Preliminary Draft for 2015 Global Business and Social Science Research Conference
Partner Selection of Venture Capital Syndication
Under Information Asymmetry
--Evidence from China
Abstract
Partner selection is one of the key topics in venture capital syndication and partnering
decisions which may ultimately influence the performance of investment. This paper
analyzes factors impacting partnering decisions in venture capital syndication using a
data set of 12349 venture capital transactions in mainland China during the period of
1989-2014. The results show that lead venture capitalists have higher probability to
cooperate with partners having similar experience level and scale. If there are direct
previous relationships between lead VCs (Venture Capitalists) and partners as well as
the partner is located in a rich cluster, they are more likely to collaborate with each
other in the future. The need for additional partner skills is anticipated to be greater in
later stages of an investment than in earlier stages. While syndicating in start-up
stages, lead VCs aim at spreading risks so they search for partners with large scale
and located in rich clusters. After early stages, lead VCs put a stronger emphasis on
combining skills of partners and pay more attention to network. This paper also
innovatively analyzes whether lead VCs with different backgrounds such as
Chinese-funded or foreign-funded have different patterns for choosing partners since
there is no research on this aspect in China. It is concluded that both Chinese-funded
leaders and foreign-funded and Sino-foreign joint funded leaders have higher
probability to cooperate with partners located in rich clusters and have previous
relationships, but only foreign-funded and Sino-foreign joint funded leaders treat
experience and scale of partners as important features.
Keywords: venture capital; syndication; partner selection; China
JEL Classification: G32; G34
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Introduction
Venture capital syndication is a form of cooperation between venture capitalists in
which two or more capitalists invest in the same venture capital program either
simultaneously or in different rounds. In reality, there is always a venture capital firm
with limited resources and funding which initiates a project firstly and then searches
partners. The venture capital firm who starts a project is called lead venture capitalist,
i.e., leader. According to Brander, Antweiler and Amit (2002), lead capitalists are
always high-level firms with abundant funding, strong financing ability and
professional talents. For other players who follow after the lead capitalist are called
following venture capitalists, i.e., followers (Dimov and Milanov, 2009). Brander,
Antweiler and Amit (2002) also mention that followers may have limited funding but
they are experts in some fields of technology and have resources that lead venture
capitalists may not have. Every player in syndication will affect the investment
profoundly because special resources that they have are the most important
requirements of success.
There are various ways in which VCs can benefit from each other through venture
capital syndication. Bygrave (1987) considers that venture capitalists can be used as
the medium to integrate resources using their own professional and extensive
experience in investment, optimize the investment management and improve
value-added ability. Moreover, venture capitalists can syndicate to reduce risks they
undertake (Wilson, 1968 and Andy Lockett & Mike Wright, 1999). Since venture
capitalists are dependent on each other, it is important to choose the most appropriate
partners and the partnering decision lies at the center of understanding why
syndication can reduce risks of VCs and add value to funded firms. However, a major
problem connected to a lead VC’s decision of whom to invite to join the syndication
is information asymmetry.
Information asymmetry refers to both trading parties in the market having varying
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degrees of knowledge about the transaction, showing an irregular and asymmetrical
distribution of information; and characterized by some economic individuals having
more information than others or there being no way to obtain information or being
unable to identify true and false (Ferrary, 2010). In order to get effective information,
the cost of information gathering is very high. For a single contract party, the
information it gained can be divided into two parts: one part is the information that
both parties can get, called public information; another part is that only a single party
can access, called private information (Elg, Ghauri and Tarnovskaya, 2008). Since
every single economic individual has its own private information and is not willing to
share with others, information asymmetry forms and it shows in two aspects: one is
single economic individual lacks cognition for the deal; another one is a single party
deliberately conceals the true information, or maybe deliberately provides false
information to deceive others.
In venture capital syndication, lead venture capitalists and followers are connected
through contracts and form a principal-agent relationship between each other (Elg,
Ghauri and Tarnovskaya, 2008). Casamatta and Haritchabalet (2007) propose that
when using venture capital syndication strategy, the lead venture capitalist becomes a
principal and followers become agent who evaluate, supervise the project and provide
value-added services. The principal-agent problem becomes more serious, particularly
when leader and followers invest in the same project in different time. Generally, the
interests of lead venture capitalists are not consistent with the interests of the
followers. Therefore, in the circumstances of information asymmetry, there is a gap of
information gained between leaders and followers, each other's behavior will affect
the operational efficiency of the venture investment project. The conditions of
information asymmetry in venture capital syndication can be divided into two aspects:
one of them is the information asymmetry of followers. Low-level followers tend to
hide their moral problems through exaggerating their abilities to appeal to lead
venture capital firms and be invited to participate in the joint venture (Pichler and
Wilhelm2001). It is hard for the lead venture capitalist to understand the real
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information of followers and it can only preliminarily discriminate through public
information in the market. The following venture capitalists know themselves very
well but it is impossible for the leader to get all these real information. Another aspect
is information asymmetry of project quality and market expectation. Pichler and
Wilhelm (2001) propose that in reality, venture capitalists are more willing to invest
in projects that are in the growing or expanding stage which means the lead venture
capitalist already know about the project well. However, the leader cannot further
invest in the project owing to limited funding or they cannot provide more
value-added services because of limited ability. In these circumstances, the leader is
going to find partners. Thus, compared with followers, the leader knows more about
the innovative level, technology and development potentiality of the project. For
followers, they need to spend very high time cost and economic cost to gain more
information.
The interest of this paper is what factors lead venture capitalists consider while
choosing partners to reduce information asymmetry in China since China is a special
market. The Chinese central government introduced venture capital investments from
the USA during The Reform of Science and Technology in 1985, which determines
not only the special role of the Chinese venture capital, fund the development of
technology, but also the developing tendency of venture capital in China, growths and
recessions are related to government policies (Chen, 2007). Today the Chinese market
has become a main one in the world. There are more and more venture firms in China
in recent years and vast foreign venture firms also flock into the Chinese market. With
the familiarity of foreign venture firms to the Chinese market, those local firms have
weaker advantages and face more fierce competition, and they have to make some
changes to investment strategies. So, syndication, a new investment model which can
gather capital quickly and manage projects jointly, is very important for the venture
capital development in China. However, since information asymmetry leads to serious
principal-agent problem, it will have negative influences on the operational efficiency
of venture capital syndications and even cause the failure of investment projects.
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Therefore, it is necessary to analyze what elements lead VCs consider when selecting
partners and use them as signals to reduce information asymmetry.
This paper studies the partner selection decision in venture capital syndication using a
sample of 12349 venture capital transactions in mainland China during the period of
1989-2014. The main research questions are which kinds of potential partners are
chosen by lead VCs and what factors influence the likelihood of collaboration. The
results suggest that the excess experience of partners plays a negative role for leaders
while the scale of potential partners plays a positive role. If there are direct previous
relationships between lead VCs and partners as well as the partner is located in a rich
cluster, they are more likely to collaborate with each other in the future. Different
skills of potential partners are needed when investing in funded firms in different
stages. While syndicating in start-up stages, lead VCs aim at spreading risks so they
search for partners with large scale and located in rich clusters. After early stages, lead
VCs put a stronger emphasis on combining skills of partners and pay more attention
to network. Finally, both Chinese-funded leaders and foreign-funded and Sino-foreign
joint funded leaders have higher probability to cooperate with partners located in rich
clusters and have previous relationships, but only foreign-funded and Sino-foreign
joint funded leaders treat experience and scale of partners as important features.
The contribution of this paper is that it analyzes the partnering decision of venture
capital syndication based on the Chinese situation. As mentioned before, the growths
and recessions of venture capital in China are highly related to government policies,
and the venture capital operates in a financial environment determined by state-owned
commercial banks, which are quite different from western countries (Chen, 2007). So
this paper studies whether Chinese-funded VCs and partly or pure foreign-funded
VCs have different considerations while choosing partners. Moreover, this paper
measures affluent levels of different provinces and municipalities according to the
current social situation where there are huge affluence gaps between provinces and
municipalities even though they have similar population size. This measurement is
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different from the traditional method which determines affluent levels merely based
on the quantity of population.
The remaining parts of this paper are organized as follows: I first review the related
literature and provide hypotheses. Next, I describe the dataset, methodology and
variables used. Following is the analysis of regressions, and finally, a conclusion,
along with potential limitations and future research.
Literature Review and Hypothesis
There is a great amount of empirical and theoretical literature analyzing
characteristics of the potential partner influencing the likelihood of collaboration. For
example, there are many papers researching whether the reputation or scale of
potential partners can affect the likelihood of collaboration. Some scholars conclude
that scale plays a positive role but others argue that it has no effect. There are also
some scholars who have found that experience is an important element for choosing
partners. Furthermore, many scholars research the different tendency of choosing
partners when lead VCs invest in funded companies at different stages. Finally, there
is no literature in China analyze whether lead VCs with different backgrounds
consider different factors when choosing partners.
Experience Level
Aoki (2000) establishes a model and classifies venture capitalists through experience.
He concludes that syndication often happens between capitalists who have abundant
experience and know each other well. Cestone (2003) mentions that capitalists with
limited experience are more willing to syndicate with experienced venture firms to
gain experience and reputation through success of projects and to gain better status for
future venture investment. Saxton (1997) also finds that experience is an important
element for choosing partners. This kind of experience can accelerate understanding
and reinforce trust between players of syndication; and then make management of
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syndication more efficient. Moreover, having more experience can urge partners to
learn from each other which is vital in syndication and can even influence the
performance of syndication.
Therefore, investment experience is a proxy for the ability of potential partners and
helps the lead VC to distinguish between high-quality and low-quality VCs in the
market. Dimov and Milanov (2009) show that high-quality VCs would enhance the
expected profit of a project by reducing the probability that partners do not live up to
expectations, and thereby increasing the chances for collaboration among a lead VC
and potential partners. This leads to the following hypothesis:
Hypothesis1: The investment experience of the potential partner VC positively affects
the likelihood of collaboration.
Scale Level
Jennings (2000) shows that scale is an important consideration while choosing
partners since scale can reflect technological level and funding ability of a company
which can help the company establish sustainable competitive advantages. In venture
capital syndication, the lead venture capitalist should have a large scale since it plays
a pivotal role in connecting funded companies and followers. Followers should also
have a large scale because it is an important consideration while leader is selecting
partners. Additionally, the large scale of partners can reinforce exchange of
information and reduce supervision costs and transaction costs; and then enhance
performance of the syndication.
However, Olav and Toby (2001) do not think scale which is a good proxy for funding
ability is an important factor to consider and analyze partner selection strategy
through investment networks. It is concluded that venture capitalists generally prefer
to cooperate with firms that are located not very far away from them. The location of
venture firms becomes an important element for success of syndication instead of
scale. That is because lead venture capitalists can gain money from any other VCs
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which means they can just select partners randomly if they merely lack money but the
reality is more complicated and there are more important factors than scale to
consider while choosing partners.
It is necessary to research whether scale is an important factor that can influence the
choice of partners in the Chinese market and the following hypothesis is formulated:
Hypothesis 2: Lead VCs have higher probability to cooperate with partners with a
larger scale.
Geographical Level
Location can be an important factor to be considered and there are many scholars
have ever researched on the cluster-based effects on likelihood of collaboration.
Streletzki and Schulte (2013) document that venture capitalists that are close by or
located in a particular cluster have higher probability to cooperate. They also point out
that the benefits of short distance between lead VCs and following VCs are the cost
reduction of pre-investment screening and the ease of post-investment control.
However, there are also disadvantages; for example, there are less potential partners
to be chosen in a certain cluster and there may be more intense competition among
VCs firms to make investments (Gilbert, McDougall and Audretsch 2008). Some
scholars researched on the geographical relationship between venture capitalists and
funded companies (Cumming and Dai 2009; Stuart and Sorenson 2003).
However, I am more interested in whether lead VCs prefer to cooperate with potential
partners in rich clusters on which fewer scholars researched. Bartkus (2004) shows
that finding a partner located in a metropolitan cluster have a positive influence on the
syndication performance. Similarly, the following hypothesis is formulated:
Hypothesis 3: Lead VCs are more willing to cooperate with partners located in rich
clusters.
Previous Relationship
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Walske and Zacharakis (2009) find that lead VC can have more precise assessments
of a partner’s quality if they have previous interactions in certain financing events
than mere observation. Moreover, the interactions may help lead VCs to know
partner’s characteristics or private information which the partner not willing to share.
Therefore, the previous relationship between lead VCs and partners should matter for
upcoming partnering decisions and the following hypothesis is formulated:
Hypothesis 4: Direct previous interactions between lead VCs and partners increase
the likelihood of collaboration.
Stages of Funded Firms
VCs usually fund companies in several rounds or stages. Christian and Christian
(2013) propose that “the need for additional partner skills is anticipated to be greater
in later stages of an investment than in earlier stages”. Kaplan and Strömberg (2004)
take more than two hundred U.S. biotech venture enterprises for ten years as research
objects, and use statistics to analyze several rounds of venture investment for the
period. They conclude that in the later stage of funded companies, what they need
most are value-added services provided by experienced venture capitalists instead of
funding. According to Mäkeläand Maula (2005), all activities such as human resource
management, development of accounting system and internationalization strategies
become more important in later stages. As the stage of the funded firm advances, the
uncertainty and ambiguity of the project decreases and this allows for improved
judgment about the managerial advice needed to support the funded firm (Lerner,
1994).
There is no doubt that partners contribute resources to the project in every round. But
as argued earlier, the need for skills of partners such as experience grows over time.
Empirical evidence shows that at the earlier stages, generally lead VCs search for
partners simply to share financial burden and have no specific requirements on ability
and quality (Manigart et al., 2005). With the development of funded firm, it asks
venture capitalists to provide value-added services and lead VCs need to find partners
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with specific knowledge and expertise. Accordingly, the following hypothesis is
formulated:
Hypothesis 5: The need for additional partner skills is anticipated to be greater in
later stages of an investment than in earlier stages.
Background of VCs
The Chinese market is very special because of its state system. Around 30 years ago,
the Chinese government imported venture capital investments from America at the
beginning of Reform and Openness period. Unlike VCs of western countries, the
earliest venture capital firms were established by the Chinese government, which are
quite different from the foreign situations. Moreover, because the Chinese venture
capital was established much later and lacks management experience, it is not as
developed as the foreign venture capitals (Chen, 2007). There is a great amount of
empirical and theoretical literature analyzing information asymmetry and partner
selection of venture capital syndication in western countries. However, there is no
literature analyzing whether Chinese-funded VCs have different considerations while
choosing partners compared with foreign-funded and Sino-foreign joint funded VCs.
Hypothesis 6: Lead VCs with different backgrounds such as Chinese-funded or
Sino-foreign joint funded have different tendency for choosing partners.
Data and Methods
Dataset and Summary Statistics
The sample complied by CV Source Database consists of 12349 venture capital
transactions that are made over different stages including start-up, early stage, and late
stage in China within the period of 1989-2014. The number of total financing events
comprises capital injections from 7194VCs. Incomplete data were deleted from
dataset. For venture capital transactions, there were original 18834 transactions, but
2335 transactions without information about VCs who invest in them were deleted.
1517 transactions with information of financing nature without VC series were
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deleted since information about different investment series is needed to distinguish
lead VCs from following VCs. Additionally, 2027 private equity and angel investment
transactions were deleted because this paper focuses on venture capital syndication.
606 data without the information about the time of the deal takes place were deleted.
Finally, 12349 transactions are left. For VCs, there were original 8341 VCs, but 22
VCs without information about venture capital type were deleted. Additionally, 1125
data without the information about date of foundation were deleted. Finally, 7194 VCs
are left.
There is a problem about how to distinguish lead VCs from following VCs and the
dataset has no such information but it is very important since this paper analyzes
factors influencing lead VCs while choosing followers. Megginson and Weiss (1991)
and Sorensen (2007) document that the lead VC usually acquires the largest stake in
the funded company. Gorman and Sahlman (1989) find that the leader spends about
10 times more time on monitoring and managing the investment. However, CV
Source provides information about neither stake of each investor nor the time each
investor spends on monitoring and managing the investment. The method used by
Christian and Christian (2013) seems more suitable. They define the lead VC as who
has the involvement in the initial financing round and at the same time invest in
maximum number of rounds. Similarly, I define the lead VC as the investor who
involves in the first round of financing. If there are more than one VCs invested in the
first round, the VC who invested in the maximum number of rounds is the lead VC.
The argument underlying this assumption is the same as in Megginson and Weiss
(1991) and Sorensen (2007) since the investor who initiates a project usually has the
largest amount of money at stake (Dimov and Milanov 2009). It is also the same as
the assumption of Gorman and Sahlman since the earlier the VCs invest in the project
the more time they spend on management and monitor. Thus, lead venture capital firm
is the one who initiate a project after feasibility research, however, due to limited
resources, it always searches for partners purposely to promote the success of the
project.
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Table 1
List of Variables
Variables
Definition
Dependent
Variable
VCs Invited
1 for active VCs whose record of deals>10, 0 for otherwise
Independent
Variables
Experience
Difference of the number of transactions between lead VCs and partners
Scale
LN of capital managed
Geography
5 for richest clusters and 1 for poorest clusters
Previous relationships
The number of previous transactions in which both the leader and the partner engaged
Start-up Stage (dummy)
1 for start-up stage of funded firms, 0 for otherwise
Early Stage (dummy)
1 for early stage of funded firms, 0 for otherwise
Late Stage (dummy)
1 for late stage of funded firms, 0 for otherwise
Chinese-funded VCs (dummy)
1 for Chinese-funded VCs, 0 for otherwise
Foreign-funded VCs (dummy)
1 for foreign-funded and Sino-foreign joint funded VCs, 0 for otherwise
Control Variables
History exit events
Average single investment
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Methodology
Logistics regressions based on binary dependent variable models have been conducted
to test hypotheses. Table 1 is a list of variables.
Dependent Variable
The purpose of this research is to explain what factors affect the likelihood of
collaboration. According to Christian and Christian (2013), although there are no
restrictions on size of the VCs in the sample, I restrict analysis to most active VCs to
avoid autocorrelation problems. Here a cutoff point of at least 10 deals over the time
period of 1989–2014 is chosen. If one of the more active VCs from the list is chosen,
it receives an entry of one; and if VCs out of the list are chosen, they receive entry of
zero. Therefore, the analysis aims at explaining which factors affect the one/zero
variable.
Explanatory Variables:
Excess Experience Hypothesis 1 states that experience of potential partners
positively affects the likelihood of collaboration. According to Christian and Christian
(2013), I calculate the cumulative number of transactions lead VCs and partners
participated in until the end of the year prior to the year in which the deal takes place.
By using this way, the causality problem can be solved because, for example, the total
number of transactions a VC made until the end of 2013 is used to explain its
partnering decision in 2014 and the partnering decision in 2014 cannot influence the
independent variables prior to the year 2014. The argument underlying this
assumption is the same as in Christian and Christian (2013) that the higher the number
of transactions VCs engaged in, the more experienced VCs are. Then, the diffidence
between the number of transactions the lead VCs and potential partners engaged in is
calculated and a negative number would thus indicate that the partner has more
experience than the lead venture capitalist.
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Scale Hypothesis 2 states that lead VC is more likely to cooperate with partners with
larger scale. According to Michal (2009), capital managed whose unit is million
dollars is used to measure the scale. The larger the capital managed, the larger the
scale of potential partners. But the figures are too large to run in the software, ln
(scale) was used in final data.
Geography With respect to hypothesis 3 and the influence of choosing a partner
located in a rich cluster on the likelihood of collaboration, I include a measure
indicating the affluence level of potential partners. Streletzki and Schulte (2013)
define the measurement as a dummy variable and the dummy takes on the value of
one if the potential partner is located in a metropolitan region in which more than one
million people live, and zero otherwise. In China, although many cities have more
than one million populations, there are huge affluence gap between them (The
Chinese Economics Report, 2013). Thus, according to the Chinese situation, I
calculated the average rankings of GDP per capital of all Chinese provinces or
municipalities from 2000-2012 released in The Chinese Economics Website and
divided them into 5 levels, then graded them according to their rankings. The richest
group got a grade of 5; the second richest group got a grade of 4, and then 3, 2, 1
(Mori et al., 2012).
Provinces / Municipalities
Grades
Shanghai, Beijing, Tianjin, Neimenggu, Zhejiang, Jiangsu
5
Fujian, Guangdong, Liaoning, Shandong, Jilin
4
Shanxi, Hubei, Hebei, Heilongjiang, Xinjiang
3
Shanxi, Hunan, Henan, Sichuan, Hainan, Anhui
2
Guangxi, Yunnan, Gansu, Guizhou
1
Previous Relationship It is hypothesized that if there are direct previous relationships
between lead VCs and partners, they are more likely to collaborate with each other in
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the future. To measure this feature, I calculate the number of previous transactions in
which both the leader and the partner engaged until the end of the year prior to the
year in which the deal takes place. For example, if the lead VC invested in a company
in the year 2000, and a partner invested in the same company in 2002, how many
previous collaborations between the lead VC and the partner prior to the year 2002 is
calculated.
Stages In hypothesis 5, I argue that the need for additional partner skills is anticipated
to be greater in later stages of an investment than in earlier stages. According to the
National Venture Capital Association classification, the first stage is the seed/start-up
stage. The second stage is the early stage, the third one is the expansion stage and the
last one is the later stage. Similarly, the CV Source database divides stages into:
start-up, early, expanding, and later stage. However, among 12349 transactions, only
133 of them are in later stages which are so small. Thus, expanding and later stages
are combining into a new category, namely “late stage” since there is no clear
distinction between expansion and later stage and expansion financing always occurs
in the later period (Hellman and Puri, 2002). I split the regressions into separate stages
and compare coefficients across the different models.
Background of VCs Hypothesis 6 indicates that Chinese-funded VCs have different
considerations while choosing partners compared with foreign-funded and
Sino-foreign joint funded VCs. In CV Source database, backgrounds of VCs are
divided into three categories: Chinese-funded, foreign-funded and Sino-foreign joint
funded. I split regressions into two categories: Chinese-funded VCs as well as
foreign-funded and Sino-foreign joint funded VCs, and compare coefficients across
the different models.
Interaction Terms:
Experience * Scale The interaction term is used to measure the interplay between
scale and excess experience of potential partners in hypothesis 1 and 2. It is
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reasonable because VCs with larger scale are more likely to be more experienced
since they have more chances to invest in projects. Similarly, experienced firms are
more likely to develop in scale.
Experience * Geography To proxy for the effect of geography in combination with
excess experience on the likelihood of collaboration, I interact the excess experience
between leaders and partners with location of VCs.
Experience* Previous relationships To measure whether the effect on likelihood of
collaboration of a change in excess experience depends on the value of previous
transactions in which both the leader and the partner engaged, I interact the excess
experience with previous relationships between leaders and partners.
Control variables:
History exit events The main purpose of a venture capital investment is to gain high
returns which matches its high risks. If the business is successful, the turnout and
profit amount of the venture capital investment will be to greater unmatched levels.
On the other hand, if the business is unsuccessful, IPO, turnover and liquidation are
main exit methods available to venture capitalists (Ferrary, 2010). I control the
number of exit events in history which may influence the likelihood of collaboration.
Average single investment I control for the average value of a single investment
since lead VCs may have higher probability to cooperate with partners who have
larger investment value.
Table 2a reports the descriptive statistics and correlation matrix for dependent and
independent variables. The mean value of VCs invited is 0.73 which means that the
majority of partners invited by lead venture capitalists are active. With respect to the
positive mean of the difference experience between lead VCs and partners, generally
lead VCs are more experienced than followers. However, the standard deviation is too
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large which is a proxy of fluctuation. The mean value of ln capital managed is quite
high referring that the partners chosen by leaders have large scale. The measure of
affluence levels of VCs’ location show that lead VCs are more willing to cooperate
with partners located in rich clusters. Among relationships, there are around
cumulative 2 ties established between the leader and the partner until the end of the
year prior to the year in which the deal takes place. The stages of funded companies
are divided into start-up investment with around 17%, the early and late stage with
around 61% and 23%, respectively. When it comes to the background of VCs, about
62% of them are Chinese-funded VCs and the left are either foreign-funded or
Sino-foreign joint funded VCs. However, there are a few multicollineartiy problems
showed by the correlations among the variables. It is noteworthy that the measures of
Chinese-funded VCs and the measures of foreign-funded and Sino-foreign joint
funded VCs are correlated. Also, the early stage of funded firms measures are highly
correlated with the late stage of funded firms measures. According to Christian and
Christian (2013), those problems can be coped with by including them separately into
regressions. The mean variance inflation factor (VIF) estimated in STATA is 1.29
which is much less than 10 and it indicates that the problem of multicollinearity does
not exist.
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Table 2a
Descriptive Statistics and Correlation Matrix
Variable
Mean
SD
1
VCs Invited
0.73
0.44
2
Experience
2.96
67.45
-0.21
3
Scale
3.20
1.80
0.01
-0.11
4
Geography
4.69
0.77
0.16
-0.06
0.01
5
Previous relationships
2.05
8.25
0.11
-0.16
0.06
0.05
6
Start-up stage
0.17
0.37
0.06
-0.08
-0.02
0.01
-0.01
7
Early stage
0.61
0.49
0.01
0.03
-0.01
-0.01
0.04
-0.35
8
Late stage
0.23
0.42
-0.04
0.01
0.03
0.00
-0.04
-0.20
-0.85
9
Chinese-funded VCs
0.62
0.49
-0.38
0.24
-0.01
-0.30
-0.13
-0.07
0.07
-0.03
10
Foreign-funded VCs
0.38
0.49
0.38
-0.24
0.01
0.30
0.13
0.07
-0.07
0.03
-1.00
11
History exit events
10.06
13.78
0.32
-0.47
0.24
0.03
0.49
0.02
0.01
-0.02
-0.34
0.34
12
Average single investment
7.79
23.00
0.06
0.02
0.28
0.07
-0.03
0.00
-0.05
0.05
-0.13
0.13
1
2
3
18
4
5
6
7
8
9
10
11
0.03
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Table 2b
estat vif
Variables
VIF
1/VIF
Experience
1.32
0.760183
Scale
1.18
0.845083
Geography
1.11
0.898150
Previous relationships
1.35
Start-up stage
Early stage
0.743255
1.15
0.872439
1.14
0.873661
Chinese-funded VCs
1.31
0.765372
History exit events
1.91
0.524496
Average single investment
1.13
Mean VIF
1.29
0.887954
Analysis and Results
Table 3 summarizes the results of regressions of the likelihood of collaboration on
various sets of regressors. Each column reports a separate regression and each
regression has the same dependent variable, VCs invited. The entries in the first
twelve rows are the estimated regression coefficients, with their standard errors below
them in parentheses. The asterisks indicate whether the t-statistics, testing the
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hypothesis that the relevant coefficient is zero, is significant at the 5% level (one
asterisk), the 1% level (two asterisks) or the 0.1% level (three asterisks). The final
three rows are summary statistics for the regression including McFadden R-squared
and LR statistics (Log Likelihood ratio) testing the hypothesis that all coefficients are
zero is significant at the 5%, 1% and 0.1% level.
Surprisingly, the results reject hypothesis 1 which means the excess experience of
followers does not play a significant role for leaders while choosing partners.
However, it becomes negatively significant after adding the scale and previous
relationships measures which means lead VCs search for partners with similar
experience and have combining skills. The results support hypothesis 2, that large
scale of following VCs, as measured by capital managed, plays a positive role in
explaining the partner selection of venture capital syndication. The larger the scale of
followers, the higher probability that they are chosen as a partner by lead VCs. Also,
the positive significance of interaction terms shows that the effect on likelihood of
collaboration of a change in excess experience depends on the scale of potential
partners. Turning to hypothesis 3, it can be found that the location measures are
positive and highly significant, but the interaction terms are insignificant. While
choosing partners, lead VCs are more willing to cooperate with VCs located in rich
clusters such as Beijing and Shanghai. Turning to hypothesis 4, it suggests that if
there are direct previous relationships between lead VCs and partners, they are more
likely to collaborate with each other in the future. However, the interaction terms are
not significant. In all cases, history exit event plays a positive role in explaining
dependent variable while the average single investment explains nothing which can be
ignored. The LR statistics in all cases confirm that at least one coefficient is
significant. In sum, the excess experience of partners is negatively significant for
leaders while the scale of potential partners plays a positive role on the likelihood of
collaboration. If there are direct previous relationships between lead VCs and partners
as well as the partner is located in a rich cluster, they are more likely to collaborate
with each other in the future.
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Table 3a
Results of Logistic Regressions Using Robust Standard Errors
Variables
H1
H2
H3
H4
ALL
Experience
-0.000594
-0.00664***
-0.011023
-0.00205*
-0.008379**
(0.000876)
(0.001902)
(0.008034)
(0.001047)
(0.007761)
Scale
Experience*Scale
0.006645***
0.134637***
(0.001902)
(0.029301)
0.00144*
0.001478*
(0.000662)
(0.000693)
Geography
0.3361***
0.325893***
(0.068205)
(0.090577)
0.002154
0.000293
(0.001651)
(0.001629)
Experience*Geography
Previous relationships
Experience*Previous relationships
Start-up stage
0.583223***
0.609604***
(0.08092)
(0.099331)
0.000922
0.000234
(0.00116)
(0.0015)
0.839548***
0.895414***
0.823955***
0.9171***
1.027782***
(0.176792)
(0.262172)
(0.177486)
(0.178222)
(0.26617)
0.477787***
0.468922***
0.479599***
0.460026***
0.453941***
(0.10193)
(0.12189)
(0.102757)
(0.103874)
(0.124554)
-1.294698***
-1.34417***
(0.098614)
(0.120145)
(0.10094)
(0.100289)
(0.127427)
0.281725***
0.212118***
0.28587***
0.262254***
0.198424***
(0.015874)
(0.015659)
(0.016119)
(0.015975)
(0.01583)
0.005213
0.004202
0.005197
0.007072
0.005618
(0.00348)
(0.003525)
(0.00334)
(0.003921)
(0.003803)
McFadden R-squared
0.343306
0.313848
0.351196
0.367354
0.343855
LR statistic
1516.395
888.3749
1551.245
1622.618
1622.618
Prob(LR statistic)
0
0
0
0
0
Early stage
Chinese-funded VCs
History exit events
Average single investment
-1.179977*** -1.212275*** -1.130394***
Notes: n=3954. p < .1; * p < .05; ** p < .01; *** p < .001
To test hypothesis 5, regressions are split into different stages to see whether the
impact of independent variables differ across stages and the results are showed in
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Table 3b. Generally, the results indicate that lead VCs have different considerations
while choosing partners in different stages. The excess experience of potential
followers is not statistically significant in the start-up stage regressions, but it
becomes negatively significant in the early stage regressions, which means lead VCs
have higher probability to cooperate with partners who have similar experience level
when leaders are investing early-stage firms. So, in early stage, excess experience of
partners is a factor that can influence the selection decision of lead VCs, however,
leaders will not consider excess experience of partners while investing in late-stage
firms. According to Christian and Christian (2013), the results favor risk-spreading
arguments where lead VCs aim at spreading risks instead of requiring partners to
provide value-added services while syndicating in start-up stage, but they may search
for more important skills of partners than excess experience when syndicating in late
stages. The scale of potential partners plays a positive role in the likelihood of
collaboration in all stage regressions, but it becomes more significant in the early and
late stage regressions. The interaction terms are insignificant. Thus, leading VCs are
more likely to cooperate with potential partners with larger scale and consider this
skill more significantly when investing in early and late stage firms. Turning to
location, it plays a positive role in influencing the likelihood of collaboration in
start-up and early stage regressions, but the characteristic is less sought after in early
stage and it becomes insignificant in late stage. Also, none of the interaction terms are
significant. When syndicating in start-up stage, leader VCs search for partners located
in rich clusters but it becomes less important after early stage. For all regressions,
previous relationships are highly significant which means lead VCs pay much
attention to ties or network while syndicating. However, none of the interaction terms
are significant. For control variables, only history exit event is statistically significant
and plays a positive role while average single investment is statistically insignificant
in most cases. Finally, LR statistics in all cases confirm that at least one regressor is
nonzero and significant in each regression. In sum, while syndicating in start-up
stages, lead VCs aim at spreading risks so they search for partners with large scale
and located in rich clusters. After early stages, lead VCs put a stronger emphasis on
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Preliminary Draft for 2015 Global Business and Social Science Research Conference
combining skills and value-added services provided by partners.
To test hypothesis 6, samples are split into partners chosen by Chinese-funded leaders
and partners chosen by foreign-funded and Sino-foreign joint funded leaders to see
whether lead VCs with different backgrounds have different tendency for selecting
partners. The results are reported in Table 3c. Generally, these two kinds of lead VCs
have marginally different tendency while choosing partners. The coefficients of
excess experience are statistically insignificant in Chinese-funded VCs regressions
while they become marginally significant in other VCs regressions. Only
foreign-funded and Sino-foreign joint funded leaders consider excess experience of
partners while syndicating. The scale of potential partners measured by capital
managed has positive effect on the likelihood of collaboration and is statistically
significant for foreign-funded and Sino-foreign joint funded leaders. In other words,
they are more likely to cooperate with potential partners with larger scale. However,
scale has no effect on the selection decision of Chinese-funded leaders. Additionally,
the interaction terms are insignificant. Notably, both locations of partners and
previous relationships with partners are highly significant for these two kinds of
leaders. However, none of the interaction terms are significant. For control variables,
both history exit event and average single investment are statistically significant and
plays a positive role for Chinese-funded lead VCs. For foreign-funded and
Sino-foreign joint funded leaders, only the history exit event measure is positively
significant. Finally, LR statistics in all cases confirm that at least one regressor is
nonzero and significant in each regression. In sum, these two kinds of lead VCs have
different considerations in syndication that only foreign-funded and Sino-foreign joint
funded leaders treat experience and scale of partners as important features.
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Table 3b
Regressions Split Into Stages
Variables
Experience
(1)
Star-up
0.000558
(0.001021)
(2)
Early
-0.004292*
(0.001847)
(3)
Late
-0.004078
(0.003494)
(4)
Star-up
-0.00535
(0.002255)
0.083973*
(0.036554)
0.00111
(0.000815)
(5)
Early
-0.005109*
(0.006561)
0.401829***
(0.069364)
0.000481
(0.001835)
(6)
Late
-0.0108
(0.007301)
0.219377***
(0.073884)
0.001849
(0.002473)
0.480899***
(0.031041)
0.022824***
(0.008483)
0.295181
730.6503
0
1817
0.136099***
(0.02119)
0.000898
(0.004217)
0.142806
106.8351
0
1352
0.191552***
(0.026382)
0.003145
(0.004147)
0.210301
125.9253
0
1785
0.482755***
(0.03927)
0.012968*
(0.012968)
0.324295
498.2783
0
1817
0.071258***
(0.016015)
0.00913
(0.007046)
0.138391
67.37766
0
1352
0.142259***
(0.023748)
0.005823
(0.004626)
0.180826
83.12721
0
1785
Scale
Experience*Scale
Geography
Experience*Geography
Previous relationships
Experience*Previous relationships
History exit events
Average single investment
McFadden R-squared
LR statistic
Prob(LR statistic)
Observations
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Preliminary Draft for 2015 Global Business and Social Science Research Conference
Table 3b
Regressions Split Into Stages
Variables
Experience
(7)
Star-up
-0.014828
(0.009228)
(8)
Early
-0.009606*
(0.063606)
(9)
Late
-0.00304
(0.0674)
0.34826***
(0.079831)
0.003218
(0.001901)
1.382349*
(0.450683)
0.00103
(0.012765)
1.392006
(0.768916)
-0.000123
(0.013615)
(10)
Star-up
-0.001431
(0.001264)
(11)
Early
-0.004836*
(0.002068)
(12)
Late
-0.003003
(0.004309)
0.493106***
(0.102813)
0.000985
(0.00126)
0.469318***
(0.031537)
0.025033***
(0.00855)
0.315576
781.1337
0
1817
1.095548***
(0.268021)
0.001248
(0.004098)
0.116828***
(0.020313)
0.002279
(0.004555)
0.20406
152.6595
0
1352
0.564492***
(0.162657)
0.000495
(0.005086)
0.175201***
(0.02649)
0.004475
(0.004474)
0.241668
144.7076
0
1785
Scale
Experience*Scale
Geography
Experience*Geography
Previous relationships
Experience*Previous relationships
History exit events
Average single investment
McFadden R-squared
LR statistic
Prob(LR statistic)
Observations
0.493354***
(0.031633)
0.020564*
(0.008523)
0.308377
763.315
0
1817
0.139542***
(0.020708)
0.001338
(0.004593)
0.172829
129.2958
0
1352
Notes: p < .1; * p < .05; ** p < .01; *** p < .001
25
0.197975***
(0.026154)
0.002664
(0.004006)
0.225752
135.1771
0
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Preliminary Draft for 2015 Global Business and Social Science Research Conference
Table 3c
Regressions Split Into Partners Chosen by State- owned Leaders and Non-state-owned Leaders
Variables
Experience
(1)
Chinese-funded
0.001028
(0.001028)
(2)
Others
-0.003803*
(0.001585)
Scale
Experience*Scale
Geography
Experience*Geography
(3)
(4)
(5)
Chinese-funded
Others
Chinese-funded
-0.004536
-0.006877*
-0.015253
(0.002265)
(0.004509)
(0.009132)
0.074189
0.290919***
(0.038155)
(0.047634)
0.001048
0.000941
(0.000816)
(0.001401)
0.293853***
(0.079422)
0.003406
(0.001883)
(6)
Others
-0.006945
(0.047033)
1.42845***
(0.424029)
0.000632
(0.009448)
Previous relationships
Experience*Previous relationships
History exit events
Average single investment
McFadden R-squared
LR statistic
Prob(LR statistic)
Observations
0.461261***
(0.031859)
0.023415***
(0.008615)
0.266969
592.1023
0
1604
0.165646***
(0.01656)
0.001315
(0.002713)
0.168725
247.2867
0
2351
0.472254***
(0.040523)
0.031096***
(0.013602)
0.30917
422.9634
0
1604
26
0.103193***
(0.013741)
0.005696
(0.003365)
0.141403
146.4523
0
2351
0.475197***
(0.032523)
0.021415*
(0.008661)
0.27878
618.296
0
1604
(7)
(8)
Chinese-funded
Others
-0.000938
-0.004475*
(0.001274)
(0.001818)
0.170219***
(0.016290)
0.001252
(0.002760)
0.190524
279.2366
0
2351
0.512917***
(0.106416)
0.000878
0.001264
0.447543***
(0.032417)
0.025844***
(0.008689)
0.289514
642.104
0
1604
0.682302***
(0.129758)
0.002228
(0.002759)
0.147198***
(0.016275)
0.00245
(0.002868)
0.207309
303.8363
0
2351
Preliminary Draft for 2015 Global Business and Social Science Research Conference
Conclusion
In venture capital syndication, venture capitalists are dependent on each other, so it is
important to choose the most appropriate partners and the partnering decision lies at
the center of understanding why syndication can reduce risks of VCs and add value to
the funded firms. However, a major problem connected to a lead VC’s decision of
whom to invite to join the syndication is information asymmetry. It is necessary to
analyze which kinds of potential partners are chosen by lead VCs and what factors
influence the likelihood of collaboration to reduce information asymmetry. This paper
studies the partner selection decision in venture capital syndication using the sample
complied by CV Source Database consists of 12349 venture capital transactions that
are made over different stages including start-up, early stage, and late stage in
mainland China during the period of 1989-2014.
The analysis highlights the importance of the excess experience of potential partners
which has a negative impact on the likelihood of collaboration after adding the scale
and previous relationships measures, and excess experience alone is not significant.
While scale of partners plays a positive role. The paper also mentions if there are
direct previous relationships between lead VCs and partners as well as the partner is
located in a rich cluster, they are more likely to collaborate with each other in the
future. Notably, combining skills of partners—similar experience level, large scale,
located in a rich cluster and have previous relationships strongly increases the
likelihood of collaboration. By splitting the sample into different stages, it is found
that lead VCs have different considerations when syndicating in different stages.
While syndicating in early stages, lead VCs search for partners with similar
experience level, however, leaders will not consider excess experience of partners
while investing in late-stage firms. Moreover, leading VCs are more likely to
cooperate with potential partners with larger scale and consider this skill more
significantly when investing in early and late stage firms. Turning to location, it plays
a positive role in influencing the likelihood of collaboration in start-up and early stage
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Preliminary Draft for 2015 Global Business and Social Science Research Conference
regressions, but the characteristic is less sought after in early stage and it becomes
insignificant in late stage. Notably, while syndicating in all stages, lead VCs pay much
attention to ties or network.
The Chinese venture capital market is very special because the earliest venture capital
firms were established by the Chinese government and its growths and recessions are
related to government policies. Samples are split into partners chosen by
Chinese-funded leaders and partners chosen by foreign-funded and Sino-foreign joint
funded leaders, and it is found that both Chinese-funded leaders and foreign-funded
and Sino-foreign joint funded leaders have higher probability to cooperate with
partners located in rich clusters and have previous relationships, but only
foreign-funded and Sino-foreign joint funded leaders treat experience and scale of
partners as important features.
In summary, venture capital syndication is a way to share resources, improve
value-added ability and reduce risks, and it is becoming a popular cooperation
tendency in the venture capital market. Partner selection is an important topic in
venture capital syndication especially in the circumstance of information asymmetry
where both trading parties in the market have varying degrees of knowledge about the
transaction, showing an irregular and asymmetrical distribution of information.
Successful partner selection can improve the performance of investment while failed
partnering decisions may bring fatal cost to not only VCs but also funded firms. It is
recommended that while syndicating in start-up stages, lead VCs should search for
partners with large scale and located in rich clusters to spread risks. After early stages,
lead VCs should put an emphasis on combining skills and value-added services
provided by partners. For Chinese-funded VCs, they should pay more attention to the
location of partners and previous relationships with them, while foreign-funded and
Sino-foreign joint funded leaders should also concern for the excess experience and
scale of partners.
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This study is with limitations and provides room for future research. Firstly, while
measuring excess experience, industry is not considered. It is possible that different
levels of excess experience are required in different industries. Future study can
measure the excess experience of partners within the given transaction-relevant
industry. Secondly, this paper does not consider trust as a variable which is a very
passive measurement. Trust between players might have strong effect on probability
of collaboration. Although the previous relationships measure network and trust to
some extent, future study can research on this topic in more detailed. Moreover, future
research can study the consequences of successful partner selection or cost of
improper partner selection. By analyzing these two aspects, one can determine which
factor brings value creation in VC syndication. Finally, this paper does not consider
time-series effect and it is possible that, for example, the partnering decisions in VC
series B can influence the decisions in series C and D. This topic is quite complicated
and machine learning methods can be used. Currently, I am still working on the
partner selection of venture capital syndication using support vector machine (SVM)
which is a very useful tool for data classification and is considered easier to be used
than Neutral Network. The time series effect will be considered by using RBF kernel
function. The purpose of that paper is to find what features influence the likelihood of
collaboration and to build a web system for finding new business partners in reality
with the help of artificial intelligence (AI) systems.
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Preliminary Draft for 2015 Global Business and Social Science Research Conference
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