Angels in the Crowd: Evidence from Online Equity Crowdfunding

Wanxin Wang and Jingyu Zhang

Imperial College London

Key Words: Online Equity Crowdfunding, Angel Investors, Entrepreneurial Financing

Executive Summary

Traditional business angels are a group of mysterious, high-networth, elite investors in private

equity markets. With rapid advances in digital technology and innovative financial services,

online fundraising platforms have been fast developing into a disruptive alternative to

traditional angel investing and entrepreneurial financing. Though there has been a growing

literature about rewards-based or microloan-based (“P2P”) crowdfunding, there is still little

work about equity crowdfunding due to data availability issues, especially for the US online

equity crowdfunding markets with data starting only from 2016Q3. In consequence, finance

academics, practitioners and regulatory authorities know very little regarding the investment

behaviour of angel investors via online equity crowdfunding.

This paper presents first-hand empirical evidence documenting the coinvestment behaviour

within angels’ online equity crowdfunding communities. Our proprietary data from a leading

equity crowdfunding platform in the UK allow us to comprehensively investigate how angel

leader-follower pairs make pledging decisions during the 2012Q3-2017Q3 period.

Here is a list of our major empirical findings that provide new insights and broaden our

knowledge about the new angel investing communities via online fundraising platforms:

Angel followers with better coinvestment experiences with an angel leader tend to

pledge sooner in the next campaign she leads.

Pledges of angel followers are positively responsive to the angel leader’s pledge.

Pledges of angel followers with better coinvestment experiences with the angel leader

are less sensitive to the angel leader’s pledge.

Angel followers are more likely to pledge to industries they never have if receiving

high-quality private information from the angel leader.

Angel leaders tend to pledge more money if their previous campaigns ended up with

unsuccessful fundraising outcomes.

Angel pairs take turns to act as the angel leader and the strength of this “role-switching”

motive depends on their coinvestment experiences and information sharing activities.

Overall, these empirical findings jointly suggest that angel leader-follower pairs form mutual

trust via their previous coinvestment, incorporate private information and previous fundraising

outcomes into their pledging decisions, and maintain their positions in angel investing

communities via sharing private information of investment opportunities.

Angels in the Crowd: Evidence from Online Equity Crowdfunding

Wanxin Wang and Jingyu Zhang1

Imperial College London

[Preliminary draft: November 2018]

Abstract

Online fundraising platforms have been developing rapidly as a disruptive alternative to

traditional angel investing and entrepreneurial financing. This paper presents first-hand

empirical evidence documenting angel investors’ coinvestment behaviour via online equity

crowdfunding. We comprehensively investigate how angel leader-follower pairs make

pledging decisions for campaigns launched at a leading equity fundraising platform in the UK.

We find that angel followers with better coinvestment experiences with an angel leader tend to

pledge sooner in the next campaign she leads. Besides confirming that pledges of angel

followers are positively responsive to the angel leader’s pledge, we further show that pledges

of angel followers with better coinvestment experiences with the angel leader are less sensitive

to the angel leader’s pledge. Further empirical evidence shows that, if receiving high-quality

private information from the angel leader, angel followers are more likely to pledge to

industries they never have. Additionally, our empirical evidence confirms that angel leaders

tend to pledge more money if their previous campaigns ended up with unsuccessful fundraising

outcomes. Finally, we find empirical evidence indicating that angel pairs take turns to act as

the angel leader and the strength of this “role-switching” motive depends on their coinvestment

experiences and information sharing activities. Overall, these empirical findings jointly suggest

that angel pairs form mutual trust via their previous coinvestment, incorporate private

information and previous fundraising outcomes into their pledging decisions, and maintain

their positions in angel investing communities via sharing private information of investment

opportunities.

Key Words: Online Equity Crowdfunding, Angel Investor, Entrepreneurial Financing

JEL Codes: G11, G24

1 Wang and Zhang are both with Imperial College Business School. We are grateful to the CEO of this leading

equity crowdfunding platform in the UK for generously sharing comprehensive campaign-related information.

We are not aware of any conflicts of interest with this platform, and this paper only represents our own views for

academic research. The findings in this paper do not represent the views or advice from this equity crowdfunding

platform. All errors are ours. Please send all correspondence to Jingyu Zhang, email: j.zhang14@imperial.ac.uk,

Doctoral Program Office, Imperial College Business School, South Kensington Campus, London, UK, SW7 2AZ.

Financial support from Imperial College London is highly acknowledged.

1

1. Introduction

Online crowdfunding platforms provide investors with easy access to early-stage investment

opportunities due to their features of low transaction fees and low information costs (Agrawal,

Catalini and Goldfarb (2014)). Traditional business angels hold regular meetings, listen to

entrepreneurs’ pitches, exchange ideas over those meetings, and make their investment

decisions.2 Unlike traditional angel investing, online crowdfunding was considered to largely

rely on the “wisdom of the crowd” rather than the expertise of traditional business angels.

Actually nowadays, online crowdfunding platforms not only attract retail investors but have

been developing very fast as a disruptive alternative for sophisticated angel investors to access

early-stage investable campaigns.

However, there is little work in the literature that directly documents angel investing behaviour

via online equity crowdfunding3; our paper aims to fill in this gap in the literature and provides

first-hand empirical evidence regarding how angel investors coinvest with their angel peers in

equity fundraising campaigns that are open to all investors (“the crowd”). In this paper, we

focus on online money-pledging angel investors with high net worth, professional expertise of

investing in certain industries, and their own circles of exchanging private information about

investable campaigns. We directly explore how they exchange information with each other and

make their coinvestment decisions for online equity fundraising campaigns. The unique

features of our data covering the 2012Q3-2017Q3 period from a leading UK equity

crowdfunding platform enable us to identify the angel leader of a campaign and her angel

2 Naturally, most associations of traditional business angels provide exclusive access to early-stage investable

campaigns only for their members. Kerr, Lerner and Schoar (2014) provide comprehensively detailed information

regarding the investment procedure for two traditional angel groups in the US. 3 We follow Freedman and Nutting (2015) and do not count “Regulation D offering platforms” as equity

crowdfunding portals since these platforms are only open to “accredited investors” and not open to the crowd.

2

followers. In particular, we present empirical evidence at the level of angel leader-follower

pairs and investigate the underlying forces that drive their coinvestment behavior.

We conjecture that angel pairs can form mutual trust through their previous coinvestment

experiences. We also argue that angel followers can obtain high-quality private information

regarding the prospect of a campaign if invited by the angel leader to have face-to-face

conversations with the campaign’s founders and co-founders. Two simplifying assumptions

are made to better understand our empirical setup. First, the angel leader of a campaign is

assumed to be perfectly informed about the campaign’s prospect. That is, there is assumed to

be no information asymmetry between the angel leader and the campaign’s founders and co-

founders. Second, the angel leader invites her angel followers to have face-to-face talks with

the entrepreneurs and acquire better-quality private information than could be otherwise

obtained via angel followers’ own social network, due diligence and research. These two

assumptions enable us to focus on the information asymmetry between the angel leader of a

campaign and her angel followers.

We find that angel followers with better coinvestment experiences with the angel leader tend

to invest sooner in the next campaign she leads. The number of successful campaigns

coinvested with the angel leader serves as the proxy for an angel pair’s previous coinvestment

experience. This effect remains if we alternatively focus on the most recent coinvested

campaign. We then investigate how angel followers react to the angel leader’s investment. Our

empirical evidence confirms that pledges of angel followers are positively related to the angel

leader’s pledge. This is consistent with a framework where angel followers interpret how much

the angel leader pledges to a campaign as a signal regarding the campaign’s future prospect.

Further empirical evidence indicates that angel followers respond differentially to the angel

leader’s pledge, depending on their own coinvestment experience with the angel leader. We

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find that pledges of angel followers with better coinvestment experiences with the angel leader

are less sensitive to the angel leader’s pledge. This evidence confirms our hypothesis that angel

followers are better able to interpret the angel leader’s pledge and evaluate a campaign’s

prospect if they have formed deeper mutual trust with the angel leader.

Next, we explore angel followers’ investment decisions in a trade-off framework between their

professional expertise and portfolio diversification motives. Angel investors usually invest in

a small number of industries where they have social networks or previous professional

experience, while portfolio diversification motives encourage them to pledge to a new industry

they have never invested in. Our empirical evidence shows that angel followers are more likely

to invest in a new industry if receiving high-quality private information from the angel leader.

That is, portfolio diversification motives are more likely to dominate professional expertise

considerations when angel followers are invited to have personal conversions with the

entrepreneurs.

Besides, we look into angel leaders’ investment behavior and investigate the effects of past

fundraising outcomes on future pledging decisions. We use the cumulative number of

successful campaigns led by an angel leader as a proxy for her past fundraising outcomes. Our

empirical evidence shows that an angel leader tends to pledge more money to her current

campaign if she has led fewer successful campaigns. This effect remains if we further control

for the fundraising outcome of her most recent campaign. These results are consistent with an

angel leader using her pledge to a campaign as a signal to her angel followers regarding the

campaign’s true quality. Our evidence supports a signaling story where an angel leader chooses

to pledge more money in her next campaign if she has relatively unsuccessful records in the

past or an unsuccessful outcome for her most recent campaign.

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Finally, we find empirical evidence consistent with angel investors taking turns to share their

own private knowledge of investable campaigns to maintain their positions within angel

investing communities. Specifically, our evidence indicates that an angel follower, having

received private information in the previous campaign coinvested with the angel leader, is more

likely to take the leader role in their next coinvested campaign. Moreover, we find that this

role-switching pattern actually works through the channel of previous successful coinvestment

experience within an angel pair. That is, this role-switching motive within an angel pair is

stronger as they have experienced a larger number of successful coinvested campaigns.

To our best knowledge, this paper is the first to provide direct empirical evidence on angel

investors’ coinvestment behavior via online equity crowdfunding platforms. 4 Anecdotal

evidence suggests that private face-to-face conversations with the entrepreneurs are fairly

important sources to acquire valuable information about investable campaigns within angel

investing communities (Freedman and Nutting (2015)). Our empirical evidence regarding the

investment behaviour of angel investors at a leading UK equity fundraising platform confirms

the importance of acquiring valuable information through such channels. Our findings about

the interactions within an angel pair also lay out the empirical foundations for future theoretical

research to model the strategic motives of such interactions in a unified framework.

Importantly, our empirical findings regarding equity crowdfunding campaigns in the UK also

contribute to the growing literature of crowdfunding in general, but online equity crowdfunding

in particular. We have obtained comprehensive campaign-related information from a leading

UK equity crowdfunding platform covering the 2012Q3-2017Q3 period. This relatively long

sample period enables us to explore how angel investors of online equity crowdfunding interact

4 Equity crowdfunding is crucially different from microloan crowdfunding. There is a growing literature of peer-

to-peer lending: Zhang and Liu (2012), Lin, Prabhala and Viswanathan (2013), Iyer et al. (2016), Wei and Lin

(2017), and many others.

5

with each other and provides empirical references for the regulatory authorities in other

jurisdictions to enact, amend or update their supervisory approaches.5

The rest of this paper will proceed as follows. Section 2 introduces the institutional background

about angel investing and online equity crowdfunding platforms. Section 3 reviews related

literature and develops hypotheses for empirical tests. Section 4 describes our datasets and

present summary statistics. Section 5 provides empirical evidence and the associated

interpretations, and Section 6 concludes.

2. Background Information

2.1 Equity Crowdfunding

Equity crowdfunding (ECF) refers to the online platforms where individuals pledge money to

early stage ventures and obtain ownership stake in return for their pledged money (Vulkan et

al. (2016) and Vismara (2016)). This newly created means of private equity financing has been

described as the “most disruptive” among all FinTech (Terry et al. (2013)). Equity

crowdfunding has been moving “mainstream” rapidly and becoming a global phenomenon,

especially after a series of regulatory changes (i.e., Jumpstart Our Business Startups Act) took

place in the US (Zhang et al. (2015)). So far, the equity-crowdfunding sector has been steadily

growing and well developed in Europe, with UK and Germany taking the lead. It has generated

a transaction value of 1,130m USD in the UK in 2018 and is expected to grow to 2,829m USD

by 2022 (Statista (2018)). Around 21% of all early-stage financing in the UK is channelled via

ECF (Beauhurst (2015)).

ECF platforms typically operate under the “All or Nothing” business model, meaning that

fundraisers, i.e. the entrepreneurs, can be granted the money raised only if the fundraising

5 For example, in the USA, the JOBS Act was enacted in 2012 with the final rules adopted in late 2015. The Title

III exemption took effects from May 2016. Researchers using US online equity crowdfunding data would have to

deal with the short sample period starting only from the second half of 2016.

6

campaign reaches its pre-set target or gets overfunded (i.e., successful). Otherwise, the

campaign is deemed failed and investors will get fully refunded. To gauge for fundraising

success, platforms typically encourage entrepreneurs to approach their existing contacts (e.g.,

family and friends, angels, and other lending bodies); entrepreneurs that have already soft-

circled certain amount of money from before going crowdfunding are usually considered as

with higher quality and are more likely to be successful.

In our studied platform, for example, entrepreneurs have a private link to their campaign

webpage where relevant introductory contents are displayed. Only investors that receive such

link via direct contacts are able to pledge money. Entrepreneurs present funds that they raise

in such way onto the webpage so that they strengthen the power of the quality signal. This

fundraising period is called private launch period and has flexible duration so that

entrepreneurs can reach out to their existing contacts until they saturate their internal resource.

Once they decide to close private launch period and start raising funds publicly at the platform

(i.e., public launch), the entrepreneurs have a fixed 60-day time frame to reach their desired

amount of money. All funds received during private and public launch are visible to all

investors once the public launch period starts.

The campaign webpage contains rich information about each start-up. We generally categorize

all observable information into two broad categories: campaign attributes and campaign

dynamics. Specifically, campaign attributes refer to those time-invariant characteristics of a

campaign that are fixed throughout its lifetime. These characteristics include campaign’s pre-

set fundraising target, share of equity offered, pre-money valuation by entrepreneurs, founder

profiles, and campaign introductory materials including narratives, images, or videos, to name

a few. Campaign dynamics refer to those time-variant measurements that are updated in real-

time manner. These dynamic features of a campaign would typically include cumulative

number of investors, cumulative amount of money raised, and news feed from the

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entrepreneurs, to name a few. In particular, each campaign has a list on its page where all the

pledges are ranked by amount and displayed in descending order. Investors can choose to reveal

their name and social network accounts (e.g., LinkedIn and Twitter) so that other potential

investors can get access to.

2.2 ECF Angels

In recent years, equity crowdfunding seems to be less about the “crowd”: Some commentators

have started to note an increasing trend of angel investors and venture capitalists (VCs) joining

ECF platforms (AIG (2016)). Angels are generally defined as “high net worth individuals who

invest their own money, either alone or with others, directly in unquoted businesses in which

there is no family connection” (Mason et al. (2016)). However, the whole angel population are

heterogeneous and are getting younger more open to digital investment opportunities as

opposed to traditional and offline-only angels (Wright et al. (2015)). Hence, micro-lending and

crowdfunding platforms are gaining popularity amongst angels seeking convenience and

passive investments in portfolio (Harrison (2017), Wright et al. (2015), Landström and Mason

(2016)). For example, the most recent study on the UK angels by British Business Bank (BBB)

and UK Business Angel Association (UKBAA) (2017) finds that 36% of UK angels have

invested via ECF. On the one hand, major ECF platforms actively encourage coinvestment

with angels and hail it as an advantage for those investing with them.

Recently, there has been a strong growth in investments made via angel networks that further

increases the impact of angel financing (Baldock and Mason (2015) and Mason et al. (2016)).

These networks are comparable to investor-led ECF platforms (e.g., Angel’s Den, Syndicate

Room) where investors pledge by following a lead investor (Agrawal et al. (2016)). As equity

crowdfunding platforms grow and evolve, even investors pledging via company-led ECF

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platforms (like the one we study) where no formal syndicates are formed are found to start

building networks (Estrin et al. (2018)).

3. Related Literature & Hypothesis Development

3.1 Signaling and Information Exchange Among Angels

Information asymmetry between investees and investors and their signaling interactions have

always been the major issue in the capital structure literature of corporate finance (Leland and

Pyle (1977), Ross (1977)). Similar to other early-stage financing markets, equity crowdfunding

is typically characterised with high level of information asymmetry between entrepreneurs and

investors (Short et al. (2017)). So far the vast literatures have studied a variety of entrepreneur-

sent signals including project valuation, ownership stake offered and retained, and

entrepreneurs’ human (Ahlers et al. (2015) and Vismara (2016a)). A more recent stream of

research has emerged focusing on inter-investor signaling and have focused on the co-existence

and interactions between large and small investors, and between investors with distinct levels

of investment- and industry-relevant expertise (Kim and Viswanathan (2018) and Vismara

(2016b)).

More importantly, recent empirical studies have recognized that the investment activities of a

segment of large ECF investors who are business angels or are wealthy yet time-constrained

individuals that behave angel-like. For example, Wallmeroth et al. (2018) have categorized

investors with average pledge size higher than EUR 5,000 as angel-like or semi-professional.

Wang et al. (2018) identified investors with top 1% portfolio (in terms of total amount invested)

and provided statistical and qualitative evidences showing that these investors can be well

classified as angels in terms of investment behaviors and demographics. More importantly, the

authors find the crucial role played by angel investment in helping campaigns gauge success

and strong effect of information exchange between angels.

9

Adopting the angel identification developed in Wang et al. (2018), this paper focuses on

signaling and information exchange specifically between angel investors in ECF market within

and across fundraising campaigns. Due to the private nature of angel investing (Prowse, 1998),

literature on angel behaviors has been relatively scarce. The rise of angel have brought certain

angel activities online and extant studies shed some light on how angel networks recruit new

members and make joint decisions networks (Mason et al. (2016)). However, how angels

exchange information and interact when making individual investments remains unexplored.

The world of finance values networks (Allen and Barbus (2008) and Pichler and Wihelm

(2001)), and many financial markets are characterised with their “strong relationships and

networks” (Hochberg et al. (2007)). To reap the benefits of cooperation, syndications are

commonly found among investment banks (Wilson (1968) and Ross et al. (1999)), in

entrepreneurial finance market among venture capitalists (Wright and Lockett (2003)) and

business angels (Paul and Whittam (2010). Investors often rely on their syndicate partners to

more accurately evaluate venture quality, spread financial risks, share management resources,

diffuse sector- and location-specific expertise, and experience better market exit (Brander et al.

(2002) and Hochberg et al. (2007)). Compared with ordinary investors who are mostly

uninformed and make occasional investments, the investment behaviors of angels are more

informed and are more likely to involve active information exchange.

With the increasing entanglement of angel population with equity crowdfunding, it is critical

to understand from a theoretical perspective how angel investors interact with each other in

this market. Specifically, anchoring at signaling and information exchange literature, we

explore angels’ behavioral drivers and propose communication intensity and reputation as two

mechanisms that are associated with a series of angel interaction patterns in the context of

equity crowdfunding.

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3.2 Communication Intensity

Classic signaling theories have put significant focus on factors explaining differing signal

effectiveness from the perspectives of signal sender and receiver (Connelly et al. (2011)). The

fact that entrepreneurs raise funds from a small circle or personal contacts during private launch

means high level of communication intensity not only between entrepreneurs and investors but

also between investors. In contrast, communications between two investors in private and

public launch period, respectively should be significantly lower. This is because the value of

crowdfunding is mainly about providing convenience with both sides of the market: it relives

investors from due diligence and costly communications and gives entrepreneurs easy access

to a large base of heterogeneous investors without the obligation to engage with them

individually (Agrawal et al. (2014) and Mollick (2014)). Consequently, differing level of

communication leads to different signal intensity and frequency, which turn into different

signal effectiveness (Carter (2006), Fischer and Reuber (2007)). We contend that signals sent

and received during campaign’s private launch period contain more intensive communications

and should be more effective than signals received during campaign’s public launch period.

3.3 Reputation and Familiarity

Signal interpretation or calibration theories (Connelly et al. (2011)) have documented that

individuals, when faced with same signals, might come up with distinct interpretations

(Highhouse et al. (2007), Perkins and Hendry (2005), Gomulya and Mishina (2016)). This is

because people assign different strengths, weights, or even meanings to the same signal based

on their own concerns, preferences, and perspectives (Suazo et al. (2009), Branzei et al. (2004),

Ehrhart and Zieger (2005)). Previous research on syndicate partner selection has pointed out

investor linkage, or familiarity, as an important individual-level factor influencing their

decisions on whether or not to cooperate.

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For example, the effectiveness of signals sent from syndicate candidates depends on their

existence and frequency of previous joint deal experience with the lead VC, though the strength

of which erodes over time (Hopp and Lukas (2013)). Venugopal and Yerramilli (2016) study

network formation between angels and further enrich inter-investor familiarity by considering

quality of previous collaboration, which is measured by start-up success. The authors argue

that not only larger quantity but also higher quality of previous coinvestment experience

between angels will beget further successful collaboration. Similar with research finding that

entrepreneurs count on social capital (Colombo et al. (2014)) and accumulate social capital

through the practice of fundraising (Buttice et al. (2017)), we contend that investors, especially

large investors, also care about linkage, which is measured by familiarity, or past coinvestment

experience. Moreover, the familiarity and reputation between lead and follower angels should

increase with the frequency of successful coinvestment occasions, resulting in higher level of

signal credibility (Connelly et al. (2010)).

3.4 Hypotheses Development

In the context of equity crowdfunding investment, angel reputation can be established via

providing high-quality information. Specifically, reputation of an angel leader could

accumulate as more campaigns she leads turned out to be successfully funded. If signaler

reputation helps improve perceived signal credibility, we expect that a signal sent by a

reputational angel leader will shorten the time needed for signal receiver to evaluate its

credibility. Therefore, our first hypothesis is about signaler (angel leader) reputation leading to

shorter reaction time of the signal receiver (angel follower):

H1. Better experiences of previous coinvestments are associated with quicker investments from

angel followers in reaction to angel leader’s pledge.

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Signaling theories document that signal costliness is a main criterion for signal receiver to

evaluate signal credibility (Connelly et al. (2011), Ramaswami et al. (2010)). One fundamental

aspect of signal quality originates from signal costliness so that by sending costly signals the

signaler can differentiate oneself from others who find it difficult to imitate (Spence (1973)).

Monetary costliness, for instance, is a direct representation of signal costliness. Signal sender’s

confidence and interest towards the start-up can be (at least partially) reflected through capital

commitments. In the framework of our online equity crowdfunding platform, angel followers

are supposed to pledge more money to a campaign if the campaign’s angel leader pledges more

money.

H2a. Pledges of angel followers increase with the angel leader’s pledge.

We also notice that there can be substantial heterogeneity in the positive association between

pledges of angel followers and that of the angel leader, depending on the previous coinvestment

experience within an angel leader-follower pair. The fundamental idea of Bayesian updating

can better illustrate this heterogeneity if angel followers consider two information sources

while making their pledging decisions: Namely, angel leader’s pledge and the mutual trust

within an angel leader-follower pair. For those angel pairs without successful experience in

previous coinvestments, angel followers have no choice but interpret the campaign’s future

prospect largely relying on how much the angel leader pledges. However, for angel pairs with

fairly successful experience in previous coinvestments, angel followers can interpret the

campaign’s future prospect not only from the angel leader’s pledge but from their mutual trust

already developed via previous coinvestments. As a result, pledges of angel followers who

have experienced successful coinvestments with the angel leader are likely to be less sensitive

to how much the angel leader pledges. This the second part of Hypothesis 2.

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H2b. The positive reaction of angel follower to investment of angel leader gets weaker as the

number of successful coinvestments between angel leader and follower increases.

Angel leader’s reputation not only influences angel follower’s reaction, but also makes

difference to her own investment behavior. For instance, higher level of reputation of an angel

leader accumulated through past successful leading experience may require lower level of

capital commitment in her next investment occasion as lead investor. The previous hypotheses

take for granted that angel followers consider the angel leader’s pledge as a signal of a

campaign’s future prospect. Nonetheless, we need to provide empirical evidence supporting

that angel leaders do have such signaling motives in the first place. Specifically, in our context,

an angel leader with better reputation is better able to attract the same level of investment from

angel followers by investing less money into a campaign compared with a less reputational

angel. Therefore, our third hypothesis is written as follows:

H3. An angel leader who has led fewer successful campaigns tends to pledge more money to

the next campaign she leads.

Angel investors are known to make habitual investments, leveraging their experience and

expertise (Wright and Westhead (1998), Maula et al. (2005), Collewaert (2012), Freedman and

Nutting (2015)). This “comfort-zone” argument indicates some persistence in angel investing,

that is, keep pledging money only to a couple of industries. However, classic portfolio theories

suggest angel investors may have motives to diversify their portfolios across different

industries as well. Mason and Landstrom (2016) argue that he easy and almost free access to a

variety of deals from different industry sectors and the low costs involved in due diligence are

likely to have stimulated angel investors to hold diversified portfolios.

The key question we hope to address regarding angel investors’ portfolio holdings is on the

relative strength of portfolio diversification motives over angels’ expertise considerations. We

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conjecture that angel followers are more likely to invest in a new industry they have never

pledged to before if they receive high-quality private information via personal conversations

with the campaign’s founders and co-founders. That is, portfolio diversification motives are

more likely to dominate angels’ expertise considerations when angel followers receive high-

quality private information regarding the campaign’s future prospect.

H4a. Angel followers tend to pledge to campaigns of a new industry sector if they have direct

communications with campaign founders and co-founders.

H4b. Better experiences of previous coinvestments with the angel leader results in higher

likelihood for angel follower to invest in campaigns from a familiar industry sector.

Finally, we think about the participants in angel investing communities by putting them into a

real-life situation where it is not the obligation of one angel investor to always share her own

private information about investable campaigns. We conjecture that angel investors have their

own “circles”, or networks, to communicate valuable information about investable campaigns.

That is, angel investors may actually take turns, from time to time, to share their own private

information with their angel peers. The angel leader of a campaign can invite her angel peers

to have personal conversations with the campaign’s founders and co-founders, substantially

increasing the chances of receiving pledges from her angel followers.6 Those angel followers

invited for personal conversations with the campaign’s founders and co-founders are expected

to be more likely to act as the angel leader in future campaigns. We also allow the strength of

this role-switching motive to be heterogeneous across angel pairs with different levels of

previous coinvestment experiences.

6 It could be the case that an angel leader is regularly engaged with the entrepreneurial community and looking

for investable start-ups, or founders and co-founders of promising start-ups are actively seeking interested capital

of angel financing from prestigious and influential angel investors. These two cases stand for two opposite

directions of information flow between the angel leader and campaign founders, and the specific direction of

information flow is beyond our paper’s scope.

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H5. An angel follower, within an angel leader-follower pair, is more likely to act as the angel

leader in future campaigns if she pledged moeny during the private launch in their last

coinvested campaign.

4. Angel Identification and Data Filteration

We study angel coinvestment behavior using investment data for 50,999 unique investors and

1,151 campaigns from July 2012 up until September 2017 at a leading UK ECF platform.7 In

terms of size, type, and business model, the platform is fairly similar to other ECF platforms

such as Crowdcube and Crowdfunder. So far, the platform has successfully attracted start-up

ventures across 14 sectors, with financial service, food & drink, digital media, entertainment,

and technology being the most popular ones. Similarly, other industry leaders, for example,

Crowdcube, has comparable sector coverage. The platform also attracts a large number of

investors, ranging from crowd investors to sophisticated and high-net-worth investors. Since

investors use multiple platforms, leading ECF platforms are likely to have similar investors.8

Therefore, we are confident that the insights generated through our analyses can be well

generalized to other equity crowdfunding platforms.

4.1 Angel Identification

We observe heterogeneity among investors in terms of portfolio size (1 to 696 campaigns) and

investment amount (10 GBP to 14.4m GBP). More importantly, the CEO of the platform

confirmed the presence of angels alongside crowd investors on the platform. Although there is

great heterogeneity across angels in terms of demographic and behavioral characteristics, it is

almost always the case that angels are wealthy individuals that typically invest large amounts

of money. We hence pick the top 1% of investors in terms of total amount pledged (i.e., total

7 An NDA prohibits us from disclosing the identity of this UK online equity crowdfunding platform. 8 The CEO of this UK equity crowdfunding platform notes: “… platforms (at least the major ones) certainly

compete with each other for deals; we also compete for investors, although many investors use multiple platforms.”

16

amount pledged greater than 66,000 GBP), and observe that the behavior of the top 1% of

investors appears similar to that of business angels outlined in two recent surveys on UK angels

(BBB and UKBAA (2017), Wright et al. (2015)). We present a comparison of several key

behavioral metrics. For example, they have similar investment frequency, size, and portfolio

breadth, and comparable level of geographical concentration and industry focus. Furthermore,

to validate our angel classification, we randomly picked a sample of 153 investors from the top

1% of investors and reviewed their LinkedIn profiles. We checked whether an investor met at

least one of the following criteria to be classified as angel: (1) having successfully founded

start-ups, (2) having taken any chief executive level roles (CEO, CFO, COO, etc.) in companies

that they do not own; (3) claiming oneself as a professional/angel investor, and (4) being a fund,

a syndicate, or an investment organization. We found at least 118 (77%) investors can be

qualified as angels based on the criteria that the literature typically uses to profile angels.

Therefore, we category the 510 investors as angel investors (N=510), while the rest as crowd

investors (N=50,489). Given that we identify angel investors based on their total amount

pledged, our approach accounts for angels who make few very large pledges and angels who

make a larger number of smaller pledges.

Moreover, the current regulatory framework requires investor self-certification as high-net-

worth, sophisticated, or everyday (crowd) investors. However, we found that a quarter of

investors classified as high-net-worth individuals made less than 500 GBP over the data period,

while 2% of those classified as crowd investors have invested more than 66,000 GBP (i.e., our

threshold for identifying angels), raising concerns regarding the reliability of the accuracy of

client self-assessment.

Furthermore, in line with existing research on angel behavior (Landström and Mason, 2016;

Wallmeroth et al., 2018), we observe heterogeneity amongst the identified angels. For example,

17

28% of angels made investments less than 100 GBP, while 3% invested higher than GPB 1m

per pledge. While 61% made fewer than five investments, 5% made more than 90 during the

entire data period. We also observe differences in the timing of investments: for instance, 342

(67%) angels made pledges prior to the public launch of the campaign while 428 (84%) made

investments during the public launch. According to the CEO of the platform, entrepreneurs

often “soft circle part of their round through offline angels” before publically launching

campaigns. It is thus likely that these offline angels treat ECF as an asset class similar to

traditional offline investments. However, investors who invest exclusively through the online

channel during the public launch may treat ECF as a different asset class. Unlike offline angels

these angels likely seek the convenience of the platform and may not be as hands-on with the

venture. Such heterogeneity amongst angels could thus lead to differences in investment

behavior. In contrast, we observe significant differences between investment behavior of angels

and crowd investors. For instance, in large campaigns angels make approximately 6 times the

pledges and pledge around 130 times the amount of crowd investors.

4.2 Summary Statistics: All Launched Campaigns

Panel A of Figure 1 presents the quarterly number of all launched campaigns in this leading

online equity crowdfunding platform in the UK. Fundraising campaigns via equity

crowdfunding, with an average fundraising goal of GBP 326,752 and pre-money valuation of

GBP 5,729,347, are usually subject to larger scales compared to those via reward-based

crowdfunding as described in Mollick (2014). Panel B of Figure 1 provides the quarterly

aggregate of money raised from all successful campaigns launched in this platform. We can

see the nominal value of this equity crowdfunding platform was somewhat quiet for a year or

18

two starting from 2012Q3, but on the track to “escalate very quickly” as predicted by Dr.

Richard Swart9 for the US counterpart of equity crowdfunding.

Panel A of Figure 2 presents the quarterly summary statistics for the funding target. We can

observe a significant upward-sloping trend for the funding target in nominal value (GBP). The

associated summary statistics of campaign valuation can be found in Panel B of Figure 2. The

campaigns in our dataset on average offer 8.6% ownership stake of the firm to equity

crowdfunding investors. Panel C of Figure 2 provides P25, Median and P75 regarding the

percentage of ownership offered. Moreover, a campaign on average receives around 16 pledges

from angel investors and around 274 pledges from non-angel retail investors. We note that a

significant portion (61.2%) of fundraising goal is fulfilled by a small number of angel investors

during the private launch period, indicating the critical and influential role played by angels

even in a seemingly democratic and crowd-based platform.10

4.3 Final Dataset for Analysis

To construct the data for model analyses, we first drop all pledges from non-angel investors

from the full dataset. We then group all angels into pairs and then keep the pairs with more

than one coinvestments records. Having coinvested in more than one campaign is necessary in

our empirical framework. Having a full time series of investor pledges for each campaign that

covers both private and public launches, we identify the angel investor that makes the first

pledge to a campaign during the campaign’s private launch period as the angel leader.11 The

other angels pledging to the same campaign are classified as her angel followers. This empirical

9 Director of Research, the Program for Innovation in Entrepreneurial and Social Finance at UC Berkeley. 10 Detailed summary statistics at the levels of campaign, angel investor, retail investor, or industries are not

reported in this paper for brevity. These summary statistics are available upon request. 11 The median number of angel investors per campaign is 3. Some campaigns may not receive any pledges from

angel investors during private launch.

19

strategy is largely based on conventions that the angel leader of a campaign has sufficient

communication and close connection with the campaign’s founder and co-founders.

Furthermore, we drop the observations where both the angel leader and follower make

investments in a campaign’s public launch period. That is, we only keep observations with a

campaign’s angel leader pledging during the private launch. As a result, our final data for

analysis consists of 5,481 observations at the level of angel leader-follower pairs per campaign,

covering 450 angel investors and corresponding to 2,276 angel pairs across 343 campaigns

from July 2012 to September 2017.12

We also find rich heterogeneity across angel pairs in many aspects of coinvestment dynamics.

For instance, the angel leader of a campaign on average contributes 18.5% to the campaign’s

fundraising target. In response to the angel leader’s pledge, angel followers on average make

investments to the same campaign approximately 21 days (median 12 days) after the angel

leader’s pledge. Rather than passively making modest investments, angel followers on average

make investments that count for 6.5% of the goal of the same campaign.

5. Empirical Evidence

5.1 Pledge Timing and Coinvestment Experience

We have developed a hypothesis of forming mutual trust within a pair of angel investors via

their previous coinvestment experience. Our dependent variable in this subsection measures

how quickly an angel follower pledges relative to the angel leader’s pledge. This variable is

expected to measure the time gap between the two pledging moments of the angel leader and

the angel follower, respectively. To absorb any unobservable features of pledging within an

12 Among 343 campaigns in our dataset, 68.5% of them (i.e. 235) are successfully funded. These campaigns span

14 industry sectors.

20

angel pair, we take the first-order difference of “time gap”: the time gap in the current campaign

minus the time gap in the previous campaign for the same pair of angel investors.

Our main explanatory variable is a proxy for mutual trust within an angel leader-follower pair.

We measure the extent of how much mutual trust has been built within an angel pair by the

cumulative number of successful coinvested campaigns (Cumulative No. of Successful

Coinvestments). We also check the result if angel pairs have “short memories” and purely focus

on the outcome of their most recent coinvested campaign. The coefficient is supposed to be

negative.

We follow the literature on crowdfunding and control for campaign level attributes, namely the

level of funds sought (Campaign Fundraising Target) and ownership stake offered to equity

crowdfunding investors (Campaign Equity Offered). Detailed variable definitions can be found

in Table 1. Besides, we control for angel leader characteristics because angel leaders may differ

from each other in many ways, possibly resulting in very different reactions from angel

followers regardless of their coinvestment experiences. Finally, taking the unobservable

heterogeneity across angel pairs into account, we include pair-level fixed effect wherever

applicable.

Our conjecture is that an angel pair form a stronger relationship of mutual trust if they have

experienced a larger number of successful campaigns. Column 1 in Table 4 supports this

conjecture by the negative coefficient before Cumulative No. of Successful Coinvestments. That

is, as the number of successful campaigns coinvested with the angel leader increases, angel

followers tend to pledge sooner to the next campaign she leads. We further confirm this

statistical association by focusing on the fundraising outcome of an angel pair’s most recent

21

coinvested campaign. That is, an angel follower tends to pledge sooner by more than one day

if the most recent campaign was successful.13

One interesting result regarding an angel leader’s network size indicates that substantial growth

of lead angels’ connections with other angels may actually dilute the “tightness” or the

“strength” of her angel investment network. Holding other factors constant, angel followers

tend to pledge slightly later for an increase in the angel leader’s network size. This finding is

consistent with a limited attention story where an angel leader with high network size in fact

spread her time and effort more thinly among all angel followers within her network.

5.2 Pledge Sensitivity and Coinvestment Experience

Beyond simply looking at timing patterns of angel investment, we also look into how

sensitively angel follower investment would respond to angel leader’s investment. Specifically,

we use “log-log” regressions to directly capture the elasticity of angel follower-leader

investment. Angel followers can interpret the angel leader’s pledge as a signal of campaign

quality and make investment decisions based on their own interpretation of this signal. When

making investment decisions, angel followers without personal trust formed via previous

coinvestment experiences with an angel leader would have to only rely on the angel leader’s

pledge to infer the true quality of the campaign. As a result, such angel followers respond more

sensitively to the angel leader’s pledge.14 Thus, we hypothesize that pledges of angel followers

respond differentially sensitively to the angel leader’s pledge, depending on their previous

coinvestment experiences with the angel leader.

13 The economic significant of this finding can be better evaluated given the summary statistics of private launch:

P25, Median, P75 are 1, 4, and 13 days, respectively. 14 This is consistent with a Bayesian updating framework where an agent relies on two information sources to

make her decision: The agent relies more on the information source with a higher precision.

22

We use the natural logarithm of how much an angel follower pledges (Angel Follower Pledge)

as the dependent variable. We use the natural logarithm of the angel leader’s cumulative

amount of investment to a certain campaign (Angel Leader Pledge) as the main explanatory

variable.15 Besides the control variables already included, we also add an interaction term

between the angel leader’s pledge and the cumulative number of successful campaigns

coinvested with an angel follower. Further, we controlled for individual investment capacity

and portfolio considerations by including cumulative amount of investment made by angel

follower and leader across all investments (Lead Angel Cumulative Amount of Investments and

Angel Follower Cumulative Amount of Investments), respectively.

The first two columns in Table 5 show that pledges of angel followers are positively responsive

to the angel leader’s pledge, and this effect is robust to controlling for their coinvestment

experience and other covariates. This result indicates that angel leaders can use how much they

invest in a campaign as a signal to their angel followers regarding campaign quality. We further

include the interaction term between angel leader pledge and the angel pair’s coinvestment

experience. Interestingly, our evidence indicates that angel followers with better coinvestment

experiences with an angel leader tend to respond less sensitively to the angel leader’s pledge.

The negative coefficient of the interaction term is statistically significant at the 10% level. This

empirical finding indicates that angel followers with no successful coinvestments experience

with an angel leader, when making pledging decisions, rely more on how much the angel leader

pledges to a campaign.

5.3 Angel Leader’s Signaling Motives

The empirical findings in section 5.2 are interpreted on the ground of angel leaders using their

pledges as a signal of campaign quality in the first place. When exploring the interactions

15 Note that investors may make multiple pledges to the same campaign over time, our dependent variable is

“updated” once a top-up investment is made by the angel leader.

23

between angel leaders and angel followers, we are particularly interested in the signaling

motives for angel leaders to determine how much to invest in the current campaign. This section

aims to provide evidence for such signaling motives. Specifically, we want to investigate

whether an angel leader’s investment decision for the current campaign is affected by her

record of fundraising outcomes for past campaigns she led.

Our hypothesis is based on a signaling story where an angel leader uses how much she pledges

to a campaign as a signal to her angel followers regarding the quality of this campaign. We

conjecture that the angel leader’s signaling motives are stronger if facing bad fundraising

outcomes of the past campaigns she led. That is, we expect, on average, angel leaders tend to

pledge more money for the current campaign if her past campaigns did not manage to hit the

funding target.

We use the nominal value of an angel leader’s pledge (Angel Leader Pledge) to measure angel

leader investment behavior. These capture how much angel leader’s level of capital

commitment when leading a new campaign. The main explanatory variable is Cumulative

Successful Led Investments serving as a proxy for the angel leader’s fundraising record. To

capture the quality of the most recent information sharing from angel leader to follower, we

additionally include an indicator variable Lag Coinvestment Successful that takes value of one

if the last campaign coinvested was successfully funded, and zero otherwise.

Table 6 shows that an angel leader tends to pledge more money to a campaign if she has a less

successful record of fundraising in the past campaigns she led. Put differently, when leading a

campaign, an angel leader with a more successful record of campaign fundraising tends to

pledge less. This is consistent with our conjecture that angel leaders use their pledge as a signal

of campaign quality to angel followers. We further include the fundraising outcome of the most

recent campaign an angel leader has led. Conditional on her past fundraising record, an angel

24

leader with a successful fundraising outcome of her most recent campaign tends to pledge 27%

less. This supplementary evidence suggests that an angel leader’s signaling motive is

particularly stronger upon the fundraising outcome of her most recent campaign.

5.4 Pledging to New Sectors

Angel investors tend to invest in the industries where they can make the best use of their

previous working experience, existing social networks or professional expertise; therefore, it

is not surprising to observe angel investors having their investment clustered within a couple

of industries. However, classic portfolio choice theory indicates that investors also consider

diversifying their investment across different instruments, asset classes, or even different

industries. That is, besides making best of their expertise, angel investors also tend to diversify

their portfolios of campaign investment.

Conditional on their investment histories, whether angel investors may want to stay within their

familiar industries (i.e. expertise considerations) or invest in a new industry they never have

before (i.e. diversification motives) depends on the relative strength of these two motives. In

particular, we argue that portfolio diversification motives are stronger for angel followers to

invest in a new industry if receiving high-quality private information about this new-industry

campaign. That is, portfolio diversification motives are more likely to dominate expertise

considerations when angel followers are invited to have personal conversations with the

entrepreneurs.

We generate an indicator variable Follower New Sector that takes value of one if the angel

follower invests in a campaign from a sector she has never invested in before at the equity

crowdfunding platform. Our main explanatory variable is an indicator Current In-In that equals

one if the current coinvestment is made within a campaign’s private launch, and zero otherwise.

Current In-In being equal to one stands for high-quality private information, because angel

25

followers who pledge during the private launch are almost surely to have the opportunities and

talk to entrepreneurs in personal conversations before the public launch.

Column (1) in Table 7 shows that it is 9% more likely for angel followers to pledge to a new

industry they have never invested in before when pledging during the private launch of a

campaign. The coefficient of the variable Cumulative No. of Successful Coinvestments in

Column (2) is negative and statistically significant, indicating the persistence of investing in

industries within their “comfort zone”. Put different, angel followers tend to avoid pledging to

a new industry they never have before when they have had a successful coinvestment

experience with the angel leader. These patterns qualitatively remain once we include both

Current In-In and Cumulative No. of Successful Coinvestments into the model.

5.5 Role-Switching Motives within Angel Pairs

An angel follower in one campaign can be the angel leader in another campaign, and this

subsection focuses on the patterns of angel investors taking turns to share their own private

knowledge of investment opportunities and lead the campaigns they believe to be good. Our

conjecture is based on a framework where angel investors have their professional and personal

networks, or their own “circles”, to share private information of investable campaigns. It’s not

the obligation of an angel leader to always provide her private knowledge of investment

opportunities for her angel peers. To maintain good relationships with other angels within their

networks, angel investors need to consider taking turns to share valuable information of

investable campaigns with their peers, at least from time to time.

In particular, when looking at angel leader-follower pairs, we want to explore the determinants

for an angel follower in a previous coinvestment relationship with the lead angel to choose the

angel-leader role in their next coinvested campaign. That is, an angel follower chooses to

provide such valuable information and act as the angel leader in the next campaign. This is a

26

role-switching pattern of angel follower turning into angel leader. We conjecture that an angel

investor’s role-switching motives are largely influenced by her previous coinvestment

experience as an angel follower and the information sharing activities within an angel pair. The

hypothesis is that, within an angel leader-follower pair, an angel follower in a previous

coinvested campaign as an information receiver during private launch is more likely to take the

angel-leader role in their next coinvested campaign.

To capture this role-switching pattern, we restructure our data so that all the coinvested

campaigns involving two angel investors are grouped into one pair without distinguishing the

lead-follower order. Next, we order all the coinvested campaign chronically so that the leader

and follower roles of the first observation is treated as the “starting values”. We then construct

an indicator variable Lead-Follow Switch that equals one if the leader-follower order is

switched in their next coinvested campaign. We used Lag In-In and Cumulative No. of

Successful Investments as our main explanatory variables. We further introduce their

interaction term to explore the channel through which this role-switching pattern works.

The empirical evidence in Column (1) of Table 8 suggests that, within an angel leader-follower

pair, an angel follower is more likely to take the role of lead angel in their next coinvestment

by 2.25 percentage points if she pledged during the private launch of their most recent

coinvested campaign. This effect remains after including Cumulative No. of Successful

Coinvestments into the model as shown in Column (3). The negative coefficient indicates that

being an angel follower in campaigns led by an angel leader generates coinvesting persistence

with this angel leader as an angel follower.

When further including the interaction term between Lag In-In and Cumulative No. of

Successful Coinvestments, we find that this role-switching pattern actually works through the

channel of previous successful coinvestment experience. The coefficient of the variable Lag

27

In-In turns statistically insignificant, suggesting that receiving private information in the

previous campaign does not affect an angel follower’s role-switching decision if this angel

follower has no successful coinvestment experience with the angel leader.

Column (4) shows that the probability differential of this role-switching pattern within an angel

pair actually depends on how many successful campaigns they have coinvested in the past. Lag

In-In has statistically insignificant effect on the probability of an angel follower’s role-

switching decision once including the interaction term in Column (4) of Table 8. That is, when

there is no previous successful coinvestment within an angel pair, the strength of such role-

switching motives is statistically insignificantly different from zero. However, if there is at

least one previous successful coinvested campaign, Lag In-In can statistically significantly

increase the probability of an angel follower’s role-switching decision for the next coinvested

campaign. Furthermore, the effect of increasing the role-switching probability grows as the

number of previous successful coinvested campaign increases. Put differently, pledging money

in the previous campaign’s private launch provides angel followers a stronger motive to switch

to the angel leader position for the next campaign as they have experienced larger numbers of

successful campaign with their angel leader.

6. Concluding Remarks

Online equity crowdfunding platforms, serving both retail investors and sophisticated angels,

have emerged as an increasingly important alternative to traditional private-equity markets.

Instead of considering the whole pool of online equity crowdfunding investors as homogeneous,

our paper identifies and focuses exclusively on angel investors out of the crowd, and provides

first-hand empirical evidence to document how angel leader-follower pairs coinvest and

exchange information. Overall, all empirical findings are consistent with a framework where

angel pairs form mutual trust, make pledging decisions, and maintain their positions within

28

angel investing communities through their previous coinvestment records and sharing private

information of investment opportunities.

Specifically, we find that angel followers tend to pledge sooner in a campaign if they have

better coinvestment experiences with the angel leader. This evidence indicates that angel

leader-follower pairs build mutual trust via previous coinvestment experiences. Besides, our

empirical evidence shows that pledges of angel followers with better coinvestment experiences

with an angel leader are less sensitive to that of the angel leader. This evidence is consistent

with our conjecture that mutual trust formed via previous coinvestment experiences helps angel

followers to better interpret the angel leader’s signal.

We also find that angel followers are more likely to pledge to industries they never have when

receiving private information regarding a campaign’s future prospect. Such private information

can be largely obtained via personal conversations with the campaign’s founders and co-

founders. Angel investors tend to invest in a couple of industries where they have professional

expertise and social network. However, portfolio diversification motives may encourage angel

followers to pledge to a new industry they have never invested in before. Our empirical

evidence indicates that angel followers face the trade-off between portfolio diversification and

their own professional expertise when making pledging decisions. The relative strength of these

two driving forces depends on whether angel followers’ private information about the

campaign under consideration.

Furthermore, we explore whether previous campaign outcomes could affect an angel leader’s

pledging decision in future campaigns she leads. We find that an angel leader tends to pledge

more money to the next campaigns she leads when facing less successful fundraising records.

This effect remains if we further include the fundraising outcome for her the most recent past

campaign. This evidence confirms the angel leader’s signaling motive regarding her next

29

campaign’s prospect. An angel leader with less successful fundraising records rationally

chooses to pledge more money in her next campaign, showing her confidence and sending a

stronger signal to her angel followers. Future research may dig deeper into the signaling games

between angel leaders and followers from a theoretical perspective.

Finally, we explore the role-switching patterns within angel leader-follower pairs. Our

conjecture is based on real-world situations where angel investors need to maintain good

relationships with their angel peers and take turns to provide valuable information about

investable campaigns. That is, angel investors have their own circles of communicating

information and take turns to share valuable information. Our empirical evidence supports this

idea. We find that, within an angel leader-follower pair, the follower is more likely to take the

role of angel leader in their next coinvested campaign if she pledged during the private launch

in their most recent coinvested campaign. In particular, further evidence confirms this role-

switching motive actually works through the channel of previous coinvestment outcomes. The

better an angel pair’s previous coinvestment records, the stronger this role-switching motive.

Overall, our paper provides first-hand empirical evidence for angel investors in the disruptively

emerging industry of online equity crowdfunding. We contribute to the literatures of angel

investors and online equity crowdfunding by exploring the interactions of angel leader-follower

pairs using our unique dataset from a leading UK equity crowdfunding platform. Nonetheless,

with digital resources rapidly developing and data collection capacity fast growing, we expect

future research to exploit big data of social media and conduct textual analyses (e.g. Chen et

al. (2014)) upon the messages digitally communicated within the online equity crowdfunding

communities.

30

References

Agrawal, A., Catalini, C., & Goldfarb, A. (2016). Are syndicates the killer app of equity

crowdfunding? California Management Review, 58(2), 111–124.

Agrawal, A., Catalini, C., & Goldfarb, A. (2014). Some simple economics of

crowdfunding. Innovation Policy and the Economy, 14(1), 63–97.

Ahlers, G. K., Cumming, D., Günther, C. & Schweizer, D. (2015). Signaling in equity

crowdfunding. Entrepreneurship Theory and Practice, 39, 955–980.

AIG. (2016). Considering potential risks and liabilities as the industry grows and matures.

https://www.aig.co.uk/content/dam/aig/emea/united-kingdom/documents/Insights/equity-

crowdfunding-thought-leadership-oct16.pdf

Baldock, R., & Mason, C. (2015). Establishing a new UK finance escalator for innovative

SMEs: the roles of the Enterprise Capital Funds and Angel Co-Investment Fund. Venture

Capital, 17(1-2), 59-86.

Beauhurst. (2015). The Deal, Q3 2015, Review of the U.K equity landscape

Branzei, O., Ursacki-Bryant, T. J., Vertinsky, I., & Zhang, W. (2004). The formation of green

strategies in Chinese firms: Matching corporate environmental responses and individual

principles. Strategic Management Journal, 25: 1075-1095.

British Business Bank & UK Business Angel Association. (2017). Business Angel Spotlight.

Carter, S. M. (2006). The interaction of top management group, stakeholder, and situational

factors on certain corporate reputation management activities. Journal of Management Studies,

43: 1146-1176

Connelly, B. L., Certo, S. T., Ireland, R. D. & Reutzel, C. R. (2011). Signaling theory: A review

and assessment. Journal of Management, 37, 39-67.

Chen, H., De, P., Hu, Y., & Hwang, B.H. (2014). Wisdom of crowds: The value of stock

opinions transmitted through social media. Review of Financial Studies, 27(5), 1367-1403.

Drover, W., Busenitz, L., Matusik, S., Townsend, D., Anglin, A., & Dushnitsky, G. (2017). A

review and road map of entrepreneurial equity financing research: venture capital, corporate

venture capital, angel investment, crowdfunding, and accelerators. Journal of

Management, 43(6), 1820-1853.

31

Fischer, E., & Reuber, R. (2007). The good, the bad, and the unfamiliar: The challenges of

reputation formation facing new firms. Entrepreneurship Theory and Practice, 31, 53-75.

Freedman, D. M., & Nutting, M. R. (2015). Equity crowdfunding for investors: A guide to

risks, returns, regulations, funding portals, due diligence, and deal terms. Wiley Finance Series.

Gomulya, D., & Mishina, Y. (2017). Signaler credibility, signal susceptibility, and relative

reliance on signals: How stakeholders change their evaluative processes after violation of

expectations and rehabilitative efforts. Academy of Management Journal, 60(2), 554-583.

Harrison, R. T. (2017). The internationalisation of business angel investment activity: a review

and research agenda. Venture Capital, 19(1-2), 119-127.

Highhouse, S., Thornbury, E. E., & Little, I. S. (2007). Social-identity functions of attraction

to organizations. Organizational Behavior and Human Decision Processes, 103, 134-146.

Iyer, R., Khwaja, A.I., Luttmer, E., & Shue, K. (2016). Screening peers softly: Inferring the

quality of small borrowers. Management Science, 62(6), 1554-1577.

Kim, K., & Viswanathan, S. (2018). The 'Experts' in the Crowd: The Role of Experienced

Investors in a Crowdfunding Market.

Kerr, W., Lerner, J., & Schoar, A. (2014) The consequences of entrepreneurial finance:

Evidence from angel financings. Review of Financial Studies, 27(1), 20-55.

Landström, H., & Mason, C. (Eds.). (2016). Handbook of research on business angels. Edward

Elgar Publishing.

Leland, H., & Pyle, D. (1977). Informational asymmetries, financial structure, and financial

intermediation. Journal of Finance, 32(2), 371-387.

Lin, M., Prabhala, N., & Viswanathan S. (2013). Judging borrowers by the company they keep:

Friendship networks and information asymmetry in online peer-to-peer lending. Management

Science 59(1), 17-35.

Mason, C., & Botelho, T. (2014). The 2014 survey of business angel investing in the UK: A

changing market place. Glasgow: Adam Smith Business School.

Mason, C., Botelho, T., & Harrison, R. (2016). The transformation of the business angel market:

empirical evidence and research implications. Venture Capital, 18(4), 321-344.

32

Maula, M., Autio, E., & Arenius, P. (2005). What drives micro-angel investments? Small

Business Economics, 25(5), 459-475.

Mollick, E. (2014). The dynamics of crowdfunding: An exploratory study. Journal of Business

Venturing, 29, 1-16.

Myers, S. (1977). Determinants of corporate borrowing. Journal of Financial Economics, 5,

147-175.

Myers, S., & Majluf, N. (1984). Corporate financing and investment decisions when firms have

information that investors do not have. Journal of Finance, 13, 187-221.

Perkins, S. J., & Hendry, C. (2005). Ordering top pay: Interpreting the signals. Journal of

Management Studies, 42, 1443-1468

Ramaswami, A., Dreher, G. F., Bretz, R., & Wiethoff, C. (2010). Gender, mentoring, and career

success: The impor-tance of organizational context. Personnel Psychology, 63, 385-405.

Ross, S. (1977). The determination of financial structure: The incentive-signalling approach.

Bell Journal of Economics, 8(1), 23-40.

Short, J. C., Ketchen, D. J., McKenny, A. F., Allison, T. H., & Ireland, R. D. (2017). Research

on crowdfunding: Reviewing the (very recent) past and celebrating the

present. Entrepreneurship Theory and Practice, 41(2), 149-160.

Spence, A. (1973). Job Market Signaling. The Quarterly Journal of Economics, 87(3), 355-

374.

Statista. (2018) Crowdinvesting (United Kingdom). Retrieved 22 June, 2018, Available at:

https://www.statista.com/outlook/377/156/crowdinvesting/united-kingdom#

Suazo, M. M., Martinez, P. G., & Sandoval, R. (2009). Creating psychological and legal

contracts through human resource practices: A signaling theory perspective. Human Resource

Management Review, 19, 154-166.

TechCrunch. (2016). Crowdfunding is dead. https://techcrunch.com/2016/05/16/equity-

crowdfunding-is-dead/

Terry, H., Schwartz, D., & Sun, T. (2013). The Future of Finance: The Socialization of Finance

(Part 3). Goldman Sachs.

33

Vismara, S. (2016a). Equity retention and social network theory in equity crowdfunding. Small

Business Economics, 46(4), 579-590.

Vismara, S. (2016b). Information Cascades Among Investors in Equity Crowdfunding.

Entrepreneurship Theory and Practice. doi:10.1111/etap.12261

Vulkan, N., Åstebro, T. & Sierra, M. F. (2016). Equity crowdfunding: A new phenomena.

Journal of Business Venturing Insights, 5, 37-49.

Wallmeroth, J., Wirtz, P., & Groh, A. P. (2018). Venture capital, angel financing, and

crowdfunding of entrepreneurial ventures: A literature review. Foundations and Trends in

Entrepreneurship, 14(1), 1-129.

Wang, W., Mahmood, A., Sismeiro, C., Vulkan, N. (2018). The Evolution of Equity

Crowdfunding: Insights from Co-investments of Angels and the Crowd. Working Paper.

Wei, Z., & Lin, M. (2017). Market mechanisms in online peer-to-peer lending. Management

Science, 63(12), 4236-4257.

Zhang, J., & Peng, L. (2012). Rational herding in microloan markets. Management Science,

58(5), 892-912.

Zhang, B., Wardrop, R., Rau, R., & Gray, M. (2015). Moving mainstream: the European

alternative finance benchmarking report. University of Cambridge and EY.

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Table 1. Variable Definitions

Variable Name Definition

Lead-Follow Switch Dummy variable that takes value of one if angel follower takes the lead instead in the current co-

investment, zero otherwise

Cumulative No. of Successful Coinvestments Cumulative number of successful co-invested campaigns per pair of angel

Lag Coinvestment Successful Dummy variable that takes value of one if the previous co-invested campaign was successfully

funded, zero otherwise

Lag In-In Dummy variable that takes value of one if angel follower made investment in campaign's private

launch period in the previous co-investment

Current In-In Dummy variable that takes value of one if angel follower made investment in campaign's private

launch period in the current co-investment

Campaign Fundraising Target Campaign's pre-set fundraising target

Campaign Equity Offered Campaign's percentage of ownership (equity share) offered to crowdfunders

Angel Leader Network Size Cumulative number of angel followers that a lead angel has co-invested with

Angel Leader Pledge Amount of first investment that lead angel invests in a certain campaign

Angel Follower Pledge Amount of investment by angel follower

Cumulative No. of Angels per Campaign Cumulative number of angel investors making investments in a campaign

Cumulative % Raised per Campaign Cumulative percentage of fundraising target fulfilled of a campaign

No. of Active Campaigns per Day Number of other fundraising (active) campaigns on the same day

Cumulative Amount Invested per Angel Cumulative amount invested by lead angel across all pairs and campaigns

No. of Days until Campaign Expiration Number of days until campaign expiration date

Cumulative No. of Unique Sectors per Investor Cumulative number of unique industry sectors that an investor has financed via ECF

35

Investor Cumulative No. of Unique Sectors

Invested

Dummy variable that takes value of one if an angel follower makes investment in a new industry

sector, zero otherwise

Δ Lead-Follow Gap Time change (in days) angel follower's reaction time to lead angel from previous to current co-

investment

Valuation Natural Log of Inferred Campaign Valuation, equal to Campaign Fundraising Target/Campaign

Equity Offered

36

Table 2. Summary Statistics

Variable Obs Mean Std. Dev. Min Max

Lead-Follow Switch 4,441 0.026 0.160 0 1

Cumulative No. of Successful Coinvestments 4,441 0.600 0.822 0 4.025

Lag Coinvestment Successful 4,441 0.866 0.340 0 1

Lag In-In 4,441 0.132 0.339 0 1

Current In-In 4,441 0.280 0.449 0 1

Campaign Fundraising Target 4,441 15.149 1.252 11.513 19.449

Campaign Equity Offered 4,441 2.136 0.600 0.087 3.928

Angel Leader Network Size 4,441 2.804 1.034 1.099 5.375

Angel Leader Pledge 4,441 9.342 3.344 0 15.016

Angel Follower Pledge 4,441 6.686 2.239 0.713 14.902

Cumulative No. of Angels per Campaign 4,441 2.114 0.780 0 4.382

Cumulative % Raised per Campaign 4,441 1.307 2.630 0 41.376

No. of Active Campaigns per Day 4,441 23.764 6.287 4 44

Cumulative Amount Invested per Angel 4,441 10.501 2.731 0 16.256

No. of Days until Campaign Expiration 4,441 2.888 1.635 0 5.765

Investor Cumulative Number of Unique Sectors Invested 4,441 1.488 0.891 0 2.890

Angel Follower Investing in New Industry Sector 4,441 0.160 0.367 0 1

Δ Lead-Follow Gap 4,441 2.409 14.818 -148.0618 145.708

37

Table 3. Pairwise Correlation Structure

This table contains the pairwise correlation for regressors included in the following tables. Variable definitions are available in Table 1.

38

Table 4. Pledge Timing and Coinvestment Experience

The lead-follow gap is the difference between the two time points when an angel leader and an angel follower pledges to a campaign, respectively. The dependent

variable is the first-order difference of the lead-follow gap within an angel pair. The included regressors are defined in Table 1. Angel pair fixed effects are

included in all regressions.

Column 1 Column 2

VARIABLES Δ Lead-Follow Gap Δ Lead-Follow Gap

Cumulative Number of Successful Coinvestments -0.707***

(0.222)

Lag Coinvestment Success

-1.096* (0.595)

Current In-In -1.898*** -1.853*** (0.439) (0.441)

Campaign Fundraising Target -0.626** -0.501 (0.303) (0.307)

Campaign Equity Offered -0.417 -0.414 (0.392) (0.393)

Angel Leader Network Size 0.0212*** 0.0133** (0.00612) (0.00571)

Cumulative No. of Angels per Campaign 2.109*** 2.034*** (0.321) (0.321)

Cumulative Percent Raised per Campaign -0.0822 -0.0880 (0.120) (0.121)

Number of Active Campaign per Day -0.00701 -0.00543 (0.0212) (0.0212)

Cumulative Amount Invested per Angel -0.157 -0.320** (0.138) (0.130)

Number of Days until Campaign Expiration -0.365*** -0.276*** (0.0973) (0.0927)

Observations 1,857 1,857

Adj R2 0.125 0.120

39

Table 5. Pledge Sensitivity and Coinvestment Experience

The dependent variable measures how much an angel follower pledges to a campaign. The included regressors are defined in Table 1. Angel pair fixed effects

are included in all regressions.

Column 1 Column 2 Column 3

VARIABLES Angel Follower Pledge Angel Follower Pledge Angel Follower Pledge

Angel Leader Pledge 0.0611** 0.0595** 0.0693** (0.0284) (0.0285) (0.0290)

Cumulative No. of Successful Coinvestments

-0.00720 0.0103 (0.00804) (0.0133)

Angel Leader Pledge X Cumulative No. of Successful Coinvestment

-0.00327* (0.00198)

Campaign Fundraising Target 0.255*** 0.256*** 0.254*** (0.0683) (0.0683) (0.0683)

Campaign Equity Offered 0.185* 0.189** 0.183* (0.0945) (0.0946) (0.0947)

Angel Leader Network Size -0.00379** -0.00346** -0.00330** (0.00155) (0.00159) (0.00159)

Cumulative No. of Angels per Campaign -0.0801*** -0.0786*** -0.0807*** (0.0291) (0.0292) (0.0292)

Cumulative Percnt Raised per Campaign 0.0193*** 0.0193*** 0.0192*** (0.00628) (0.00628) (0.00628)

Number of Active Campaign per Day -0.00968 -0.00820 -0.00596 (0.0142) (0.0143) (0.0143)

Cumulative Amount Invested per Angel 0.0215 0.0161 0.0123 (0.0272) (0.0279) (0.0279)

Number of Days until Camapign Expiration 0.0776 0.0804 0.0906 (0.0619) (0.0620) (0.0622)

Observations 4,441 4,441 4,441

Adj R2 0.024 0.024 0.026

40

Table 6. Angel Leader Pledge as Signal of Campaign Quality

The dependent variable measures how much the angel leader of a campaign pledges. The included regressors are defined in Table 1. Angel pair fixed effects

are included in all regressions.

Column 1 Column 2

VARIABLES Angel Leader Pledge Angel Leader Pledge

Cumulative Successful Led Investments -0.691*** -0.683*** (0.0604) (0.0605)

Lag Coinvestment Successful

-0.270* (0.154)

Valuation 1.138*** 1.125*** (0.0549) (0.0554)

Campaign Equity Offered 1.091*** 1.071*** (0.100) (0.101)

Cumulative % Raised per Campaign 0.00105 0.00218 (0.0182) (0.0182)

No. of Active Campaigns per Day 0.0369*** 0.0370*** (0.00926) (0.00925)

No. of Days until Campaign Expiration -0.0912** -0.0850** (0.0361) (0.0363)

Cumulative No. of Angels per Campaign 0.680*** 0.691*** (0.107) (0.107)

Angel Leader Network Size -0.749*** -0.745*** (0.100) (0.100)

Observations 2,276 2,276

Adj R2 0.389 0.389

41

Table 7. Pledging to New Sectors and Information Quality

The dependent variable is an indicator that equals one if an angel follower pledges to a new sector she has never invested in before, and zero otherwise. The

included regressors are defined in Table 1. Angel pair fixed effects are included in all regressions.

Column 1 Column 2 Column 3

VARIABLES Follower New Sector Follower New Sector Follower New Sector

Current IN-IN 0.0912***

0.0326*

(0.0210)

(0.0190)

Cumulative No. of Successful Coinvestments

-0.413*** -0.409***

(0.0175) (0.0177)

Investor Cumulative No. of Unique Sectors Invested -0.0239*** -0.000633 -0.00174

(0.00405) (0.00370) (0.00375)

Campaign Fundraising Target 0.00277 0.0170 0.0166

(0.0148) (0.0132) (0.0132)

Campaign Equity Offered 0.0116 -0.00932 -0.00297

(0.0260) (0.0230) (0.0233)

Angel Leader Network Size 0.000191 0.00347*** 0.00344***

(0.000419) (0.000400) (0.000401)

Observations 2,988 2,988 2,988

Adj R2 0.352 0.480 0.481

42

Table 8. Role-Switching Motives within Angel Leader-Follower Pairs

The dependent variable is an indicator that equals one if, within an angel leader-follower pair, an angel follower in her last campaign switches to act as the angel

leader in the next campaign. The included regressors are defined in Table 1. Angel pair fixed effects are included in all regressions.

Column 1 Column 2 Column 3 Column 4

VARIABLES Lead-Follow Switch Lead-Follow Switch Lead-Follow Switch Lead-Follow Switch

Lag In-In 0.0225***

0.0265*** -0.00836 (0.00737)

(0.00746) (0.0129)

Cumulative No. of Successful Coinvestments

-0.00877*** -0.0106*** -0.0134*** (0.00314) (0.00318) (0.00329)

Lag In-In X Cumulative No. of Successful

Coinvestments

0.0355***

(0.0107)

Campaign Fundraising Target 0.00136 0.00102 5.11e-05 -5.93e-05 (0.00229) (0.00231) (0.00232) (0.00232)

Campaign Equity Offered 0.00279 0.00371 0.00425 0.00369 (0.00433) (0.00435) (0.00435) (0.00435)

Angel Leader Network Size 0.000218 0.000206 0.000279 0.000218 (0.000940) (0.000940) (0.000939) (0.000938)

Cumulative No. of Angels per Campaign 0.000200 0.000320 0.000285 0.000282 (0.000397) (0.000398) (0.000397) (0.000397)

Cumulative Percent Raised per Campaign 0.00343*** 0.00405*** 0.00398*** 0.00414*** (0.000883) (0.000898) (0.000897) (0.000898)

Number of Active Campaign per Day 0.000243 -0.000595 3.78e-05 -1.70e-05 (0.00154) (0.00153) (0.00154) (0.00154)

Cumulative Amount Invested per Angel -0.0276*** -0.0294*** -0.0277*** -0.0254*** (0.00391) (0.00388) (0.00390) (0.00396)

Number of Days until Camapign Expiration 0.0204*** 0.0243*** 0.0224*** 0.0207*** (0.00277) (0.00278) (0.00283) (0.00286)

Observations 4,441 4,441 4,441 4,441

Adj R2 0.0284 0.0281 0.0694 0.0332

43

Figure 1. Launched Campaigns, 2012Q3 – 2017Q3

Panel A presents the quarterly number of all campaigns launched in a leading UK online equity

crowdfunding platform for the period 2012Q3 – 2017Q3. Any launched campaign may or may not

reach its target funding, and campaigns not reaching their own target are classified as unsuccessful.

Investors get fully refunded from the unsuccessful campaigns which they have pledged to. Panel B

provides the quarterly aggregate of money raised from all successful.

Panel A. Quarterly Number of Launched Campaigns

Panel B. Quarterly Aggregate of Money Raised (Billions of GBP)

0

10

20

30

40

50

60

70

80

90

Successful Unsuccessful

0

0.5

1

1.5

2

2.5

3

44

Figure 2. Funding Target and Valuation, 2012Q3 – 2017Q3

This figure includes all campaigns launched in a leading UK online equity crowdfunding platform for

the period 2012Q3 – 2017Q3. Funding Target in Panel A and Valuation in Panel B are both in nominal

values (1000s GBP). Median and the 25th and 75th percentiles are provided in each graph. Any launched

campaign may or may not reach its target funding. Campaigns not reaching their own target are

classified as unsuccessful. Investors will get fully refunded from the unsuccessful campaigns which

they have pledged to. Percentage Equity Offered in Panel C is the ratio of Funding Target and Valuation

per campaign.

Panel A. Quarterly Summary Statistics of Funding Target (1000s GBP)

Panel B. Quarterly Summary Statistics of Valuation (1000s GBP)

0

100

200

300

400

500

600

P25 Median P75

0

1000

2000

3000

4000

5000

6000

7000

P25 Median P75

45

Panel C. Quarterly Summary Statistics of Percentage Equity Offered

0

5

10

15

20

25

P25 Median P75