# data[].attributes.headline
REAL OPTION ANALYSIS OF VENTURE CAPITAL INVESTMENTS

# data[].attributes.subject
REAL OPTION ANALYSIS OF VENTURE CAPITAL INVESTMENTS

# data[].attributes.attachment_link.readable_text
Abstract

The number of ventures with a market value of one billion USD or more has considerably increased during the last decade. Driven by new technologies and business models, these ventures became an integral part of our daily life. Particularly, the number of unicorns based in China and other regions outside the US raised during recent years whereas the phenomenon was initially limited to the US region. Existing research has mainly focused on descriptive approaches to examine the rise of these ventures but lacks knowledge on the drivers of this phenomenon. We address these research gaps and investigate the underlying factors that foster the emergence of such high-valued ventures. Our results present several economic environmental as well as investor-related factors that impact the likelihood for a venture to achieve a market valuation of more than one billion USD. Subsequently, we derive theoretical and practical implications that may foster the future emergence of new high-valued ventures, covering regulatory, investor- and venture-specific aspects.

Introduction

During the last decade a comparably new development began to evolve in the field of venture capital (VC) with the increasing occurrence of so called "unicorns". Originally introduced by Aileen Lee (Brown and Wiles 2015) the term "unicorns" describes companies that have always been private, have received at least one funding round of institutional capital, are not a divisional buyout of a public company and have an estimated market value of one billion USD or more (Brown and Wiles 2015). In fact, it could be observed that an increasing number of ventures refuse to go public but remain privately owned instead. In order to cover their capital requirements, extended sources of private financing are necessary. So in contrast to previous periods, the median amount of total capital raised by unicorn firms in private financing rounds exceeds the median of technology based IPOs in the meantime (Brown and Wiles 2015). That is why several studies refer to these financing events as "private IPOs" (Kensinger et al. 2000; Milanesi 2012; Brown and Wiles 2015). Although the public and media interest in this particular subgroup of VC backed companies increased since the 2010s, academic research on this topic remained rather limited.

To a certain extent, this lack of studies might be given due to the general scarceness of data in the area of VC (Kaplan and Lerner 2016) which also persists in the case of unicorns. Especially as some studies see the possibility to retain sensitive financial and operational data private for a prolonged time period as an advantage of private ownership (Fenwick and Vermeulen 2015) compared to IPOs and related disclosure procedures. Furthermore, unicorns are still quite rare compared to the remaining universe of ventures. However, the lack of research is quite striking as today's unicorns are expected to disrupt large industry sectors (Divan 2016) which will bring major changes to everybody's daily life. Due to this, politics and governmental institutions recognized the importance of this disruptive potential and started to examine the evolution and location behavior of unicorns (Simon 2016). However, as the current research on unicorns is still rather nascent, one needs to focus on the more general literature and research that discusses high valuation levels in the area of VC. This is in particular the case as existing research that examines unicorns specifically is mainly limited to descriptive studies which report the rise of this phenomenon but do not identify factors that drive the probability for ventures to achieve multibillion USD valuations.

Previous research has identified that a startup's valuation is based on numerous factors including factors that can be observed (e.g. industry, VC investor's quality) as well as on factors that are not publicly known (e.g. company's sales and assets) (Gompers et al. 2006). Due to the better availability of data and more meaningful results, existing research was conducted mainly considering observable factors. For example, Nahata ( 2008), Ebbers and Wijnberg ( 2012), Phalippou ( 2010) and Cumming and Dai ( 2011) found that the characteristics of investors and their respective quality have a strong impact on the valuation levels of portfolio companies, whereas other studies see the degree of innovation of companies as dominant factor for ventures to become successful (Da Rin et al. 2011). Furthermore, Miloud et al. ( 2012) find sales growth to be a decisive criterion when assessing high valuation levels especially in the high technology industry. Interestingly, Bergemann et al. ( 2008) show that high valuations in the venture's starting days do not necessarily lead to high valuation levels in later funding rounds which gives some indication that stakeholders along the venture's lifecycle have a material influence on the success probability and ultimately on the valuation levels. In contrast to this finding, Rungi et al. ( 2016) find that the valuation for ventures, which exceeds the ten billion USD threshold in later funding rounds, is already significantly higher in the second funding round which points to the special nature of unicorns.

However, given the extremely high valuation levels and the meteoric rise of unicorns as well as their unique and strong impact on all stakeholders, we presume that unicorns do not follow entirely common valuation patterns as they remain a very special phenomenon. Taking this into consideration, we conclude that the valuation and success factors of unicorns differ from factors that influence typical and common ventures, making unicorns a very special phenomenon which requires research on the question which factors drive the likelihood of becoming a unicorn.

In order to establish a better understanding of unicorns and their valuation levels, we shed light on the question what drives the probability for young ventures to become a unicorn by identifying differentiating characteristics of ventures. Further, we answer in a second analysis the question what factors are decisive that some unicorns outperform "common" valuation levels. By providing answers to these questions, the contribution of our work is twofold. First, we contribute to the growing literature regarding the growth and emergence of ventures and more specifically on determinants for venture valuation (Lerner 2010; Grilli and Murtinu 2014; Mason and Brown 2014). Second, we provide novel insights how investors' characteristics and economic conditions impact the likelihood for ventures to become a unicorn and hence identify critical influences for venture success. Thereby, our findings can be generalized to a certain extent and transferred to the more general question which conditions influence the success probability of young ventures and how they can be supported on their growth path. Further, our results are relevant to the valuation literature, as we confirm the hypothesis that the growth strategy has an impact on valuation levels (Salamzadeh and Hiroko 2015; Gompers et al. 2016; Meglio et al. 2017) but show simultaneously that other factors might be overestimated in prior research.

Theoretical considerations and hypotheses development

Reviewing the list of unicorns, the often-cited concentration of unicorns in certain geographical areas becomes obvious. More than three-quarter of the current unicorns are based in the US and China. The number of China-based unicorns increased continuously since 2014, whereas the number of unicorns founded in the US remains rather stable (Fig. 1).

Number of "unicorn births"

Further, by reviewing US- and China-based unicorns in detail, a clear domination of certain local areas becomes obvious. Whereas in the US a clear domination of the Silicon Valley, followed by the New York area is observable, most unicorns are based in Beijing and Shanghai in China.

However, the speed of the rise of unicorns seems to decrease after reaching its peak in 2017. Figure 1 shows the slowdown as the number of ventures which became a unicorn in 2018 was 26% below that of 2017.

The majority of all unicorns operate in the consumer and technology sector followed by unicorns that are active in the field of financial services (Fig. 2). Similar to the origin of unicorns, the majority of unicorns' initial lead investors are based in the US, however, we see an increase of investors based outside the US over time.

Industry distribution of unicorns in 2018

Although, we find an increasing public awareness for unicorns, this special form of VC-backed companies has received only limited attention in the current academic discussion so far. Therefore, it seems reasonable to review the more general literature on VC and startup valuation first and subsequently to deduce relevant aspects for unicorns. By doing this, we identify three potential general areas of influence that might impact the success as well as the valuation of ventures and which can serve as a basis for deducting hypotheses.

First, we find investor-related factors in existing literature as an influencing category for the success probability of ventures. Experience and investor's skills have a direct influence on the performance of a VC-backed company (Gompers et al. 2006; Bottazzi et al. 2008; Bengtsson and Wang 2010; Achleitner et al. 2013). Especially in the case of venture financing, this aspect seems to be important as VC investors provide usually additional advice in comparison to pure financial backing (Bonini et al. 2012; Jackson et al. 2012).

Hypotheses development

Based on the review of existing literature, we develop four hypotheses which we consider as most relevant when assessing whether ventures will achieve a unicorn status and the level of their respective valuation.

Investor reputation

A firm's reputation can be described as an intangible asset that is based on public recognition of the quality of a firm's activities and outputs (Shapiro 1983; Rindova et al. 2005; Lee et al. 2011). In particular, reputation, which results from a VC firm's prior experience and performance, is economically important (Achleitner et al. 2018) as it can generate future rents by reducing information asymmetries among different actors (Hsu 2004). In the case of ventures and their respective investors that means that entrepreneurs are able to select superior investors based on their reputation although they did not previously interact with them. For investors, the reputation construct comprises their experience, their network capabilities and the level of direct assistance which they provide to portfolio firms (Hsu 2004). Thereby, highly reputed investors can generate superior performance through two general levers (Haagen 2008; Tykvová 2018). First, it is presumed that they invest in superior ventures (selection effect) and second, their strong operational support for their portfolio companies [e.g. mentoring, financial assistance (MacMillan et al. 1989)] facilitates the successful development of the venture (treatment effect). Given the current levels of capital inflow and the availability of financial capital, reputation gains increasing institutional and investors' awareness for certain venture capitalists as financial capital per se is not a differentiating factor among the VC investor universe (Hsu 2004). As a consequence, investors that possess a higher reputation create a higher visibility for themselves (Megginson and Weiss 1991; Krishnan et al. 2011) and attract ventures of superior quality which in turn have a higher probability to achieve a unicorn status.

Finally, a reputable investor group bears also a signaling effect for other investors in subsequent funding rounds. Future investors are more open to commit higher amounts of capital if they find highly-reputable existing investors among the venture's group of investors. The reputation thereby takes on the role of a certification of the venture (Megginson and Weiss 1991). Of course, this effect leads to a rather increasing valuation and therefore increases the probability for ventures to achieve a unicorn status.

Taking these aspects into consideration, we derive the following hypothesis:

Hypothesis 1

A high investor reputation increases a venture's probability to achieve unicorn status.

Economic clusters

Past research has identified several factors that foster the establishment of new ventures in certain geographical areas, leading to clusters of innovation. Originally, three players were identified that facilitate the creation of clusters: universities and research institutions, large companies and the government (Leydesdorff and Etzkowitz 1996). However, recent research demonstrates that the establishment of clusters is way more complex and applies complex network theory to shed some light on the operating principles of innovation clusters (Ferrary and Granovetter 2009). Generally, clusters provide unique conditions for new ventures and consist of a network of companies that are geographically concentrated (Khan and Ghani 2004) which can be described as an ecosystem that promotes the creation of companies and enhances competitiveness (Lemarié et al. 2001; Carlsson 2002; Yamawaki 2002; Sternberg and Litzenberger 2004). Clusters show higher growth rates than other regions and past research found that the performance of young firms benefits from being founded within such clusters (Gilbert et al. 2008; Delgado et al. 2010). Subsequently, we assume that being founded within one of these ecosystems influences also the occurrence of unicorns.

However, beside Silicon Valley and Route 128, New York is the third area that has been identified as a regional area that produces a considerable number of new ventures majorly in the fintech area due to its strong financial sector (Romanelli and Khessina 2005; Zhang 2009).

Hypothesis 2

Ventures that are founded within a technological innovation cluster possess a higher likelihood to become a unicorn.

Corporate venture capital

As we consider unicorns as highly innovative, the more general findings regarding the innovativeness and corporate-backing are transferable without any limitations to unicorns as a special subset of ventures. Furthermore, established corporate investors can reduce the widespread uncertainty for new technologies that are introduced by innovative ventures. By backing these ventures, incumbents generate a technology endorsement effect as they signal markets that they are convinced by a technology which is a strong argument for other market participants to apply these products (Paik and Woo 2017). Thus, receiving funding from a CVC investor can be regarded as a certification for the venture's products or services. As many unicorns have developed disruptive and innovative products and services, we assume that the technology endorsement effect should hold especially for this subgroup of ventures. In particular, the endorsement as well as the certification effect is supposed to have a positive influence on the development of ventures by lowering market entry barriers for them and enabling stronger sales growth.

Further, Chesbrough ( 2002) found that CVC investors are more patient when it comes to returns and exits. In fact, corporate investors have less time constraints in their investment horizon (Paik and Woo 2017) which enables ventures and subsequently later unicorns to spend more time to develop products and services which could lead to a superior quality of the latter which should also be beneficial for the development of unicorns. Furthermore, more patient CVC investors are willing to remain invested for a longer time than independent VC funds with a finite lifetime and hence enable high follow-on funding rounds instead of promoting an exit through an IPO which gives unicorns the possibility to grow (Wadhwa and Phelps 2009; Guo et al. 2015).

Past research has found some evidence that CVC can outperform independent VC investors at least if there is a strategic fit between the venture and the investor (Morck 2000). Taking the different influences and effects into consideration we derive the following hypothesis:

Hypothesis 3

CVC-backing increases a venture's probability to achieve unicorn status.

Growth strategy

In general, it is difficult to answer why, e.g. Airbnb, achieves a post-money valuation of $ 30bn without having any relevant assets and even more interesting for what they spend almost $ 4bn of funding without having a research-intensive or high-tech business model (Filloux 2014). But having a closer look at unicorns and prospective candidates, it becomes obvious that a lot of their valuation is based on market shares and strong growth rates.

One of the reasons for this development is that investors often accept growth metrics as proxies for value and hence are primarily interested in increasing users, engagement, customer acquisition, retention rates and revenues. Thereby, investors are comfortable to absorb high losses if they see a chance to lock-in "a position to generate quasi-monopolistic profits and, by extension, enormous capital gains" (Kenney and Zysman 2019, p. 46).

As a result, unicorns become often-as Ramadan et al. ( 2014) call them-"category king" as they define, develop and dominate new markets.

Though, ventures are required to achieve a "critical" size from a funding perspective in order to have sufficient financial resources and the capabilities to execute acquisitions. Hence, although inorganic growth strategies and potentials related to it might already exist in the very beginning of a venture, they become only obvious when being executed in a later funding round of a venture.

This leads to the following hypothesis:

Hypothesis 4

Inorganic growth is driving post-money valuation of unicorns.

Data

As our research addresses two different questions, we perform two independent analyses with different data samples. First, we conduct a hierarchical logistic regression analysis in order to examine which factors are driving the likelihood for ventures to achieve a unicorn status, i.e. a post-money valuation of more than one billion USD. Second, we conduct a linear regression in order to assess our hypothesis that the post-money valuation of unicorns is primarily driven by their inorganic growth strategy. For both types of analysis, we construct a unique dataset as described in the following.

Dataset

Our sample comprises 258 venture-backed companies valued with one billion USD or more based on their post-money valuation. The list and post-money valuation of identified companies was retrieved from CB Insights global unicorn club list as per Nov 4th 2018 (CB Insights 2018). Due to missing data, we needed to reduce our sample size to 215 individual unicorns in our analysis. In particular, we face missing founding dates as well as latest funding dates which are required to control for the economic environment during this time and to construct our peer group. Furthermore, for ten unicorns, the initial investor was not disclosed and hence the assessment of reputation not possible.

Remaining data on company and funding round details is also derived from Crunchbase, a free online database which was founded in 2005 and which provides information about technology companies, investors, and funding details majorly focused on the US market.

Though the Crunchbase database is comparably young, it becomes increasingly popular in VC research (Werth and Boeert 2013; Homburg et al. 2014; Croce et al. 2016; Schlichte et al. 2019). Further, the coverage of Crunchbase is on a comparable level to other more established data sources like Thomson One Private Equity (Werth and Boeert 2013) or data recorded by the National Venture Capital Association (Block and Sandner 2009; Huang and Zhan 2015). Potential biases, arising from the geographical focus of Crunchbase, have been considered and taken care of by cross-checking the completeness of the company list with other sources, e.g. CB Insights. Further, we conduct a web search to validate our data by comparing the unicorns' websites with the data in our sample. In particular, we check the unicorns' founding locations in order to reduce potential bias that might arise from any relocation developments. In addition, we compare the disclosed post-money valuation of each company in the sample in the Crunchbase and CB Insight database. Thus, we cannot detect any systematic bias but confirm the high data coverage of Crunchbase. Numbers for GDP growth in respective countries were taken from World Bank's World Development Indicators database.

Variables

Dependent variable

We apply two dependent variables for our research. For the analysis of factors that drive the likelihood to achieve a valuation above one billion USD we take a dummy variable which equals one if the respective company is a unicorn and zero otherwise (logit model). This modeling is particularly necessary as no reliable post-money valuation is available for most ventures and ventures that are valued below one billion USD.

Further, we take the post-money valuation to examine influences in the group of unicorns (linear regression model). We consider that the post-money valuation of unicorns as dependent variable fits best our research purpose although we are one of the first studies using this variable as success measure. Another advantage of our selection is that the post-money valuation is a continuous variable compared to previously used binary variables, thus providing an information gain. However, the post-money valuation of ventures is only available in a reliable manner for unicorns. Hence, we apply this variable only for unicorns in the respective analysis but not for peers or analyses that include peers. Due to the high skewness of the post-money valuation in our sample, we apply the natural logarithm of the post-money valuation.

Independent and control variables

Historically, the age of an investor was argued to be a good proxy for the reputation of an investor. However, this does not consider any success-related components except the survival of an investor.

The reputation ( Inv_IPO Share) was calculated following a similar approach like Nahata ( 2008) by taking the average of the yearly percentage share of IPOs backed by a specific investor compared to all IPOs in the same year. For calculating the reputation, we refer to a timespan from 1990 until 2018.

In addition, we include a dummy variable indicating whether the venture received funding by a CVC firm or not ( CVCinvestor) in order to assess the influence of the latter and to test the hypothesis whether ventures that receive funding from CVC investors exhibit a higher likelihood to become a unicorn.

To answer the question whether the foundation of ventures within innovation clusters influences the likelihood to become a unicorn, we incorporate dummy variables in our analysis. These dummy variables equal one if the venture is located within the respective region and zero otherwise ( SiliconValley_Target, Boston_Target, NewYork_Target, Beijingarea_Target, Shanghai_Target).

To address the question whether strong and aggressive growth is driving the valuation of unicorns, we use the number of add-on acquisitions (#Addon_acquisitions) to account for non-organic growth as there is evidence for the existence of a "winner-takes-it-all" premium (Simon 2016; Stayton and Mangematin 2018) for industry/market-dominating ventures.

To account for additional influences on the likelihood for new ventures to become a unicorn, we include the following control variables in our model. A lot of recently founded ventures operate in internet-related businesses in a B2C setting (Li and Mahoney 2011). Therefore, we include a dummy variable indicating if the respective unicorn's or peer's business model is related to the internet or mobile apps as well as a dummy variable that indicates if the unicorn or peer acts in the B2B sphere. The variable equals one if the company's business is app or internet related and zero otherwise ( Internetorapprelated). The B2B variable ( B2B) equals one if the company operates in the B2B sphere and zero if the company operates a B2C business model. Further, we include a dummy variable that equals one if the respective unicorn or peer operates a platform business model and zero otherwise ( Platform). In addition, we control for the founding team size (#founders) following Bhawe et al. ( 2017) and Ratzinger et al. ( 2018) in order to account for the effects evolving from the diversity of founding teams (Eesley and Roberts 2012), particularly as founders' characteristics and team size have been identified as influencing factors in past research (Bernstein et al. 2017; Rungi et al. 2016).

We include the GDP growth in the year of founding ( GDP_growth_founding) to control for general market effects on the respective founding behavior. Further, we control for the age at latest funding ( Age). To control for the effect of overall economic conditions, we include the GDP growth during the latest funding round in the respective country ( GDP_growth_latestfunding).

Descriptive statistics

Table 1 presents descriptive statistics of the variables used in the empirical analysis of all unicorns we have identified (Panel A). Further, Table 1 presents descriptive statistics for unicorns and identified peers (Panel B) and for the respective peers (Panel C) separately. T-tests for the comparison of means indicate that unicorns and respective peers differ to some extent, depending on the variables considered in our analysis.

Table 1 Descriptive statistics and comparison of means

Unicorns are more often backed by CVC investors compared to non-unicorns. In addition, the share of IPO exits is considerably higher for unicorn's initial lead investors compared to non-unicorn lead investors. Comparing the entire unicorn sample of 235 cases with the relevant peers identified shows no significant difference.

Empirical analysis and results

As described, we conduct two types of analysis which are presented below, in order to examine our hypotheses. To test for multicollinearity of variables, we refer to the variance-inflation-factors (VIF). The mean VIF in all models is below an acceptable range of 10, indicating that multicollinearity is not a major issue (Gujarati 2003).

Logit model

To test our hypotheses and in order to examine the factors that separate non-unicorns from ventures with a valuation of more than one billion USD, we conduct a logistic regression analysis in line with Jain ( 2001). Hence, we model the probability of a venture to become a unicorn depending on a set of control variables and our hypothesized effects regarding investor reputation, being placed in a cluster and the existence of CVCs among the investor group. Our model is therefore formalized as follows:

Here, y i is the occurrence of becoming a unicorn (0/1) depending on a ventures' characteristics x i.

The results of the logistic regressions are presented in Table 2. Thereby, Panel A reports the results as marginal effects at the means, whereas Panel B reports the results as average marginal effects which is seen as superior when realistically interpreting the results. In model 1 to 5, we present the regressions for independent variables and controls separately, whereas model 6 contains the full model of all independent variables and controls. Since a valuation of more than one billion USD (unicorn) is indicated by a value of one, significantly positive coefficients denote an increase in the likelihood for ventures to become a unicorn. For all logit models, model fit was assessed by the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Thereby, lower levels represent a better model fit (Akaike 1998). In order to test our first hypothesis, we include investor reputation in our model. The logit estimates show that the likelihood to become a unicorn is positively impacted by the investor's reputation ( Inv_IPO Share) when including our controls (model 2) as well as in the full model (model 6). Though, the effect remains statistically insignificant.

Table 2 Determinants of achieving unicorn status

Reviewing the influence of being founded within a cluster presents ambiguous results. We find a significant (1% level) positive relationship for the likelihood to become a unicorn for Silicon Valley based ventures ( SiliconValley_Target) (model 3) which holds also in our full model (model 6). Significant effects from other clusters like Route 128 ( Route128_Target) or Beijing ( Beijing_Target) are not observable within our dataset. This result underlines to some extent the uniqueness of Silicon Valley when it comes to the foundation of innovative and successful ventures. In particular, it demonstrates that clusters exhibit different characteristics and finally different quality levels regarding the support of young ventures.

Furthermore, the logistic regression shows that the likelihood of becoming a unicorn is positively influenced by the existence of a CVC investor ( CVC Investor). This effect is significant on a 0.1% level (model 4) and is line with hypothesis 3. Further, these findings persist in our full model (model 6) at the same significance level.

Finally, we cannot find any statistically significant effect that the number of add-on acquisitions (#Addon_acquistions) impacts the likelihood to become a unicorn, although the estimates show positive coefficients (model 5) which holds also in the full model (model 6).

In the full model of all independent and control variables taken together, the combination of effects becomes obvious. Although the average marginal effect does not consider the non-linear trend of the probability curve of logit regressions, the respective coefficients are more suitable to present a more vivid effect of the independent variables on the dependent variable than, for example, odds ratios (Hoetker 2007). Therefore, we present our results using average marginal effects in Table 2 panel B.

Within our full model, we see that the foundation of a venture in the Silicon Valley area ( SiliconValley_Target) increases the predicted probability to become a unicorn, on average, by 14.7% (model 6). As already outlined, a statistically significant effect is only observable for Silicon Valley based ventures (5% level). For ventures that are based in New York ( New York_Target) or Route 128 area ( Route128_Target) the model exhibits positive relationships but only on statistically insignificant levels. Surprisingly, we find negative coefficients for the predicted probably to become a unicorn (models 3 and 6) for ventures that have been founded in Beijing area ( Beijingarea_Target) and Shanghai ( Shanghaiarea_Target), though on statistically insignificant levels. Hence, the relevance of clusters seems to differ between different countries and the existing clusters possess different levels of development, whereas the Silicon Valley seems to be superior to others (Castilla 2003; Rosenthal and Strange 2003).

The predicted probability for ventures to become a unicorn is increased, on average by 28.1% (model 4) and 27.9% (model 6) if a corporate venture investor is part of the venture's investor group ( CVCinvestor). This strong positive and highly significant (0.1% level) effect supports hypothesis 3 and gives a clear indication that corporate investors either select superior ventures or provide better support for their portfolio companies than pure financial investors do. Interestingly, the number of founders (#founders) shows a significant positive effect (5% level in models 1 to 5 and 10% level in model 6) throughout all models. The positive impact of the number of founders on the predicted probability to become a unicorn hints on the widely stressed argument that a higher diversity within founding teams leads to higher performance (Beckman et al. 2007; Vogel et al. 2014).

Linear regression

In order to test our hypothesis that aggressive inorganic growth and subsequent add-on acquisitions drive the post-money valuation, we conduct a linear OLS regression analysis applying robust estimators to cope with heteroscedastic data. We incorporate this separate analysis to test hypothesis 4 as this hypothesis presents a slightly different perspective than the remaining hypotheses. Hypothesis 4 considers the question which type of strategy investors value once a venture has achieved a unicorn status which differs from the question what drives the likelihood of ventures to become a unicorn. In order to avoid potential bias that might arise from the timing dynamics, as ventures do not execute add-on acquisitions at the very beginning of their lifetime but rather once they have become more mature and especially once they achieved a unicorn status, we decided that the post-money valuation as a dependent variable as well as a linear regression model is most appropriate following Seppä and Laamanen ( 2001) and Heughebaert and Manigart ( 2012). In particular, we use a linear regression model instead of a truncated regression model as we consider only unicorns as relevant for our research question and hence other ventures with a post-money valuation below one billion USD are not part of the sample. Due to the skewness of the post-money valuation, we apply the natural logarithm of this variable in our model. Subsequently, we derive the following regression equation:

The results of the linear regression are presented in Table 3. Basically, we consider the same variables as in our logit analysis but add the number of add-on acquisitions for the reasons outlined above. Model fit was assessed by adjusted R-square, whereas higher values determine a better model fit. As we apply the natural logarithm of the post-money valuation of unicorns as dependent variable, positive coefficients indicate a supporting influence between variables and the respective post-money valuation. For the sake of completeness, we present the same model structure as in the logit analysis but focus on the results of models 5 and 6. Model 5 includes the number of add-on acquisitions while considering all control variables. Model 5 shows that the number of add-on acquisitions (# Addon_acquisitions) is positively linked to the post-money valuation at a high significance level (0.1% level). This effect also holds in the full model (model 6) and is in line with the often-cited assumption that the current venture and unicorn market is determined by a "winner-takes-it-all" approach and gives strong support for hypothesis 4 that aggressive and inorganic growth of unicorns is a value-determining factor. Although being outside of the focus of this analysis, we find no other significant effects that influence the post-money valuation of unicorns except for the foundation within the Silicon Valley area ( SiliconValley_Target) which exhibits a positively significant effect (1% level) (model 6). Obviously, unicorns that are based in the Silicon Valley area are valued higher by their investors independently form their age or general economic conditions as we have controlled for both factors.

Table 3 Determinants of post-money valuation

Extended analysis and limitations

Second to test the robustness of the investor reputation results, we replace the IPO share as proxy for the investor reputation by the age of the investor following Nahata ( 2008). Further, we consider relationships between CVC investors and the venture's business model. In order to examine whether CVC investors are primarily interested in ventures that could help them to adapt their own business models to a changing economic environment, we test for moderating effects between the existence of a CVC investor and a special type of business model (platform and internet or app related). However, based on our data sample we do not find any significant effects concerning these assumptions.

Further, we perform our analysis limited to certain geographic regions. Therefore, we create subsamples of our original dataset based on the origin of the ventures and respective unicorns. As we apply two different datasets for models 1 and 2, we rerun our analysis on four subsamples (two for China and two for the US). We focus on these two countries as most unicorns are domiciled in these countries whereas the socio-political conditions are completely different and hence diverging effects might be observed. As a result in the logit model, we find a stronger influence of the existence of a CVC investor on the likelihood to become a unicorn for China-based unicorns and a positive significant effect for the number of founders for US-based ones. Both effects are statistically significant at a 0.1% and 1% level respectively. The remaining results of the logit analysis are in line with the results of our full sample (Table 3). Running again our linear regression model (Table 3) in order to test hypothesis 4 on China and US-based subsamples separately, reveals that the number of add-on acquisitions as a value-determining effect loses its significance for China-based unicorns. However, the sign of the effect remains positive. For the US-based subsample, the significance of the add-on effect increases.

Finally, we conduct again our two types of analysis (logit and linear regression) for those unicorns with a post-money valuation of more than two billion USD as unicorns valued exactly or slightly above one billion USD threshold might provide a bias as their valuation could be primarily artificially increased due to publicity reasons. The linear regression model, limited to the subsample of unicorns that are valued above two billion USD, shows consistent results with the full sample. For the logit regression, we observe that the significance of the Silicon Valley effect diminishes but remains significant on a low level (10% level). We see two potential explanations for this effect. On the one hand, the Silicon Valley cluster support could primarily be important for less mature ventures or respective unicorns. On the other hand, one might argue that investors in the Silicon Valley area tend to provide ventures artificially high funding to enable them to pass the one billion USD threshold for publicity reasons which would increase the number of unicorns in Silicon Valley. However, we assume this explanation to be less relevant as the higher competition among VC investors within the Silicon Valley area should rather adjust such biased valuations.

Based on our analysis, we consider our results as robust in general. However, our robustness checks show that the degree of influence of considered factors varies depending on the origin and valuation level of the respective unicorn.

As every empirical study, our results are facing certain limitations. First, as unicorns are a very new phenomenon, the sample size is limited and there is currently no possibility to perform any long-term investigations. Closely linked to this limitation, scarcity of available data remains another limitation for our results which has also impacted previous studies (Homburg et al. 2014; Kaplan and Lerner 2016), due to the private ownership of unicorns. In particular, as the majority of all unicorns are headquartered in China or the US, there are no obligations for them to disclose annual reports or financials statements. Hence, our analysis is only able to measure the success of unicorns on a proxy level from an outside and third-person perspective.

Second, due to limited disclosure requirements, current research lacks detailed information on the ownership structure. To get deeper insights in the ownership and stock structure, it would be important to cope with the very common use of preferred liquidation rights in the field of unicorns. Especially unicorns with a valuation around one billion USD tend to sell stock with preferred liquidation rights to overcome the one billion USD valuation threshold for publicity reasons. But by selling stocks with preferred liquidation rights, the implicit and risk-adjusted valuation should be lower and probably in certain cases below one billion USD (Bartlett 2016).

Third, as the majority of unicorn investors are comparably young, they lack a proven track record. Hence, it is rather difficult to assess the performance of investors particularly in a long-term view.

Conclusion and discussion

In this paper, we investigate the factors which drive the rising of VC-backed companies with a valuation of more than one billion USD, so called unicorns. Therefore, we follow a two-fold approach by examining factors that lead to a trespass of the one billion USD valuation threshold and the question which growth strategy is favored for unicorns by investors, separately. Based on the descriptive analyses, we observe that unicorns are a locally concentrated phenomenon with more than fifty percent based in the US. However, the share of China-based unicorns has been rapidly increasing during the last two years whereas activities in other countries remain on a comparably low level.

Based on our empirical results, we cannot find any significant influence of investor's reputation on the likelihood for ventures to become a unicorn. In particular, early investors have a higher influence as they are working in closer relationships with their portfolio companies and exhibit a stronger operational involvement (Hsu 2004; Stayton and Mangematin 2018). As we cannot find this effect to be significant in our analysis, we assume that this is due to the fact that the construct of investor reputation is hardly measurable. Hence, new measures of investor's reputation and quality should be considered in future research.

Besides that, we find limited support for hypothesis 2 that the foundation within a cluster increases the likelihood for ventures to become a unicorn as we find this effect to be significant only for the Silicon Valley area which indicates superior characteristics of this cluster in particular compared to other clusters in the US and China. Probably, this includes a self-reinforcing effect, as successful ventures and unicorns attract other ventures and investors, providing ultimately better infrastructure and leading to a highly innovative environment. The geographical proximity of VC investors might have an additional positive influence in this context on the likelihood of receiving further funding and allows investors to identify promising ventures earlier which results in locally concentrated unicorn births. Past research has identified that the political and regulatory environment has a direct effect on the likelihood of unicorn births (Wright et al. 2005; Safari 2013), however we see this effect as significant primarily on an inter-country level.

Finally, our results indicate that aggressive and inorganic growth strategies measured by the number of add-on acquisitions are driving the level of valuation and hence are a favored strategy from an investor's perspective. This finding confirms the assumption that investors accept growth rates as a proxy for value. As an extension to existing research (Kenney and Zysman 2019) this assumption does not hold only for platform-based business models but seems to be applicable to all unicorns. Furthermore, we find that inorganic growth is fostered by investors as we do not find any significant impact that add-on acquisitions increase the likelihood to become a unicorn per se but only that these drive post-money valuation levels. Of course, this implies that investors take the availability of potential add-on targets into consideration when investing in unicorns.

As profitability fades into the background and investors are willing to absorb high exceptional losses, we conclude that growth is one of the key drivers for valuation and return on investment based on the assumption that today's unicorns will turn in highly profitable companies some time in the future following role models like Facebook.

By comparing the results of both analyses (Logit and OLS regression) additional detailed conclusions can be drawn on the interconnection of the factors that drive the likelihood to become a unicorn and factors that influence the post-money valuation.

Our results show that the foundation in the Silicon Valley area has a positive impact on the likelihood to become a unicorn as well as on the post-money valuation. This underpins the exceptional position of the Silicon Valley cluster in today's unicorn ecosystem. However, whereas we assume that good entrepreneurial conditions for high quality ventures i.e. best-in-class research institutes, strong investor network, skilled labor force etc. have a positive impact on the likelihood to become a unicorn, the post-money valuation levels are probably also driven by the existence of large VC investors in the Silicon Valley area. With extremely high levels of committed capital, they are able to participate in or to drive multibillion funding rounds.

Second, as we cannot find any significant influence of CVC investors on the post-money valuation but a strong positive impact on the likelihood to become a unicorn, this finding indicates that ventures benefit primarily from CVC support before they become a high-valued unicorn which is in line with the findings of Park and Steensma ( 2012). Furthermore, this finding indicates that CVC investors act as a signal to other market participants besides providing operational and financial support. However, we conclude that this effect decreases once the venture has achieved a unicorn valuation because unicorns have usually already established a strong footprint with regard to market and stakeholder awareness. In addition, the influence of an individual investor decreases usually during follow-on funding rounds due to larger investor universes and diluted shareholdings. This effect is in line with our result that the existence of a CVC investor does not directly impact the post-money valuation of unicorns.

Third, the number of add-on acquisitions, i.e. an inorganic growth strategy, only seems to influence the post-money valuation whereas we do not find any significant impact on the overall probability to become a unicorn. We assume that the major reason for this effect is that unicorns need to achieve a certain level of maturity in terms of organizational development and funding before acquisitions are executed on a large scale. That means investors value acquisitions and strong growth rates predominantly in later funding rounds and hence increase their funding and subsequently the post-money valuation. Furthermore, the larger funding rounds prepare the ground for additional large acquisitions which enable unicorns to gain considerable market shares and hence to boost their post-money valuation. Clearly, this effect comes only limited into play when assessing the likelihood of ventures to become a unicorn. To some extent this effect can be regarded as a self-enforcing process once the venture or unicorn has reached a certain threshold in terms of market share and received funding. At this point, investors seem to be willing to finance the further development of the unicorn towards an absolute market leader.

Third, we add to the literature on CVC. The existing literature has already described the benefits of CVC extensively (Park and Steensma 2012; Rossi et al. 2017). However, our results highlight the interrelation between corporate investments and the success probability of ventures even for the most successful type of ventures, i.e. unicorns.

Besides the theoretical contribution of our findings, we see also clear practical implications. Our results enable decision-makers and governments to adjust their policy in order to foster the emergence and growth of ventures. Further, our analyses have implications for founders and key executives of ventures as the results can be considered when conducting funding rounds and subsequently choosing new investors. In addition, our results help founders and entrepreneurs when it comes to the question of choosing the right location as well as finding the right strategy for their venture in order to maximize the likelihood to become a member in the club of unicorns if they wish so.

The main question that remains in current research is how the valuation of unicorns will develop in the future and if there will be any impairments necessary or if the rise of unicorns will remain as an ongoing development (Fenwick and Vermeulen 2015). In particular, the majority of all unicorns is still showing high cash-burn rates and negligible or only intangible assets (Zimmerman 2016). Further, some of the current unicorns even lack a business model and generate only minor revenues but offer just great customer growth rates or growth potential. In fact, it will turn out if the current unicorn valuations are justified when the VC investors exit the unicorns via an IPO or private sale. However, there is certain consensus among venture capitalists that today's unicorns are overvalued (Gompers et al. 2016) especially as today's unicorns seem to be different from previous ones like Google or Facebook, which in fact were cash-generative and with a positive income in an early status (Cable 2017). Given that and taking into consideration that we were facing a slowdown of unicorn births in 2018, certain studies assume that we achieved already the peak of the unicorn trend (CB Insights August 4, 2016) or stand right in front of the bust of a unicorn bubble (Fitzgerald 2015; Vardi 2016).

However, the further development could rather be driven by the general VC inflow which is highly linked to general financial capital markets (Li and Mahoney 2011). Hence, high capital inflows to the VC market could enable further private initial public offerings (PIPO) which in some sectors exceed public IPOs in both frequency and dollar volume (Brown and Wiles 2015).

In the course of our study, we found an additional phenomenon which could be hardly assessed due to the available data. Currently, the role of preferred liquidity stock and its impact on valuation is rather unclear as only limited research has been conducted so far in this field. However, there is a clear indication that this special stock class has a strong influence especially for the unicorns with a valuation slightly above one billion USD and hence it could be worth to consider that phenomenon in future research purposes (Zörgiebel 2016a). This holds also for special term sheets that are used in venture financing rounds which are favorable for investors in particular in later funding rounds but which are usually detrimental to the interests of founders and early stage investors. However, research on those aspects is limited due to scarcity of data and particularly financials in the field of unicorn research. Overcoming this hurdle can open additional room for research purposes on a company level which should be linked with an investigation of the preferred liquidity stock phenomenon.

However, it remains unclear if growth and increasing market shares will remain key drivers for investor's valuation in the future. As we include only the initial investor, the question what drives the future valuation has to be left for future research. Nevertheless, despite of any potential valuation issues, there is a high probability that some of the unicorns will develop and offer very disruptive technologies (Fenwick and Vermeulen 2015) which will change traditional business models for ever.

References

1. Borell, M., and Heger, D. 2013. Sources of Value Creation Through Private Equity-Backed Mergers and Acquisitions: The Case of Buy-and-Build Strategies. ZEW -Centre for European Economic Research Discussion Paper No. 13-094

2. CB Insights. 2018. The Global Unicorn Club (including whisper valuations) Current Private Companies Valued At $1B+. https://www.cbinsights.com/research-unicorn-companies. Accessed 4 Nov 2018.

3. Filloux, F. 2014. The Ripple Effects of Disruptive Models. https://mondaynote.com/the-ripple-effects-of-disruptive-models-b9eb56407f6b#.6rlhy5fy7. Accessed 14 Mar 2017.

4. Gompers, P., Gornall, W., Kaplan, S., and Strebulaev, I. 2016. How Do Venture Capitalists Make Decisions?. NBER Working Paper Series, No. 22587.

Google Scholar

5. Häussler, C., Harhoff, D., Müller, E. 2008. To Be Financed or Not - The Role of Patents for Venture Capital Financing. Working paper, ZEW - Centre for European Economic Research Discussion Paper No. 09-003.

6. Morck, R. (ed.). 2000. Concentrated corporate ownership. Chicago: University of Chicago Press.

Google Scholar

7. Ramadan, A., Lochheard, C., and Peterson, D. 2014. Behind Uber's soaring value | Fortune.com. https://fortune.com/2014/12/11/behind-ubers-soaring-value/. Accessed 14 Mar 2017.

8. Romain, A., van Pottelsberghe de Potterie, B. (eds.). 2004. The determinants of venture capital: Additional evidence. Frankfurt am Main: Dt. Bundesbank.

Google Scholar

9. Solomon, G. 2015. To Burn or Not to Burn-Going Long. https://goinglongblog.com/to-burn-or-not-to-burn/. Accessed 14 Mar 2017.

Download references

Author information

Affiliations

Institute of Entrepreneurship, Technische Universität Darmstadt, Hochschulstr. 1, 64289, Darmstadt, Germany

Carolin Bock & Christian Hackober

Corresponding author

Correspondence to Christian Hackober.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and Permissions

About this article

Download citation

# data[].attributes.attachment_link.description
The number of ventures with a market value of one billion USD or more has considerably increased during the last decade. Driven by new technologies and business models, these ventures became an integral part of our daily life.

# data[].attributes.headline
Essays on new and established types of entrepreneurial finance:venture capital, business angels, and the emergence of initial coin offerings (ICOs)

# data[].attributes.subject
Essays on new and established types of entrepreneurial finance:venture capital, business angels, and the emergence of initial coin offerings (ICOs)

# data[].attributes.attachment_link.readable_text
Grow your career

Whether you're writing your first CV or deepening your technical knowledge, our library of trainings and tools is full of ways to sharpen your digital skillset.

Grow your business

From bringing your business online for the first time to growing its reach internationally, our library of online learning and tools can help you take your business further.

Helpful tools for small business owners

Growth stories

Meet people all over the UK who are using Google's tools to adapt and grow their business or career.

"The Google Project Management Certificate was such a confidence boost that I want more!"

How a Google Career Certificate helped Jelena train for a new career with no experience.

"I don't have a tech background, so my Google mentor walked me through every step of running a digital business."

Simone Haynes created a storytelling tool that helps children become the heroes of their own comics - with support from Google

"Using Google Analytics to see how people were using the website helped make it more accessible."

Grant Reilly couldn't buy the bag he needed for his diabetes essentials, so he designed his own.

# data[].attributes.attachment_link.title
Training & Tools to Grow Your Business or Career, from Google- Grow with Google

# data[].attributes.attachment_link.description
Grow your business or career with Google's wide range of online courses, digital training and tools. Explore how we help Britain grow.

# data[].attributes.attachment_link.description
The skills required to advance your career and earn your spot at the top do not come easily. Now there’s a more rewarding approach to hands-on learning that helps you achieve your goals faster. Earn points, levels, and achieve more!

# data[].attributes.attachment_link.description
The Partnership on AI was established to study and formulate best practices on AI technologies, to advance the public's understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.

# data[].attributes.attachment_link.provider_name
Forums - Portal of Robotics and Artificial Intelligence

# data[].attributes.attachment_link.description
Portal of robotics and artificial intelligence, drones, wearables