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Financing Hightech Startups
Georg Licht
Centre for European Economic Research (ZEW)
Industrial Economics and International
Management
Mannheim
DIMETIC PhD Workshop
July 8, 2009
Pécs, Hungary
Outline
Some examples of startups in high tech
How are entrepreneurial ventures financed?
Business Angels
Venture Capital
Banks
Miltenyi
The autoMACS™ Separator and the autoMACS Pro Separator are benchtop automated magnetic cell sorters for the isolation of virtually any cell type from any species based on MACS Technology (IPR for MACS is owned by Miltenyi)
Biotech firm in Bergisch Gladbach (mid-sized town close to Cologne)Leading firm in magnetic cell separation („MACS technology“) and cell analytics & measurement.
• Started in 1989• Spin-off from University of
Cologne, Institute for Genetics (Prof. Andreas Radbruch)
• Founder: Stefan Miltenyi(Ph.D. in Physics)
• Today: 1100 employees• Locations: Bergisch
Gladbach, Teterow, Boston (and in more then 10 other countries
• Financing: Venture capital
Metaio
Leading in development of Augmented Reality Technology. Unique software platform to combine interaktiv solutions and application in mixed real and virtual worlds
Application: Marketing (e.g. furniture, cars,..), automation, factory planing,
Application possible via internet, mobile phones, PCs, .. Started in 2003 in Munich Spin-off from Munich Technical University Today: 50 employees Sales and development units in San Francisco and
Seoul Financing: Cash flow, founding teams equity +
government R&D money
COPS
The Anatomy of a Large-Scale Hypertextual Web Search Engine
Sergey Brin and Lawrence Page {sergey, page}@cs.stanford.edu
Computer Science Department, Stanford University, Stanford, CA 94305
Abstract
In this paper, we present ….., a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. ….. is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at … To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want. Keywords: World Wide Web, Search Engines, Information Retrieval
Academic citation literature has been applied to the web, largely by counting citations or backlinks to a given page. This gives some approximation of a page's importance or quality. PageRank extends this idea by not counting links from all pages equally, and by normalizing by the number of links on a page. PageRank is defined as follows:
We assume page A has pages T1...Tn which point to it (i.e., are citations). The parameter d is a damping factor which can be set between 0 and 1. We usually set d to 0.85. There are more details about d in the next section. Also C(A) is defined as the number of links going out of page A. The PageRank of a page A is given as follows:
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
Note that the PageRanks form a probability distribution over web pages, so the sum of all web pages' PageRanks will be one.
PageRank or PR(A) can be calculated using a simple iterative algorithm, and corresponds to the principal eigenvector of the normalized link matrix of the web. Also, a PageRank for 26 million web pages can be computed in a few hours on a medium size workstation. There are many other details which are beyond the scope of this paper.
Born 1973 in Moskau
BA U. of Maryland
MA Stanford
Ph.D expected 97/98
Father:Prof. in Maths
Born 1973 Ann Arbor
BA U. of Michigan
MA Stanford
Ph.D expected 97/98
Father:Prof. in IT/Computer Sciences
Based on this information would you invested in these two PhD candidates 800K US-$? (to transform the paper into a workable program and a firm to commercialize this program?
Financial Constraints
• Asymmetric Information: Entrepreneur and financing institutions (Banks, Private Equity, Venture capital, Individuals) face different sets of information about the technology, market, market development, etc. („ex ante“)
• Moral Hazard:Entrepreneur‘s behaviour can not be observed fully (after the financing contract) or change her behavior („ex post“)
• How to overcome these problems?
How are young ventures financed?
Distribution of Financial Resources Used
Young Hightech-Firms in Germany 2007
65,7
15,4
4,0
1,9
6,12,1 5,2
Cohort 2005/2006
43,2
35,8
5,8
1,5
7,6
6,0 1,5
Cohort 2000/2001
Cashflow Owner Family & Friendes Outside equityBanks Public support Others
Source: ZEW HT-Survey 2007
33,636,837,3
52,727,5
43,3
24,19,2
17,7
5,95,6
3,0
14,117,0
27,2
46,846,9
52,3
18,511,3
4,1
2,40,01,6
14,29,4
7,0
18,314,9
11,7
17,622,2
47,0
13,213,614,5
3,91,52,7
2,66,66,1
5,412,3
8,2
17,422,2
5,4
0,90,00,5
20,66,7
4,0
Kontokorrentkredit
Längerfristige Bankdarlehen
Förderdarlehen der KfW
Förderdarlehen der Förderinstitute der Länder
Mittel von Verwandten, Freunden etc.
Zuschüsse der Bundesagentur für Arbeit
Beteiligungskapital
Mezzanine-Kapital
Sonstige Quellen
0% 30% 60% 90% 0% 30% 60% 90%
Häufigkeitsanteil Volumenanteil
STW & HTW TDL & Software Nicht High-Tech
Share of firm using source
Contribution of sourceto volume of financing
Bank overdraft / Short term bank loan
Long term bank loan
Loan from KfW
Loan local government banks
Family & friends (& fools)
Federal labour office (startup from unemployment)
Business Angels, Private Equity, Venture capital
Mezzanine loans
Other external sources
HT-Manufact. HT-Service/Software NonHighTech - industries
Use of External Sources of Finance
KfW/ZEW: Start-up Panel 2008
Stage of Company DevelopmentSeed: The idea/concept stage. Company proves a concept
and qualifies for start-up capital.
Start-Up: Company completes product development and initial marketing.
Early Stage: Expansion of company that is producing and delivering products or services.
Expansion: Product or service is in production and commercially available. The company demonstrates significant revenue growth, but may or may not be showing a profit.
Later: Product or service is widely available. Company is generating ongoing revenue; probably positive cash flow. It is more likely to be, but not necessarily profitable.
Demand for External Funds and Company Development
Stage
Source
Demand
Supply
500K €25K €
100K € 2000K €
Pre-Seed Seed / Start-up
FFF/Government
AngelsOwner/Government Venture Funds
Later Early
EquityGapLack of
information / Matching
Capital gap
Financial structure of firms with outside equity
- Average values for startups with outside equity from 2005-2006 cohort -
thereof
Cashflow
Founders resources
Others
Banks
Family & Friends
Public subsidies
Third parties
22%
24%
43%
4%
4%
Private investors
VCOther enterprises
Public moneyOther outside
74%
6%
9%
9%
thereof
Cashflow
Founders resources
Others
Banks
Family & Friends
Public subsidies
Third parties
22%
24%
43%
4%
4%
Financial structure of firms with outside equity
- Average values for startups with outside equity from 2005-2006 cohort -
Business Angel Finance
Business Angels
Rich individuals
Investing their own money
Aiming at profit
Investing in small companies not listed at a stock exchange
No family ties
(sometimes philanthropic motivation)
Investing in seed and early stages
Investment size: 20k Euro to 250k Euro (as a rule)
Definition of Firms with Equity Financing by Private Investors &
Business Angels
Private Investors: Individuals investing in young firms (incl. Investments via BA Fonds or BA networks)
Business Angels: Private Investors providing money and additional support services for their portfolio companies
Firm management values the support as „helpful“(Advice, Contacts, Infrastructure, Administration, R&D, Production, ..)
Role of Business Angels & VCin financing HT-Start-ups
Hightech-StartupsAlle Unternehmens-
gründungen
5% BA-financed firms
Firms with passive, privateInvestors
3%
Source: ZEW-Survey 2007
2,5%
VC
Share of high-tech firms having these types of financing
~18000 startups in
Hightech-sectors =
7% of all startups
BA-Financing in Germany About 5% of HT-Startups have BA financing (+ 3% with equity
by „passive“ private investors)More important for university spinoffs (9%)
Average 1,9 BA per portfolio firm
BAs invest in early stages (41% during year of start-up, even 7% before start-up);But also investment in expansion phase (31% invested 3 years after start-up or later)
Average investment size 100 000. € (Median 30 000 € ) per firm.HT sector receives in 2005 about 190 Mio. € (0,0085% BIP)
Average share of BA: 26% of total equity
Quelle: ZEW-Hightech-Gründungspanel 2007
Human capital of founding team is significantly larger (65% vs. 48% have university degrees).
Firms found by a team
University spin-offs und R&D intensive firms
BA portfolio companies utilized more often technologies developed by founders or develop in-house, hold patents and have a larger share of sales with new products
Difference between BA-financed and companies financed by other private investors are smal (similar selection criteria of both groups)
Which firms are typically financed by BAs?
Support by Business Angels
Multiple answers possibleSource: ZEW HT Survey
Areas of support by BAs
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Production / R&D
Commercial areas(e.g. book keeping)
Member of board
Infrastructure /facilities
Contacts, networking
Coaching / Advice
as share of firms with equity holding by BAs
Deeply involvedInvolved Slightly involvedHardly involved
How portfolio companies value the support by BAs?
Remark: These are conditional probabilities because only those firms are considered which have received some „slight support“ in these areas. Source: ZEW HT Survey 2007
0% 20% 40% 60% 80% 100%
Production / R&D
Commercial areas (e.g. book keeping)
Member of board
Infrastructure / facilities
Contacts, networking
Coaching / AdviceVery helpfulHelpfulPartly helpful
as share of firms with equity holding by BAs
How BAs and portfolio find each other?
Aktive Search By chance
BA-financing 27% 73%
Other private investors
40% 60%
Total 31% 69%
Share of firms by means of type of search and investor (only firms where contact lead to investment
Average duration of search: <= 1 month for 50% of private investors
<= 1 month for 35% of BA portfolio companies
Who was helpful in finding a private investor?
Source: ZEW HT Survey 2007
Multiply answers possible
The contact resulted to … resulted from …
Business Angels Passive, private investor
By aktive Search
By chance By aktive Search
By chance
private contacts 89% 95% 95% 96%
BA networks 12% 1% 8% 0%
Chamber of commerce / start-up or technology centres
4% 2% 2% 1%
Business plan competitions / entrepreneurship competitions
9% 8% 11% 1%
Special entrepreneurship fairs / conferences / professional meetings
2% 10% 8% 0%
Internet 7% n.v. 8% n.v.
Others 13% 6% 7% 3%
Success probability for various channels?
Slource: ZEW-HAT survey 2007
Relation between contract points, which turned into an equity investment, and all contact points tried to receive an investment
0% 10% 20% 30% 40% 50% 60%
Private contacts
B.plan competiton etc.
Fairs for entrepreneurs
BA networks
Internet
Chamber of Commerce / Technology centre
Others
aktive Suchezufälliger Kontakt
Why no agreement with BAs is reached?
Sou
rce:
ZEW
-HA
T S
urv
ey 2
00
7
Mu
ltip
le a
nsw
ers
possib
le
Share of enterprises having contract with a potential private investor
0% 5% 10% 15% 20% 25% 30% 35%
Other reasons
No agreement about conditions
Too large share is demanded
Investment size
Too risky
Insufficient growth potential
Business plan
Existing founders
Inkompetence of investor
Pers. Differences
No money needed
Venture Capital Financing
A Typical VC Fund
General Partner(VC Firm)
1% of Capital2.5% Mgmt. Fee
20% Carry
Starter A
Starter B
Starter C
Starter D
Highflyer A Profitable exit B No gain C
Total loss D
Limited Partners99% of Capital80% Carry€€
Expertise
€ € €
Track record of owners / founders
Owners / founders invest their own money
IPR (Patents, ….)
Government R&D support
Links to other organisations
(BAs)
How Firms signal their type
Careful and extended due diligence / Highly selective
Staggered contracts / Milestone payments
Multiple rounds of financing
Hands-on management
Specific governance rights (e.g. right to dismiss CEO)
Involvement in board
Fix income (e.g. management fee) + residual claim
How VCs Overcome Problems Resulting from Asymmetric Information and Moral
Hazard
Size of VC Market in Selected Countries
0
1
2
3
4
5
6
7
8
9
10
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
USA
Großbritannien
Deutschland
Frankreich
Japan
VC / BIP (in %)
Source: EVCA ; NCVA
USAUKGermanyFranceJapan
Venture Capital Market in Germany
Share of Segments in Total VC Investments in %
2000
49,39
38,34
12,27
2005
0,52
23,78
75,70
1995
4,36
15,99
79,65
Seed
Start-upsExpansion
Source: BVK 2006
Banks and Financing of SMEs
Dominance of Loans- Supply Side Explanations -
Continental European “Relationship-Banking”: “Three-pillar-model”: Large private banks, Small
(regional) private banks, Public/Community banks Strong regional anchorage Traditionally: pricing of loans not risk adequate
Strong position of creditors in case of default (Insolvency)
Dominance of Loans - Demand Side Explanations -
Size-related restrictions regarding certain financing options(Significant fixed costs related to the size of loans)
Fiscal treatment of loans vs. equity
Outstanding position of the entrepreneur-personality Accentuation of operative business vs. financing-
management Comparable low knowledge of financing-issues in
medium-sized companies No systematic analysis of financing alternatives High preference in the entrepreneurial freedom of
decisions
Main Reason to Start a New Firm
Gründungen insgesamt
22,5%
8,5%
9,5%
12,8%
43,0%
2,2% 1,5%
Gründungen mit Marktneuheiten
30,1%
36,5%
16,9%
7,5%
8,4%0,1%0,6%
All startups Startups with market novelty
36,5%
selbstbestimmt arbeiten Umsetzung einer konkreten GeschäftsideeAusnutzen einer entdeckten Marktlücke keine alternative unselbstständige BeschäftigungAusw eg aus Arbeitslosigkeit Forcierung durch ehemaligen Arbeitgebersteuerliche Anreize
Being his own master
Exploitation of a new market (niche)
Way out of unemployment
Utilising favourable tax treatment
Commercialisation of new business model/idea
No other opportunity in the labour market
Forced by previous employer
KfW/ZEW: Start-up Panel 2008
Further ReadingRECOMMENDED Gompers, P. And J. Lerner (1999), The Venture Capital Cycle, MIT Press: Boston Freear, J., Sohl, J. and Wetzel, W., 1994, Angels and non-Angels: are there differences?,
Journal of Business Venturing, 9, 85-94. UN Economic Commission For Europe (2007), Financing Innovative Development.
Comparative Review of the Experiences of UNECE Countries in Early-Stage Financing, New York and Geneva.
Shane, Scott (2008), The Illusions of Entrepreneurship – The Costly Myths that Entrepreneurs, Investors and Policy Makers Live By, Yale University Press: New Haven. (esp. chapter 5: How are New Businesses Financed?)
Gorman/Sahlman (1989): What do Venture Capitalists do? in: Journal of Business Venturing, 4. Jg., S. 231-248
ADDITIONAL LITERATURE Vise, David A., Mark Malseed (2005), The Google Story – Inside the Hottest Business,
Media and Technology of Our Times, Delacorte Press/Random House: New York. . Sahlman (1990): The Structure and Governance of Venture-Capital Organizations, in:
Journal of Financial Economics, 27, 473-521. Kaplan, S. and Zingales, L. (1997) ‘Do investment - cash flow sensitivies provide useful
measures of financing constraints?’, Quarterly Journal of Economics 112, 169-216. Hubbard, R.G. (1998): ‘Capital-Market Imperfections and Investment’, Journal of Economic
Literature, 36, 193-225.
Definition of Hightech Industries
Cutting Edge
-Pharma-Biotech-Spec. Chemisty-Electronics-Control tech.-Automation-Telecom
R&D intensive Industries
-Chemistry-Mechanical I.-Engineering-Automotive-Consumer Elec.-Medical devices
Knowledge intensive Services
-Telecom services-R&D services-Software-Information services-Technical consulting-Technical labs
900 (4,9%) 1.500 (8,2%) 16.000 (86,9%)Estimated number of annual start-ups
Startups in these industries = 7% of all start-ups
Definition of Hightech Industries
Cutting Edge
-Pharma-Biotech-Spec. Chemisty-Electronics-Control tech.-Automation-Telecom
R&D intensive Industries
-Chemisty-Mechanical I.-Engineering-Automotive-Consumer Elec.-Medical devices
Knowledge intensive Services
-Telecom services-R&D services-Software-Information services-Technical consulting-Technical labs
900 (4,9%) 1.500 (8,2%) 16.000 (86,9%)
Estimated number of annual start-ups
ICT-Hardware Software
30% 25%
Econometric Evidence for Financial Constraints
Modigliani-Miller theorem: In the absence of taxes, bankruptcy costs, and asymmetric information, and in an efficient market, the value of a firm is unaffected by how a firm is financed.
Variety of explanation why MM does not hold Hence: Search for evidence that (free) cash-flow has
an impact on size and structure of investments of firms (e.g. R&D)
Regression-based evidence is available for a large number of countries
SMEs & young companies are more restricted
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