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The Rise of the Unicorns. How Media Affects Startup Valuations

Scientific Essay 2016 38 Pages

Business economics - Investment and Finance

Excerpt

Table of contents

1. Introduction

2. Literature Review
2.1. VC Valuation Drivers
2.2. Unicorns
2.3. Effects of Media on Organizations and Investors
2.4. Technology and Media
2.5. Media and VC Valuations
2.6. Hypotheses

3. Data and Methodology
3.1. Data
3.2. Methodology
3.2.1. Model (1) – LN Media Coverage / Day
3.2.2. Model (2) – % of Non-PE/VC Investors
3.2.3. Model (3) – Unicorn
3.3. Exceptional Media Coverage

4. Results
4.1. Univariate Results
4.2. Multivariate Results
4.2.1. Media Coverage
4.2.2. Non-PE/VC Investors
4.2.3. Unicorns
4.3. Robustness Section
4.3.1. Sample Split, Sub-Sample and Other Robustness Checks
4.3.2. First and Last Time Unicorns
4.3.3. Two-Stage Least-Squares Regression
4.3.4. Different Valuation Measures

5. Conclusion

6. References

Abstract

Within the last years, start-ups have achieved extraordinary high valuation levels which have never been seen in such dimensions before. These high-valued start-ups with valuations above or equal to US$1bn are also called unicorns. Similarly, media coverage of start-ups has increased significantly. In this paper the impact of media coverage on global unicorn valuations between 1990 and October 2015 is empirically analyzed. In addition, the impact of technology advancements on the media and start-ups is discussed. The here presented results indicate that technology advancements increase media coverage for start-ups. Investors which are typically not primarily active in the VC market are most affected by increasing media coverage. Start-up and especially unicorn valuations are driven to a large extent by increasing media coverage before a funding round. These results add new insights on the driving factors of start-up valuations and are consistent across a variety of different regression models and robustness checks.1

1. Introduction

Unicorns are a rare species. According to fairy tales, it is nearly impossible to see at least one of them in your lifetime. That is the reason why journalists, investors, entrepreneurs and other market participants call start-ups with a valuation above or equal US$1bn unicorns (Lee, 2013). With only a couple of employees, a basic business idea, no or only marginal revenues (not to speak of profitability), it should be hard to attract venture capital (VC) funding and achieve sky-rocketing valuation levels. Not even in the Dot-com phase, start-ups achieved valuations levels in the dimensions we see them nowadays. Nevertheless, times seem to have changed. As of October 2015, according to Crunchbase2, 153 start-ups are in the so-called “unicorn club”. Altogether, these VC-funded start-ups offer a current post-money valuation of about US$529bn with a total funding of US$79bn. This is about 10% of the entire NASDAQ 100 index or more than 40% of the German DAX 30 index market capitalization3. So, unicorns seem not to be too rare. Apparently, they are popping up in a nearly weekly manner or as the Fortune magazine stated: “They seem to be everywhere.” (Griffith and Primack, 2015)

Where are these unicorns coming from? What factors are relevant for this high valuation levels? This paper tries to empirically investigate the effect of media coverage on start-up valuations. Empirical evidence shows that media coverage is especially affecting investor behavior in an environment of high uncertainty (Hillert et al. 2014). Uncertainty is highly pronounced in the area of start-ups and VC valuations. Here, especially the effect for high valued start-ups (unicorns) is analyzed. This is the first empirical study which focuses explicitly on the unicorn phenomenon. In doing so, I first analyze the driving factors behind media coverage. Thereafter, I try to answer the question which investors are affected the most by the media. In the end, the effect of media coverage on unicorn valuations is tested. Univariate and a variety of multivariate regression analyses and robustness checks are used. Certain statistical and sample-related challenges are addressed. The global sample is primarily based on Thomson VentureXpert, Crunchbase and LexisNexis data for the time between 1990 and 2015.

The here presented results indicate that media coverage is positively affected by technological advancement. The technological change induced by the internet, mobile business, cloud computing or social media fosters the speed of communication and the amount of available information. Information asymmetries might be lowered. Apparently, non-PE/VC investors which are typically not very experienced in the field of VC investments are majorly affected by exceptional media coverage. These investors tend to be invested in start-ups with more media coverage and also with unicorn valuations. This finding might indicate some kind of valuation overreactions in the area of unicorns.

Based on the results, the rise of the unicorns seem to be significantly affected by increasing media coverage. High levels of media coverage might close information gaps between founders and investors. Lower information asymmetries might lower risk levels and increase valuations. In addition, based on Petkova et al. (2013), media coverage also serves as legitimacy for start-ups. Legitimacy should be more pronounced when media coverage is high. Brown and Wiles (2015) provided first descriptive findings that increasing later stage investments (so called “private IPOs”) and available VC capital are replacing IPOs in the start-up sector. I provide the first empirical and supportive findings on this relation. Moreover, I show that especially media coverage is one of the key drivers within unicorn transactions. As a result, media coverage might serve as a channel through which technology change affect financing and valuations of start-ups.

This paper contributes to different literature streams in the following way: First, it provides new evidence on a direct relation between technological change and media coverage of start-ups. Second, the findings extent the literature of media influence on start-up investors. Third, the paper adds new insights to the knowledge of media effects on valuation. Especially for extreme situations like current unicorn valuations. Forth, media coverage as a potential channel of how technology advancements affect start-up financing and valuation levels is introduced. Fifth, descriptive findings on the major drivers of the unicorn phenomenon based on Brown and Wiles (2015) can be supported based on first empirical tests in that area.

The paper is structured as follows: in Section 2, an overview of the literature and theories is provided. Based on that, hypotheses are developed thereafter. Section 3 deals with the used data and methodology to test the developed hypotheses. The results are presented in Section 4. Section 5 concludes.

2. Literature Review

A lot of opinions, theories and commentaries are stated in public media which try to explain the unicorn phenomenon. In order to understand the drivers of VC valuations, the effect of technology on media and the effect of media on investors and valuations, the literature of these topics is discussed as follows: First, a general overview of known and possible valuation drivers in the VC area is provided. Second, potential reasons and findings on unicorns are discussed. Third, the impact of technology on media is illustrated. Forth, media effects on organizations and investors are summarized. Fifth, an overview of potential effects of media on valuation levels is provided. In the end, hypotheses are developed based on the presented literature findings.

2.1. VC Valuation Drivers

What is known about the drivers of start-up valuations and VC activity? Firstly, there is a direct link between the entire public market valuation and VC funding for start-ups. Within hot markets, more capital for VCs is available (Nanda and Rhodes-Kropf, 2013). According to Gompers et al. (2008), valuations and VC activity is associated with public equity markets. Based on Jeng and Wells (2000), increasing IPO valuations lead VCs to raise more funds. Especially for younger VCs, this effect is particularly strong (Kaplan and Schoar, 2005). Also the returns of VCs are highly correlated with entire market returns (Cochran, 2005 and Kaplan and Schoar, 2005). In addition, VC activity increases with increasing economic growth (Gompers and Lerner, 1999).

Secondly, it is important to understand the implications of increasing VC activity (fundraising and investments) for risk and valuation levels. As Gompers and Lerner (2000) point out, capital inflows into VCs increase the valuation levels of new investments. Additionally, VCs invest in more risky firms in hot periods (Nanda and Rhodes-Kropf, 2013; Gupta (2000) and Nanda and Rhodes-Kropf, 2014). According to Nanda and Rhodes-Kropf (2013), there is a causal relation between hot markets and the increase in VC capital towards shifting investments to more novel and innovative start-ups. Costs of experimantation are lower.

Based on the previously discussed literature, the public market affect fundraising and investments, which also has a direct effect on valuation and risk levels of the portfolio companies. Furthermore, other drivers within the VC industry or the VCs itself affect valuations. Based on an equilibrium model of Inderst and Müller (2004), valuation is also driven by contracting and bargaining as well as outside options and scarcity of VC. Within the model, capital market characteristics affect the relative supply and demand for capital. These characteristics affect bargaining powers and ownership shares. This affects the pricing and value creation in start-ups. Valuation levels increase and VCs invest in start-ups with lower quality. This would be in line with the findings of Gupta (2000), who describes that VCs invest in lower quality firms in hot times. In addition to that, Cumming and Dai (2011) find a convex U-shaped relationship between fund size and valuation.

Next to these findings, there is also some empirical evidence that VC investors overreact or are engaged in some kind of herd behavior. Under certain circumstances, VCs simply follow the investment decisions of other market participants and ignore private information. Managers might do so due to reputational reasons as it might be harmful for them for being perceived in the market as a contrarian. Overreactions and misvaluations can be the consequence (Scharfstein and Stein, 1990). Similarly, according to Gupta (2000), the volatility within the VC industry is a symptom of overreaction by VCs and entrepreneurs to perceived investment opportunities.

From a more general perspective, valuation can also be driven by a variety of other factors. Based on Gompers and Lerner (1999), R&D expenditures, reputation of a VC, but also taxes can have an effect on VC fundraisings which affect valuation levels as previously described. With regard to R&D expenditures or taxes, Harford (2005) shows that economic, regulatory or technology shocks can lead to merger waves. This is not directly linked to the VC industry, but provide a potential explanation for VC investments and valuation levels.

Another model is based on Miller (1977) on valuation effects for IPOs. According to the model, a limited supply of companies increase valuation levels when the "true" value is uncertain, short selling constraints and heterogeneous believes are prevalent. Only the most optimistic investors with the highest valuations receive a certain share in a company.

2.2. Unicorns

The literature focusing on unicorns is relatively new with a very limited number of articles. Brown and Wiles (2015) analyze the unicorn phenomenon on a descriptive basis. They point out that capital markets for private equity investments are changing. An increasing liquid and available later stage VC investment market (or as they call it “private IPO” market) might be responsible for the high valuation levels and the appearance of unicorns. IPOs are postponed as private funding is available and less expensive. The major forces which drive the private IPO market are costly IPO regulations, IPO costs, analyst coverage, low interest rates and search for yield from investors, poor performing small IPOs and mainstream acceptance for private equity investments. In addition, based on Gao et al. (2013), they mention a close connection between technology advancements, reduced market entry barriers for technology companies and unicorn valuations. With respect to technological advancements and globalization, Gao et al. (2013) point out that with increasing speed of communications, technology and global markets, small companies need to grow faster than in the past due to the “winner-take-all” principal. Only the fastest growing companies with the highest market share survive. This has implications how companies finance themselves.

Similar to Brown and Wiles (2015), a variety of experts and journalists perceive new technologies like the internet, cloud computing and the increasing usage of mobile devices as key driving force of unicorn valuation (Grabow, 2015; Bender et al., 2015; Thompson, 2015 and Griffith and Primack, 2015). Another force might be the so called “fear-of-missing-out” (FOMO) mentioned by Frier and Newcomer (2015), Janeway (2015), Thompson (2015) and Gurley (2015). As Amazon, Apple, Facebook or Google partially demonstrated, technology markets seem to be often characterised by a “winner-take-all” principal, i.e. only the largest market player survives and takes the major market share. With a new or more innovative business model, service or technology, new entrants try to disrupt the prevailing system. VCs might fear to miss the next Google, Facebook or Apple. Missing out such a “blockbuster” deal would have negative implications on fund performance and reputation expressed in lower levels of future VC fundraisings. In that relation, Facebook’s IPO in 2012 with a valuation of about US$122bn is often seen as a “super-unicorn” or a catalyst for other start-up valuations (Lee, 2013).

Liquidation protections are additionally affect unicorn valuations. In a recent study of Fenwick & West LLC, Kramer et al. (2015) analyze the terms of various unicorns. Based on the results, investors seem to have implemented significant downside liquidation protections within the contracts. Liquidation preferences, IPO conversion provisions or anti-dilution adjustments protect the investors in case of further funding rounds, acquisitions or IPOs. Higher values can be accepted more easily by the investors, as the transactions bear less risks. Brown and Wiles (2015) also refer to liquidity protections as important driver of valuations. In a case study about the unicorn Square, Inc. Rauch (2016) illustrates the valuation effects of such protections. As a result, the valuation levels should be treated carefully as a direct comparability with other transactions and protections might not be possible.

2.3. Effects of Media on Organizations and Investors

After having discussed valuation driving factors in the VC and unicorn environment, the potential effects of the media on organizations and valuations should be discussed next. Based on a variety of studies, media per se can have an effect on companies, investors and valuations. However, the effect is studied in only a few VC related articles.

In general, media tries to find broad topics in order to reach as many interested people as possible (Carroll, 1985). According to Suchman (1995), there is a need of new organizations to be perceived and appear desirable in order to gain legitimacy. This might be achieved via the media. Media coverage provide information about organizations to a large number of stakeholders (Petkova et al., 2013). In addition, media coverage reduces information asymmetries (Tetlock, 2010) and directly affects information collection, processing and interpretation of investors (Engelberg and Parsons, 2011 and Tetlock, 2007). Media coverage and legitimacy might be beneficial as companies with legitimacy have better access to resources like financial capital, employees or business partners (Deeds et al., 2004; Williamson, 2000; Williamson et al., 2002 and Pollock and Gulati, 2007).

How does the media help to increase legitimacy? Attention is a scarce resource (Ocasio, 1997). Communication processes contribute to attention and organizational legitimacy (Suchman, 1995). Media can be viewed as intermediary and is responsible for allocation of attention to new organizations: The media actively selects specific news about certain topics, events, actors, companies, etc. which increase public attention (Hoffman and Ocasio, 2001; Kennedy, 2008 and Pollock et al., 2008). With this selection mechanism, media channels public attention towards specific organizations and away from others (Rindova et al., 2007). The public opinion about an organization is reflected in media coverage. Therefore a measure of legitimacy is provided (Baum and Powell, 1995 and Elsbach, 1994). The legitimating role of media in the context of broad stakeholder audiences with limited information about young companies is analyzed by Pollock and Rindova (2003) for IPO investors. The role of an intermediary when media coverage is perceived as an external “critic” is important for stakeholders who have to make decisions under uncertainty. This increases the legitimacy and credibility of new organizations especially within an environment of limited information (Zuckerman, 1999). In addition, firms which are using Twitter for communications are more likely to increase the perceived quality of the firm and reduce uncertainty (Fischer and Reuber, 2014). Consequently, media is a distinct legitimation mechanism (Petkova et al., 2013).

2.4. Technology and Media

Globalization and technological change affect companies, investors and other market participants in many ways. Moreover, technology also affects the information availability and perception. Technology in form of the internet changes the media business landscape and usage of media (Küng et al., 2008 and Dimmick et al., 2004). The internet affects the consumption and perception of news, news content, the user itself, regulation and business models (Küng et al., 2008). The media industry has undergone a fundamental shift over the past decade. New online distribution channels have been created. But not only had the internet changed the media market and user behavior. Also mobile devices like smartphones affect the consumption of news (Xu et al., 2014).

2.5. Media and VC Valuations

So far, the effects of technology on media and media on organizations were illustrated. Next, I want to describe what is known about the effects of media on valuation levels also focusing on the VC and start-up field. Important factors for a VC due diligence have been analyzed in many studies. Nevertheless, the effect of external perception of start-ups on VC valuations are less covered in the literature. Even though more information (also coming from external media sources) within a due diligence process might affect valuations. Based on the findings of Petkova et al. (2013), media attention in early stages of new organizations affect the perceived valuation of well-informed experts like VCs. VCs benefit from external indicators of public recognition of start-ups and they incorporate media coverage in their due diligence processes. According to Petkova et al. (2013), VCs and the perceived valuation of start-ups are affected by media coverage in two ways: First, media coverage signals public interest which might positively influence stakeholders like customers, employees, etc. Second, new information become widely available. This reduces information-provision costs and provide legitimacy and credibility. Interestingly, news with positive as well as negative tone have a positive effect on VC funding. Berger et al. (2010) find supporting results as even negative reviews about books of new and unknown authors increase their sales. Publicity increases awareness. Media channels public attention toward specific organizations and away from others. From a stakeholder perspective, this increases the importance and valuation of these organizations (Rindova et al., 2007). However, the causality can also be the other way around. A positive effect of valuation on public attention is described by Demers and Lewellen (2003).

To the best of my knowledge, the study from Petkova et al. (2013) analyzing 398 U.S. based start-ups between 1997 and 2004 is the only study conducted with the focus on the effect of media on VC funding. Additionally, there is empirical evidence that marketing can have a postive effect on valuation levels. In the field of IPOs, Cook et al. (2006) find a close relationship between involvement of investment banks, marketing of IPOs and valuation. Well promoted IPOs induce sentiment investors and consequently increase the valuation. Receiving media attention is beneficial even when the content is negative. Higher attention creates awareness and lead the focus on specific organizations and away from competitors. In line with Cook et al. (2006), Pollock and Rindova (2003) show that media coverage before an IPO increase valuation levels and liquidity.

Hillert et al. (2014) find that media coverage can have an effect on investors and can lead to overreactions and biases especially in an environment of high uncertainty. They provide evidence that media coverage affects investors and increase momentum in stock returns. In line with Daniel et al. (1998), Hillert et al. (2014) indicate that high media coverage might induce investor overreactions as signals from the media confirm (contradict) investor’s initial private information. This might be considered as evidence of one’s own skills and overconfidence is more pronounced. Disconfirming news will be largely neglected.

2.6. Hypotheses

Based on the above described findings and theories, I derive three hypotheses in order to describe the effect of technology on media, the effect of media on investors and the effect of media on start-up valuations. The aim is to provide evidence on unchartered driving factors which can explain the unicorn phenomenon. In doing so, I combine the three research fields of technology, media and VC valuations. Oftentimes a causal relation cannot directly been stated and analyzed here, but the logic of the hypotheses and market mechanisms is assumed to work as follows: Technology and innovations have advanced significantly over the last years. With technological advancements like the internet, mobile phones, cloud computing or social media platforms the amount of available information on firms increased. Information can be distributed globally in a faster way than in the past. Blogs and websites covering news on firms foster the effect and increase media coverage for firms. Especially in a technology oriented field like start-ups, this effect is assumed to be very much pronounced.

Hypothesis 1: Technological advancements increase media coverage of start-ups.

Information are especially valuable in an opaque and uncertain environment (Hillert et al., 2014). The valuation and the decision to invest in a certain start-up takes place in such an opaque and uncertain situation. Information on the start-up are limited and information asymmetries between the founders and the investor are quite large. A growing amount of information via increased media coverage might be able to lower these information asymmetries. Especially inexperienced non-PE or VC investors in the field of start-ups rely on and might be stronger affected by the increasing amount of information.

Hypothesis 2: Non-PE/VC investors are stronger affected by media coverage than PE/VC investors.

Based on Petkova et al. (2013), media can affect a start-up in the following ways: First, media coverage and attention on a large scale is a scarce resource (Ocasio, 1997) and signals that the start-up, its products, the technology, the founder team, or other aspects are of interest and relevance for stakeholders. Second, large scale media coverage act like an information intermediary (Zuckerman, 1999), which reduces information-provision costs. In an environment of limited information and expertise, media coverage brings a credibility and legitimacy advantage to the covered start-up. These effects might favor market access, customer acceptance, increasing sales, hiring employees, finding investors and increase valuation. Therefore, increasing media coverage is assumed to be positively related to start-up valuations.

Hypothesis 3: Increasing media coverage has a positive effect on start-up valuations.

As media coverage and technology increased significantly over the last years, the assumed positive relation between media coverage and valuation should be explicitly prevalent in the area of unicorn valuation. The effect of media attention might be another important explanatory variable for unicorns, next to the already mentioned driving forces like “private IPOs” (Brown and Wiles, 2015), driven by low interest rates, high stock market valuations, new investors and the search for returns.

Based on the described theories, I test the stated hypotheses with a specific data set and methodology which are described in the next section.

3. Data and Methodology

3.1. Data

In order to analyze and test the proposed hypotheses, I use the Thomson VentureXpert database for information on global VC-backed transactions between 1990 and October 2015 like dates, companies, funding amounts, rounds, investors and valuations. Furthermore, total investments and fundraisings are drawn from this database. Incomplete or missing data for unicorns are manually corrected or added by using Crunchbase data4. Industry financials and stock market data are drawn from Compustat and CRSP. Additionally, information on the global IPO market are provided by ThomsonOne. Interest rates are accessed via WRDS from the Federal Reserve Bank. In total, the sample consists of 14,497 VC deals with total VC investments of about US$1,013bn and 276 transactions with disclosed valuations above or equal to US$1bn (i.e. unicorns). A sample overview over time is provided in Table 1

(Table 1)

Similar to Petkova et al.(2013), I hand collected the media coverage information for 8,356 VC transactions from LexisNexis. Due to missing information which are needed for the baseline regression and lack of data prior 1995, 6,141 transactions were excluded. By grouping duplicates and excluding non-business news, the number of articles, which include the name of the start-up are counted. The used time period begins with the last previous funding round or 12 months before the investment date until one day before the investment date. To account for media coverage related to a previous funding round, a time gap of one month after the last funding round was included. 12 months are used as proxy for the average time between different funding rounds based on Gompers & Lerner (2004). A graphic illustration of the different time periods is provided in Figure 1.

(Figure 1)

For certain start-ups, adjustments for the searched company name needed to be applied. For example, by searching for the media coverage for the company “Box”, the searched name needed to be adapted to “Box Inc”. Otherwise, non-related articles would be included. In total, I made 1,829 adjustments. Non-directly attributable articles could be found for 52 companies. In accordance with Petkova et al. (2013), I divided the total media coverage of a company by the days of the total media coverage time period. As a result, the sample includes media coverage information for 8,294 transactions with an average of 0.17 articles per day (median: 0.04; minimum: 0; maximum: 60.87).

In order to measure the effect of technological change and advancement, I construct the same time trend variable as described in Gao et al. (2013). They introduce a time trend variable in order to account for the increasing importance of economies of scope and the speed to the product market. This time trend has a significant negative effect on IPOs of small companies. As they describe, a direct measure like to number of granted patents would be ideal. However, patent data suffers from effects from changes in the patent law (Gao et al., 2013). The variable equals 0.01 in the first quarter of 1990 and increases by 0.01 for each quarter onward.

For further robustness tests, I also use a variety of different measures for technological advancements: First, I use the quarterly sold units of Apple’s iPhone. Mobile technology is key for information availability, new business ideas and the speed of communication. Especially in the years, starting from 2007, the mobile technology grew strongly on a global basis. Second and similar to Boulton et al. (2016), I use the internet penetration (i.e. internet users per 100 people) from the World Bank of the major regions North America, Europe, APAC (Asia and Pacific) and the rest of the world. Internet penetration might be a measure for technology advancements and information availability. Third and again similar to Boulton et al. (2016), daily newspaper circulation in the U.S. from the Editor and Publisher International Yearbook serves as negative measure of technological change. As internet news are cheaper, faster and everywhere accessible, newspaper circulation rates decreased significantly with the rise of the internet. Fourth, Google’s advertising revenues are taken into consideration. Google is a global internet company with both a search engine and also mobile phone software. Hence, Google’s advertising revenues from these sources might serve as an indicator for technological change, growing mobile business and the increasing flow of information. Another feature of these measures for technology advancements is the exponential characteristic which might be advantageous over the linear Time Trend variable.

The occurrence of unicorns and transaction valuations over time are illustrated in Figure 2. In addition, the current 15 largest unicorns with a last disclosed (most prevalent) valuation since 2012 are displayed in Table 25.

(Figure 2)

(Table 2)

3.2. Methodology

First, the hypotheses are tested by using univariate differences-in-means tests and multivariate logit, OLS and firth model regressions thereafter. The major dependent variable for hypothesis (1) is the natural logarithm of media coverage per day (LN Media Coverage / Day). For hypothesis (2), I use the percentage of non-PE or VC investors per funding round (% of Non-PE/VC Investors), which are bank affiliates, endowments, pension funds, government programs, individuals, insurance firm affiliates, investment managers, non-private equity firms, SBICs, service providers, university programs and other investors. These investors are typically not active in the PE or VC business and are assumed to have less experience and information compared to PE funds, VC funds, corporate VCs or incubators. For hypothesis (3), the major dependent variable is a dummy indicating if a deal is a unicorn (private VC funded transaction with a valuation at transaction above or equal to US$1bn) or not. Consequently, one portfolio company can be included several times, but only with one specific transaction value. Additionally, the natural logarithm of transaction value also serves as dependent variable in further tests using robust OLS regressions.

For robustness reasons, I also use unicorn dummy variables indicating only the first or the last unicorn valuation at transaction a company achieved. In additional tests, I split the sample by time, analyzing deals pre and post the Facebook IPO which is treated here as a catalyst or compare only large transactions (deals with valuations at transaction above US$250mn). To address potential statistical problems arising out of the small share of unicorn deals compared to non-unicorn deals, I use the firth model. The firth model is based on a penalized likelihood approach to reducing small-sample bias in maximum likelihood estimation. Especially within logistic regressions, penalized likelihood also has the attraction of producing finite, consistent estimates of regression parameters when the maximum likelihood estimates do not even exist because of complete or quasi-complete separation.

[...]


1 Goethe University Frankfurt, Finance Department, House of Finance, Theodor-W.-Adorno-Platz 3, 60323 Frankfurt am Main, Germany. Phone: +49-(0)69-798-33717. E-mail: zoergiebel@finance.uni-frankfurt.de.

2 Crunchbase is one of the world’s most comprehensive publicly available dataset of start-up activity. The dataset includes about 650k profiles of people and companies in the start-up and VC industry.

3 As of 26. October 2015 based on Thomson Reuters Eikon.

4 Crunchbase data as of 26.10.2015.

5 As of 26.10.2015.

Details

Pages
38
Year
2016
ISBN (eBook)
9783668949881
Language
English
Catalog Number
v469422
Institution / College
University of Frankfurt (Main) – House of Finance
Grade
1,0
Tags
Unicorns Startups Valuation Media

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Title: The Rise of the Unicorns. How Media Affects Startup Valuations