1,592) (1,421) (1,485) (1,502) (1,498)

Duration between two rounds -0,384 -0,411 0,437

(2,432)** (2,781)*** (2,405) **

Duration between creation and 1st round -0,241 -0,339 0,206

(2,114) ** (2,124)** (2,093)**

Number of VC at the 1st round 0,087 0,103 0,203 0,220 -0,153 -0,163

29

(2,554) ** (2,491)** (2,743)*** (2,782) *** (2,509) ** (2,485)**

Number of new VC/olds VC 0,675 0,653 0,548 0,552 -0,514 -0,509

(1,429) (1,451) (1,984) ** (1,974)** (1,402) (1,414)

Syndication Size 0,432 0,529 0,514 0,542 -0,456 -0,485

(3,682) *** (3,728) *** (3,634)*** (3,592)*** (3,564) *** (3,551)***

Computer 0,672 0,703 0,821 0,915 -0,893 -0,923

(3,124)*** (3,085)*** (3,245)*** (3,482)*** (3,143) *** (3,186) ***

Information retrieval services 0,347 0,289 0,357 0,381 -0,394 -0,429

(2,893) *** (2,832) *** (2,831)*** (2,808)*** (2,851) *** (2,910) ***

Commercial -0,652 -0,532 -0,623 -0,504 -0,279 -0,355

(1,076) (1,321) (1,209) (1,164) (1,159) (1,245)

Industry 0,113 0,192 0,104 0,203 -0,219 -0,306

(1,410) (1,419) (1,502) (1,619) (1,552) (1,498)

30

Software 0,621 0,702 0,592 0,601 -0,596 -0,634

(2,762) *** (2,752) *** (2,815)*** (2,828)*** (2,803) *** (2,896) ***

Medical 0,456 0,509 0,589 0,546 -0,603 -0,587

(2,357) ** (2,406) ** (2,523) ** (2,492) ** (2,501) ** (2,523)**

Age of firm 0,609 0,543 0,636 0,691 -0,496 -0,414

(2,125) ** (2,295) ** (2,235)** (2,328)** (2,301) ** (2,290) **

Bubble 0,738 0,692 0,624 0,694 0,704 0,721

(2,534) ** (2,551) ** (2,579) ** (2,584) ** (2,567) ** (2,595) **

Year Dummies Yes Yes Yes Yes Yes Yes

Adjusted R2 44,8% 43,1% 45,2% 44,6% 42,4% 45,7%

Observations 884 884 884 884 884 884