Recent developments in crowdfunding

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Recent Developments
in Crowdfunding
Essex Business School – 4 June 2018
A BLGDRC Conference

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Welcome
University of Essex
Professor Jerry Coakley

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Debt Crowdfunding
(P2P Lending)
Chair – Professor Claudia Girardone
University of Essex

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Intermediary Within Intermediary:
Business Loan Risk Pricing in a P2P Platform
Professor Oleksandr Talavera (Swansea)
Co-author: Mustafa Caglayan (Heriot-Watt)

Outline
• P2P lending in UK
• Ratesetter.com
• Descriptive Statistics
• Preliminary results
• Preliminary conclusions
6

The history of P2P lending in the UK
• Zopa: The first P2P loan provider in the world
• Top three P2P platforms: RateSetter, Zopa, and FundingCircle
had issued over £ 700 million of loans by 2014
• The UK government invested a large number of amount into
business loan via P2P platforms (e.g. £ 20 million in 2012 and
£ 40 million in 2014)
• The P2P industry has been regulated by FCA since 2014
• The Uk P2P lenders lent over £ 3.2 billion in 2016
7

Dynamics of consumer vs business loans for
FundingCircle/Zopa/RateSetter
9

Geographical distribution of business loans for
Funding Circle/RateSetter
10
Region FundingCircle Ratesetter
East of England 1,913 1,412
London 7,396 4,064
Midlands 6,815 1,762
North East 4,949 242
North West 5,953 2,091
Northern Ireland 1,033 41
Scotland 2,778 68
South East 12,187 3,198
South West 5,442 8,772
Wales 1,633 367
Yorkshire and The Humber 813
Total 50,099 22,830

Dynamics of interest rates/term/maturity for
FundingCircle vs RateSetter (Business loan only)
11

Data: Ratesetter Loanbook
• The sample is constructed using the loan listings of a leading UK P2P
platform, RateSetter.com,
• The loanbook database provides 482,801 loan listings over period
from 2010m9 to 2017m12.
• Each listing provides loan specific information including the annual
interest rate, the amount of loan, the period of repayment, the
borrow type (business or individual), use of funds and various pieces
of borrower characteristic information (such as sector and region).
• We limit our analysis to business loans only: our sample contains
almost 23 thousand loan listings over 2013-2017.
12

Ratesetter: Consumer vs Business Loans
13

Descriptive Statistics
(1) (2) (3)
VARIABLES Mean sd p50
Log(Amount) 9.726 1.493 9.616
Interest Rate, % 4.325 1.230 4.160
Maturity (Month) 16.401 12.850 12.000
Indirect 0.680 0.467 1.000
Log(Business Loans) 6.078 0.644 6.075
Log(Consumer Loans) 9.071 0.519 9.273
Defaulted 0.005 0.071 0.000
Secured 0.153 0.360 0.000
14

The purpose of business loans: RateSetter
15
Loan purpose Frequency Percent
Business loan 2,716 11.89
Loans to lending businesses for consumer loans 7,893 34.56
Loans to lending businesses secured against HP arrangement
223 0.98
Loans to lending businesses secured against business asset 2,630 11.52
Loans to lending businesses secured against property 7,290 31.92
Property development 2,042 8.94
Refinancing of existing lending facility 36 0.16
Other 6 0.02
Total 22,836 100.00

Direct vs indirect borrowing
17
Direct borrowing (4,778
obs)
Indirect borrowing
(10,143 obs)
mean sd mean sd diff
Log(Amount) 10.85 1.40 9.20 1.22 1.65
Interest Rate, % 4.80 1.59 4.10 0.94 0.70
Maturity 23.27 19.30 13.16 5.89 10.11
Log(Business Loans) 5.94 0.60 6.14 0.65 -0.21
Log(Consumer Loans) 9.22 0.37 9.00 0.56 0.22
Defaulted 0.01 0.11 0.00 0.04 0.01
Secured 0.31 0.46 0.08 0.27 0.24

Econometric Specification
𝐿𝑜𝑎𝑛𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑖 = 𝛾 + 𝜆𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡𝑖 + 𝑋𝑖 𝛿 + 𝜖𝑖
where i indexes loans.
The dependent variables (𝐿𝑜𝑎𝑛𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑖):
- interest rate
- loan amount
- maturity.
The key variable of our interest is 𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡𝑖 and it equals to one when
we observe a loan to a financial intermediary, and to zero when loan is
received directly by a firm.
18

Geographical distribution
Region Direct Indirect Total
Channel Islands 2 0 2
East Midlands (England) 251 643 894
East of England 476 923 1,399
London 1,659 2,398 4,057
North East (England) 89 153 242
North West (England) 424 1,666 2,090
Northern Ireland 41 0 41
Other 4 0 4
Scotland 54 14 68
South East (England) 843 2,355 3,198
South West (England) 301 578 879
Wales 180 187 367
West Midlands (England 221 646 867
Yorkshire and The Humber 233 580 813
Total 4,778 14,921
19

Control variables
𝑋𝑖 denotes the set of control variables:
- the number of business loans in the same month,
- the number of consumer loans in the same month,
- default dummy
- secured dummy.
- the number of loans issued by Zopa in the same region and month.
- the number of loans issued by Funding Circle in the same region and
month.
- region, sector, and repayment type (bullet, amortizing, and interest
only) dummy variables.
- We estimate the model implementing OLS methodology with robust
standard errors.
20

Prelim
results:
21
Interest Log(Amount) Maturity
(1) (2) (3)
Indirect 0.678*** 0.619*** 8.377***
(0.051) (0.059) (0.405)
Log(Business Loans) 0.485*** -0.035 -1.094***
(0.022) (0.026) (0.182)
Log(Consumer Loans) -0.459*** 0.066 -0.188
(0.043) (0.051) (0.350)
Log(Zopa Loans) -0.006 -0.033 1.142***
(0.036) (0.042) (0.294)
Log(FundCircle Loans) -0.106*** -0.106*** 1.163***
(0.032) (0.037) (0.257)
Defaulted 0.197* -0.100 -0.128
(0.115) (0.134) (0.929)
Secured -0.227*** -0.501*** 1.890***
(0.034) (0.039) (0.272)
Log(Amount) 0.036*** 1.050***
(0.007) (0.058)
Maturity 0.038*** 0.022***
(0.001) (0.001)
Interest Rate, % 0.049*** 2.497***
(0.010) (0.065)
Obs. 14,101 14,101 14,101
R2 0.39 0.44 0.63

Ratesetter response:
…in January this year we stopped making unsecured loans to
businesses, and now focus solely on hire-purchase finance secured
against assets for businesses (we are still continuing to provide
unsecured personal loans too).
22

So what?
23
Region Direct Indirect Total
Channel Islands 2 0 2
East Midlands (England) 251 643 894
East of England 476 923 1,399
London 1,659 2,398 4,057
North East (England) 89 153 242
North West (England) 424 1,666 2,090
Northern Ireland 41 0 41
Other 4 0 4
Scotland 54 14 68
South East (England) 843 2,355 3,198
South West (England) 301 578 879
Wales 180 187 367
West Midlands (England 221 646 867
Yorkshire and The Humber 233 580 813
Total 4,778 14,921

Conclusion
Our preliminary results suggest that the intermediaries that operate
within an online platform lends
• at a higher interest rate,
• gives higher total loans,
• with longer time to maturity.
24

Further analysis
• Propensity score matching
• Competition and premium for intermediation
25

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P2P Lending and Capital Structure
Dr. Winifred Huang (Bath)
Co-authors: Jerry Coakley (Essex)
Daniel Tsvetanov (UEA)

Outline
 Introduction
 Objective and Contributions
 Research Design
 Findings
 Conclusions

UK Small Business
Centre for Economic and Business Research (Aug, 2016)
 Small business makes up half of our GDP: 2016 GDP is £1.922 trillion
 Provides 60% of private sector employment Since 2011, bank lending to small
businesses has declined 18%. In 2016, 50% more loans happened via direct lending
platforms.
 Driven by fintech, crowdfunding brings lenders (investors) and borrowers together via
internet platforms.

Typology of Crowdfunding
What is Peer-to-Peer (P2P) lending?
 Part of the crowdfunding phenomenon
 Linked to general rise of P2P markets eg Uber,
AirBnB
 These bring buyers and sellers together
Crowdfunding brings lenders (investors) and borrowers
together via internet platforms
Driven by fintech - application of big data and digital
technologies like machine learning to finance
Part of the alternative finance revolution: Broader than
crowdfunding includes challenger banks, cryptocurrencies
etc

Alternative finance to
small, private UK firms
Part of the crowdfunding phenomenon, Peer-to-Peer (P2P) lending provides alternative
sources of finance to SMEs and entrepreneurs, while it provides investors with a good
return.
Three main types of UK P2P lending:
1. P2P business lending (1-5 years) - Funding Circle is the leader
2. 2. P2P invoice nance (<12 months) - MarketInvoice is the leader
3. 3. P2P Consumer lending - Zopa is the leader

UK P2P Business Lending 2010-2016

Funding Circle (FC)
Between 2010 - June 2016
 About 72.5k investors have lent to UK businesses
 28.8k businesses have accessed finance
 40.2k loans funded
 The lending and borrowing added £2.7 billion to the UK economy
 It creates 40k jobs
 There are 2,200 new-built homes
 10% of lending goes to the North East

Mechanics of P2P lending
via Funding Circle
 SMEs apply online to FC
 Must be trading for 2+ yrs and have 1 year’s filed accounts
 Loan type: unsecured (up to £350k; PG) or secured (up to £1m)
 FC uses machine learning to evaluate SME (application + other eg risk) - loan
assigned to one of 6 risk bands: A+ to E If loan approved
 Advertised for up to 14 days on FC site - investors pledge sums
 All or nothing - application closed once loan sum reached
 If not, application is deemed unsuccessful

P2P lending to unlisted SMEs
Cosh, Cumming, and Hughes (Economic Journal, 2009)
 They examined all the outside entrepreneurial capital sources of private UK firms (1996-
1997).
 Privately held UK firms attempt to obtain external funds in addition to internal funds. Small
firms are more likely to finance from private individuals.
Brav (JF, 2009) studied funding of medium-large SMEs in the UK (1997-2003).
 Private firms depending almost entirely on debt finance have higher leverage ratios and
tend to avoid external capital markets.
 Private equity is more costly than public equity due to information asymmetry and the
desire to maintain control.
Cole (FM, 2010) I studied US private firm capital structure.
 Private US firms employ less leverage than public firms (different to Brav, 2009)
 Leverage of these private firms is negatively related to firm age (different to public firms,
Frank & Goyal, 2009)

Objective
This paper focuses on private SMEs
 Provide balance sheet but no P&L or cash flow information - typically ineligible for
bank term loans
 More opaque and riskier than listed SMEs
 P2P business lending has grown rapidly in the UK
 P2P loans accounted for 14% of UK SME lending in 2015
This paper studies the role of alternative finance (the profit crowdfunding in a medium
term - P2P loans) in corporate financing decisions.

Contributions
Unique linked data
 P2P loan data for 934 SMEs (2010-2015) from Funding Circle - UK unicorn
 Linked to financial and firm data from FAME
 2/3 of loans have a 5-year maturity Contributions to entrepreneurial finance
literature
 Investigates the drivers of P2P debt vs bank debt for SMEs
 Debt ratios sensitive firm characteristics (tangible assets, size) and profitability (ROA)
but not growth or capital expenditure
 P2P lending adds a new layer of external debt for firms heavily dependent on debt
finance

Sample & UK P2P Descriptive Stats
 934 unique small and privately held firms that were financed by Funding Circle from
2010 to 2015
 Final sample: 3,979 firm-years (1,465 firm-years with P2P debt and 2,514 firm-years
without P2P debt)
 Median age of firms with P2P debt is 10 years - young but not start ups
 Average maturity is 4.3 years
 Two thirds of the P2P debt raised has a maturity of 5 years
 Average (net) leverage is 25.6 (18.3) percent
 Vast majority (80%) of sample P2P loans were raised late in sample (2014 and 2015).

Sample & UK P2P Descriptive Stats

Determinants of leverage (OLS)
Firms' debt ratios are
sensitive to P2P debt
and to firm
characteristics like
profitability, asset
tangibility and debt
composition, but less
sensitive to firm size

Decision to issue or retire capital
(Multinomial Logit)
When private firms
have a financing
deficit, they are likely
to issue either debt
or request more
equity capital than
retire debt or
repurchase equity.

The choice of issuing/holding
P2P debt or not (Probit)
The larger the target
leverage deviations,
the higher the
probability of firms
issuing or having P2P
debt.

Financing choices (Probit)
Hypothesis: Debt is preferable to equity capital
The larger the target leverage deviations, the higher the probability of
firms issuing or having P2P debt.

Remarks
 One of the first studies of P2P business loans using both platform + SME financial
and other data
 P2P debt with a mean maturity of >4 years fills an important medium term funding
gap for unlisted SMEs
 It's a new debt layer in the pecking order for private SMEs
 These firms are more likely to issue P2P debt when they are financing their deficits
and when deviations from target debt ratios are higher than their actual debt ratios.

Thanks!
 Thank you very much for your time.
 We welcome any questions or comments.

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Marketplace Lending: Business Models
and Regulation in Australia and the UK
Professor Alistair Milne (Loughborough)
This research has been supported by British-Academy Leverhulme 2017-2018
small grant SG161157

Policy context
• Rapid growth marketplace (P2P) lending
Consumer $bn 2013 2016
China 3.85 136.54
US 2.81 20.00*
UK 0.29 1.17
RoW
Business $bn 2013 2016
China 1.44 58.18
US 0.34 1.30
UK 0.14 1.23
RoW
Source: various reports of Cambridge Centre for Alternative Finance

Ongoing research:
tentative conclusions
• Marketplace lending (“loan based crowdfunding”)
– Very different from equity crowdfunding
– Best viewed as part of the “Alternative fixed income” asset class
– Main appeal to institutional investors
• Modern platform technologies support viable non-bank loan
intermediation on relatively small scale
– Perhaps $250mn/£250mn outstanding loans
– Compare bank balance sheets of $500bn +
• Risk assessment, esp for consumer lending, relies on co-opetition
– sharing of data, not a ‘distinctive capability’
– standardised risk metrics, limit “race to the bottom”
• This may be a direction of travel
• Customer experience (borrower, retail investor) key
– Banks struggling with legacy
• But banks protected by regulation
• Case of functional approach to regulation
– And limits on deposit insurance

One taxonomy of alternative
finance business models
Business model Description
Market place consumer lending Individuals/ institutions loan to consumer
Balance sheet consumer lending Platform entity loans to consumer
Market place business lending Individuals/ institutions loan to business
Balance sheet business lending Platform entity loans to business
Market place real estate lending Individuals/ institutions lend secured on property
Real estate crowdfunding Individuals/ Institutions take equity in real estate
project
Equity based crowdfunding Individuals/ Institutions take equity in business
Reward-based crowdfunding Funding in exchange for non-monetary rewards
Donation-based crowdfunding Funding for philanthropic reasons
Source Ziegler et. Al. (2017) The America’s Alternative Finance
Benchmarking Report, Cambridge Centre for Alternative Finance
My discussion focuses on market place lending (but not real estate). Many
differences between platforms even within these categories.

Ongoing interview research
Australia and UK: objectives
• Focus on business models and regulation
– Goal: obtain insight on medium-term trends
• Explore case for functional regulation
– See Merton (1995a,1995b)
– Institutional boundaries between business models
becoming fluid
• Do we want regulation to protect traditional models
• A “Cambrian explosion”
– Many different business models,
– Some will survive – those with scale and
“distinctive capabilities” (Kay (1995)).

Marketplace lending: the 16 functions
Operation Strategy Execution Regulation
1. Investor base X
2. Borrower segments X
3. Customer engagement and marketing
4. Identity and fraud prevention C Y
5. Loan application processing
6. Credit assessment C
7. Borrower protection/ responsible lending Y
8. Risk categorisation C
9. Matching of investors and loans
10.Loan resale and access to funds C
11.Diversification and loss protection
12.Default and collections C
13.Fiduciary duties and asset segregation Y
14.Investor communication Y
15.Costs, charging and profitability
16.Servicing and operational continuity C Y

Interviews
• Seven in Australia (not thincats)
– SocietyOne
– Ratesetter
– Bigstone
– Kikka/ Enably (balance sheet lender)
– True Pillars
– WISR
– MoneyPlace
• One so far in UK
– Ratesetter

Platform positioning
• Investor base
– Institutional
– High net worth “sophisticated” individuals
– Retail
• Borrower segments
– Prime personal unsecured
– Higher worth personal unsecured
– Short term property finance
– SME Invoice finance
– SME working capital
– SME ‘asset finance’ for vehicles/ equipment
– SME medium term loans
• Costs, charging and profitability
– Private equity
– Public listing
– Scale and consolidation?

Regulated functions
• Identity and fraud protection (KYC, ALM)
• Borrower protection/ responsible lending
• Fiduciary duties/ asset segregation
• Investor communication
– esp. for retail investors
• Servicing and operational continuity
– esp. for retail investors

Some functions (C) a choice:
competition or collaboration
• Identity and fraud protection
• Credit assessment
• Risk categorisation
• Loan resale and access to funds
• Default and collections
• Servicing and operational continuity
Issue: do risk functions (in italics) become
standardised. My view, very possibly yes,
driven by competition for investor funds.

Distinctive capabilities
associated with remaining functions
• Customer engagement & marketing
• Loan application processing
– Not fully automated esp for SMEs
• Matching of investors and loans
• Diversification and loss allocation
– Choice of marketplace lending or balance
sheet lending
– Issues around ‘deposit insurance’

Ongoing research: tentative conclusions
• Marketplace lending (“loan based crowdfunding”)
– Very different from equity crowdfunding
– Best viewed as part of the “Alternative fixed income” asset class
– Major appeal to institutional investors
• Modern platform technologies support viable non-bank loan intermediation on
relatively small scale
– Perhaps $250mn/£250mn outstanding loans
– Compare bank balance sheets of $500bn +
• Risk assessment, esp for consumer lending, relies on co-opetition
– sharing of data, not a ‘distinctive capability’
– standardised risk metrics, limit “race to the bottom”
• This may be a direction of travel
• Customer experience (borrower, retail investor) key
– Banks struggling with legacy
• But banks protected by regulation
• Case of functional approach to regulation
– And limits on deposit insurance

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New Developments in Crowdfunding –
Views from the Inside
Chair – Professor Jerry Coakley
University of Essex

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The Collaboration of Platforms and
Traditional Investors
Tom Britton
Co-Founder – SyndicateRoom

Institutional “Collaboration”
with the Crowd

1. Acquisition – of new customers
2. Advertising – cheap brand exposure
3. Advocation – creating loyal customers
4. Awareness – winners and losers
Types of “Collaboration”

Acquisition “Collaboration”

Advertising “Collaboration”

“Advocation” Collaboration

“Awareness” Collaboration
Part 2 – A company in trouble

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Development of a secondary
share market at Seedrs
Debra Burns
Senior Compliance Manager - Seedrs

Seedrs is one of the world’s largest platforms for
investing in and raising early-stage and growth capital
633
funded
deals
Single
shareholder
Pan –
European

Primary market with no plans to include a secondary offering

But our investors and shareholders started creating one anyway…

With or without us? Let’s build them a product

And it was all pretty well-received

That was one year ago… so how’s it looking now?

Volume & value to date
1651
Buyers &
Sellers
£1.45M
Traded
348
Companies

What are the benefits of a secondary market?

How does the selling process work?

How did we get here?
Listening to our clients
Overcoming
challenges
Testing & development
…And keep on
listening
Building solutions

Our next trading cycle opens tomorrow

Bringing the product out of Beta

The important bits
Investing involves risks, including loss of capital, illiquidity, lack of dividends and dilution, and should be done only as part of a diversified
portfolio. Please read the Risk Warnings before investing.
Given that there is no liquid, public secondary market for most of these investments, it may be difficult to sell them at all. Where performance figures include conversions from
another currency, those figures may increase or decrease as a result of currency fluctuations. With regard to the Seedrs Secondary Market, not all shares will be eligible for the
Secondary Market and, even if they are, the ability to buy and sell shares will depend on demand. It can be difficult to find a buyer or seller, and investors should not assume that
an early exit will be available just because a secondary market exists.
This document has been approved as a financial promotion by Seedrs Limited ("Seedrs"), which is authorised and regulated by the Financial Conduct Authority (No. 550317). It is
not an offer to the public. The summary information provided in this document is intended solely to provide an overview of the fund it describes, and any investment decision with
respect to the fund should be made on the basis of the full information package, which be made available before any investment in the fund is confirmed. Seedrs is a limited
company, registered in England and Wales (No. 06848016), with its registered office at Churchill House, 142-146 Old Street, London EC1V 9BW United Kingdom. All investment
activities take place within the United Kingdom, and any person resident outside the United Kingdom should ensure that they are not subject to any local regulations before
investing.
Seedrs does not make investment recommendations to you. No communications from Seedrs though this document or any other medium, should be construed as an investment
recommendation. Further, nothing in this document shall be considered an offer to sell, or a solicitation of an offer to buy, any security to any person in any jurisdiction to whom or
in which such offer, solicitation or sale is unlawful. Seedrs does not provide legal, financial or tax advice of any kind, and nothing in this document constitutes such advice. If you
have any questions with respect to legal, financial or tax matters relevant to your interactions with Seedrs or its affiliates, you should consult a professional adviser.

Thank you!
Debra Burns
Debra.Burns@seedrs.com
Investing involves risks, including loss of capital, illiquidity, lack of dividends and dilution, and should be done only as part of a
diversified portfolio. Please read Risk Warnings before investing. Seedrs is authorised and regulated by the Financial Conduct
Authority (FCA). Seedrs Limited is a limited company, registered in England and Wales (No. 06848016), with registered office at
Churchill House, 142-146 Old Street, London, EC1V 9BW

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Break
in room EBS 2.63
Refreshments are available

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Equity Crowdfunding
Chair – Professor Neil Kellard
University of Essex

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Follow-on equity crowdfunding in the UK
Professor Jerry Coakley (Essex)
Co-authors: Aristogenis Lazos (Essex)
Jose Linares-Zegarra (UEA)
BLG Centre and Essex Finance Centre

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Basics of ECF
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❑ 3 agents in basic ECF model
- Unlisted startups seeking pre-IPO equity
- Crowd of investors – small retail/ large professional
- ECF internet platforms
- Lead investor (eg BA) is recent development
❑ ECF campaigns
• A promising startup wants raise equity
• 30/ 60 day window to raise target funds
• Funded iff reach/ exceed target, zilch otherwise
• First follow-on campaign is the next ECF campaign after a succsssful initial campaign
• UK ECF market is largest and most developed
• Helped by prospectus exemptions (ECF amount < 5m euros) EU Directive/
Regulation

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➢ Crowdcube 2011
- One of 1st ECF platforms
- Market leader
➢ Seedrs 2012
- Pioneered nominee a/c and secondary
market
- Andy Murray is backer!
➢ Syndicate Room 2014
- BAs do the DD on projects
- Act as lead investors
Top 3 ECF Platforms
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Growth of ECF in UK
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Motivation
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❑ ECF is a new primary market for unlisted startups
• Includes initial and follow-on (FO) campaigns
• Differs from P2P lending which directly competes for loans with commercial
banks
❑ Follow-ons - main source of outside equity for those with successful initial
campaign
• Lower information asymmetries acw initial ECF raises
• Some similarities to SEOs on AIM but also quite distinct eg EU has separate
exemption provisions for SEOs
• Responding to 2nd equity gap Wilson et al (JCF 2018)
• Help small firms on journey to IPO

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Preview of findings
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1. Determinants of FO campaigns
• Target capital, Lead investor, Nominee a/c, and
Overfundung all increase probability of a FO
• Complements/extends Signori & Vismara (2018) study of events (including
FO) in successful ECF firms
2. Determinants of successful Follow-ons
• Overfunding in initial offering, Initial raise/ FO Goal, Quick FO (social
capital of Buttice et al. 2017) all increase the probability of FO success
• Complements Hornuf et al. (2018) study of private investment by VC/BA
in successful ECF firms

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Literature
@BLGDataResearch
❑ ECF literature is sparse
• Seminal studies by Ahlers et al., 2015; Hornuf and Schwienbacher, 2016;
Vismara, 2016; 2017; Vulkan et al., 2016
❑ FO ECF events/ campaigns
• Signori & Vismara (JCF 2018) - 212 Crowdcube firms
• More likely with Quick success, less with Age, Dual
shares, No. investors for sample of 54 FOs
• Hornuf et al (2018) study of private funding of 412 UK & German firms
• Buttice et al (ETP 2017) study serial funding using Kickstarter data
and use concept of internal social capital (network of contacts) – this
fades quickly

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Data
@BLGDataResearch
❑ Large sample of 790 (668 successful) initial campaigns from Top 3
UK platforms April 2011– Dec 2017
❑ 106 firms (succ/ unsucc) 1st FOECF
campaigns Nov 2011- Dec 2017
❑ 80/106 firms successful in 1st FO ECF

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Initial (Follow-on) ECF campaigns
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Backers
Mean
195 (295)*
Median
117 (119)
Duration 45 (39) 38 (36)
Target £259k (454k)*** £125k (250k)
Amount raised £389k (593k)** £150k (267k)
Amt-to-goal 1.44 (1.35) 1.21 (1.14)

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Methodology
@BLGDataResearch
❑ 2-stage Heckman
• Sample selection bias in our study since FOs are only
observed after successful initial campaigns
• Illustrate for determinants of FO ECF campaigns
➢ 1st stage selection model
• Probit for probability of a successful ECF initial
campaign
𝑃 𝐼 𝐸 𝐶 𝐹 = 1 = 𝜙 𝐼 ( 𝑋 𝐼 𝛽 𝐼 + 𝜇 𝐼 )
• N = 790: IECF = 1 for 668 successful initial campaigns
• Employ #competing campaigns as instrument

@BLGDataResearch
Methodology
@BLGDataResearch
❑ 2-stage Heckman
➢ 2nd stage outcome model
• Probit for 106 successful & unsuccessful first FO ECF campaign
𝑃 𝐹𝑂 𝐸𝐶𝐹 = 1|𝐼 𝐸𝐶𝐹 = 1 = 𝜙 𝐹 𝑂 (𝑋 𝐹𝑂 𝛽 𝐹𝑂 + 𝜇 𝐹 𝑂 )
• N = 668: FOECF = 1 for 106 first FO campaigns
• NB For success of FO campaigns research question, use 2nd stage
probit with FOECF = 1 for 80 successful first FO campaigns

@BLGDataResearch
1. Determinants of FOs
@BLGDataResearch
❑ Posit that these are driven by initial campaign & platform
characteristics
• First FO campaigns are more likely with
- high Target capital
- Overfunding (high Amount-to-goal)
- Lead investor
- Nominee a/c (protects investor rights )

Table 3 Determinants of follow-on ECFs
P s e u d o R2 0 .2 6 0 .1 0
1s t s t a g e
Fir m a g e 0 . 0 0 1
T a r g e t capital - 0 . 1 2 * * *
- 0 . 0 6 * * *
.000 1 * * *
-0 .0 0 3
0 .0 3
0 .0 1
-0 .0 0 0 1
Duration
Backers
Lead investor Nominee
dum Amt-to-target
-0.007
0.10**
-0.03
0.0001
0.27***
0.12***
0.25***
C o m p e t e c a m p - 0 . 8 5 * * *
Mills ratio -0 .0 9 0.3 4 * * *
0 .1 6
@BLGDataResearch

@BLGDataResearch
Determinants of FO Results
@BLGDataResearch
❑ Interpretation
• Complement the Signori & Vismara (2018) findings (consistent on voting
rights, target k)
• They find Quick success, Target k have positive impact but Age, No investors,
Voting rights have negative impact
• Extend their study by finding Overfunding and Lead investor are
significant drivers also

@BLGDataResearch
2. Probability of FO success
@BLGDataResearch
❑ Driven by the characteristics of and links the initial campaign
• FO campaigns are more likely to succeed
-the higher the overfunding (Amount/Goal)
- for quick FOs (<1 year) Buttice et al (2017)
-the higher initial raise/ FO target – former acts as a reference
point
- for younger startups

@BLGDataResearch
Table 5 Probability of follow-on success
Pseudo R2 0.15
Firm age -0.03** -0.02***
London dummy -0.05 -0.03
Duration -0.05 -0.02
Backers 0.0001 0.0001
Amount/goal 0.47***
Amount/FO goal 0.10***
Quick follow on 0.09***
Mills ratio -0.13 0.06
0.35

@BLGDataResearch
Probability of FO success
@BLGDataResearch
• Novel results on FO success relating to aspects of
the initial campaign
• Overfunding, Initial raise/ FO Goal, Quick FO (social capital
of Buttice et al. 2017) all increase the probability of FO
success
• Age has negative impact – young startups
more likely to enjoy FO success

@BLGDataResearch
Conclusions
@BLGDataResearch
❑ 790 initial & 106 UK FO campaigns 2011-17
• FOs involve higher targets & larger raises
❑ Results
• Those on FO determinants complement & extend those
of Signori & Vismara (2018)
• Reveal some novel determinants of FO success linked to initial campaign
characteristics like overfunding
❑ FO offerings play a key role in providing outside equity for young
fast growing startups
• Helping to fill the second equity gap identified by
Wilson et al. (2018)

@BLGDataResearch
@BLGDataResearch
@BLGDataResearch
Any questions?

@BLGDataResearch
Equity Crowdfunding in Germany and the
UK: Follow-up Funding and Firm Failure
Professor Lars Hornuf
(University of Bremen, MPI for Innovation and Competition, CESifo)
Co-authors: Matthias Schmitt (MPI for Innovation and Competition)
Eliza Stenzhorn (University of Bremen)

Literature
• Most research has focused on the success factors of ECF campaigns ...
• Ahlers, Cumming, Guenther, & Schweizer, 2015; Hornuf & Schwienbacher, 2018a, 2018b;
Ralcheva & Roosenboom, 2016; Vismara, 2017; Vulkan, Åstebro, & Sierra, 2016
• ... or the determinants of crowd engagement
• Agrawal, Catalini, & Goldfarb, 2015; Block, Hornuf, & Moritz, 2018b; Hornuf & Neuenkirch,
2017; Vismara, 2016
• Little is known, however, about the ability of crowdfunded firms to build
enduring businesses.
• Hornuf and Schmitt (2016) analyze the success and failure of crowdfunded firms in Germany
and the UK
• More firms in Germany than the UK managed a crowd-exit through a significant VC round,
but somewhat fewer firms ultimately failed in the UK.
• Signori and Vismara (2018) investigate follow-up funding and firm failure by calculating the
return on investments for 212 successful ECF campaigns that obtained financing on
Crowdcube.
Hornuf, Schmitt Stenzhorn, Equity Crowdfunding in Germany and the UK
138

This Paper
• We test whether some of the factors affecting follow-up funding and firm
failure known from the BA/VC financing literature are important in ECF as well.
139

Motivation
• By identifying criteria predicting follow-up funding and firm failure in ECF, we aid
the crowd and professional investors in making better investment decisions.
• Making the factors that contribute to the success and failure of ECF more salient
not only benefits various investor types but also helps stabilize and establish a
new market segment of entrepreneurial finance and helps reduce the prejudice
against ECF among traditional investors.
• Helping portal managers and investors differentiate lemons from potentially
enduring businesses might also foster economic growth and employment.
140

Preview of Findings
• We find that British firms have a lower chance of obtaining follow-up funding
through outside BAs/VCs
• But British firms have a relatively higher likelihood of surviving three years after
the ECF campaign than German firms.
141
Follow-up funding
• # subsequent successful ECF campaigns (+)
• # senior management team members (+)
Control
• # VC investors (+)
• firm age (-)
Firm failure
• # subsequent successful ECF campaigns (-)

Hypotheses
• Hypothesis 1 Management team size increases the firm’s probability of receiving
follow-up funding and decreases the probability of firm failure:
• Allows specialization in decision-making and entrepreneurial activities (Eisenhardt and
Schoonhoven, 1990; Ahlers et al., 2015)
• Hypothesis 2 A higher average age of the management team increases the firm’s
probability of receiving follow-up funding and decreases probability of firm failure
• Human capital theory suggests experience comes with age
• Young people have lesser or uncertain skills and abilities, and higher employer-to-employer
turnover (McGee, Dowling, and Megginson, 1995; Johnson, 1978)
142

Hypotheses
• Hypothesis 3 Ownership of patents and trademarks increases the firm’s probability
of receiving follow-up funding and decreases the probability of firm failure:
• Provide an effective signal to potential investors about the firm’s innovativeness and brand
value (Hsu and Ziedonis, 2013; Haeussler, Harhoff, and Mueller, 2014; Block et al., 2014)
• Allows firms to reap monopoly profits from their intellectual property
• Hypothesis 4 High crowd participation in an ECF campaign increases the firm’s
probability of receiving follow-up funding and decreases the probability of firm
failure:
• The certification effects positively influences the decision of a VC to fund the startup (Kaminski,
Hopp, and Tykvova, 2016)
• Number of backers in reward-based crowdfunding positively affects the product-market
performance (Stanko and Henard, 2017)
143

Data
• For the period from August 1, 2011, to September 30, 2016, we hand-collected
data on 426 firms that ran at least one successful ECF campaign.
• Plattforms: Crowdcube and Seedrs (N= 285) + 12 German platforms (N= 141)
• We merged the information about the ECF campaign characteristics with additional
information about firm characteristics from Bureau van Dijk (BvD) Orbis and Zephyr;
Thomson Reuters Eikon; and Crunchbase, the German company register
(Unternehmensregister) and the UK Companies House.
144

Dependent Variables and Models
• We use four different dependent variables in our study.
• The first variable measures whether a firm received follow-up funding by BAs/VCs.
• The second dependent variable measures whether a firm failure occurred.
• The third variable measures the time until follow-up funding by BAs/VCs after the firm’s first
successful ECF campaign.
• The fourth dependent variable captures the time until firm failure—that is, the time the firm
went insolvent, was liquidated, or was dissolved—after the firm’s first successful ECF
campaign.
• We estimate a probit model that identifies factors influencing the probability of
whether a startup firm will receive follow-up funding or a firm failure occurred.
• Thereafter, we examine when the follow-up funding takes place or firm failure
occurred by performing a Cox proportional hazards model.
145

146
N Mean S.D. Median Minimum Maximum Yes
Difference
UK - Germany
Events
Follow-up funding by BAs/VCs 426 0.150 0.358 0 0 1 64 -0.132***
Firm failure 426 0.059 0.221 0 0 1 25 -0.592***
Senior management team
# senior management team
members 426 3 2 2 1 12 . 2***
Average age of senior management 426 43 9 42 25 72 . 5***
Trademarks and patents
Number of filed patents 426 0.110 0.617 0 0 8 . -0.058
Number of granted patents 426 0.049 0.376 0 0 6 . -0.064+
Number of granted trademarks 426 0.531 1.418 0 0 19 . -0.553***
ECF campaign characteristics
# of subsequent successful
campaigns 505 0.214 0.551 0 0 4 . 0.110**
Total amount of capital raised 505 461,899.80 808,182.00 203,559.00 140,614.00 8,642,694.00 . -230,497.80**
Total amount of funding target 505 2,788,411.00 560,305.40 1,228,954.00 12,192.15 8,009,061.00 . 307,453.10***
Number of investors 505 320 383 200 120 3736 . -132***
Business valuation 505 375,591.10 808,738.20 1,669,867.00 8,932.83 8,505,571.00 . 185,149.10**
Ratio amount raised to funding
target 505 0.668 0.285 0.711 0.033 1.112 . 0.405***

147
N Mean S.D. Median Minimum Maximum Yes
Difference
UK - Germany
Controls Variables
Number of VC investors 505 0.253 0.742 0 0 7 . -0.034
Number of BA investors 505 0.343 1.042 0 0 12 . -0.565***
UK firm 426 0.669 0.471 1 0 1 285 .
LLC form with no capital
requirements 426 0.050 0.212 0 0 1 20 0
Age of the firm at end of first
campaign 426 2 3 2 0 18 . 1**
Share of female senior management 426 0.152 0.284 0 0 1 . 0.113***
Number of employees 426 4.594 5.398 3 1 62 . -3***
Firm located in a large city (>1m) 426 0.622 0.485 1 0 1 265 -0.206

Follow-up Funding by BAs/VCs
148

Follow-up Funding by BAs/VCs
Table shows results of the regressions on follow-up funding. Variable definitions are reported in Table A1 in the Appendix. The dependent variable in
column (1) is whether the firm received follow-up funding by a BA/VC investor or not, and in columns (2)–(4) the duration until the firm received
follow-up funding by a BA/VC investor. The method of estimation in column (1) is a probit model (coefficients reported are average marginal effects)
and in columns (2)–(4) Cox, exponential, and Weibull models, respectively (coefficients reported are hazard ratios). Standard errors are clustered at the
industry level and are reported in parentheses. Significance levels for coefficients: + p<0.10, * p<0.05, ** p<0.01 *** p<0.001.
Duration Analysis
(1) (2) (3) (4)
Probit Cox Exponential Weibull
Number of senior management team members 0.022*** 1.222*** 1.310*** 1.253***
(0.006) (0.071) (0.078) (0.072)
Average age of senior management -0.003 0.978 0.922*** 0.974
(0.002) (0.016) (0.017) (0.017)
Number of filed patents 0.002 0.992 0.942 0.998
(0.016) (0.146) (0.200) (0.155)
Number of granted patents -0.089+ 0.534 0.522 0.510
(0.047) (0.306) (0.451) (0.301)
Number of granted trademarks 0.008 1.038 1.007 1.054
(0.010) (0.057) (0.062) (0.054)
Number of subsequent successful campaigns 0.016 1.752** 1.262 1.504*
(0.023) (0.360) (0.232) (0.271)
Total amount of capital raised 0.003 1.001 0.963 0.990
(0.007) (0.028) (0.031) (0.025)
Total amount of funding target 0.002 1.025 1.088** 1.044+
(0.008) (0.028) (0.034) (0.027)
Total number of investors -0.009 0.973 0.949 0.974
(0.007) (0.041) (0.054) (0.040)
Business valuation 0.000 1.000 1.004 0.997
(0.002) (0.017) (0.028) (0.019)
Ratio of amount raised to funding target -0.152+ 0.369 0.021*** 0.319
(0.086) (0.243) (0.022) (0.235)
Control variables
Number of VC investors 0.047* 1.408* 1.469* 1.406*
(0.022) (0.203) (0.225) (0.203)
Number of BA investors 0.009 1.036 1.053 1.020
(0.014) (0.062) (0.068) (0.059)
Main
Results
Follow-up
Funding
149

Main
Results
Follow-up
Funding
150
(0.007) (0.028) (0.031) (0.025)
Total amount of funding target 0.002 1.025 1.088** 1.044+
(0.008) (0.028) (0.034) (0.027)
Total number of investors -0.009 0.973 0.949 0.974
(0.007) (0.041) (0.054) (0.040)
Business valuation 0.000 1.000 1.004 0.997
(0.002) (0.017) (0.028) (0.019)
Ratio of amount raised to funding target -0.152+ 0.369 0.021*** 0.319
(0.086) (0.243) (0.022) (0.235)
Control variables
Number of VC investors 0.047* 1.408* 1.469* 1.406*
(0.022) (0.203) (0.225) (0.203)
(0.014) (0.062) (0.068) (0.059)
UK firm 0.851*** 0.499* 2.129* 0.520*
(0.048) (0.164) (0.708) (0.171)
LLC form with no capital requirements 0.017 1.119 0.736 1.073
(0.018) (0.178) (0.199) (0.193)
Age of the firm at the end of first campaign -0.017** 0.840* 0.840+ 0.840*
(0.006) (0.067) (0.077) (0.071)
Share of female senior management 0.020 1.008 1.222 0.989
(0.052) (0.506) (0.582) (0.475)
Number of employees 0.004 1.024+ 1.009 1.022
(0.003) (0.013) (0.015) (0.014)
Firm located in a city bigger than 1 million inhabitants 0.038 1.411 1.191 1.388
(0.036) (0.464) (0.405) (0.480)
Largest portals dummy Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes
Observations 505 505 505 505
Days at risk 253711 253711 253711
Number of follow-up funding events 82 82 82 82
Number of firms 426 426 426 426
Pseudo-R2
0.212 0.091
Log-likelihood -176.425 -421.489 -291.686 -266.944
-

Firm Failure
151

Main
Results
Firm
Failure
152
Firm Failure
Table presents the results of the regressions on firm failure. Variable definitions are reported in Table A1 in the Appendix. The dependent variable in
column (1) measures whether a firm failure occurred and in columns (2)–(4) the duration until firm failure. The method of estimation in column (1) is
a probit model (coefficients reported are average marginal effects) and in columns (2)–(4) Cox, exponential, and Weibull models, respectively
(coefficients reported are hazard ratios). Standard errors are clustered at the industry level and are reported in parentheses. Significance levels for
coefficients: + p<0.10, * p<0.05, ** p<0.01 *** p<0.001.
Duration Analysis
(1) (2) (3) (4)
Probit Cox Exponential Weibull
Number of senior management team members 0.001 0.940 0.847 0.954
(0.005) (0.254) (0.287) (0.275)
Average age of senior management 0.001 1.004 0.928* 1.002
(0.002) (0.040) (0.031) (0.039)
Number of filed patents -0.019 0.797 0.894 0.819
(0.017) (0.609) (0.550) (0.594)
Number of granted patents 0.017* 1.382 1.719 1.347
(0.008) (0.776) (0.815) (0.684)
Number of granted trademarks -0.002 0.918 0.902 0.936
(0.007) (0.107) (0.129) (0.114)
Number of subsequent successful campaigns -0.050* 0.143*** 0.385 0.142**
(0.025) (0.080) (0.345) (0.088)
Total amount of capital raised -0.001 0.864 0.831 0.846
(0.004) (0.136) (0.160) (0.144)
Total amount of funding target 0.001 1.211 1.294 1.224
(0.004) (0.168) (0.254) (0.189)
Total number of investors -0.006 0.967 0.772* 0.964
(0.005) (0.091) (0.100) (0.096)
Business valuation 0.002 1.039+ 1.062** 1.043+
(0.001) (0.022) (0.020) (0.024)
Ratio of amount raised to funding target 0.040 1.149 0.027** 1.159
(0.042) (0.793) (0.036) (0.881)
Control variables
Number of VC investors 0.012 1.839+ 2.189* 1.751
(0.011) (0.639) (0.819) (0.621)
(0.008) (0.138) (0.161) (0.158)

Main
Results
Firm
Failure
153
(0.004) (0.136) (0.160) (0.144)
Total amount of funding target 0.001 1.211 1.294 1.224
(0.004) (0.168) (0.254) (0.189)
Total number of investors -0.006 0.967 0.772* 0.964
(0.005) (0.091) (0.100) (0.096)
Business valuation 0.002 1.039+ 1.062** 1.043+
(0.001) (0.022) (0.020) (0.024)
Ratio of amount raised to funding target 0.040 1.149 0.027** 1.159
(0.042) (0.793) (0.036) (0.881)
Control variables
Number of VC investors 0.012 1.839+ 2.189* 1.751
(0.011) (0.639) (0.819) (0.621)
(0.008) (0.138) (0.161) (0.158)
UK firm -0.170*** 0.086*** 0.462 0.080***
(0.020) (0.026) (0.318) (0.024)
LLC form with no capital requirements -0.021+ 0.648 0.413* 0.597
(0.012) (0.239) (0.176) (0.231)
Age of the firm at the end of first campaign -0.001 0.945 1.020 0.949
(0.004) (0.152) (0.135) (0.151)
Share of female senior management -0.003 0.798 0.899 0.789
(0.037) (0.809) (1.280) (0.832)
Number of employees -0.000 1.017 0.956 1.016
(0.001) (0.030) (0.054) (0.034)
Firm located in a city bigger than 1 million inhabitants 0.005 1.004 1.061 1.007
(0.013) (0.351) (0.372) (0.377)
Largest portals dummy Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes
Days at risk - 253711 253711 253711
Number of failures 26 26 26 26
Number of firms 426 426 426 426
Pseudo R2
0.246 0.171 - -
Log-likelihood -77.271 -112.581 -98.994 -79.883

Robustness
154
• Several robustness checks have been conducted and results remain stable
• We find that mediation is taking place, but the share being mediated is
economically small
• We can thus directly interpret the effect of our explanatory variable number of subsequent
successful campaigns on firm failure.
• Before examining whether campaigns receive follow-up financing or face
insolvency, we might need to examine which characteristics lead to ECF success
• Running a Heckman selection model we show that after controlling for sample selection, the
unobservables are not correlated with the unobservables in the second stage.
• We estimate accelerated failure time models with an exponential and Weibull
distribution. The Weibull model displays similar results for the number of
subsequent successful campaigns.

Conclusion and Outlook
155
• We find that British firms have a lower chance of obtaining follow-up funding
through outside BAs/VCs
• But British firms have a relatively higher likelihood of surviving three years after
the ECF campaign than German firms.
• Assuming that our UK firm dummy captures differences in control rights, our
results show that control by the crowd is important for firm performance.
• The presence of London as a financial center might be an indicator of more
financial sophistication among investors (Vulkan et al. (2016) show that 38
percent of all pledges come from investors located in London).
• The tax advantages in the UK might in fact trigger riskier investments.

Variables
158
Appendix
TABLE A1
Table reports the definitions of variables. If variables capture a money amount, the EUR/GBP exchange rate as of the date of the ending of the
campaign is used.
Variable Description Source
Dependent variables
Follow-up funding by BAs/VCs
Firm failure
Dummy variable equal to 1 if the firm received follow-up funding after a successful ECF
campaign and 0 otherwise
Dummy variable equal to 1 if the firm went into insolvency, was liquidated, or was dissolved
and 0 otherwise.
BvD Orbis, BvD Zephyr, Thomson Reuters Eikon,
Crunchbase, press releases
Unternehmensregister (GER), Companies House (UK)
Time until follow-up funding by
BAs/VCs
Event until follow-up funding by BAs/VCs at time t after the firm’s first successful ECF
campaign.
Time until firm failure Event until firm failure at time t after the startup’s first successful ECF campaign (i.e., the
firm went insolvent, was liquidated, or was dissolved.
Explanatory variables
Management
Number of senior management team
members
Number of senior managers of the firm. BvD Orbis
Average age of senior management Average age of senior managers of the firm. Age: BvD Orbis
Share: Calculation by the authors
Number of filled patents Number of filled patents by the firm. BvD Orbis, PATSTAT
Number of granted patents Number of granted patents owned by the firm. BvD Orbis, PATSTAT
Number of trademarks Number of trademarks owned by the firm. BvD Orbis
Campaign characteristics
Total amount of capital raised Total amount of capital raised during an ECF campaign in Mio. EUR. ECF portal
Total amount of funding target Total amount of the funding target in an ECF campaign in Mio. EUR. ECF portal

Variables
159
Management
Number of senior management team
members
Number of senior managers of the firm. BvD Orbis
Average age of senior management Average age of senior managers of the firm. Age: BvD Orbis
Number of filled patents Number of filled patents by the firm. BvD Orbis, PATSTAT
Number of granted patents Number of granted patents owned by the firm. BvD Orbis, PATSTAT
Number of trademarks Number of trademarks owned by the firm. BvD Orbis
Campaign characteristics
Total amount of capital raised Total amount of capital raised during an ECF campaign in Mio. EUR. ECF portal
Total amount of funding target Total amount of the funding target in an ECF campaign in Mio. EUR. ECF portal
Total number of investors Total number of ECF investors of the firm. ECF portal
Business valuation Pre-money valuation of the firm in Mio. EUR. ECF portal
Ratio of funding to funding target Ratio of funding to funding target. Calculation by the authors
Number of subsequent successful
campaigns
Number of subsequent successful ECF campaigns after the first successful campaign of the
firm.
ECF portal
Control variables
Firm characteristics
UK firm Dummy variable equal to 1 if the firm ran an ECF campaign in the UK and 0 otherwise. ECF portal
campaign
Age of the firm at the end of first ECF campaign. Foundation: BvD Orbis
Age: Calculation by the authors
Legal form with no capital
requirements
Dummy variable equal to 1 if the firm’s legal form does not have capital requirements and 0
otherwise.
Share of female senior management Share of female senior managers of the firm. Gender: BvD Orbis
Number of employees Number of employees at the time of the ECF campaign. ECF portal
City with more than 1 million
inhabitants
Dummy variable equal to 1 if the firm is located in a city with at least 1 million inhabitants
and 0 otherwise.
BvD Orbis
Year dummies Year dummies of ECF campaigns on the platform. ECF portal
Largest portals Dummy variable equal to 1 if the ECF campaign took place on one of the five largest
platforms: Crowdcube (UK), Companisto (GER), Innovestment (GER), Seedmatch (GER),
and Seedrs (UK).
ECF portal

Variables
160
34
campaigns firm.
Control variables
Firm characteristics
UK firm Dummy variable equal to 1 if the firm ran an ECF campaign in the UK and 0 otherwise. ECF portal
campaign
Age of the firm at the end of first ECF campaign. Foundation: BvD Orbis
Age: Calculation by the authors
Legal form with no capital
requirements
Dummy variable equal to 1 if the firm’s legal form does not have capital requirements and 0
otherwise.
Share of female senior management Share of female senior managers of the firm. Gender: BvD Orbis
Number of employees Number of employees at the time of the ECF campaign. ECF portal
City with more than 1 million
inhabitants
Dummy variable equal to 1 if the firm is located in a city with at least 1 million inhabitants
and 0 otherwise.
BvD Orbis
Year dummies Year dummies of ECF campaigns on the platform. ECF portal
Largest portals Dummy variable equal to 1 if the ECF campaign took place on one of the five largest
platforms: Crowdcube (UK), Companisto (GER), Innovestment (GER), Seedmatch (GER),
and Seedrs (UK).
ECF portal
Financials
Number of VC investors Current number of VC investors. BvD Orbis, BvD Zephyr, Thomson Reuters Eikon,
Number of BA investors Current number of BA investors. BvD Orbis, BvD Zephyr, Thomson Reuters Eikon,

@BLGDataResearch
Keynote Address
Chair – Professor Geoffrey Wood
University of Essex

@BLGDataResearch
Investors' choice between cash and voting rights:
evidence from dual-class equity crowdfunding.
Professor Douglas Cumming
(Schulich School of Business, York University, Ontario)
Co-authors: Michele Meoli (Bergamo)
Silvio Vismara (Bergamo, Augsburg)

Managers of dual-class firms could use the insulation from the
disciplining effect of the market for corporate control to enjoy the
perquisites of control (Grossman and Hart, 1988)
Investors may be reluctant to invest in inferior voting shares
because they anticipate the risk of expropriation (Bebchuk and
Zingales, 2000)
Empirical evidence, however, is mixed: Smart et al. (2008) vs
Bohmer et al. (1996), Cox and Roden (2002)
Chemmanur and Jiao (2012) argue that dual-class equity deliver
to talented executives the opportunity to focus on value
maximization without distractions from outsiders
page 164
Pros & cons of dual-class equity

A large literature studies corporate governance of IPO-firms
Equity crowdfunding platforms allows firms to raise capital in
similar, though less regulated, way to IPO
While collective action problems limit investors’ monitoring
incentives, entrepreneurs can be tempted to engage in self-dealing
Investors in equity crowdfunding cannot even rely on third-party
certification mechanisms, such as the endorsement by prestigious
underwriters, to discern the quality of the offerings
In the absence of a secondary market, underpricing cannot be used
to limit adverse selection problems (Rock, 1986)
page 165
CG in equity crowdfunding

A-class thresholds - example

Our sample is made of 491 firms listed in the period 2011-2015
page 167
A-class thresholds - distribution
0
5
10
15
20
25
30
35
Threshold frequencies (£)

Professional investors include high net worth investors (i.e.,
annual income over £100,000 or net assets over £250,000) and
certified sophisticated investors (i.e., business angels,
professionals in the private equity sector, or directors of a
company with an annual turnover of at least £1 million)
Others are “restricted investors” that cannot invest in
crowdfunding more than 10% of their net assets (FCA Policy
Statement PS14/4)
page 168
Professional investors

Professional investors bid in ¼ of the offerings with no voting
rights or with thresholds up to £5,000
They bid in ½ of the offerings with a threshold above £ 5,000
page 169
Professional investors
0
5
10
15
20
25
30
35
%
Professional Investors at the offering
No professional Professional

All thresholds, all professional investors’ bids
page 170
Threshold level and professional investors’ bids (1/2)
0
200000400000600000
0 50000 100000 150000
VR threshold
professional_bid VR threshold

Threshold ≤ £50,000, professional investor’s bids ≤ £200,000
page 171
Threshold level and professional investors’ bids (2/2)
0
50000
100000150000200000
0 10000 20000 30000 40000 50000
VR threshold
professional_bid VR threshold

Probability of success (e.g. Ahlers et al., 2015)
Presence of professional investors
- Dummy variable (1 if a professional investor bid shares)
- Measure of bid concentration, calculated as an HHI (i.e. HHI=1 if all
the offering is subscribed by only one investor)
- Average size of bids from non-professional investors
Probability of follow-on offering: dummy that identifies firms that
raised additional capital after their initial crowdfunding offering
(source: Crunchbase, up to January 31, 2017)
page 172
Dependent variables

We consider the presence and the amount of the threshold to
obtain A-shares
In line with the corporate finance literature (e.g., Faccio and Lang,
2002), we measure the degree of separation between ownership
and control as the ratio of voting to cash-flow rights
Cash-flow rights (C) are measured at the end of the offering as
the controlling shareholder’s percentage ownership of the profits
and dividends of the firm, as in Faccio and Lang (2002)
V/C is the post-offering ratio between the controlling shareholder
voting and cash-flow rights, where voting rights are estimated
using the procedure used by La Porta et al. (1998)
page 173

Calculating V
• Controlling shareholder voting rights (V)
• V is equal to 1 if no right is distributed (only B-shares are issued).
• If A-shares are issued, the calculation of V depends on the existence of a
threshold for the attribution of voting rights.
• If no threshold is set, V is simply given by 1 minus the percentage of equity
offered (and is equal to C, cash flow rights).
• If a threshold is set, we cannot determine ex ante whether the participants to
the offering will receive voting shares or not, but we can proxy this effect, by
reducing the number of equity offered that are expected to be distributed. In
practice, in this case we calculate the following:
• V = 1 − [(equity_offered) ∗ 1 −
Threshold
Target_Amount
]
• where the parameter 1 −
𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑
𝑇𝑎𝑟𝑔𝑒𝑡
runs from 0 (if the threshold is set so high
that no voting right is actually delivered) to 1 (when no threshold is set), and
consequently V can have, at best, a value equal to C, while it is greater if a
threshold is set
pagina 174

Age is the age (in months) of the company
Positive sales equals 1 if the company has already reported
positive sales
Patents equals 1 if the company owns or is filing patents
TMT size is the firm’s number of management team members
Non-executives equals 1 if there are non-executives
Founder experience if the founder’s number of previous work
experiences
SEIS equals 1 if the offering is eligible for the Seed Enterprise
Investment Scheme (SEIS) tax relief
Target capital is the amount of capital to be raised in the offering,
in thousands of British pounds
Exit IPO equals 1 if the firm declares the intention to conduct an
IPO in the near future
Time trend and industry controls included in regressions
page 175
Control variables

Univariate analysis. A- vs B-shares
A-shares B-shares Difference
(voting rights) (no voting rights) A-shares -
405 obs 86 obs B-shares
mean median mean median mean median
Success (%) 37.53 0.00 43.02 0.00 -5.49 0.00
Professional investor (%) 26.12 0.00 33.93 0.00 -7.81 0.00
C (%) 85.55 87.00 86.30 87.50 0.74 -0.50
V/C 1.11 1.10 1.14 1.13 -0.03*** -0.03**
Age (years) 2.94 2.63 3.09 1.95 -0.15 0.68
Positive sales (%) 53.05 100.00 51.11 100.00 1.94 0.00
Patents (%) 9.16 0.00 2.22 0.00 6.94 0.00
Non-executive directors (%) 9.38 0.00 10.46 0.00 -1.08 0.00
Founder experience (no.) 3.49 3.00 5.20 4.00 -1.71*** -1.00***
SEIS (%) 39.69 0.00 22.22 0.00 17.47** 0.00**
Target capital (£k) 230.97 150.00 288.40 100.00 -49.87 50.00
Exit IPO (%) 21.76 0.00 11.11 0.00 10.64 0.00

Univariate analysis. Successful vs unsuccessful
Successful Unsuccessful Difference
189 obs 302 obs Success - Unsucc.
mean median mean median mean median
Professional investor (%) 41.80 0.00 10.12 0.00 27.38*** 0.00***
C (%) 85.21 86.00 86.22 90.00 -1.01 -4.00*
V/C 1.11 1.11 1.11 1.10 0.00 0.01
Age (years) 2.71 1.97 3.63 2.12 -0.92** -0.16*
Positive sales (%) 61.29 100.00 39.67 0.00 21.62*** 100.00***
Patents (%) 8.06 0.00 8.26 0.00 -0.20 0.00
Non-executive directors (%) 12.65 0.00 14.28 0.00 -1.63 0.00
Founder experience (no.) 4.04 3.00 3.28 2.00 0.76* 1.00**
SEIS (%) 65.05 100.00 59.50 100.00 5.55 0.00
Target capital (£k) 249.10 145.00 226.96 150.00 22.13 -5.00
Exit IPO (%) 18.30 0.00 23.10 0.00 -4.8 0.00

The threshold to obtain A-shares is observable only for A-shares
issues: Heckman selection model
First step: probit on the likelihood of issuing voting rights in the
campaign (A-shares dummy, 491 obs)
Identification conditions chosen similarly to Gompers et al.
(2016), by adding TMT size (a proxy of internal competition for
control), number of M&As in the same industry (a proxy of the
market for corporate control), and a Mimicking variable (namely,
the probability to issue A-shares calculated as the ratio of
crowdfunding campaigns which offered voting rights amongst all
previous offerings in the previous year)
page 178
Econometric analysis: first step

Inverse Mill’s Ratio estimated in the 1st stage included in the 2nd
Second step (405 obs): instrumental variable approach to address
endogeneity among CG variables: mimicking variables
Three equations for CG variables and one for outcome variable
CG variables are the threshold amount (Equation 2), the
controlling shareholder’s cash flow rights (Equation 3), the voting
to cash-flow rights (Equation 4)
page 179
Econometric analysis: second step

Model
A-shares= α1 + β1,1 TMTsize + β1,2 M&Aindustry +
+ β1,3 Mimicking + δ1 Controls + ε
Threshold= α2 + β2 Mimicking + ρ1Mill′s +δ2 Controls + ε
C= α3 + β3 Mimicking + ρ2Mill′s + δ3 Controls + ε
V/C= α4 + β4 Mimicking + ρ3Mill′s + δ4 Controls + ε
Outcome= α5 + γ1 C+γ2 V/C + ρ4Mill′s + δ3 Controls + ε

ResultsA-shares Threshold (ln) C V/C Success
C - - - 1.104**
(0.508)
V/C - - - -5.551**
(2.492)
Threshold (ln) - - - 0.015
(0.109)
Age -0.064 -0.058 0.018*** -0.018*** -0.253**
(0.121) (0.079) (0.005) (0.006) (0.114)
Positive sales 0.111 0.022 0.002 0.005 0.905***
(0.203) (0.127) (0.008) (0.009) (0.185)
Founder experience -0.111*** -0.041** 0.000 -0.002 -0.015
(0.025) (0.020) (0.002) (0.002) (0.051)
Target capital -0.101 0.302*** -0.012*** 0.008 -0.045
(0.107) (0.091) (0.005) (0.005) (0.124)
TMT Size 0.091**
(0.045)
M&As in the industry -0.215*
(0.106)
Pr. A-shares 3.275***
(0.867)
Pr. Threshold 0.749** 0.134* 0.039 -
(0.354) (0.069) (0.079)
Pr. C -3.123 1.042*** 0.048 -
(2.468) (0.302) (0.117)
Pr. V/C 5.555 -0.386 0.072** -
(10.890) (0.232) (0.039)
Inverse Mill’s ratio -0.395 -0.044*** 0.039*** 1.223
(0.377) (0.013) (0.015) (1.669)

Economic significance of C and V/C on success
• For a one-standard deviation change in "C", equal to 8%, there is an increase
in the probability of success of 3%;
• For a one-standard deviation change in "V/C", equal to 0.07, there is a
decrease in the probability of success by 12%.
pagina 182

Results
Professional
investors
Bid concentration
(HHI)
Average bid
(restricted inv.)
C 4.148** 0.115 -0.043
(2.112) (0.143) (2.577)
V/C -5.515*** 0.091 2.352
(2.057) (0.135) (2.427)
Threshold (ln) 0.247** 0.014** -0.050
(0.115) (0.007) (0.61)
Age -0.429*** -0.006 0.103
(0.133) (0.008) (0.143)
Positive sales 0.323* 0.022* -0.097
(0.179) (0.013) (0.225)
Non-executive directors -0.057 -0.017 -0.717**
(0.292) (0.019) (0.334)
Founder experience -0.038 0.002 -0.043
(0.034) (0.002) (0.038)
SEIS -0.518** -0.025* 0.339
(0.242) (0.015) (0.269)
Target capital 0.381*** -0.015* 1.006***
(0.132) (0.008) (0.151)
Inv. Mill’s Ratio -0.044 -0.022 1.967***
(0.049) (0.040) (0.721)

The sample is truncated twice – i.e. (1) only some offerings
include voting rights; (2) only some of the campaigns succeeded
Trivariate probit model, a model analogue to the bivariate probit
with sample selection but with three equations, due to the two
truncations (Carréon Rodriguez and Garcìa Menéndez, 2011);
estimated as in Cappellari and Jenkins (2013)
The equation for A-shares is the same as in the previous model,
while in success equation we also include a variable counting the
Competing offers, i.e. the number of offerings open in the same
equity crowdfunding platform (Crowdcube) at the time of the
opening of each campaign
page 184
Econometric analysis: follow-on offerings

Follow-on Offerings
• We only have two companies that went for an IPO after a successful
crowdfunding offering: Bis Sofa and Freeagent.
• For this reason, we believe that the regression should consider all "positive
events" for follow-on offerings, without distinguishing between the specific
types (e.g., IPOs, M&As, seasoned equity offering, ..)
pagina 185

Results
A-shares Success Follow-on
C - 1.211** 0.509*
(0.572) (0.296)
V/C - -0.488** -0.653*
(0.234) (0.389)
Threshold (ln) - 0.012 0.278
(0.109) (0.210)
Age -0.064 -0.243** -0.402*
(0.121) (0.116) (0.276)
Positive sales 0.111 0.904*** 0.430*
(0.203) (0.208) (0.271)
Founder experience -0.111*** 0.063* 0.041
(0.025) (0.033) (0.048)
SEIS 0.098 -0.323** 0.262
(0.241) (0.189) (0.417)
Target capital -0.101 -0.182** 0.070
(0.107) (0.091) (0.220)
Competing offers - -0.013*** -
(0.005)
Log-likelihood 0.140 0.174 0.178
Observations 491 405 152

Separation of ownership and control matters for the success of
the offerings (and weakly for the long-term success)
Thresholds matter to professional investors
page 187
Conclusions

Results - Heckman model on voting right decision
First step Second step (A-shares threshold)
A-shares Ln(amount) Threshold>0 Block threshold
(probit) (OLS) (probit) (probit)
(1) (2) (3) (4)
… … … … …
Founder experience -0.111*** 0.042** 0.014** 0.009**
(0.025) (0.020) (0.007) (0.004)
Target capital -0.101 0.391*** 0.022 0.141***
(0.107) (0.083) (0.032) (0.025)
TMT size 0.091** - - -
(0.045)
Firms in the industry -0.155* - - -
(0.086)
Pr. A-shares 3.275*** - - -
(0.867)
Inverse Mill’s ratio - -0.387 -0.448*** 0.235*
(0.396) (0.151) (0.120)
Pseudo (adjusted) R2 0.140 (0.136) 0.109 0.240

Results - GSEM on offering success
No. obs.: 491. Log-likelihood: -581.4
(1) (2) (3)
C V/C Success
C - - 1.642***
(0.566)
V/C - - -1.361***
(0.497)
Age 0.018*** -0.017*** -0.210**
(0.005) (0.006) (0.106)
Positive sales 0.003 0.004 0.724***
(0.008) (0.009) (0.165)
SEIS -0.019** 0.017 -0.369**
(0.009) (0.011) (0.182)
Target capital -0.013*** 0.011** -0.213**
(0.005) (0.005) (0.093)
Pr. C 1.129*** 0.379 -
(0.394) (0.452)
Pr. V/C -0.124*** 0.066** -
(0.027) (0.031)

Heckman (threshold – success)
The first stage
(omitted) is a probit
model on the
likelihood of issuing
A-shares (as before)
The second stage is
a system of four
equations estimated
using GSEM
No. obs.: 405
Log-likelihood:
-408.6
(1) (2) (3) (4)
Threshold (ln) C V/C Success
C - - - 1.243**
(0.596)
V/C - - - -0.521**
(0.240)
Threshold (ln) - - - 0.015
(0.109)
Age -0.039 0.019*** -0.017*** -0.273**
(0.079) (0.005) (0.006) (0.120)
Positive sales 0.018 0.005 0.004 0.891***
(0.127) (0.008) (0.009) (0.193)
SEIS 0.028 -0.019** 0.019* -0.079
(0.149) (0.009) (0.011) (0.218)
Target capital 0.390*** -0.010** 0.010* -0.244*
(0.080) (0.004) (0.005) (0.138)
Pr. Threshold 0.849** 0.069 0.065 -
(0.429) (0.053) (0.061)
Pr. C -3.026 1.169*** 0.339 -
(2.458) (0.353) (0.391)
Pr. V/C 4.856 -0.142*** 0.072** -
(10.764) (0.060) (0.040)
Inverse Mill’s ratio -0.395 -0.044*** 0.039*** 0.439***
(0.377) (0.013) (0.015) (0.169)

Heckman (threshold – type of investor)
(1) (2) (3)
Professional
investor
Bid concentration
(HHI)
Average bid
(non professional)
C 4.148** 0.115 -0.043
(2.112) (0.143) (2.577)
V/C -5.515*** 0.091 2.352
(2.057) (0.135) (2.427)
Threshold (ln) 0.247** 0.014** -0.050
(0.115) (0.007) (0.61)
Age -0.429*** -0.006 0.103
(0.133) (0.008) (0.143)
Positive sales 0.323* 0.022* -0.097
(0.179) (0.013) (0.225)
Non-executive directors -0.057 -0.017 -0.717**
(0.292) (0.019) (0.334)
SEIS -0.518** -0.025* 0.339
(0.242) (0.015) (0.269)
Target capital 0.381*** -0.015* 1.006***
(0.132) (0.008) (0.151)
Inv. Mill’s Ratio -0.044 -0.022 1.967***
(0.049) (0.040) (0.721)

We gathered information about 207 professional investors among
those that made their profile public in the platform and identified
177 professional investors by matching Crowdcube data to
Crunchbase
We distributed the survey electronically to these professional
investors between September and November 2016 and obtained
153 responses (out of 384, response rate of 39.8%)
Participants asked to state their agreement using 7-point Likert
Potential social desirability bias: complete confidentiality assured
Non-response bias: no difference between early and late
respondents (assumed similar to non respondents) using ANOVA
We see no reason to believe that the sample is biased toward
investors with different preferences with regard to voting rights
page 192
Survey

89% of respondents declared to observe the provisions about
voting rights in their crowdfunding decision (mean response: 5.13,
statistically different from the neutral mid-point response of 3.5 at
the 1% significance level)
92% of respondent are more likely to invest in offerings with
voting rights rather than without voting rights
72% of respondents declared that they pay attention to the
presence of threshold to obtain voting rights
68% declared that they are more likely to invest in offerings that
deliver voting rights above a certain threshold as compared to
those that deliver voting rights to every investor
page 193
Survey - results

D . C U M M I N G , F . H E R V É , E . M A N T H É , A . S C H W I E N B A C H E R
J U N E 2 0 1 8
Hypothetical Investment Bias

Motivation
 RQ: Are non-binding investment commitments informative?
 Are individuals reliable when they make investment commitments in
that they do what they said they would do?
 Novel context: equity crowdfunding, where investors are asked to make a non-
binding announcement about their investment intention into a true
entrepreneurial startup.
 Context of investment intentions:
 E-voting on equity crowdfunding platforms (WiSEED)
 Platforms: (i) outsource part of the due diligence process to the crowd; (ii)
pre-collect investment commitments
 Non-binding commitment, voluntary participation, but impacts
decision to have a campaign;
 Only cost may be to reduce the effectiveness of the selection process

Related literature
 Equity crowdfunding: Ahlers et al. (2015), Guenther et al.
(2016), Hervé et al. (2016), Hornuf and Schwienbacher
(2017), Vismara (2017)
 Existing studies only consider crowd investors as individuals who
provide funds to startups
 Hypothetical bias (Murphy et al., 2005; List and Gallet,
2001; Döbeli and Vanini, 2010):
 Difference between actual investment and initial intention
 Honesty: mostly experiments or questionnaires (Arbel et
al., 2014; Dieckmann et al., 2016)

Hypotheses (1 / 3)
 Crowd investors (voters) may be subject to a ‘hypothetical bias’ when asked
how much they would invest (Murphy et al., 2005; List and Gallet, 2001):
 Individuals report a higher WTP in a purely hypothetical situation as compared
to when they are put in a real situation.
 They overstate by a multiple of two to three (Murphy et al., 2005).
 Hypothesis 1: Voters overstate their intended investment.
 Brown and Taylor (2000) and Gilligan (1982) find that women are less
prone to the hypothetical bias: Women and men have different ways of
thinking about moral problems.
 Hypothesis 2: The overstatement of intended investment is stronger for
men than for women.

Hypotheses (2 / 3)
 Social capital as level of trust (Guiso et al., 2004)
 The more people trust others, the more they are susceptible to
cooperate and be committed to what they initially said.
 Thus, the hypothetical bias will vary with investor characteristics.
 Trust affected by
 Higher social capital is positively correlated with education and
wealth (Guiso et al., 2004).
 More educated investors are less inclined to retract as they will have a
better appreciation for the costs of retracting (Guiso et al., 2004); less
fraud (Cumming et al., 2015).
 Lower income makes it more difficult to honor investment
commitments due to the prevalence of financial constraints, rendering
retractions more commonplace.

Hypotheses (3 / 3)
 Alternative hypothesis is that people change their mind
because they received other, better opportunity.
 Need to control for time elapsed between voting and campaign start.
 Also, they may deliberately lie at the time they make
commitment. Similarly, there could be an informational
channel
 Here, there are (almost) no costs related to lying.
 But are there any gains?

Empirical Setting: WiSEED
 Launched in 2008 as first French equity crowdfunding platform
 €72 million for 150 companies as of February 2017
 All members are individuals (> 70,000 members)
 Varying minimum tickets (starts from EUR 100), with pooled
investment
 3-step project selection process (since September 2011):
 Internal committee selection (1,200 projects per year)
 E-votes: selection by members of WiSEED (roughly 400 in our sample)
 Project selected if >100 voters and >EUR 100,000 of investment intentions (min.
of 25% by current investors); last due diligence by platform
 Funding model: mix between “keep-it-all” and "all-or-nothing"

e-vote interface on Wiseed
Default is €100.

Sample (1 / 2)
 Initial sample: all the members/campaigns that took place on
WiSEED since its start
 71,915 registered members (extraction date: September 30, 2016)
 Filters:
 We exclude real-estate campaigns
 Campaigns that were still ongoing at time of the data collection
 Projects/campaigns that had no e-voting
 52,901 votes cast by 23,827 different members (32% of registered
members) in 397 different startups/projects.
 The first vote was cast on September 14, 2011.
 64 out of the 397 eventually ran a campaign.

Sample (2 / 2)
 Investments (full pop.): amount of each investment made, incl.
the exact date
 Investors (full pop.): date of registration, gender, date of birth,
location (postal code and name of town) and entire set of
investments and votes made across campaigns
 Start-ups (64 only): minimum ticket, location of the start-up,
year of incorporation, industry, and desired funding goal
 INSEE’s data (French National Statistical Agency) matched with
investors’ postal codes

Summary Statistics
 Members
 Votes
 Investments
 Campaigns

Members Statistics
Variables No. Obs. Mean Median Std. Dev. Min Max
Nbr. Votes Cast Since Registration 71,915 0.736 0 4.365 0 339
Member Voted at least Once (1=yes) 71,915 0.331 0 0.471 0 1
Nbr. Investments Since Registration 71,915 0.221 0 1.427 0 91
Member Invested at least Once (1=yes) 71,915 0.072 0 0.258 0 1
Member is a Man (1=yes) 71,909 0.808 1 0.393 0 1
Member Lives in France (1=yes) 71,915 0.946 1 0.226 0 1

Voting statistics
Overall Grade (1 to 5 stars) 52,891 4.34 5 1.05 1 5
Total Grade (-11 to +11) 52,901 3.56 3 4.29 -11 11
Nbr. Plus Grades (0 to +11) 52,901 4.36 4 4.14 0 11
Nbr. Minus Grades (0 to + 11) 52,901 0.72 0 1.45 0 10
Intended Investment (€) 52,901 661.0 100 1,783.5 0 50,000
Amount Invested (€) 20,445 193.4 0 1,434.5 0 99,998.1
Diff. Intested - Invended (€) 20,445 -557.8 -100 2,031.6 -50,000 94,998.1
Member Did Invest After Voting (d) 52,901 0.06 0 0.24 0 1

Distr. Intended Investments
Intended Investment | Freq. Percent Cum.
-----------------------+---------------------------------------
EUR 0 | 20,273 38.32 38.32
EUR 100 [DEFAULT] | 8,188 15.48 53.80
EUR 200 – 500 | 8,896 24.16 77.96
EUR 600 – 1,000 | 6,270 11.85 89.81
EUR 1,100 – 2,500 | 2,602 4.92 94.73
EUR 2,600 – 5,000 | 1,721 3.25 97.98
EUR 5,100 – 10,000 | 596 1.13 99.11
Above EUR 10,000 | 471 0.89 100.00
-----------------------+---------------------------------------
Total | 52,901 100.00

Investment statistics
Amount Invested (€) 15,866 1,123.0 500 3,843.2 100 279,990
Intended Investment (€) 3,309 934.0 500 1,989.7 0 50,000
Member Lives in France (d) 15,866 0.93 1 0.26 0 1
Member Did Cast Vote (d) 15,866 0.21 0 0.41 0 1

Campaign statistics
Variables
No.
Obs.
Mean Median Std. Dev. Min Max
Funding Goal (€) 64 312,203.1
300,00
0
177,030.
6
50,000 750,000
Nbr. Votes Received 64 268.92 217 190.18 51 1306
Sum of Intended Investments (€) 64 210,515.6 177,500
163,364.
3
14,600
1,057,40
0
Amount Raised during Campaign
(€)
64 261,300
200,80
0
206,833.
5
25,600 976,700
Ratio "Amount Raised / Sum Int.
Inv."
64 1.602 1.233 1.439 0.131 8.394
Ratio "Amount Raised / Funding
Goal"
64 1.112 0.689 1.290 0.116 5.954
Successful Campaign (d) 64 0.281 0 0. 453 0 1

Determinants of the Transformation Rate
 Dep. Var. = Amount Invested, in € (OLS)
 Main Expl. Var. = Intended Investment (in €), so
that its coefficient is the “transformation rate”
 Definition: fraction of intended amount that is eventually
invested if the campaign takes place (i.e., ratio of actual over
intended investment amount)
 Follow-up Analysis:
 Dep. Var. = dummy if invested after vote (Probit)

Full sample analyses
(1) (2) (3) (4) (5) (6) (7)
Intended Investment (in €) 0.183*** 0.183*** 0.187*** 0.188*** 0.188*** 0.188*** 0.187***
Intended Investment == €0 74.780*** -5.632 -15.324 -8.544 -14.812 -3.222
Intended Investment == €100 28.456*** -27.157 -29.516 -26.479 -30.756 -27.08
Total Grade (-11 ; +11) -5.207*
Evaluation Criteria (d) Yes
Grade Nbr. Plus (0 ; 11) -5.776**
Grade Nbr. Minus (0 ; 11) -5.238
Nbr. Stars (1-5 stars) 5.622
Member is a Man (d) -15.093 -19.285 -23.159 -14.157 -13.487
Nbr. Votes Cast -0.080* -0.081** -0.074* -0.085** -0.081**
Average Grade of Votes 15.142 20.185 20.552 19.748 14.512
Time Between Voting Period and
Campaign Start (year)
-69.40*** -68.70*** -69.30*** -68.79*** -69.09***
Minimum Ticket (€) 0.069** 0.069** 0.069** 0.069** 0.069**
Funding Goal (in €1000) 0.329*** 0.329*** 0.329*** 0.333*** 0.330***
Industry Fixed Effects
No, nor
constant
No, nor
constant
Yes Yes Yes Yes Yes
Year Fixed Effects (Voting)
No, nor
constant
No, nor
constant
Yes Yes Yes Yes Yes
Nbr. Obs. 20445 20445 18220 18220 18220 18220 18220

Full sample analyses
 Similar results when:
 Adding campaign fixed effects
 Controlling for self-selection (Heckman)
 Splitting the sample between new/old members (2 months at
time of campaign start)
 For different levels of investment intentions
 Different results: transformation rate is lower for
 Men (consistent with H2); 0.136 vs. 0.375
 Members with less trust (lower education, lower income)

(8) (9) (10) (11)
Full Sample (incl.
Campaign FE)
Full Sample
(Heckman)
Full Sample
(Heckman)
Full Sample
(Heckman)
Intended Investment (in €) 0.183*** 0.187*** 0.188*** 0.187***
Intended Investment == €0 -5.079 -6.218 -15.989 -3.71
Intended Investment == €100 -24.571 -26.199 -28.577 -25.965
Total Grade (-11 ; +11) -5.237*
Nbr. Stars (1-5 stars) 3.766 5.313
Member is a Man (d) -12.879 22.402 18.484 24.047
Nbr. Votes Cast - - -0.090* -0.091* -0.091*
Average Grade of Votes - - 16.179 21.227 15.977
-1007.605 -71.556** -70.890** -71.103**
Minimum Ticket (€) - - 0.068*** 0.068*** 0.068***
Funding Goal (in €1000) - - 0.342*** 0.342*** 0.343***
Industry Fixed Effects No Yes Yes Yes
Year Fixed Effects (Voting) No Yes Yes Yes
Lambda -178.43 -180.035 -176.767
Nbr. Obs. 18988 18220 18220 18220

(1) (2) (3) (4) (4) (5)
Men Only
Women
Only
Full
Sample
Membership
< 2 months
Membership
≥ 2 months
Full
Sample
Intended Investment (in €) 0.136*** 0.375*** 0.365*** 0.160*** 0.214*** 0.165***
Intended Investment == €0 -46.836** 167.902 -15.851 -25.255 28.619 15.989
Intended Investment == €100 -73.550*** 144.497 -41.96 -30.34 -9.935 1.014
Intended Inv. * Man -0.225***
New Member (d) 22.648
Intended Inv. * New Member 0.076
Nbr. Stars (1-5 stars) 11.316 -4.219 11.873* 15.127 11.938 12.158
Member is a Man (d) 0.000 0.000 134.97*** -11.089 -52.567 -8.954
Nbr. Votes Cast -0.049 -0.211 -0.072* -0.066 -0.09 -0.022
Average Grade of Votes 30.906*** -43.096 23.430* 2.561 27.140* 25.947
Time Between Voting Period
and Campaign Start (year)
-45.0*** -228.1*** -67.8*** -137.6*** -40.4** -63.2***
Minimum Ticket (€) 0.094*** -0.115 0.076** 0.064 0.075** 0.035
Funding Goal (in €1000) 0.361*** 0.07 0.335*** 0.176 0.445*** 0.278***
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects (Voting) Yes Yes Yes Yes Yes Yes
Nbr. Obs. 15645 2575 18220 7671 10549 14625

Int. Inv. =€0
Int. Inv.
=€100
Int. Inv. ≤
€1000
Int. Inv. =€100 -
€1000
Int. Inv. >
€1000
Intended Investment (in €) - - - - 0.252*** 0.252*** 0.174***
Intended Investment == €0 - - - - 50.588*** - - - -
Intended Investment == €100 - - - - 9.147 6.143 - -
Nbr. Stars (1-5 stars) 9.598* 4.276 7.324* 5.709 -38.389
Member is a Man (d) 58.084*** 35.783*** 35.195*** 18.118 -355.075
Nbr. Votes Cast 0.036 0.024 -0.071*** -0.099*** -0.097
Average Grade of Votes -6.777 2.318 15.841** 28.696*** 25.857
Time Between Voting Period
and Campaign Start (year)
-38.46*** 17.284 -25.80*** -14.738 -431.85***
Minimum Ticket (€) 0.060** -0.001 0.040*** 0.014 0.178
Funding Goal (in €1000) 0.025 0.048 0.162*** 0.230*** 1.539***
Industry Fixed Effects Yes Yes Yes Yes Yes
Year Fixed Effects (Voting) Yes Yes Yes Yes Yes
Nbr. Obs. 5021 3114 16038 11017 2182

Household
Rev. < 50p
50 ≤
Household
Rev. < 90p
Household
Rev. ≥ 90p
Educ. <
75p
75 ≤
Educ. <
90p
Educ. ≥
90p
Intended Investment (in €) 0.040** 0.373*** 0.156*** 0.118* 0.267*** 0.257***
Intended Investment == €0 -89.610*** 140.827 -42.386 -22.417 62.734 65.749
Intended Investment == €100 -116.35*** 154.858* -69.741** -73.339 62.747 51.429
Nbr. Stars (1-5 stars) 38.780*** 5.14 2.249 39.489*** 9.67 15.745
Member is a Man (d) 1.807 -49.548 -0.489 40.405 -67.664 -37.586
Nbr. Votes Cast 0.005 -0.265** -0.079 -0.063 -0.114 -0.094
Average Grade of Votes 16.254 61.060* 10.275 54.968** 22.52 27.88
-22.629 -1.137 -86.043*** 41.866 -56.046* -31.218
Minimum Ticket (€) 0.004 0.128 0.084** 0.022 0.047 0.035
Funding Goal (in €1000) 0.232*** 0.404*** 0.375*** 0.359*** 0.254*** 0.286***
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects (Voting) Yes Yes Yes Yes Yes Yes
Nbr. Obs. 3130 2512 12578 1355 4595 5664

Determinants of Investment by Voters
 Dep. Var. = Dummy if the voters did invest (Probit)
 In general qualitatively similar results, but:
 impact of investment intention is economically very small

Does Lying explain our results? (1 / 2)
 A potentially alternative hypothesis is that
individuals deliberately lie, which would mean they
already know at time they vote that they will not
invest what they report during the vote.
 The economic approach argues that individuals will tell the
truth if the gains from being honest are larger than the
possible costs of lying.
 These costs increase with the probability of being detected as a
liar and with the severity of punishment (Rosenbaum et al.,
2014).

Does Lying explain our results? (2 / 2)
 Two main reasons why lying is unlikely to explain our
results.
 What are the gains that would induce them to lie? Help the
entrepreneur if he is a friend? => Less 1% have intentions >
€10,000.
 We would otherwise expect a much lower transformation rate for
large investment intentions. => For the subsample of investment
intentions larger than €10,000, we get transformation rates of 0.55
to 0.60.
 Rather, these are more likely wealthy investors such as
business angels.

Campaign Success
 Dependent Variables:
 Amount Raised (OLS)
 Dummy whether Funding Goal was achieved (Probit)
 [Ratio Amount Raised / Funding Goal (OLS)]
 Main explanatory variables:
 Cumulated Commitments
 Grades, both averages and variation

Campaign success
(1) (2) (3) (4) (5) (6) (7) (8)
Intended Investment (in
€1000), total
652.8*** 473.4*** 329.0* 492.2*** 0.001** 0.001** 0.001* 0.001**
Total Grade (-11 ; +11),
Average
68655.8 0.095
Total Grade (-11 ; +11), Std.
Dev.
-25814.8 0.093
Nbr. Stars (1-5 stars),
Average
-56117.1 -0.431
Nbr. Stars (1-5 stars), Std.
Dev.
-384958.6* -0.625
Voting Member is a Man,
fraction
1022867.4 -0.917
Voting Member is a Man, Std.
Dev.
-1168873.8 -0.152
Funding Goal (in €) 0.459** 0.478** 0.431** -0.000 -0.000 -0.000
Industry Fixed Effect Yes Yes Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes
Nbr. Obs. 62 62 62 62 62 62 62 62

Summary
 Bulk of investments comes from non-voters.
 Investment intentions are roughly at level of final investments (around
EUR 200,000)
 Voters: +/- 20% of intended investments (aggregate) are transformed if
a campaign takes place
 Voters only invest part of what they say:
 Many retract; but those who invest largely invest what they said;
transformation rate quite stable
 Support for hypothetical bias, and in relationship with social capital
 It remains difficult to predict campaign success with information
obtained from the e-votes, except overall investment intentions.

contributions
 RQ: When do voters invest what they said they would invest?
=> transformation rate; are grades informative?
 First, we contribute to the literature on hypothetical bias.
 We provide new empirical tests, in a unique setting where agents
make ‘true’ decisions.
 Second, for crowdfunding literature: examine how crowd
investors help the platform in screening projects.
 Existing studies only considered crowd investors as individuals who
provide funds to startups.
 Third , for crowdfunding literature: evidence of cognitive
biases on the side of crowd investors

Concluding remarks
 First study on pre-campaign steps in equity crowdfunding
 E-voting enables extending participation of the crowd in
crowdfunding
 Externalization of due diligence and collection of investment
preferences
 Usefulness depends on ‘reliance’ of voters; i.e., whether they will do
what they said they will do
 We also contribute more broadly to the literature on
hypothetical bias.
 We provide new empirical tests, in a unique setting where agents
make ‘true’ decisions.

@BLGDataResearch
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University of Essex
Professor Jerry Coakley

Recent developments in crowdfunding

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