Becoming a mature company : the influence of timing on a venture capitalists funding decision towards new tech ventures

(1)

Master Thesis

“Becoming a mature company - The influence of timing on a venture capitalists

funding decision towards new tech ventures”

Name: Matthias Gunsch (11374276)

Date of submission: Final version, 23rd June, 2017

Degree: MSc, Business Administration – Entrepreneurship and Innovation management Institution: University of Amsterdam – Amsterdam Business School

Supervisor: Dr. Tsvi Vinig

(2)

II

Statement of originality

This document is written by Student Matthias Gunsch who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

(3)

III

List of tables and figures:

Figure 1: Risk of loss of investment 5

Figure 2: Understanding differences in startup financing stages 8

Figure 3: Real GDP growth 10

Figure 4: Time-to-IPO of new ventures 20

Figure 5: Amount of IPOs per quarter 22

Figure 6: The effect of the financial crisis on venture capital 23

Figure 7: Dot-com stock market performance, 1997-2001 27

Figure 8: The impact of funding rounds on the time-to-IPO 34

Table1: Coefficient analysis: funding_rounds, foundation_area, amount of investment 23

Table 2: Descriptive statistics, funding_rounds 24

Table 3: Descriptive statistics, founded_recession and amount_of_investment 25

Table 4: Results of Univariate model, founded_recession and funding_rounds 26

Table 5: Results of Univariate model, founded_recession and amount_of_investment 26

Table 6: Descriptive statistics, founded_recession and time_to_IPO 28

Table 7: Report, founded_recession, amount_of_investment and funding_rounds 29

(6)

VI

Table 9: Descriptive statistics, impact of funding_stage on time_to_IPO 32

Table 10: Coefficient analysis between funding_rounds and time_to_IPO 35

Table 11: Coefficient analysis between amount_of_investment and time_to_IPO 35

Table 12: Distribution of otherforms_funding 37

Table 13: Descriptive statistics, impact of otherforms_funding on time_to_IPO 37

Table 14: Results of Univariate model for the effect of funding_rounds on time_to_IPO 38

Table 15: Model summary, time_to_IPO, lead_investor_early and high_rep_funding 40

Table 16 : Coefficient analysis, time_to_IPO, high_rep_funding, lead_investor_early 41

Table 17: Coefficient analysis, amount_of_investment, lead_investor_early,

high_rep_funding 42

(7)

1

Abstract:

Venture capital is supposed to be one of the most important financial instruments nowadays, providing start-ups with capital. Those start-ups would otherwise not be able to get investments due to the high risks they are exposed to. In this thesis, I observed the role of timing in the venture capital industry. This is especially important, as research in this field lacks behind and no extensive studies of this coherence have been in existence until this time. To get this insights, data of 130 randomly selected US and Europe based companies, which are publicly listed, had been observed. Through testing a number of venture capital related variables like the time to IPO, the number of funding rounds and the impact of recessions, findings suggest that a well-timed funding strategy can indeed put a new venture in a beneficial market position among others. Especially the number of funding rounds a company runs through during its lifetime seem to make a huge contribution to the future success of a new venture, while others are not affecting the potential success of companies at all. Through the extensive analysis of all these variables, this thesis is providing a number of recommendations to entrepreneurs, how to time their strategy to structure funding along their lifecycle, as well as some future implications for researchers in this field.

(8)

2

Introduction:

We are living in a time of fast growing technological progress, changing business practices

and advancing globalization, leading to strongly developing markets in which more and more

companies are founded every day. The problem hereby is, according to previous research

findings, that new ventures have remarkably higher failure rates than established or mature

companies, with an estimated failure rate of 40% in the first year and even 90% during the

first 10 years (Shepherd, Douglas, & Shanley, 2000). This is due to the fact that a lot of

startups have “problems to establish effective work roles, relationships with outside suppliers

and buyers, and bases of influence, endorsement, and legitimacy” timely (Chang, 2004a, p.

724). Additionally, most new ventures are facing unstable financial situations during their

first years and react harder on recession times.For this reasons and because most startups are

of small size and lack resources, numerous entrepreneurs are dependent on support from

outside the organization, as a good idea isn’t enough anymore.

These resources mostly come in the form of capital, which means outside investment,

respectively funding. There are multiple forms of funding in existence, but in my thesis I am

going to concentrate on venture capital, (hereby VC) as this is one of the fastest growing and

most common financial instruments in the startup sector nowadays. Obviously there are

certain principles how VC funding is structured, but sometimes actors have to drift away

from the typical procedure, as VC financing should happen in a timely and seamless way,

without interrupting the growth process of a new venture (Davila, Foster, & Gupta, 2003). By

comparing numerous successful companies, whereby a company’s IPO was used as an

indicator for the success, I am going to examine what coherence timing and the VC funding

process stand to each other. For my data analysis I am using data from US and Europe based

(9)

3

of the 500 most successful multinational enterprises (MNEs) had been placed within the

“triads”, that are composed of the US, Europe and Japan and secondly, a concentration on only the two biggest regions is valuable in terms of better comparability.

Before even starting with the analysis of my data, it can be said that, according to the

literature and the therein specified terms and findings, there has to be such a coherence in

existence, that still hasn’t been researched extensively so far.

The main focus during my data analysis will be put on both low- and high tech-startups as a

high percentage of them exhibits fast growth and an extraordinarily high innovative behavior.

This helps me to measure the influence of funding on the startup life cycle more precisely and

provides me with versatile data to analyze. In this thesis tech startups are defined as follows:

“A business model defines a series of activities, from procuring raw materials to satisfying the final consumer, which will yield a new product or service in such a way that there is net

value created throughout the various activities” (Chesbrough, 2007, p. 12). If this net value is

created by the use of technology, no matter if hard- or software, I defined the new venture as

a tech company.

Research question:

Referring to the introduction the research question, which has to be answered, reads to be

followed:

What is the impact of timing on a tech startups venture capital funding process, in order to

become a successful and mature company?

Following sub-question may have to be answered during research:

Are there times during the year in which the probability to receive funding is exceptionally higher than during other periods and what are the reasons for it?

(10)

4

Literature review

The VC industry:

“Venture capital involves the financing of new or radically changing firms which contrast in many important informational ways to established companies quoted on a stock market,

notably the problem of asymmetric information. VC companies are attracted to this kind of

investment because of the specific skills they perceive themselves to possess” (Robbie &

Mike, 1998, p. 522). Additionally, venture capital is an important source of funding for the

ongoing operations of the enterprise (Hsu, 2007) and venture capital (VC) investments have

been increasing globally. Not only the number of deals increased, but also the involved

capital (Nahata, Hazarika, & Tandon, 2014) accelerated over the last years, making VC one

of the most important financial sources for startups. Nonetheless, the venture capital sector is

a relatively new source to gain investments for young companies and plays a significant role

in providing capital to a wide variety of enterprises worldwide nowadays. Venture capital

firms distinguish from classical financial institutions like banks in a number of

characteristics. Especially the risk, that is related to a VC investment, is very much higher

than that of a bank, dealing with investments in the classical sense. Additional to that, most

venture capital companies don’t only offer money, but also “engage in a number of

value-adding activities, including monitoring, support and control”, further distinguishing them

from a bank’s operations (Bottazzi, Da Rin, & Hellmann, 2008, p. 489). This is the reason why VC investments are always connected to serious adverse selection risk and an extensive

screening of the companies, a venture capitalist invests in, is necessary. Obviously, VC

companies also gain more experience over time and therefore become faster in analyzing and

screening of the companies they invest in. Bottazzi et al. (2008) in this regard mention the

terms high- and low reputation VCs and their potential influence on the success of a growing

(11)

5

Figure 1: Risk of loss of Investment (Ruhnka & Young, 1987, p. 181)

entrepreneurs, as this could give one company an advantage towards another company, which

might be similar or equal in multiple aspects. “Once an investment is made, the investment is

illiquid and strongly dependent on a small number of entrepreneurs or managers” (Fried &

Hisrich, 1994, p. 28). If the business, they invested in, fails, their investment is fully exposed

(Mason & Stark, 2004).

This high risk, that comes with VC investments, is also proofed by Ruhnka and Young

(1987). According to their findings, investors still do have a risk of slightly more than 20%,

even in the exit stage of the funding process.

So how are VC companies operating? “Venture capital firms provide privately held

“entrepreneurial” firms with equity, debt, or hybrid forms of financing, often in conjunction with managerial expertise” (Amit, Brander, & Zott, 1998, p. 442). This form of investment is

necessary, as “small private growth companies typically do not have cash flows to pay

interest on debt or dividends on equity” (Cumming, Fleming, & Schwienbacher, 2005a). The

money VCs use for their investments is usually generated out of very large institutions such

as pension funds, financial firms, insurance companies and university endowments. This

institutions expect a return on their investments by the venture capitalists over the lifetime of

their investments (Zider, 1998). For this reason venture capitalists have to make sure that

their investment in an innovative company is successful. This is why VCs don’t mainly invest

(12)

6

“industries that are more competitively forgiving than the market as a whole” (Zider, 1998). Investors search for industries with high growth and innovation potential to lower their risk

and generate fast returns for their investments.

Today the technology sector is exhibiting all these characteristics. Through the technological

improvement, changing policies and the increasing possibilities, thousands of tech startups

arise worldwide every year, making this industry even more interesting for VC companies.

Besides venture capital, multiple other forms of investment exist entrepreneurs can ask for.

Additional to that, the different forms of funding are not mutually exclusive. Just because a

startup already received venture capital doesn’t mean that additional funding through private

equity, a secondary market investment or other forms of capital aren’t an additional option to

generate capital. During the data analysis, this terms have to be considered, as a number of

companies are relying on those additional forms of investments, besides of venture capital.

The effect of this other forms of monetary aid on the timing of funding and the lifetime of the

financing cycle will be observed later in this paper.

Funding process:

The funding process is very complex and extensive knowledge about the industry is needed,

both for the investors and the entrepreneurs to successfully wrap up a deal. This is because

both actors involved in the process want to maximize their potential benefits, ensure future

success, increase growth and at the same time lower costs. For the entrepreneur a number of

complex issues and regulatory requirements make it important to extensively think about the

right mode and time of market entry as they are facing “different payback periods, possibly

influencing the firm’s future performance” (Zahra & Hayton, 2008, p. 198). For the venture

capitalist, it is firstly important to satisfy the financier and to pay back investments, and

(13)

7

Here an interconnection with the field of globalization can be recognized as a shift from

focusing on the right entry mode to the right entry timing and speed took place. In this field

Hitt, Li, & Xu (2016) discuss the time lag between the foundation and the initial international

expansion of a company, while in the course of this thesis, the timing and speed from the

foundation until its IPO is relevant. This again shows that timing plays a relevant role in the

VC industry and interdisciplinary theories support this importance. For this reason, venture

capital investment is separated in different phases, during all of which, extensive screening of

the company, a venture capitalist invests in, takes place.

Based on the startup financing cycle, three respectively four stages can be identified. Early

stage, which consists of seed and startup financing, later stage (expansion) and mezzanine

(typically last round before going public) (Jeng & Wells, 2000). It is important to consider,

that literature is using different terms for this stages or label them differently. To make an

analysis easier and more precise, I pooled this stages into only two categories: Early stage

and later stage.

While the shown graph is using 1st stage, 2nd stage and 3rd stage as terminology, other sources use 1st round or A-series. The meaning and the function of the stage, however, stays the same. In the following, I am going to use the terms Series A, Series B etc., as this is also the notion

(14)

8

Figure 2: Understanding differences in startup financing stages (Novoa, n.d.)

In Figure 2, the startup financing cycle is showed in more detail as a deep understanding of

the different stages is necessary. It shows, that in the beginning stage of a startup, typically

negative revenues are generated, as it takes time and money to successfully enter the market.

During this time entrepreneurs are seeking for external money through seed investors by

using patents as a signal of success and use FFF money (Friends, family and founders) to

show their commitment (Conti, Thursby, & Rothaermel, 2013).

This initial phase to build the company and reach the break-even point has indeed an

influence on the further success, but I want to investigate the next phase, as this is the phase

were venture capital companies usually enter the game and help entrepreneurs to grow their

business successfully.

Theoretically, startups can receive funding during more than the three stages depicted in the

graph (typically Series A, B, C), even though the stages shown represent the “optimal”

procedure. If a company obtains funding by a VC during one of the later stages (Series D, E,

F, G…), this might be an indicator for financial troubles. But it can also be associated with positive issues like financing an exceptional event or the “support of continuing growth

(15)

9

have to start the funding process with Series A financing, but can also ask for initial funding

during a later series. This means that their business is already more mature and that the

entrepreneur has a higher chance of generating more money for less percent of the new

ventures shares.

As the revenue of a company increases and the company evolves over time, it is obvious that

the mode of investment changes too. This modes or stages are distinguished by the amount of

money startups receive, the purpose the money is used for and the number of shares the

investor gets (Gompers, 1995). This percentage of shares changes every round. Described by

an simplified example, this means that, if an investor offers an entrepreneur one million euros

for 10% of the company’s shares, he estimates the companies value around ten million euros. If another investor offers the same amount of money for only 5% of the shares during a later

funding stage, an easy calculation shows that the estimated value of the company doubled.

This numbers are not entirely right, as the already existing value of the company has to be

counted in and a number of additional variables have to be minded, but it is good enough to

get an idea how the industry works. This is why a funding decision has to be planned and

timed. Getting funding during multiple rounds or getting funding by multiple investors during

one round means giving away shares every time. The valuation of a company, a VC company

invests in, is a process that takes time and isn’t made over night. This long time periods VCs need for the screening of a company they invest in, entrepreneurs should plan to get funding

timely.

Hypotheses

:

Literature shows that stronger and weaker funding periods exist (Nanda & Rhodes-Kropf,

2012). This can be attributed to a number of reasons, whereby external events seem to be the

(16)

10

recessions, which are times of economic downturn, negatively affect the VC industry, as it is

hard for VC companies to find sponsors in times, in which financial problems dominate the

market. As the “amount of funds raised by VC firms is strongly dependent on a vibrant

market for IPOs”, an explanation for this phenomena is found. During times in which the

market is unstable and financial products fluctuate in their value, fewer companies will plan

their IPO, as a high profit is the main goal. Further, it can be said that in times of unstable

financial situations shareholders and private investors might not be willing to spend money to

buy shares of a company - or at least not to high prices.

Nanda & Rhodes-Kropf (2012), however, found that startups that had been funded during

stronger funding periods tend to fail more often, but are valued higher at IPO and had more

highly cited patents than startups funded in less active, or weaker, periods. Another fact,

Nanda & Rhodes-Kropf (2012) observed, is that most successful companies are founded

during a recession. Hereby, I put special attention on the Dot-Com bubble from 1998 to 2001

and the early 2000’s recession that hit the European union in 2000/01 and the US between 2002 and 2003. The GDP growth during this times can be obtained from Figure 3.

Figure 3: Real GDP growth. Source OECD Factbook 2010 and Eurostat (Lerner & Tåg, 2013, p. 165)

(17)

11

Talking about recession phases, it is also importnat to take the financial crisis of 2008 into

consideration, as it constitutes one of the events with the highest impact on the global

financial sector in the recent past and was followed by a large number of new ventures

disappearing. The graph shows, that every of this events had an impact on the real GDP

growth and a significant underperformance is concentrated in this times. This

underperformance can be explained by “behavioral factors such as market timing by both the

entrepreneur and the venture capitalist” (Coakley, Hadass, & Wood, 2007, p. 1424). Another

important influence, these recession times have on the venture capital market and start-ups,

is, that during this times IPO underpricing is a common consequence, leaving companies

behind with a lower IPO pricing compared to the real market value. Especially during the

Dot-Com bubble, this underpricing reached astronomical levels. Representative for the EU,

the graph shows Sweden’s numbers. Another event, that made the bubble worse, was, that the VC market grew extraordinarily in the time between 1995 and 2000, which led to an

exceptionally high number of new companies in the market, making the industry even more

unstable. This leads to the first hypothesis that has to be answered in this thesis.

H1: During times of recessions, significantly more companies are founded than in times without.

H1a: External effects like crises, have an impact on the number of funding stages a company runs through.

H1b: External effects like crises have an impact on the overall amount of investment a company receives during its lifecycle.

Nonetheless, in the decade ending in 2000, a huge increase in the number of IPOs could be

located, especially in Europe. In this period, “nearly 160 IPOs in 1999 and 147 IPOs in 2000

(18)

12

to 1982, Germany saw only 19 IPOs, an average of less than one firm each year. The huge

fluctuations in volume from period to period suggest that market timing considerations are

relatively more important than the life-cycle considerations” (Ritter, 2003, p. 422). This is

another proof for the importance of timing in the venture capital sector.

In one of their articles, Chemmanur, Krishnan, & Nandy (2011, p. 4037) question if VC

backing and the “associated efficiency gains affect the probability of a successful exit”. The

most common exit strategies for startups are typically an IPO or an acquisition of the startup

by another, mostly more mature, company (Davila et al., 2003). There are indeed a number of

further exit options for companies, but IPOs are very often what venture capitalists aim for

when investing in new ventures. At the same time, an “IPO represents an outside option for

highly profitable ventures” per definition (Cumming et al., 2005a, p. 80).

Acquisitions are incidentally a very common form of exit, especially in the tech sector, but

are not necessarily a sign for success. This is why an acquisition is not a suitable tool to

declare a company as successful and is therefore not used as a measure in the course of this

thesis. Instead of acquisitions, IPOs are the most frequently chosen way of concluding VC

investments and as a measurement for success (Barry, Muscarella, Peavy, & Vetsuypens,

1990). This is why I also consider it as a measurement for success in my thesis.

The question that Chemmanur et al. (2011) introduced, is answered by Chang (2004b) and

Gompers (1995). Chang (2004) argues that the more money an internet startup raises, the

faster the startup will have an IPO. Gompers (1995) complements this findings by arguing

that firms, that successfully go public, run – by tendency – through a higher number of

financing rounds. It can also be seen that the amount of money companies receive by

(19)

13

stages will generate significantly less money per round then later stage companies. Sapienza

(1992) explains as follows:

“As ventures are less likely to have established relationships with suppliers, distributors and buyers during earlier stages, the CEOs might be untested as they are facing a shortage of

trained stuff, investments in this early-stage startups are risky and unpredictable”. That is

why the amount of funding in early stages is usually less than in later stages.

“Companies become progressively less risky as they develop, and the venture capitalist will accept a proportionately smaller incremental equity position for a given amount of investment

at each successive stage” (Barry et al., 1990, p. 450). That lets me assume that if an investor

takes the risk of investing in an early stage and the earlier in the lifetime of a company

funding’s can be generated, the higher the probability of a successful exist is, and based on Chang (2004), that early entrants exhibit higher IPO rates.

H2: The phase in which a startup receives funding by a VC has an impact on the future success and the performance of the company.

Cumming et al. (2005a) further argue that, “during times of high market liquidity, the

likelihood of investing in new ventures increases, but on the other hand the likelihood that

this investments are early-stage investments, decreases”. They therefore recognize a negative

relationship between the liquidity of an exit market and the likelihood of early-stage funding.

This concurs with the findings of Gompers (1995). According to Gorman & Sahlman (1989),

the typical duration for an investment horizon is five to seven years. After that time, investors

expect an appropriate return for their investments. In other words, investors typically expect a

seven years period of time to exit. Staged financing is therefore the best tool for this purpose,

(20)

14

expected net present value turns negative. Further, it gives the entrepreneur a “stronger

incentive to create value”, as the consequence would be to lose an important investor (Barry

et al., 1990, p. 450). This seven years of time to IPO or shorter could, however, only be

achieved by 41 companies in my sample, which corresponds 30% of all companies. Based on

my data, this shows that an IPO of seven years or shorter is a quite optimistic goal. To

understand this, I tested, if there exists a coherence between the number of funding rounds,

the amount of invested money and the companies, that took shorter than seven years to go

public and couldn’t find a scenario that favors such a short time to IPO.

Chang (2004c) argues, that the more money a startup receives, the higher its growth rate.

Therefore, it will have its IPO faster than other non-VC backed companies. Gompers (1995)

concludes that “the duration of financing declines for later stage companies while the average

amount of financing per round rises”. This can be traced back to the fact, that older

companies might have more information about their business that they can offer to the VC

company. Extensive information about the company lowers the investment risk many times.

H3: The more funding stages a company passed through, the shorter is the time-to-IPO. H3a: The number of funding stages has a higher impact on the time-to-IPO than the amount of money raised.

H3b: The amount of money raised has a higher impact on the time-to-IPO than the number of funding stages a start-up went through.

This hypotheses still have to be proofed in the course of the data analysis. Giot &

Schwienbacher (2007) however give me an indication that H3b is right.

In a simulation, they increased the funding in the first funding round from 10 million USD to

(21)

15

amount of money entrepreneurs receive, is worth more than the number of funding rounds

they went through, might be the explanation for a phenomenon, we will explore closer during

the data analysis part of this thesis. Many companies received their initial funding during

later stages and therefore renounced seed and early stage capital. This means, that the

company already developed a certain level of maturity before receiving money. Under this

circumstance, asking for funding during a later stage makes sense and can be beneficial. As

later stages exhibit lower risk and are normally connected with higher amounts of funding,

this strategic consideration has a non-negligibly high impact on timing, or constitutes the

actual funding decision. This is also in line with Hypothesis 2.

As the investment process is well structured, but still varies from company to company, I

admit that there must be a connection between timing and the funding decision to generate

benefits for both, the VC company and the entrepreneur. This again concurs with Giot and

Schwienbacher (2007), as it is from high importance for both, the VC company and the

entrepreneur, to know, how long it takes for the company to cash out, therefore describing the

“actual timing of the exit” as an important dimension of the whole VC process.

As determined in the introduction, other forms of funding also influence the funding process

and the time-to-IPO. Therefore, I need to formulate another hypothesis to measure the real

influence of this exceptional investments during the life-cycle of a company.

H4: Other forms of investments like private equity or secondary market investments influence the time-to-IPO of a company negatively.

The assumption I have to prove, is, whether it is true that it will take longer for a company to

go public, if it receives other forms of funding additionally to venture capital. This would

somehow be logical, as the money received through venture capitalists is obviously not

(22)

16

additional investments might also help entrepreneurs to generate faster and more sustainable

growth. The real impact, additional forms of funding therefore have, needs to be observed

more closely.

The staging of the funding process and the term “time-to-IPO” are further concepts that let me assume that the right timing during the venture capital investment process can generate

important competitive advantage. As VCs always leave themselves the possibility to exit an

investment if it is not profitable and startups fundamentally aim for fast growth, we have to

investigate this connection of timing and venture capital.

H5: Receiving funding by a high reputation venture capital company has a positive impact on the firms future success

High reputation venture capital companies play an important role in the venture capital

sector. “Reputation is frequently acknowledged as a source of competitive advantage”. The

value and reputation of a firm can, among others, be measured based on the ability to create

economic profits (Fernhaber & McDougall‐Covin, 2009, p. 280). In the venture capital sector, this means to invest in promising ventures, obtain fast growth and reach for a high

valuation at IPO. High reputation VC financing offers are three times more likely to be

accepted by entrepreneurs as they know that “performance benefits can be realized by the

reputable producer’s affiliates through a process of certification” (Hsu, 2004, p. 1808).

Another benefit of high reputation VCs is, that through their experience and trustworthiness

in the market, they help entrepreneurs to attract further investors. I assume that being part of a

high reputation VC company’s investment portfolio is a characteristic for a new ventures quality, which attracts further investors in the market to go on investing in such ventures. The

(23)

17

Methodology and dependence of variables

:

Information was retrieved from academic journals proposed to us by the ABS journal list and

will be found in the most common online databases for scientific journals. This information

was used to acquire the needed theoretical knowledge about startups, venture capital and the

variables that make timing a relevant part of the VC funding process and constituted the basis

for this thesis. To underline, proof and explain the theoretical findings I used existing

statistics about venture capital and retrieved data from online databases and other online

sources. The website crunchbase.com offers extensive data about thousands of companies,

including information about their IPO, their funding history, their lifecycles and the

extraordinary events and investments that took place during the lifetime of this companies

until their IPO.

I collected data from 150 companies which had their IPO between 2004 and 2017 and

generated my own dataset. To guarantee trustable data and to avoid diversification of the

data, I focused exclusively on US and European companies. This will help me to find

evidence about the right timing of the funding decision. By collecting this data and analyzing

it, I will get a timeline that shows me, among others, during which phase and at what time in

the funding process the most investments had been made, and which factors are directly

impacting the timing of a startups business routine. This makes an analysis about the reasons

for the existence of stronger and weaker funding periods possible. If further information was

needed or unclear data had to be explained in more detail, additional data was retrieved from

the VC databases Thomson One and Zephyr.

In sum, it can be said that I used the quantitative research approach, as data can be generated

in a relatively standardized way. As non or only little research had been done in this subject

(24)

18

with upfront. Through an extensive analysis of this data and the creation of an own dataset to

compare all companies, a clear picture of the industry will emerge. I will also include and

consider the influences external effects like crises trigger and analyze their effect on the

venture capital market. The variables that I’m using in this thesis are described and connected

as follows :

Dependent variables:

My first hypothesis describes the assumption that during recession times significantly more new ventures are being founded. Through selecting the foundation dates of new ventures from crunchbase.com, I measure how many successful companies had been founded during this times. The number of funding rounds (funding_rounds) as well as the amount_of_investment (the overall amount of investment a startup received during the lifecycle) constitutes my dependent variable here. Hypothesis 2 is based on the assumption that the phase, during which a new venture receives funding, has an impact on the future success of a new venture. IPO is my measurement for success as an IPO represents an outside option for highly profitable ventures per definition (Cumming, Fleming, & Schwienbacher, 2005b). Therefore the time_to_IPO is my dependent variable here. Hypothesis 3 and 4 are also both addressing the time_to_IPO as measured by the firm’s age at IPO as dependent variable (Yang, Zimmerman, & Jiang, 2011). In hypothesis 5, both the time_to_IPO and the amount_of_investment are constituting the dependent variables

Independent variables:

The recession times I used as the independent variable in hypothesis 1, are the Dot-Com bubble from 1995 to 2001, the early 2000’s recession and the financial crisis of 2008 (Ivashina & Scharfstein, 2010). This factors are also representing external effects and are summarized to the variable founded_recession, which tells us if a company was founded

(25)

19

during an economic downturn or not. In hypothesis 2, the stage of funding is the independent variables. Four stages (Early stage (seed and start-up), later stage and mezzanine) can be identified, but are narrowed down to two categories as already explained in the introduction of this paper (Jeng & Wells, 2000). Crunchbase.com provides data about the funding stage, during which a company got funded, and the amount of funding the company received per stage, which makes an analysis of this hypothesis possible. This variable is also representative for Hypothesis 3. The amount of money raised by new ventures overall and per stage, can also be retrieved from crunchbase.com. In Hypothesis 4, we assume that additional investments like private equity, which is different to VC in the way as PE companies strictly focus on later-stage investments, that are typically control-oriented transactions involving mature companies (Moon, 2006), influences the time-to-IPO. Therefore, the independent variable therefore is otherforms_funding. In hypothesis 5, the variable high_rep_funding, which indicates if a venture was funded by high reputation VC company, is representing my independent variable.

Moderator and control variables:

In Hypothesis 1, the amount of investment is used as control variable. Hypothesis 2 uses the age of a company when receiving initial funding (company_age) as a moderating variable. In Hypothesis 4, the variable otherforms_funding, which describes the additional forms of funding a company received during their lifecycle, is used as control variable. In Hypothesis 5 the variable lead_investor_early, describing if the startup was funded by a high reputation VC, which also operated as lead investor in the lifecycle is included as a control variable.

(26)

20 0 20 40 60 80 100 120 140 0 5 10 15 20 25 30 35 40 45 50 Company ID Ti m e -to -IPO

Time-to-IPO/company

Data analysis:

Overall analysis of the sample:

The final and adjusted sample consists of 130 randomly selected tech companies, including

both high- and low-tech ventures. As it can be seen in Figure 4, most companies have their

IPO within 5 to 15 years after foundation. The average time to IPO in my sample is 10.16

years, with some extreme aberrations. If the database is adjusted and the company with 43

years of time to IPO is excluded, constituting an extreme outlier, the average time to IPO is

only 9.9 years. Every year in my sample can be divided into four quarters, showing that one

third of all IPOs took place during the 2nd quarter of the year (April, May, June), while all the other IPOs are relatively equally distributed across the other three quarters with

approximately 22 percent each.

In my sample I analyzed companies that had their IPO in the 14 years between 2004 and 2017. Only for testing the percentage of companies that are founded in a recession, we have to extend the time frame by 6 years as the early 2000s recession and the “Dot-Com Bubble” took place before my sample period. The 14 years are only limiting the timeframe for the

Extreme outlier

(27)

21

IPOs but obviously a lot of companies had been founded before that period of time. As a reminder, the “Dot-Com Bubble” took place between 1998 and 2001, the early 2000s recession, which mainly hit the US between 2001 and 2003 and the financial crisis, whose peak was reached in the third and fourth quarter of 2008. In my sample, 74 companies had been founded during stable times. In comparison to that, 56 companies had their foundation during one of those recession times. The 5.5 years of recession times amount of approximately 27.5% of the adjusted overall time in my sample. The companies founded during this 5.5 years of recession times account for 43% of all companies in my sample.

This easy calculation tells me that during recession times, procentually more companies are founded than in times, during which a stable economy prevailed. Interestingly, the time period in which the “Dot-com Bubble” disrupted the market, exhibits an exceptionally high number of new ventures entering the market. 44 companies of all companies founded in recession times account for the period from 1998 to 2001, corresponding 79% of the companies that went into business during recession times and 34% of all companies in my sample. This was also one of the reasons for the burst of the bubble, as the internet sector earned over 1000 percent returns on its public equity in the two years period from early 1998 throughout February 2001 (Ofek & Richardson, 2003), followed by a strong decrease in the valuation of this recently funded tech startups. That left a big number of investors behind, experiencing huge losses and lead to a wave of companies going bankrupt.

(28)

22

Hypothesis 1:

Through this findings we can answer Hypothesis 1: My sample shows a weak coherence

between recession times and the foundation of companies in general. Taking the fact that

during the recession times, that account for 27.5% of my sample, 42% of all companies had

been founded. Interestingly, even though my sample is randomly selected, most of the

companies are California based, or even from Silicon Valley. Shepherd & Zacharakis (2001,

p. 60) mention several reason for this high concentration of companies in this area, especially

pointing out the high concentration of venture capital companies. The fact, that the majority

of the 10 high reputation VC companies I used in my sample, are Silicon Valley based is

supporting this assumption. Further, they mention a “talent pool of knowledgeable

professionals, universities and research institutions, a professional service infrastructure, and

customers as well as lead users of innovation” as main reasons for rapid growth. This tells

me, that not only the timing, but also the location of a new venture might play a certain role

for future success. The following table 1 shows, that the foundation_area has a positive

Figure 5: Amount of IPOs per quarter

Quarter 1 Quarter 2 Quarter 3 Quarter 4 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Quarters Per ce n t

IPOs per Quarter

(29)

23

impact on the number of funding rounds (funding_rounds) a company runs through. This lets

assume that new ventures, that are based in, by nature, innovative areas, have a higher

probability of getting funded.

Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 6.375 .511 12.474 .000 foundation_area -1.421 .311 -.379 -4.576 .000 2 (Constant) 5.809 .516 11.264 .000 foundation_area -1.274 .300 -.339 -4.239 .000 amount_of_investment 2.451E-6 .000 .280 3.496 .001

a. Dependent Variable: funding_rounds

In this one-way ANCOVA, I further used the variable amount_of_investment as a covariate,

recognizing a further significant relationship. The foundation and operations of a company in

an innovative area have an indirect influence on the timing and the success of a company.

Only because a company is founded in an innovation hub, doesn’t necessarily mean that the

0 5 10 15 20 25 30 35 40 Fu n d in g r o u n d s Period

Funding rounds

Figure 6: The effect of the financial crisis on venture capital

(30)

24

IPO will be reached faster – I also couldn’t find significant evidence – but as the probability

increases to run through more funding rounds, the likelihood of survival increases. Talking

about the influence of timing on the future performance of a new tech venture, one thing has

to be pointed out. Financial downturns, that have a negative influence on a firms success in

the long term, can be spotted easily (Block & Sandner, 2009; Brandstätter, 1997). Both,

before the “Dot-Com bubble” and the financial crisis, the venture capital market increased in a tremendous pace. The number of funding rounds as well as the invested amount of money

reached a peak, shortly before the crash happened (Ljungqvist & Wilhelm, 2003). Recessions

are therefore predictable, if a comprehensive knowledge about the market is in existence.

Figure 6 shows the extreme increase of funding rounds in the period before the crisis

happened. Then, during the crisis, 38% less funding had been effected. The data shows that a

company, which was founded in a recession runs on average through less funding stages and

also the amount of invested money is lower across the whole lifecycle.

Descriptive Statistics

Dependent Variable: funding_rounds

founded_recession Mean Std. Deviation N

0 4.26 2.500 74

1 3.96 2.097 56

Total 4.13 2.331 130

(31)

25

After running the analysis, it is clear that the foundation of a company during a recession

(founded_recession) has no significant effect on both the number of funding rounds (p =

0.329) and the amount of invested money (p = 0.417) a company receives. Therefore, we can

say, that the timing of the formation of a company has no impact on the future success, even

if we can see that companies that are founded during a recession, run through less funding

stages on average than companies that are not.

To find further evidence, I used the control variable type_of_recession, consisting of 4

categories. The categories are 0, not_in_recession, 2, dot_com, 3, early_2000s and 4,

financial_crisis and explain during which period of economic downturn the company was

founded. Through this, I can say that even the type of recession has no significant impact on

the number of funding rounds a company runs through during its lifecycle (p = 0.432). Data

shows, that the different recession times admittedly exhibit different averages of time-to-IPO,

but the fluctuation is relatively low, so that the influence, as mentioned above, is not

statistically significant. I therefore have to disagree with Nanda and Rhodes-Kropf (2012).

According to my criteria for success, I cannot proof the statement that most successful

companies are founded in recession.

Dependent Variable: amount_of_investment

founded_recession Mean Std. Deviation N

0 152758.72 316573.720 74

1 114196.07 161732.835 56

Total 136147.12 261214.333 130

(32)

26

Tests of Between-Subjects Effects

Dependent Variable: funding_rounds

Source

Type III Sum of

Squares df Mean Square F Sig.

Corrected Model 6.119a 2 3.059 .559 .573 Intercept 194.944 1 194.944 35.640 .000 type_of_recession 3.392 1 3.392 .620 .432 founded_recession 5.252 1 5.252 .960 .329 Error 694.658 127 5.470 Total 2919.000 130 Corrected Total 700.777 129

a. R Squared = .009 (Adjusted R Squared = -.007)

Based on these findings it can be said that it has no impact on the number of investments,

which corresponds the number of funding rounds, whether a company is founded in a

recession or not, as well as the amount of money a start-up receives.

Considering the recession times separately, the impact on the overall number of funding

rounds in the market gets visible, as the number of funding rounds decreases during this

times, but the effect is counterbalanced through peak times.

Table 4: Results of Univariate model, founded_recession and funding_rounds

(33)

27

The same can be applied when I tested the influence of founded_recession on the

amount_of_investment, saying if it has an impact on the overall amount of money a company

receives, if the venture is founded during a recession. Here again, I used the

type_of_recession as a control variable and the findings are again not significant. (Sig.>0.05).

A further decline in the average amount of funding for companies that are founded during a

recession can be recognized, but as the coherence is not significant we cannot assume that

this is the rule.

Figure 7 shows the evolution of the Dot-com stock index between 1997 and 2001. Interesting

is the strong impact of the “Dot-com Bubble”. One third, or exactly 44, of every company in

my sample was founded during or shortly after the bubble, still feeling the impacts of the

market crash. This exhibits a strong accordance with the first hypothesis. But are the

companies that are founded during this recession times also more successful than others?

This question is important as I want to explore the impact of timing on the future success of

companies. The average time-to-IPO of the companies founded in recession times (founded

in recession (1), founded during stable times (0)) is 9.80 years. That is, on average, 0.36 years

faster than the average of my complete sample. Compared to that, the average time to IPO of

all companies founded during the “Dot-Com Bubble” is 10.27 years.

(34)

28

Table 6: Descriptive statistics, founded_recession and time_to_IPO

This would mean, that companies founded during this period reach their IPO on average later

than the companies that are founded during economic stable periods. The financial crisis and

the early 2000s recession whereas form an initial situation, in which a faster IPO could be

expected.

In sum it can be said that there is only a very little increase of newly founded ventures during recession times in overall. The reason for this high number of new foundations during the time of the bubble was that internet stock prices soared at that time and entrepreneurs started seeing this as an opportunity for fast growth and quick money. Thousands of newly founded companies raised tens of billions of dollars from venture capitalists and hundreds of these start-ups subsequently went public, raising additional billions till the bubble burst by 2000 (Hendershott, 2004). All this deliberations are necessary and important as the conditions in which a firm is born, may have a substantial effect on its performance (Geroski, Mata, &

(35)

29

Portugal, 2010). This might also be the reason for the longer time to IPO, which can be observed for companies founded during the “Dot-Com bubble”.

The 130 companies in my sample ran through 539 funding rounds in total, which is an average of 4.13 rounds per company. Additionally, a number of other forms of investment had been deployed, which we will elaborate later in the analysis. Of this 539 funding rounds, only 91 took place during recession times. Taking the fact that recession times account for only 5.5 years and 90 funding rounds took place during that times, I can confirm Hypothesis 1a. The number of investments indeed decreases during economic downturns. Mathematically 148 funding rounds should have taken place in this 5.5 years’ time period to correspond the average. For Hypothesis 1b we can see that the average amount of investments indeed decreases if a company had been founded during a recession, but the findings for this variables are not significant, because of which the validity of this assumption has to be denied. This is again proofed by the analysis run in SPSS.

Hypothesis 2:

Companies in their early stage are associated with a higher level of uncertainty as already discussed earlier. At the same time, this companies entail greater learning opportunities by using the invested money for R&D (Research and development). Therefore companies in their seed or early stage, will receive funding faster than companies that are already more

(36)

30

experienced and can therefore be dedicated to a later stage in their lifecycle (Li, 2008). Further Li (2008, p. 498) determines that “the timing of staging is critical for both the entrepreneur and the venture capitalist”. This lets me assume that Hypothesis 2 is right. This section is meant to proof this assumption. The staging of venture capital investments is a critical task, as a well-timed investment provides startups with liquid assets for future expansion and growth. The fact, that staging constitutes a critical task, makes it necessary to extensively think about the impact of earlier or later stage investments on a firms future performance.

According to my definition, I declare Series A and Series B investments as early stage investments, while every other Series, starting with Series C, will be seen as later stage investment. Additionally we rate the Seed stage as early stage investment as long as the investment is done by a VC company and not by a private investor. This hypothesis is tested by using time_to_IPO as dependent variable, funding_rounds, which is devided into 0 (early

stage), 1 (later stage), 2 (same funding rounds in both stages), 3 (no funding) as independent

variable and company_age, which states whether the company was older than one year old

when receiving initial funding (1) or younger (0), as moderator. What can be seen in the

following table 8 is that, companies, that had been younger than one year when receiving initial funding reached their IPO up to 4 years earlier than companies that had been older. Here, I rely on averages, which doesn’t show me unevenness in the data, but it is sufficient to show the impact of the firms maturity on the time-to-IPO. To test my hypothesis, I ran a one-way ANOVA with a moderator variable to test an interaction effect between founding_stage and company_age. Again, my dependent variable is the time_to_IPO. To run this test, I had to adjust my dataset and delete one company from my sample as no clear information could be found about whether the funding took place during the early or the later stage of the lifecycle. To avoid a distortion of the data, I therefore conducted all the necessary tests for this

(37)

31

hypothesis with only 129 instead of the full 130 companies from my sample. It was important to answer this question, as an entrepreneur can freely decide at what time it is necessary to ask for funding, as long as the current financial situation allows it. As early stage and later stage investments are distinguished from each other in a number of attributes (Gompers, 1995), I tested if it makes a difference whether a new venture received funding during the early stage or during the later stage of its lifecycle. The output from SPSS statistics depicted in table 8 shows me that the stage in which a company receives funding has a significant impact on the time_to_IPO. However my moderating variable shows a significant effect too. Despite the significant effect, the company_age has on the time_to_IPO, the interaction between funding_stage and company_age does not provide further significant evidence to proof my hypothesis.

Dependent Variable: time_to_IPO

Source

Type III Sum of

Corrected Model 618.735a 6 103.123 4.788 .000 Intercept 6932.472 1 6932.472 321.910 .000 funding_stage 249.509 3 83.170 3.862 .011 company_age 207.471 1 207.471 9.634 .002 funding_stage * company_age 6.457 2 3.229 .150 .861 Error 2627.327 122 21.535 Total 16367.000 129 Corrected Total 3246.062 128

a. R Squared = .191 (Adjusted R Squared = .151)

We now know, that the impact of the funding stage on the time to IPO is significant. The following table provides more detailed information about this correlation. Companies in category 2, in which the funding rounds are equally distributed across the early and the later stage, have the shortest average time to IPO. A surprising fact is, that early stage funding

(38)

32

seems to be more impactful than later stage funding. This could probably be related to the fact mentioned earlier, that early stage funding provides faster growth possibilities to companies.

funding_stage company_age Mean Std. Deviation N

0 0 7.09 2.343 11 1 11.00 7.820 27 Total 9.87 6.905 38 1 0 8.22 2.375 27 1 11.05 4.049 41 Total 9.93 3.731 68 2 0 6.83 1.169 6 1 9.73 2.195 11 Total 8.71 2.339 17 3 1 17.17 4.997 6 Total 17.17 4.997 6 N/A 1 20.00 . 1 Total 20.00 . 1 Total 0 7.75 2.284 44 1 11.40 5.670 86 Total 10.16 5.091 130

Hypothesis 3:

To answer the third hypothesis it has to be remembered that the average time to IPO is 10.16 years and the average number of funding stages per company is 4.13. I divided my sample into four categories:

 Companies that needed less time-to-IPO or exactly the average time and went through more funding stages than average.

(39)

33

 Companies that needed more time-to-IPO than average and went through less funding stages than average

 Companies that needed less time-to-IPO or exactly the average time and went through less funding stages than average at the same time

 Companies that need more time-to-IPO and went through more funding stages than average at the same time

The type of companies mentioned first in this enumeration is relevant to answer this hypothesis. If a company goes public in a shorter time than the average and runs through more stages at the same time, the assumption that “the more funding stages a company passed through, the shorter is the time-to-IPO” is proofed. In contrast to that, the second type of companies in this list gives counterevidence, as this means that less funding stages are effectively associated with a longer time-to-IPO. The last two types of companies in my sample are the outlier groups, as less time-to-IPO together with fewer funding stages the company went through as well as more time-to-IPO with more funding stages are not paralleling my hypothesis.

(40)

34

Figure 8: The impact of funding rounds on the time-to-IPO

The analysis of the data shows that 36% of all companies in the sample received a higher number of investments than average and went public in less than or in exactly 10.16 years. In contrast to that, 23% of the companies went through less than four funding rounds and therefore had to wait for their IPO longer than the average 10.16 years.

Summed up, this companies together account for approximately 59% of my sample. The fact, that the companies from category 1 and 2 represent nearly 60% of my sample, shows, that there must be a positive relationship between the rounds of funding and the time-to-IPO. Figure 8 visualizes my relevant data regions. The intersection is showing the average time-to-IPO as well as the average number of funding rounds.

This analysis can be underpinned with a regression analysis I ran through SPSS, contrasting

the dependent variable time_to_IPO with the independent variable funding_stages. As table

11 can show the coefficients analysis shows that the number of funding stages indeed has a

significant effect on the time to IPO.

0 2 4 6 8 10 12 0 10 20 30 40 50 Fu n d in g r o u n d s Time to IPO

(41)

35

This means, that the more funding stages a company runs through, the faster it will reach its

IPO. Further I want to find out, if the amount of funding a new venture received, has an

impact on the speed of going public.

Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 12.874 .870 14.801 .000 funding_rounds -.671 .183 -.310 -3.670 .000

a. Dependent Variable: time_to_IPO

For this purpose I merged the funding data of all the companies in the sample collected from crunchbase.com with data from Thomson One to get the exact amount of venture capital funding throughout the lifecycle of a new venture. This data is adjusted in a way that venture funding and all other forms of funding are quoted separately

Coefficientsa

Mode Unstandardized Coefficients

Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 10.637 .498 21.373 .000 amount_of_investment -3.496E-6 .000 -.179 -2.063 .041

a. Dependent Variable: time_to_IPO

Testing the effect of the amount_of_investment as the independent variable on the

time_to_IPO as the dependent variable, I could find out that a significant coherence between

these two variables could be found. This tells us that the amount of money a company

received has an impact on the speed to exit via an IPO.

Table 11: Coefficient analysis between amount_of_investment and time_to_IPO Table 10: Coefficient analysis between funding_rounds and time_to_IPO

(42)

36

Hypothesis 4:

It was already mentioned that, additionally to venture capital, other forms of “investments” play a role in the funding history and lifecycles of companies. The four most important types of investment that emerged during the data collection are:

Private Equity (PE): According to the literature, PE can be divided in two groups of investors. Those are formal (VC) and informal (business angels (BA)) (Bruton, Filatotchev, Chahine, & Wright, 2010). Previous research though shows, that even if PE is used as a hypernym of VC, one main difference can be spotted from the collected data. Private equity normally appears in a later stage of the lifecycle of a new venture and is commonly connected with a higher investment and a lower level of risk exposure. This can also be seen in Figure 1. Different kinds of investors have a different impact on the potential success of a new venture, especially because private equity normally is accompanied by a higher number of shares in the company (Thakur, 2015).

Post IPO equity: “VC-backed companies exhibit superior post-IPO performance, compared with non VC-backed companies” (Wang, Wang, & Lu, 2003, p. 2016). But a number of market conditions and external influences can make it necessary for new ventures to ask for additional funding to stay competitive even after their IPO. That is, in this context, what we understand as Post IPO equity trough private investors.

Debt financing: “Companies should be inclined to use debt to finance assets in place and equity to finance growth” (Hovakimian, Opler, & Titman, 2001). “In principal, companies needing new finance should issue equity if they are above their target debt level and debt if they are below” (Marsh, 1982). This target levels are defined by each company independently. Nonetheless, debt financing distinguished from equity, as it means to lend

(43)

37

money from a financial institution and pay it back with higher interest rates within a certain time, instead of exchanging shares for money.

Secondary market investment: IPOs normally take place in the primary market. This means, that companies sell their shares to the public and investors for the stock price their shares exhibit. In the secondary market, this happens “after-market clearing prices” are established (Mauer & Senbet, 1992). This means that entrepreneurs, who don’t want to wait for their IPO or need money quickly without entering a new funding stage, can sell their shares to investors on the private market instead of offering them to the public. The same applies for employees of a new venture, who want to get rid of their shares, but want to cash out instead.

Post IPO debt: This investment form constitutes a form of “Debt financing” with the only difference that it takes places after a successful IPO.

In my sample, further additional forms of funding played a role, but appeared so few times so that we don’t have to attach value to them:

Private Equity 15

Debt financing 20

Post IPO Equity 23

Secondary market investment

15

Grant 4

Convertible note 1

Post IPO debt 7

Table 12: Distribution of otherforms_funding

(44)

38

In table 13 the distribution of the additional forms of funding in my dataset is stated. The descriptive statistics table contains the following variables: 0 (no other forms of funding), 1

(one additional form of funding) and 2 (a combination of 2 or more additional kinds of funding). The simple calculation shows that additional forms of investments lower the time to

IPO. The mean is 10.16 years, while the mean for variable 1 and 2 is 9.92 years respectively

9.80 years, while the mean for variable 0 is 10.42 years. But we need further information to

clearly say, if a coherence between additional forms of funding and the time to IPO can be spotted. In my analysis, time_to_IPO constitutes the dependent variable, otherforms_funding is the independent variable and the variable funding_rounds is used as control variable to see if the number of funding rounds has further impact. Table 12 provides information about which other forms of funding played a role in my sample and how often they appeared. From table 14 it can be seen, that these other forms of funding don’t have an impact on the time to IPO, while the control variable, the number of funding rounds is impacting the time to IPO significantly. This is not very surprising, as Hypothesis 3 already showed this positive coherence.

Source

Type III Sum of

Corrected Model 363.739a 3 121.246 5.127 .002 Intercept 4741.888 1 4741.888 200.505 .000 funding_rounds 354.496 1 354.496 14.989 .000 otherforms_funding 14.450 2 7.225 .305 .737 Error 2979.869 126 23.650 Total 16767.000 130 Corrected Total 3343.608 129

a. R Squared = .109 (Adjusted R Squared = .088)

Becoming a mature company : the influence of timing on a venture capitalists funding decision towards new tech ventures

Master Thesis