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.

The End

Thanks for your attention

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