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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. b 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: silvio.vismara@unibg.it.

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“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/the-

press-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 Non-

Disclosures 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.

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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

2 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.

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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 ]

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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

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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

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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

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£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.

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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).

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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

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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 value-

maximizing 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).

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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.

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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

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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).

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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%.

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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

17

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

18

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).

19

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.

20

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 post-

offering 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 cross-

check 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.

21

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 post-

offering 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

22

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

23

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

24

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 non-

executive 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

25

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 firm-

and 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

26

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,

13 A one unit change in the independent variable changes the hazard ratio by (eβ – 1). 14 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.

27

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

15 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.

28

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 firm-

and 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

29

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), non-

executives (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

30

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

31

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

32

offer from BBC’s Dragons’ Den. An open research question is how initial crowdfunding equity

offerings compare with private equity deals.

33

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38

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.

39

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.

All Post-offering scenarios SEOs

sample SEOs Active Failed Public Private M&A

(212 obs) (64 obs) (126 obs) (22 obs) (47 obs) (22 obs) (3 obs)

Return (%) 8.8 63.5*** 0.0** -100.0*** 69.1 61.2 48.8

Age (months) 67.3 52.3* 79.3*** 41.4* 49.7 62.3 30.8

Diversification (no.) 1.2 1.2 1.2 1.1 1.1 1.2 1.3

Positive sales (%) 53.3 62.5* 54.8 18.2*** 53.2*** 81.2** 100.0

Non-executive directors

(%) 9.0 18.8*** 4.8*** 4.5 12.8** 36.4*** 66.6**

Patents (%) 8.0 12.5 6.3 4.5 8.5 13.6 33.3

Target capital (£k) 197.9 207.9 206.1 122.3 180.2 194.2 553.3**

Equity offered (%) 14.8 13.1* 15.5 15.4 13.4 12.2 9.3

Voting rights (%) 75.5 62.5*** 80.2* 86.4 57.4 72.7 100.0

SEIS (%) 46.7 56.3* 42.1 45.5 59.6 59.1 0.0**

No. investors (no.) 144.9 174.5 141.7 77.2* 137.1* 179.6 778.0***

Quick success (%) 10.4 12.5 9.5 9.1 14.9 18.2 0.0

Professional investor (%) 6.6 12.5** 4.8 0.0 4.3*** 31.8*** 33.3

SEOs (no.) - 1.6 - - 1.7 2.1*** 1.7

Time to first SEO (months) - 24.7 - - 26.2 19.8* 16.4

Total capital raised (£k) - 2,309.3 - - 697.8** 1,975.8 29,082.5***

SEO size (£k) - 1,955.7 - - 455.9* 1,113.8 28,590.3***

SEOs within 1 year (no.) - 1.0 - - 1.0 1.5*** 1.3

40

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.

First step SEO Failed SEO Failed SEO Failed SEO Failed

(probit) (1) (2) (3) (4) (5) (6) (7) (8)

Age -0.46*** -0.24 -0.56** -0.25 -0.56** -0.20 -0.63** -0.30 -0.52**

(-3.96) (-1.51) (-2.31) (-1.57) (-2.31) (-1.28) (-2.42) (-1.59) (-2.19)

Diversification -0.11 0.81*** -1.06 0.79*** -1.06 0.87*** -0.93 0.83*** -1.09

(-0.79) (3.03) (-1.05) (2.98) (-1.05) (3.20) (-1.05) (3.17) (-1.10)

Positive sales 0.74*** 0.80** -1.73** 0.89** -1.73** 0.90*** -1.70** 0.83** -1.60**

(3.55) (2.32) (-2.26) (2.51) (-2.27) (2.59) (-2.19) (2.33) (-2.14)

Non-executive dir. -0.10 1.21*** -0.10 1.48*** -0.10 1.27*** -0.25 1.10*** 0.07

(-0.36) (2.99) (-0.08) (3.55) (-0.08) (2.99) (-0.20) (2.72) (0.06)

Patents 0.28 1.00** -0.93 1.25** -0.93 1.16** -0.54 0.92* -0.94

(0.89) (2.03) (-1.61) (2.40) (-1.59) (2.34) (-0.77) (1.70) (-1.64)

Target capital -0.41*** 0.36** -0.31 0.59*** -0.30 0.34** -0.32 0.35** -0.31

(-3.44) (2.24) (-0.80) (2.98) (-0.64) (2.10) (-0.81) (2.13) (-0.84)

Equity offered 0.16 -3.28* -4.05 -3.82** -4.05 -3.69* -4.24 -3.27* -3.71

(0.13) (-1.79) (-0.89) (-2.07) (-0.87) (-1.94) (-0.98) (-1.73) (-0.83)

Voting rights -0.50* -0.51 1.37* -0.50 1.37* -0.60* 1.43* -0.48 1.38*

(-1.89) (-1.60) (1.87) (-1.50) (1.87) (-1.94) (1.72) (-1.45) (1.87)

SEIS -0.34 1.14*** -1.47*** 1.15*** -1.47*** 1.16*** -1.49*** 1.17*** -1.48***

(-1.46) (2.74) (-2.64) (2.67) (-2.63) (2.71) (-2.69) (2.74) (-2.69)

No. investors -0.48** -0.01

(-1.99) (-0.01)

Quick success 1.01*** -0.82

(2.63) (-1.38)

Professional inv. 0.59 -17.93***

(1.07) (-23.55)

Mills ratio -0.81 -1.17 -0.69 -1.17 -0.96* -1.25 -0.68 -1.17

(-1.32) (-1.03) (-1.11) (-0.98) (-1.72) (-1.07) (-1.08) (-1.07)

Competing offerings -3.36***

(-7.36)

Constant 23.22***

(8.25)

Industry effects Yes Yes Yes Yes Yes Yes Yes Yes Yes

Pseudo R-squared 0.33

Wald Chi-squared 1,322.5 991.6 1,226.3 991.6 1,429.8 798.4 1,300.7 2,567.7

41

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.

Public Private Public Private Public Private Public Private

(1) (2) (3) (4) (5) (6) (7) (8)

Age -0.45** 0.26 -0.46** 0.24 -0.41* 0.25 -0.39* 0.17

(-1.97) (1.03) (-2.02) (1.00) (-1.80) (1.09) (-1.71) (0.41)

Diversification 0.59 1.04* 0.56 0.99* 0.62 1.03* 0.59 1.44***

(1.51) (1.81) (1.43) (1.69) (1.52) (1.81) (1.48) (2.73)

Positive sales 0.06 3.08*** 0.13 3.20*** 0.14 3.09*** 0.03 4.07***

(0.15) (3.45) (0.30) (3.45) (0.32) (3.37) (0.08) (2.97)

Non-executive dir. -0.53 3.13*** -0.31 3.31*** -0.51 3.12*** -0.39 2.77***

(-0.75) (3.45) (-0.43) (3.99) (-0.73) (3.46) (-0.51) (3.59)

Patents 0.38 2.30** 0.64 2.62** 0.55 2.28** 0.53 2.76*

(0.57) (2.57) (0.88) (2.54) (0.82) (2.52) (0.78) (1.89)

Target capital 0.38** 0.08 0.55*** 0.28 0.35* 0.08 0.39** 0.07

(2.04) (0.18) (2.65) (0.60) (1.82) (0.18) (2.16) (0.12)

Equity offered -3.68 -3.53 -4.01* -4.15 -4.03* -3.59 -3.77* -1.38

(-1.60) (-0.74) (-1.74) (-0.84) (-1.72) (-0.74) (-1.68) (-0.29)

Voting rights -0.60 -0.11 -0.61 -0.07 -0.74* -0.10 -0.59 0.23

(-1.53) (-0.18) (-1.51) (-0.12) (-1.92) (-0.16) (-1.52) (0.30)

SEIS 0.69* 1.89 0.67* 1.77 0.70* 1.90 0.69* 2.15

(1.81) (1.54) (1.70) (1.51) (1.74) (1.51) (1.82) (1.55)

No. investors -0.42 -0.46

(-1.36) (-0.86)

Quick success 0.99** -0.15

(2.29) (-0.10)

Professional inv. -1.11 2.47***

(-0.89) (3.22)

Mills ratio -0.45 -0.61 -0.33 -0.41 -0.53 -0.61 -0.53 0.81

(-0.53) (-0.49) (-0.38) (-0.34) (-0.71) (-0.48) (-0.61) (0.69)

Industry effects Yes Yes Yes Yes Yes Yes Yes Yes

Wald Chi-squared 3,931.9 955.7 3,659.9 2,555.6 3,359.6 1,478.8 3,220.8 1,315.8

42

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.

(1) (2) (3) (4)

Age -0.35* -0.36* -0.20 -0.41**

(-1.90) (-1.93) (-1.22) (-2.05)

Diversification 0.76** 0.84** 0.78*** 0.78**

(2.18) (2.30) (2.63) (2.04)

Positive sales 0.73*** 0.77*** 0.82*** 0.80***

(2.72) (2.70) (2.80) (3.15)

Non-executive dir. 0.91* 1.01** 0.99** 0.52

(1.90) (2.18) (2.16) (1.15)

Patents 0.35 0.54 0.56 0.39

(0.85) (1.18) (1.41) (0.77)

Target capital 0.28* 0.45** 0.20 0.22

(1.71) (2.21) (1.25) (1.40)

Equity offered -5.19** -5.58** -4.63* -4.76**

(-2.20) (-2.33) (-1.95) (-2.10)

Voting rights -0.02 0.02 -0.19 0.02

(-0.06) (0.04) (-0.55) (0.04)

SEIS 1.06*** 1.06*** 0.99** 1.12***

(2.66) (2.60) (2.47) (2.75)

No. investors -0.32

(-1.52)

Quick success 1.06***

(3.16)

Professional inv. 2.06***

(4.06)

Mills ratio -0.70 -0.62 -0.80 -0.57

(-0.97) (-0.82) (-1.21) (-0.96)

Constant -3.40 -3.65 -3.63 -2.60

(-1.48) (-1.46) (-1.53) (-1.10)

Industry effects Yes Yes Yes Yes

Ln(alpha) -0.95 -0.94 -13.66 -0.96

(-1.09) (-1.11) (-0.75) (-1.45)

Wald Chi-squared 17,387.4 15,173.1 16,587.4 3,606.7

43

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)

(1) Age 1

(2) Diversification 0.01 1

(3) Positive sales 0.22*** 0.05 1

(4) Non-executive directors 0.12* 0.03 0.16** 1

(5) Patents -0.02 0.04 0.03 -0.03 1

(6) Target capital 0.27*** -0.01 0.34*** 0.24*** 0.06 1

(7) Equity offered -0.22*** -0.06 -0.12* -0.10 0.08 0.15** 1

(8) Voting rights -0.04 0.05 0.02 0.03 0.05 0.04 0.01 1

(9) SEIS -0.44*** -0.11 -0.37*** -0.13* 0.07 -0.42*** 0.07 0.12 1

(10) No. investors 0.20*** 0.03 0.27*** 0.27*** 0.12* 0.63*** -0.05 0.1 -0.26*** 1

(11) Quick success -0.11 -0.07 -0.12* -0.05 0.01 -0.05 0.03 0.05 0.12* -0.07 1

(12) Professional investor 0.13* -0.08 0.21*** 0.25*** -0.01 0.21*** -0.06 -0.03 -0.17** 0.17** -0.09