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101 Seiten, Note: 1,7
Table of Contents
List of Tables
List of Figures
2.1 Literature and definitions
2.1.1 How the literature evolved
2.1.2 Focus areas
2.2.1 Geographic characteristics
2.2.3 Target audience
2.2.4 Talent funnel
2.3 Startup and unicorn
2.4.1 Venture capital
2.4.2 Corporate investors
2.5 Valuation methods and down-rounds
2.5.1 General trends and recognized limitations
2.5.2 The use of traditional valuation methods in venture capital
2.6 Endnote theory
3.1 Study design
3.2 Sample selection
3.3 The interviews
4.1 Analysis of the interviews
4.2 Clustering factors
4.2.1 Overview table
4.2.2 Ecosystem factors
4.2.3 Startup factors
4.2.4 Investor factors
4.2.5 Valuation methods and down-rounds factors
5.1 Contrasting the literature with the interviews
5.1.1 Comparing ecosystem factors
5.1.2 Comparing startup factors
5.1.3 Comparing investor factors
5.1.4 Comparing valuation methods and down-rounds factors
5.2 The ideal unicorn aspirant, which factors have the highest impact at what time
5.2.1 Founding stage
5.2.2 Initial financing stage
5.2.3 Later stages and exit
5.4 Future research
The valuation of a startup results out of a highly complex interplay of different factors which can be assigned to four dimensions – namely, valuation methods and down-rounds, the ecosystem, the startup, and the investor. Breaking down this process into the deep lying and often hidden factors requires to examine the existing literature and to look even further beyond it. Therefore, active startup investors and entrepreneurs were interviewed, collecting hands-on knowledge from professionals directly involved in the negotiation process. In comparing theory and expert knowledge, it could be shown that the findings from the existing literature have to be considerably expanded by additional valuation drivers. Among the most crucial new insights is, that the negotiation dynamics play a substantial role on the final value achieved at the end of an investment round. For better overview these factors are sorted into the four above stated dimensions. Furthermore, for practical relevance, the discovered factors are assigned to three different development stages of a startup. This results in a guidance for entrepreneurs who aspire to build a unicorn startup by highlighting the relevant valuation drivers they should consider over time.
Besides, an initial set of influencing factors causing down-rounds is identified by the expert interviews. Such down-rounds may lead to the fall of unicorns. This topic has not yet been adequately approached by literature and gives room for further exploration.
Table 1: GAFA investments and acquisitions from January 2012 to October 2014, adapted from (Fabernovel, 2014)
Table 2: Identified factors extracted from the existing literature
Table 3: Factors stemming from the interviews
Table 4: Comparing factors from the literature to factors from the interviews
Table 5: Factors which were solely recognized by the interviewees
Table 6: Factors which are relevant in the respective financing stage
Figure 1: Development of startup valuation focused research, shown is the number of articles published in the respective time interval, own illustration, based on findings of Köhn (2018)
Figure 2: VC funds raised (in USD nillion) in US and Europe by stage focus, 2010-2014, adapted from (Murray, 2015)
Figure 3: Distribution of investments into venture capital backed companies from 2013 to 2017, by continent, adapted from (KPMG, 2017)
Figure 4: The evolution of Silicon Valley: successive waves, adapted from (Blank, 2015)
Figure 5: The fastest unicorns: from first the financing round to a billion dollar valuations, 2009 to 2016, adapted from (CBinsights, 2016)
Figure 6: The unicorn universities, adapted from (M. Armstrong, 2017)
Figure 7: The link between the predicted value of a company and the number of its registered trademarks, own illustration, based on findings of Block et al. (2014)
Figure 8: Median deal size of venture capital backed companies worldwide from 2010 to 2017, by series (in million U.S. dollars), adapted from (KPMG, 2017)
Figure 9: Global venture capital investment from 2008 to 2014 (in billion U.S. dollars), adapted from (Ernst & Young, 2015)
Figure 10: Illustrative relationship among fund size, valuation, and exit probability, own illustration, based on findings of Cumming and Dai (2011)
Figure 11: Interviewed investor characteristics
This research paper was created to serve several areas justice and shed light on processes in the startup environment that have not been concisely documented in a scientific way. The overarching question of this paper was “The rise and fall of startup unicorns – Which factors influence high startup valuations.” It is aimed to provide a new, more comprehensive view on the valuation drivers of startups by including the dynamics of negotiation during the actual valuation process.
This question entails several areas that must be assessed in order to answer it adequately. Accordingly, the three areas that this paper sought to investigate are the following:
1. What are the factors that impact the valuation of a startup – especially relating to startup unicorns.
2. Which factors increase the likelihood that a highly valued startup (startups valued at more than USD 100 millions) is engaging in a down-round.
3. From a practical point of view, which factors are the most important valuation drivers of a startup in each stage of its development.
At this point, non-scientific work, which elaborates on several factors that influence the valuation of a startup exists widely, for example in online magazines such as Mashable or Forbes.com (Fankhauser, 2013; Hudson, 2015). However, from a scientific perspective, each of the above stated research areas is missing information.
Concerning the first area mentioned above, one can find multiple research papers, even some which were summarizing previous studies, e.g. (Bottazzi & Da Rin, 2002; Köhn, 2018). Nevertheless, the researchers either concentrated on a different scope or only particular topics. As could be shown by the present study, Köhn’s study, which was the most comprehensive the author found in this field, did not include all factors and exposed major shortcomings regarding practical research. For instance, his work was not based on expert interviews, giving it a rather statistical, non-practical character. This led to missing out on further evaluating the dynamic character of financing rounds. Other researchers, for example Thursby et al. (2011), focused on very specific topics and their influencing factors on startup valuations. In their case, they evaluated the signaling effect that technology startups have on external investors. Consequently, the approach of the present thesis was to compile a comprehensive picture out of all major factors that could have an impact on a startup’s valuation, in particular with focusing on high startup valuations.
The second area of the research question was devoted towards finding factors that might enhance the probability for a startup to go through a down-round and thereby decrease in valuation. The existing literature on this topic was found to be rather limited. Eventually, only two factors could be extracted from these sources. Specifically, these two factors were the link between a rise in long-term debt and down-rounds (Hand, 2005) as well as the effect on financing rounds from overall negative market conditions, e.g. recessions (Buch, Gustafsson, Drvota, & Sundberg, 2011). Hence, the present thesis looked at expert opinions in order to enlarge the understanding of the factors driving down-rounds. In addition, this topic was seen as important to obtain a holistic picture, as startups ought to bypass factors that are driving down-rounds to increase their chances of becoming a unicorn.
The scope of the third area exceeds the mere answering of the research question to increase this paper’s practical relevance. It is understandable that the valuation drivers of a startup will vary over the lifetime of a venture. However, to the best of the author’s knowledge, the existing literature gives only very little recommendations to startups about which factors they should be especially aware of at which point in time. Yet, in a number of research papers, the factors were loosely connected to the time within a startup’s lifecycle. For instance, one source (Hand, 2005). stated that calculative valuation methods gain importance as the startups matures. Therefore, a startup lays a larger focus on factors related to the valuation method used later in its lifetime. Apparently, the time related influence of factors has not been adequately addressed by existing literature. As a consequence, the present paper started out to fill this gap and aimed to provide a guidance to startups on which valuation drivers they should care the most by going through each development stage.
In order to cover these just described three areas of the question effectively, this paper was designed to follow five essential steps.
First, the existing literature was examined carefully and in total 127 sources that focused on one of the three areas, were analyzed.
Second, the factors that were found in these research papers were collected and clustered into four main dimensions: ecosystem, startup, investor, and valuation related factors. The dimension “ecosystem” compromises all factors that are connected to geographical areas, including the market size of a region or the available venture capital of a country. The dimension “startup” entails all startup or more detailed unicorn specific factors, such as the characteristics of the founding team or the industry it is operating in. The dimension “investor” is divided into the different kinds of investors and also gives an overview over the development of the venture capital field – hereto belongs for example the signaling effect for a startup from an invested VC fund according to its reputation. Lastly, the valuation methods are gathered in the dimension “valuation methods and down-rounds”, where also enablers for down-rounds belong to. At the end of the theory chapter, a table with all the relevant factors was set up to provide an overview of the findings.
Third, as described above, the existing literature is missing out on some of the dynamics involved in startup investment rounds. Consequently, experts in startup valuation, i.e. four startup investors and one serial entrepreneur, were interviewed concerning the three main areas with a special weight on the negotiation process and down-rounds.
Fourth, factors were extracted from these interviews and subsequently sorted into the same four dimensions which were used to group the factors stemming from the literature. At the end of the findings chapter, a table with the relevant factors, which were revealed through the interviews, was created. It gives overview over the findings of the interviews and make these factors easily comparable to the factors which were found in the literature review.
Fifth, the findings of the interview part and the literature were compared and rationales for potential divergences were discussed. Ultimately, the resulting factors were presented over the timeline of a startup’s development. This way, readers receive a practical guidance on which factors are most critical at what stage. By following this guideline, prospective founders could maximize their chances of building a highly valued venture.
This section is dedicated to illustrate a comprehensive view on the existing literature concerning potential valuation drivers for startups in general and unicorns specifically. According to the found sources, the valuation drivers can be clustered into four dimensions – in particular these are: the startup, the various kinds of investors, the valuation methods and down-rounds, and the respective ecosystem all stakeholders are operating in. Further groups, such as talent fueling the startups, the audience buying from the startup, and business partners can be attributed to the four main dimensions in order to keep this overview structured more clearly. One circumstance that has to be kept in mind, is that in this article all identified factors might undergo changes over time, and so far, undiscovered factors could be recognized as relevant in future research.
The scholar Köhn (2018) thoroughly studied the empirical literature on the determining factors of startup valuations in the VC context prior to 2017. For his evaluation, he selected a total of 57 articles according to several criteria – most importantly: only studies concerning the valuation of venture backed startups were considered and a quality cut-off for the articles included in the SCImago Journal Rank (SJR ≤ 0.337) was strictly adhered to – much similar to earlier researchers such as Bouncken et al. (2015). Eventually, the selected articles show a significant growth over the last few decades, making this research field in general rather young.
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Figure 1 Development of startup valuation focused research, shown is the number of articles published in the respective time interval, own illustration, based on findings of Köhn (2018)
Concerning the focus of the existing research, one has to distinguish between the geographical, the stakeholder, and the scientific field.
Evaluating the 57 articles identified by Köhn (2018), the majority, i.e. 35, concentrates on US samples. European and Asian literature focused samples follow with 13 and three respectively. Other parts of the world are only covered incidentally in international surveys. Thus, specific South American and African unicorn factors are practically unresearched and not in the focus of this thesis. This finding is also in line with the number of unicorns in these regions: Two in Africa and five in South America (CB Insights, 2017; Nxtp.Labs, 2015). The geographically US centered research, relying heavily on the US based VC databases, has also been observed by Da Rin et al. (2011).
Aside from the geographical aspect, according to Köhn, the mentioned articles are also leaning towards the startup’s specific valuation drivers, e.g. (Block, De Vries, Schumann, & Sandner, 2014; Joshua Lerner, 1994; Moghaddam, Bosse, & Provance, 2016), or VC’s, e.g. (D. Cumming & Dai, 2011; Heughebaert & Manigart, 2012). Additionally, several scholars concentrated on the external environment valuation factors including the institutional and cultural setting (e.g. (Batjargal & Liu, 2004; D. J. Cumming & Walz, 2010) or VC fund inflows. Lastly, also a finding stemming from Köhn’s analysis, is, that research regarding startup valuations in the VC context is dominated by the field of management. Accordingly, the Journal of Business Venturing (n=7) and the Management Journal (n=5) are representing the majority of articles in Köhn’s sample. In his eyes, this finding can be explained by the complexity of the field of startup valuation. More tangible determinants seem to be superior to non-tangibles, hence, the research field of management might be more relevant.
There are multiple definitions concerning the term startup. In this article a startup is a young company (i.e., less than ten years old (Simon, 2016)). Furthermore, startups are private companies which are growth oriented (Kollmann & Kuckertz, 2010; Morris, Schindehutte, & Allen, 2005). The original term unicorn in the startup context was first publicly used by Lee, A. (2013) to describe startup companies which have had their market capitalization valued, typically during investment rounds with external investors, at one billion dollar or more. By choosing this word, Lee intended to symbolize the rarity of such a startup company.
However, as unicorns started out as regular startups, their entire development has to be taken into account. Therefore, investors from all different investment stages were interviewed, from early stage to later stages. Hence, the majority of the factors which were revealed in this paper are applicable to small ventures as well as to unicorns.
In the life circle of a startup the first investor typically is called an Angel investor, who is commonly found in the personal network (friends or family) of the founder. Eventually, the first investment will be exhausted, and a subsequent investment will be needed to accel and ensure growth of the venture. These investments most often come from VC funds, which in turn raise capital from their own investors and acquire equity stakes in high growth and high risk startups (Veugelers, Pottelsberghe, Véron, & Bogdanowicz, 2012). Thus, VC is a form of private equity investment (Ritter, 2015).
Furthermore, in this paper it is often referred to early stage and later stage startups. Early stage startups are defined here as startups during their seed and their series A investment stage. Later stage startups refer to startups in their series B or subsequent investment rounds.
Additionally, in this paper, a valuation is assigned to a startup when it receives an investment from third party investors and cannot be self-given by the founders without external stakeholders agreeing.
This chapter is dedicated to factors impacting high startup valuations which can be found in the setting startups and investors are operating in, i.e., their external environment. At first, several external environments around the globe, especially the European and American, will be examined. Next, universal trends will be discussed and put into the context of startup valuation. Subsequently, the focus will shift to other stakeholders such as the target audience and the potential talent a startup can aim at to hire.
At the beginning of this chapter, a quick overview regarding the worldwide unicorn distribution is appropriate. This helps to identify regions which are prone to yield unicorns and regions which are not. CB Insights (2017) assessed all on the 26th of May 2017 existing 197 unicorns. It came to the conclusion that more than half of the worldwide unicorn number can be found in the US (54%), followed by China (23%), India (4%), the UK (4%), Germany (2%), and South Korea (2%). All other countries have only two or less startups valued at USD one billion dollar or more. In order to shed light on the reasons behind these major discrepancies, a comparison among the three geographic areas: US, Asia, and Europe will follow.
An extensive white paper was published by multiple authors from a variety of universities in Europe and in the United States (Fleisch et al., 2015). This particular study (Fleisch et al., 2015) examined differences of the two ecosystems and their impact on startup valuations. First, time seems to be a key reason why the US has become a hub for highly valued startups. Silicon Valley was formed after experiencing different impulses from the late 1940s to this date, while reaching a critical mass of settled startups in 1990. In its history, but particularly from the 1990s onwards, a relatively high number of start-ups turned into major successful companies. These in turn keep nurturing the ecosystem with funding and other resources, including human resources.
This phenomenon contributes to the incomprehensive coverage of all investment stages a startup potentially goes through (Fleisch et al., 2015). Looking at Europe, there is an over proportional number of small funds which help to start a company, so that the founders can get their idea off the ground. In contrast, there are only a few medium sized B- and C- round funds which can finance and accelerate growth. These funds are needed to provide the necessary resources startups necessitate to become unicorns with global relevance (Bottazzi & Da Rin, 2002). In the illustrations below, a comparison between Europe and the US regarding the VC funds per investment stage raised is highlighted.
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Figure 2 VC funds raised (in USD nillion) in US and Europe by stage focus, 2010-2014, adapted from (Murray, 2015)
The reduced available capital is also seen as one of the factors forcing higher multiple investor expectations on European startups. In the US, the average yearly revenue per unicorn was USD 129 million opposed to USD 315 million for EU based unicorns. Accordingly, the average revenue multiple was 46.0x for US based unicorns and 18.1x for EU based unicorns making US based unicorns about three times higher valued while generating about three times less revenues compared to EU unicorns (Madhvani, Casartelli, Maerz, & Indekeu, 2016).
The researchers (Fleisch et al., 2015) further elaborate on the risk taking culture across the two regions. Concerning people as well as investments, risk embracing decisions and thereby fostering change is not common in Europe. Learning by trial and error is seen as a sign for an uncontrolled process which is not desirable - eventually resulting in a risk averse culture. This setting represents not the best soil for groundbreaking and pioneering innovations that change whole industries and create startup unicorns.
Nevertheless, Simon (2016) notices that the European startup environment has already improved by introducing tax shelter concerning investments into small and medium sized businesses as well as by improving the digital infrastructure. In his view, it seems that an additional factor is that the access to capital in each investment stage is starting to increase. Besides that, universities have begun cooperating with startups more closely to transfer their knowledge and findings into the economy. Moreover, the collaborative white paper (Fleisch et al., 2015) argues for an improvement of Europe’s startup conditions: Berlin is gaining international visibility and various investment funds as well as incubators are establishing themselves. Meanwhile, more European countries produce unicorns – e.g., Luxembourg, Denmark, and Switzerland have each produced their first unicorn (Madhvani et al., 2016) – and the cumulative value across all European unicorns increased from $122 billion in 2015 to $131 billion in 2016. However, due to the mentioned factors, Europe is still significantly lagging behind the US in terms of unicorn growth.
Asia, especially the Eastern region, is developing rapidly its startup ecosystem. Below, the diagram shows the investment distribution into VC backed companies among the regions Americas, Europe, and Asia Pacific.
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Figure 3 Distribution of investments into venture capital backed companies from 2013 to 2017, by continent, adapted from (KPMG, 2017)
It outlines the accelerated growth of Asian VC investments in comparison to Europe and the Americas. Nevertheless, the Americas are still leading the field in terms of available venture capital, giving its startups more financing options (Hellmann, 2000).
Besides the rising financing opportunities in Asia, also the potential customer base has been growing vastly (Huang, 2015). Referring to a Credit Suisse Report (2015), China’s middleclass has already overtaken the Americans by size and so has the number of internet users.
Likewise, India recognized the need for entrepreneurship as a vehicle to change the economy for the better and help employ roughly one million people who are entering the country’s workforce each month (Sarkar, 2016). In this context, Prime Minister Dr. Narendra Modi, announced on 15th August 2015 the initiative “Start Up India, Stand Up India” which gives incentives to Indian investment companies to finance startups.
Hence, the external ecosystem factors to yield unicorns are improving immensely in Asia.
Global trends stemming from various patterns and changes are impacting the entire startup and investor landscape. One of the largest evolutions can be observed in technology. The researcher Simon (Simon, 2016) considers that different generations of startups use the opportunities of different technological waves. The prominent 115% rise in the number of US based unicorns between 2013 and 2015 (Lee, 2013, 2015) gave Simon room to argue that the current technological wave (Internet/mobile) could have reduced the cost of entry significantly. In the previous technology phase, i.e. the Internet wave 1995-2005, see Figure 5, startups such as Amazon required extensive financial resources in order to build up logistics and infrastructure to disrupt the book market. In contrast, smartphone applications can currently be developed and introduced to the market at a much lower price point. Taking Silicon Valley as an example, five technological trends can be identified. These impacted several metrics, from employment rates over startup growth to available capital. In the illustration below, which shows the different technological stages, Simon proposed to call the last depicted technological wave mobile networks instead of social networks.
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Figure 4 The evolution of Silicon Valley: successive waves, adapted from (Blank, 2015) 9
Apart from the reduction in costs to start, more growth opportunities have arisen (Simon, 2016). Simon recognized, that in the 1990s, telecom operators started to diversify and tap into new industries by starting their broadband networks (e.g., through selling data and video bundles). Following this phase in the 2000s, IT companies used the internet as a platform to offer products and services such as search engines, e-marketplaces, and later social media. Subsequently, the third phase began approximately after 2010, when existing Internet businesses as well as traditional firms witnessed new market dynamics. The digital and physical world started to merge (B. Williamson, 2015) and the line between them further blurred (e.g. Uber bridges the digital and physical world to improve transportation). Williamson called this sequence “the transformation of everything else”, as not only software businesses utilized the internet, but also the rest of the economy. Startups are pushing the boundaries of these various undisrupted fields with plenty of opportunities and the ones leading the field of a business sector can turn into a unicorn. The transformation of everything also gives room to startups to expand their business in accordance with the growth and disruption of the specific markets in which they are operating (Simon, 2016).
The third omnipresent trend which is recognized by researchers, is a certain covariance between the public market valuations and the private market valuations of companies in the same sector, especially regarding competitors (Gompers & Lerner, 2000). As a specific example, Lerner and Hand (Hand, 2005, 2007; Joshua Lerner, 1994) examined the valuations of private and public biotech firms. The researchers came to the conclusion, that an increase in the valuation of public biotech companies would trigger an upward movement in the valuation of the respective private companies. Thus, they stated the observation that the valuations of publicly listed firms are used as indications to value privately listed firms and their economic potential. Accordingly, the valuation of private companies, is highly reliant on the current market situation in the public markets and thus the mindset and action of investors acting in the public markets.
In addition, if the public markets or specific industries go through a recession, valuations of private and public companies in all or the respective industry decrease. Hence, a startup that is raising capital in such a timeframe typically does so at an undervalued valuation (Köhn, 2018).
One factor that is seen as crucial for the development of a unicorn and that is primarily dependent upon the geographic environment, is the target audience of the startup, i.e., the buyers (Beise, 2004). Particularly, the size of the home market is critical to decide which strategy to pursue. Beise observed that the majority of successful European unicorns in his sample chose to internationalize its product or service very quickly. In contrast, successful Chinese and US unicorns followed a “two-step” approach. The first step would be to concentrate on the home market, and the second to open up globally. This phenomenon of a big enough market was also recognized by Simon (Simon, 2016). In his sample, all examined unicorns chose the global sales route first. The mentioned collaborative white paper (Fleisch et al., 2015) stressed the ongoing fragmentation of Europe and argued for more common regulations and laws, eventually uniting Europe from a business perspective. The aim behind this suggestion was to create a big enough market so that ambitious startups would also start primarily with the European market first before targeting other continents. Similar implications regarding the decisive character of a country’s regulations and their effects on the business environment were made by North (1991).
Another stakeholder group, which is also highly dependent on the environment the respective startup is operating in, is the available talent a company is able to acquire (Fleisch et al., 2015). Ultimately, there are three identified parts to find in the literature. Foreign talent that is streaming in to the local talent pool, the local talent pool itself and the startup’s ability to acquire top talent from this combined talent pool. Concerning foreign talent inflow, the whitepaper found that international startup hotspots such as Silicon Valley or Tel Aviv draw in a talented workforce and ambitious founders. The researchers (Fleisch et al., 2015) identified multiple causes in these ecosystems that explain this observation: Extensive financial resources, a proven unicorn track record, and close by value chain partners. Regarding the development of local talent, the researchers value educational measures as the most conclusive to foster homegrown talent and a more risk-taking mindset. Explicitly, the consortium suggests improvements in four key areas: Rewards for universities for generating startups, incentives for spending on research and development, education in computer programming and maker skills, and measures against the fear of failing.
In order to acquire the top talent from this combined pool of candidates, two researchers (Hsu, 2007; Wasserman, 2017) view the personal networks of entrepreneurs as an important factor. These networks are associated with higher valuations as they gain an advantage for entrepreneurs in the recruiting process. Hsu therefore suggests, that through the entrepreneur’s signaling effect, less effort from investors and less monetary resources would be required for acquiring top talent.
In this chapter, the focus lies on specific factors that are foremost specific to startups and unicorns. Since the literature entails significantly more data and articles on startup valuation in general than on the subcategory unicorn, the majority of factors will be discussed concerning startups in general. Due to the fact that unicorns are later stage startups, the factors, which concentrate on them, are presented in this article.
Hence the first part of this chapter is dedicated to factors impacting later stage factors, whereas the second part is related to unicorns.
The first factor potentially influencing the valuation and the further development of a startup is its size. Almeida et al. (2003) did not state which firm characteristic determines the size of a company in their article. Therefore, this paper defines size as the number of employees working in a company, a simplified definition of Jiang’s (2003) suggestion. Almeida et al. (2003) were not entirely certain whether a large or a small size would be more beneficial for a venture. On one side they argued that with larger size, companies have a higher ability to engage with external knowledge through more ties to the external environment. Thus, gaining the advantage of better exploiting the acquired external knowledge internally if a firm does not grow inward looking and ignores external data sources – as the scholars Levinthal and March warn (1993). Almeida et al. (2003) noticed on the other side, that a startup does not in every case increase its utilization when its size grows. In their opinion, this could stem from a decrease in informal mechanisms such as mobility and agility of the startup. Other researchers (Davila, Foster, & Gupta, 2003) pointed to industry statistics (e.g., (Bradstreet & Dun, 1998)) and discussed the twin liabilities of newness and smallness. Davila et al. (2003) argued that if these two characteristics were truly liabilities, then a startup would benefit from a rapid growth as this would eliminate at least one of the two.
Examining the age constraint, other scholars (Yoo, Yang, Kim, & Heo, 2012) argue against the age of a company as a liability. Data collections regarding the time for current and former startups to reach the unicorn status, support the researchers’ finding as highlighted below.
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Figure 5 The fastest unicorns: from first the financing round to a billion dollar valuations, 2009 to 2016, adapted from (CBinsights, 2016)
Examining past rapid growth as a factor on its own had been done by the team around Davila as well as by Cox and Camp (2001). The researchers concluded, that past growth is a reliable indicator of future growth, hence, a possible future higher valuation.
Beise (2004) took the growth factor even further and discovered that past growth alone does not drive valuation, but also the way it had been achieved. The scholar uses the term “lead markets” to describe countries which first adapt a certain innovation successfully. Beise’s findings reveal that "Innovations that have been successful with local users in lead markets have a higher potential of becoming adopted world-wide than any other design preferred in other countries". As a consequence, a growth profile like this will most likely drive a startup’s valuation up.
The next hard factor in discussion is the location of a startup (Valuation Houlihan & Ventureone, 1998). A comprehensive US study by the companies Valuation Houlihan & Ventureone revealed that there is a significant difference in valuation connected to the location of a startup – e.g. startups on the east and west coasts of the USA receive higher valuations than in the rest of the country. This finding is in line with the distribution of unicorn startups in the US as examined by Simon (2016).
Besides the location, it is also the industry the startup operates in that impacts its valuation. Sievers et al. (2013) deliver evidence, even if not entirely significant, that the German life science and traditional high-tech industry startups are valued at a discount compared to internet startups. Backing up this finding, Miloud et al. (2012) conducted a representitive (for the French market) study including 102 French startups from 18 different industries. This study found that VCs value ventures higher that operate in widely differentiated industries and industries with high growth rates.
The subsequent part of this subchapter is dedicated to factors concerning the founding team. In general, a startup achieves higher valuations when there is more than one founder (Hsu, 2007) – A study (Chaiyochlarb, 2007) assessing 21 former unicorns suggested 2.09 as a success proven number. Hsu, among others (Lee, 2013), regard previous founding experience as an valuation enhancer. Hsu continues with the founders personal network and brand. If these have an extended reach, the signaling effect of the entrepreneur increases, with it the ability to hire first-class executives and thus the company’s valuation. Particulary in China, entrepreneurs connected to Chinese VCs during earlier ventures are assigned valuation premiums on new companies (Batjargal & Liu, 2004). Batjargal and Liu stated that this observation is in line with the Chinese concept of guanxi – that strong social ties are from utmost importance.
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Figure 6 The unicorn universities, adapted from (M. Armstrong, 2017)
Consequently, VCs see risk reducing measures in this concept and thus assign higher valuations to these startups. Besides, not only the entrepreneur but also the startup itself can have a signaling effect which helps to attract external investors (Thursby et al., 2011). Thursby et al. measured this effect by assessing the patents of a startup and the founders technological background.
Moreover, the founders’ level of education and alma mater are seen as decisive factors by several researchers (e.g., (Hsu, 2007), (Sievers et al., 2013), (Lee, 2013)). Often founders with a doctoral degree or who have studied at least for a significant period of time at a particular university are more likely to be funded and can negotiate higher valuations.
Another factor concerning the founders is in respect to their experience in the industry they are emerged in and in their management experience (Miloud et al., 2012; Wasserman, 2017). In addition, Yoo (2009) found that the market value of a startup increased when technological competency is present in the venture. This can be connected to a finding by Lin and Yoo (Lin, 1998; Yoo et al., 2012). They stated that the market value increased if the technology and market uncertainty decreased. It is important to notice here the cultural differences between the US and Asia concerning the sources that information comes from (Wright et al., 2004). In Asia, information regarding the market or technology coming from the founder is given significantly less value than in the US.
Davila et al. (2015) examined 66 startups around the globe and found that VCs assign a premium to startups which introduced management control systems and made use of strategy- implementing systems. For the VC, this signals improved decision making and execution by the startup. In an earlier study (Davila & Foster, 2005), Davila discovered a similar positive effect on a startup valuation if management accounting systems were in place. Lastly, other factors include: the gender, the need for achievement, and personal goals in life which can profoundly impact the performance of a startup and consequently also its valuation (Cooper, 1993; Herron & Robinson, 1993).
Another factor that can drive valuations up are the strategic alliances a startup has established. Strategic alliances are powerful means to signal the status of the startup to investors and to gain facilitated access to resources. Depending on the partners, alliances often also reduce information asymmetries (Frankel & Li, 2004; Miloud et al., 2012; Nicholson, Danzon, & McCullough, 2005; Uzzi, 1996). Nicholson et al., showed in the sector of biotech startups, that ventures with strategic alliances with established pharmaceutical companies receive higher valuations. A similar pattern was discovered by Miloud et al. and Sievers et al. (2013) concerning German and French startups. Also, Moghaddam et al. (2016) revealed the correlation between a rising valuation of a startup and its alliances among US based software ventures. However, he added that too many strategic alliances could destroy the positive effect and could even have an adverse impact on the valuation of the company. The author recognized the reasons causing this phenomenon in the missing ability to handle a vast number of alliances due to the respective startup’s limited resources and capabilities.
Another observation made by Aggarwal et al. (2012) is the link between online blog coverage and the valuation of a startup. Blog coverage can contribute to a startup’s value in the form of inexpensive marketing and positive signals about a startup to VCs. As more VCs become interested in a specific startup, the competition to invest rises and thus also the startup’s valuation.
In this subchapter, intellectual property in the form of patents or trademarks and when these are likely to increase the valuation of a startup are discussed.
Researchers (Block et al., 2014; Greenberg, 2013) support the argument that intellectual property in non-software based businesses in general increases the value of a company as it reduces asymmetric information. In case the entrepreneurial finance market were sufficient, there should be no difference in valuation whether the respective patent is pending or granted. Nevertheless, Greenberg (2013) carried out a significant study examining 317 Israeli technology across multiple industries differentiating between pending and granted patents. He discovered that before the patent is granted, knowledge is still unprotected and needs to be kept undisclosed. This in turn could trigger asymmetric distribution of information (Frankel & Li, 2004) between a startup and an investor and adverse selection could occur which could lead to a lower valuation. Results from Greenberg’s research revealed indeed that granted patents have an additional value adding factor opposed to pending patents. His research also showed that there is no correlation between patents and the valuation of software firms. However, there are differences in how highly patents are valued, linked to the respective industry the startup is emerged in. In accordance to Greenberg’s (2013) finding, patents contribute the most to the valuation of a startup in the life science sector, followed by the communications and semiconductor industries. Lerner (1994) also demonstrated the positive effect on the company’s valuation relevant patents have, in particular for US based biotech companies. More proof for the valuation driving patent factor was delivered by Hand (2005) which was also the case for biotech startups, Armstrong et al. (2006) companies across industries, and Hsu et al. (2013) for semiconductor companies.
Block et al. (2014) analyzed besides patents also trademarks as a potential factor driving startup valuation since they also let startups protect their intellectual property and underline their market and growth direction. The team conducted the first representative study focused on the impacts of trademarks on a startups valuation. They converted their results into an inverted u- shaped curve which is illustrated below in figure 8. The curve shows the post-money valuation over the number of trademarks.
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Figure 7 The link between the predicted value of a company and the number of its registered trademarks, own illustration, based on findings of Block et al. (2014)
The researchers inferred, that holding multiple trademarks tend to increase the valuation, yet, too many trademarks would lead to a decline in valuation. The decline was according to the researchers caused by additional work load and coordination efforts for incremental better intellectual property protection.
In addition, the breath of a certain trademark can be identified by the chosen nice classes, providing information about scope and the future plans of a startup – a signaling effect which can increase valuations. Nevertheless, registering for too many Nice-Classifications diminishes the signaling effect and does not impact the valuation of a startup positively. In general, the authors concluded that the effect of trademarks on the valuation declines in respect to the maturity of a startup.
In this subchapter, unicorn specific factors, which have been assessed by scholars, are presented. Simon (2016) extracted data from the website of the VC fund Atomico in 2015, which had listed all back then known unicorns, to shed light on the topics of industries and locations in the context of unicorns. It showed, that from the reviewed 182 software focused unicorns, 40 were in the E-commerce/retail sector, 36 in the enterprise app sector, and 20 in the social sector. However, there were considerable regional differences: China’s unicorns for instance are mainly consumer focused – especially gaming which plays a substantial role in Asia. Accordingly, one could argue that there are certain industries, which are more prone to be funded in some countries than in others.
Concerning the growth of unicorn, Simon (2016) stated that unicorns typically grow organically, while only a minority grew through mergers and acquisition deals. Furthermore, Simon argued for the so called “category king” factor as one of the most decisive unicorn characteristics. A category king is a company that “defines, develops and dominates new markets” (Ramadan, Lochhead, & Peterson, 2014; Ramadan & Peterson, 2016). Another VC investor (Solomon, 2015) observed, that for these companies valuations rise exponentially - e.g., Salesforce was worth USD 40 billion (2013), when the next biggest competitor UpShot was sold for under USD 100 million.
Investopedia (Fuhrmann, 2011) calls the creation of category kings the establishment of virtual monopolies. The portal comes also to the conclusion that startups in such positions typically see a significant valuation increase.
A number of researchers (e.g. (Fan, 2016)) and founders, e.g. Stewart Butterfield the founder of Slack (Griffith & Butterfield, 2015) believe that reaching the one billion USD mark itself entails a signaling effect that impacts potential customers, the press, and employees positively. Other sources (Filloux, 2014) claim, that unicorns which are based on technology which is rather easy to imitate such as Uber or Airbnb are passing the one billion USD valuation mark and simultaneously the category king status by acquiring market share with the raised capital. These companies are continuing with this strategy until they are the industry standard and thus have established their position in the market.
Investors are essential in the process of creating unicorns. They not only provide financial resources, but also various other assets and services all aimed at increasing a startups valuation and accelerating growth. Thus, as investors considerably influence a startup’s success, this separated chapter is dedicated to how they interfere with the valuation of a startup especially in later stages. It is crucial to call out the motivational difference between an entrepreneur and an investor: The entrepreneur’s motivation can be rather diverse (e.g., besides financial motivation, desirability for self-employment, self-efficacy (Segal, Borgia, & Schoenfeld, 2005)), whereas the investor’s primarily is to improve financial returns. The latter can be achieved in multiple ways, such as, engaging in product market competition, licensing, or partnering with corporations through alliances or acquisitions (Baum & Silverman, 2004; Block et al., 2014). Examining the types of investors in unicorns through the database of CBInsights (2018) showed that venture capital/ private equity funds, large corporations, and institutional investors are the most active in this regard. There are differences in the valuation amounts within the group of investors dependent on the respective type. These differences are mainly tied to the average bargaining power and investment strategy different investors have (Heughebaert & Manigart, 2012). Manigart and Heughebaert found that generally, university or governmental VCs offer lower valuations than independent or corporate VCs. The researchers observed that the latter two have a similar track record concerning the valuation amounts. In contrast, Sievers et al. (2013) states that corporate investors do not impact German startup valuations considerably, whereas Hand (2005) reports that they do influence US based startups positively. With these facts in mind, the first part of this chapter is focusing on independent VC investors, the most relevant group in this context. A subchapter dedicated to corporate investors is following.
The most prominent type of investor in the context of startups is the VC, a subcategory of private equity. As indicated in the two diagrams below, venture capital has experienced an increase in the median deal size as well as in the total capital invested.
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Figure 8 Median deal size of venture capital backed companies worldwide from 2010 to 2017, by series (in million U.S. dollars), adapted from (KPMG, 2017)
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Figure 9 Global venture capital investment from 2008 to 2014 (in billion U.S. dollars), adapted from (Ernst & Young, 2015)
Lerner (Josh Lerner, 1998) roots this trend back to the alternative new ventures as innovation vehicles opposed to traditional R&D investments present for corporate companies. In particular, he compared the smaller investments into venture backed companies to the larger R&D investments by corporations such as IBM. This led to the expansion of venture capital including large funds such as Kleiner, Perkins, Caufield & Byers refocusing on that area.
The VC investment process without the work subsequent to an investment consists out of screening, evaluation, structuring, and the deal itself, where each of these phases can have a variety of impacts on the valuation of a venture (Kollmann & Kuckertz, 2010; Petty & Gruber, 2011)
According to Davila et al. (2003), Venture capital funds have gained experience and expertise in dealing with investments made in high uncertainty. In line with the finance theory, researchers (Manigart, Wright, Robbie, Desbrières, & De Waele, 1997; Pintado, de Lema, & Van Auken, 2007) show that increases in risk and uncertainty need to be outbalanced by a higher return of an investment. Consequently, the valuation of a startup at the time of investment must be reduced, so that an elevated return can be attained more securely. Following this line of thinking, VCs are required to aim for high investment returns – dependent on the fund, 30% per year is a common benchmark. A VC tries to maintain such a rapid growth and hence supports its invested ventures in the scaling process, yet not all investments will perform at such a rate. Typically, some will achieve higher rates and carry the investments which miss the chosen benchmark.
Gompers and Lerner (2000) report that the amount of funding available to VCs in certain ecosystems is linked to an increase in startup valuations in this ecosystem. They also showed that this increase was neither caused by a better risk profile from startups nor by improved cash flow projections. The authors viewed the increased supply in the VC industry as a competition enhancer amongst them, which in turn would raise the startup valuations.
VC firms do not only supply a startup with financial resources, but also bring management skills and reputation as well as a signaling effect to other stakeholders. A startup which receives funding by a VC and herewith passing the VC screen, often throughout multiple rounds and with different VC firms, signals the quality of the firm to the inside and outside of the startup. The startups can increase its reputation which reduces uncertainty and in turn decreases transaction costs (Davila et al., 2003; O. Williamson, 1979). Potential stakeholder groups that can be influenced by the signaling effect to the startup’s advantage include prospective high quality employees, customers, or negotiating new alliances making them choose the respective startup instead of competitors (Davila et al., 2003).
The strength of the signaling effect caused by the acceptance of a VC varies among the VCs. Different VCs have different reputations and their ability to add value to a venture can vary substantially. Some VCs’ involvement can even trigger a negative effect on the value of a venture (Fitza, Matusik, & Mosakowski, 2009). The reputation and ability of value add can be determined in multiple ways - e.g., the experience and history in terms of age, the amount of capital being managed, the number of IPOs, the frequency of joining the startups’ boards, the IPO capitalization amount, or the size of the fund (Bengtsson & Sensoy, 2011; Kaplan, Steven, & Schoar, 2005; Krishnan, Ivanov, Masulis, & Singh, 2009). By studying 246 startups, Hsu (2004) empirically evaluated how a VC’s reputation and the valuation offer of a startup by a VC are linked together. Ultimately, he found out that entrepreneurs are three times more likely to accept an offer from a highly reputable VC. Furthermore, the ventures were willing to accept a 10%-14% discount in valuation in order to get funded by a VC with a high reputation. These observations are also in line with the discoveries by Falik et al. (2016) who show that Israeli entrepreneurs put more emphasis on the valuation when dealing with less reputable VCs to outweigh the decreased “extra-financial” benefits. Hsu concluded, that entrepreneurs view the “extra-financial” benefits as entailing more financial value in the long-term than actual capital. Further scholars (Bengtsson & Sensoy, 2011) prove these findings and even show that throughout the investments in one company an experienced VC keeps adding value with the same level of effort (Welpe, Dowling, & Picot, 2010).
Besides the mentioned venture backed startup’s signaling effect on potential business partners, VCs themselves usually have an established network to specific partners reducing search costs and time to reach them. The partners within their business networks are commonly other startups, other investors, or manifested companies (Gulati, Nohria, & Zaheer, 2000). In general, VCs recognize two main benefits in intellectual property and alliances: To minimize information asymmetries and as a factor to add value to the respective startup (Block et al.,2014).
Cumming and Dai (2011) analyzed based on empirical evidence how VC characteristics impact their bargaining power. There were two main findings drawn: First, the relationship between the size of a fund (i.e., one criterion for reputation) and a firm’s valuation can be described by a convex curve. Second, when they compared the venture’s performance, defined as the likelihood to perform a successful exit over the fund size, they received a concave curve as an outcome as illustrated below.
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Figure 10 Illustrative relationship among fund size, valuation, and exit probability, own illustration, based on findings of Cumming and Dai (2011)
As previously elucidated, larger funds usually are in the position to offer a startup a lower valuation due to the knowledge of startups that these funds have a higher probability to exit them successfully. On the contrary, when the VC fund gets too large, the likelihood of a successful exit decreases and the valuation the VC has to offer starts to increase again. Cumming and Dai demonstrate that this phenomenon is at least partly connected to the limits of human resources. VCs commonly neglect increasing their workforces in proportion to their fund size growth. As a consequence, their bargaining power is reduced because they only have limited attention for the sourcing and screening process as well as for further developing portfolio companies. In consequence to these limitations, they have to pay an increased price for investments with comparable quality. This is because, on the one hand the investment opportunity inflow is restricted and on the other hand, portfolio companies receive only inadequate support.
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