Eects of syndication network on specialisation and performance of venture capital rms

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Eﬀects of syndication network on specialisation and

performance of venture capital ﬁrms

Qing Yao1,4, Shaodong Ma2, Jing Liang2, Kim Christensen4,

Wanru Jing3, Ruiqi Li2,∗

1School of Systems Science, Beijing Normal University, Beijing 100875, China

2UrbanNet Lab, College of Information Science and Technology, Beijing University

of Chemical Technology, Beijing 100029, China

3E-link Ventures, Shanghai 200050, China

4Blackett Laboratory & Centre for Complexity Science, Imperial College London,

London SW7 2AZ, UK

∗Author to whom any correspondence should be addressed.

E-mail: lir@buct.edu.cn

Abstract The Chinese venture capital (VC) market is a young and rapidly expanding

ﬁnancial subsector. Gaining a deeper understanding of the investment behaviours of

VC ﬁrms is crucial for the development of a more sustainable and healthier market and

economy. Contrasting evidence supports that either specialisation or diversiﬁcation

helps to achieve a better investment performance. However, the impact of the

syndication network is overlooked. Syndication network has a great inﬂuence on the

propagation of information and trust. By exploiting an authoritative VC dataset

of thirty-ﬁve-year investment information in China, we construct a joint-investment

network of VC ﬁrms and analyse the eﬀects of syndication and diversiﬁcation on

specialisation and investment performance. There is a clear correlation between the

syndication network degree and specialisation level of VC ﬁrms, which implies that the

well-connected VC ﬁrms are diversiﬁed. More connections generally bring about more

information or other resources, and VC ﬁrms are more likely to enter a new stage or

industry with some new co-investing VC ﬁrms when compared to a randomised null

model. Moreover, autocorrelation analysis of both specialisation and success rate on

the syndication network indicates that clustering of similar VC ﬁrms is roughly limited

to the secondary neighbourhood. When analysing local clustering patterns, we discover

that, contrary to popular beliefs, there is no apparent successful club of investors. In

contrast, investors with low success rates are more likely to cluster. Our discoveries

enrich the understanding of VC investment behaviours and can assist policymakers in

designing better strategies to promote the development of the VC industry.

1. Introduction

Venture capital (VC) has been an essential source of ﬁnancing for start-up companies

deemed to have a high growth potential with innovative technologies or business

models [1–3]. In the past a few decades, we have witnessed an enormous increase

arXiv:2211.00873v1 [physics.soc-ph] 2 Nov 2022

Eﬀects of syndication network on specialisation and performance of venture capital ﬁrms2

in VC investment activities and start-ups supported by VC, including Tesla, Amazon,

Apple, Facebook, Google, Alibaba, and Whole Foods, which have signiﬁcantly impacted

domestic and global economies. Gaining a deeper understanding of investment

behaviours of VC ﬁrms are crucial for the more sustainable and healthier development

of the industry and economy [4, 5].

On the one hand, given the ultimately limited knowledge of industries or investment

stages and resources of individual entities, being specialised is a natural choice to

obtain a good ﬁnancial performance for VC ﬁrms [6]. Previous evidence indicates

that specialised VC ﬁrms tend to outperform generalised ones on the quality of capital

allocation across industries, as specialists may be more responsive to signals of speciﬁc

industries [6]. Specialists can diversify their portfolios to become generalist. However,

generalised VC ﬁrms may encounter diﬃculties in redeploying capital into industries

with better opportunities [7–9].

On the other hand, previous literature also suggests that the role of networking

(which can lead to diversiﬁcation) is crucial for the investment activities and

performance of VC ﬁrms. Gompers et al. [10] surveyed hundreds of VC ﬁrms on ‘How do

venture capitalists make decisions?’ and attributed the success more to the management

team than to business-related characteristics (e.g., product or technology). However,

similar case studies are limited to a local view focusing on individual VC ﬁrms. It does

not have a global perspective that appreciates the importance of interactions between

entities, e.g., either between individuals in management teams in diﬀerent companies or

collaborations between VC ﬁrms [11–13]. Joint-investment relation between VC ﬁrms

(i.e., investing in the same start-up company at the same time) form complex collective

interaction patterns that inﬂuence the ﬂow of information and trust [14,15]. Hochberg

et al. [11] examined U.S.-based VC funds and found that better-networked VC ﬁrms

experience signiﬁcantly better fund performance, as indicated by a higher fraction of

investments that are successfully exited through an initial public oﬀering (IPO) or

mergers and acquisitions (M&A) [11], and concluded ‘whom you know matters’. A

better networking with other VC ﬁrms can generally bring insightful information [14],

more contacts, more deal ﬂows, and more resources [16–18]. That means a better

networked VC can beneﬁt from mutual complementarity [19] and diversity [20, 21],

which help the ﬁrm invest against uncertainties of market and policy, free ridings and

opportunism behaviours [11,21–26]. Additionally, a better networking will build a VC

ﬁrm reputations [27–29]. This suggests that networking positively aﬀects the investment

performance, as VC ﬁrms can beneﬁt from mutual knowledge and skill complementing

from collaborators [19] and diversity of investments [20,21].

There seems to be a fundamental contradiction between specialisation and

generalisation in previous literature, thus, a comprehensive understanding of the relation

between network eﬀect on specialisation/generalisation (diversiﬁcation) and investment

performance needs to be developed. In this work, we exploit an authoritative dataset

that records over 160,000 detailed investment activities involving more than 31,000 VC

ﬁrms and 50,000 start-up companies in the Chinese VC market since 1985. We ﬁrst

Eﬀects of syndication network on specialisation and performance of venture capital ﬁrms3

construct the syndication network of VC ﬁrms, where two VC ﬁrms are connected if they

have made joint-investment in the same start-up company in one deal, i.e., at the same

time. We reveal that better networked VC ﬁrms tend to have a more diversiﬁed spectrum

of investments in terms of both investment stages and industries. In this project, we treat

the co-investing VC ﬁrms as ‘friends’ of each other, because, they have common interests

and are connected through the same start-up companies. In addition, when entering a

new industry, VC ﬁrms are more likely to collaborate with new friends, which might

indicate that new friends bring about new knowledge, information, or other resources.

When we analyse the correlation patterns of VC ﬁrms in the syndication network, we

ﬁnd a strong clustering eﬀect that VC ﬁrms with similar specialisation levels tend to

neighbour each other. Such clustering pattern is generally limited to the second order

of neighbourhood. In addition, generalists are typically of a stronger local clustering,

and the local clustering eﬀect is weakening along with the increase of specialisation

level. While for the investment performance, there is no obvious successful club where

successful VC ﬁrms cluster together, but poor performance VC ﬁrms are surrounded by

similar entities, that is, VC ﬁrms which perform poorly cluster together.

2. Methods

2.1. Herﬁndahl Index for evaluating the degree of specialisation

The Herﬁndahl Index, also known as Herﬁndahl–Hirschman Index (HHI), was ﬁrst

proposed to measure the competition between companies based on the market share

of each ﬁrm [30]. With a similar formalisation, HHI later got adopted to evaluate the

degree of specialisation of VC ﬁrms, which is the sum of the squares of the percentage

of all previous investments of a VC ﬁrm in each industry [6] or in each investment

stage. For example, if a VC ﬁrm i(VCi) invests in αindustry with a fraction

pα

i=nα

i/Pmi

α=1 nα

i, α = 1,2, . . . , mi, where nα

iis the number of investments the VCi

makes in αindustry, miis the total number of industries VCientered (which can be

diﬀerent across VC ﬁrms), and Pαnα

i, is the total number of investments the VCihas

made. The counting of investment numbers is illustrated in Fig 1(a). Then its HHI of

industry, Hind

i, is

Hind

α=1

(pα

i)2.(1)

The most specialised VC ﬁrms (mi= 1) have Hind

i= 1, and the most diversiﬁed or

generalized ones have Hind

i= 1/mi, which corresponds to the situation with equal

number of investments nα

ifor each industry α= 1,2, . . . , mi. The formalisation for the

HHI of investment stage Hstage

iis the same as Eq.(1) with pα

ithe fraction of investments

in a certain stage α.

Eﬀects of syndication network on specialisation and performance of venture capital ﬁrms4

2.2. Moran’s Index in networks

The Moran’s Index (Moran’s I) is widely used to measure the spatial autocorrelation of

concerned variables [31–33], and here we adopt it to measure autocorrelation between

the variables of VC ﬁrms in the syndication network. The Moran’s I for a variable with

Nobservations xiis deﬁned as:

i=1

j=1

wij (xi−¯x)(xj−¯x)

|W|σ2

,(2)

where Nis the number of VC ﬁrms indexed by i;xiis the variable of interest of node i

(e.g., it can be HHI of stage or industry, or success rate of a VC ﬁrm); ¯x=1

NPN

i=1 xi

is the mean of x;σ2

i=1

(x−xi)2/N is the variance of that variable. wij is the spatial

weight between nodes iand jand wii = 0. The sum is |W|=PN

i=1 PN

j=1 wij . We can

deﬁne a spatial weight matrix Wwith entries wij . Literature, especially in geosciences,

use this measurement to identify the geographic diﬀerence in or characterise the regional

variations of the variables of interest. It is a measurement that can combine relational

information (i.e., how close the two entities are) and non-relational information (i.e.,

any individual performance of an entity/VC ﬁrm).

A network’s topological measurements are similar to the geometrical measurements,

i.e., the shortest path length between two nodes characterises how ‘close’ they are.

Therefore, we can deﬁne Moran’s I using network measurements to evaluate the

topological position’s impact on the non-network variables of interest, taking the

performance of VC ﬁrms, for example.

The local Moran’s I of a node ion networks in this paper is deﬁned as:

Ilocal

i=N

|W|

xi−¯x

σ2

j=1

wij (xj−¯x),(3a)

I=1

i=1

Ilocal

i.(3b)

To make further investigation on the eﬀects of topological distance on the

autocorrelation of variables, we deﬁne four diﬀerent weight matrices Wbased on

diﬀerent levels of neighbourhood: considering the nearest neighbours (i.e., ﬁrst-order

neighbourhood), further integrate the second-order, third-order, and all reachable

neighbours, respectively. The detailed deﬁnitions are:

W(1)

ij =A(1)

ij ,(4a)

W(2)

ij =A(1)

ij +1

2A(2)

ij ,(4b)

W(3)

ij =A(1)

ij +1

2A(2)

ij +1

3A(3)

ij ,(4c)

W(L)

ij =A(1)

ij +1

2A(2)

ij +1

3A(3)

ij +1

4A(4)

ij +· · · +1

LA(L)

ij ,(4d)

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摘要：

EectsofsyndicationnetworkonspecialisationandperformanceofventurecapitalrmsQingYao1;4,ShaodongMa2,JingLiang2,KimChristensen4,WanruJing3,RuiqiLi2;1SchoolofSystemsScience,BeijingNormalUniversity,Beijing100875,China2UrbanNetLab,CollegeofInformationScienceandTechnology,BeijingUniversityofChemicalTechn...

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Eects of syndication network on specialisation and performance of venture capital rms

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