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CROWDINVESTING AND VENTURE CAPITAL: COMPLEMENTS OR COMPETITORS?

EMPIRICAL EVIDENCE FROM THE EUROPEAN CROWDINVESTING CAMPAIGNS

Giuliana Borello*, Veronica De Crescenzo† and Flavio Pichler‡

Abstract

Crowdinvesting platforms are specialized websites that allow average investors to invest in firm’s

capital with valuable campaigns and business ideas. In this paper, we examine the behavior of

investors and firms that raise capital through crowdinvesting platforms. Our sample is a unique and

hand-collected data set of 490 campaigns promoted on CrowdCube, Companisto, Fundedbyme,

Invesdor, and Seedmatch since their inception to July 2015. We first show that crowdinvesting

increases the supply of risk capital in Europe, in particular in early-stage firms. We also show that

crowdinvesting bridges the funding gap of early-stage firms, in particular those operating in

traditional industries (considered not attractive for VC market). Second, we find that the average

investors consider the social impact of the crowdinvesting campaign, democratizing investment into

firms. Third, we evidence that crowdinvesting does not reduce private placement, which is a proxy

for venture capital. Overall, our results indicate that crowdinvesting complements venture capital

JEL codes: G21, G24, G11, E51

Keywords: crowdinvesting, venture capital, funding gap, early-stage.

* Giuliana Borello (Corresponding author) Department of Business Administration, Università di Verona, via Cantarane 24, 37129 Verona and School of Banking and Finance and Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123 Milan, Italy. Tel. +39 02 7234 2989, E-mail: giuliana.borello@unicatt.it, giuliana.borello@univr.it. † Veronica De Crescenzo, Assistant Professor in the Department of Business Administration, Università di Verona, via Cantarane 24, 37129 Verona. Tel. +39 045 8028273 E-mail: veronica.decrescenzo@univr.it ‡ Associate Professor in the Department of Business Administration, Università di Verona, via Cantarane 24, 37129 Verona. Tel. +39 045 8028273 E-mail: flavio.pichler@univr.it

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1. Introduction

Crowdinvesting1 allow a crowd of potential investors (hereafter, crowd) to invest in capital of firms

with valuable campaigns and business idea. This new form of financial instrument started in the

United Kingdom in 2011 as a reaction to the global financial crisis. Its entrance was the result of the

spread of information and communication technologies and the dawn of the knowledge economy.

Since the recent financial crisis, early-stage firms and established firms raising finance for expansion,

faced difficulties to get funding. In particular, the funding gap for early-stage firms increased

significantly (Dapp, 2013; Hagedorn and Pinkwart, 2013; 2016) that facilitated the growth of

crowdinvesting as a new source of capital. In Europe, more so than in the United States and other

developed countries, crowdinvesting has contributed to the growth of early-stage firms that are a

leading source of economic growth and job creation.

Crowdinvesting is still a recent phenomenon. The literature on the subject is minimal but expanding

and most of the studies have only recently appeared. To date the literature has focused on the signaling

in equity crowdfunding (Parker, 2014; Ahlers et al., 2015; Vismara, 2015a; Colombo et al., 2016),

geographic dispersion (Agrawal et al., 2013), platforms as contractual mechanisms (Hornuf and

Schwienbacher, 2014a), and corporate governance mechanisms as voting rights (Cumming et al.,

2015; Hornuf and Neuenkirch, 2015).

Using a unique data set of 490 campaigns promoted on five European platforms, we focus on the role

played by European crowdinvesting in filling the funding gap of firms not financed by venture capital

(VC). The size and quality of the data set provide a highly representative picture of the European

crowdinvesting actvity.

1 As Ahlers, Cumming, Günther and Schweizer (2013) Klöhn and Hornuf (2012), we refer to this activity when firms thought crowdfunding platform issue equity and mezzanine capital because in some jurisdictions not necessarily involve a financial contract that falls under the legal definition of a security. Crowdinvesting is a subcategory of crowdfunding, which is profoundly different from other subcategories of this novel form of entrepreneurial finance (Schwienbacher and Larralde, 2012; Agrawal et al., 2013; Mollick, 2014).

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In the first stage of the analysis, we provide evidence on the effect of crowdinvesting on the funding

of early-stage (seed and startup) firms in Europe. Using a subsample of the data set of 157 financed

campaign and 55 non-financed campaigns promoted on Fundedbyme, Invesdor, and Seedmatch, we

find that crowdinvesting funded both early-stage and expansion firms operating in all kinds of

industries. Since the financial crisis, venture capitalists have become less willing to take risks than

they were before the crisis; thus, they began to invest in lower risk firms, typically firms in later stages

of development. Our analysis shows that crowdinvesting funded early-stage firms operating in

traditional (or Non –High Tech) industries and considered not attractive for the VC market); then we

suggest that crowdinvesting is able to bridges the funding gap of these firms.

We also examine if crowdinvesting democratizes investment into firms. Kaufman et al. (2013) state

that crowdfunding democratizes entrepreneurship because it provides a mechanism for entrepreneurs

to raise capital and provides an opportunity for the crowd to invest in firms that share their values and

interests (of a financial and social nature). The democratization facilitates both groups’ respective

interests. Therefore, to establish if the crowdinvesting democratizes investment into firms we analyze

whether the social impact of the firm’s campaign influences the crowd. We use the conscious

emotional bias in the investment decision as proxy for the social impact. We find that home, frame,

herding biases in particular affect the crowd.

In the second stage of the analysis, we use the full hand-collected data set of 490 funding campaigns

promoted between 2011 and 2015 on the five largest crowdinvesting platforms operating in Europe:

Companisto (Germany), CrowdCube (United Kingdom), Fundedbyme (Sweden), Invesdor (Finland),

and Seedmatch (Germany). We use this data set to disentangle the effect of a campaign’s success

from the tax rate and VC market. From a policy point of view, because crowdinvesting has the natural

ability to cross borders due to the prevalence of internet access (Vismara, 2015b), the contribution of

taxation to the development of crowdinvesting is crucial to define. We find that the funds raised by

each campaign are negatively affected by the national tax rate. This result indicates that

crowdinvesting should be addressed in a harmonized way by European policy makers in order to

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establish a new level playing field aimed at supporting both the growth of early-stage firms and free

competition in the European Union.

Further, we report that crowdinvesting does not affect the country’s private placement activity, which

is a proxy for the VC market.

This paper contributes to the literature in three ways. First, we provide evidence on the effect of

crowdinvesting on the funding of early-stage firms. Second, we assess that the crowd operates

towards the democratization into firms. Third, we show that crowdinvesting complements VC.

The paper is organized as follows: Section 2 presents a background of the characteristics and

relevance of crowdinvesting in Europe. Section 3 details the research hypotheses. In Section 4 we

describe the data and the preliminary analysis. We report the empirical analysis in Section 5, and

Section 6 is the conclusion.

2. Background

1.1 Characteristic and relevance of crowdinvesting

As reported by the European Commission (2014), since the global financial crisis broke out in 2007,

the access to finance is one of the most pressing problems for European small and medium enterprises

(SME). The Commission reports a deterioration in public financial support (-13%), access to loans

(‐11%), trade credit (-4%), and in the willingness of investors to invest in equity (-1%). Since the

crisis, VC funds have had difficulties in finding investors. Investors in VC funds are typically pension

funds, insurance companies, and large banks (Gompers and Lerner, 1998). New regulations have

forced most banks and insurance companies to decrease the share of their investments in risky assets

such as VC funds, often by selling stakes to alternative parties. The crisis had also affected the market

for IPOs. The VC firms faced severe exit challenges (Cumming and MacIntosh, 2003; Cumming,

2008) that in turn also reduced the supply of money for VC funds. The crisis has led to a strong

decrease in VC activity in both the size of the investments and the number of deals (Block and

Sandner, 2009; De Vries and Block, 2010). Not surprisingly, venture capitalists became less willing

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to take risks than they were before the crisis; thus, they began to invest in lower risk firms, typically

firms in later stages of development. Consequently, the funding gap for early-stage firms has

increased significantly (Dapp, 2013; Hagedorn and Pinkwart, 2013; 2016) that creates an opportunity

for crowdinvesting to become a new source of capital for firms.

In the global economy, where markets are internationalized, crowdinvesting platforms allow

entrepreneurs and crowd to reduce both costs and risks. In fact, the entrepreneurs that raise capital

become more competitive and creative thanks to the capital raised with the participation of the crowd

(Belleflamme and Lambert, 2014). The individual investor buys a firm’s capital quotes and then

shares the risks with the rest of the crowd the entrepreneurship risks, which is distributed among

investors. Because the single investments are small compared to traditional investments, the “risk

equivalents” for the investor remain low (Lehner, 2014, 2013; Schwienbacher and Larralde, 2012).

Through crowdinvesting campaign, the entrepreneur decides how much capital he or she would like

to raise in exchange for a percentage of the firm’s capital. Therefore, the investors obtain pro-rata

shares (usually ordinary shares) from which they receive a financial return in terms of equity based

revenue or profit sharing. This exchange does not necessarily involve a transfer of control, in some

cases crowdinvesting can take a form that does not require giving up control or voting rights. The

firm decides whether to assign voting rights to investors. In the German crowdinvesting market2,

investors hold a mezzanine finance instrument, such as profit participation notes, cooperative

certificates, convertible bonds, and profit participating loans named partiarisches Darlehen, which

are typically senior to common shares and junior to other liabilities. Other securities sold by

crowdinvesting platforms include convertible bonds, participating notes and cooperative certificates

(Hornuf and Schwienbacher, 2014a). Typically, the financed firms rarely pay dividends during the

first years because the profits are re-invested in the business to fuel growth and build shareholder

value. Further, these shares cannot be sold easily because they are unlikely to be listed on a secondary

2 In German, the sale of equity carrying voting rights through crowdfunding platform is not legal; so the platforms have profit-sharing arrangements.

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trading market. The fact that crowdinvesting platforms directly allow the investment in these complex

and non-standardized financial instruments by a crowd of non-professional3 investors requires more

attention from policy makers. In particular, non-professional investors might make emotional

investment decisions instead of rational investment decisions.

The crowdinvesting market is quite new but it has exhibited strong growth in the past few years,

reaching a significant size in volume of raised funds and number of platforms, which peaked at 1,250

worldwide in 2014 (Massolution, 2015). This new alternative investment practice is currently more

active in Europe than in other world regions. In 2015 in the United Kingdom alone, crowdinvesting

reached £245 million in equity investments, which is equivalent to 15.6% of the total UK seed and

venture equity investment (Zhang et al., 2016). This growth confirms the validity of the approach and

raises expectations as to the benefit of its further expansion.

1.2 The role of the crowdinvesting platform in financial intermediation

Especially during the recent financial crisis, early-stage (seed and startup) firms and established

firms that raise finance for expansion faced difficulties in obtaining funds, although they are

recognized as a leading source of economic growth and job creation. Such firms with unpredictable

cash flows find crowdinvesting more suitable than other conventional instruments from traditional

financial intermediaries.

The firms collect capital from a highly dispersed, heterogeneous, and large number of investors

(Agrawal et al., 2015; Lehner, 2014, 2013). The individual investments are relatively small compared

to venture capitalists (VC) and business angels but can be a large amount in sum (Schwienbacher and

Larralde, 2012; Lehner, 2014, 2013; Sannajust et. al, 2014;). The crowdinvesting has numerous

similarities and differences with the equity market. Among the similarities is the problem of

information asymmetries. The information asymmetries involve the fact that crowdinvesting can

3 As reported by Hornuf and Schwienbacher (2015a) professional investors are usually accredited or qualified investors. To become an accredited investor under the national regulation, professional investors need to meet certain wealth and/or experience requirements. The bulk of the investors in crowdinvesting are non-accredited.

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suffer from problems of hidden information (adverse selection) and hidden action (moral hazard).

Hidden information problems occur when firms do not provide sufficient information to allow

potential funders to make informed investment decisions. Every financial system promotes allocative

efficiencies in the saving investment process that require potential investors to have extensive

computational capabilities, which might not be feasible for average investors. All potential investors,

and not just a substantial portion of them, should be able to evaluate the firms seeking funds and to

implement the portfolio strategy correctly. Some crowdinvesting platforms address this problem by

adopting a uniform price auction and relying on professional investors to set an appropriate price.

Nevertheless, the uninformed price imposes a cost on the professional investors that are devoting time

and resources to correctly valuing the equities, thus making them less willing to enter. Moreover, the

crowdfunding platforms carry out the screening activity before posting the campaign online and

allowing the fundraising to begin (Belleflamme et al., 2015). Another mitigation technique is that of

restricting the turnout of investors to those who are considered professional investors, that is, VC and

private equity funds and institutional investors (Belleflamme et al., 2014).

Hidden action problems arise after the firms close the campaign because fundraisers might use the

collected money for purposes other than the original campaign or before the full amount is raised.

Sahlman (1990) and Jensen (1993) find that VC solve the corporate governance and monitoring

problem through extensive initial due diligence on start-up firms. Crowdfunding platforms can avoid

such moral infractions by fundraisers by transferring the money to the firm only after the fundraising

campaign is successfully completed (Belleflamme et al., 2015). Further, they can constantly follow

up on the outcomes of campaigns (ex-post monitoring). The first is regularly adopted by the

platforms, whereas the latter is not clearly and uniquely defined by the platform. As suggested by

Belleflamme and Lambert (2014) the crowdinvesting campaign can also be used either to improve

the business strategy by supporting mass customization or user-based innovation, as a way for the

producer to gain a better knowledge of the preferences of its consumers, or as a promotion strategy.

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Among the differences between crowdinvesting and the traditional equity market, we rely on the type

of financial instrument issued by the firm in coordination with platform rules. Usually, equity

investment into seed and start-up firms often requires tailored contracts to align the interests of the

entrepreneur to those of the investors. For example, VC and business angels use various covenants in

their contracts, such as anti-dilution provisions that protect against down rounds, tag-along rights that

facilitate exit opportunities, and liquidation preferences that secure higher priority in the distribution

of value (Hornuf and Schwienbacher, 2014a). Moreover, in order to reduce the risk exposure and

increase control over the entrepreneur’s behavior, early-stage investors often split their investments

into tranches that are conditional on the attainment of defined milestones. All of these mechanisms

are difficult to replicate in the crowdinvesting setting.

Platforms play a central role in providing intermediation services (Bessy and Chauvin, 2013). By

assessing and certifying firms, campaigns, and investors, they contribute to building the trust that is

essential to the achievement of the democratization of investment. The crowdinvesting platform acts

as a direct financial intermediary because the crowd invests directly in securities issued by the firms.

The platforms act as a pure search service in which they operate merely as matchmakers, bringing

investors and firms together. The platform profits come from charging three type of commission fees

for their services: the listing fee, subscription fee, and the success fee. When a firm applies to the

platform to raise capital for a campaign, the platform charges a listing fee to the firm for displaying

their offering. A potential investor has to register in order to see detailed information about each

campaign and to invest in one. The registration on the platform can require a subscription fee. The

success fee is a percentage of the financed amount in case the funding goal is fully committed (all-

or-nothing model4 ), but if the target amount is not reached, the platform does not charge the success

fee.

Existing crowdinvesting platforms exhibit very heterogeneous organizational structures and

4 According the all-or-nothing model, the firms are funded only when the target amount, requested by the firm, is completely reached (Cumming and Johan, 2013).

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market designs. This heterogeneity can be due to three factors (Hornuf and Schwienbacher, 2014a).

First, crowdinvesting grew rapidly. Second, crowdfunding platforms might decide to differentiate

among themselves to reduce competition and attract specific investors and firms. Third, heterogeneity

might be a consequence of the different regulatory regimes surrounding crowdinvesting in the

different countries in which it operates.

Crowdinvesting offerings in the form of equity, debt, or mezzanine financing typically involve

financial securities and should thus be subject to securities regulation (see Bradford, 2012; Klöhn and

Hornuf, 2012). In contrast, in some European countries, the regulators do not classify crowdinvesting

contracts as financial securities (see Hornuf and Schwienbacher, 2014a). This new financing

instrument benefits from the growing involvement of the crowd that have different levels of capacity

to evaluate each campaign and firm. As a general rule, financial instruments and exchange laws

protect investors against fraudulent offerings through adequate disclosure by the issuer (Hazen,

2012). Currently, European countries are defining heterogeneous national legal frameworks related

to crowdinvesting. Conversely, because crowdinvesting has the natural ability to cross borders due to

the prevalence of internet access (Vismara, 2015b), this empirical evidence has clear implications for

intermediaries and policy makers that seek to help potential investors to identify high quality

investment opportunities in order to ensure the growth of early-stage firms and to maintain a low rate

of fraud.

3. Hypotheses and predictions derived from the prior literature

Crowdinvesting play a crucial role in the economy for at least three reasons. Firstly, it finance early

stage firms giving them the opportunity to raise capital. Secondly, crowdinvesting finance the future

growth of firms and consequently enhance the local and national economy. Thirdly, crowdinvesting

contribute to building the trust between firms and crowd that is essential to the achievement of the

democratization of investments.

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In this paper, we disentangle the effect of a campaign’s success from the firm’s characteristics, the

platform’s market design, the tax rate, and the private placement market.

Gompers (1995) highlights that venture capitalist concentrate investments in early-stage

companies and high-technology industries where information asymmetries are significant and

monitoring is valuable. Since the crisis, the VC activity decreased in both the size of investments and

the number of deals (Block and Sandner, 2009; De Vries and Block, 2010). VC become less willing

to take risks than they were before the crisis; thus VC began to invest in lower risk firms, typically

firms at later development stages. If the crowd finance early-stage firms operating in traditional (or

Non –High Tech) industries (considered not attractive for VC market) we can affirm that

crowdinvesting bridges their funding gap.

Hypothesis 1: Crowdinvesting bridges the funding gap of early-stage firms.

As evidenced by Berger and Udell (1998), the internal capital provided by the firm’s founders are

most critical at the early stages when information opacity are most acute, because in the small firms

the juxtaposition of ownership and management may pursue non-value maximization behavior to

reduce risk. Colombo et al. (2014) reports that firms that have been financed through the reward

crowdfunding make the greatest investment of human capital early in a firm’s lifecycle and later in a

firm’s lifecycle.

Using the classification made by Jeng and Wells (2000), we consider three stages of firm’s

lifecycle: seed, startup, and expansion. These three stages are defined with reference to the stage of

development of the firm. In the literature there is not a clear division between each stage. We measure

these stages with the firms’ age measured as the months elapsed between its founding date and the

month of its first financing campaign. Firms that are less than or equal to one year of age are at the

seed stage; firms that are more than one year and less than three years of age are at the startup stage;

and firms that are more than three years of age are at the expansion stage. Investments in either the

seed or startup stage are also referred to as early-stage firms.

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Potential investors might know more about expansion firms because these firms are associated

with lower agency costs and therefore they might be willing to invest more money in them. We

assume that the average investor knows that he or she has no contractual force with the firm’s

corporate governance, so he or she has to trust the management firm and their operating choices.

Therefore, we hypothesize that the crowd evaluates the business ideas related to the campaign without

excessively considering the firm’s stage of development.

Hypothesis 1a: The likelihood of a campaign’s success varies across a firm’s lifecycle

Florida and Kenney (1988) define VC as the “technological gatekeepers accelerating the process

of technological change.” A wide theoretical and empirical literature exists that emphasize the

advantage of VC in financing high-tech firms compared to other sources of finance (Berger and Udell,

1998; Rajan and Zingales, 1998; Gompers and Lerner, 2001; Deli and Santhanakrishnan, 2010;).

Crowdinvesting brings together a geographically dispersed, heterogeneous, and large number of

investors (Lehner, 2014, 2013; Agrawal et al., 2015) with firms from all kinds of industries.

Therefore, we hypothesize that crowd decrease the individual preference for one industry and

therefore decrease the industry specialization that is typical of VC. In contrast to VC, we assume that

the likelihood of a campaign’s success is not greater for high-tech firms than other kind of industries.

Hypothesis 1b: The probability of a high-tech campaign being financed by the average

investor or venture capital is the same.

As Parker (2014) suggests, “it is always optimal for an informed investor to invest in the

campaign with the greatest amount of investment so far, since that campaign is associated with the

greatest expected number of positive, informed signals.” We expect that the crowd might perceive

that firms that seek a larger amount of funds are less risky for two reasons: non-professional investors

prefer to share their entrepreneurial risk with a largest crowd and they might believe that good

business ideas require bigger investments to be initiated.

Hypothesis 1c: The likelihood of a campaign’s success is linked to the amount of financing

demanded.

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In the “Wisdom of Crowds,” Surowiecki (2004) states that in many cases, the crowd can perform as

well as or better than a limited number of experts in the field. This better performance occurs when

individuals from diverse backgrounds, with expertise in different fields, bring various pools of local

knowledge together. This diverse knowledge permits all members of the community to benefit from

the feedback of the individual. Crowdinvesting’s advantage over other forms of entrepreneurial

finance is this use of the “wisdom of the crowd The participation of many individuals generates

information through the aggregation of individual decisions that cannot be obtained from a single

individual or investor (Girotra et al., 2010; Bayus, 2013; Kelley and Tetlock, 2013; Lyon and Pacuit,

2013; Hakenes and Schlegel, 2014; Hornuf and Schwienbacher, 2014b, 2015b;). Agrawal et al.

(2013) evidence that in the United States, the reward crowdfunding democratizes access to capital

and business idea. The authors suggest (without empirically testing) that although reward

crowdfunding has many differences with crowdinvesting, the same evidence could be found on it.

Ramsey (2012) defines crowdinvesting as ‘the process of raising money to help turn promising ideas

into business realities by connecting investees with potential supporters’ whereby the crowd is asked

to bridge the funding-gap for a business idea to be initiated’. The crowd, comprised of non-

professional investors (principally household) and institutional investors, is encouraged to invest

savings in firms that might generate goods or services that could realize different benefits in terms of

economic, social, and emotional returns. Therefore, to establish if crowdinvesting democratizes

investing, we analyze whether the social impact of the campaign influences the crowd. We use the

emotional bias in the investment decision as a proxy of the social impact because it can consciously

influence the rational decisions of individuals. We find in particular that the crowd is affected by

home, frame and herding biases. Contrary to the equity market where these behavioral biases are

empirically confirmed and defined as not being rational decisions, we support the idea that each

component of the crowd makes conscious decisions that are not strictly based on a financial

evaluation.

Hypothesis 2: Crowdinvesting democratizes investment into firms.

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Home bias occurs in financial investments in terms of the asset holdings and investment decisions

(Ahearne et al., 2004; Cooper and Kaplanis 1994; Coval and Moskowitz, 1999; Dziuda and Mondria,

2012; Graham et al., 2009; Karlsson and Nordén, 2007; Sorenson and Stuart, 2001). According the

behavioral finance, individuals who hold too little of their wealth in foreign assets suffer from home

bias (Lewis, 1999). From a theoretical point, no differences should exist in a world of efficient

financial markets (Fama, 1970). Stuart and Sorenson (2003) reports that equity investment is

geographically concentrated, and then it is difficult to get funded far away from an industry's center.

Sorenson and Stuart (2005) report that the average distance between lead VC and target firm is

approximately 70 miles. Similarly, Sohl (1999) and Wong (2002) report that business angels locate

close to the entrepreneurs they finance (more than 50% are within half a day of travel). In general,

the research considers home bias to be a suboptimal behavior in decision making that leads to

economic inefficiencies in the marketplace. Home bias is especially important for early-stage

entrepreneurs and the investors who finance them (Sohl 1999).

In Europe during the last two decades, two factors should have contributed to a reduction in home

bias in favor of cross-border investments: the launch of the European common market and the

development of technological innovation that decreases the transaction costs and information

asymmetries between firms and investors. Crowdinvesting has the natural ability to cross borders due

to the prevalence of internet access (Chemmanur and Fulghieri, 2014; Vismara, 2015b).

We refer to home bias as the preference of the investors for campaigns in their home country.

According to our point of view, and supported by the results of Colombo et al. (2014) on the reward

crowdfunding in the United States, we expect that the crowd has more similar interests and views

with local entrepreneurship. We also expect that the investor looks at the social impact of the firm

that might enhance the domestic economy. For this reason, in contrast to previous authors, we expect

that the crowd might still prefer domestic firms that discloses a “home bias” investment decision.

Hypothesis 2a: The home bias affects the investment decisions of the average investor.

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Next to economic determinants, VC supply and demand depend also on the tax policy

(Keuschnigg and Nielsen, 2003; Da Rin et al, 2006). From the policy point of view, because

crowdinvesting has the ability to cross borders because of the Internet (Vismara, 2015b), the national

tax rate can have an effect on the development of crowdinvesting.

Investors obtain a greater financial return if the firm operates in a country with a lower fiscal rate.

We use the country’s tax rate for a medium-size firm, which comes from the World Development

Indicators, as a proxy for the fiscal impact. We expect that the capital raised by each campaign is

negatively related to the country’s tax rate. Moreover, we test if the home bias is stronger than the

fiscal impact and if the tax rate is a substitute for the home bias.

Hypothesis 2b: The average investor prefers the campaigns of firms that operate in a

country with a lower fiscal impact (or greater fiscal incentive).

We define the frame bias as when the media consciously and positively influences investors

over the rational information provided in the business plan. The crowd could be more interested in

firms that are able and willing to update their investors thought different social media contents

(pictures, video and updates) to overcome the monitoring problem. The crowd obtains information

from other investors who can post comments when making an investment (Vismara, 2015b). This is

in contrast to VC who maintain a close relationship with the firm after the investment by frequently

visiting and talking to the management.

We expect that early-stage firms have changed the way they communicate to potential investors,

for example, simplifying their business plans and stressing other media that are more simple to

understand by non-professional investors and that more easily show the social impact of their

business. We estimate that the crowd make their investment decisions by jointly using the business

plan (rational information) and the media.

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Hypothesis 2c: The frame bias affects the investment decisions of the average investor. The

likelihood of a campaign’s success is linked both to the business plan and to the media posted on the

web site.

The average investor might consider the investments decisions of other investors without

evaluating the attractiveness of the campaign itself. This phenomenon, known as the herd bias

(Scharfstein and Stein, 1990), was recently observed in crowdlending (Herzenstein at al., 2011b; Lee

and Lee, 2012; Zhang and Liu, 2010) might be particularly relevant in crowdinvesting (Vismara,

2015b).

We hypothesize that the crowd herd together according to the platform reputation and presence

of Professional investors in the crowd.

Platform with a large number of financed campaigns attract investors that are willing to provide

capital into firms through a successful platform and consequently the firms are more interested to

promote their campaign on platforms that have a large number of registered investors. As the crowd

and firms prefer to seek capital in the more successful platform we proxy the platform reputation

(Platform_reputation) as the percentage of financed campaigns. Moreover, we look at the likelihood

of campaign’s success respect to other open campaigns in the same funding windows. We expect that

campaign’s success is negatively affected by the competitors campaigns open on the same platform,

but are not affected by the campaigns open in other platforms.

The crowd is composed by investors with expertise in different fields that bring a pool of local

knowledge together generating the known ‘wisdom of the crowd’. Some platforms give the

opportunity to their investors to reveal the identity to the rest of the crowd when they support a

campaign. As, the component of the crowd co-invest with anonymous and declared Professional

investors in the campaign they should not be influenced by their investment choice. In contrast,

Vismara (2015a) evidences the information cascades play a crucial role in the success of

crowdinvesting campaigns. We hypotheses to confirm his results. Besides, we obtain the information

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regards the participation of Professional investors (as institutional investors and the crowdinvesting

platform) merely by Invesdor and Seedmatch, we expect to confirm that their presence influence the

crowd.

Hypothesis 2d: The herd bias affects the investment decisions of the average investor

To identify the activity of VC, we use as a proxy for the yearly country data on the private

placement market provided by Thomson One. For our analysis, we consider both the number of firms

financed through private placement (PP_Ncompanies) and the overall amount raised (PP_amount) in

each country and year. As anticipated, we estimate if crowdinvesting competes or complements the

VC. If private placement is negatively related with the crowdinvesting, we should affirm that

crowdinvesting competes with VC otherwise we should affirm that crowdinvesting complements VC.

Hypothesis 3: Crowdinvesting complements VC.

4. Data and preliminary analysis

Based on a unique and hand-collected data set, we use detailed information on 490 campaigns by

firms published in the five of the largest crowdinvesting platforms operating in Europe: Companisto

(Germany), CrowdCube (UK), Fundedbyme (Sweden), Invesdor (Finland) and Seedmatch

(Germany). A common characteristic across these platforms is that crowdinvesting is well developed

in their countries. The data set is comprehensive but we do consider possible selection bias. The

information used in this analysis is all available to the crowd on each platforms’ dedicated website

page. When the potential investor clicks on the campaign name, he or she is directed to a page

containing a description of that firm (Firm). The firm’s founders provide information about

themselves in a biography section, which also depicts their prior jobs and work experience. Usually,

the business plan, pictures, and videos complement this section. The value of the firm’s business is

imperfect and disclosed over time with updates that usually help the firm gain more visibility on the

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platform’s website. The other immediately available information is the real time amount of capital

raised, the percentage of target capital, the number of investors, and how many days remain before

the close of the funding campaign.

Investments might differ in that they can occur directly or indirectly. In direct investments, investors

buy and hold securities directly from the firm through the crowdinvesting platform. In indirect

investments (used only by Companisto as of July 2015), investors give money to the platform that is

transferred to a special purpose vehicle (SPV). The SPV is then the “final investor” in the firm because

it invests the money collected from investors. Other efforts to address this issue to date have included

the introduction of an intermediary between the crowd and the issuing firm that is able to perform

these tasks, or the limiting of investors to only qualified ones.

Our observation units are cross-sectional at the campaign level, but a second level (the month

of the first crowdinvesting investment) exists.

Table 1 summarizes a variety of explanatory variables used in the empirical analyses.

To account for the skewness in the distribution of the firms’ ages and investment sizes, we take

the natural logarithm of a firm’s age (Log (1+Firm Age) expressed in months and the natural

logarithm of the investment size (Log (1+Investment size)). To capture the nature of a firm’s future

growth opportunities, we initially rely on the ICB industry classification for each campaign, and

successively we define a D_Tech dummy variable that equals one if the business campaign belongs

to the technology or telecommunication industry (or briefly, high technology).

In accordance with Hornuf and Schwienbacher (2015b), we control for a potential “Blockbuster

Effect,” where one campaign with a large number of investors steals potential investments from other

campaigns. Therefore, we calculate the number of campaigns that are accepting investments on the

same funding windows on the platform (competitors_platform) and the total number of campaigns

that are accepting investment on the same funding windows on all five platforms of our sample

(competitors).

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The platforms are successful if they have a good reputation that we derive by the crowd and their

willingness to provide capital to the firms. Because the average investors and firms prefer to seek

capital from a more successful platform, we represent the platform’s reputation (Platform_reputation)

as the percentage of financed campaigns in each month.

To identify the VC activity, we use as a proxy the yearly country data on the private placement

market provided by Thomson One.5 We consider both the number of firms that were financed by

private placement and the overall amount in the country in which the firms launched the campaign.

In the first stage, to estimate the determinants of a campaign’s success, we retrieve the data from

Fundedbyme, Invesdor, and Seedmatch on funded and non-funded campaigns. The data set comprises

212 funding campaigns from each platforms’ website since their inception to July 2015. Table 2

shows the number of campaigns per platform per year. This sample consists of campaigns both

successfully and unsuccessfully financed (i.e., a campaign that did not reach the target amount).

Tables 3 and 4 provide summary statistics at the campaign level, as well as the correlations for

different variables. Table 3 indicates that 157 out of 212 campaigns were successfully financed. The

crowd financed a greater percentage of campaigns promoted by domestic firms (D_Domestic) with a

business plan (D_Business Plan) and with a reward6 (D_Reward). The average investment size of

financed campaigns is €253,842. The campaigns that were not financed, raised on average €40,060

that was not transferred to the firm because the campaign was not successfully completed. Concerning

the media contents, the presence of updates seems to be the strongest determinant between financed

and not financed.

Table 5 provides a univariate analysis on the percentage of financed campaigns. Panel A of Table

5 reports that most of the campaigns succeeded in raising the amount of money they sought, even

5 Private placement data originates from the Thomson VentureXpert database (formerly known as Venture Economics), which has been widely used in the entrepreneurial financing literature (Bygrave, 1989; Sorenson and Stuart, 2001; Gompers and Lerner, 2004; Hochberg et al., 2007). 6 In many cases, the firm recognizes a “reward” to their investors. The rewards are related both to the campaign product/service and to the investment size of each investor. There may be different types of rewards: such as the finished product/service, a gadget, a discount, or participation in an event.

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though the rate of success differs among the platforms: from 50.87% for Fundedbyme to 96.47% for

Seedmatch. On average, 74% of campaigns were successfully financed. The Pearson ꭓ2 indicates that

there is a statistically significant relation between the platform and the percentage of financed

campaigns. In Panel B of Table 5, we report the percentage of financed campaigns across the three

stages of development. The Pearson ꭓ2 and the Spearman correlation report that a statistically

significant relation does not exist between the firm’s stage of development and a campaign’s success.

This result could be preliminary evidence that crowdinvesting supports all types of firms in terms of

the stage of development and therefore bridges the funding gap of early-stage firms. In Panel C of

Table 5, we first rank the probability of a campaign’s success into four quartiles based on investment

size. Consistent with our prediction, there is an increase in the percentage of financed campaigns

across the four-investment size quartiles (with an exception through the second and third quartiles).

This preliminary analysis might lead to an incorrect inference about the campaign’s success because

it does not control for some interaction among the explanatory variables. For example, campaign

success does not appear to be related to the stage of development, but we find that the firm’s stage of

development is also highly correlated with the investment size (Spearman correlation = 0.1464). The

omission of investment size could lead to incorrect inferences in the unconditional analysis, about the

relation between the campaign success and the firm’s stage of development. To address this, we

analyze the campaign success using a binomial approach in the next section.

The Panel D of Table 5 reports a greater percentage of financed campaigns classified as domestic

(D_Domestic) that emphasizes the preference of the crowd toward domestic campaigns compared to

non-domestic. This preliminary result supports the literature that highlights the importance of home

bias.

The Panel E of Table 5 reports that the crowd do not specialize in high-tech campaigns (D_Tech).

The percentage of campaigns in the high-tech industry is almost equal to these operating in the non-

tech industry. These results is in contrast with the extensive literature in VC. That highlight that VC

20

concentrate investments in early stage companies and high-technology industries where information

asymmetries are significant and monitoring is valuable (Gompers, 1995).

In the second stage of the analysis, we add campaign information from another two platforms:

CrowdCube, the oldest crowdinvesting platform in the world, and Companisto. In contrast to the first

stage, we are able to exclusively collect the successfully financed campaigns on Companisto and

CrowdCube for the period from 2011 to July 2015 (Table 9). Figure 1 shows the number of financed

campaigns and the total investment volume (in million €) for each platform in each year. The figure

shows that the CrowdCube platform successfully completed 278 funding campaigns. Table 10 reports

the predominance of domestic campaigns for CrowdCube and the two German platforms

(Companisto and Seedmatch), which might be due to country-specific crowdinvesting regulation.

All the platforms enable entrepreneurs to set a second, third, and fourth round to obtain additional

funds (Table 11). Tables 12 and 13 report the summary statistics and correlation matrix of the full

sample.

5. Empirical analysis

In this section, we present binomial and Tobit models to estimate the likelihood of a campaign’s

success. The success is measured as a dependent variable that uses i) the dummy variable Funded

that equals one if the campaign is successfully funded by the crowd, ii) the number of investors (N.

Investors), and iii) the funding amount raised that represents the overall investment size at the end of

each campaign.

5.1 Campaign’s success: binomial approach

In order to provide quantitative evidence that crowdinvesting bridges the funding gap and

democratizes investment into firms, we identify which variables affect the campaign’s probability of

being successfully funded by the crowd that discloses the preferences of the crowd. First, we use the

subsample described in Tables 3 and 4. Because the dependent variable Financed equals one if the

21

campaign is funded and zero otherwise, we follow a binomial approach by using a logit to identify

the probability of success. In Table 6, we report the four specifications of the analysis, whereas in

Table 7 we report the marginal effects.7

Observing the marginal effect of specification (1), we find that the campaign promoted by

domestic (D_Domestic) firms leads to an 8.67% increase in the likelihood of success. This evidence

shows that crowdinvesting cannot reduce the economic frictions associated with investing in early-

stage campaigns over long distances. Clearly, the crowd prefer domestic campaigns that benefit the

national economy and also have a social return. This result confirms our hypothesis on home bias.

The effect of this variable is of considerable magnitude if compared with D_Business Plan where a

campaign with a detailed business plan has a 9.45% more probability of being funded. The presence

of the reward (D_rewards) seems to not influence the crowd. The media contents (pictures, video and

updates) posted on the campaign page, shows that only the presence of updates (D_Updates)

positively affects the success at the 5% level, whereas pictures (Pictures_quartile) show a negative

relation at the 5% level. This result is in line with Hornuf and Schwienbacher (2015b).

The Year negatively affects the likelihood of a campaign’s success because of the increased

competition between campaigns. This hypothesis seems to be confirmed in specifications (2) and (3)

in which the Year is substituted with the number of campaigns promoted in the three platforms of our

subsample (Competitors) and the number of campaigns promoted in the same platform

(Competitors_platform).  

Specification (4) shows the same relation as in the previous specification. The coefficient for

D_Tech is significant at the 5% level that shows a greater probability of success for campaigns not

related with the high-tech industry; therefore, we can affirm that the crowd do not prefer high-tech

firms like VC does. The firm’s age (Log(Firm Age)) has a negative and significant coefficient at the

5% level for the campaign’s success, probably due to the preference for early-stage firms. This

7 The marginal effect of the logit estimation is consistent with the probit estimation. The probit estimation is provided in Appendix A.4, A.5.

22

hypothesis seems to be confirmed in specification (5) in which the Log(Age) is substituted with the

stage of development. The coefficient shows that on average seed firms have 21.76% more

probability to be funded than the expansion firms; and consequently, the startup firms have 10.88%

more probability to be funded than the expansion firms. These results confirm Hypotheses 1 that the

crowdinvesting principally finances early-stage firms operating in tradition industry (or non high-

tech industry). Moreover, we split the specification (5) into seed, start-up, and expansion subgroups.

We find that the factors that contribute to campaign funding by the crowd are not clearly different

between the three stages of development. Overall, these results indicate that crowdinvesting bridges

the funding gap of early-stage firms operating in traditional industries.

5.2 Campaign success in terms of dimension of the crowd

In this section, we provide empirical support for the concept that a good business idea attracts

more investors. The “Wisdom of Crowds” introduced by Surowiecki (2004), states that in many cases,

the crowd permits all members of the community to benefit from the feedback of the individual. In

crowdinvesting, the participation of many individuals at a campaign, generates information through

the aggregation of individual decisions that cannot be obtained from a single individual or investor

(Girotra et al., 2010; Bayus, 2013; Kelley and Tetlock, 2013; Lyon and Pacuit, 2013; Hakenes and

Schlegel, 2014; Hornuf and Schwienbacher, 2015, 2014;).

Given Hypothesis 2d, we test if investors are looking for confirmation from other individuals,

because we expect to find evidence of herding. In Table 8, we use the number of investors (N.

Investors) to estimate the factors that increase the wisdom of the crowd on the funding campaign.

Applying an ordinary least square (OLS), we again show that average investors prefer domestic

campaigns. In contrast with the previous analysis, we find that neither the business plan nor the media

influences the wisdom of the crowd. However, the presence of a reward seems to distort the wisdom

23

of the crowd; the coefficient D_Reward is strongly positive and statistically significant at the 1%

level in all the specifications.

As in the previous estimation, the Year negatively affects the likelihood of a campaign’s success

because of the increased competition between campaigns. The hypothesis is confirmed in

specification (3) in which the Year is substituted by the number of campaigns promoted in the same

platform (Competitors_platform).  

5.3 Campaign’s success in terms of investment size

In this section we conduct a deeper exploration of the factors contributing to the raising of capital.

The funding amount represents the overall investment size raised by each campaign. The sample used

to investigate the campaign’s success in terms of investment size is the overall sample of 490

campaigns. We consider that the campaign that does not reach the target amount has an investment

size equal to zero. To account for the skewness in the distribution, we take the natural logarithm of

the investment size (Log (1+Investment size)). Because the dependent variable LogFinanced takes

only a positive value and because there are several campaigns that were not financed (see Figure 2),

we model our outcome variables using a Tobit model that is censored at 1% on both tails (Table 14).

The sign of the independent variables is in line with the previous analysis. The coefficient for

D_Tech is significant at the 5% level and shows that the campaigns not related with the high-tech

industry can raise more capital through crowdinvesting; confirming again Hypothesis 1b that

crowdinvesting bridges the funding gap of firms operating in traditional industries. The coefficient

for Log(Firm Age) is positive and statistically significant at the 5% level in all of the specifications.

This finding indicates that the investment size increases with the stage of development; older firms

require greatest investment to expand their business.

The coefficient for D_Domestic is positive and statistically significant at the 1% level in all of the

models that confirms the home bias (Hp. 2a). In specification (4) the country’s Tax rate negatively

24

affects the investment size. The coefficient for D_Domestic is significantly different than the

coefficient for the Tax rate; therefore, the fiscal impact does not substitute for the home bias (H 2b).

The listing fee applied by the platform on the campaign influence negatively the investment size.

To assess if the crowd is affected by frame bias we test the joint effect of pictures, (Picture-

quartile), video, and updates (D_Updates), and we observe that the overall effect is statistically

significant at the 10% level. This result confirms Hypothesis 2c.

The coefficient for N.Investors is positive (statistically significant at the 1% level) and shows the

ability of the crowd to reach a large amount of capital. The crowd seems to invest more in campaign

in which is evident the presence of a Professional investors, confirming Hypothesis 2d on the herd

bias. This result is in line with Hornuf, and Schwienbacher (2015b)

As expected, the number of campaigns promoted in the same platform (Competitors_platform)

affect negatively LogFinanced because the amount available by the crowd for investment is spread

across the campaigns, supporting the diversification policy. The coefficient of Platform_reputation

(significant at 10% level) evidences that the reputation of the platform positively affect the amount

raised by campaign confirming Hypothesis 2d. All these finding confirm that each component of the

crowd makes consciously investment decisions that are based on emotional bias. Considering the

social impact of the campaign, proxied by the conscious emotional biases, the crowd is able to

democratize the investment into firm.

Observing the private placement data we find that the PP_amount is statistically significant at the

1% level but the coefficient is positive and nearly equal to zero. In contrast PP_Ncompanies has a

negative coefficient (significant at 1% in specification (2) although it is extremely small). This finding

supports the idea that crowdinvesting does not reduce private placement activity in terms of the

amount invested, even though the VC has been more selective since the financial crisis begin.

Therefore, we assume that crowdinvesting complements VC, confirming Hypothesis 3.

We also report means and maximums of the VIFs to show the absence of strong collinearity

among the explanatory variables in the different specifications. All of our specifications show

25

maximum VIF values of below four and in all cases the mean VIF is below two that shows no

collinearity problems exist in our estimations.

6. Conclusion

This study provides the first empirical analysis of the relation between crowdinvesting and VC. With

a sample of 490 campaigns, we show that crowdinvesting bridges the funding gap of early-stage firms

operating in traditional industries that are considered not interesting by VC, that it democratizes the

investmentinto firms, and that it does not compete with the VC market.

Consequently, we can affirm that the crowd operate better than VC in his activity because they

support the early-stage firms that contribute to the development of the local economy and in this way

to future economic growth. The investor protection is relevant, but it is also significant to maintain

the social nature of the instrument. Therefore, safeguarding the investor could be more crucial when

looking at both ex ante selecting the worthy firm/campaign and the instrument that can be issued in

the platform, and ex post regularly monitoring the firm. In contrast to the VC that operates with the

originate to hold model, generally, the platforms operate with originate to distribution model;

therefore, it does not hold the risk connected with the firm but it spreads it across the average

investors. To overcome this issue we suggest to define deeper the role of the platform. In particular,

we propose to emphasize the transparency of its activity, reporting periodically the screening and the

monitoring activity and the analysts’ team.

Our result and the relative implications are strictly confined to the sample of platforms analyzed and

the lack of data on the default rates of the firms financed through the crowdinvesting. Consequently,

we hope that further research will explore the ex-post crowdinvesting by focusing on the short- and

long-run performance and crowdinvesting default rate.

26

References

Agrawal, A., Catalini, C., and Goldfarb, A. (2013). ‘Some Simple Economics of Crowdfunding’ Innovation Policy and the Economy, 14, NBER, University of Chicago Press.

Agrawal, A., Catalini, C., and Goldfarb, A. (2015).’ Crowdfunding: Geography, Social Networks, and the Timing of Investment Decisions’ Journal of Economics & Management Strategy 24 (2:, 1530-9134. http://dx.doi.org/10.1111/jems.12093

Ahearne, A. G., Griever, W. L., and Warnock, F. E. (2004), ‘Information costs and home bias: an analysis of US holdings of foreign equities’, Journal of International Economics 62(2): 313-336.

Ahlers, G.K.C., Cumming, D., Günther, C. and Schweizer, D. (2013). ‘Equity Crowdfunding’, working paper Available at: http://ssrn.com/abstract=2362340.

Ahlers, GK, Cumming, D, Günther, C, and Schweizer, D. (2015). ‘Signaling in equity crowdfunding’ Entrepreneurship: Theory and Practice 39(4): 955–980.

Audretsch, D. B., Lehmann, E. E., Paleari, S., and Vismara, S. (2016), Entrepreneurial finance and technology transfer, The Journal of Technology Transfer, 1-9.

Bayus, B.L. (2013) ‘Crowdsourcing New Product Ideas over Time: An Analysis of the Dell IdeaStorm Community’, Management Science 59 (1): 226 - 244.

Belleflamme, P., and T. Lambert, (2014) ‘Crowdfunding: some empirical findings and microeconomic underpinnings’, Forum Financier - Rev. Bancaire Financière 4: 288–296.

Belleflamme, P., N. Omrani and M. Peitz (2015) ‘The economics of crowdfunding platforms’, Information Economics and Policy 33: 11-28.

Belleflamme, P., T. Lambert and A. Schwienbacher (2014) ‘Crowdfunding: tapping the right ’, Journal of Business Venturing 29, 5: 585–609.

Berger, A.N., and Udell, G.F., (1998), ‘The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle’, Journal of Banking & Finance 22 (6–8): 613-673.

Bessy C., and Chauvin, P.M., (2013) ‘The Power of Market Intermediaries: From Information to Valuation Processes’, Valuation Studies 1(1): 83–117.

Block, J., and Sandner, P., (2009).’ What is the effect of the financial crisis on venture capital financing? Emperical evidence from US internet start-ups’, Venture Capital - An International Journal of Entrepreneurial Finance 11: 295-309.

Bradford, S. (2012) ‘Crowdfunding and the Federal Securities Laws’, Columbia Business Law Review: 1-150.

Bygrave, W. D. (1989), ‘The entrepreneurship paradigm (I): a philosophical look at its research methodologies’, Entrepreneurship Theory and practice 14(1): 7-26.

Chemmanur, T.J., and Fulghieri, P. (2014), Entrepreneurial finance and innovation: An introduction and agenda for future research, Review of Financial Studies 27 (1): 1-19.

Colombo M. G., C. Franzoni and C. Rossi-Lamastra (2014) ‘Internal Social Capital and the Attraction of Early Contributions in Crowdfunding’, Entrepreneurship Theory and Practice, 75-100, January.

Colombo M., Cumming D. J., and Vismara S., (2016), ‘Governmental venture capital for innovative young firms’, Journal of Technology Transfer 41(1): 10-24.

Cooper, I., and Kaplanis, E. (1994), ‘Home bias in equity portfolios, inflation hedging, and international capital market equilibrium’, Review of Financial Studies 7(1): 45-60.

27

Coval, J. D., and Moskowitz, T. J. (1999), ‘Home bias at home: Local equity preference in domestic portfolios’, The Journal of Finance 54(6): 2045-2073.

Cumming, D. J., and Johan, S. (2013), ‘Demand driven securities regulation: Evidence from crowdfunding’, Venture Capital: An International Journal of Entrepreneurial Finance 15: 361–379.

Cumming, D. J., and Macintosh, J., G. (2003), ‘A cross-country comparison of full and partial exits’, Journal of Banking and Science, 27: 511-548

Cumming, D. J., Leboeuf, G., and Schwienbacher, A. (2015), ‘Crowdfunding models: Keep-it-all vs. all-or-nothing’, SSRN working paper.

Cumming, D.J., (2008), ‘Contracts and Exits in Venture Capital Finance’, Review of Financial Studies 21: 1947-1982.

Da Rin, M., Nicodano, G., and Sembenelli, A. (2006), ‘Public policy and the creation of active venture capital markets’, Journal of Public Economics 90(8): 1699-1723.

Dapp, T.F. (2013), ‘Crowdfunding. An alternative source of funding with potential’, Deutsche Bank Research, Banking & Technology Snapshot, March, available at www.dbresearch.com.

De Vries, G., and Block, J. (2010), ‘Venture capital syndication in times of crisis: evidence from the financial crisis and the dot-com crisis’, mimeo available at http://ssrn.com/abstract=1628649.

Deli, D. N., and Santhanakrishnan, M. (2010), ‘Syndication in venture capital financing’, Financial Review 45(3): 557-578.

Dziuda, W., and Mondria, J. (2012), ‘Asymmetric information, portfolio managers, and home bias’, Review of Financial Studies 25(7): 2109-2154.

European Commission (2014) ‘Unleashing the potential of crowdfunding in the European Union’, Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, March, available at http://ec.europa.eu/internal_market.

Fama, E. (1970), ‘Efficient capital markets: a review of theory and empirical work’, Journal of Finance 25(2): 383-417.

Florida, R. L., and Kennedy M. (1988), ‘Venture capital-financed innovation and technological change in the USA’, Research Policy 17: 119-137.

Girotra, K., Terwiesch, Ch. and Ulrich, K.T. (2010) ‘Idea Generation and the Quality of the Best Idea’, Management Science 56 (4): 591 - 605.

Gompers, P. A. and J. Lerner (1998), ‘What Drives Venture Capital Fundraising?’ Brookings Papers: Microeconomic, 149-204

Gompers, P. A., and Lerner, J. (2004), ‘The venture capital cycle’, MIT press.

Gompers, P. A.. (1995), ‘Optimal Investment, Monitoring, and the Staging of Venture Capital’, The Journal of Finance 50(5): 1461–1489.

Gompers, P., and Lerner, J. (2001), ‘The venture capital revolution’, The Journal of Economic Perspectives, 15(2), 145-168.

Graham, J. R., Harvey, C. R., and Huang, H. (2009), ‘Investor competence, trading frequency, and home bias’, Management Science 55(7): 1094-1106.

28

Hagedorn, A., and A. Pinkwart (2013), ‘Crowdinvesting as a financing instrument for startups in Germany. A critical platform analysis’, HHL Working Paper No. 120, August.

Hagedorn, A., and A. Pinkwart (2016,) ‘The financing process of equity-based crowdfunding: an empirical analysis’, in D. Brüntje and O. Gajda (eds.), Crowdfunding in Europe. State of the art in theory and practice, 71–85, Berlin, Springer.

Hakenes, H., and Schlegel, F. (2014), ‘Exploiting the Financial Wisdom of the Crowd--Crowdfunding as a Tool to Aggregate Vague Information’, available at SSRN 2475025.

Hazen, T. L. (2012). ‘Crowdfunding or Fraudfunding? Social networks and the securities laws–Why the specially tailored exemption must be conditioned on meaningful disclosure’, Social Networks and the Securities Laws–Why the Specially Tailored Exemption Must be Conditioned on Meaningful Disclosure, North Carolina Law Review, May.

Herzenstein, M., Dholakia, U. M., and Andrews, R. L. (2011), ‘Strategic herding behavior in peer-to-peer loan auctions’, Journal of Interactive Marketing 25(1): 27-36.

Hochberg, Y. V., Ljungqvist, A., and Lu, Y. (2007), ‘Whom you know matters: Venture capital networks and investment performance’, The Journal of Finance 62(1): 251-301.

Hornuf, L., and Neuenkirch, M. (2015), ‘Pricing the Value of Cash Flow Rights in Crowdinvesting: An Analysis of Innovestment Backers’, available at SSRN 2624908.

Hornuf, L., and Schwienbacher, A. (2014a), ‘Which securities regulation promotes crowdinvesting?, Munich Discussion Paper No. 2014-26, available at: http://ssrn.com/abstract=2412124.

Hornuf, L., and Schwienbacher, A. (2014b), ‘Crowdinvesting–Angel Investing for the masses?, Handbook of research on venture capital, 3.

Hornuf, L., and Schwienbacher, A. (2015a), ‘The emergence of crowdinvesting in Europe: with an in-depth analysis of the German market’ SSRN: http://ssrn.com/abstract=2481994

Hornuf, L., and Schwienbacher, A. (2015b), ‘Funding Dynamics in Crowdinvesting’, available at SSRN: http://ssrn.com/abstract=2612998

Jeng, L. A., and Wells, P. C. (2000), ‘The determinants of venture capital funding: evidence across countries’, Journal of Corporate Finance 6(3): 241-289.

Jensen, M. C. (1993), ‘The modern industrial revolution, exit, and the failure of internal control systems, The Journal of Finance 48(3): 831-880.

Karlsson, A., and Nordén, L. (2007), ‘Home sweet home: Home bias and international diversification among individual investors’, Journal of Banking & Finance 31(2): 317-333.

Kaufman, Z. D., T. W. Kassinger, and H. L. Traeger, (2013), Democratizing Entrepreneurship: An Overview of the Past, Present, and Future of Crowdfunding, Bloomberg BNA Securities Regulation & Law Report 45, 5: 208-217, Available at SSRN: http://ssrn.com/abstract=2211698

Kelley, E. and Tetlock, P.(2013) ‘How Wise Are Crowds? Insights from Retail Orders and Stock Returns’, Journal of Finance 68: 1229-1265.

Keuschnigg, C., and Nielsen, S. B. (2003), ‘Taxes and venture capital support’, European Finance Review 7(3): 515-539.

Klöhn, L., and Hornuf, L. (2012) ‘Crowdinvesting in Deutschland: Markt, Rechtslage und Regulierungsperspektiven‘, Journal of Banking Law and Banking, 24: 237-266.

29

Lee, E. and Lee B. (2012), ‘Herding Behavior in Online P2P Lending: An Empirical Investigation’, Electronic Commerce Research and Applications 11(5): 495– 503

Lehner, O.M, (2014), ‘Finance, Risk and Accounting Perspectives’, Venture Capital: An International Journal of Entrepreneurial Finance 16 (3): 185-188

Lehner, O.M., (2013), ‘Crowdfunding Social Ventures: A Model and Research Agenda’, Venture Capital: An International Journal of Entrepreneurial Finance 15 (3): 289-311

Lerner, J. (1995) ‘Venture capitalists and the oversight of private firms’, The Journal of Finance 50, 1: 301–18.

Lewis, K. K. (1999), Trying to explain home bias in equities and consumption, Journal of economic literature 37(2): 571-608.

Li, E., and J.S. Martin (2014) ‘Crowd sourcing in capital formation: an empirical investigation’, FIRN Paper No. 2517273, November.

Lyon, A., & Pacuit, E. (2013), ‘The Wisdom of crowds: methods of human judgement aggregation’, In Handbook of Human Computation, 599-614, Springer New York.

Massolution (2015) ‘2015 CF Crowdfunding industry report’, April

McDonald, J. F., and Moffitt, R. A.. (1980). The Uses of Tobit Analysis. The Review of Economics and Statistics, 62(2), 318–321.

Megginson, W. L., and Weiss, K. A. (1991) ‘Venture Capitalist Certification in Initial Public Offerings’. The Journal of Finance, 46: 879–903. doi:10.1111/j.1540-6261.1991.tb03770.x

Mollick, E. (2014), ‘The dynamics of crowdfunding: an exploratory study’, Journal of Business Venturing 29, 1: 1–16.

Parker, S. C. (2014), ‘Crowdfunding, cascades and informed investors’, Economics Letters 125: 432-435

Petersen, M. A. and Rajan, R. G. (2002) ‘Does Distance Still Matter? The Information Revolution in Small Business Lending’ The Journal of Finance, 57: 2533–2570.

Rajan R. and Zingales L. (1998), ‘Financial dependence and growth’, American Economic Review 88: 559–586.

Ramsey, Y.A. (2012), ‘What the heck is crowdfunding?, Business People, November: 54–7.

Sahlman, W., A. (1990), ‘The structure and governance of venture-capital organizations’, Journal of Financial Economics, 27: 473-521

Sannajust, A., Roux, F., and Chaibi, A. (2014), ‘Crowdfunding In France: A New Revolution?’ Journal of Applied Business Research 30(6): 1919.

Scharfstein, D. S., and Stein, J. C. (1990), ‚Herd behavior and investment’, The American Economic Review, 465-479.

Schwienbacher, A. (2015) ‘Crowdinvesting in the Netherlands: market analysis, securities regulation and policy options’, DSF Policy Paper, No. 48

Schwienbacher, A., and Larralde, B., (2012), ‘Crowdfunding of small entrepreneurial ventures’, in The Oxford Handbook of Entrepreneurial Finance, D.J. Cumming (Ed.), Oxford University Press.

Shane, S., and Cable, D. (2002), ‘Network ties, reputation, and the financing of new ventures’, Management Science 48(3): 364-381.

30

Sohl, J. E. (1999), ‘The early-stage equity market in the USA’, Venture Capital: An international journal of entrepreneurial finance 1(2): 101-120.

Sorenson, O., and Stuart, T. E. (2001), ‘Syndication networks and the spatial distribution of venture capital investments’, American journal of sociology, 106(6): 1546-1588.

Sorenson, O., and Stuart, T. E. (2005), ‘The evolution of venture capital investment networks’, Federal Reserve Bank of Atlanta.

Stuart, T., and Sorenson, O. (2003), ‘The geography of opportunity: spatial heterogeneity in founding rates and the performance of biotechnology firms’, Research policy 32(2): 229-253.

Surowiecki, J. (2005), ‘The wisdom of crowds’, Anchor.

Vismara S. (2015a), ‘Information cascades among Investors in equity crowdfunding’, SSRN Working Paper, April, available at http://ssrn.com/abstract=2589619

Vismara S. (2015b), ‘Equity Retention and Social Network Theory in equity crowdfunding’, SSRN Working Paper, September, available at http://ssrn.com/abstract=2654325

Wong, A. (2002), ‘Angel Finance: The Other Venture Capital’, Working Paper University of Chicago.

Zhang, B., Baeck, P., Ziegler, T., None, J., and Garvey, K. (2016), ‘Pushing boundaries: the 2015 UK alternative finance industry report’ NESTA, available at http://www.nesta.org.uk/publications/pushing-boundaries-2015-uk-alternative-finance-industry-report.

Zhang, J., and Liu, P. (2012), ‘Rational herding in microloan markets’, Management science 58(5): 892-912.

31

FIGURES AND TABLES

 

TABLE 1 DEFINITION OF VARIABLES Variable Definition

Campaign's variables_ Firm Firm’s name

D_Domestic = 1 if the firm is established in the same country of the platform; = 0 otherwise

Financed = 1 if the campaign raised the target amount; = 0 otherwise

Country_firm Country in which the firm is established

Firm Age Difference between the first investment and the firm's founded date

Log (Firm Age ) Log (1+ Firm Age)

D_Reward = 1 if there is some reward in addition to financial return; = 0 otherwise

D_Business Plan =1 if the business plan is available for investors; = 0 otherwise

Video Number of video posted for each campaign

Pictures Number of pictures posted for each campaign

Pictures (quartile) Pictures quartile (Q1=smallest, Q4=largest)

D_Updates = 1 if updated information are posted after the first investment; = 0 otherwise

D_Tech = 1 if the firm operate in the Technology or Telecommunication Industry according the ICB classification; = 0 otherwise

Professional: = 1 if institutional investors or the platform invested in the campaign and the information is visible to other potential investors; = 0 otherwise

D_Institutional investors = 1 if institutional investors invested in the campaign and the information is visible to other potential investors; = 0 otherwise

D_Platform participation = 1if the platform invests in the campaign and the information is visible to other potential investors; = 0 otherwise

Competitors_platform Number of campaigns that are seeking capital in the same platform /months

Competitors Number of campaigns that are seeking capital in the five platform of our sample (Companisto, CrowdCube, Fundedbyme, Invesdor and Seedmatch)/months

N. Investors Number of investors in each campaign

Investment size Raised funding amount in EUR

Log (Investment size) Log (1+ Investment size)

Year The year of the first investment

Stage of development =1 if the firm is defined SEED (company age less or equal than one year); =2 if the firm is defined STARTUP (with more than one year and less than three year of company age; =3 if the firm is defined EXPANSION (with more than three years of company age)

Funding window (months) Identifies the time slot (expressed in months) used by the campaign to seek capital in the platform

Platform's variables

Platform Platform’s name

Country_Platform Country in which the platform is established

Platform_reputation Percentage of financed campaigns at time t by each platform

D_Listing fee =1 if the platform charges a listing fee at the firm for displaying their campaign; = 0 otherwise

Country's variables:

PP_amount Private placement amounts raised in range (EUR Mil) in the same country / year. Source: Thomson One

PP_Ncompanies Number of companies financed through private placement in the same firm country / year. Source: Thomson One

Tax rate It measures the amount of taxes payable by medium-size businesses after accounting for deductions and exemptions, expressed as a share of commercial profits in the same country/year. Source: World Development Indicators

32

TABLE 2 NUMBER OF CAMPAIGNS BY PLATFORM AND YEAR

Platform Name 2011 2012 2013 2014 (July) 2015 All YearsTotal

financ=0 not financ=1 0 1 0 1 0 1 0 1 0 1

FUNDEDBYME 7 14 20 22 16 8 43 44 87INVESDOR 1 4 9 3 12 2 9 9 31 40SEEDMATCH 3 27 1 22 1 22 1 8 3 82 85

Total - 3 - 28 12 45 24 56 19 25 55 157 212ꭓ2 test for equality proportion 47.4418

(p-value) 0.000

TABLE 3 SUMMARY OF STATISTICS Variables Obs Mean St. Dev. Min Max Obs Mean St. Dev. Min Max Campaign's variables Financed Non Financed D_Domestic 157 0.879 0.327 0.000 1.000 55 0.527 0.504 0.000 1.000D_Business Plan 157 0.917 0.276 0.000 1.000 55 0.800 0.404 0.000 1.000D_Reward 157 0.548 0.499 0.000 1.000 55 0.218 0.417 0.000 1.000Pictures 157 8.306 5.793 0.000 39.000 55 6.673 3.596 2.000 19.000Video 157 1.127 0.838 0.000 5.000 55 1.091 1.023 0.000 4.000D_Updates 157 0.650 0.479 0.000 1.000 55 0.182 0.389 0.000 1.000Log (Investment size) 157 11.936 1.004 8.418 14.914 55 9.365 1.990 0.000 12.744N. Investors 157 224.535 264.141 1.000 1827 55 24.764 38.020 0.000 206Log (Firm Age) 157 3.242 0.856 0.693 5.313 55 3.129 0.856 0.000 4.920Stage of development 157 2.083 0.698 1.000 3.000 55 2.055 0.705 1.000 3.000D_Tech 157 0.197 0.399 0.000 1.000 55 0.200 0.404 0.000 1.000Professional: 157 0.051 0.221 0.000 1.000 55 0.000 0.000 0.000 0.000D_Institutional investors 31 0.161 0.374 0.000 1.000 9 0.000 0.000 0.000 0.000D_Platform investor 113 0.027 0.161 0.000 1.000 12 0.000 0.000 0.000 0.000

Year 157 1.471 1.016 1 5 55 2.127 0.747 3 5Competitors 157 16.497 10.888 1.000 45.000 55 16.600 6.652 3.000 36.000Competitors_platform 157 5.981 3.825 0.000 24.000 55 7.018 3.291 1.000 15.000Funding window (months 157 2.210 1.359 0.000 6.000 55 1.818 0.796 1.000 3.000 Platform variable D_Listing fee 157 0.478 0.501 0.000 1.000 55 0.945 0.229 0.000 1.000Platform_reputation 157 0.857 0.164 0.506 1.000 55 0.633 0.113 0.500 0.973

33

TABLE 4 Correlation Matrix

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19)

(1) Financed 1

(2) D_Domestic 0.3770* 1

(3) D_Business Plan 0.1621* -0.0398 1

(4) D_Reward 0.2898* 0.3425* 0.2716* 1

(5) Pictures 0.1341 0.0944 0.0730 0.2314* 1

(6) Video 0.0181 -0.1394* 0.1822* 0.1544* 0.0798 1

(7) D_Updates 0.4108* 0.2952* 0.2290* 0.4971* 0.2641* 0.2006* 1

(8) N. Investors 0.3594* 0.2976* 0.0780 0.4373* 0.2785* 0.1583* 0.3257* 1

(9) Professional 0.1172 0.1028 -0.0855 -0.1836* -0.1765* -0.1662* 0.0384 -0.1021 1

(10) Log (Investment size) 0.6484* 0.2956* 0.0756 0.3612* 0.3363* 0.1090 0.3864* 0.5526* 0.0305 1

(11) Year -0.2897* -0.2340* -0.0391 -0.3134* 0.2253* 0.1502* -0.1985* 0.0443 -0.0033 -0.0545 1

(12) Competitors -0.0046 -0.0365 0.0368 -0.0584 0.2602* 0.0698 0.0270 0.0061 0.0843 0.0819 0.4932* 1

(13) Competitors_platform -0.1227 -0.3292* 0.0803 -0.0804 0.0501 0.2355* -0.0178 -0.1194 -0.1135 -0.0512 0.3130* 0.5722* 1

(14) Log (Firm Age ) 0.0583 0.0181 0.0755 0.0582 0.1587* 0.0289 0.0174 0.1818* 0.1144 0.1081 0.2097* 0.2493* 0.1114 1

(15) D_Tech -0.1163 -0.1481* 0.0006 -0.0137 -0.1373* 0.0315 -0.0572 -0.0711 0.0359 -0.1381* 0.0442 -0.0788 -0.0238 -0.1298 1

(16) Stage of development 0.0178 0.0563 0.0387 0.0761 0.1915* 0.0238 0.0210 0.1537* 0.1207 0.1464* 0.2098* 0.2407* 0.1097 0.8757* -0.1292 1

(17) Funding window (months)

0.1380* 0.1101 0.0192 0.1852* 0.1914* 0.0055 0.2114* 0.0552 0.1021 0.1440* 0.0200 0.5990* 0.3050* 0.1436* -0.1143 0.2082* 1

(18) D_Listing fee -0.4183* -0.4247* -0.2923* -0.8244* -0.2723* -0.1955* -0.5995* -0.4413* 0.1620* -0.4352* 0.4319* 0.1392* 0.1721* 0.1043 0.0393 0.0610 -0.1839* 1

(19) Platform_reputation 0.5434* 0.5094* 0.1859* 0.6965* 0.1949* 0.0329 0.5101* 0.4097* 0.0050 0.4893* -0.5397* -0.1318 -0.3677* -0.0621 -0.0932 -0.0198 0.3010* -0.8492* 1 This table presents the correlation coefficients across selected variables, as defined in Table 1. Correlations are provided for the subsample of 212 capital funding campaign retrieved from Fundedbyme, Invesdor , and Seedmatch. The * indicates that the correlation has statistical significant at the 5% level

34

TABLE 5 UNIVARIATE ANALYSIS OF THE LIKELIHOOD OF CROWDINVESTING

Panel A: Campaign success across platforms

            ꭓ2 test for equality proportion

Spearman correlation

Fundedbyme Invesdor Seedmatch (p-value) (p-value)

Stage 50.57% 77.50% 96.47% 47.4418 0.3495

          (0.000) (0.000)

Panel B: Campaign success across stage of development

            ꭓ2 test for equality proportion

Spearman correlation

Seed Startup Expansion (p-value) (p-value)

Stage 74.42% 74.07% 73.77% 0.0055 -0.0051

          (0.997) (0.9412)

Panel C: Campaign success across investment size quartile (Q1=smallest, Q4=largest)

             Spearman correlation

   Q1 Q2 Q3 Q4 (p-value)

Investment size 22.64% 88.89% 88.37% 96.23% 0.5628

          (0.000)

Panel D: Campaign success across domestic and non-domestic definition

Non-Domesticꭓ2 test for equality proportion

(p-value) Domestic

Firm’s country 17.37% 82.63% 30.1324

            (0.000)

Panel E: Campaign success across high-technology and non-high-technology industry

Non-High-

Technologyꭓ2 test for equality proportion

(p-value) High-

Technology

Industry’s classification 77.33% 66.13% 2.8663

            (0.090)

35

TABLE 6 DETERMINANTS OF FUNDING SUCCESS (LOGIT ESTIMATION) FINANCED

(1) (2) (3) (4) (5) (5) (5) (5) VARIABLES All All All All All SEED Startup EXPANSION D_Domestic 1.1263* 0.4795 0.1878 0.3528 0.7343* -1.6238 1.6562*** 0.1312

(0.5835) (0.4224) (0.4432) (0.4596) (0.4351) (1.0986) (0.6290) (0.9388) D_Business Plan 1.2277* -0.5099 -0.4561 -0.1137 -0.3478 0.2108 0.3250 -4.6396**

(0.7049) (0.5362) (0.5417) (0.5512) (0.5447) (1.0698) (0.7684) (2.2624) D_Reward -0.4887 0.4018 0.3668

(0.6245) (0.4548) (0.4587) Pictures (quartile) -0.5407** -0.3722* -0.3922** -0.3953* -0.4309** -0.7133 -0.7881** -0.2805

(0.2652) (0.2004) (0.1987) (0.2042) (0.2019) (0.5861) (0.3325) (0.4189) Video -0.3325 -0.3161 -0.2322

(0.2919) (0.2038) (0.2134) D_Updates 1.3973** 2.0008*** 1.9727*** 1.8160*** 1.8438*** 2.1009* 2.2764*** 2.6454**

(0.5789) (0.4933) (0.4891) (0.4541) (0.4502) (1.0898) (0.7352) (1.0475) Log (Investment size) 1.5440*** 0.2009*** 0.2320*** 0.3737*** 0.3026*** 0.3048** 0.0809 0.5286**

(0.2637) (0.0697) (0.0745) (0.0951) (0.0839) (0.1453) (0.1030) (0.2392)

Year -

0.0082*** (0.0014)

Competitors -0.0347* (0.0206)

Competitors_platform -0.1234** -0.1197** (0.0526) (0.0543)

Log (Firm Age) -0.5381** (0.2217)

D_Tech -0.9339** -0.9106** -0.1730 -0.7015 -1.9201* (0.4121) (0.4053) (0.9626) (0.5875) (1.0135)

Stage of development -

0.8351*** (0.2759)

Observations 212 212 212 212 212 44 108 60 Chi-squared 61.61 56.35 59.29 59.56 60.16 12.70 32.11 14.42

Prob >chi2

0.0000

0.0000

0.0000

0.0000

0.0000

0.0480

0.0000 0.0252 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

36

TABLE 7 MARGINAL EFFECTS BINOMIAL LOGIT ESTIMATION LOGIT (1) (2) (3) (4) (5) (5) (5) (5) VARIABLES All All All All All SEED Startup EXPANSION D_Domestic 0.0867** 0.0662 0.0253 0.0448 0.0956* -0.2318 0.1955*** 0.0142 (0.0435) (0.0576) (0.0595) (0.0581) (0.0553) (0.1443) (0.0642) (0.1015) D_Business Plan 0.0945* -0.0704 -0.0613 -0.0144 -0.0453 0.0301 0.0384 -0.5007** (0.0524) (0.0732) (0.0723) (0.0699) (0.0707) (0.1527) (0.0906) (0.2087) D_Reward -0.0376 0.0554 0.0493 (0.0476) (0.0624) (0.0614) Pictures (quartile) -0.0416** -0.0514* -0.0527** -0.0502** -0.0561** -0.1018 -0.0930** -0.0303 (0.0198) (0.0268) (0.0258) (0.0251) (0.0253) (0.0797) (0.0363) (0.0444) Video -0.0256 -0.0436 -0.0312 (0.0223) (0.0276) (0.0284) D_Updates 0.1076** 0.2761*** 0.2652*** 0.2306*** 0.2401*** 0.2999** 0.2687*** 0.2855*** (0.0424) (0.0597) (0.0578) (0.0501) (0.0511) (0.1369) (0.0748) (0.0928) Log (Investment size) 0.1189*** 0.0277*** 0.0312*** 0.0475*** 0.0394*** 0.0435** 0.0095 0.0571*** (0.0139) (0.0089) (0.0091) (0.0104) (0.0097) (0.0169) (0.0121) (0.0208)

Year -

0.0006*** (0.0001) Competitors -0.0048* (0.0028) Competitors_platform -0.0166** -0.0152** (0.0067) (0.0066) Log (Firm Age) -0.0683** (0.0269) D_Tech -0.1186** -0.1186** -0.0247 -0.0828 -0.2072** (0.0502) (0.0507) (0.1372) (0.0680) (0.0963)

Stage of development -

0.1088*** (0.0333) Observations 212 212 212 212 212 44 108 60

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

37

TABLE 8 OLS VARIABLES N. Investors N. Investors N. Investors N. Investors D_Domestic 65.2799* 30.7891 4.1347 -5.5958 (37.2109) (39.8208) (40.2859) (40.3651) D_Business Plan -28.4311 -132.3134*** -119.7080*** -114.8955** (44.8742) (44.7886) (44.4456) (45.4039) D_Reward 115.0054*** 155.5503*** 152.8652*** 157.4054*** (33.2381) (35.4503) (34.6827) (35.1583) Pictures (quartile) 19.2935 22.4981 21.6362 22.2225 (13.4816) (15.2026) (14.4886) (14.6264) Video 24.5066 15.9687 24.2649 (15.9855) (17.2540) (17.3231) D_Updates -8.9435 32.5168 30.9204 38.1221 (33.1885) (35.1518) (34.6383) (35.0877) Log (Investment size) 56.6764*** 13.1375** 16.7780*** 16.9799** (8.9419) (5.6099) (5.7198) (6.5526) Year -0.3051*** (0.0499) Competitors -1.9840 (1.5225) Competitors_platform -11.2252*** -10.3591** (4.0471) (4.0733) Log (Firm Age ) 4.9767 (16.2437) D_Tech -2.1527 (36.2709) Platform_reputation Observations 212 212 212 212 R-squared 0.594 0.524 0.5373 0.5331 F-test 37.34 28.05 29.61 5.671 Prob > F 0.000 0.000 0.000 0.000 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

TABLE 9 NUMBER OF CAMPAIGNS FINANCED BY PLATFORM AND YEAR Platform Name 2011 2012 2013 2014 July 2015 Total COMPANISTO 0 7 17 11 6 41CROWDCUBE 12 19 58 91 57 237FUNDEDBYME 0 0 14 22 8 44INVESDOR 0 1 9 12 9 31SEEDMATCH 3 27 21 23 8 82

Total by year 15 54 119 159 88 435

38

TABLE 10 DOMESTIC FIRM BY PLATFORM’S COUNTRY

Firm's country Domestic Total 0 =Not Domestic 1 =Domestic

DE (Companisto - Seedmatch) 0 126 126 0.00% 100.00%

FIN (Invesdor) 0 40 40 0.00% 100.00%

SWE (Fundedbyme) 45 42 87 51.72% 48.28%

UK (Crowdcube) 1 236 237 0.42% 99.58%

Total 46 444 490 10.22% 89.78%

TABLE 11 MULTIPLE ROUND BY PLATFORM Platform Name Round 1 Round 2 Round 3 Round 4COMPANISTO 38 2 1 CROWDCUBE 210 21 6 1FUNDEDBYME 83 4 INVESDOR 37 3 SEEDMATCH 72 12 1 Total 440 42 8 1

TABLES 12 SUMMARY OF STATISTICS Investment size expressed in foreign currency were converted in € at the monthly average exchange rate of the month in which the firm received the full funding amount Variable Obs Mean St. Dev. Min MaxLog (Investment size) 490 10.84793 3.993005 0 15.91232N. Investors 490 220.5041 330.6207 0 2702D_Domestic 490 0.9061224 0.2919564 0 1D_Business Plan 490 0.7836735 0.4121602 0 1D_Reward 490 0.5530612 0.4976846 0 1Pictures 490 8.34898 6.821313 0 86Video 490 1.071429 0.6805808 0 7D_Updates 490 0.7918367 0.4064092 0 1D_Tech 490 0.1714286 0.3772682 0 1Log (Firm Age) 490 3.041634 1.030416 0 5.313206Competitors 490 45.4551 25.13902 0 177Competitors_platform 490 15.14694 13.1696 0 53Professional 490 0.0163265 0.1268575 0 1Funding window (months) 490 2.544898 1.318045 0 14Platform_reputation 463 2.143477 3.067325 0 30PP_amount 490 15349.26 14176.2 71 35667PP_Ncompanies 490 77.17802 68.03922 3 226Tax rate 490 40.71265 7.360123 32 52.1

39

TABLE 13 Correlation Matrix

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)

(1) Log (Investment size)

1

(2) N. Investors 0.3352* 1

(3) D_Domestic 0.4796* 0.1803* 1

(4) D_Business Plan 0.0258 0.1896* -0.1011* 1

(5) D_Reward 0.2528* 0.0165 0.2595* 0.0262 1

(6) Pictures 0.1355* 0.2895* 0.0678 0.2393* 0.1478* 1

(7) Video 0.0267 0.2082* -0.1309* 0.1500* 0.0401 0.1078* 1

(8) D_Updates 0.5517* 0.2013* 0.4210* -0.0252 0.3681* 0.1546* 0.1056* 1

(9) D_Tech -0.0518 0.0205 -0.0021 0.0812 -0.0159 -0.04 0.0558 -0.0335 1

(10) Log (Firm Age) 0.011 0.1035* -0.0406 0.1315* -0.021 0.0811 0.0432 -0.0564 -0.083 1

(11) Competitors 0.1807* 0.2788* 0.0685 0.2434* 0.0073 0.2615* 0.0761 0.1568* -0.0878 0.1243* 1

(12) Competitors_platform

0.2991* 0.0146 0.1509* 0.1155* 0.2047* 0.0692 -0.076 0.3286* -0.1121* 0.0093 0.5504* 1

(13) Professional 0.0222 -0.0676 0.0415 -0.0105 -0.1433* -0.0988* -0.1320* -0.0529 -0.0159 0.0831 0.0054 -0.1079* 1

(14) Funding window (months)

0.2085* 0.1823* 0.1651* -0.0724 0.0634 0.0941* 0.0659 0.2694* -0.0484 -0.0183 0.5694* 0.1448* 0.0201 1

(15) Platform_reputation0.2875* 0.6197* 0.1612* 0.1824* -0.0581 0.2350* 0.1595* 0.1598* 0.0273 0.0068 0.1886* -0.1991* 0.0017 0.2257* 1

(16) PP_amount 0.3896* -0.0815 0.2879* -0.2233* 0.2896* -0.0156 -0.1375* 0.4692* -0.1437* -0.1018* 0.2439* 0.8026* -0.1368* 0.1199* -0.2417* 1

(17) PP_Ncompanies 0.2674* -0.2219* 0.2778* -0.5601* 0.2262* -0.1435* -0.1120* 0.3867* -0.1139* -0.1719* -0.0978* 0.2497* -0.1058* 0.2043* -0.2526* 0.6931* 1

(18) Tax rate -0.3797* 0.1180* -0.3752* 0.2354* -0.2525* 0.0406 0.2320* -0.4350* 0.1648* 0.0657 -0.2060* -0.7143* -0.0206 -0.1102* 0.2819* -0.9084* -0.7061* 1

This table presents the correlation coefficients across selected variables, as defined in Table 1. Correlations are provided for the full sample of 490 funding capital campaign retrieved from Crowdcube, Companisto, Fundedbyme, Invesdor, and Seedmatch. The * indicates that the correlation is statistical significant at the 5% level

40

TABLE 14 DETERMINANTS OF FUNDING AMOUNT (TOBIT ESTIMATION) The dependent variable is LogFinanced, the natural logarithm of the financed amount (in euros) raised. We report Tobit regressions. All the variables are defined in Table 1. VARIABLES (1) (2) (3) (4) (5) (6) N. Investors 0.0018*** 0.0019*** 0.0018*** 0.0016*** 0.0018*** 0.0019***

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) D_Domestic 1.1962*** 1.0248*** 1.0528*** 1.3817*** 0.8600*** 0.8159***

(0.2383) (0.2409) (0.2332) (0.2734) (0.2446) (0.2396) D_Business Plan 0.0807 0.1441

(0.1451) (0.1622) D_Reward -0.0942 -0.0791

(0.1290) (0.1187) Pictures (quartile) 0.0019 0.0275

(0.0563) (0.0528) Video 0.0337 0.0049

(0.0862) (0.0829) D_Updates 0.5352*** 0.4967** 0.4601** 0.5368** 0.5334*** 0.5743***

(0.2039) (0.2028) (0.2035) (0.2165) (0.2043) (0.2021) D_Listing fee -0.9550*** -0.8669*** -1.0575*** -1.2959*** -0.7814*** -0.7819***

(0.2276) (0.2079) (0.2159) (0.2266) (0.2387) (0.2191) Competitors -0.0004 0.0030

(0.0029) (0.0024) Competitors_platform 0.0274*** 0.0194*** 0.0276*** 0.0319***

(0.0057) (0.0061) (0.0045) (0.0045) D_Tech -0.3111** -0.2518* -0.2986** -0.2932** -0.1893 -0.1931

(0.1472) (0.1474) (0.1453) (0.1443) (0.1384) (0.1384) Log (Firm Age) 0.1210** 0.1269** 0.1157** 0.1312** 0.1134** 0.1194**

(0.0543) (0.0537) (0.0535) (0.0527) (0.0502) (0.0502) Tax rate -0.0231*

(0.0123) Professional 1.1245** 1.3947***

(0.4450) (0.4363) Funding window (months) -0.0845**

(0.0428) Percentage financed 0.0588**

(0.0233) PP_amountraised 0.0000*** 0.0000***

(0.0000) (0.0000) PP_Ncompanies -0.0015 -0.0028**

(0.0014) (0.0011) Constant 9.3086*** 10.5061*** 9.5375*** 9.1693*** 9.4028*** 9.6804***

(0.3887) (0.7065) (0.3499) (0.3880) (0.4293) (0.3467)

Observations 490 490 490 463 455 455 Uncensored 434 434 434 407 417 417 Left-censored 55 55 55 55 37 37 LL -751.4 -750.1 -748.7 -682 -665.5 -667.4 Chi-squared 372.5 375 377.8 416.8 351.2 347.3 Prob >chi2 0 0 0 0 0 0 McFadden's pseudo R-squared 0.199 0.200 0.201 0.234 0.209 0.206 Sigma 1.181 1.178 1.175 1.129 1.067 1.072 Maximum VIF 3.41 3.62 3.07 3.01 3.61 3.02 Mean VIF 1.64 1.88 1.55 1.61 1.88 1.81 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

41

FIGURE 1 NUMBER OF FINANCED CAMPAIGNS AND TOTAL VOLUME RAISED (IN MILLIONS €) BY PLATFORM AND YEAR

FIGURE 2

12

37

19

1

27

17

58

21

13

22

11

91

42

15

24

6

57

24

11 9

0

20

40

60

80

100

0

5

10

15

20

25

30

35

40

45

50

CROWDCUBE

SEED

MATC

H

COMPANISTO

CROWDCUBE

INVESDOR

SEED

MATC

H

COMPANISTO

CROWDCUBE

FUNDED

BYM

E

INVESDOR

SEED

MATC

H

COMPANISTO

CROWDCUBE

FUNDED

BYM

E

INVESDOR

SEED

MATC

H

COMPANISTO

CROWDCUBE

FUNDED

BYM

E

INVESDOR

SEED

MATC

H

2011 2012 2013 2014 2015

N.  finan

ced firms

Total amount invested

 (million €)

0.2

.4.6

.81

Density

8 10 12 14 16LogFinanced

42

A. APPENDIX

TABLES A.1 ICB FIRM INDUSTRY ICB Firm’s N. % 2710 Aerospace & Defense 3 1.42% 2770 Industrial Transportation 2 0.94% 3350 Automobiles & Parts 1 0.47% 3530 Beverages 8 3.77% 3570 Food Producers 7 3.30% 3720 Household Goods & Home Construction 11 5.19% 3740 Leisure Goods 14 6.60% 3760 Personal Goods 19 8.96% 4530 Health Care Equipment & Services 3 1.42% 5370 General Retailers 17 8.02% 5550 Media 15 7.08% 5750 Travel & Leisure 17 8.02% 6570 Mobile Telecommunications 20 9.43% 8630 Real Estate Investment & Services 1 0.47% 8770 Financial Services 10 4.72% 9530 Software & Computer Services 35 16.51% 9570 Technology Hardware & Equipment 7 3.30% 5330 Food & Drug Retailers 3 1.42% 1770 Mining 1 0.47% 8980 Equity Investment Instruments 1 0.47% 2790 Support Services 5 2.36% 8530 Nonlife Insurance 2 0.94% 2750 Industrial Engineering 1 0.47% 2730 Electronic & Electrical Equipment 1 0.47% 4570 Pharmaceuticals & Biotechnology 2 0.94% 0530 Oil & Gas Producers 1 0.47% 3700 Personal & Household Goods 2 0.94% 3000 Consumer Goods 2 0.94% 0580 Alternative Energy 1 0.47% Total 212 100.00%

TABLE A.2 ICB FIRM SUPERSECTOR Dummy_ICB Supersector FUNDEDBYME INVESDOR SEEDMATCH Total % 37 Personal Household Goods: 22 8 16 46 21.7% 95 Technology 16 3 23 42 19.8% 65 Telecommunication 14 6 0 20 9.4% (53 + 55 +57) Consumer Services 14 12 27 53 25% % 75.9% 72.5% 77.6% 75.9% 75.9% Other 21 11 19 51 24.1% % 24.1% 27.5% 22.4% 24.1% 100%

43

TABLE A.3 FIRM’S COUNTRY BY PLATFORM’S COUNTRY Firm’s Country/ Platform

COMPANISTO CROWDCUBE FUNDEDBYME INVESDOR SEEDMATCH Total

CHINA 0 0 1 0 0 1DE 41 0 3 0 85 129DEN 0 0 5 0 0 5ES 0 0 3 0 0 3EST 0 0 1 0 0 1FIN 0 0 14 40 0 54IT 0 0 1 0 0 1MALTA 0 0 1 0 0 1NETHERLAND 0 0 1 0 0 1NOR 0 0 11 0 0 11SWE 0 0 42 0 0 42UK 0 236 3 0 0 239USA 0 1 1 0 0 2Total 41 237 87 40 85 490

44

TABLE A.4

DETERMINANTS OF FUNDING SUCCESS (BINOMIAL PROBIT ESTIMATION) PROBIT

(1) (2) (3) (4) (5) (5) (5) (5) VARIABLES All All All All All SEED Startup EXPANSION

D_Domestic 0.7061** 0.2790 0.1142 0.2423 0.4287* -0.9285 0.9839*** -0.0102

(0.3222) (0.2481) (0.2588) (0.2597) (0.2469) (0.6058) (0.3456) (0.5490) D_Business Plan 0.7522* -0.2594 -0.2335 -0.0037 -0.1419 0.0990 0.1111 -1.6095**

(0.3887) (0.2955) (0.2995) (0.3045) (0.2936) (0.5854) (0.4129) (0.7564) D_Reward -0.2898 0.2829 0.2741

(0.3373) (0.2548) (0.2568) Pictures (quartile) -0.2917** -0.2016* -0.2142* -0.2048* -0.2235** -0.3880 -0.3837** -0.1317

(0.1430) (0.1134) (0.1116) (0.1129) (0.1116) (0.3408) (0.1655) (0.2359) Video -0.1875 -0.1742 -0.1342

(0.1614) (0.1197) (0.1226) D_Updates 0.8546*** 1.0529*** 1.0301*** 0.9550*** 0.9880*** 1.1434* 1.1632*** 1.5083***

(0.3149) (0.2555) (0.2551) (0.2425) (0.2395) (0.5851) (0.3691) (0.5516) Log (Investment size) 0.8589*** 0.1102*** 0.1299*** 0.1977*** 0.1590*** 0.1756** 0.0381 0.2100**

(0.1306) (0.0392) (0.0418) (0.0504) (0.0449) (0.0829) (0.0548) (0.0966) Year -0.0046***

(0.0007) Competitors -0.0191

(0.0118) Competitors_platform -0.0692** -0.0665**

(0.0291) (0.0298) Log (Firm Age) -0.2953**

(0.1215) D_Tech -0.4709** -0.4681** -0.0957 -0.3268 -1.1217**

(0.2343) (0.2319) (0.5603) (0.3326) (0.5384) Stage of development -0.4524***

(0.1542)

Observations 212 212 212 212 212 44 108 60 Chi-squared 78.49 73.50 77.05 80.48 80.48 16.10 43.55 20.61 Prob >chi2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0132 0.0000 0.0022 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

45

TABLE A.5 MARGINAL EFFECTS BINOMIAL PROBIT ESTIMATION

PROBIT (1) (2) (3) (4) (5) (5) (5) (5) VARIABLES All All All All All SEED Startup EXPANSION Domestic 0.0981** 0.0675 0.0271 0.0555 0.1008* -0.2342 0.2135*** -0.0020 (0.0430) (0.0595) (0.0613) (0.0590) (0.0569) (0.1429) (0.0658) (0.1097) D_Business Plan 0.1045** -0.0628 -0.0554 -0.0009 -0.0334 0.0250 0.0241 -0.3217** (0.0524) (0.0712) (0.0708) (0.0698) (0.0690) (0.1477) (0.0897) (0.1473) D_Reward -0.0403 0.0685 0.0650 (0.0465) (0.0610) (0.0603) Pictures (quartile) -0.0405** -0.0488* -0.0508* -0.0469* -0.0526** -0.0979 -0.0833** -0.0263 (0.0195) (0.0270) (0.0260) (0.0254) (0.0257) (0.0835) (0.0346) (0.0467) Video -0.0261 -0.0422 -0.0318 (0.0223) (0.0286) (0.0289) D_Updates 0.1187*** 0.2548*** 0.2444*** 0.2187*** 0.2324*** 0.2884** 0.2524*** 0.3015*** (0.0413) (0.0567) (0.0561) (0.0512) (0.0512) (0.1345) (0.0732) (0.0866) Log (Investment size) 0.1193*** 0.0267*** 0.0308*** 0.0453*** 0.0374*** 0.0443** 0.0083 0.0420** (0.0132) (0.0091) (0.0093) (0.0106) (0.0099) (0.0180) (0.0119) (0.0179) Year -0.0006*** (0.0001) Competitors -0.0046 (0.0028) Competitors_platform -0.0164** -0.0152** (0.0067) (0.0066) Log (Firm Age) -0.0676** (0.0269) D_Tech -0.1079** -0.1101** -0.0241 -0.0709 -0.2242** (0.0528) (0.0536) (0.1412) (0.0719) (0.0932) Stage of development -0.1064*** (0.0346) 212 212 212 212 212 44 108 60

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

46