Returns on Investments in Equity Crowdfunding
Andrea Signori a, Silvio Vismara b,
Abstract
This paper quantifies for the first time the return on investments in equity crowdfunding. Using an
augmented dataset with combined information from Crowdcube, Crunchbase and the Companies
House, we study the population of 212 successfully funded initial equity offerings on UK
crowdfunding platform Crowdcube from inception (2011) to 2015. We find that 10% of these firms
failed, while 30% pursued one or more seasoned equity offerings, either in the form of private equity
injection (22%), follow-on offering on the same platform (10%), or as target in a merger or acquisition
(1%). The expected annualized return for an initial crowdfunding investor is 8.8%, implying an
overall annual value creation of 25 £m. Among the determinants of post-campaign outcomes, the
presence of non-executives, patents and tax incentives are associated with seasoned offerings, while
none of the companies initially backed by professional investors have subsequently failed.
Keywords: Crowdfunding, Equity Offerings, Security issuance, Private Equity; Public Equity;
Entrepreneurial Finance.
JEL Code: G32; G33; G34; L26.
a
Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123 Milano, Italy.
University of Bergamo, Department of Economics and Technology Management, via Pasubio 7b, 24044 Dalmine (BG),
Italy.
* Contact author: Silvio Vismara, Department of Economics and Technology Management, University of Bergamo, Italy;
viale Marconi 5, 24044 Dalmine (BG), Italy. Ph. +39.035.2052352. Email: [email protected].
1
b
“The goal of the campaign isn’t the campaign; it’s life after the campaign”
[Chris Hawker, United Inventors Association of America, Director]
1.
Introduction
“For the first time,” proclaimed President Obama when he signed the JOBS Act, “ordinary
Americans will be able to go online and invest in entrepreneurs that they believe in”.1 The great deal
of attention devoted by regulators to equity crowdfunding, where a large number of small investors
bid shares of firms listed on a web platform, is mirrored in a growing academic literature. Most of the
papers focus on the campaigns, whose identified success factors include the characteristics of the
business and its top management team (Ahlers, Cumming, Günther, and Schweizer, 2015), the
properties of the offering (Vismara, 2016a), as well as its funding dynamics (Hornuf and
Schwienbacher, 2015; Vismara, 2016b). The ultimate goal of crowdfunding, however, is to build an
enduring business. A successful campaign is therefore a start, not an end. This study is the first to
investigate what happens next, and to quantify the returns from investing in equity crowdfunding.
External investors back projects in equity crowdfunding to receive a return on their investment.
Previous research has indeed shown that non-financial motives and the presence of physical and
experiential rewards do not play a significant role in this context (Cholakova and Clarysse, 2015,
Vismara, 2016a). Shares of firms listed on crowdfunding platforms, however, are not easy to trade.
The development of a secondary market is indeed prevented by the small size of the potential free
float and by serious concerns about investor protection. The JOBS Act, for instance, prohibits a
secondary market during the first year of issuance. In the absence of liquid secondary markets,
crowdfunders have the opportunity to realize returns, or to assess the potential returns, on their
1
The quotation reported is from the White House Press Release, accessed at https://www.whitehouse.gov/thepress-office/2012/04/05/remarks-president-jobs-act-bill-signing. The Jumpstart Our Business Startups (JOBS)
Act, enacted in April 2012, includes the Capital Raising Online While Deterring Fraud and Unethical NonDisclosures Act (CROWDFUND Act), which authorizes equity crowdfunding. In October 2015, the Securities
and Exchange Commission (SEC) adopted final rules, set to become effective 180 days after publication in the
Federal Register.
2
investments only in the presence of post-offering deals. These can occur in the form of additional
equity offerings or mergers and acquisitions (M&As). In a parallelism with the literature on Initial
Public Offerings (IPOs), we focus on Seasoned Equity Offerings (SEOs) and differentiate between
firms that raise capital through further crowdfunding offerings (i.e., public offerings) and private
placements. The latter involve business angels (BAs) and venture capitalists (VCs).
Since market failure can arise from collective action problems that limit investors’ incentives
to purse ex-ante due diligence and ex-post monitoring, the assessment of returns on investments in
equity crowdfunding is crucial to the future of this market. The lack of third-party certification
mechanisms typically at work in other entrepreneurial finance settings, such as endorsement by
prestigious underwriters in IPOs, makes it more difficult to avoid adverse selection problems. These,
in turn, may lead to a suboptimal equilibrium where only low quality ventures list on crowdfunding
platforms (Agrawal, Catalini, and Goldfarb, 2013). Moreover, since crowdfunding is often a one-time
event for entrepreneurs, their reputational incentive to behave correctly is low. They can be tempted
to shirk and engage in self-dealing in the aftermarket, if not to pursue outright frauds. Independently
from moral hazard problems, crowdfunded firms may also fail soon after the campaign because of
the risky nature of the projects or the overconfidence of their proponents. Investors would lose their
money in the event of failure of the company they invested in, whatever the reason for such failure.
In equilibrium, the risk of such eventuality is priced into the asset class.
Risky assets like the shares offered in crowdfunding promise high returns. Coherently, the
inspection of the business plans of crowdfunding firms reveals that most companies exhibit optimistic
estimates.2 The UK Financial Conduct Authority (FCA) raises the concern that these estimates are
based on “misleading or unrealistically optimistic impression of the investment” (FCA, 2015, p. 8).
To date, however, companies’ life after the campaign is totally unexplored. Since understanding
In Crowdcube, for instance, companies are required to provide a “Financial Snapshot”, which summarizes
the key numbers of their business plan in a clear and consistent format across all pitches.
2
3
whether and which successful campaigns have the ability to become successful investments is of
primary importance for investors, an empirical answer to this question is long overdue.
We fill this gap by quantifying the returns on investments in equity crowdfunding. To do so,
we identify three post-offering scenarios. First, if the company fails, investors lose their entire amount
of money, realizing a -100% return. Second, if the company is still active but has not been involved
in any deal, investors are unable to assess the returns from their investment because a valuation of the
company has not yet been observed. In this case, we set the returns to zero. Last, if the company has
completed a SEO or has been targeted in an M&A, independently from whether the offering is made
of shares sold by existing shareholder or by newly issued shares, the return on the investment can be
quantified by comparing the valuation of the company at the initial offering with that implied by the
subsequent transaction. In these cases, we calculate the internal rate of return (IRR), a widely
employed measure of investment performance used in the finance literature (e.g., Kaplan & Schoar,
2005).
Our empirical setting is based on the population of 212 firms that successfully raised equity
capital in the UK crowdfunding platform Crowdcube in the period 2011 to 2015. We construct an
augmented dataset by relying on Crowdcube, Crunchbase, and the UK Companies House as data
sources. We then track these companies till the end of March 2016 and, in line with the scenarios
discussed above, we categorize them as (1) failed, (2) active, and (3) SEOs. Among seasoned
offerings, we then distinguish among (a) public offerings, if the company launched a new
crowdfunding offering, (b) private, if the company has received financing from VCs or BAs, and (c)
M&A if the company has been targeted. Figure 1 depicts the possible scenarios faced by a company
at the end of its first offering.
[ FIGURE 1 ]
4
We find that 22 of our 212 sample firms have failed (10.4%). This is a low percentage, if
compared to the statistics of 56% failure to return capital reported for UK business angel investments
(NESTA, 2009). Among our crowdfunding companies, 64 (30.2%) have raised further capital in
SEOs, of which 47 (22.2%) raised additional public equity in the same platform, 22 (10.4%) secured
private equity from BAs or VCs, and 3 (1.4%) were targeted in M&A deals. By comparing these
proportions with the SEO and delisting rates of firms going public on London’s Alternative
Investment Market (AIM), we find that crowdfunded companies are less likely to either fail or raise
further capital. Vismara, Paleari, Ritter (2012) find that 46.0% of them are delisted, mostly due to
take-over, while 52.6% conduct at least one SEO (on a longer time horizon). Doukas and Hoque
(2016) document that the average firm going public on the AIM complete more than 2 SEOs within
the first three years of the IPO.
Finally, we calculate the average annualized return to equity crowdfunding investors by
weighting the returns associated with each scenario by the corresponding probability of occurrence.
We find that the expected return for investors in an initial crowdfunding equity offering is positive at
8.8%, with 63.5% being the return associated with companies who conduct SEOs.
The paper is organized as follows. In Section 2, we elaborate the theory. Section 3 presents the
institutional setting for equity crowdfunding in the UK. Section 4 motivates our decision to use
Crowdcube as empirical setting and describes data, variables, and methodology used in the study. In
Section 5, we report the results. Section 6 concludes and discusses the implications of our findings.
2.
Post-campaign scenarios
In a world in which writing, issuing, and enforcing contracts consumes resources, and
information is asymmetric and its acquisition costly, properly functioning financial systems should
5
reduce these information and transaction costs (Cumming and Vismara, 2016). To achieve such goal,
entrepreneurial finance markets confront three problems. First, entrepreneurial ventures are by
definition a risky class, as it is difficult to assess their business potential and to predict how they will
perform. Second, information asymmetry and related adverse selection problems are extreme in the
entrepreneurial setting, due to the difficulties faced by investors in discerning the quality of new
ventures and, relatedly, by entrepreneurs in conveying quality signals. Third, raising funds from
external investors separates ownership and control. The related agency costs impact equity
crowdfunding like any other financial market (Cumming, Meoli, Signori, & Vismara, 2016). The
more imperfect the contractual solutions adopted to prevent these issues, the higher the likelihood
that potentially value-creating transactions will not be completed.
Equity crowdfunding markets are less equipped to overcome information asymmetry problems
than other entrepreneurial finance settings. BAs and VCs rely heavily on due diligence predicated on
face-to-face interactions and personal relationships. In IPOs, underwriters are in charge of pricing
and allocating shares. Crowdfunding investors cannot neither rely on the certification of such
intermediaries, nor on their services or those of financial analysts. This may generate reluctance to
invest in crowdfunding projects, with potential investors being willing to do so only if compensated
by a discount. This could eventually ingenerate an Akerlof-type of market failure, resulting in
vanishing markets because the only equilibrium price would be zero.
The weak individual-level incentives to perform due diligence increase the possibility of low
quality firms listing in crowdfunding platforms. Vismara (2016b) documents that crowdfunding
investors free-ride on the investment decisions of previous backers and bid in the projects with the
greatest number of investments in an information cascade fashion. With the crowdfunding community
systematically underinvesting in due diligence, low quality projects will be pooled with high quality
ones. At the extreme, crowdfunding may deliver conditions that can be exploited by criminals in an
attempt to profit from fraud. As leading source of Internet fraud, Internet auctions have been
6
characterized by the information system and criminology literature as highly criminogenic
environments (Chua, Wareham, and Robey, 2007; Newman and Clarke 2003). Moreover, relative to
platforms such as eBay and Airbnb, the lack of repeated interaction in equity crowdfunding increases
the potential for fraud (Agrawal et al., 2013).
On the other hand, the transparency and traceability granted by the web-based structure of
crowdfunding can reduce the risk of fraud. Crowdcube, for instance, reports to have already
conducted 36,927 anti-money laundry checks. At an informal level, the crowdfunding community
may help to identify and expel perpetrators. Community-level controls, however, become effective
only if accompanied by authorities-based formal control (Chua et al., 2007). Regulation on equity
crowdfunding incorporates existing anti-fraud measures and requires intermediaries to comply with
specific, additional measures. In the US, the CROWDFUND Act has powerful anti-fraud provisions,
empowering state authorities as well as investors to sue issuers and other parties who engage in
wrongdoing (Schwartz, 2016). In the UK, the FCA supervises investment-based crowdfunding
platforms by monitoring their websites and reviewing monthly management information (FCA,
2015).
To date, there is no empirical evidence able to justify support to one or the other position, as no
previous study has tracked firms after they successfully secured equity capital through crowdfunding.
As terms of comparison, we now discuss available evidence in related markets, namely reward-based
crowdfunding and traditional IPOs. These comparisons, however, have some limitations. On one
hand, equity-based crowdfunding is intrinsically different from reward-based crowdfunding in terms
of the nature of its proponents and the size of the deals. While in equity crowdfunding, the proponent
is by definition a company, reward-based campaigns are launched mostly by individuals (Colombo,
Franzoni, & Rossi-Lamastra, 2015). The governance and organizational implications of the process
of raising capital through crowdfunding are arguably different. The monetary value of an equity
crowdfunding campaign is typically higher. For instance, the average target funding in our sample is
7
£197,962, while in Kickstarter, the world’s largest reward-based platform, it is less than $15,000 (see,
e.g., Colombo et al., 2015; Kim and Viswanathan, 2014). On the other hand, equity offering on
traditional stock markets (i.e., IPOs) are arguably less risky than those taking place in crowdfunding
platforms. While firms going public typically are at least a few years old, equity crowdfunding
projects are typically proposed by first-time entrepreneurs soon after their conception.
2.1. Survival rates
Mollick’s (2014) study of Kickstarter finds that less than 5% of the projects fail to deliver their
products, and 75% deliver with a delay. Small projects are more likely to fail to deliver rewards, while
the characteristics of the proponent do not affect the chance of a project succeeding. Cumming and
Johan (2016) offer some examples of documented cases of fraudulent behavior in reward-based
crowdfunding in the US. A few headline-grabbing frauds have been reported also in equity
crowdfunding. For instance, in February 2016, the claims management group Rebus went into
administration less than a year after raising £816,790 via an equity crowdfunding round from over
100 investors (at the time of listing, Rebus forecasted profits of 12 £m by 2017/18).
The comparison with IPO literature is less straightforward, as the survival profile of IPOs is
shaped by the delisting decision, that might be motivated by different, not necessarily negative,
reasons. For instance, firms can delist from a second-tier market to transfer to a main market, or be
taken-over due to their attractiveness as acquisition targets. Delistings can therefore even represent a
desirable outcome.3 Vismara et al. (2012) show that approximately one fourth of the delistings
occurred among European IPOs over the last two decades are associated with an M&A, and that
3
Differences in methodology, sample size and period, and definition of involuntary delisting make
comparisons between studies challenging and limit our ability to draw strong conclusions. For instance, some
studies categorize mergers and acquisitions as censored survivors (e.g., Jain and Kini, 2000), while others
categorize them as non-survivors (e.g., Carpentier and Suret, 2011). Moreover, determinants of delisting rates
depend on the identification of the event. For instance, Vismara and Signori (2014) find that the number of
patents significantly increases a firm’s likelihood of being delisted due to take-over, while it decreases the
likelihood of voluntary delisting. On the other hand, more R&D-intensive firms are more likely to be delisted
due to take-over, but also due to unmet regulatory requirements.
8
transfers from different markets are also frequent. Espenlaub, Khurshed and Mohamed (2012) and
Pour and Lasfer (2013) document that a significant fraction of delistings from the London’s AIM are
voluntary. Broadly speaking, the delisting rate five years after the IPO ranges from 9% to 48%, with
firm quality shaping the variance. For instance, larger firms are better equipped to survive, as
originally found by Seguin and Smoller (1997). Fama and French (2004) argue that the lower survival
rates of firms listed more recently is due to then higher percentage of lower profitability and higher
growth firms going public. This argument is coherent with recent studies on the decreasing annual
numbers of firms going public (Gao, Ritter and Zhu, 2013; Ritter, Signori and Vismara, 2013) and
increasing probability to get acquired soon after the IPO (Chemmanur, Signori and Vismara, 2016).
2.2. Seasoned equity offerings
By providing a first market assessment of the project and evidence of connection with active
supporters, firms are expected to benefit from a successful crowdfunding campaign, both in the
product and the financial market. So far, however, the only available study on the “success after the
success” in (reward-based) crowdfunding is that by Mollick and Kuppuswamy (2014).4 As discussed
in the introduction, firms listing on equity crowdfunding platforms propose optimistic growth
expectations that are, however, difficult to meet without the injection of additional capital.
Successfully crowdfunded firms that subsequently do not engage in further deals are unlikely to
become high-growth firms. Moreover, these “financially inactive” firms do not mitigate adverse
selection and moral hazard concerns through the progressive reduction in information asymmetries,
as VC stage financing typically does (Gompers, 1995).
Central to corporate finance literature, our paper is related to studies on stage financing. Instead
of providing all the necessary capital upfront, VCs invest in stages. Since the seminal study by
4
In a survey of 158 successfully funded projects in Kickstarter, they find that around 18% of the respondents
raise outside risk capital in the forms of VC or BA investing, while 8% through additional reward-based
crowdfunding campaigns. The proponents’ specific industry experience and the presence of “a substantially
complete business plan before fundraising” are the main predictors of outside funding (Mollick and
Kuppuswamy, 2014, p. 12).
9
Sahlman (1990), a large literature has modeled such a staggered infusion of capital over time as a
control mechanism used by VCs, who retain the option to abandon investee firms whose performance
turns out to be worse than expected. Hellmann’s (1994) multi-stage model, for instance, explains how
stage financing provides incentives for active monitoring. Staging capital, indeed, allows the VC to
gather information and monitor the progress of the firms, while the threat (put option) of exit creates
incentives for the entrepreneur to maximize value, eventually at the cost of inducing short-termism.
A similar line of thought applies to studies that relate initial to seasoned public offerings (Welch,
1989).
2.3. Merger and Acquisitions
Searching for potential acquirers is costly as it entails consumption of time and resources. The
literature on IPOs has demonstrated that a sequential divestiture strategy through IPO may be
attractive relative to a direct sale if it serves to raise the firm’s profile and reduce uncertainty (Shen
and Reuer, 2005). The contextual move from the private to the public domain, indeed, increases the
level of a firm’s disclosure and eases access to information. This makes the process of going public
responsive to adverse selection problems by increasing the amount of information available on firms,
especially for those that suffer from a larger degree of ex-ante information asymmetry (Mantecon and
Thistle, 2011). We argue that similar arguments hold for equity crowdfunding offerings, which, with
regard to increasing firm visibility and disclosure, resemble IPOs.
One important benefit of first going public and then being acquired is that, by successfully
going through the due diligence process associated with an IPO, the firm may be able to reduce the
asymmetric information faced by potential acquirers, thus enabling it to obtain a higher valuation in
a post-IPO acquisition, compared to the valuation it can obtain in a direct acquisition as a private
firm. A second important benefit is that the firm may be able to invest the external capital raised in
the IPO in its growth opportunities and thus obtain a higher valuation in an eventual post-IPO
acquisition, compared to the scenario where it is acquired as a private firm, in which case its valuation
10
may be lower due to the inability to fully implement its growth opportunities because of the financial
constraints faced as private firm. Coherently with these arguments, recent finance studies have
confirmed that a growing number of firms are acquired soon after their IPO (Gao et al., 2013;
Chemmanur et al., 2016; Signori and Vismara, 2016)
A parallel stream of research in entrepreneurship (e.g., Audretsch and Lehmann, 2007;
Bonardo, Paleari and Vismara, 2010; Lehmann, Braun and Krispin, 2012) has modeled the market
for corporate control as a matching mechanism between established and entrepreneurial firms with
regard to assets (Gans and Stern, 2000; Granstrand and Sjölander, 1990), innovation (Acs and
Audretsch, 1988; Hall, 1990), and labor specialization (Lichtenberg et al., 1987; Lichtenberg and
Siegel, 1990). These studies argue that the decision to be acquired by incumbents is often valuemaximizing for entrepreneurial firms. Everything else equal, for the reasons discussed above, this
possibility is higher for listed than private firms.
2.4. Professional investors
Private seasoned equity offerings comprehend capital infusions from BAs and VCs that take
place after the closing of crowdfunding campaigns. However, professional investors invest also
during the campaigns. BAs are frequently found to operate in equity crowdfunding platforms
(Enterprise Research Centre, 2014), and Crowdcube reports that 9 VCs invested in campaigns
launched on their platform.5 Additionally, the UK Government has invested £1.25m in 9 offerings
through the London Co-Investment Fund. As “superior principals”, these professional investors are
likely to benefit from an information advantage over small investors.6 Small investors in
5
Independent VCs that invested in campaigns on Crowdcube include Balderton Capital, British Business
Bank, DN Capital, Episode1, Forward Partners, Index Ventures, MMC Ventures, Octopus Investments,
Passion Capital, Wellington Partners. Other sources confirm the participation of professional investors in UK
equity crowdfunding campaigns. NESTA (2014) report that institutional investors embracing alternative
finance is becoming a ‘feature of the UK market’.
6
Corporate finance literature defines small investors as those who (1) invest relatively small amounts of
money, and (2) receive a relatively small stake of a company in return. This definition is in line with that of
restricted investors used in crowdfunding (see next Section).
11
crowdfunding have limited opportunity to perform due diligence due to the presence of fixed costs
(Agrawal et al., 2013).7 Moreover, they are reported to lack the experience and the capability to
evaluate different investment opportunities (Ahlers et al., 2015). This is not the case of professional
investors, who are more likely to possess a more accurate set of information that allows them to select
high quality offerings. If this is the case, we would expect those offerings in which they invest to be
less likely to fail and more likely to raise further capital. This would provide support to the intertwined
relation between traditional funding models and equity crowdfunding portrayed by Agrawal et al.
(2013), with professional investors ‘picking the winners’.
The scope of the benefits brought by the presence of professional investors extends beyond the
financial aspect. BAs and VCs are indeed expected to provide portfolio companies with bundles of
value-added activities (Gompers and Lerner 2001). These include both direct and indirect benefits,
such as coaching and access to network, and certification effect to third parties (e.g., customers,
skilled workers, alliance partners, and financial intermediaries). The presence of professional
investors can help to develop a better strategy and gain market shares more quickly. Consumer
research posits indeed that the support of influential individuals accelerates the diffusion of products
and innovations (Valente, 1995). Along this line of arguments, firms with offerings backed by
professional investors are expected to outperform other firms because of both selection and treatment
effects.
More broadly, the distinction between small and professional investors is of current interest.
First, one of the most sought after benefits of a successful crowdfunding campaign is the proof of the
market demand for the product or service delivered (NESTA, 2014). This can be related, as discussed
7
Crowdfunders receive a relatively small stake in a company in return for their investment. As reported in
Vismara (2016b), the average equity stake offered in Crowdcube is 13.8%, whereas the average number of
investors in successful campaigns is 152.5. This means that the average investor acquires less than 0.1% of the
company’s equity. On the contrary, professional investors tend to invest relatively substantial amounts of
money. For instance, the crowdfunding offering of Sugru, creator of the world’s first moldable glue, raised
1m£ in a single investment.
12
above, to the presence of influential investors, but is also directly related to the visibility that stems
from being listed and successfully funded on a public crowdfunding platform. Successful campaigns
are indeed covered by media, in particular when the number of investors is remarkably high or when
large amounts of money are quickly raised. For instance, all the major finance newspapers reported
the news that Crowdcube self-crowdfunded £1.2m in only 16 minutes in 2014, or that Pressery
reached the target capital in less than 2 hours in its 2015 offering.
Second, recent changes in traditional stock markets further motivate the attention towards the
different roles played by professional and retail investors in equity crowdfunding platforms. Over the
last two decades, three quarters of the IPOs in Europe took place in second markets, such as London’s
AIM. As reported in Vismara et al. (2012), most of these IPOs are offered exclusively to institutional
investors, and are equivalent to private placements, which frequently raise only a few million euros
and rarely develop liquid trading. With institutional investors being allocated the largest fraction of
IPO shares (Aggarwal, Prabhala and Puri, 2002), crowdfunding investors are likely to be more diverse
than shareholders of newly listed companies.
3.
Equity crowdfunding in the UK
The United Kingdom has the most developed pure equity crowdfunding market, both in terms
of number and size of the offerings. Its success factors are, at least partly, the same as those of other
entrepreneurial finance markets, where the British VC industry is the most developed in Europe
(EVCA, 2015) and London’s AIM the most successful second-tier market (Vismara et al., 2012).
Coherently, the Depth of Capital Markets Index, developed by Groh, Liechtenstein, and Lieser (2012)
ranks the UK at the fourth position worldwide, the only European country among the top five.
Moreover, the regulation of equity crowdfunding in the UK is often put forward as an important
13
ingredient of its development, so that it serves as a model for other legislations (Steinhoff, 2014).8
We focus below on two aspects, namely the conditions required by investors and the tax incentives
crafted for them.
The regulation of equity crowdfunding is currently defined in the UK by the FCA’s Policy
Statement PS14/4, which delegates the FCA to “mitigate the liquidity risk investors face when
investing in the equity or debt securities of small and medium enterprises which are difficult to price
and for which there is no, or only a limited, secondary market”. To this extent, three types of investors
are allowed to trade on crowdfunding platforms, namely high net worth, professional, and restricted
investors. High net worth investors are defined under preexisting rules as someone with annual
income of at least £100,000 or net assets of at least £250,000. An investor is certified as professional
if a qualified firm assesses the investor’s capability to understand the risks associated with engaging
in non-readily realizable investments, or in presence of a “Self-Certified Professional Investor”
statement in which the investor declares to be a member of a BA network, to have worked in the
business finance sector over the previous two years, or to have served as a director of a company with
at least £1 million in revenues. If (and only if) an investor is neither high net worth nor professional,
then the fraction of money invested in non-readily realizable investments, including crowdfunded
securities, cannot exceed 10% of her net assets. In such cases, the platform requires to certify that
they are informed regarding investment opportunities and risks or have received independent advice.
There are significant tax incentives for investing in small businesses in the UK. Two
overlapping incentive programs are of particular interest to crowdfunding investors, as testified by
the high visibility given to these programs on all the platforms. The Enterprise Investment Scheme
(EIS) provides a tax deduction of 30 percent of the cost of shares purchased in qualifying private
8
Some other countries, such as Germany, allow for certain profit-sharing arrangements, but forbid the sale of
shares carrying voting rights through crowdfunding platforms. In countries where pure equity crowdfunding
is permitted, such as France and Italy, the amount of capital raised to date is considerably lower than in the
UK (Vismara, 2016a).
14
companies with a maximum tax benefit of £300,000. The Seed Enterprise Investment Scheme (SEIS)
provides additional incentives by exempting shares up £150,000 in value from capital gains taxes.
This amount is set as the standard maximum issuance sought by UK crowdfunding platforms such as
Seedrs and Crowdcube, and exceptions can be granted only to issuers who present a “compelling
proposition” subject to the platform’s approval. EIS and SEIS are subject to a three year minimum
holding period, with the relief being clawed back if shares are disposed earlier. This could create an
inventive to postpone exit from crowdfunded firms.
4.
Research design
4.1. Crowdcube
In light of the arguments discussed in the previous Section, the UK experience provides us with
a unique opportunity to explore equity crowdfunding, also as a possible predictor of what will likely
happen in the US. The largest equity crowdfunding platform in the UK is Crowdcube, on which we
focus on.9 Established in 2011, Crowdcube is, as of April 2016, the world’s largest platform, with
£150 million successfully raised from more than 270,000 investors in 105 countries. Each project’s
business plan is vetted before listing (according to Crowdcube statistics, the due diligence team, on
average, verifies 28 entrepreneur claims for each admitted project), whereas no ongoing reporting is
required to the company. Like other equity crowdfunding platforms, Crowdcube works in the
traditional “all-or-nothing” fashion (Cumming, Leboeuf, and Schwienbacher, 2014). Only if the
target amount is reached, the campaign is successful and investors become direct shareholders in the
company.
Crowdcube provides the most useful empirical setting to our aims. First, it represents the largest
sample size and has the longest trading history amongst UK crowdfunding platforms. Second, the
9
According to AltFi (2015), Crowdcube has raised more capital than all other competing platforms. Beauhurst
names Crowdcube as the leading equity investor in 2015 and the most prolific investor in the e-commerce
sector. Crowdsurfer estimates Crowdcube’s share in the UK investment crowdfunding market in 2015 at 52%.
15
direct involvement of a large number of small investors makes this platform particularly suitable for
studies in corporate finance. Most of them have indeed used this empirical setting (e.g. Cumming et
al., 2016; Ralcheva and Roosenboom, 2016; Vismara 2016a, 2016b). We now motivate our choice
by commenting on the alternatives. According to AltFi (2015), SyndicateRoom is the second equity
crowdfunding platform in the UK, with a funded equity volume of 37.7 £m as of September 2015.
SyndicateRoom, however, is not a pure equity crowdfunding platform, as it requires at least 25% of
the target capital to be already committed by BAs and/or professional arms-length investors. Seedrs
is the third platform with 36.0 £m, followed by VentureFounders with 11.4 £m. Both these platforms
use a unified nominee structure, meaning that the platforms themselves remain the representative of
their investors throughout the investment period, rather than each individual backer becoming a
shareholder.
4.2. Sample and data
We construct an augmented dataset by relying on Crowdcube, Crunchbase, and the UK
Companies House as data sources. From Crowdcube, we identify the equity offerings that make up
our sample, covering the platform since its inception in 2011 to the end of 2015. Starting from the
population of 318 successfully funded offerings, we exclude 12 mini-bond offerings and 94
“seasoned” equity offerings, i.e. follow-on campaigns conducted by companies that had already
raised in funds in Crowdcube. The final sample is made of 212 initial equity offerings.
We then monitor these companies from the closing date of their initial offering till the end of
March 2016, using Crunchbase to identify capital infusions and the UK Companies House to identify
failures. Crunchbase is a database of startup companies operated by TechCrunch that records
information about their characteristics and relevant events. We collect information on the equity
offerings carried out by each company, including the type of transaction and the identity of the
investors. Crunchbase is increasingly used in entrepreneurial finance studies (see, e.g., Cumming,
Walz, Werth, 2016; Hellmann and Thiele, 2015). The Companies House is a government agency
16
acting as the official registrar of UK companies. All companies incorporated and dissolved in the UK
are required by law to make this information publicly available through the Companies House and
file their annual accounts.
We categorize our sample companies in one of the three post-offering scenarios as follows.
First, we classify as ‘SEOs’ those companies who raised additional capital subsequent to their initial
crowdfunding offering, according to Crunchbase. Second, ‘Active’ companies correspond to those
who are listed as ‘active’ at the Companies House at the end of the observation window, but did not
raise further capital. Third, ‘Failed’ companies refer to those who are listed as ‘dissolved’, ‘in
administration’, ‘in liquidation’, or whose compulsory filing of accounts is overdue by more than 6
months at the Companies House, or if their website indicates that trading has ceased. Finally, we
further distinguish SEO companies based on the type of transaction, namely (1) public SEO, if the
company launched a new crowdfunding offering, (2) private SEO, if the company received financing
by a BA or VC, and (3) M&A, if the company (or a minority stake) was acquired by another firm.
VC and M&A transactions have been cross-checked in Thomson Financial SDC.
4.3. Returns
Whilst raising further capital in a SEO at a higher valuation does not represent an opportunity
for crowdfunding investors to cash out and realize positive returns, the occurrence of such transaction
is an important indication that this possibility may arise at a future date. Most importantly, when a
company undertakes a SEO its valuation becomes observable, thereby allowing investors to assess
the performance of their investment to date. To this extent, we compute the annualized IRR, a
common indicator used to assess investment performance in the private equity literature (see, e.g.,
Kaplan and Schoar, 2005; Harris et al., 2015).
We set the return on investing in a failed company to -1, assuming no money is returned to
investors in case of failure. For active companies, we set the return to zero, given the absence of
equity transactions that would allow to observe the valuation of the company, thereby preventing to
17
quantify any return to shareholders. For SEOs, we determine the return an investor would earn by
buying a fraction of equity in the initial crowdfunding offering and subsequently selling this fraction
in the SEO at the new valuation. Consider for instance a company who launches an initial
crowdfunding offering in January 2014 raising £10,000 for 10% of its equity, and then issues another
8% of equity in a new offering (i.e., a public SEO) for the same amount of capital in June 2015. The
equity position of the investors in the initial offering gets diluted due to the new equity issued by the
company in the second crowdfunding offering.10 Collectively, their stake decreases from 10% to
0.1/1.08 = 9.3%. The new valuation of the company as of June 2015 can be inferred from the terms
of the SEO (£10,000 for 8% of equity gives a £125,000 total valuation), which would allow initial
investors to raise 9.3% of £125,000, equal to £11,574.1 by selling their shares. This results in an
annualized IRR of 10.9%. The same procedure is adopted in the computation of the returns from
private SEOs. In all these cases, indeed, we find that offerings to venture capitalists and business
angels are entirely made of newly issued shares.
Finally, there is no dilution in case of an M&A, as in this case shares are directly sold by existing
shareholders (secondary market). Examples of M&A deals are E-Car Club and Camden Town
Brewery. E-Car Club was bought out by Europcar in July 2015, approximately two years after its
March 2013 initial equity offering. Camden Town Brewery was acquired by Anheuser Busch InBev
in a deal worth about £85m. The acquisition took place in December 2015, approximately 8 months
after the closing of Camden’s initial equity offering, delivering an annualized internal rate of return
of 111.9% to crowdfunding investors. All the M&As of crowdfunded firms in our sample delivered
positive returns for crowdfunding investors. This is why, differently from the IPO papers which treat
take-overs as delistings, we clearly distinguish M&As as a peculiar type of event.
10
All the equity crowdfunding offerings in our sample are made of newly issued shares (primary market).
18
4.4. Variables
To define the set of independent variables employed in the study of the post-offering outcomes,
we first rely on those used in published studies on the success of equity crowdfunding campaigns
(i.e., Ahlers et al., 2015; Vismara, 2016a).11 Company-specific variables are measured at the launch
of the initial offering. We define firm age and diversification by referring to the incorporation date
and the number of SIC codes reported in the Overview section of the Companies House website.
Then, since the quality of the company crucially affects the likelihood of going through each of the
post-offering scenarios, we employ a number of important quality proxies aimed at controlling for
this effect. First, we include the positive sales dummy, equal to one if the company has already
reported positive sales prior to the initial crowdfunding offering, according to the financial snapshot
published on the pitch webpage. Second, we control for the presence of non-executive directors in
the company’s board, given their potentially valuable advisory role, by setting a dummy equal to one
if the company has appointed at least a non-executive director. Third, we include the patents dummy,
which equals one if the company owns or is currently filing patents, according to information reported
in the pitch details.
We control for offering-specific effects by including in our regression the amount of target
capital and the fraction of equity offered. Ahlers et al. (2015) and Vismara (2016a) show indeed that
these variable impact on the probability of success of an equity offering. Moreover, since different
classes of shares can be issued depending on whether they carry voting rights or not (Cumming et al.,
2016), we include a voting rights dummy which equals one if they do. Finally, we identify offerings
that are eligible to the SEIS tax incentive by setting the SEIS dummy equal to one.
Finally, we study the effects of investor participation in the offering on post-campaign
outcomes. Differently from the firm- and offer-specific variables above, such investor-specific
11
A correlation matrix among all the independent variables is available in Appendix A. The variance inflation
factors (VIFs) associated with each model specification all fall well below the acceptable threshold of 10,
indicating multicollinearity is not a concern.
19
characteristics are measured at the end of the initial crowdfunding campaign. First, we include the
number of investors who participated in the offering, used in Ahlers et al. (2015) and Vismara (2016a)
as a proxy for the success of the offering. Second, we define a quick success dummy, equal to one if
the target capital was raised within the first 20 days of the offering. Since these companies have been
able to reach the target amount in up to one third of the standard duration of an offering (set to 60
days), we investigate the effect that such a remarkable success may have on the occurrence of postoffering scenarios. Third, we monitor the participation of professional investors in the initial offering
by including the professional investor dummy, equal to one if a BA or VC has invested in the
company. Professional investors are identified by Crowdcube, as they typically disclose their identity
by linking their profile to webpages or professional social networks (Vismara, 2016b). We crosscheck these data in Crunchbase.
4.5. Methodology
We estimate a company’s likelihood of going through each of the three above post-offering
scenarios while taking into account the time elapsed since the initial offering. An appropriate way to
model this situation is to estimate hazard rates, i.e. the probability that a given scenario occurs at a
given time, conditional on the fact that neither this nor other alternative scenarios have occurred
before that time. We do so by employing a competing risks proportional-hazard duration model (Fine
and Gray, 1999), fitted using maximum likelihood. This technique allows to determine the hazard
rate for a scenario of interest in the presence of other possible competing scenarios.12
In our setting, we consider a SEO as an event of interest with failure being the competing event,
and vice-versa, in order to investigate the determinants of both scenarios. ‘Active’ companies
12
The competing risks model is more suitable to our setting than other possible alternative models. First,
standard duration models such as Kaplan-Meier treat observations going through competing events as
censored. In our setting, this would imply that failed companies could presumably still conduct a SEO in the
future, which is clearly not the case. Second, the traditional Cox proportional hazard model can be adapted to
account for multiple events, but interpreting the results would become difficult due to the impact of an
explanatory variable on the hazard rate of a given event, which is a nonlinear function of its impact on the
hazard rates of other events, as well as of their respective baseline hazards.
20
correspond to the right-censored observations. The time to occurrence of the event is measured
starting from the closing date of the initial crowdfunding offering as reported in Crowdcube. For
failed companies, the event date is the failure date or the commencement date of the first insolvency
case if the company is in administration or liquidation, according to the Companies House website.
For SEOs, we use the date of completion of the second crowdfunding offering or of the private deal
as recorded in Crunchbase. We then repeat our analysis by distinguishing public and private SEOs,
with M&As being included in the latter category. If a company conducts multiple SEOs, its scenario
is determined by the first event. Our company-, offering-, and investor-specific measures are the
explanatory variables. For ease of interpretation, we report coefficients instead of subhazard ratios in
the results. A positive coefficient indicates that an increase in a given variable increases the hazard
rate relative to its baseline level (i.e., when all explanatory variables are set to zero), therefore making
the occurrence of a given scenario more likely (i.e., making the time to event shorter). Conversely, a
negative coefficient indicates that an increase in the explanatory variable decreases the hazard ratio
and makes the occurrence of a given scenario less likely (i.e., makes the time to event longer). Last,
we also estimate a zero-inflated negative binomial regression with a company’s number of SEOs
within one year of the initial offering as dependent variable. The choice of this model is motivated
by the fact that the largest fraction of observations (both active and failed companies) have zero postoffering SEOs. Moreover, the distribution properties of this variable make the negative binomial
preferable over a Poisson model, which requires no overdispersion.
The fact that our analysis is conducted on companies that completed a successful initial offering
raises the concern that selection in the successful and unsuccessful samples is not random. As a
consequence, unobservable characteristics that determine the success of a company’s initial offering
may also be correlated with the likelihood of going through a given post-offering scenario, thereby
producing biased estimates. We therefore address this issue by adopting a two-step Heckman
procedure. In the first step, we model the probability of conducting a successful initial crowdfunding
21
offering by means of a probit regression with the success dummy as dependent variable. We follow
the model specifications used by Ahler et al. (2015) and Vismara (2016a) to model the success of
crowdfunding campaigns, and include the log of company age, the positive sales, non-executive
directors and patent dummies, the log of target capital, the fraction of equity offered, and the voting
rights, SEIS, and industry (1-digit SIC) dummies as independent variables. Furthermore, since the
Heckman model requires instruments to avoid identification being driven by the non-linearity of the
first step, we identify the competing offerings variable as instrument, defined as the log of 1 plus the
number of offerings active and available on Crowdcube at the launch date of the considered offering.
This measure is an important determinant of offering success (Vismara, 2016b) but is presumably
uncorrelated with a company’s likelihood of going through a given post-offering scenario. In the
second step, we then run our competing risks and count models while correcting for selection bias
thanks to the inclusion of the inverse Mills ratio, estimated in the first step, among the independent
variables.
5.
Results
5.1. Returns
We calculate the average annualized return to equity crowdfunding investors, considering the
different outcomes for each firm. First, when companies fail, investors lose all their money. Out of
212 sample firms, 22 have failed (10.4%), with investors realizing a -100% return. Second, the return
from investing in companies that have neither failed nor pursued further financial deals is
unobservable. In these 126 cases (59.4%), the return is set to zero. Most importantly, we calculate the
return for each firms that is involved in equity deals after the initial crowdfunding campaigns. As
reported in Table 1, the average return earned from investing in 64 (30.2%) companies that have
raised further capital in SEOs is 63.5%.
By weighting the returns associated with each scenario by the corresponding probability of
occurrence, we find that the expected return on investments in initial equity crowdfunding offerings
22
equals 8.8%. The average target capital of successful offerings is about £200,000, corresponding to
an issuance of 14.8% of a company’s equity (Table 1). This means that the average initial valuation
of firms successfully listing on Crowdcube is 1.3 £m. The aggregate capitalization of this population
of firms is then equal to 283.5 £m, and an average return of 8.8% implies an annual value creation of
25 £m.
We further distinguish SEOs between public, private, and M&As. The subsample of public
SEOs is made of 47 companies (22.2%) that, after successfully completing an initial equity offering,
raise additional equity on the same platform. We find that they do so at a high premium. The average
internal rate of return of crowdfunding follow-on offerings is indeed 69.1%. In our sample, 22
companies (10.4%) secured private equity from BAs or VCs after listing on Crowdcube. On average,
the return on these investment is 61.2%. 3 companies were targeted in M&A deals, with a 48.8%
average return.
Last, we describe the successfully crowdfunded firms that make up our sample. As reported in
the first column of Table 1, these firms are on average 5.6 years old (67.3 months) and most of them
(53.3%) have positive sales at the time of listing. The percentage of firms that employ non-executive
directors or that hold patents is quite low, respectively 9 and 8 percent. On the contrary, three out of
four companies offer to crowdfunding investors shares that carry voting rights. The average number
of these investors is 145, and ten percent of these offerings are closed within only 20 days of offerings.
Professional investors back only 6.6% of successful offerings.
5.2. Univariate tests
We now investigate whether the identified post-campaign outcomes are associated with
different firm characteristics. We begin with a univariate setting. In subsequent analyses, we switch
to a multivariate setting to include possible determinants and control factors simultaneously. Table 1
gives the results of our first broad analysis. First, we explore whether and how firms that failed after
a successful crowdfunding campaign differ from those that are still alive. We perform a t-test on the
23
difference in mean between this group of 22 firms that subsequently fail and the rest of the sample,
and find that only 18.2% of failed firms reported positive sales before launching their initial offering,
compared to an average of 53.3% for the entire sample. The number of investors backing initial
offerings that subsequently fail is, on average, lower than the rest of the sample (77.2 investors vs
144.9 for the full sample). Second, we focus on SEO firms. We find that the fraction of firms that
have non-executive directors is larger (18.8% vs. 9.0% for the full sample), while the fraction of firms
that deliver voting rights to investors is smaller (62.5% vs. 75.5% for the full sample). A larger
fraction of professional investors back companies that subsequently pursue SEOs (12.5%), while
none of the firms they back have subsequently failed. Last, firms that have neither failed nor raised
further capital (labelled active firms) are older and less prone to hire non-executives. On average,
active firms are 79.3 months old at listing (57.3 for the full sample) and only 4.8% of them have nonexecutive directors (vs 9.0%).
Finally, we inspect whether different types of SEOs are associated with different types of firms.
We find that firms that pursue public SEOs often do not have positive sales at listing. These firms
were less able than the rest of the sample to attract professional investors, as only 4.3% of their
offerings were backed by professional investors. On the contrary, about one third (31.8%) of the firms
raising private equity in seasoned offerings were able to do so. Private SEO firms are also more likely
to have non-executive directors. The three firms that are targeted in M&As were highly successful in
their initial crowdfunding equity offering, raising on average £553,300 from 778 investors.
So far, we investigated offering and firm characteristics on the base of post-campaign scenarios.
We now move to the outcomes by analyzing the subsample of 64 firms that completed seasoned
offerings within, distinguishing by SEO type. On average, these firms engage in 1.6 SEOs, with
private SEOs being more frequent (2.1). Firms typically go for a SEO after about two years from the
initial crowdfunding offering. The total amount of capital raised by the average firm in subsequent
24
seasoned offerings equals 2.3 £m, although this value is driven by large M&As. Each follow-on
crowdfunding offering (public SEO) allows to raise £455,900 on average.
[ TABLE 1 ]
5.3. Multivariate tests
We now turn to a more detailed analysis that makes use of multivariate analyses to identify the
determinants of post-offering outcomes. The results of our competing risks regressions are reported
in Table 2. Evidence from the first step of the Heckman model confirms on a larger sample the results
documented by Vismara (2016b). More robust firms are more likely to succeed to raise capital in
crowdfunding campaigns. Indeed, age and traction (positive sales) are both significant predictors of
success (p<0.01). On the other hand, success is more difficult to achieve when the amount of capital
to be raised is larger, and in presence of a larger number of competing offerings (p<0.01). The number
of offerings active and available on Crowdcube at the launch date of the considered offering is used
as instrumental variable, since it affects the probability of success of the campaign but does not
influence the likelihood of post-campaign outcomes.
We now move to the second step of the Heckman model, which is a competing risks regression
on post-offering scenarios. First, we discuss the results of Models 1 and 2, which include only firmand offering-specific independent variables. We find three strong determinants of the probability to
pursue a SEO, that, as motivated above, we interpret as successful outcomes. Results in Model 1
show indeed that more diversified firms, firms with at least one non-executive director, and offerings
eligible for the SEIS tax relief are more likely to succeed in raising additional capital in follow-on
offerings. First, business diversification results in higher SEO probability as the company’s
prospective to successfully engage in equity deals is enhanced from its appeal to a larger pool of
25
investors (p<0.01). Model 1 documents that a one unit increase in diversification, corresponding to
an additional SIC industry in which the firm operates, increases the hazard rate of conducting a SEO
by 2.25 times.13 Our interpretation is that companies operating in various industries are of interest for
a larger number of industry-specialized funds. Alternatively, more diversified firms may be more
likely to engage in SEOs thanks to a more robust and less uncertain business model. We note,
however, that also the positive sales variable affects the probability of SEOs (p<0.05), following its
high impact on the probability of success of crowdfunding campaigns. Firms with traction (positive
sales) at listing are also less likely to fail (p<0.05, Model 2), while diversification does not affect the
risk of failure.
Second, the presence of non-executive directors enhances the chances to get involved in SEOs
(p<0.01). The magnitude of the coefficient implies that having at least a non-executive director in the
board raises the hazard rate of conducting a SEO by 3.35 times.14 Third, we find that tax incentives
matter. Firms that benefit from the SEIS tax shield are both more likely to engage in follow-on
offerings (p<0.01, Model 1) and less likely to fail (p<0.01, Model 2). The hazard rate of undertaking
a SEO is indeed 3.13 times higher for these firms, while that of failing is 77% lower. This result
points to a success for fiscal policies meant to stimulate the growth of the private equity industry (for
a review, see Colombo et al., 2016).
We then find that successfully crowdfunded firms with patents are more likely to raise capital
in seasoned offerings (p<0.05). At a first glance, this is a puzzling result. Previous studies on equity
crowdfunding offerings find indeed that patents are not a predictor of the success of the campaign.
Ahlers et al. (2015) report that companies that registered patents do not have higher chances of
securing funding in Australian platform ASSOB. In a study closely related to the present one, Vismara
(2016b) confirms that patents do not increase the success rate of offerings on Crowdcube. Likewise,
A one unit change in the independent variable changes the hazard ratio by (e β – 1).
We acknowledge that this is likely to be an endogenous results, as firms that hire non-executives are probably more
prone to growth through external strategies.
26
13
14
in the first step of our Heckman model, we find that patents do not affect the probability of reaching
the target capital. After the campaign, however, patentee firms are 2.72 times more likely to do SEOs
than other firms. Our results, combined with evidence from previous studies, are consistent with
Rajan’s (2012) model of standardization following differentiation. Rajan argues that the soft variables
(e.g., founder’s human capital) are necessary early on to differentiate startups. To produce significant
net present value, indeed, ventures have to differentiate from the ordinary. However, as time goes by,
they will find it increasingly difficult to raise follow-on finance against differentiated assets. For this
reason, an entrepreneur has to commit to undertake a second transformation, standardization, that will
make the human capital embodied in the firm replaceable. Property rights are at the core of this
standardization process that gives outside financiers residual rights over going-concern surplus.15
The structure of the offering is found to have little impact on the probability to engage in SEOs.
While larger initial offerings are more likely to be followed by additional offerings (target capital,
p<0.05), such events are less frequent after offerings that grant smaller fractions of equity (equity
offered, p<0.10). Last, the inverse Mills ratio is not significant, suggesting that our results are not
suffering from success bias.
We move our attention now to the determinants of failure. First, as anticipated above, Model 2
shows that firms that benefit from the SEIS tax shield are less likely to fail (p<0.01). Second, we find
that firms with positive sales at the times of the initial offering (p<0.05) and older firms (p<0.05) are
less likely to fail. We therefore confirm the role of sales as a proxy for robustness of firms going
public (Chemmanur et al., 2010). Third, we find weak evidence that firms granting voting rights
attached to the shares offered have lower survival profiles (p<0.10). This result offers further support
to the view that the decisions to deliver voting rights in crowdfunding matters, although it does not
Rajan’s (2012) model extends the property rights theory developed by Grossman and Hart (1986) and Hart and Moore
(1990), that refer to “residual rights of control” conferred by ownership that cannot be determined by explicit contracts.
Rajan (2012) reports a number of examples and findings of other papers that support this model. For instance, Kaplan,
Sensoy, and Stromberg (2009) document 46% of firms have patent rights at the business plan stage, going up to 60% at
the time of the IPO, and 66% soon after. More of the intellectual property of the firm is embedded in formal patents as it
undergoes subsequent financing rounds. Further evidence is found in the increased patenting activity after VC injection.
27
15
directly influence the campaigns’ probability of success (see Cumming et al., 2016, for an empirical
analysis).
In Models 3 to 8, we add to our regression the variables measuring the effects of investor
participation in the offering on post-campaign outcomes. Differently from the firm- and offer-specific
variables, such investor-specific characteristics are measured at the end of the initial crowdfunding
campaign. First, in Models 3 and 4 we include the log of the number of investors who backed each
offering and find this variable to be a negative predictor of the probability to perform SEOs (p<0.05).
A one standard deviation increase in this variable corresponds to a 38% decrease in SEO hazard rate.
Second, in Models 5 and 6 we add the quick success dummy, equal to one if the target capital was
raised within the first 20 days of the offering. Firms with quick success in the initial equity
crowdfunding offering have a higher probability of follow-on offering (p<0.01). Controlling for firmand offering-specific factors, the hazard rate for these firms is 2.75 times higher than the rest of the
sample. Third, in Models 7 and 8, we focus on the presence of professional investors in the initial
offering. While their initial participation is not a significant predictor of subsequent offerings, their
presence is strongly associated with survivorship (p<0.01). Indeed, as previously discussed, none of
the firms they back have subsequently failed.
[ TABLE 2 ]
In Table 3, we replicate the type of competing risks analysis by considering private and public
SEOs as events of interest. This allows us to discern the determinants of follow-on crowdfunding
offerings from those of private equity capital injection. Results in Model 1 show that younger firms
(p<0.05) and larger offerings (p<0.05) are more likely to raise capital through sequential
crowdfunding campaigns. Younger firms are arguably those that benefit more from the progressive
28
reduction in information asymmetries granted from a crowdfunding stage financing strategy. On the
other side, results in Model 2 show that private equity is raised by firms with patents (p<0.01), nonexecutives (p<0.01), and traction (p<0.01). These results support Rajan’s (2012) sequential model of
differentiation and standardization, with VCs prioritizing firms with structured corporate governance
mechanisms and intellectual property rights. With regard to investor-specific variables, we find that
initial offerings that quickly reach the target capital are more likely to be followed by further
crowdfunding campaigns (p<0.05, Model 5). Moreover, not surprisingly, private equity investors tend
to target firms whose initial offerings were already backed by professional investors (p<0.01, Model
8). The size effect of this results is high, as the presence of professional investors in the initial offering
increases the hazard rate of raising further private equity capital by 11.8 times.
[ TABLE 3 ]
5.4. Consecutive seasoned offerings
Among the 64 firms that raise additional capital after the successful closing of their initial
crowdfunding campaign, the average number or SEOs is 1.6. To understand which type of firms
engage in a higher number of seasoned offerings, we estimate a zero-inflated negative binomial
regression with a company’s number of SEOs within one year of the initial offering as dependent
variable. Results, reported in Table 4, show that more diversified firms (p<0.05), firms with traction
(p<0.01), and firms eligible to the SEIS tax shield are those that pursue more SEOs. The fraction of
equity offered in the initial campaign is instead negatively correlated with the number of subsequent
SEOs (p<0.05). This evidence may be related to the fact that firm owners are less likely to put control
at risk further diluting their stake if a significant fraction of equity has already been issued in the
initial offering. Furthermore, the fraction of equity offered can be interpreted as a signal of firm
29
quality in light of the commitment of the original shareholders (Ahlers et al., 2015, Vismara, 2016a),
whereby these firms find it more difficult to do SEOs (Table 2) and, when they do so, they do it once.
Last, initial offerings that are quickly successful (p<0.01, Model 3) and that attract professional
investors (p<0.01, Model 4) are followed by more seasoned offerings.
[ TABLE 4 ]
6.
Conclusions
This paper is the first to conduct an examination of the return on investments in equity
crowdfunding. We find that a significant fraction of companies that raised funds through
crowdfunding have gone on to raise further funds at higher valuations. Whilst this does not secure a
monetary return to initial crowdfunding investors, it is an important market feedback indicating that
there is prospect of return. Having found an expected positive return at an aggregate level is good
news if we compare initial equity crowdfunding offerings to initial public offerings occurring on
traditional stock markets, which on average underperform (Ritter, 1991). As argued by Ritter (2013,
p. 8), “it is unlikely that investors will earn high average returns on crowdfunding investments,
although the returns may be higher than the -30% earned on purchases of state lottery tickets”. This
paper provides novel evidence that addresses this concern.
The distribution of returns on equity crowdfunding investments is likely to present similarities
with IPOs, with the skewed distribution of returns offering the chance to gain extremely high rewards
if one is able to identify the “next Google”. Among IPOs, indeed, the mean return usually exceeds
the median because of a few big winners. Over the period 1980–2000, however, the top 100 IPOs
earned over 1,000% in their first three years of trading (Field and Lowry, 2009). Investors who are
confident in their ability to identify such winners invest in this asset class, forecasting low-probability
30
events. Similarly, we find that crowdfunding investors could gain as much as 371% if they were able
to pick the best equity offering. On the contrary, they face a one out of ten probability to lose all their
money, by investing in firms that fail soon after the offerings. This paper offers a first evidence of
factors that are associated with positive returns, such as diversification, non-executive directors and
tax incentives, and factors associated with failure, such as lack of traction at listing (no previous
sales).
The implications of this paper, as discussed above, are mainly for investors. However, our
findings generate interesting implications also for practitioners and policy makers. For entrepreneurs
that use equity crowdfunding, for instance, the data suggest that hiring non-executive directors
facilitates follow-on offerings. With respect to policy implications, our data highlight the fact that
firms benefiting from tax incentives face a 2.9 times higher probability to raise additional capital in
SEOs, while that of failing is 76% lower. This result points to a success of UK fiscal policies, that
could serve as a reference for other countries in fostering crowdfunding. Most importantly, our results
provide insights on how to lessen the concerns about the collective action problems that could
undermine the development of equity crowdfunding markets, or even vanish them in an Akerlof-type
of failure. For instance, their long-term success would be enhanced if the platforms were to facilitate
some tracking of previous campaigns. At present there is no central repository for such information
and the platforms themselves offer varying levels of disclosure.
The results of this study offer avenues for further research along this direction. Managers of
some crowdfunding platforms have declared the intention to develop a secondary market aimed at
offering the possibility to trade shares of crowdfunded firms. They have also announced increasing
control aimed at detecting frauds. Whether and how they can succeed in these endeavors is crucial to
the development of the industry as well as of research in the field. Finally, this study is at the crossroad
between public and private equity. As an example, Flavourly, a food and drink subscription club,
raised £515,000 from 339 crowdfunding investors, soon after turning down a £75,000 private equity
31
offer from BBC’s Dragons’ Den. An open research question is how initial crowdfunding equity
offerings compare with private equity deals.
32
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Figure 1. Post-offering outcomes of crowdfunding projects. Post-offering scenarios of 212 funded offerings
on Crowdcube from 2011 to 2015. The observation date is end of March 2016. The sum of public, private, and
M&A exceeds the number of SEOs because 8 companies are included in multiple categories.
38
Table 1. Descriptive statistics. Average values of variables by post-offering scenario (left-hand side) and
SEO type (right-hand side). The last five variables refer to the subsample of SEO companies. Return is the
annualized IRR of the investment based on the valuation implied by the seasoned offering. Age is the age (in
months) of the company. Diversification is the company’s number of SIC codes. Non-executive directors
equals 1 if the company has at least one non-executive director. Positive sales equals 1 if the company has
already reported positive sales. Patents equals 1 if the company owns or is filing patents. Target capital is the
amount of capital to be raised in the offering. Equity offered is the fraction of equity offered. Voting rights
equals 1 if voting rights are attached to the equity offered. SEIS equals 1 if the offering is eligible for the Seed
Enterprise Investment Scheme (SEIS) tax relief. All these variables are measured at the launch of the offering.
No. investors is the number of investors who participated in the offering. Quick success equals 1 if the target
capital was reached within 20 days of the launch of the offering. Professional investor equals 1 in case a
professional investor (as identified from Crowdcube) participated in the offering. SEOs is the number of SEOs
completed by the company during the observation window. Time to first SEO is the time elapsed from the
closing date of the initial offering to the first SEO. Total capital raised is the amount of money raised by the
company during the observation window. SEO size is the amount of money raised in a SEO. SEOs within one
year is the number of SEOs completed within one year of the closing date of the initial offering. Monetary
values are in thousands of British pounds. *, **, and *** indicate significance at the 10%, 5%, and 1% levels,
respectively, of the t-test for the difference in means between the corresponding group and the rest of the
sample (SEOs) in the left-hand (right-hand) side of the table. Z-test of equal proportions is used for dummy
variables.
Return (%)
Age (months)
Diversification (no.)
Positive sales (%)
Non-executive directors
(%)
Patents (%)
Target capital (£k)
Equity offered (%)
Voting rights (%)
SEIS (%)
No. investors (no.)
Quick success (%)
Professional investor (%)
SEOs (no.)
Time to first SEO (months)
Total capital raised (£k)
SEO size (£k)
SEOs within 1 year (no.)
All
sample
(212 obs)
8.8
67.3
1.2
53.3
Post-offering scenarios
SEOs
Active
Failed
(64 obs) (126 obs)
(22 obs)
63.5***
0.0**
-100.0***
52.3*
79.3***
41.4*
1.2
1.2
1.1
62.5*
54.8
18.2***
Public
(47 obs)
69.1
49.7
1.1
53.2***
SEOs
Private
(22 obs)
61.2
62.3
1.2
81.2**
M&A
(3 obs)
48.8
30.8
1.3
100.0
9.0
18.8***
4.8***
4.5
12.8**
36.4***
66.6**
8.0
197.9
14.8
75.5
46.7
144.9
10.4
6.6
-
12.5
207.9
13.1*
62.5***
56.3*
174.5
12.5
12.5**
1.6
24.7
2,309.3
1,955.7
1.0
6.3
206.1
15.5
80.2*
42.1
141.7
9.5
4.8
-
4.5
122.3
15.4
86.4
45.5
77.2*
9.1
0.0
-
8.5
180.2
13.4
57.4
59.6
137.1*
14.9
4.3***
1.7
26.2
697.8**
455.9*
1.0
13.6
194.2
12.2
72.7
59.1
179.6
18.2
31.8***
2.1***
19.8*
1,975.8
1,113.8
1.5***
33.3
553.3**
9.3
100.0
0.0**
778.0***
0.0
33.3
1.7
16.4
29,082.5***
28,590.3***
1.3
39
Table 2. Determinants of post-offering outcomes. Two-step Heckman selection model on the determinants
of post-offering scenarios. The first step is a probit regression with the success dummy as dependent variable.
Competing offerings, defined as the log of 1 plus the number of offerings active and available at the launch
date of the considered offering, is the instrumental variable. The second step is a competing risks regression
on post-offering scenarios. In SEO models, the hazard rate of completing a seasoned offering is the latent
dependent variable, and failure is the competing event. In Failed models, the hazard rate of company failure is
the latent dependent variable, and SEO is the competing event. Age, Target capital, and No. investors are in
logarithm. Industry (1-digit SIC level) fixed effects are included. Heteroskedasticity corrected clustered robust
t-statistics are in parentheses below the coefficients. The number of observations is 212. ***, **, and * indicate
significance at the 1, 5, and 10 percent levels respectively.
Age
Diversification
Positive sales
Non-executive dir.
Patents
Target capital
Equity offered
Voting rights
SEIS
First step
(probit)
-0.46***
(-3.96)
-0.11
(-0.79)
0.74***
(3.55)
-0.10
(-0.36)
0.28
(0.89)
-0.41***
(-3.44)
0.16
(0.13)
-0.50*
(-1.89)
-0.34
(-1.46)
SEO
(1)
-0.24
(-1.51)
0.81***
(3.03)
0.80**
(2.32)
1.21***
(2.99)
1.00**
(2.03)
0.36**
(2.24)
-3.28*
(-1.79)
-0.51
(-1.60)
1.14***
(2.74)
Failed
(2)
-0.56**
(-2.31)
-1.06
(-1.05)
-1.73**
(-2.26)
-0.10
(-0.08)
-0.93
(-1.61)
-0.31
(-0.80)
-4.05
(-0.89)
1.37*
(1.87)
-1.47***
(-2.64)
No. investors
SEO
(3)
-0.25
(-1.57)
0.79***
(2.98)
0.89**
(2.51)
1.48***
(3.55)
1.25**
(2.40)
0.59***
(2.98)
-3.82**
(-2.07)
-0.50
(-1.50)
1.15***
(2.67)
-0.48**
(-1.99)
Failed
(4)
-0.56**
(-2.31)
-1.06
(-1.05)
-1.73**
(-2.27)
-0.10
(-0.08)
-0.93
(-1.59)
-0.30
(-0.64)
-4.05
(-0.87)
1.37*
(1.87)
-1.47***
(-2.63)
-0.01
(-0.01)
Quick success
SEO
(5)
-0.20
(-1.28)
0.87***
(3.20)
0.90***
(2.59)
1.27***
(2.99)
1.16**
(2.34)
0.34**
(2.10)
-3.69*
(-1.94)
-0.60*
(-1.94)
1.16***
(2.71)
Failed
(6)
-0.63**
(-2.42)
-0.93
(-1.05)
-1.70**
(-2.19)
-0.25
(-0.20)
-0.54
(-0.77)
-0.32
(-0.81)
-4.24
(-0.98)
1.43*
(1.72)
-1.49***
(-2.69)
1.01***
(2.63)
-0.82
(-1.38)
Professional inv.
Mills ratio
Competing offerings -3.36***
(-7.36)
Constant
23.22***
(8.25)
Industry effects
Yes
Pseudo R-squared
0.33
Wald Chi-squared
SEO
(7)
-0.30
(-1.59)
0.83***
(3.17)
0.83**
(2.33)
1.10***
(2.72)
0.92*
(1.70)
0.35**
(2.13)
-3.27*
(-1.73)
-0.48
(-1.45)
1.17***
(2.74)
Failed
(8)
-0.52**
(-2.19)
-1.09
(-1.10)
-1.60**
(-2.14)
0.07
(0.06)
-0.94
(-1.64)
-0.31
(-0.84)
-3.71
(-0.83)
1.38*
(1.87)
-1.48***
(-2.69)
-17.93***
(-23.55)
-1.17
(-1.07)
-0.81
(-1.32)
-1.17
(-1.03)
-0.69
(-1.11)
-1.17
(-0.98)
-0.96*
(-1.72)
-1.25
(-1.07)
0.59
(1.07)
-0.68
(-1.08)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1,322.5
991.6
1,226.3
991.6
1,429.8
798.4
1,300.7
2,567.7
40
Table 3. Private vs public SEOs. Second step of the Heckman model with competing risks regression on
SEO type. In Public models, the hazard rate of completing a public seasoned offering is the latent dependent
variable, with private SEO and failure being the competing events. In Private models, the hazard rate of
completing a private seasoned offering is the latent dependent variable, with public SEO and failure being the
competing events. Age, Target capital, and No. investors are in logarithm. Industry (1-digit SIC) fixed effects
are included. Heteroskedasticity corrected clustered robust t-statistics are in parentheses below the coefficients.
The number of observations is 212. ***, **, and * indicate significance at the 1, 5, and 10 percent levels
respectively.
Age
Diversification
Positive sales
Non-executive dir.
Patents
Target capital
Equity offered
Voting rights
SEIS
Public
(1)
-0.45**
(-1.97)
0.59
(1.51)
0.06
(0.15)
-0.53
(-0.75)
0.38
(0.57)
0.38**
(2.04)
-3.68
(-1.60)
-0.60
(-1.53)
0.69*
(1.81)
Private
(2)
0.26
(1.03)
1.04*
(1.81)
3.08***
(3.45)
3.13***
(3.45)
2.30**
(2.57)
0.08
(0.18)
-3.53
(-0.74)
-0.11
(-0.18)
1.89
(1.54)
No. investors
Public
(3)
-0.46**
(-2.02)
0.56
(1.43)
0.13
(0.30)
-0.31
(-0.43)
0.64
(0.88)
0.55***
(2.65)
-4.01*
(-1.74)
-0.61
(-1.51)
0.67*
(1.70)
-0.42
(-1.36)
Private
(4)
0.24
(1.00)
0.99*
(1.69)
3.20***
(3.45)
3.31***
(3.99)
2.62**
(2.54)
0.28
(0.60)
-4.15
(-0.84)
-0.07
(-0.12)
1.77
(1.51)
-0.46
(-0.86)
Quick success
Public
(5)
-0.41*
(-1.80)
0.62
(1.52)
0.14
(0.32)
-0.51
(-0.73)
0.55
(0.82)
0.35*
(1.82)
-4.03*
(-1.72)
-0.74*
(-1.92)
0.70*
(1.74)
Private
(6)
0.25
(1.09)
1.03*
(1.81)
3.09***
(3.37)
3.12***
(3.46)
2.28**
(2.52)
0.08
(0.18)
-3.59
(-0.74)
-0.10
(-0.16)
1.90
(1.51)
0.99**
(2.29)
-0.15
(-0.10)
Professional inv.
Mills ratio
Industry effects
Wald Chi-squared
-0.45
(-0.53)
Yes
3,931.9
-0.61
(-0.49)
Yes
955.7
-0.33
(-0.38)
Yes
3,659.9
-0.41
(-0.34)
Yes
2,555.6
41
-0.53
(-0.71)
Yes
3,359.6
-0.61
(-0.48)
Yes
1,478.8
Public
(7)
-0.39*
(-1.71)
0.59
(1.48)
0.03
(0.08)
-0.39
(-0.51)
0.53
(0.78)
0.39**
(2.16)
-3.77*
(-1.68)
-0.59
(-1.52)
0.69*
(1.82)
Private
(8)
0.17
(0.41)
1.44***
(2.73)
4.07***
(2.97)
2.77***
(3.59)
2.76*
(1.89)
0.07
(0.12)
-1.38
(-0.29)
0.23
(0.30)
2.15
(1.55)
-1.11
(-0.89)
-0.53
(-0.61)
Yes
3,220.8
2.47***
(3.22)
0.81
(0.69)
Yes
1,315.8
Table 4. SEO activity. Second step of the Heckman model with zero-inflated negative binomial regression on
the number of SEOs completed within one year of the initial offering (second step of the Heckman selection
model). Age, Target capital, and No. investors are in logarithm. Industry (1-digit SIC) fixed effects are
included. Heteroskedasticity corrected clustered robust z-statistics are in parentheses below the coefficients.
The number of observations is reduced to 198 companies that closed their initial offering before the end of
March 2015. ***, **, and * indicate significance at the 1, 5, and 10 percent levels respectively.
Age
Diversification
Positive sales
Non-executive dir.
Patents
Target capital
Equity offered
Voting rights
SEIS
(1)
-0.35*
(-1.90)
0.76**
(2.18)
0.73***
(2.72)
0.91*
(1.90)
0.35
(0.85)
0.28*
(1.71)
-5.19**
(-2.20)
-0.02
(-0.06)
1.06***
(2.66)
(2)
-0.36*
(-1.93)
0.84**
(2.30)
0.77***
(2.70)
1.01**
(2.18)
0.54
(1.18)
0.45**
(2.21)
-5.58**
(-2.33)
0.02
(0.04)
1.06***
(2.60)
-0.32
(-1.52)
No. investors
Quick success
(3)
-0.20
(-1.22)
0.78***
(2.63)
0.82***
(2.80)
0.99**
(2.16)
0.56
(1.41)
0.20
(1.25)
-4.63*
(-1.95)
-0.19
(-0.55)
0.99**
(2.47)
1.06***
(3.16)
Professional inv.
Mills ratio
Constant
Industry effects
Ln(alpha)
Wald Chi-squared
(4)
-0.41**
(-2.05)
0.78**
(2.04)
0.80***
(3.15)
0.52
(1.15)
0.39
(0.77)
0.22
(1.40)
-4.76**
(-2.10)
0.02
(0.04)
1.12***
(2.75)
-0.70
(-0.97)
-3.40
(-1.48)
Yes
-0.95
(-1.09)
17,387.4
-0.62
(-0.82)
-3.65
(-1.46)
Yes
-0.94
(-1.11)
15,173.1
42
-0.80
(-1.21)
-3.63
(-1.53)
Yes
-13.66
(-0.75)
16,587.4
2.06***
(4.06)
-0.57
(-0.96)
-2.60
(-1.10)
Yes
-0.96
(-1.45)
3,606.7
Appendix A. Correlation matrix. ***, **, and * indicate significance at the 1, 5, and 10 percent levels respectively for the difference from zero of the correlation
coefficients.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
Age
Diversification
Positive sales
Non-executive directors
Patents
Target capital
Equity offered
Voting rights
SEIS
No. investors
Quick success
Professional investor
(1)
1
0.01
0.22***
0.12*
-0.02
0.27***
-0.22***
-0.04
-0.44***
0.20***
-0.11
0.13*
(2)
(3)
(4)
1
0.05
1
0.03
0.16**
0.04
0.03
-0.01 0.34***
-0.06
-0.12*
0.05
0.02
-0.11 -0.37***
0.03 0.27***
-0.07
-0.12*
-0.08 0.21***
1
-0.03
0.24***
-0.10
0.03
-0.13*
0.27***
-0.05
0.25***
43
(5)
(6)
(7)
1
0.06
1
0.08
0.15**
0.05
0.04
0.07 -0.42***
0.12* 0.63***
0.01
-0.05
-0.01 0.21***
1
0.01
0.07
-0.05
0.03
-0.06
(8)
(9)
(10)
(11)
1
0.12
1
0.1 -0.26***
0.05
0.12*
-0.03 -0.17**
1
-0.07
0.17**
1
-0.09