Venture Capital as an Asset Class

© 2020 Sugarfree Ventures Holding UG
All Rights Reserved, Author: Ben Scherer
A Quantitative Walk through
Venture Capital
Quantitative Insights, Interpretations and Strategies

Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Risk of Loss
Content
Power law
Evidence
Drilldown
Stock Selection
& Competition
Syndication
Early vs Late Stage
Other / Misc.
Power Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss
Section 1:
Asset Class Performance
Section 2:
Class Specialty – Power Law
Section 3:
Diversification and Fund-of-Funds
Section 4:
Fund Size and Manager Maturity
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
Part 1:
Venture Capital in Numbers: Core Dimensions
Investor Sentiment
Start-Up
Volume
Investment
Volume per Stage
# of Funds
Funnel
Funnel
Development
Conversion
Ratios
Funnel
Visualizations
Section 5:
Venture Capital Funnel
p. 6 - 25 p. 26 - 37 p. 38 - 48 p. 49 - 62 p. 63 - 72
Continued in Part III
Section 1

Content
Section 1:
Seed Market
Section 2:
Top End of US VC Market
Section 3:
European VC Market
Part 2:
Details on Seed and Europe
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
- % Revenue Generating
Fundraising
- Interround Multiples
Valuations
- Multiples
Market
Development
Market
Activity
Valuation
Trends
Selection Bias
/ Multiple Expansions
US vs EU
Liquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds
p. 74 - 86 p. 87 - 99 p. 100 - 118

Content
Section 1:
OpModel Intro
Section 2:
Basic Operations
Section 3:
Specialization
Part 3:
Venture Capital Operating Models: Past, Present, Future
Section 4:
Quant Funds
Buy and Sell
Companies
Value Creation
Model
Adapting to
Funnel Ecosystem
Example
Funnel
Generic
Fund Strategies
p. 120 - 127

Part I
Venture Capital in Numbers
Core Dimensions

Section 1:
Asset Class Performance

Data Source: Preqin, Private Equity and Venture Capital Spotlight, March 2018
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Introduction : Risk/Return Profiles in PE Buyout (Growth Stage)
Venture
Performance
7
Tech and Venture aren’t just a thing
▪ Buyout risk/return profile in technology belongs
to the most attractive segments for the PE class,
but provides still limited allocation space when
comparing against all buyouts.
▪ The return opportunities when moving from late
stage buyouts to the earlier stages look even more
promising and are the reason why Venture Capital
continues to thrive.
▪ In our study, we want to look at all the little quant
features of the asset class and provide interpretations
on recent developments and shifts.
Risk of Loss

Data Source: Cambridge Associates Report: The case for venture capital, Invesco
Top Quartile Performance in Asset Classes
▪ Private Equity is and remains the king of risk/return of
most asset classes and is comparable with the stability
and risk protection of the real estate asset class.
▪ Notably, Venture Capital in its entirety has shown even
more promising.
▪ Over the last 25 years and looking at different return
horizons (horizon IRRs), the asset class has outperformed
all other asset classes every time in the top quartile.
▪ Reason enough to study the sector more deeply.
Top Quartile Performance in Venture is clearly unrivalled when looking at Horizon Pooled IRRs
Venture
Performance
8
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Data Source: US VC Index Benchmark Statistics, Cambridge Associates 2018
Horizon Pooled Returns vs Modified PM Equivalents
▪ PMEs are calculated by comparing allocation into e.g.
a Venture Capital Index vs. a stock market index
▪ US Capital Index CA 2018 shows VC has outperformed
US public markets for several periods
▪ Significant long-term above-average returns in 15 – 30
years horizon
Info: The entire capital drawn down over a horizon of vintages
by the VC asset class is then assumed to be allocated at the
fund creation date. The general holding period is calculated
and the return (on Fund level/TVPIs) is compared with the
Cash on Cash / MOIC return of a similar investment into the
index with same holding period. The resulting MOIC return
is translated into an IRR return.
Venture
Performance
9
Benchmarking IRRs by using Public Market Equivalents (PMEs)Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Data Source: US VC Index Benchmark Statistics, Cambridge Associates 2018
Horizon Pooled Returns vs Modified PM Equivalents
▪ Similar comparison adding Dow Jones to the benchmark
shows also strong performance against largest capitalized
index
Venture
Performance
10
Benchmark Index and PME for Dow JonesBenchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Data Source: White Paper Report, Alumni Venture Group 2018
Top Quartile Returns VC vs Public Markets in Time Horizons
▪ Comparing medians, the VC asset class clearly outperformed
the public markets.
▪ Looking at Top Quartile performance, however, the S&P has
provided similar returns.
▪ Due to liquidity aspects of public markets, being consistently
invested in the top quartile is ex-ante a lot more difficult than
investing in top quartile Venture. The return expectation among
top funds is more predictable.
▪ This is exactly the reason why a larger dispersion in returns is
visible when moving away from top quartile.
Venture
Performance
11
Top Quartile Performance of Venture Index vs. S&P 500Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Looking at different investment horizons
▪ Showing the general venture and top quartile index in this
study from Alumni Ventures Group, both perform similar in
the longer horizon. But in shorter horizons the top quartile
clearly lead the benchmarks.
▪ In all horizons, the Index analyzed outperformed the public
market equivalents in S&P 500 and Russel 2000
Pooled IRR: Aggregates several funds into one joint investment index.
Horizon IRR: Aggregates all capital inflows at beginning of period and all outflows at end of period to calculate a fund IRR.
PM Equivalents: Uses the amount and timing of inflows and outflows of a PE fund or PE pool to assess the return of a
strategy investing total inflow into an index to measure outflow at the exit date.
Venture
Performance
12
Drilldown on Top Quartile Horizon IRR Performance of Venture Index vs. S&P 500Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Dotcom Bubble skews 20+ years return statistics
▪ While in this view, the IRRs still show a better return for
the Top Quartile and General Venture index over all time
horizons, the long-term profile clearly shows a strong skew
for the top quartile caused by the Dotcom era.
▪ Interestingly enough, the general Index did not fare in the
same way. In times of bubbles, the best performing funds
capture all the returns.
▪ In general, the VC top quartile offers 7% better returns than
the PME and the general asset class offers 1-2%.
Venture
Performance
13
Caveat on the Dotcom BubbleBenchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Drilling down deeper into VC IRRs based on Quartile
Data Source: Alumni Venture Group, Index Like Venture Investing
https://cdn2.hubspot.net/hubfs/3925488/Sales%20Materials/Focused%20Funds%20Sales%20Materials/AVG%20Index-like%20Venture%20Investing.pdf
Clear shortfall of IRRs in the Bottom Quartile
▪ A key issue of Limited partners investing into the VC asset
class is to avoid investing into bottom quartile funds. Doing
so can greatly diminish the benefits of the asset class.
▪ On a fund level, while some GPs have sufficient power in
their brands and strong operating models that allow them
to consistently rank in the top two quartiles, most funds on
the single vintage rely on luck. The returns of these funds is
driven entirely by chance of having two or three outperformers
in their portfolio to drive the return of the fund.
▪ LPs typically find it harder to get access to top performing
GPs who usually source from existing network and rely on a
good relationship with the LP.
▪ For new LPs not having access to high performing GPs and
their vintages, a more diversified approach offers protection
from bottom quartile funds. It still requires insights and
proper due diligence to avoid a majority of funds in the
index drifting into the lower quartile.
Venture
Performance
14
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Returns of Different Classes within Venture Capital
▪ Early Stage index strongest IRR returner which has several
drivers ranging from smaller specialized funds achieving
better selection and value-add and multiple expansions in
early to later stage are not met by similar expansions in the
later stage. The later and growth stage have less strong val.
Expansions and far higher competition on very few invest.
Opportunities.
▪ Multi-Stage funds typically perform better than late or
expansion funds due to a stronger exposure to the early
stage returns.
▪ The size and allocation strategies of multi-stage funds,
however, gears the overall performance more towards the
later stage returns.
▪ Only focused early stage can yield the overall returns.
Venture
Performance
15
Returns of Different Investment Stages ComparedBenchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

High Frequency PME IRRs and IRR volatility
Volatility of Pooled IRR vs S&P 500 and Dax
▪ Pooled IRRs, with the exception of the Dotcom era,
have reacted less strongly to any public market shock.
▪ This is in line with lower correlation
Venture
Performance
16
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Cumulative/Chained IRRs (Pooled) VC vs S&P 500 vs Dax
▪ Chaining the high frequency IRRs from the PME benchmark
shows that IRRs are pro-cyclical, lagging and slightly less
volatile than public market strategies
▪ The cumulative return from the high frequency strategy
also yields almost consistently higher performance in VC
starting after the Dotcom recovery in 2014.
Venture
Performance
17
Cumulative IRRs from the High Frequency PME AnalysisBenchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

IRR Volatility vs Hedge Funds
Data Source: Cambridge Associates Q3 2019 US VC Report; S&P 500 - Yahoo Finance
Hedge Funds vs VC vs S&P 500
▪ While Hedge Funds, marketing themselves as low-beta,
high-alpha vehicles, the volatility in IRRs from this PME
study shows that Venture provides a far more stable and
consistently above 0% IRR.
Venture
Performance
18
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Public Equity vs. Venture Capital vs Fund-of-Funds Volatility
Data Source: White Paper Report, Alumni Venture Group 2018, VC - Pitchbook Benchmarks Q1 2019; S&P 500 - Yahoo Finance; Internal Calculations
Stable and Low Volatility in Venture Capital
▪ In a similar comparison looking at PME IRR volatility
between VC, Fund of Funds and the S&P 500, the
study shows that the VC index has the lowest and most
stable overall standard deviation.
▪ Fund-of-Funds compared against the entire index do
show slightly more pronounced changes in the standard
deviation due to concentration risk.
Venture
Performance
19
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Asset Class Correlations
Data Source: Cambridge Associates Report: The case for venture capital, Invesco
Correlation of Venture Capital with other Asset Classes
▪ Low correlation with VC with PE, Real Estate and other
non-alternative investments (70% corr.) makes it an
attractive addition to investors seeking asset class
diversification.
Key Features:
▪ Pro-Cyclical
▪ 70% correlation with PE and RE
▪ < 5% correlation with Large Cap Equity
▪ -10% correlation with Core Bonds
Venture
Performance
20
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Venture
Performance
Data Source: Weidig, T., Kemmerer, A., & Born, B. (2005). The Risk Profile of Private Equity Funds of Funds. In: The Journal of Alternative Investments
Diversification and skills of GPs almost eliminates risk of loss
▪ While direct investments into companies bore a fairly high
MOIC, being wrong most of the time is the norm. The risk
of loss of capital in total is 30%, and in partial 42%. Investors
typically lose 85% of their capital in all their loss investments.
▪ Single VC fund vintages already greatly reduce the risk of
loss by having more disciplined investment strategies, a more
competent value building process and the ability to find buyers
for any underperforming investment. While partial losses still
belong to the business model (30%), the overall loss on the
portfolio is on average limited to 4% !!!
▪ Adding the capability of a Fund-of-Funds investor further
reduces the risk of partial loss down to 1%. The result is a
0% probability of total loss, a 1% of partial loss and an average
loss on the portfolio similar to the VC funds with 4%.
21
Risk of LossBenchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Investor Sentiments: What are investors looking at in their portfolios?
Investor Expectation on VC stages
▪ Investors have increasingly positive expectations about VC
investing. In particular in early stage and fund-of-funds.
▪ This trend will likely accelerate Post-Covid19
Venture
Performance
22
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Investor Sentiment: Asset Class appetite
Investors view Venture Capital second most interesting
▪ Reputation of Venture Capital ranks second after very
oiled machine of Small- to Mid-Cap Buyout market
▪ Leading 11% against large cap buyouts
▪ Fund-of-Funds still weak with 14%
Venture
Performance
23
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Investor Sentiment: Preqin study on general interest in the asset class
Data Source: Preqin Special Reports, PE Fund of Funds November 2017
PE Strategies by Limited Partners next 12 Months
▪ Preqin survey to investors Q3 2016 vs Q3 2017 shows
Venture Capital and Fund-of-Funds investing are among
the four most favored asset classes for investors
Venture
Performance
24
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Investor Sentiment: The shift of liquidity towards private markets
Data Source: Prequin; McKinsey - Global Private Markets Review, 2020
Entire PE Asset Class outperformed markets clearly
▪ Asset values in Private Equity have grown rapidly
in the last 20 years providing a clear shift in liquidity
towards private assets
Venture
Performance
25
Benchmark
Returns
Quartile
Drilldown
Volatility
Correlations
Investor
Sentiment
Risk of Loss

Section 2:
Unique Characteristics : The Power Law

Power
Law
Power-Law is key feature of the VC market
▪ Returns on direct investment portfolios are not following
a normal distribution. Most of the returns are generated
by a very small set of outliers
▪ This is known as the power law in venture capital and has
lead to different strategies to adapt the reality of venture
investing
▪ Some models focus on pure diversification without any
particular selection strategy, called “spray and prey” models.
The argument goes that any larger diversified portfolio can
perform against a high conviction portfolio of only few
selected companies.
▪ Other models, especially in later stage, try to identify the
most likely outliers early on and invest diversified into the
upper half of the total start-up portfolio
▪ Again others, focus on entering even more late stage and
compete on the identified outperforms, trying to capture
the outlier multiple expansions in the latest stage to get
returns.
Data Source: https://medium.com/@adiezbarroso/power-law-in-venture-capital-why-portfolios-matter-8d3fb2afac5e
https://blog.usejournal.com/power-laws-in-venture-capital-why-the-long-tail-matters-22e057c6fa34 27
The Power Law that applies to Venture CapitalPower law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Power
Law
Most companies (75%+) exit below 200 Million USD
▪ 54% companies exit below 50 Million
▪ Only 4% exit at billion dollar valuations
Global tech exit valuations,
2016
28
The distribution of Exit returns shows clear power lawPower law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Power
Law
Allocation shows Stock Selection does not work
▪ Correlation ventures clustered a portfolio of companies
and studied how many companies returned on different
clusters of multiples.
▪ Most companies (64%) returned <1X MOIC and show
that too many companies get funded that are not able
to return the capital invested. A clear example that VC is
in 64% of cases not able to deploy capital on a risk-adjusted
basis. The number grew rapidly towards 2019.
▪ A second majority sits in the 1x – 3x range and only gives
its investors non-competitive returns. This can be caused
by either excess capital investments in the later stage, or
by a majority of investments not exiting at a reasonable
multiple, given the time and capital needed to gear these
companies towards Exits.
▪ Less than 12% of the exits returns MOICs above 5X and
indicate that competition over high performing companies
is likely more fierce and also adds to the multiples by
driving up valuations in the later stage.
▪ Despite immaturity of asset class, the overall index still,
as we saw, outperforms others on the index and top
quartileData Source: https://medium.com/correlation-ventures/venture-capital-no-were-not-normal-32a26edea7c7
1%
3%
6%
9%
31%
51%
2% 3%
7% 6%
18%
64%
>20X 10-20X 5-10X 3-5X 1-3X <1X
Distribution of Realized US VC Outcomes over Past Decade
2008 - 2019
% of Dollars % of Financings
29
Allocation to different performance clusters shows: stock selection does not workPower law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Power
Law
Top 100 VC Investments return majority of VC Index Return
▪ Given the before data with most capital allocated to low
performers and few high performers overall in the system,
it is evident that the top performers are returning the Index.
▪ In a study from Cambridge associates, the top 100 investments
in the studied portfolio returned almost 70% - 80% of total index
and did so consistently.
Data Source:
1) https://marginalfutility.substack.com/p/the-power-law-in-venture-capital
2) http://cambridgeassociates.com/wp-content/uploads/2015/11/Venture-Capital-Disrupts-Venture-Capital.pdf
30
Power Law Impact on VC Index ReturnsPower law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Power
Law
MOIC Contributions of Top 100 Companies
▪ Top 10 % / Top 10 companies of Top 100 Investments returned
largest share of the Top 100 Cohort, often more than 50%
▪ Top 11 – 20 companies contributed only 15% - 20% of the
cohort
▪ Remaining 80% only returned up to 30% of the total cohort
Data Source:
1) https://marginalfutility.substack.com/p/the-power-law-in-venture-capital
2) http://cambridgeassociates.com/wp-content/uploads/2015/11/Venture-Capital-Disrupts-Venture-Capital.pdf
31
Even in the Top 100, >50% of returns generated from the top 10%Power law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Early Stage Deals are key bread winners of MOIC returns
▪ Returns of the top 100 companies mostly benefitted early
stage investors, almost hitting 2 times the MOICs in later
stage investments.
▪ Competition in early stage is fierce with a very high chance
of average funds not being competitive in bids for the most
attractive and visible companies
▪ This leads to a crowding out effect and adverse selection
bias for most funds. Funds need to invest in even more
than 100 companies to have a 1% probability of hitting a
larger returner
▪ Most tier 2 – tier 4 funds hence focus on entirely different
portfolio dynamics. Having lower targeted exit multiples,
but a clear exit funnel for a higher share of companies they
invest in and taking in less risk on potential super returners
Data Source: http://cambridgeassociates.com/wp-content/uploads/2015/11/Venture-Capital-Disrupts-Venture-Capital.pdf
Power
Law
32
Early Stage Investments contribute more strongly to Fund ReturnsPower law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Power
Law
Apart from failed funds (<1X), all funds share the weight
▪ Most funds, according to this study, share an equal weight
of failing or moderately successful deals.
▪ The outlier performance on the fund level hence clearly
is driven by the minority share of extreme outliers.
▪ The share of low performers tells a clear and convincing
story that asset selection capability and brand visibility has
no impact on the overall portfolio. Otherwise top GPs
would identify more winners (selection) and would be
easily competing for access to these winners (brand).
Data Source: https://www.ben-evans.com/benedictevans/2016/4/28/winning-and-losing
33
Most funds have equal share of low returnersPower law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Power
Law
The same study shows the impact of winners
▪ While e.g. 70% of the lower performing companies are
equally distributed over all quartiles of vintages, the
top performing funds typically do not only have one,
but several outliers
▪ With stock selection and brand being off the table as a
reason for this, the result indicates that the power to
build, grow, market and adequately capitalize these
high performers is the key differentiator for those top
performing GPs.
34
The Winners make the FundPower law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Power
Law
Companies built around those power laws
Spray and Pray Diversification underperforms selection
▪ Y Combinator with 632 early stage deals with very low pre-
screening quality only has 0.95% of unicorns in rooster
▪ Sequoia and SV Angels with 307 and 441 portfolios have far
larger share of unicorns explaining fund returns
▪ Y Combinator sources promising start-ups in the Pre-Seed
stage and invites diversified portfolio into its Accelerator.
▪ Sequoia invests slightly later with Seed institutional rounds,
picking best performers overall in the global Venture Space
Good Selection and Fund Sizing to cover entire funnel matters
▪ Good brand and premium valuations as well as full coverage
of the VC investment funnel can help source best companies
globally into investment funnel of tier 1 US VC (Sequoia)
▪ Leading US VCs come with sufficient capital to enter and stay
in the early stage and cover investments up till Exit stage
Data Source: https://marginalfutility.substack.com/p/the-power-law-in-venture-capital
35
Nevertheless, some GPs consistently manage to acquire outliers in their portfoliosPower law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Power
Law
Example Syndication Chart of Alumni Ventures Group
▪ Investors try to syndicate their best deals with the best
investors with that deal (Tier 1 Investors)
▪ If no syndication, they try to obtain follow-on funding
from tier 1 investors
▪ While this does not reduce portfolio concentration
risk and does not offer diversification . . .
▪ . . . it increase the probability of adequate funding,
venture support and network power to fully empower
the built-up of winning companies
“Great companies are not born, they are made.”
- Great Teams
- Great Products
- Great Markets
- Great Business Model
- Great Investors
- Great Venture Support
- Great Management
36
How does syndication play into the overall picture?Power law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Power
Law
37
EMPTYPower law
Evidence
Top 100 Performance
Early vs Late Stage
Stock Selection
Syndication
Other / Misc.

Section 3:
Diversification and Fund of Funds
38

Diverse
Portfolios
80% < $200m
67% < $100m
54% < $50m
vs
Only 4%
unicorns
Global tech exit valuations,
2016
Return Distribution is key to understanding fund vintages
▪ We can recap from the power law section that it is almost
impossible to predict winners and stock selection does not
work.
▪ Some part of the portfolio performance rests in the ability
to build the outliers in the portfolio to generate return from
the very few top candidates in the portfolio
▪ How to get top candidates into the portfolio? If stock
selection does not work, the answer is in diversification of
the portfolio:
▪ Single funds might go multi-stage or broaden their
investment portfolio by managing more deals per
assets under management and partners available,
or by increasing syndication to use joint know-how
and building capacity and lower the commitment
per deal that otherwise exhausts partners
▪ Fund-of-Funds investors might spread their invest
over several VCs that show strong characteristics.
▪ Diversifying and doing so early is a key strategy of more
passive investors that want to capitalize on the power awl.
Power Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss
Fund-of-Funds
- Other
39
Recap on and diversification edge of the Power Law

Diverse
Portfolios
Global
Average Exit
valuation:
$117m
Acquisitions of venture-backed companies, globally Average Exit Value explains performance of larger funds
▪ With $117m average exit returns and the skew of the power
law on the previous slide, investors investing tickets of up
to 20 million at 100 million valuations need to win on all their
investments to return around 1.2x MOIC. Covering fees and
carried interest, the risk increases.
▪ Early stage investors taking 15 – 30% of fully diluted shares
investing at 10 – 20 Million USD valuations are far more likely
to return 3-5x on top performing investments. Reducing their
exposure to underperformers, fund MOICs reaching 2-3x are
more likely. This requires strong vertical exposure or strong
cross-regional exposure to good companies.
40
HeadlinePower Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss

Diverse
Portfolios
Probability
Number of
deals
Source: SR analysis on the entire cohort of early stage deals in 2011-2014, data provided by Beauhurst
As the portfolio size increases the probability of 3x returns rises
while it lowers for returns <1x Portfolio Size: Quo Vadis
▪ While the statistics provided here indicate that a larger
portfolio shows higher expected returns, and might cause
business models such as Y Combinator or 500 Startups,
executing such a portfolio requires access to very strong
follow-on investors to drive the performance.
▪ A larger later-stage fund struggling with follow-on investors
requires a substantial amount of partners under management
which is economically almost impossible to achieve. Funds
operating with 20 – 30 partners almost never outperform the
total of 10-20 funds with 2-3 partners and a more reduced
portfolio.
▪ For LPs, this statistic promotes a diversified index approach
over a highly concentrated super-size portfolio
41
A study on the impact of diversification on fund TVPIsPower Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss

Diverse
Portfolios
AngelList: How portfolio size affects early-stage venture returns, Nigel Koh/Abraham Othman
https://angel.co/pdf/lp-performance.pdf
Linear Correlation between # investments and median IRR
▪ Confirms the prior analysis on fund sizes.
▪ The dispersion shows, however, that diversification also
requires a consistent investment model, as each individual
portfolio might still perform strongly under the pooled
market performance (black line)
▪ Investing into diversified portfolios hence requires a
consistent commitment to the asset class
42
Linear Correlation of # of Investments and Median IRRsPower Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss

Diverse
Portfolios
Data Source: Preqin 2014 - National Bureau of Economic Research 2017
Concentration Risk – Manager Capability Risks
▪ Fund managers significantly pose different risk for the
investors which are oftentimes hard to assess/measure
▪ Fund size and ability to effectively use management fees
creates risk around use of resources / lack of resources
▪ Access to specific markets, maximum size of portfolio and
weak approach to diversification drives concentration risk
▪ Expertise in fund management and experience in the domain
can increase risk of identifying and managing portfolio
43
Explanation: GP Concentration RiskPower Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss

Diverse
Portfolios
Data Source: A tale of two squirrels by Matt H. Lerner
OUTCOME 50 COMPANIES 100 COMPANIES 200 COMPANIES 500 COMPANIES
WHOLE FUND 6.34x 5.52x 5.53x 5.49x
WITHOUT BEST
COMPANY
3.25x 3.51x 4.44x 5.08x
CHANGE -49% -36% -20% -7%
COMPARISON OF TOP QUARTILE RETURNS ACROSS VARIOUS PORTFOLIO
SIZES WITH AND WITHOUT THE TOP COMPANY
Concentration Risk – Under diversification and Power law
▪ Looking at return data net of the top performing
company shows clearly that top performers are
the key contributors to the fund return
▪ While investment managers have to remain keen
on sticking in – and not early divesting from – their
top performing companies, leveraging their expertise
and network to maximize the follow-on valuations and
exit valuations of their best performing company,
the data also shows that the risk of losing out on the
total fund performance decreases with the size of
the portfolio.
44
Explanation: Portfolio Concentration RiskPower Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss

Diverse
Portfolios
Data Source: A tale of two squirrels by Matt H. Lerner
Single Fund vs Fund-of-Funds IRR Performance
▪ Concentration risk on manager and portfolio level is
cause for stronger performance of Fund-of-Funds
strategy.
▪ While not subtracting individual fund manager
capability in executing a return strategy on its
vintage or overall manager level, a diversified approach
increases probability of outlier vintages and spreads
and reduces manager risk
45
Diversification Benefit: Results from Fund-of-FundsPower Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss

Diverse
Portfolios
Data Source: Preqin 2014 - National Bureau of Economic Research 2017
Fund-of-Funds KPIs
▪ All studies show a consistent result in the FoF strategy
▪ Fund multiples disperse marginally between median
and average providing consistent and stable MOIC
▪ IRRs consistently show values between 6 – 7.5 % and
provide the highlight of the asset class.
Database Period # of FoFs
Burgiss 1987-2007 294
Burgiss 1997-2007 280
Prequin 1987-2007 543
Prequin 1997-2007 499
46
Return KPIs of Fund-of-FundsPower Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss

Diverse
Portfolios
Single Fund vs Fund-of-Funds IRR Performance
▪ Direct Investments into Venture Capital has potential of
higher MOIC due to less fees and GP concentration risk.
But typically has the highest cost in selecting, winning
and managing the investment successfully.
▪ Single VC funds have lower administration overhead and
provide more stable returns, but offer the lowest MOIC
due to portfolio concentration risk in their vintage fund
generations.
▪ While Fund-of-Funds comes with the highest overall mgt.
fee and double-carry mechanisms, the overall overhead of
managing a diversified portfolio of start-ups via direct
investments into GPs is lowest. The diversification effect
yields the highest MOIC by lowering GP and portfolio
concentration.
47
Return KPIs of Fund-of-FundsPower Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss

Diverse
Portfolios
Diversification and skills of GPs almost eliminates risk of loss
▪ While direct investments into companies bore a fairly high
MOIC, being wrong most of the time is the norm. The risk
of loss of capital in total is 30%, and in partial 42%. Investors
typically lose 85% of their capital in all their loss investments.
▪ Single VC fund vintages already greatly reduce the risk of
loss by having more disciplined investment strategies, a more
competent value building process and the ability to find buyers
for any underperforming investment. While partial losses still
belong to the business model (30%), the overall loss on the
portfolio is on average limited to 4% !!!
▪ Adding the capability of a Fund-of-Funds investor further
reduces the risk of partial loss down to 1%. The result is a
0% probability of total loss, a 1% of partial loss and an average
loss on the portfolio similar to the VC funds with 4%.
Power Law
Recap
Diversification
Evidence
Concentration
Risks
Fund-of-Funds
- Returns
Fund-of-Funds
- Risk of Loss
48
Risk of Loss

Section 4:
Fund Design: Fund Size and Manager Maturity Impact on Performance

Fund
Design
Data Source: Preqin Special Reports, PE Fund of Funds November 2017
Fund Size:
▪ Early Stage Advantage:
▪ Smaller funds invest in better performing early stage
▪ Larger funds either (a) diversify more, or (b) invest later stage
▪ The Downside of Later Stage Funds
▪ Later stage in general lower returns
▪ Competition over tier 1 deals is high. High adverse selection risk
▪ The Downside of Large Early Stage Funds
▪ Manager capabilities do not scale with diversification. Better: many smaller funds
▪ Manager sourcing capabilities not scaling into the right channels
Emerging Managers:
▪ Smaller funds more often managed by emerging fund managers
▪ Emerging managers more focused on their expertise and value-add
▪ Emerging managers with higher incentive to performance, active venture builders
▪ Later stage more focused on metrics
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
50
Summary Insights

Fund
Design
Smaller Funds show higher TVPIs and MOIC
▪ TVPI distribution shows funds below $200 Million in size
exhibit a sizable higher chance of generating TVPIs > 1.5x
▪ The reason being generally stronger multiple-expansions
in the earlier stage
▪ On the contrary side, the TVPI performance of the individual
fund vintage is likely less risky. The smaller fund performance
statistics are likely driven by the top quartile, while the larger
fund performance distributes more into the 2nd and 3rd quartile.
The reason being greatly reduced risk in the later stage that the
larger funds are exposed to more.
Data Source: Kauffman Foundation VC Portfolio Analysis
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
51
Smaller Funds outperform larger ones on TVPIs

Fund
Design
Clear relationship of Fund Size and TVPI Performance
▪ In this sample, a small number of 10 funds in the below
$400 Million size category returns TVPIs above 1.5x
▪ A larger share of small funds still performs equally low
around 1.0x TVPI, similar to larger funds
▪ The fund returns hence equally follow of power law like
pattern
Data Source: Kauffman Foundation VC portfolio analysis
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
52
TVPIs of individual funds shows a clearer picture

Fund
Design
Funds returning in Billion Range rather rare
▪ The two highest performers in TVPI exhibit fund
commitments below $200 Million
▪ No fund above $800 Million in commitments is able
to return above $ 2 Billion.
▪ Three funds managing a commitment of $2B+ return
less than $1B in nominal terms. Likely due to (a)
general performance of larger and more later stage
focused funds, and (b) increased issues in placing the
committed funds into viable investments. They risk either
limited the fund performance by allocating excess dry
powders or over-committing capital into their deals
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
53
Individual TVPIs based on nominal value of fund

Fund
Design
Pooled IRR performance
▪ While IRR performance has to be assessed with the caveat
of some later stage funds gearing their IRRs by investing into
companies they can exit very quickly – here TVPIs make more
sense -, the picture still speaks clearly in favor of smaller funds.
▪ Funds larger than 1 Billion hardly make IRRs larger than 2 %.
This is driven by the very late stage offering less chances of
expanding multiples and large investments being more likely
comparatively illiquid, allowing less gearing of IRR metrics.
Data Source: Prequin; The White Paper Report, Invesco 2015
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
54
Pooled IRRs show similarly clear picture: Smaller funds return higher IRRs

Fund
Design
Horizon Pooled IRRs net to LP (Fund Size < $100m)
▪ Looking at IRRs net to LPs and over different time horizons,
small funds in total have almost completely performed well
for their LPs, providing net IRRs larger than typical preferred
interest around 5% - 7%.
▪ While this return shows that GPs might have lower incentives
given the fairly low carried interest coming from their invests,
the data shows markets for smaller funds are efficient in
setting preferred returns and GPs being able to return slightly
above set target to LPs.
Data Source: PE and VC Impact Investing, Cambridge Associates 31 March 2020
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
55
Horizon Pool IRRs net to LPs in early stage close to Preferred Interest

Fund
Design
The Rise of Nano VCs (<$25m)
▪ Micro VCs in the US increase steadily in the recent
years
▪ Clearly shows desire of highly networked and sourcing
capable GPs to enter the cap table early and fund the
young companies towards later institutional rounds
▪ This also is in line with general data showing that the
average time till institutional funding and the total
capital deployed before a Series Seed or A round by
a larger fund are increasing.
▪ This also partially is a result of increasing maturity,
network and skill know-how and liquidity flowing
into the asset class.
▪ For investors, this opens opportunities to enjoy even
stronger multiple expansions in their portfolio by
investing diversified into earlier stage Micro VCs
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
56
Trends: The rise of Pre-Seed Nano VCs

Fund
Design
Micro VCs ($25m - $100m)
▪ A similar development is visible in the number of micro VCs
forming in the market.
▪ As bridge investors, they might take stronger risk positions
into diversified early stage portfolios
▪ As co-investors for larger Seed VCs, they help spread the
risk of a concentrated investments and allow them to take
on more companies at lower commitment in their portfolios
▪ Finally, syndication with micro VCs can bring additional networks
to potential hires, adds a critical and highly motivated eye to
the sourcing and investment management activities and overall
brings in more total hours of attention of experienced VCs
into the start-up, which can boost performance and also
lower the risk of conflict
Data Source: Small VC funds continue to raise, Crunchbase news 2019
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
57
Trends: The rise of Seed Co-Invest Micro VCs

Fund
Design
Capital Gain Contribution – Emerging Firms vs Established
▪ The rise of micro VCs brings in not only more bridge money
and additional capital and diversification of the cap table,
it also brings in first time GPs
▪ Emerging GPs (1st/2nd time managers) also bring in the
highest level of motivation and typically a deeper operational
edge than established VCs.
▪ Emerging managers historically outperformed established
managers in their capital gain contributions to the overall
index (1995 – 2012 time window)
▪ The gap has drastically increased in recent years with an
extreme dispersion in 2011
Data Source: http://cambridgeassociates.com/wp-content/uploads/2015/11/Venture-Capital-Disrupts-Venture-Capital.pdf
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
58
Not only small, but emerging and motivated

Fund
Design
Emerging Funds consistently rank among top performers
▪ Looking at industry performance rankings, the emerging
micro and nano VCs top the TVPI charts
▪ Reasons can include any of the already covered material
from exposure to better multiple-expansions to being
closer to operations and value creation, to being more
diversified with smaller exposure to individual invests.
Data Source: Venture Capital Disrupts Investing - Cambridge Associates 2020
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
59
Emerging Managers rank among the top performers

Fund
Design
Data Source: First-Time Fundraising Barometer, BVCA 2018
Richard Clarke-Jervoise, Head of the Stonehage Fleming Private Capital Investment team, says: “I think the most
compelling first-time funds are those that show a commitment to building a firm right from the outset. This
will mean demonstrating that they aim to be on a par with the best across all areas of their business rather
than just focusing on investment performance.”
Ken Cooper, Managing Director of the British Business Bank: “First-time managers are usually hungry for
success, they don’t carry the baggage of earlier funds so tend to be fully-focused on making a success. If that
balances out the lack of experience then we see good results.”
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
60
LPs on Emerging Managers: More Hungry and More Focused

Fund
Design
Data Source: Venture Capital Disrupts Investing - Cambridge Associates 2020
Willingness to Invest in First Time Funds
▪ Willingness to invest in 1st time funds increased by 15%
from 2011 to 2016
▪ Some prefer emerging managers coming out as spin-offs
of more established funds (11%), indicating that LPs still
believe that credibility in managing funds is critical. And
hope to capitalize more on emerging managers bringing
strong networks at higher risk tolerance and incentive.
▪ The declining numbers in the study show that the market
has become more certain in looking at emerging managers
(less will consider, more will actually invest), and are in
general more open to investing (will not vs will)
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
61
Investors take notice and increasingly take interest in investing in emerging managers

Fund
Design
Caveat when it comes to allocation
▪ Despite the rise of willingness to invest in emerging managers,
and the rise of Micro VC / Nano VC funds, the total number of
funds created in this category relative to established funds has
declined from 36% to 26%
▪ This indicates that the growth in emerging funds does not
emerge from a shift towards emerging funds, but the general
liquidity in the VC market is driving the dynamic of new fund
creation.
▪ The increased willingness to invest in first time funds can
then be understood by higher maturity of emerging VCs and
higher comfort of LPs with investing into the riskier parts of
the asset class.
▪ Liquidity, overall, will naturally prefer more mature and
established funds with allegedly proven operating models
and will prefer to place larger amounts of capital in a smaller
set of larger and established funds.
Data Source: Prequin; The White Paper Report, Invesco 2015
Introduction
Smaller Funds
- Performance
Smaller Funds
- Market Dynamics
Emerging Managers
- Performance
Emerging Managers
- Characteristics
Investor Sentiment
- Emerging Managers
62
Liquidity and Allocation Preferences
36% 26%

Section 5:
Venture Capital Funnel
63

Venture
Funnels
Data Source: TopTal (Link), Crunchbase data 64
Start-Up Creation World Wide
The World of Start-Ups and Investors
▪ Every year, 20.000 to 40.000 start-ups are created worldwide
(Crunchbase data)
▪ 15.000 Seed investments on average are made per year,
when including Angels into the Seed investing pool. Roughly
25%% to 65% hence never receive any funding. This requires
15.000 people or more to look at start-ups, negotiate with
start-ups and eventually deploy capital into start-ups.
▪ Annual Early stage investments (Series A + B) are around
7500 per year. This implies approx. 5000 Series A
investments. A survival rate from incubation to Series A of
25% to 12.5%. Approx. 85% of start-ups do not make it this
far. And yet, 5000 Series A stage investors need to look at
deals every year and make a judgement call on whether to
invest.
▪ The supply of start-up ideas spans a financing ecosystem
from the first Angel investment to the potential Unicorn IPO.
Start-Up
Volume
Investment
Volume per Stage
# of Funds
Funnel
Funnel
Development
Conversion
Ratios
Funnel
Visualizations

Venture
Funnels
Data Source: Crunchbase Global Venture Report, Global Data 65
Global Deal Capacity of the Venture Capital Ecosystem by Stage of Investment
0
2000
4000
6000
8000
10000
12000
2017 H1 2017 H2 2018 H1 2018 H2 2019 H1 2019 H2 2020 Hq
Angel / Seed Early Late Growth
# Deals
Numbers of investment in each stage reveals GP market
▪ Growth stage with $100m+ rounds fairly flat around 57 – 70
deals per quarter and divided by few later stage Venture
funds funneling companies towards final stages of growth
before preparing outsized exits into public markets, buyout
or corporate M&A. Likely maximum number of 25 - 45 active
lead taking funds globally. With <120 total co-investors.
▪ Late stage activity with approx. 500 deals per quarter and
average of $75m capital deployed indicates a wider set of
active market participants comprising of late stage funds,
investment banks and institutional direct investments. With
lead investors requiring approx. 120 – 180 funds globally to
process the stable funnel of deals. Net of Growth funds,
additional 80 – 140 funds. Regionalized activity very likely, as
well as corporate involvement. With <300 co-investors.
▪ Extreme around Seed funding with up to 5.000 deals per
quarter or 10.000 deals per year. With average 5 years
investment periods and 3.5 deals per investor per year, a
range of approximately 2900 – 3500 early stage investors
cover the early phase of the venture funnel globally
Start-Up
Volume
Investment
Volume per Stage
# of Funds
Funnel
Funnel
Development
Conversion
Ratios
Funnel
Visualizations

Venture
Funnels
Data Source: Guesstimates and data from Crunchbase, DealRoom 66
Estimating Deal Capacity by applying the power law to VC counts per stage
Estimated Global VC Ecosystem Funnel
▪ Summarizing previous slide, approximately 3.000 – 7.000
Seed VCs moving down to approx. 45 – 70 Growth VCs.
▪ A Growth VC would build relationships with a maximum of
the 200 Late Stage VCs to source deals and position Growth
fund for finding exit opportunity.
▪ Late Stage VCs likely not able to have relationship with 2.050
Early Stage VCs (Dunbar’s Law). Clustering of ecosystem now
starts. While top global companies will be funneled inde-
pendent from ecosystem clustering, more specialized
clusters emerge to support companies in geographically,
vertically or otherwise focused funding trajectories and Exit
strategies.
▪ The highest clustering being in the clearly locally operating
Seed VCs who will likely concentrate larger parts of their
portfolios on companies whose Exit funnel – within 3-5 years
and 2-3 follow-on rounds – they can reasonably predict and
handle.
3,900
1,500
130 25
3,500
2,000
400 110
7,400
3,500
530
135
Seed VCs Early VCs Late VCs Growth VCs
Min Variance
Start-Up
Volume
Investment
Volume per Stage
# of Funds
Funnel
Funnel
Development
Conversion
Ratios
Funnel
Visualizations

Venture
Funnels
Data Source: CB Insights, International Unicorn Club 67
Unicorns come from all over the world from different ecosystems
Unicorns emerge from everywhere
▪ Historic data shows that almost all mature ecosystems in the
world were able to produce unicorns
▪ China, the US and India have shows that large ecosystems
with maturing financial and consumer markets can produce
excessive numbers of large companies
▪ The result of this insight that global capital is increasingly
competing for access to companies from around the world
and can gain access by either spreading their operations to
new regions or by building and supporting local earlier stage
GPs and outsourcing the sourcing and competition over
access to these managers.
▪ This explains large parts of the funnel as the global venture
market pushes increasing levels of liquidity into emerging
markets into an increasing number of specialized and hyper
regional super-early stage vehicles.
Start-Up
Volume
Investment
Volume per Stage
# of Funds
Funnel
Funnel
Development
Conversion
Ratios
Funnel
Visualizations

Venture
Funnels
Data Source: Start-Up Genome 68
As ecosystems around the world keep growing, trend in funnel will continue
Growing # of mature ecosystems implies need for more funnel
▪ With every new ecosystem maturing to a level that the
creation of new start-ups leads to a sizable amount of
potentially viable businesses and outliers, new funds emerge
naturally around this ecosystem to fund promising
companies and connect them to the global venture
ecosystem
▪ While this regionalization of VC activity also leads to more
active regional M&A markets where local strategic investors
aim to purchase local winners into their corporate portfolios,
and thereby taking volume out of the global funnel, it also
fuels the competition of regional players for access to the
global venture ecosystem. Funneling a promising start-up to
become a potential unicorns still reaps larger potential
capital gains than funneling a company towards a < $ 100
Million regional exit early.
▪ The trend of rising ecosystems will hence spur a trend for
rising numbers of GPs across the funnel from Seed to
Growth stage.
Start-Up
Volume
Investment
Volume per Stage
# of Funds
Funnel
Funnel
Development
Conversion
Ratios
Funnel
Visualizations

Venture
Funnels
Data Source: Crunchbase Global Venture Report 69
Exits from the Financing Funnel indicate local strategies exist
100%
47%
41%
11%
1%
Seed Out to Early Out to Late Out to Growth Survivors
The clustering element also is visible in the share of companies
who exit the financing funnel after a particular stage. VCs with
exposure to their unique portfolios need to identify local strate-
gies that allow them to source for a particular build and sell
model in the local market.
Post-Seed Exit metrics pose unique challenge
▪ With 47% companies leaving the venture financing funnel
after their Seed stage investment, distribution of exit
scenarios drives the chase for strategies in the market:
▪ Liquidations 65%
▪ Fire Exits 20%
▪ < 1 MOIC Exits 8%
▪ Low MOCI Exits > 5%
▪ Shareholder / Management Buyout < 2%
Post Early Exit towards 0.8 – 2.2 MOICs
▪ With 44% exiting the Venture Funnel as a norm, Seed and
Early stage VCs have to funnel their investments quickly
into a more local but still acceptable Exit to Mid-Cap M&A.
Post Late Exit towards offering 2.2 – 8.0 MOIC
Companies exiting from the funnel still need care
Start-Up
Volume
Investment
Volume per Stage
# of Funds
Funnel
Funnel
Development
Conversion
Ratios
Funnel
Visualizations

Venture
Funnels
Data Source: Crunchbase Database 70
Visualization of Funnel
Angel Pre Seed Seed Series A Series B SeriesC Series D Series E Series F Series G Series H Series I Series J
Global 1666 1279 6900 7540 5724 3791 2322 1366 673 312 129 24 8
Europe 607 727 3175 3050 2255 1651 1082 719 399 205 82 11 5
USA 926 1001 4784 5398 4477 3209 2067 1301 653 303 124 23 7
Number of Investors on Crunchbase
Data available on Crunchbase or Dealroom show funnel
The number of investors committing capital in later Series of
funding is steadily decreasing. While numbers might be inflated by
(a) earlier stage investors remaining part of the later stage deals
without contributing capital, (b) multi-stage funds being part of
different brackets, and (c) some later stage-rounds being focused on
bridging underperformers towards Exit, the overall picture tells a
convincing story of a decreasing number of investors being part of
the funnel.
▪ Clear highest density in the early stages in Seed to Series B rounds
▪ While many companies might already exit at this stage and provide returns to their investors
for companies that do no have the capacity or skill to grow to multi-billion dollar companies,
the remainder is funneled to an continuously decreasing number of more globally operating
funds.
▪ The dynamics can also be explained by the need to set-up early stage investors in various
ecosystems around the globe to capture regional opportunities. The sourcing of very early
stage companies from a global hub would be less efficient compared to having regional
specialists living and breathing and working with the regional sourcing, venture building and
identification of early exit strategies to more regional stragic buyers.
Start-Up
Volume
Investment
Volume per Stage
# of Funds
Funnel
Funnel
Development
Conversion
Ratios
Funnel
Visualizations

Data Source: Crunchbase Global Venture Report 71
Visualization of Funnel
Full Funnel GPs
Several Funds
Multi-Stage GPs
with Focused Funds
Single-Stage
Single-Fund
1 invest,
1 Follow-on
Exemplary Overview of Stage-based Strategies
Different Allocation models over stages
Earlier stage funds typically focused on basic venture building and
spray and pray models aiming at building diversified port-folios of
high potentials in a particular niche. Specialized single stage funds
bring in network and operational know-how around specialized
verticals or business models. Later in the funnel, VCs become
brokers of networks and focus more on building return strategies on
more mature businesses. Some funds operate full funnel strategies
to capitalize on the entire growth of their leading portfolio
companies.
▪ Multi-stage funds typically invest diversified in the early stage and continue to move the best
performers of their portfolio through additional vehicles under management by the GP.
▪ Single-stage funds capitalize on their unique expertise, networks and access to regional
opportunities and serve as brokers of their insights to potentially larger co-investors.
▪ Companies typically try to obtain access to the best single-stage investors that bring most value
at and de-risking for the exit funnel, while also seeking multi-stage funds for additional
capabilities and increasing their chances of convincing investors that they can grow beyond
being a regional, low multiple portfolio company. Full-funnel funds might in addition come in
later on to add their more broad and generic insights into the process and bring in additional
capital and know-how and insights.
Start-Up
Volume
Investment
Volume per Stage
# of Funds
Funnel
Funnel
Development
Conversion
Ratios
Funnel
Visualizations
Venture
Funnels

Data Source: Sugarfree Ventures Proprietary Research
Example Schematic View of the funneling process
Start-Up
Ecosystems
Angel
Investors
Seed
Investors
Series A
Investors
Series B
Investors
Exit Channels
Regional companies
in regional clusters
Regional angels tracking
local ecosystem in all sectors
Regional and cross-
regional VCs
National and
cross-national
International
and Global
International and global on top performers,
more regional and small in low performers
1 Bn+ Exit Channel: IPO, Mega-Exit
300 Mn – 900 Mn Exit Channel: Small IPO / Exit
100 Mn – 200 Mn Exit Channel: Cross-National M&A
40 Mn – 100 Mn Exit Channel: National M&A
< 40 Mn : National M&A and Liquidation
Tier 1
Tier 2
Tier 2
Tier 3
Tier 1
Tier 1
Tier
2
Tier 4
Tier
3
Tier
4
Company moves through funnel
?
?
?
Start-Up
Volume
Investment
Volume per Stage
# of Funds
Funnel
Funnel
Development
Conversion
Ratios
Funnel
Visualizations
Venture
Funnels

Part II
Details on Seed and Europe
Drill Down

Section 1:
Seed Stage Sector Dynamics
74

Seed
Investing
Data Source: Preqin; The White Paper Report, Invesco 2015
75
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
75
Todays VCs favor lower valuations in early stage to reflect pre-traction risk

Seed
Investing
The Cost of Creating a Start-Up Have Radically Decreased
▪ This created both hype and an explosion of activity in
the world of Start-Ups and Ecosystems.
▪ An increased worry from national policy makers around
the world facing the need for embracing the new world
of digital, data and technology as they try to cope with
the structural drawbacks of their regional economies;
also lead to an increase in research and entrepreurial
funding.
This lead also to a rise in micro VCs. Finding companies in
the large ecosystem, coaching them, financing them and
brokering them to more mature VCs has become a viable
market.
Data Source: Preqin; The White Paper Report, Invesco 2015
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
76
Lower cost of starting up

# of Start-Up Ecosystems is Increasing
▪ As the trajectory continues, millions of start-ups get
created around the world within thousands of “incubation”
centers.
▪ An increasing number of ecosystem clusters has hence
developed, tying together various catalyst: advisors,
consultants, freelancers and service firms (IT, marketing,
sales, vendure studios) cater towards start-ups.
▪ Government funding drives the development of ecosystems
around high schools and universities and sponsors more
commercialization initiatives around corporate and
private research organizations.
Data Source: Start-Up Genome
Seed
Investing
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
77
The low cost started a start-up boom that drives emergence of ecosystems

Seed
Investing
US Start-Up Graduation to Series A underpins picture
▪ Local US ecosystem becomes less relevant for producing
strong Series A companies. Competition is global
▪ Requires an index investor in Seed stage to think more
broadly in how to cover best companies in variety of
ecosystems
Data Source: https://sethlevine.com/archives/2015/09/charts-of-the-day-the-complexity-of-raising-series-a.html
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
78
Not all start-ups equal. In the US, the number of Start-ups reaching Series A has been falling

Rivalling Competition over the Funding Funnel
▪ Larger funds increasingly struggle to source within the
entire global range of 45+ ecosystems
▪ Medium-Sized regional funds try to act as brokers towards
larger global tier 1 funds while building networks to the
smaller early stage funds which serve as sources
▪ Meanwhile larger tier 2 funds emerge on regional or
cross-regional level to absorb companies that did not
make it to tier 1 stage, but might have a defined exit strategy
▪ Competition among these funnels can mean that the first
investor crowds out a pool of later stage investors by serving
as almost exclusive broker to a defined set of follow-on funds
▪ While outperformers can still make it to the global level later,
the funnel competition requires most funds to support the
creation of more vertical-focused or regional micro VCs
▪ It is almost impossible for a UK VC to join a Korean VC round
Seed
Investing
Ecosystem – E.g. German Berlin Ecosystem
Sourcing
Investors
Rival Networks
Series A Funnel
Larger
Funds
Global Tier 1
Cross Regional Tier 2
Local Tier 3
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
79
As VC market expands, competition and clustering a key feature

Early Stage is most active in the # of deals
▪ With more early-stage funds in the market, risk in the
Seed and early stage is held captive in these vehicles
▪ To supply later stage funds with a reasonable amount
of deal flow, early stage funds have to cover more
deals and compete in building valid companies
▪ From the funnel perspective, the liquidity in the market
increasing will push the emergence of more early stage
funds and more deals being made in this stage.
▪ Players mostly are comprised of former entrepreneurs,
angels, venture development specialists.
By giving small stakes to the early stage investors, the
larger funds can step in after small stakes have been
invested and the quality of the venture has been pre-
established.
Data Source: Credit Suisse VC market Report Q4 2017
Seed
Investing
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
80
Early Stage most active in deals – also visible in # of VC funds (VC funnel Section)

Increasing valuation follows simple mechanic
▪ Number of Seed deals fairly constant in the US, but
slowly decreasing from 1.3k to 1.0k
▪ Capital overall also sourcing outside of the US in
other developed and new emerging markets
▪ Increase in general liquidity still sufficient to trigger
a constant decrease in valuations from 1.2m in 2014 Q1
to 1.9m in 2019 Q4
Data Source: Pitchbook-NVCA Venture Monitor 3Q 2019
Seed
Investing
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
81
More Liquidity and Less Deals explains development of Valuations

Average Angel rounds remain small at around 0.5m
▪ The price of ideas and early pre-Seed ventures and
capital invested remains low and constant with an
average of 0.5m invested
▪ Without any reasonable measure of pricing risk and
potential outlook, the model focuses on deploying
capital in to reasonably sound teams with promising
ideas and spread a standard amount of investment
needed to start basic operations and show results
▪ In Seed stage, with some pre-selection having passed,
the amount of capital has decreased and reflects the
increasing demands of Series A investors to invest into
reasonably developed and mature operations with
strong indicators of future success.
Data Source: Pitchbook-NVCA Venture Monitor 3Q 2019
Seed
Investing
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
82
Angel Rounds have remained flat, deploying standardized chunks on diverse portfolios

Series A valuations meanwhile developed more strongly
▪ Higher liquidity in the market and increased competition
of particular investment types on average has raised the
Series A valuation from 12.0m to 33.0m*
▪ The field has remained very competitive and Series A
is the strongest gate keeper for access to later stage
funding in the funnel
* Sample of top performing companies in leading VC
Funnel in the US market
Data Source: Wing 2019 V21 Analysis - Peter Wagner, Wing Ventures
Seed
Investing
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
83
Series A is gatekeeper to progression in funnel. Valuations grew stronger. But getting in is hard

Quartile Performance on Seed Stage
▪ Top quartile Seed raises at roughly 2.3x bottom quartile
while median at 2x of bottom quartile.
▪ Dispersion of quartiles is fairly stable over time and all
are growing over time.
▪ With application of power law to Seed stage investing,
it follows that (a) liquidity is the driver of the valuations,
and (b) all quartiles have a share of survivors.
That explains the multiple expansion discussed later.
Pitchbook-NVCA Venture Monitor, June 30 2019
Seed
Investing
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
84
Performance in Pre-Seed matters: Sizable variation in Seed valuations based on Quartile

Quartile Performance on Series A Stage
▪ Multiple Expansion clearly visible. Top quartile now at
400x of bottom quartile performance.
▪ Market clearly expects lower performers to either die
at higher rate or exit at far lower valuations.
▪ Lower quartile likely sub 100M candidates, if surviving,
medium in 80M to 300M range. Upper quartile likely in
300M to 10B range.
Pitchbook-NVCA Venture Monitor, June 30 2019
Seed
Investing
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
85
Compounded Performance matters more: Range of Series A valuations is extremely large

US vs UK Seed Valuations
▪ UK: 26% stake with 1.2 Million Fresh Money into a 4.2 Million
company
▪ US: 32% stake with 4M into 12.5 Company
▪ A large part likely explained by a larger market in the US
with far better ability to defend a strong growth case in
the domestic market.
▪ Other typical reasons include quality of the pre-funding
ecosystem, the available pre-institutional funding and
general availability of liquidity, talent and ease of doing
business.
Data Source: AVC London Co-Investment Fund, June 2018
Seed
Investing
Market
Development
Market
Activity
Valuation
Trends
Multiple
Expansions
US vs EU
86
Caveat: Europe looks still quite different

Section 2:
Seed Stage Sector Dynamics – High Performer Segment

Median Pre-Money Valuations in Seed and Series A
▪ Valuations have increased similarly, but not as sharply as
cumulative funding. Implying founders have to shell out
more personal funds or might lose more equity as they
are building their companies towards institutional rounds.
▪ In Series A and B, valuations increased sharper than cash
inflows, implying that more liquidity and competition on
deals drives up prices paid during the transaction.
▪ Growth Multiples show slight multiple compression in the
Series Seed stage and imply higher dilution given the higher
cash amounts raised with less aggressively growing. E.g.
2.9x valuation multiple vs . 3.9x cash invested multiple on
next page. Series A multiples have expanded and Series B
multiples, again have compressed, but less aggressively.
Data Source: Wing, Peter Wagner 2020 V1 Analysis
Seed
Dynamics
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
88
Median Pre-Money Valuations increased 300% between 2010 and 2019

Average Pre-Money Valuations in Seed, Series A and Series B
▪ With more cash needed to finance company to next stage
of funding, valuations increased, too
▪ Seed today at Series A valuations of 2010. Series A today
at Series B valuations of 2010.
▪ Other factor playing into development also likely due to
more liquidity in the market. As more capital needs to
deployed, larger valuations allows more cash to deployed
at each stage in the investment funnel. Thereby also
reducing the risk at each funding stage and helping the
maturity of each stage
Wing 2019 V21 Analysis - Peter Wagner, Wing Ventures
Pre-money
valuation ($M)
Seed
Dynamics
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
89
Average Pre-Money in Seed almost quadrupled in this study

Share of Jumbo Rounds increasing
▪ With more capital allocated in Pre-A rounds, the filtering
effect on quality companies has become stronger.
▪ Top performers have a more visible trajectory towards
outstanding growth and are capable of raising more
funds at higher valuations
▪ The risk from competing post Seed investors looking
towards placing their capital into top-performing
companies is focused on sizing the exact amount of
cash raised and understanding the allocation and execution
capabilities of the jumbo investments.
Seed
Dynamics
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
90
Share of Jumbo Rounds in Early Stage is increasing

Valuation increase larger in later stage
▪ Most likely explanation a combination of excess
liquidity in this stage coming from higher institutional
▪ Some portion likely due to power law. Good ideas
generate revenue fast.
▪ Some portion likely due to increase pressure to
build traction and market know-how
▪ Also, liquidity in the market makes funding more
probable and invites more angel investors early on
▪ Last possible reason could be, that in competition
over access to better investors and higher probability
of getting funding, founders also might seek more
investment from family and friends and take up credit
to finance the early stage of the company
Data Source: Venture Capital Positively Disrupts Intergenerational Investing - Cambridge Associates, Jen 2020
Seed
Dynamics
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019*
Seed 1,0 2,3 3,8 2,1 3,8 2,8 3,3 3,8 4,0 4,7 5,0 5,4 5,7 6,3 7,0 7,5
Series A 6,0 6,4 6,7 7,0 7,0 6,0 6,0 7,0 8,3 9,3 11,7 13,5 15,0 15,0 20,0 22,5
Series B 16,4 17,6 18,0 20,2 19,2 16,4 18,5 21,0 21,9 26,5 31,5 38,8 37,4 40,3 59,8 68,0
Series C 24,2 30,6 37,1 42,8 41,0 27,8 34,9 45,0 45,0 54,0 55,6 73,8 80,0 83,7 115,0 140,0
Series D + 40,4 44,8 69,0 78,0 78,6 50,1 65,9 83,5 91,9 100,1 140,6 174,9 137,0 207,5 318,5 360,0
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
91
Strongest Explosion of Valuations in recent years in Series D+ segment (Mega Rounds)

Average Cumulative Capital Raised Prior to Funding Round
▪ Increase of liquidity in the market has lead to increases in
cumulative funding in each investment stage.
▪ Competition in the Pre-Seed stage and Angel activity
likely casing the pre-Seed financing volume increasing.
▪ Companies have to deploy more cumulative funding,
both in external and revenue-based funding to be com-
petitive in the higher investment rounds.
▪ A resulting benefit for later stage investors is the higher
maturity of the asset before making an investment de-
cision.
Seed
Dynamics
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
92
Average cumulative Capital Raised before Seed and Series A is increasing steadily

Fundraising for better performing Companies
▪ Study shows that companies in the better US technology
segment require 6.3 Million USD (vs 3.3 Million on Avg.)
before getting to Series A level and take in 12.1 Million USD
in sequential rounds in their Series A stage.
▪ With equal targeted shares in holdings after investment, this
implies a stronger multiple expansion in Series A
Seed
Dynamics
2010 2011 2012 2013 2014 2015 2016 2017 Growth Multiple
Cumulative Seed Capital 1,4 1,5 2,2 2,0 2,7 3,7 4,4 6,3 4.5x
Average Size of Series A 4,9 6,9 8,2 7,5 9,6 10,1 10,7 12,1 2.5x
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
93
Companies doubled their total funds raised in 2017 in Series A

Medians Funds Raised in Round increasing similarly
▪ With higher valuations and higher requirements for
companies to mature before raising later stage rounds,
more cash can be infused into the companies.
▪ Overall, more liquidity can now enter companies at similar
dilution. The market can hence allocate more capital in the
early stage Venture funnel while reducing the risk for later
stage investors.
▪ As a side effect, execution and accretive allocation of funds
raised has become a competitive factor. The system more
quickly determines which ideas have both proven the market
and teams have shown they are capable of deploying capital.
Seed
Dynamics
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
94
Median funds raised increasing in Seed and Series A, growing 250% - 300% since 2010

# of Sequential Funding Rounds per Stage
▪ Increasing number of funding rounds in each stage implies
that companies no longer raise capital for the entire stage.
▪ Lead investors taking the major risk at the beginning of each
stage open the company up for the stage and evaluate if the
company is performing under the expectations of the stage.
▪ Lead investor than can enter follow-on round and increase
share in the company – for high performers – or can choose
to add new investors to the cap table to diversify its risk
in being a single shareholder in the stage and to bring in
new know-how and networks to drive the value creation
process or move company towards a particular exit funnel.
Seed
Dynamics
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
95
Companies increasingly raise extension rounds before moving into next stage of funding

Years from Inception to Series X financing increased
▪ On average, companies need to raise 1.2 Million USD
in pre-Seed funding. And they have roughly 20 months
(1.7 years) to get to Seed funding stage. A number that
has been increasing over recent years, showing a higher risk
aversion of Seed investors. More Angels need to step in.
▪ They have roughly 13 months to close sequential rounds
of Seed funding up to 2.1 Million and get close a Series
A round 2.39 years after inception of the company.
▪ With 4 years to Series B and 1.7 Years in Series A stage, a
company has around 20 months in Series A to reach Series
B maturity
Seed
Dynamics
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
96
The time it takes to reach maturity for certain stage increases

Share of Revenue-Generating companies increasing
▪ With more capital at hand and more time to maturing
to different stages of rounds, revenue has also become
a key differentiator
▪ Only 12% of companies raising their Series B have no
revenue – rare capital intense companies and companies
with high network effects and a free business model.
▪ Only 49% of companies in Seed stage are pre revenue vs
91% in 2010
Seed
Dynamics
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
97
For each stage, the share of companies being revenue-generating is increasing

Double Triple, Triple Double SaaS Growth Framework in Effect
▪ SaaS company growth after reaching the first million in ARR
is ideally following a trajectory of tripling ARR for two se-
quantial years, then doubling. A similar thinking underlies the
valuations.
▪ Valuations at Series A stage typically are 3.0x that of the
Series Seed stage valuations.
▪ Series B valuations are 2.8x – 3.0x of Series A valuations.
Seed
Dynamics
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
98
Inter-Round valuations show that companies need to triple their valuation between stages

Median Inter-Round Financing Comparison
▪ Cash raised in Series B financing only 3.2x of Series A
financing in 2019, down from 5.0x in 2010
▪ Indicates that largest and smallest follow-on financing
have come down. Companies more prudently aim at
sizing follow on rounds conservatively and prevent
dilution. Less aggressive risk taking and forward looking
capital deployment. The larger “Jumps” in capital deployment
now moved to a later stage in Series C and onward.
Seed
Dynamics
Valuations
- Trends
Fundraising
- Funds Raised
Fundraising
- # Rounds / Stage
Fundraising
- Timelines
Fundraising
Fundraising
Valuations
- Multiples
99
The Cash raised in each round inter-round is within the same range

Topic :
European Funds

European
VC
Data Source: Europe Stepping Up to Global Tech Leadership, Dealroom.co June 2020
101
Global Liquidity flowing into European VC ecosystemLiquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

European
VC
Capital Deployed in Different Countries
▪ US leading with 86 Billion USD overall deployed in market
▪ China trailing with equally 39 Billion USD
▪ Largest European market in UK with mee 7.8 Billion USD
▪ Germany only ranking 5th, with 2.7 Billion USD deployed
Capital Deployed per Capita
▪ Highest per capita dollar invested in Singapore.
Totaling 1.4 Billion USD and 5.6 Million residents and yielding
250 USD per capita.
▪ Israel with 2nd highest per capita share, 2.1 Billion USD for a
country with less than 8.8 Million Residents. Excludes the
amount of investment from government funded R&D and
yields 238 USD per capita.
▪ India still in early stage with mere 5 Billion USD dollar
meeting a population of 1.3 Billion people and thereby
showing the lowest per capita deployment of merely
3.50 USD per capita. China in comparison with 30.76 USD
per capita.
Data Source: Global Private Market Review, McKinsey 2020
102
Capital Deployment per Country: Nominal and Per CapitaLiquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

European
VC
Capital Invested in Europe vs US and Asia
▪ Asia remains an active ecosystem with SE Asia
being in recent focus and India on rising trajectory,
with minor uncertainty around China and its visibility
and impact in the economy and largest volatility.
▪ Europe on steady trajectory attracting liquidity, likely
getting a boost with renewed focus in recent years,
new policies on innovation and increasing appetite
of US investors in chase of alternative markets to China
and US core market-
▪ In 2019:
▪ Asia with 4.4 Billion residents, brings a per capita
VC deployment of 14 USD
▪ Europe with roughly 740 Million residents looks at
per capita deployment of 46 USD, or 3.2x Asia
▪ US with 328 Million residents has a deployment of
362.5 USD per capita, or 7.8x that of Europe
Data Source: Dealroom.com; The State of Europea Tech Report, Atomico 2019
103
Capital Deployment per Region: Nominal and Per CapitaLiquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

European
VC
Europe with fastest growth rate in capital invested
▪ Europe experienced clear upward trajectory in terms of
capital invested in 5-year horizon. Up 124% (+39% vs 2019)
▪ This is a clear result from being clearly underfunded given
the overall capital base, level of development of its major
economies, size of population and quality of education.
This metric will likely continue to remain high or even
accelerate as EU markets integrate further and global
capital is capitalizing on a maturing ecosystem and lower
valuations offering higher returns via expansion of multiples
▪ Asia, in comparison, is focused on core regions in East and
South East Asia and India. South East Asia with varied capital
base and education systems. Regions like central Asia, Eastern
Russia and the Middle east, if counted to Asia, will likely see
less inflow of capital, dampening the growth prospects.
104
Capital Deployment Growth RatesLiquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

European
VC
Capital Allocation in different stages
▪ The later stage markets are clearly weak in Europe compared
to the US. Most companies not moving their core operations
to the US still struggle expanding towards a global footprint.
Thereby reducing the number of opportunities to allocate
sizable amounts of later stage and growth capital.
▪ Market is continuously developing in the early stage areas
in Angel/Seed and Early VC. More companies already Exit
the VC funnel in this stage by being acquired by strategic
investors or exit towards the US funnel, with global players
from the US market stepping in to further develop companies
towards later stage exits.
105
Capital flowing mostly to Seed and Early stage
? Read report
Liquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

European
VC
Data Source: European Venture Report 2020, Pitchbook
106
In per deal terms, late stage more active with Angel/Seed being smallest
Capita allocated to later stage
▪ B2C sectors such as Media, Consumer Goods fairly low
in the overall portfolio. B2C talent and ability knowingly
not in focus in heavily deep tech and research driven
Europe. Likely due to the diversity of cultures and landscapes
and the fairly small domestic market for pure English language
companies. Makes it hard to test scale of business model.
▪ More low-tech products in Europe mostly focused on software
and B2B products, with some share still in eCommerce and
MarTech
? Read report
Liquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

European
VC
107
Deal Counts : First Institutional rounds vs. Follow-Ons
Capital Allocation in different stages
▪ The later stage markets are clearly weak in Europe compared
to the US. Most companies not moving their core operations
to the US still struggle expanding towards a global footprint.
Thereby reducing the number of opportunities to allocate
sizable amounts of later stage and growth capital.
▪ Market is continuously developing in the early stage areas
in Angel/Seed and Early VC. More companies already Exit
the VC funnel in this stage by being acquired by strategic
investors or exit towards the US funnel, with global players
from the US market stepping in to further develop companies
towards later stage exits.
Liquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

European
VC
108
Software and Health dominant positions in European VC Portfolios
Sector Composition of European VC Portfolios
▪ Strongest sector with > 40% clearly software
▪ Hardware Tech and Energy fairly low in the portfolio, including
NanoTech and Quantum technology
▪ Health, Pharma and Biotech decreasing in overall share
▪ Consumer goods equally in the lowest share segment
Liquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

European
VC
Data Source: The Data: Enterprise & Deep Tech VC in Europe & Israel- Medium, Angular Ventures 15 August 2020
109
Software: Enterprise and Deep-Tech leading vs Consumers
Software: Consumer vs. Enterprise/Deep-Tech
▪ Enterprise and Deep-Tech with steady 60 – 75% of the
entire market in capital deployed
▪ However, consumer is steadily increasing in total dollar
volume deployed and might provide a mid-term angle
Liquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

European
VC
110
US participation in early still very low
US participation in early stage rounds
▪ Numbers show that US participation in Seed deals in Europe
remains painfully low at 3%, showing little appetite in being
part of the higher risk end of the European market
▪ While promising companies might attract first investments
in the early Venture stage (7% vs 3%), the majority of rounds
taking in US capital happens in growth stage
Liquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

European
VC
111
US investments focused on Northern and Wester Europe
US Participation in different regions
▪ Eastern and Southern Europe clearly not in focus of US
investors and hardly increasing
▪ Focus clearly on Nordic region (e.g. UK, Nordics) and
increasing share going to Western region.
Liquidity
Liquidity by
Stage
Liquidity by
Sector
US Capital
in Europe
Global Capital
in Europe
Intra Europe
Comparisons
Exit Activity
European VC
Funds

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