who invests using “accredited crowdfunding?” evidence from...

Proceedings of the 14th European Academic Research Conference on Global Business,

Economics, Finance & Social Sciences (EAR19France Conference)

Paris-France. July 5-7, 2019. Paper ID: P938

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www.globalbizresearch.org

Who Invests Using “Accredited Crowdfunding?” Evidence from

506(c) Filings

Eric Fricke,

California State University – East Bay, USA.

E-mail: [email protected]

Scott Fung,



M. Sinan Goktan,



___________________________________________________________________________________

Abstract

The JOBS Act of 2012 required the SEC to facilitate a new way for small private companies to

raise capital directly from the general public by selling stock or other securities in order to

increase access to capital, spur business growth and create jobs. This method, generally known

as “accredited crowdfunding” is subject to criticism regarding the type of potential investees

this new market might attract and the possibility that it might become a market for lemons. Our

paper provides the first set of empirical evidence regarding the characteristics and the type of

investees in this market to see if the market is on track to serve its original purpose or if it is

evolving into a market for lemons.

___________________________________________________________________________

Key Words: private equity, crowdfunding, 506(c), venture capital.

JEL Codes: G24, G28, D80




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1. Introduction

The term “crowdfunding” is being heard more frequently in the media in recent years. It is

commonly defined as a method of raising small amounts of capital from a large group of

investors through public platforms such as the internet. Small startups initially entered this

arena by offering small gifts, early product prototypes, or the promise of serving a greater good

in order to attract potential investors to raise capital. They initially did not offer equity like

claims to potential investors to avoid the expense of securities regulation. In April 2012, the

JOBS act directed the Securities and Exchange Commission (SEC) to facilitate securities-based

crowdfunding in order to increase access to capital so that companies can grow and hire. Among

different rules introduced in the new bill, one is particularly important; the new Rule 506(c).

This paper examines the implications of Rule 506(c) and provides early evidence on how this

new source of funding is developing, shedding some light on this new but promising area of

funding for private businesses.

The 506(c) rule expands the ability of issuers to crowdfund significant amounts of capital

from “accredited” investors and thus becomes a serious alternative to private placements, which

are currently the main sources of funding for private businesses. Accredited investors are

defined as having a minimum net worth of $1 million (excluding their residence), income of

$200,000 a year (or $300,000 with their spouse), or officers and directors of the issuer or various

institutions having more than $5 million in assets. It is estimated that there are 8.7 million US

households (7.4% of all households in the country) that qualify as accredited investors. By

limiting access to “accredited investors” the SEC is trying to protect public investors from these

highly opaque and risky investments that have very little disclosure requirements. Even with

this restriction in place, the potential pool of eligible investors represents a huge opportunity

for companies to access capital.1 Whether this new market for private capital will attract quality

investments or turn out to be a market for lemons is a question that needs examination.

To our knowledge, empirical finance literature in this area is scarce, even though legal

literature has debated the implications of this new rule for some time. Amongst the benefits of

this new law is the fact that investees can access capital more easily and cheaply by directly

soliciting registered investors in public. In the common alternative method, which is a private

placement under Regulation D, a financial intermediary sets the company/investee with

1 After the 506(c) rule addition to Reg D, we believe that this current funding structure can

significantly change. Rather than going through a typical Rule 506 Reg D funding using a financial

intermediary to match with potential registered investors, investees can now broadly advertise their

offering in public and directly solicit offers from accredited investors. This would shift the business from

the traditional 506 Reg D market to an “accredited crowdfunding” market. Given the fact that the current

market for 506 Reg D funding is $700 billion (almost half of all private business funding), the

implications of such a shift in the financial markets would be quite significant.




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registered investors and charges sales commissions for this service, which is approximately 5%

of the offer size. Given the fact that the median size of a Regulation D offering is $5 million,

this means that investees can potentially save $250,000 with the 506(c) rule that lets investees

seek investors through public soliciting.2

Besides the potential legal benefits of equity crowdfunding, some papers argue that equity

crowdfunding might create a market for lemons where a lack of reporting requirements and

transparency leads to a proliferation of low quality equity offerings that ultimately hurt

investors.

To be able to make a distinction on these two opposing arguments, one needs to observe

how the market has behaved in its early stages and what type of companies chose to participate

in this market. This paper provides empirical evidence regarding the financing structure of

equity crowdfunding and evaluates crowdfunding decisions and outcomes in this rapidly

changing environment. We provide empirical evidence regarding the characteristics and the

type of investees in this market to see if the market is on track to serve the purpose that it was

originally designed for or if signs of a market for lemons exist.

Our findings include the following sets of results. First, we examine the offering size and

capital raising of crowdfunding firms. Results reveal that crowdfunding firm’s offering size

increases with firm age, shares in pooled investment fund, mergers, tech firms, etc. Equally

important, we examine the success of fund raising and find that the success of fund raising

increases with older firms, merger-related offerings, tech firms, and smaller minimum

investments. Fundraising expenses and payment of proceeds to insiders tend to decrease

offering success. Overall, these findings support the idea that observable firm characteristics,

such as a firm’s age and industry, are associated with the success of fund raising efforts of

crowdfunding firms.

Our second set of results looks at crowdfunding attempts by VC backed companies, and,

more specifically, the quality of the VC backed companies. We find that, overall, VC backed

companies are more likely to raise funds through 506(c)’s. Additionally, we find that offerings

are more likely filed as 506(c)’s if they are smaller, have higher VC firm investments, are

backed by younger VC companies, and draw proportionately more capital from 506(c)

investors. Additionally, offerings are more likely to file as 506(c)’s if they have lower minimum

2 To put the magnitude of this new rule into perspective, if we look at the total Venture Capital

(VC)/angel capital invested in 2012, it was about $26.5 billion. On the other hand, Rule 506 Reg D

private placement market was $800 billion, and the 144A market was about $700 billion. So, although

VC capital is significant at the very early stages of the deal pipeline, it is only 1.5% of the total capital

that is raised towards the funding of technology based economic development. Following the seed

funding of VCs, Reg D Rule 506 funding is typically raising almost half of the capital needed for these

technology based private companies in the deal pipeline and Reg D, 144A fundings provide the other

half, enabling private businesses to sell shares to qualified institutional buyers.




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investment requirements, have lower sales and commission fees, use a greater proportion of

proceeds to invest in the business versus paying off earlier investors, and offer equity or debt

rather than convertible debt. If the company receives more funding rounds, if the number of

IPOs completed by the VC Firm is greater and the amount invested in company is greater, the

company is more likely to receive 506(c) funding. These results show that older VC firms

managing younger funds who invest larger amounts in younger firms tend to be more likely to

be involved in 506(c) funding. Consistent with asymmetric information theory, a separating

equilibrium seems to exist in the market of crowdfunding firms – i.e., quality crowdfunding

firms with VC are more likely to raise funds through 506(c)’s.

In essence, our study provides the following contributions. First, the crowdfunding market

remains an emerging and understudied area in the finance literature. We complement existing

literature by examining offering firm characteristics and investee types in the crowdfunding

market. Belleflamme, Lambert, and Schwienbacher (2014) provide a theory on the

entrepreneur’s choice of forms of crowdfunding. Signori and Vismara (2017) examine the UK

crowdfunding platform and quantifies the return on investment in equity crowdfunding.

Schweizer and Zhou (2017) examine financing characteristics of seven leading US-based real

estate crowdfunding platforms and find that projects with higher investment risk on average

have higher expected returns. Our study contributes to the existing literature by providing new

findings on the firm characteristics associated with more successful crowdfunding efforts.

Second, our study contributes to the literature that examines investees in the crowdfunding

market. Lin, Sias, and Wei (2019) examine institutional investors in online crowdfunding and

find that their portfolios do not necessarily outperform those of retail investors. Hornuf and

Schwienbacher (2018) show that investors make investment decisions in crowdfunding firms

based on information provided by the investment behavior and comments of other crowd

investors. Our study highlights the material role of VC companies in the crowdfunding market.

Our findings also contribute to the study of Ambrosio and Gianfrate (2016), which show that

crowdfunding and VC can be substitutes or complements, depending on the rounds of

financing. We show that the characteristics of VCs, such as their age and number of IPOs

completed by the VC, also play an important role in understanding the relation between VC

financing and crowdfunding.

Third, existing studies find that due diligence screens out lower quality projects and

mitigates information asymmetry between issuers and funders. For example, Cumming and

Zhang (2018) use Canadian crowdfunding platforms to examine whether crowdfunding

platforms provide active and effective intermediaries. Our results extend these studies by

showing that greater transparency and signaling are still valid channels that improve the lending

practices in the crowdfunding market, similar to the banking and VC industries.




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Overall, our findings provide new insight into the characteristics and outcomes (e.g., fund

raising) of crowdfunding firms as well as the importance of VC backed companies in the

crowdfunding market – these areas are relatively unexplored in the existing literature. Our

findings can also be extended to the information opacity problem of small businesses [see, e.g.,

Cole, Goldberg, and White (2004)]. Last, our study contributes to the literature on financing of

early-staged startup firms including investor syndication and the JOBS Act. Hellmann and

Thiele (2015) provide a theory of the interaction between angel and venture capital markets.

Dambra, Field, and Gustafson (2015) find that the JOBS Act has led to a 25% increase in IPOs

and firms with high proprietary disclosure costs increase IPO activity the most and are more

likely to take advantage of the JOBS Act’s de-risking provisions. Our results show that the

JOBS Act is not really a substitute but a complement for other sources of private equity.

This paper is organized as follows. Section 2 presents the crowdfunding setting and

hypotheses. Section 3 presents sample and data. Section 4 reports empirical results. Section 5

concludes.

2. The Crowdfunding Setting and Hypotheses

2.1 Theory and Empirics of Crowdfunding

The institutional setting and economic features of crowdfunding (see Parsont (2014)) offer

an opportunity to examine investments in highly opaque private firms with high uncertainty

and no market liquidity. First, crowdfunding provides an ideal setting to test the theory of

adverse selection (‘lemons problem’) of corporate finance wherein outside investors may not

distinguish the qualities of the firms. While existing literature mainly uses IPO and equity

markets to support the adverse selection problem in outside financing (Myers and Majluf

(1984)), the asymmetric information problem is accentuated in the setting of crowdfunding

because investors do not have or have very little information of past performance of firms at

the beginning of crowdfunding.3 Thus, investors might have to rely on other signals rather than

the actual information derived from the investments themselves.

In addition to asymmetry information problem, crowdfunding provides an interesting

setting to evaluate moral hazard problems between the manager and outside investors. In the

crowdfunding setting, there is no secondary market and hence no market liquidity. This implies

that investors cannot exert governance through exit/trade (see, e.g., Admati and Pfleiderer

(2009), Edmans and Manso (2001), Edmans (2009), and Edmans, Fang, and Zur (2013)) and

3 Hilderbrand, Puri, and Rocholl (2017) provide evidence on the adverse incentives in crowdfunding

market.




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the only governance channel is monitoring and intervention.4 Appendix A presents a model to

illustrate the theoretical insight and adverse selection (the lemons problem) in crowdfunding

market – i.e., in the presence of information asymmetry, the market is left with low-quality

firms.

Second, the crowdfunding setting enables us to focus on examining factors that determine

financing choices (before observing ex post performance of the startup firms) since the 506(c)

rule provides an external shock to examine the financing opportunity set of a company in its

early stages. Furthermore, the crowdfunding setting relieves a significant portion of the

endogeneity concerns as firms seeking crowdfunding do not provide information of past

performance (i.e., the effect of firm performance on financing, a typical feedback effect that

complicates the endogeneity relation between financing and performance).

Utilizing the unique setting of crowdfunding, we perform two sets of empirical analyses.

In the first, we examine the characteristics and outcome of crowdfunding firms. In the second,

we evaluate crowdfunding decision and outcome from the perspective of the VC industry to get

a sense of what type and quality of VC backed companies choose to enter the crowdfunding

arena.

2.2 Hypotheses

As discussed in the previous section, our first set of analyses examine both the

characteristics and outcomes of crowdfunding firms as well as the characteristics that

significantly effect the crowdfunding choice and its success rate. Thus, our first hypothesis is

provided as follows:

Hypothesis 1: Offering Success of Crowdfunding Firms is a Function of Observable

Firm Characteristics.

Our second set of analyses focus on crowdfunding attempts by VC backed companies,

which is relatively unexplored in the existing literature. Since getting data on quality is scarce

for crowdfunding companies, we rely on the VC industry signals to proxy for quality since VC

backed company data is more available for those crowdfunding companies that also received

VC funding. First of all, receiving VC backing is a clear sign of quality since the VC firm takes

on the role of verification of quality before investing in a company. Thus, receiving

crowdfunding should be easier for companies that already have VC funding since new investors

will use this external signal to make their choice on investment.

4 The unique features of crowdfunding can shed new light on investors’ governance [Edmunds (2014)].

For example, crowdfunding is characterized by a large number of small investors where monitoring will

be difficult due to a free-riding problem [Grossman and Hart (1980) and Shleifer and Vishny (1986)].




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Hypothesis 2: VC Backed Companies are More Likely to have Success Raising Funds

Through 506(C)’S Due to Certification.

This second hypothesis gives us the opportunity to examine how the VC backed companies

that receive both venture capital and crowdfunding compare to other VC backed companies

that did not attempt to receive any crowdfunding. This gives us the chance to compare the

quality of the VC backed companies in the crowdfunding domain to those that are not. If the

crowdfunding market is a market for lemons, we would expect relatively low quality VC backed

companies that have grim prospects to enter the crowdfunding arena to secure future funding.

On the other hand, if the crowdfunding market has a separating equilibrium, higher quality

companies that received funding from highly reputable VC firms are more likely to enter the

crowdfunding arena to secure extra funding, presumably at more favorable terms to the

company. Thus, our third hypothesis is as follows:

Hypothesis 3: VC Backed Companies that Receive Funding from More Reputable VC

Firms are More Likely to Raise Capital from the Crowdfunding Market.

To further support Hypothesis 2 above, we examine various characteristics of VC firms

that invest in crowdfunding market. We test whether their VC firms have taken more firms to

IPOs and whether these companies are raising more money. We also test whether crowdfunding

firms are funded by older VC firms – i.e., companies have participated in the 506(c) are funded

by older VC firms, are investing more money into their companies on average. In contrast, we

test whether younger funds invest in relatively younger companies.

3. Data and Sample

We gather the 506(c) filing data using the www.thecrowdcafe.com website, which gathers

data from the 506(c) filings to the SEC. Our data includes filings from 2/14/2013 to 9/29/2014.

Since information about private companies is limited, proxying for their quality is difficult.

Since our analysis requires an external signal of quality for the company requiring 506(c), we

rely on the subset of the 506(c) sample that receives private equity backing. This allows us to

test and compare among the universe of private equity firms and companies, what type of

private equity firms and companies choose to go through the 506(c) process. During this period,

there are approximately 900 companies that filed 506(c) with the SEC and among these

companies, 72 of them were private equity backed. These 72 companies were backed by 189

private equity firms. For the 72 private equity backed companies, we gather data from SDC

Platinum about the funds invested in each company and their firms. Since 506(c) is in effect for

a very short period, we do not have data on how these companies perform after the filing so

rather than testing the “causal effect” of 506(c) on company performance, this research will

focus on the “selection” of 506(c) filing among private companies.

http://www.thecrowdcafe.com/




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In order to identify what type of companies select to raise capital, we gather data from

1983-2014 from SDC on all the private equity funds, firms, and companies that they are

backing. We merge this data with the 506(c) data from www.thecrowdcafe.com to identify the

companies and funds that seek such funding. Using the SDC VentureXpert database, we are

able to identify the age of the company when it receives its first round of investment, the total

amount invested in the company, the age of the fund, the firms investing in the company at the

first round of company investment, the total number of IPOs completed by the firm in the past,

the industry of the company, the number of rounds, and the number of funds invested in the

company. From the SEC filing, we are able to identify the following for companies that received

506(c) funding: form of payment-whether it is debt equity or any other form of capital, number

of investors participating in the funding, the amount requested for funding, the amount

successfully raised, minimum amount needed to join the fund, the sales commissions to be paid

from the fund raising, and whether any employee is to be paid from the fund that will be raised.

4. Empirical Analysis and Results

Our paper provides empirical evidence regarding the accredited crowdfunding market

characteristics and the type of investees. Our findings include the following two sets of results:

(i) Offering size and success of fund raising; and (ii) Funding from VC firms.

4.1 Summary Statistics and Characteristics of Crowdfunding Firms

Next, we evaluate the crowdfunding decision and outcome from the perspective of the VC.

We examine various characteristics of VC firms that invest in crowdfunding market. Tables 1

through 6 use the merged data of 506(c) transactions with the SDC data that includes all VC

backed companies. These analyses allow us to compare accredited crowdfunding investments

that have VC backing to other VC backed investments that do not enter the crowdfunding

arena..

Table 1 presents summary statistics for the complete set of 506(c) transactions in our

sample and shows that the actual funds raised as a percentage of the amount offered is 37.3%

on average. The average number of years since organization of the entity is about 3 years.

When firms do not report revenue, a significant positive impact on offering amount is seen

and we report the descriptive statistics for the differences in these transactions in Table 2. Firms

that do not report revenue tend to have smaller offerings, be slightly older, be more tech

focused, and have a higher probability of VC backing.

Table 3 lists the descriptive statistics for the differences in characteristics of VC backed

companies that also receive crowdfunding compared to those VC backed companies that do not

receive crowdfunding. Table 3 shows that firms that filed for 506(C) funding are about 5 years

younger than firms that do not file for 506(C) funding (although the difference is not statistically

significant). Moreover, the number of firms and the number of funds in their syndicate are




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significantly larger for firms that filed for 506(C). Importantly, the total venture capital amount

invested is significantly larger for firms that filed for 506(C).

Table 4 provides another important finding and shows that 66% of firms conducting 506(c)

offerings had VC backing. This result suggests that VCs are significant participants among

crowdfunding firms.

4.2 Offering Amount and Fund Raising by Crowdfunding Firms

Using regression analyses, we now re-examine the determinants of offering size and fund

raising of crowdfunding firms. Tables 5 and 6 look at the complete set of 506(c) transactions

in our sample to identify which characteristics determine the offering size and success of the

offering.

According to our results in Table 5, regression (1), we see that older firms, investment

funds, tech firms, and VC backed firms tend to have larger offerings. In regression (2), we

include revenue and if the firm did not report revenue to the regression. When revenue is

included in the regression, the predictive power of the age and tech variables is made

insignificant.

The regression in Table 6 (percent of offering raised) shows how successful a firm is in

raising the planned offering amount. Larger offerings tend to raise less. Older firms raise more,

while debt offerings raise less compared to equity offerings. Merger related offerings tend to

be more successful. Fundraising expenses and payment of proceeds to insiders tend to decrease

offering success. Tech firms and smaller minimum investments tend to be more successful.

Overall, both univariate and multivariate analyses findings support Hypothesis 1 and shows

that observable firm characteristics that correlate with quality of investment are associated with

the success and fund raising of crowdfunding firms.. We can clearly see that as companies are

clearer about how the money will be spent and as the company becomes more mature and tries

to raise a conservative amount, the crowdfunding market reacts more positively.

In addition, in all regressions in Tables 5 and 6, VC backed companies are significantly

more likely to succeed in raising capital. This is consistent with Hypothesis 2 and shows that a

credible external certification provided by the VC investment significantly increases the

success of crowdfunding efforts.

4.3 VC Backed Crowdfunding

To test H3, we now focus only on companies that are VC backed and are attempting to

raise crowdfunding.

In Table 7, we model the log of percentage of funds successfully raised. We see that higher

amounts requested for funding result in less successful offerings, companies that receive more

money from their private equity firms are raising more, younger companies are raising more,

companies that can draw from more investors can raise more, companies that have a lower




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minimum investment amount raise more, and companies that don’t pay sales commissions are

able to raise more. Interestingly, VC firm experience does not play a significant role.

In Table 8, we examine how the 506(c) companies compare to the others. We see that

these companies are receiving more rounds of financing from their funds, their VC firms have

taken more firms to IPOs and those companies that are raising more money are also more

likely to seek 506(c) funding.

In Table 9, we examine how funds whose companies have participated in 506(c)’s compare

to other funds who have not participated in 50(c)’s. Our results indicate that the 506(c)

participating funds are backed by older VC firms, are investing more money into their

companies on average, and are younger funds investing in relatively younger companies.

These results can be interpreted as the possibility that VC backed companies that enter the

crowdfunding market tend to have higher quality compared to the overall VC backed company

set.

Overall, these findings support Hypothesis 3 and suggest that contrary to a market for

lemons, we see a separating equilibrium where companies backed by more reputable VCs are

entering the crowdfunding arena. Our findings also contribute to the study of Ambrosio and

Gianfrate (2016) by showing that the characteristics of VCs, such as their age and number of

IPOs completed by the VC, also play an important role in understanding the relation between

VC financing and crowdfunding.

In all of our models, we see that companies that receive funding from multiple investors,

those that receive money from more funds of more experienced VC firms, and those that invest

more money into their companies have a higher chance of raising money. These findings can

be interpreted as evidence of monitoring effect (by VCs) in a crowdfunding market. As

discussed in Section 2, crowdfunding is characterized by a large number of small investors

where monitoring will be difficult due to a free-riding problem. While the existing studies (such

as Carletti, Cerasi, and Daltung (2007), Chen, Harford and Li (2007), Brav, Jiang, Partnoy, and

Thomas (2008), Edmans, Fang, and Zur (2013), Crane, Michenaud, and Weston (2014))

document the presence of monitoring by investors in different firm settings, our study applies

the unique setting of crowdfunding and our findings suggest that investors choose to invest in

companies where they observe some level of monitoring by credible investors such as VCs.

Last, our findings are also consistent with the theoretical model of crowdfunding (see Appendix

A) – i.e., a separating equilibrium can exist when the high-quality firms obtain crowdfunding

in the presence of lower marginal cost of monitoring cost (e.g., in the presence of VCs’

investing and monitoring).




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5. Conclusion

The JOBS Act of 2012 provided a new way for small private companies to raise external

capital directly from the general public. This new way, termed “accredited crowdfunding,” is

subject to criticism regarding the type of investees this new market might attract and the

potential to become a market for lemons. However, empirical study done in this area within the

finance literature is relatively unexplored. Our study examines the outcomes of crowdfunding

in the context of corporate finance literature. Specifically, our study provides new empirical

evidence regarding the firm characteristics and the type of investees in this market to see if the

market is on track to serve the purpose that it was originally designed for or if signs of a market

for lemons exist.

First, our findings reveal which factors contribute to the success of raising capital through

crowdfunding. Our findings suggest that 506(c) companies’ offering size increases with older

firms, when shares in pooled investment fund are offered, and when proceeds are used for

investments in mergers or tech companies. The success of fund raising is higher for older firms,

merger-related offerings, tech firms and those that require less minimum investments.

Fundraising expenses and payment of proceeds to insiders tend to decrease offering success.

Second, our findings highlight the importance of VC backed companies in crowdfunding

market. Compared to other companies raising private equity through the VC industry, 506(c)

companies are younger, receive more funding from their VC firms, and receive VC funds from

more firms and funds in their syndicate. Interestingly, VC firm experience does not play a

significant role in the amount raised. While these companies are receiving more rounds of

financing from their funds, their VC firms have taken more firms to IPOs and these companies

are raising more money.

Overall, our results do not find evidence of a “lemons problem” in the crowdfunding

market. To the contrary, companies raising capital in the crowdfunding market tend to have

significantly higher quality. Our findings suggest that observable signals of quality seem to help

companies successfully raise capital. Our findings also suggest that in the case of uncertainty,

a separating equilibrium may exist wherein high quality companies seem to get funded in the

crowdfunding market.

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Table 1: Summary Statistics

Amount Offered is the planned dollar value of raised funds. Percentage Raised is the actual

funds raised as a percentage of the amount offered. Age is the number of years since

organization of the entity. Equity is an indicator variable with a value of zero (no equity offered)

or one (equity offered). Debt is an indicator variable with a value of zero (no debt offered) or

one (debt offered). Options is an indicator variable with a value of zero (not offered) or one

(offered) if options, warrants or other rights to acquire another security are offered. Fund is an

indicator variable with a value of zero (no fund shares offered) or one (fund shares offered) if

the offering is for shares in an investment fund. Merger is an indicator variable with a value of

zero (not merger related) or one (merger related) if the offer is made in connection with a

business combination transaction, such as a merger, acquisition or exchange offer. Corp is an

indicator variable with a value of zero (not a corporation) or one (a corporation) if the entity is

a corporation or other entity such as a limited liability corporation or general partnership. Tech

is an indicator variable with a value of zero (a non-technology firm) or one (a technology firm)

if the firm is in a technology-based industry (computers, telecommunication, other technology,

or biotechnology). VC is an indicator variable with a value of zero (not a VC-backed) or one

(VC-backed) if the entity is funded by venture capital. Revenue is the reported annual revenue.

Expense is the sales commissions and finders’ fees paid by the entity. Use of Proceeds is the

offering proceeds used to pay firm officers, directors, or promoters. Min Invest is the minimum

investment accepted from any outsider.

N MEAN Q1 Q2 Q3 Std. Dev.

Amount Offered 847 72.2 0.750 2.000 6.000 1,720

Percentage Raised 879 0.373 0.000 0.120 0.890 0.424

Age 897 3.060 0.000 2.000 7.500 3.022

Equity 958 0.737 0.000 1.000 1.000 0.441

Debt 958 0.215 0.000 0.000 0.000 0.411

Options 958 0.161 0.000 0.000 0.000 0.367

Fund 958 0.013 0.000 0.000 0.000 0.111

Merger 915 0.040 0.000 0.000 0.000 0.197

Corporation 958 0.578 0.000 1.000 1.000 0.494

Tech 958 0.294 0.000 0.000 1.000 0.456

VC 958 0.075 0.000 0.000 0.000 0.264

Revenue 501 4.096 0.000 0.500 0.500 14.70

Expense 895 7.501 0.000 0.000 0.000 221.0




14


Use of Proceeds 894 0.152 0.000 0.000 0.030 0.983

Minimum Investment 896 0.154 0.000 0.010 0.025 1.486

Table 2: Sample Statistics by Revenue Reporting Status

Amount Offered is the planned dollar value of raised funds. Percentage Raised is the actual

funds raised as a percentage of the amount offered. Age is the number of years since

organization of the entity. Equity is an indicator variable with a value of zero (no equity offered)

or one (equity offered). Debt is an indicator variable with a value of zero (no debt offered) or

one (debt offered). Options is an indicator variable with a value of zero (not offered) or one

(offered) if options, warrants or other rights to acquire another security are offered. Fund is an

indicator variable with a value of zero (no fund shares offered) or one (fund shares offered) if

the offering is for shares in an investment fund. Merger is an indicator variable with a value of

zero (not merger related) or one (merger related) if the offer is made in connection with a

business combination transaction, such as a merger, acquisition or exchange offer. Corp is an

indicator variable with a value of zero (not a corporation) or one (a corporation) if the entity is

a corporation or other entity such as a limited liability corporation or general partnership. Tech

is an indicator variable with a value of zero (a non-technology firm) or one (a technology firm)

if the firm is in a technology-based industry (computers, telecommunication, other technology,

or biotechnology). VC is an indicator variable with a value of zero (not a VC-backed) or one

(VC-backed) if the entity is funded by venture capital. Revenue is the reported annual revenue.

Expense is the sales commissions and finders’ fees paid by the entity. Use of Proceeds is the

offering proceeds used to pay firm officers, directors, or promoters. Min Invest is the minimum

investment accepted from any outsider.

Total Sample Revenue Reported Revenue Not Reported

N Mean N Mean N Mean

Amount Offered 847 72.2 458

118 389 17.9

Percentage Raised 879 0.373 480 0.284 399 0.480

Age 897 3.060 501 2.91 396 3.25

Equity 958 0.737 501 0.813 457 0.656

Debt 958 0.215 501 0.232 457 0.197

Options 958 0.161 501 0.158 457 0.164

Fund 958 0.013 501 0.016 457 0.009

Merger 915 0.040 501 0.046 414 0.034

Corporation 958 0.578 501 0.563 457 0.595




15


Tech 958 0.294 501 0.238 457 0.357

VC 958 0.075 501 0.012 457 0.144

Revenue 501 4.096 501 4.096 0 .

Expense 895 7.501 500 0.184 395 16.8

Use of Proceeds 894 0.152 501 0.167 393 0.133

Minimum Investment 896 0.154 501 0.122 395 0.194

Table 3: Summary Statistics by 506(c) Filing Status

See Table I for variable descriptions. The p-values are presented for the mean and median tests of each

variable. *, **, *** denote significance at 10%, 5%, and 1%, respectively.

506(c) filing Mean Q1 Q2 Q3 Std dev

Age of firm at comp first inv. Yes 15.95 9 14 19 11812

No 21.13 11 15 20 20451

p-value 0.14 0.04**

Average firm inv. in comp. Yes 8366 2628 6698 9859 1,557

No 8066 2538 4976 8654 4,293

p-value 0.81 0.03**

Number of firms inv. in comp Yes 8.9 4 6 12 6.85

No 5.2 2 4 7 4.82

p-value 0.00*** 0.00***

Number of funds inv. in comp Yes 10.9 4 8 13 9.3

No 6.3 2 4 8 6.3

p-value 0.00*** 0.00***

Total amount invested in comp Yes 95804 3492 16803 65351 275950

No 40041 1900 7715 27085 202881

p-value 0.02** 0.00***

Total prior firm IPOs Yes 25.96 0 9 40 39.0

No 19.34 2 9 23 29.3

p-value 0.00*** 0.98

Company Age at first investment Yes 5.8 1 2 5.5 13.4

No 11.1 1 3 12 22.3

p-value 0.08* 0.05**




16


Table 4: Summary Statistics by VC Backing Status

506(C) COMPANIES MEAN MEDIAN N

VC BACKED 0.66 0.85 72

NON VC BACKED 0.36 0.08 1511

Table 5: Offering Amount Regressions

Log(Amount Offered) is the log of the planned dollar value of raised funds. Age is the number

of years since organization of the entity. Debt is an indicator variable with a value of zero (no

debt offered) or one (debt offered). Options is an indicator variable with a value of zero (not

offered) or one (offered) if options, warrants or other rights to acquire another security are

offered. Fund is an indicator variable with a value of zero (no fund shares offered) or one (fund

shares offered) if the offering is for shares in an investment fund. Merger is an indicator variable

with a value of zero (not merger related) or one (merger related) if the offer is made in

connection with a business combination transaction, such as a merger, acquisition or exchange

offer. Corp is an indicator variable with a value of zero (not a corporation) or one (a corporation)

if the entity is a corporation or other entity such as a limited liability corporation or general

partnership. Tech is an indicator variable with a value of zero (a non-technology firm) or one

(a technology firm) if the firm is in a technology-based industry (computers,

telecommunication, other technology, or biotechnology). VC is an indicator variable with a

value of zero (not a VC-backed) or one (VC-backed) if the entity is funded by venture capital.

Log(Revenue) is the log of reported annual revenue. RevenueNotReported is an indicator

variable with a value of one if the offering did not report revenue information and zero if it did.

Results include year fixed-effects with robust standard errors in parentheses. *** p<0.01, **

p<0.05, * p<0.1

Log(Amount Offered)

(1) (2) Age 0.0385* 0.0350 (0.0232) (0.0231) Debt -0.282 -0.284 (0.191) (0.192)

Options -0.243 -0.254 (0.222) (0.221)

Fund 2.720*** 2.811*** (0.573) (0.590)

Merger 0.616 0.653 (0.391) (0.398)

Corp -0.0969 -0.133 (0.151) (0.151)

Tech 0.284* 0.216 (0.152) (0.155)

VC 0.659** 0.556* (0.314) (0.317) Log(Revenue) 0.010




17


(0.0138) Revenue Not Reported 0.457** (0.181) Constant 14.43*** 14.24*** (0.140) (0.179) Observations 830 830

R-squared 0.042 0.051

Table 6: Offering Amount Percent Raised Regressions

Percentage Raised is the actual funds raised as a percentage of the amount offered. Age is the

number of years since organization of the entity. Debt is an indicator variable with a value of

zero (no debt offered) or one (debt offered). Options is an indicator variable with a value of

zero (not offered) or one (offered) if options, warrants or other rights to acquire another security

are offered. Fund is an indicator variable with a value of zero (no fund shares offered) or one

(fund shares offered) if the offering is for shares in an investment fund. Merger is an indicator

variable with a value of zero (not merger related) or one (merger related) if the offer is made in

connection with a business combination transaction, such as a merger, acquisition or exchange

offer. Corp is an indicator variable with a value of zero (not a corporation) or one (a corporation)

if the entity is a corporation or other entity such as a limited liability corporation or general

partnership. Tech is an indicator variable with a value of zero (a non-technology firm) or one

(a technology firm) if the firm is in a technology-based industry (computers,

telecommunication, other technology, or biotechnology). Log(Amount Offered) is the planned

dollar value of raised funds. Log(Min Invest) is the log of the minimum investment accepted

from any outsider. Log(Expense) is the log of the sales commissions and finders’ fees paid by

the entity. Log(Use of Proceeds) is the log of offering proceeds used to pay firm officers,

directors, or promoters. VC is an indicator variable with a value of zero (not a VC-backed) or

one (VC-backed) if the entity is funded by venture capital. Log(Revenue) is the log of reported

annual revenue. RevenueNotReported is an indicator variable with a value of one if the offering

did not report revenue information and zero if it did. Results include year fixed-effects with

robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Percentage Raised (1) (2) Age 0.0224*** 0.0211*** (0.0047) (0.0048) Debt -0.0751** -0.0804* (0.0327) (0.0322) Options 0.0246 0.0184 (0.0376) (0.0381) Fund -0.0631 -0.0246 (0.0823) (0.0738) Merger 0.271*** 0.293*** (0.0795) (0.0798) Corp -0.0168 -0.0246 (0.0296) (0.0293) Tech 0.0645** 0.0512* (0.0307) (0.0308)




18


Log(Amount Offered) -0.0342*** -0.0388*** (0.0070) (0.0072) Log(Min Invest) -0.0141*** -0.0119*** (0.0034) (0.0035) Log(Expense) -0.0121*** -0.0108*** (0.0026) (0.0026) Log(Use of Proceeds) -0.0125*** -0.0104*** (0.0025) (0.0025) VC 0.170** 0.159** (0.0783) (0.0799) Log(Revenue) 0.0060** (0.0025) Revenue Not Reported 0.156*** (0.0344) Constant 0.969*** 0.929*** (0.105) (0.104) Observations 826 826 R-squared 0.216 0.234

Table 7: VC Backed Firm Offering Amount Percent Raised Regressions

Ln (% company

raised)

OLS

IPO (after 506C) dummy -0.048

(0.33)

Number of funds invested in company 0.004

(0.14)

Amount offered -0.00

(0.02)**

Number of prior IPOs completed by firm 0.00

(0.28)

Total amount invested in company by firms 0.00

(0.09)*

Company age at first round of investment -0.008

(0.00)***

Number of 506C investors invested in

company

0.006

(0.00)***

Min required investment for 506C -0.00

(0.00)**

Sales and commission fee paid for 506C -0.000

(0.00)***

Use of proceeds 0.000

(0.04)**

Type of security (A) 0.089

(0.02)**

Type of security (B) 0.166

(0.00)***

Type of security (C) -0.127

(0.11)

Observations 190

R2 0.45




19


Table 8: Likelihood of VC Backed Firms Seeking 506C Funding

Table 9: Likelihood of VC Funds Seeking 506C Funding

506C backed company dummy

OLS

Number of funds invested in company 0.012

(0.22)

Number of rounds funds invested in

company

0.000

(0.10)*

Venture buyout dummy 0.080

(0.20)

Venture disbursement dummy -0.150

(0.33)

Number of prior IPOs completed by firm 0.002

(0.00)***


(0.57)

Total amount invested in company by firms 0.000

(0.01)**

Industry controls Yes

Observations 89427

R2 0.07

Funds whose companies involved in

506C

OLS

Number of funds invested in company -0.019

(0.16)

Number of companies fund invested -0.002

(0.13)

Firm age of the fund 0.000

(0.00)***

Fund age -0.000

(0.00)**

Fund average company investments 0.000

(0.07)*

Firm average company investments -0.000

(0.20)


(0.02)**

Total amount invested in company by fund -0.000

(0.16)

Industry controls Yes

Observations 94686

R2 0.16




20


Appendix A. Theoretical Model of Crowdfunding

We present a simple, two-period model to illustrate the crowdfunding process and gain

insight into the underlying theory and problems of the crowdfunding market. At time 0,

investors decide whether to invest in the startup firm. At time 1, large investor (S) may perform

monitoring and firm value is observed at time 2. Let V denote the firm value at time 2. Assume

that there are two types of investors, namely large investors (L) with shares and small

investors (S) with shares (1 ). Further, assume that only large investors (L) are

strategic/active investors that can perform monitoring (i.e. under new Rule 506(c), the

accredited investors will be large investors in this model). The value of monitoring is given by

V with a monitoring cost c(V), which is a convex cost function of large investors’ investment

in the startup firm with c(V) > 0 and c(V) > 0. Note that the monitoring cost c(V) will be

fully incurred by the large investors and used as a parameter to model ‘free-riding’ problem

(example of monitoring cost can be illiquidity cost or opportunity cost).

After monitoring, V + V denotes the firm value with monitoring and/or other value-adding

effects from investors. In other words, V denotes the monitoring benefit and/or other value-

adding opportunities from large investors (e.g. due to Rule 506(c)). If large investors perform

monitoring of the startup firm, the value to large investors is:

(V + V ) c(V)

In general, large investors will invest if (V + V) c(V) > 0, i.e. when (V + V ) (e.g.

with high , V or both) is sufficiently high to justify the cost of monitoring c(V).5 In contrast,

the value to smaller investors, who do not monitor, is:

(1 – ) (V + V )

The above conditions illustrate the problem of free-riding in the Rule 506(c) setting in

which large investors can improve value but the costly intervention and monitoring is solely

incurred by large investors (e.g. if the monitoring cost c(V) is too large, large investors will

not invest). To illustrate the adverse selection problem in crowdfunding, we assume that there

are two types of startup firms: high quality firms with V = 𝑉𝐻 and low quality firms with V =

𝑉𝐿 . High quality firm has higher firm value than lower quality firm with 𝑉𝐻 > 𝑉𝐿 . In the

presence of information asymmetry, large and small investors cannot distinguish the good firms

and the bad firms.

Adverse selection (the lemons problem) exists if the market is left with low quality firms.

In this case, the value to large investors becomes:

5 Large investors will pick optimal share (*) to invest with (V + V ) = c(*V) V.




21


(𝑉𝐿 + V ) c(𝑉𝐿 )

In general, large investors will invest if (𝑉𝐿 + V ) c(𝑉𝐿 ) > 0 but adverse selection (i.e.

with low value of 𝑉𝐿 ) may prevent large investors from investing. The adverse selection

problem also applies to the case of small investors – i.e. when 𝑉𝐿 is too low, smaller investors

will not invest as (1 – ) 𝑉𝐿 becomes too low to be incentive-compatible. In this case,

crowdfunding will be subject to the adverse selection problem.6

For a separating equilibrium, there will be the differential cost function (i.e. 𝑐𝐻(.) < 𝑐𝐿(.)),

which implies that the “single-crossing” property is met and that it’s more value-adding for the

high-quality firm “H” to obtain crowdfunding in the presence of a monitoring cost; in contrast,

the low-quality firm “L” will not mimic the high-quality firm due to the higher marginal cost

of monitoring. Given this intuition, the low quality firm “L” will not choose the same level of

crowdfunding due to the higher monitoring cost of a low quality firm (𝑐𝐿(. )), the outsider will

infer that the firm that seeks larger funding will be the high quality firm “H”.

6 To overcome adverse selection problem, large investors will have incentive to monitor with

(𝑉𝐻 + V ) c(𝑉𝐻) > 0

A separating equilibrium exists wherein large investors will monitor high quality firms and low quality

firms will not be invested in by large investors. The separating equilibrium exists when the following

conditions are satisfied:

(𝑉𝐻 + V ) c(𝑉𝐻) > 0

(𝑉𝐿 + V ) c(𝑉𝐿 ) < 0

A separating equilibrium exists when the high-quality firm value (𝑉𝐻) is sufficiently high, value-adding

from monitoring (V)is significant, or both. In contrast, firm value (𝑉𝐿 ) and value-adding from

monitoring (V) will be low for the case of low quality firm.




22


Appendix B – Data Collection

Crowdfunding Research Data Collection Project

This research will study a new area of company fundraising known as crowdfunding. A new

SEC rule, 506(c), applies to this type of fundraising. We will collect data from many 506(c)

filings and put the data into an Excel spreadsheet. This data collection project details the

instructions on how to do this.

Basically, we have a list of company 506(c) filings in an Excel spreadsheet. For your assigned

companies from the Excel list, you will find each company listed in a data table at

www.thecrowdcafe.com/506c-data. You will right click the source link given for each company

in the table, copy the link into the Excel spreadsheet, and then left click the source, which will

take you to the 506(c) form at the SEC.

You will then proceed to look up data in the 506(c) form and copy it into our Excel spreadsheet

for each company.

Detailed Instructions

1. Open the 506(c) data Excel spreadsheet provided to you and locate your section of assigned

companies. Choose one company to start data collection.

2. Go to the following website link: http://www.thecrowdcafe.com/506c-data/

3. Inside the web data table and to the right of the "Issuer" column heading, click on the little

triangles. This will sort the data table alphabetically by issuer name. The site does not let you

see all companies at one time. So at the bottom of the data table, you can use the navigation

links to move around to find your company. Also, you can type the first couple numbers or

letters of your company name into the search bar on top of the data table to find your company.

4. Once you find your company, right-click on the "Source" link to the right of the issuer name

and click copy web address. Paste the web address into the Excel spreadsheet under the Source

Link heading.

5. Go back to data table and click on "Source." This will take you to the 506(c) form at the SEC

web site.

6. The 506(c) form is organized into numbered sections. Under Section 1, Issuer's Identity, note

the box checked for Year of Incorporation/Organization. Input 1 (Over five years ago), 2

(within last five years), or 3 (yet to be formed) in the Excel file under the Year of Incorporation

column. If you input a 2 into Excel, then also input the specified year under Specify Year in

Excel. Otherwise, the Specify Year column should be left blank.

The following example would input 2 under Year of Incorporation and 2013 under Specify

Year.

Year of Incorporation/Organization

http://www.thecrowdcafe.com/506c-data

http://www.thecrowdcafe.com/506c-data/




23


Over Five Years Ago

X Within Last Five Years (Specify

Year) 2013

Yet to Be Formed

7. Also under the section 1, Issuer's Identity, note the box checked for Entity Type. A check

in the first box would have a "1" entered in Excel under Entity type. The second box would

have a "2" entered in Excel (see below) and so on. For the example below you would enter a

2 under the Issuer Identity column in Excel.

Entity Type

Corporation

X Limited Partnership

Limited Liability Company

General Partnership

Business Trust

Other (Specify)

8. In Section 5, Issuer Size, record in Excel the data provided under Revenue range and, if

given, Aggregate Net Asset Value Range using 1 for the first box, 2 for the second box and so

on. If no data is given, leave the Excel cell blank. In example below, 1 is entered in Excel

under the Revenue range column and nothing is entered under Aggregate Net Asset Value

Range column.

5. Issuer Size

Revenue Range OR Aggregate Net Asset Value Range

X No Revenues No Aggregate Net Asset Value

$1 - $1,000,000 $1 - $5,000,000

$1,000,001 - $5,000,000 $5,000,001 - $25,000,000

$5,000,001 - $25,000,000 $25,000,001 - $50,000,000

$25,000,001 - $100,000,000 $50,000,001 - $100,000,000




24


Over $100,000,000 Over $100,000,000

Decline to Disclose Decline to Disclose

Not Applicable Not Applicable

9. In Section 7. Type of Filing, record the box checked (1=New Notice, 2=Amendment,

3=First Sale Yet to Occur) and, if given, the Date of First Sale.

10. In Section 8. Duration of Offering, record whether the “Yes” or “No” box is checked by

entering “Yes” or “No” under the Duration of Offering column.

11. In Section 9. Type of Security Offered, record the box checked (1=Equity, 2=Debt, etc.)

12. In Section 10. Business Combination Transaction, record whether the “Yes” or “No” box

is checked by entering “Yes” or “No.”

13. In Section 11. Minimum Investment, record the exact number provided in the form. In the

example below input 10000 into Excel file under Min Invest. If the number given in the form

is 0, enter 0 in Excel.

Minimum investment accepted from any outside investor$10,000USD

14. In Section 14. From the first box, enter the information given into Excel under Non-

Accredited Investors. From the second box, enter the total number of investors who already

invested into Excel under Number of Investors.

15. In Section 15. Sales Commissions & Finder’s Fees Expenses enter the amount given for

Sales Commissions into the Excel under the Sales Commissions column and the amount given

for Finder’s Fees into Excel under the Finder’s Fee column.

16. In Section 16. Use of Proceeds, enter the amount given.

17. Go to next company in your list.

-END-

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