Venture Capital Growth in
Portfolio-level Returns
November 2021
Richard Nigel Inciong, CFA
Contents
We analyze data from over then thousand deals
Crunchbase deals to determine the impact of
several factors drivers to portfolio-level returns of
early stage venture investments. These factors
are:
1)
2)
3)
4)
Introduction ......................................................................................... 3
Background ........................................................................................ 4
Preliminary Definitions ........................................................... 4
Introduction to Power Laws (Othman, 2019) .... 5
Capital Asset Pricing Model (CAPM)....................... 6
Number of investments within the portfolio
Portfolio Age, and
Loss due to startup failure rate.
Investment Horizon
This allows us to examine the magnitude of
impact of these factors to a hypothetical early
stage venture capital portfolio. Our results suggest
that there is indeed an empirically measurable
positive impact to portfolio-level returns from the
increasing number of investments
BETA Coefficient ................................................................... 6
Crunchbase Data ....................................................................... 7
Individual Investment Returns ......................................... 7
Portfolio Simulation Methodology ................................ 9
Assumptions on Loss............................................................10
Correlation & Beta to Overall Market Returns ..........11
Results .................................................................................................. 12
Effect of Different Portfolio Sizes ................................ 12
Impact of Portfolio Age ....................................................... 15
Impact of Investment Horizon........................................16
Closing Thoughts .......................................................................... 17
References ........................................................................................ 18
KEY TAKEAWAYS
Our analysis based on investing in a randomized VC
portfolio suggests that :
 Expected portfolio returns increase with the
number of companies giving approx. 5bps
higher IRR for each company made
 Expected portfolio returns tend to stabilize at 3+
years with approx. 50% chance of at least 5% IRR
 Majority of companies do not see positive
returns but are made up for by outsized gains
from a small portion of portfolio
 Timing of Investments does play in impact to
portfolio returns supported by demonstrating a
negative -0.66 beta to S&P returns
Portfolio IRR vs. Portfolio Size
(n=1000/series)
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
0.0%
25
50
Mean IRR
75
Median IRR
100
Introduction
In this paper, we use Crunchbase data of early-stage investments to determine whether there is an impact
on portfolio-level returns from several factors including:
1)
2)
3)
4)
Number of investments within the portfolio,
Portfolio Age
Loss due to startup failure rate (which in our paper is based on our assumptions)
Investment Horizon
Based on our analysis, our results suggest that making an increasing number of seed-level VC investments
can increase the overall level of portfolio returns. This differs from conventional equity investments where
increasing the number of investments generally only serves to decrease return volatility. We approximate
the impact of this to be 5bps to portfolio-level returns from each additional seed-level VC investment made.
While the number of investments made make the most significant & measurable impact, other factor do
impact VC portfolios. We see that portfolio returns between 1-3 years generally do not see appreciable
returns due to the irregular nature of VC funding. Failures in the VC space also make a large impact to overall
returns. Given the lack of information on failures, assumptions were used in the analysis which also shows
that differing levels of failure rates can have outsized impacts on the portfolio returns. Also, the investment
horizon and/or time period unsurprisingly makes a difference.
Another interesting result of our analysis shows that overall correlation to the market is low (-0.11) and has a
beta lower than 1 (-0.66). This shows that including VC investments can be shown to have a positive impact
as part of a diversified investor portfolio.
Venture Capital Growth in Portfolio-level Returns | Page 3
Background
In this section we provide definitions of our core concerts, introduce power-law distributions and an existing
academic model of venture capital returns, and discuss the Crunchbase data used in the results.
Preliminary Definitions
A portfolio’s Internal Rate of Return (IRR) is the rate of growth r that equilibrates between incoming and
outgoing cashflows.
𝑣 ∙ (1 + 𝑟) =
|𝑣| ∙ (1 + 𝑟)
In this paper we will quote IRRs on an annualized basis. For overall clarity, we will often abuse language and
refer to the quantity 1 +r as the “IRR” of the investment, particularly in the case of draws from a power-law
distribution.
An portfolio’s return multiple m is the combined sum of the distributed and residual values of the
investment divided by the total amount invested. Formally:
𝑚 ≡
∑
∑
𝑣
|𝑣|
An investment’s effective duration is defined as the amount of time such that the investment’s IRR implies its
return multiple. Formally, d is an investment’s effective duration that solves:
𝑚 = (1 + 𝑟)
Venture Capital Growth in Portfolio-level Returns | Page 4
INTRODUCTION TO POWER LAWS (OTHMAN, 2019)
The seminal quantitative work on modern power laws is Clauset et al. (2009). Taleb (2001, 2007) also
discusses power laws at length from a less quantitative perspective, focusing on their relationship to finance,
culture, and society.
A power-law distribution (with shape parameter α > 1) is distributed according to the probability density
function:
𝑓(𝑥) ≡
𝛼−1 𝑥
𝑥
𝑥
In the case where xmin = 1 power law PDF reduces to simply:
𝑓(𝑥) = (𝛼 − 1)𝑥
There are three cases to consider depending on the shape parameter α, each of which can have different
implications for portfolio construction and assessment:
•
•
•
When α > 3, the distribution has finite mean and finite variance. With finite variance, the Central Limit
Theorem holds and so important portfolio theory concepts like the Sharpe Ratio have meaning. If
investments draw their returns from a distribution with α > 3 and investors select randomly among
them, then the number of investments in a portfolio does not affect that portfolio’s expected mean or
expected median return.
When 2 < α ≤ 3, the distribution has finite mean but unbounded (infinite) variance; as a result, the
Central Limit Theorem does not hold. If investments draw their returns from such a distribution,
making more investments at random will increase a portfolio’s expected median, but not expected
mean, return.
When α ≤ 2, both the mean and variance are unbounded. If investments draw their returns from such
a distribution, making more investments at random will increase both a portfolio’s expected median
and expected mean return.
This last case of an “escaping” distribution is extremely difficult to make intelligible. Such distributions are far
outside the realm of our typical experience. In our opinion, the most illuminating description of such a
distribution comes from Taleb (2001), who describes a “Refugee Probability Distribution”: for each additional
day that a refugee spends outside of their homeland, the number of days that they can expect to wait to
return increases by more than one. The heavy tails of power laws are generally thought of as their most
unintuitive property. However, another unintuitive property is that the power-law distribution, but unlike a
Gaussian or uniform distribution, has qualitative properties (i.e., the existence of moments) that are highly
dependent on its shape parameters.
Venture Capital Growth in Portfolio-level Returns | Page 5
CAPITAL ASSET PRICING MODEL (CAPM)
The Capital Asset Pricing Model (CAPM) describes the relationship between systematic risk and expected
return for assets, particularly stocks. CAPM is widely used throughout finance for pricing risky securities and
generating expected returns for assets given the risk of those assets and cost of capital. The formula for
calculating the expected return of an asset given its risk is (Kenton, 2021):
ERi=Rf+βi(ERm−Rf)
where:
ERi=expected return of investment
Rf=risk-free rate
βi=beta of the investment
(ERm−Rf)=market risk premium
BETA COEFFICIENT
The Beta coefficient is a measure of sensitivity or correlation of a security or an investment portfolio to
movements in the overall market. We can derive a statistical measure of risk by comparing the returns of an
individual security/portfolio to the returns of the overall market and identify the proportion of risk that can be
attributed to the market. (Beta Coefficient, 2021). We can calculate beta using two formulas:
1.
Covariance/Variance Method:
𝐵𝑒𝑡𝑎 =
𝐶𝑜𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 (𝑅 , 𝑅 )
𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 (𝑅 )
Where:
𝐶𝑜𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 =
1
𝑁−1
1
𝑁
(𝑅
𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 =
(𝑅
,
−𝑅
−𝑅
,
,
,
) × (𝑅
,
−𝑅
)
,
)
Re = Individual Equity Return
Rm = Overall Market Return
2.
Correlation Method:
𝐵𝑒𝑡𝑎 = 𝜌
,
×
𝜎
𝜎
Where:
𝜌 , = correlation between Re and Rm
𝜎 = Standard Deviation of Re
𝜎 = Standard Deviation of Rm
Venture Capital Growth in Portfolio-level Returns | Page 6
Crunchbase Data
The dataset used for the analysis was constructed in several steps to align with the analysis requirements.
The analysis is meant to simulate a portfolio of investments into various companies’ seed rounds and tracking
the overall value of the portfolio.
Initially, Transactional data was pulled from the crunchbase website from companies in Canada & the U.S. as
well as included a company valuation. This initial step included 9052 separate funding transactions
between 2000 and 2021.
Secondly, due to IPO transactions not being part of the Crunchbase transactional data, IPOs of companies
between 2000 and 2021 were pulled separately and added to the dataset. This included 1707 IPO rounds.
Lastly, to narrow the dataset to those relevant to the analysis, funding rounds were filtered based on listing
their first initial seed round between 2010 – 2018. The final dataset includes 1891 companies which meet this
criteria and 2528 total funding rounds related to these companies.
Individual Investment Returns
Figure 1 shows the power law fit for the return multiples of winning investments.
As expected, return multiples are consistent with an extreme lower law distribution. This fit was made using
the excel trendline feature using the power-type trendline.
Return Multiple for individual Winning investments for x>1
Normalized Frequency of Returns > x
1
0.1
0.01
y = 0.9617x-0.633
0.001
1
10
100
1000
10000
100000
1000000
Return Multiple
Figure 1 - Return Multiple for individual Winning investments for x>1
Generally in the standard model, investment duration and IRRs are drawn independently, implying that there
should be no correlation between the two quantities. However, the Crunchbase data indicated that there is a
Venture Capital Growth in Portfolio-level Returns | Page 7
weak negative correlation between the two quantities (-8.6% correlation). This relationship is shown in Figure
2.
That said, the majority of investments (76%) only show one round of funding which would require
assumptions to be made on losses. These loss assumptions are expected to have an outweighed effect on
portfolio level returns with regards to the effect of portfolio age vs. portfolio returns.
Annualized Return vs. Duration (>1Y) (n=487)
2000%
1800%
1600%
Annualized Return, r
1400%
1200%
1000%
800%
600%
400%
200%
0%
1.000
3.000
-200%
5.000
7.000
9.000
11.000
13.000
Duration
Figure 2 - IRR vs. Effective Duration
Venture Capital Growth in Portfolio-level Returns | Page 8
Portfolio Simulation Methodology
To simulate the return of a hypothetical VC portfolio, a Microsoft excel model was built to measure the
Return Multiple of an entire portfolio of investments monthly from in the initial investment. Each investment
within the portfolio is selected randomly based on specified criteria. I.e. having their first seed round within
the specified time horizon of the simulated run. There were four time horizons used which are specified in
Table 1 – Time Horizons used in simulations and number of companies in the set.
Table 1 – Time Horizons used in simulations and number of companies in the set
Time Horizon (First Seed Round)
2010-2012
2013-2015
2016-2018
2010-2018
Number of Companies in Set
217
709
965
1891
The Return multiple of the portfolio was measured monthly with each companies’ value updated based on
Crunchbase funding round data. i.e. if there was a subsequent funding round after the initial seed with a new
valuation, the company’s value within the portfolio would be updated to reflect the new valuation. It should
also be noted that each run included 100 companies with portfolio values also being recorded for portfolio
sizes of 75,50, and 25. Essentially, each smaller size being a subset of the larger.
However, given the vast majority of companies only had a single seed round (76%), these companies values
were modelled based on specific loss assumptions to reflect the inherent risk in VC portfolios. These loss
assumptions are further detailed in the section Assumptions on Loss.
Figure 3 – Example simulation run of Portfolio of 100 Investments vs similar portfolio of S&P investments
shows an example of a simulated run on a portfolio.
900%
Portfolio of 100 Value - VC vs. S&P 500
800%
Return Multiple
700%
600%
500%
400%
300%
200%
100%
0%
1-Jun-10
1-Jun-11
1-Jun-12
VC Portfolio
1-Jun-13
Date
1-Jun-14
S&P 500
Figure 3 – Example simulation run of Portfolio of 100 Investments vs similar portfolio of S&P investments
Venture Capital Growth in Portfolio-level Returns | Page 9
Assumptions on Loss
A significant portion of venture investments do not see a return and a large percentage of startups fail. This is
partially reflected in the Crunchbase data where the majority (76%) of companies have only raised a single
seed round with no subsequent rounds recorded. Due to the limitations of data within the space, it is difficult
to ascertain whether companies have closed down or merely stopped listing. To simulate the loss inherent in
VC portfolios, companies which only list a single seed round of funding are modelled to lose value using a
straight-line amortization (24 or 48 months) from the initial seed round to a specified loss magnitude (50% or
100%). Other sources indicate that approximately 50% of investments fail after the first 5 years. As such for the
results section, the 48 months amortization and 50% loss magnitude are used for analysis.
Table 2 – Impact of Loss Assumptions to Power Law Coefficients (90% Interval)
Loss Assumptions on Single Round investments
SL-Amort Period
Loss Magnitude
24 Months
100%
48 Months
100%
24 Months
50%
48 Months
50%
Power Law Coefficients (90% Interval)
K
Exponent
0.5089
.800
0.6704
.690
0.6419
.847
0.8805
0.633
Impact of Loss Assumptions to 5Y Return Multiple
100000
Return Multiple
10000
1000
100
10
1
0%
10%
20%
30%
0.1
40%
50%
60%
70%
80%
90%
100%
Normalized Frequency of Portfolios > Return Multiple
24Mo/100%Loss
48/100%Loss
24Mo/50%Loss
48Mo/50%Loss
Figure 4 – Impact of Loss assumptions to 5Y Return Multiple
Impact of Loss Assumptions to 5Y Return Multiple CDF (90% Interval)
Return Multiple
25
5
1
5%
0.2
15%
y = 0.5089x-0.8
24Mo/100%Loss
Power (24Mo/100%Loss)
25%
35%
y = 0.6704x-0.69
45%
55%
65%
y = 0.6419x-0.847
Normalized Frequency of Portfolios > Return Multiple
48/100%Loss
24Mo/50%Loss
Power (48/100%Loss)
Power (24Mo/50%Loss)
75%
85%
95%
y = 0.8805x-0.633
48Mo/50%Loss
Power (48Mo/50%Loss)
Figure 5 - Impact of Loss Assumptions to 5Y Return Multiple (90% Interval)
Venture Capital Growth in Portfolio-level Returns | Page 10
Correlation & Beta to Overall Market Returns
Diversification can play an important role in an investor’s portfolio to producing stable growth. Venture
Capital as an asset class can be shown to have a low beta to the overall market and therefore the argument
could be made that it’s inclusion in an investor portfolio of different asset classes can reduce systematic risk.
For this analysis, each portfolio of VC investments was compared against a portfolio of S&P 500 investments
and this methodology is more summarily explained in the section Portfolio Simulation Methodology. The 1year return of 4000 VC portfolios was compared against 4000 S&P 500 portfolios giving the following
distribution:
Beta - VC 1Y Return vs. S&P 500 1Y Return
10.00
VC Portfolio Return Multiple
9.00
8.00
7.00
6.00
5.00
4.00
3.00
y = -0.6658x + 1.7608
R² = 0.0121
2.00
1.00
1
1.05
1.1
1.15
1.2
1.25
1.3
S&P 500 Return Multiple
Figure 6 – 1Y Returns vs 1Y S&P Returns
Based on two methods of calculating beta, VC returns are determined to have a beta of -0.66 to overall
market returns, respectively. This is shown in Table 3 – VC Beta calculation.
Table 3 – VC Beta calculation
Covariance
Variance
Beta - Covariance Method
Value (N=4000)
-0.0014041
0.0021094
-0.6656278
Correlation
Std, VC
Std, Market
Beta - Correlation Method
-0.1100836
0.2777025
0.0459178
-0.6657664
Venture Capital Growth in Portfolio-level Returns | Page 11
Results
This section details the impact of different factors to the portfolio level returns from different factors.
Effect of Different Portfolio Sizes
If it is assumed that VC investments follow a power-law distribution with a shape parameter of less than α <2,
then both the mean and variance are unbounded. Therefore, if investments follow this distribution it is
expected that making more investments at random will increase both a portfolio’s expected median and
expected mean return. This is seen to be the case with VC returns as seen from Crunchbase data. Table 5
clearly shows as the portfolio size of randomized investments increases then so too does the portfolio’s
expected mean return as well as the median return multiple.
5 Year Portfolio Return of Different Portfolio Sizes (n=1000/series)
50000
Return Multiple
5000
500
50
5
0.50.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
Normalized Frequency > Return Multiple
25
50
75
100
Figure 7 - 5 Year Portfolio Return of Different Portfolio Sizes (n=1000/series)
On the left end of Figure 7, it can seen that there is a small (<5%) probability of outsized returns, which we will
define from this point as greater than 1000%. The jump from the rest of the returns is similar to a Bernoulli
distribution. It can be seen that the probability of outsized returns increases with portfolio size. This is intuitive
as with a greater number of investments in the portfolio, the greater likelihood that one would provide these
outsized returns. However, as can be seen in Table 4 – Probability of Outsized Returns, the average return of
the portfolio is also lower.
Table 4 – Probability of Outsized Returns
Probability
1.6%
2.8%
3.5%
4.0%
Avg 5Y Return Multiple
12,667
6,489
4,227
3,309
Venture Capital Growth in Portfolio-level Returns | Page 12
However, given the irregular behaviour of the outsized returns compared to the rest of the distribution, it is
easier to focus on the 90% interval. This is focusing on the same distribution while excluding 5% of results on
both sides which would exclude the asymmetrical pattern seen for the outsized returns. This should prove
representative for the majority of returns that an investor might expect from VC portfolios.
Normalized Frequency > Return Multiple
5 Year Portfolio Return (n=1000 / series) of different portfolio sizes (90% Interval)
64.0%
0.5
1
2
4
8
16
32.0%
16.0%
8.0%
4.0%
2.0%
25
50
75
100
y = 0.6373x-1.505 y = 0.7491x-1.587 y = 0.8306x-1.609
R² = 0.9511
R² = 0.9557
R² = 0.9539
Return Multiple
Power (25)
Power (50)
Power (75)
y = 0.9106x-1.662
R² = 0.9525
Power (100)
Figure 8 – Probability Distribution of 5Y portfolio Returns of different portfolio sizes
As can be seen in Table 5 and Table 6, as portfolio size increases, so too does the level of expected return.
Increasing portfolio size from 25 to 100 increases the average expected return multiple at the 90% interval
from 152% to 178%.
Table 5 – Shape Parameters and Expected Return Multiples of Portfolios of different sizes (90% Interval)
Portfolio Size
Shape Parameters
25
50
75
100
K
0.6373
0.7491
0.8306
0.9106
-α
1.505
1.587
1.609
1.662
Expected Return Multiple (90%
C.I.)
Mean
Median
152%
122%
163%
133%
172%
139%
178%
143%
With respect to portfolio IRRs, it would also seem there is direct impact to the expected IRR vs size of
portfolio with each successive step. This relationship is shown in Table 6 and Figure 9. Averaging these
results gives the approximation Figure 10 - 5 Year Portfolio Return of S&P 500 Portfolios of different sizesof a
5bps increase in both mean and median returns per additional investment made.
Table 6 - Expected IRRs of Portfolios of different sizes (90% Interval)
Portfolio Size
Expected IRR (90% C.I.)
Mean
Median
25
50
75
100
6.5%
8.4%
9.6%
10.5%
4.0%
5.9%
6.8%
7.4%
Venture Capital Growth in Portfolio-level Returns | Page 13
5 Year Portfolio IRR (n=1000 / series)
0%
0%
0%
1%
2%
3%
6%
Normalized Freq
0%
13%
100.0%
26%
51%
50.0%
25.0%
12.5%
6.3%
3.1%
IRR Annualized
25
50
75
100
Figure 9 – Probability distribution of Annualized Return IRR, r
It is also noteworthy to show similar portfolios made of investments in S&P have no apparent impact from the
number of investments made into the S&P this is shown in Figure 10.
5 Year Portfolio Return CDF (n=4000)
Normalized Fequency > m
100.0%
1
2
50.0%
25.0%
12.5%
6.3%
3.1%
Return Multiple
25
50
75
100
Figure 10 - 5 Year Portfolio Return of S&P 500 Portfolios of different sizes
Venture Capital Growth in Portfolio-level Returns | Page 14
Impact of Portfolio Age
Given seed investments in the VC space only change in value when there are subsequent funding rounds
and time between funding rounds can be long, it can take time for portfolio to show appreciable returns. As
such, the age of the portfolio can play a significant role in the level of annualized returns within the portfolio.
As would normally be expected from most investment portfolios, the return multiple increases with portfolio
age shown in Figure 11 reflecting the impact of compounding returns.
Return Multiple of Various Portfolio Ages (N=4000)
Normalized Frequency > x
80.00%
40.00%
20.00%
10.00%
0.5
1
2
5.00%
4
8
Return Multiple
60
48
36
24
12
Figure 11 - Return Multiple of Various Portfolio Ages
When looking at annualized returns on the log scale, negative returns cannot be shown and are excluded.
What is of note is that as portfolio age increases, the probability of a portfolio producing non-negative returns
increases from less than 20% to close to 80% from 1 to 5 years. This is likely due to the fact that loss
assumptions are present which cause the majority of investments to decay in value for the first few years.
Additionally, many “winning” investments only show returns once there is a subsequent round of funding
which can take multiple years. The most interesting fact here is the annualized portfolio returns do tend to
stabilize after 3 years, however this is most likely due to the investment horizon for all portfolios being 3
years, at a minimum.
IRR of Various Portfolio Ages (N=4000)
Normalized Frequency > x
80.00%
0.0%
40.00%
20.00%
0.0%
0.0%
0.0%
0.1%
0.2%
60
0.3%
0.6%
1.3%
Annualized Return
48
36
2.6%
5.1%
10.2%
20.5%
10.00%
41.0% 81.9%
5.00%
24
12
Figure 12 - IRR of Various Portfolio Ages
Venture Capital Growth in Portfolio-level Returns | Page 15
Impact of Investment Horizon
Also present in the analysis results is investments of different time horizons to try to avoid biases in time
period selection. As can be seen in Figure 13, each time period does have somewhat of an impact to portfolio
returns. Overall, the 3-year investment horizons do show different shapes to overall returns but overall
following the same trend. The one exception is the 2010-2018 Seed period which does show lower returns
overall due to the averaging effect of spreading out investments.
Normalized Frequency > m
5 Year Portfolio Return of VC Portfolio by Horizon(n=1000 / series)
100.0%
50.0%
0.5
1
2
4
8
25.0%
12.5%
6.3%
3.1%
Return Multiple
2010-2012 Seed
2010-2018 Seed
2013-2015 Seed
2016-2018 Seed
Figure 13 – 5 Year Portfolio Return of VC Portfolio by Horizon
When comparing to market returns in Figure 14, returns are much more consistent and tighter in spread with
the exception of the 2010-2018 Seed due to similar factors.
5 Year Portfolio Return of S&P Portfolio by Horizon (n=1000 / series)
100.0%
Normalized Frequency > m
0.5
1
2
4
8
50.0%
25.0%
12.5%
6.3%
3.1%
Return Multiple
2010-2012 Seed
2010-2018 Seed
2013-2015 Seed
2016-2018 Seed
Figure 14 - 5 Year Portfolio Return of S&P Portfolio by Horizon
Venture Capital Growth in Portfolio-level Returns | Page 16
Closing Thoughts
Overall, the VC space is a highly competitive market where the source of returns comes from a small fraction
of companies. Based on the results found throughout this paper it would seem prudent that to maximize the
return of a portfolio of VC companies the following should be considered:




Maximizing the number of companies within the portfolio. Analysis shows that there is a
measurable increase in expected returns with the number of investments within the portfolio.
Higher returns are not expected within the first few 3 years. This is expected as generally VC
investments have low liquidity and longer holding periods.
High failure rates are expected. The majority of companies within the VC space do not see positive
returns and/or fail. However, with a large & diverse enough portfolio, this is mitigated with a small
number of outsized winners
Using VC companies can help diversify a portfolio. VC companies do show some negative
correlation & beta to the overall market. Using a portfolio of VC investments help diversify an
investor’s overall portfolio.
Additionally, while the impact of screening companies was not explored, any portfolio would benefit from
screening companies that due not fit an individual’s investment thesis.
Venture Capital Growth in Portfolio-level Returns | Page 17
References
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Beta Coefficient. (2021). Retrieved from Corporate Finance Institute:
https://corporatefinanceinstitute.com/resources/knowledge/finance/beta-coefficient/
Clauset, A., Shalizi, C. R., & Newman, M. E. (2009). Power-law distributions in empirical data. SIAM review,
51(4):661–703.
Kenton, W. (2021, March 31). Capital Asset Pricing Model (CAPM). Retrieved from Investopedia:
https://www.investopedia.com/terms/c/capm.asp
Othman, A. (2019). Startup Growth and Venture Returns. AngelList.
Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. New York: Random House Group.
Venture Capital Growth in Portfolio-level Returns | Page 18