Valuing Internet Start-ups

Selected Issues and Adjustments of the Multiple Valuation Method


Seminar Paper, 2013

30 Pages, Grade: 1,5


Excerpt

Table of Contents

List of Abbreviations

List of Tables and Figures

1. Introduction
1.1. Problem Definition and Objectives
1.2. Course of Investigation
1.3. Author’s Valuation Experience

2. Internet Startups
2.1. Firm Characteristics
2.2. Market Characteristics

3. The Multiple Valuation Method
3.1. Introduction
3.2. Reasons of Popularity
3.3. Generic Multiples in Detail
3.4. Issues

4. Conducting an Admissible Comparable Companies Analysis
4.1. Approach
4.2. Obtain Forecast Data
4.3. Select Comparable Companies
4.4. Ensure Consistency & Uniformity
4.5. Reduce Statistical Bias
4.6. Adjust for Differences Between Peers
4.7. Determine a Valuation Range
4.8. Adjust for Survival
4.9. Adjust for Illiquidity
4.10. Crosscheck

5. Outlook and Conclusion
5.1. Practical Relevance of Complex Models
5.2. Conclusion

Reference List

Appendix

List of Abbreviations

illustration not visible in this excerpt

List of Tables and Figures

Table 1: Liftbox Inputs

Table 2: Comparables Universe

Table 3: Raw Peer Group Multiples

Table 4: Adjusted Peer Group Multiples: Chapter 4.3 Option

Table 5: Adjusted Peer Group Multiples: Chapter 4.3 Option 2 &

Table 6: Internet Company Failure Rates

Table 7: Liftbox’ Final Valuation Range

Figure 1: CY2014E EBITDA Multiple & Margin Regression

Figure 2: CY2014E Sales Multiple & Growth Regression

Figure 3: EV/EBITDA Multiple Range

Figure 4: EV/Sales Multiple Range

1. Introduction

1.1. Problem Definition and Objectives

During the height of the 1997-2000 dot-com bubble valuations of internet startups skyrocketed, seemingly regardless of their profitability, product significance or even compliance to applicable law (Lowenstein, 2004). It was argued that new drivers such as revenues and growth potential rather than cash flows were the foundation of value creation in the new economy. Metrics like website traffic, reach and page views were utilized to estimate future valuations and public companies were ranked on how fast they burned through their IPO capital. The bubble burst 2000-2001 when shareholders lost confidence in their still unprofitable investments, which began to run out of cash. Internet companies at that time constituted c. 8% (Willoughby, 2000) of the entire U.S. stock market, and the rapid decrease in market value of companies significantly contributed to the early 2000s recession (Lowenstein, 2004). The burst of the dot-com bubble left both the business and academic world at loss to explain how the values they had conjointly attributed to internet ventures could have been so far off the mark.

The middle of the last decade indicated a comparably calm sentiment in the market for internet ventures. Notable dynamic returned after 2007 when the exemplary rise of high profile companies like Facebook, Twitter and Groupon once again shifted investor attention to the sector (The Economist, 2011). Reduced cost and increased availability of technology had spurred in the meantime many entrepreneurs to develop online software that should prove to disrupt every aspect of modern life in the near future (Romanova, Helms, & Takeda, 2012). Subsequently, subscriber figures and sales of internet firms began to soar, which created numerous above average return opportunities for investors. Internet software companies were able to acquire c. $8.3 billion of venture financing in the United States in 2012 alone, which is up 105% from $4.1 billion in 2009 (National Venture Capital Association, 2013). Once again, the issue of valuing high growth internet ventures has become important. However, Damodaran(2011) concurs with Goldenberg and Goldenberg(2009) and Ho et al. (2011) that this is still one of the most challenging areas of business valuation.

Thus, the purpose of this paper is to provide the reader with a better understanding of internet company valuation by reviewing and analyzing relevant literature and discussing industry best practices. It focuses on valuing internet startups using the multiple valuation method in the form of comparable companies analysis, also known as trading comps and aims to showcase a reliable application of this approach.

1.2. Course of Investigation

In order to elaborate an admissible approach to comparable companies analysis based internet startup valuation, this paper is divided into five chapters. After the introduction in chapter one, which also features a comment on the author’s practical valuation experience, chapter two and three are based on an extensive review of scientific literature, newspaper articles and relevant internet sources. Chapter two covers operational and financial characteristics of internet startups and their market and highlights relevant trends in technology and economy. Chapter three introduces the multiple valuation method and points out difficulties of applying it on internet startups. Afterwards, chapter four presents best practices and enhancements of the multiple valuation method based on an exemplary case study. Reliable measures to adjust the comparable companies analysis for the special characteristics of internet startups are elaborated. In chapter five, the practical relevance of complex valuation models is evaluated as an outlook and conclusions are drawn.

1.3. Author’s Valuation Experience

From May until August 2012, the author completed an internship at the financial advisory firm DC Advisory Partners GmbH in Frankfurt/Main. Working in the mergers and acquisitions (M&A) department his focus was on technology, media and telecommunications transactions in the European small- and mid-cap market. At that time, most of DC Advisory’s clients were small and medium-sized enterprises (SMEs) opting for a buyout and private equity investors seeking exit opportunities. Among other tasks, his scope of work covered the identification of takeover targets, market analysis and company analysis applying discounted cash flow (DCF), multiple and leveraged buyout (LBO) valuation models. The companies he analyzed in detail included two mature internet companies from the e-tail and online travel sectors, two growth stage internet companies active in insurance services and gifting and one online-security startup. At DC Advisory the multiple valuation method was preferred above other approaches because it yields valuations relevant to the current market situation.

2. Internet Startups

2.1. Firm Characteristics

The first issue in order to analyze internet startups is to establish an appropriate definition of the object in question. In this paper they are defined as a young ventures that aim to generate their major revenue share online where no physical contact with the customer takes place and that develop a scalable business model (Blank, 2012; Turban, King, Veihland, & Lee, 2006). Further, there are frameworks available to more meticulously differentiate the mass of internet startups according to their inherent characteristics. Harbott (2012) devises a range of seven internet business models which differ in their form of business conduct and revenue model, and names prominent examples. These are namely providers of community (e.g. Facebook), content (e.g. Google), ecommerce (e.g. Amazon), infrastructure (e.g. Rackspace), marketplaces (e.g. eBay), services (e.g. Dropbox) and software (e.g. Salesforce). Laudon and Laudon (2011) suggest a comparable approach, but devise other business model segmentations combined with a separate differentiation between revenue models. Because internet businesses tend to constantly evolve and explore new activities, there are numerous other business model classifications available (e.g. Wirtz, Schilke, & Ulrich, 2010) and no academic consensus on this topic can be found (Krüger & Struwig, 2012).

Due to their low age, internet startups feature limited operational and financial histories, often providing only one or two years of data. This situation can be exacerbated by the fact that these histories may have little operating detail in them. Profits can be negative and revenues small or nonexistent for idea-stage companies (Damodaran, 2009). Moreover, the primary assets of internet startups are often of intangible nature, consisting of proprietary software code, brand reach, talent of the founders or patents (Athanassakos, 2007). For companies reporting in accordance to European accounting standards this often implies that assets are not represented on the balance sheet with their true and fair value (Hering & Olbrich, 2006). Further, startups often rely on private and thereby illiquid equity, sometimes featuring a complex structure of claims if different rounds of venture financing are involved (Damodaran, 2009).

Successful internet startups can grow at a more rapid rate than traditional businesses because their underlying technology is easier to scale. They also tend to be able to generate more revenue per employee than brick-and-mortar businesses due to their technology leverage (Hand, J. R. M., 2000). However, internet startups feature a lower survival rate than other ventures. A survey based on data from the American Small Business Development Center indicates that c. 63% of them fail during their first four years of operation compared to an average of c. 48% amongst other businesses (Harden, 2013).

2.2. Market Characteristics

The market in which internet companies compete is highly dynamic and still in an early stage of its development (Andreessen, 2011). World internet penetration has almost doubled from c. 1.37 billion users in 2007 to c. 2.50 billion in 2012, with the most significant increase originating from Africa and Asia (ITU World Telecommunication, 2013). This growth has been fueled by increasing availability of cheap, high performance hardware making the market accessible to a wide audience and is expected to continue as the internet further infuses every aspect of modern life. What Schwartz and Moon (2001) and other of their contemporary authors quoted as one of the highest risk factors of internet ventures in 2000, fixed technology investments, is not relevant anymore. Hosting an internet application today costs approximately a 100th of what it did in 2000(Laudon & Laudon, 2011). Even large scale services like Dropbox(Dropbox, Inc., 2013) or Instagram (Instagram, Inc., 2012) serve millions of users by renting Amazon S3 cloud space and thereby keep fixed investments low. Hence, low entry barriers and steadily ongoing technology innovation create a fertile ground for future growth in this sector. Especially in developing countries, the most rapid increase in internet penetration and digital wiring of the economy is expected to occur in the midterm. This opens plenty of business opportunities for foreign as well as local entrepreneurs, as exemplarily demonstrated by the recent boom of online shopping in China (Chang, Chen, & Dobbs, 2013) and the vibrant Chinese startup scene (Custer, 2013).

Because the internet industry as a whole is still in an early stage of development, a description of the operational characteristics it can achieve in maturity would only be speculative (Zarzecki, 2010). Andreessen (2011), currently one of the most influential internet entrepreneurs and venture capitalists in the United States, points out that “we are in the middle of a dramatic and broad technological and economic shift in which software companies are poised to take over large swathes of the economy” (p. 1). This considered and combined with the commonly accepted notion that internet startups compete in the winner takes it all markets (Laudon & Laudon, 2011), the prevalent hype concerning some contemporary ventures becomes easier to retrace. It is at last an emotional component that supports the hope of investors that a valuation target can become the new Facebook or Google (Romanova et al., 2012) and it lies solely in the arbitrary discretion of an analyst to assess the probability of this outcome. Even if the probability that a target becomes a major force in the market is negligible, there is sometimes the possibility to sell to a larger player with a top of the range valuation. For instance, Google alone acquired 52 other internet companies worth c. $1.1 billion in 2012 (Excluding Motorola Mobility), in a pursuit to extend its talent and intellectual property portfolio or simply to shut down rivaling services (Empson, 2013b). This form of conduct offers the opportunity of high returns to lucky seed, series A/B or growth stage investors. For example, due to its mapping war with Apple and Microsoft, Google paid c. $970 million for the four year old social mapping service Waze in 2012; nearly $850 million of that constituting goodwill (Empson, 2013a).

3. The Multiple Valuation Method

3.1. Introduction

In the corporate valuation field, there are many techniques available to assess the value of a company. Amongst other existing approaches the multiple valuation method is the most widely used amongst practitioners. Approximately 67% of analysts employ it for valuations associated with leveraged buyouts, initial public offerings, stock analysis and merger and acquisition activities (Demirakos, Strong, & Walker, 2004). A multiple based comparable company analysis typically uses a peer group of companies, which are considered to be comparable to the valuation target. The comparable companies are identified based on criteria such as industry membership, size, growth indicators, maturity and other key financial indicators (Tu, 2010) . In a relatively simple analysis of the market values and financial performance of the peer group companies, ratios are calculated, the peer group multiples. Subsequently, synthetic peer group multiples are obtained by averaging the peer group multiples. The enterprise or equity value of the valuation target then is estimated by multiplying the target company’s value driver with its corresponding synthetic peer group multiple (Liu, Nissim, & Thomas, 2007).

3.2. Reasons of Popularity

There are several reasons for the multiple valuation method being widely-used amongst financial analysts. Studies have empirically shown that it manages to produce valuations that are similar to DCF analysis in terms of accuracy (Feltham & Ohslon, 1995 ; Gilson, Hotchkiss, & Ruback, 2000). However, using the multiple valuation method without adjustments requires analysts to make no initial assumptions concerning target beta, cost of capital, or risk and is thereby less calculation intensive than a DCF analysis (Tu, 2010). Therefore, a relative valuation is comparably intuitive and easier to present to clients and customers. The multiple valuation method is further preferred because it does not determine the target valuation intrinsically like a DCF analysis, but is based on extrinsic market data. This implies, that the it does not express a theoretical value but what the market would actually pay for the target company at present (Damodaran, 2006).

3.3. Generic Multiples in Detail

In order to make firm values comparable on a mutual basis, their need to be standardized. This is achieved by computing ratios between the firm values and corresponding measures of financial performance. To be of explanatory value, these measures of financial performance need to stand in a logical relationship with the market value observed or, in other words, be drivers of value (Damodaran, 2011).

While some sectors may employ sector-specific valuation multiples, common choices for value drivers include various measures of cash flow, book value, earnings, and revenues (Liu et al., 2007). The most widely used ratios explain the enterprise value or equity value of a firm; the enterprise value multiples and equity value multiples. Enterprise value multiples employ the enterprise value in the nominator and a value driver that flows to both debt and equity holders, such as sales or EBIDTA, in the denominator. For equity value multiples, the denominator must be a value driver that flows only to equity holders, such as net income (Rosenbaum & Pearl, 2009). In practice, equity value multiples are sometimes preferred because the value relevant base does not need to be further adjusted for the market value of net debt, as it is the case with enterprise value (Liu, Nissim, & Thomas, 2002).

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Details

Title
Valuing Internet Start-ups
Subtitle
Selected Issues and Adjustments of the Multiple Valuation Method
College
European Business School - International University Schloß Reichartshausen Oestrich-Winkel
Grade
1,5
Author
Year
2013
Pages
30
Catalog Number
V264878
ISBN (eBook)
9783656543985
ISBN (Book)
9783656544715
File size
1167 KB
Language
English
Notes
The theoretical discourse is complemented by a case study. It walks the reader through the multiple valuation of a fictional cloud-service start-up and critically assesses every step.
Tags
Valuation, Corporate Finance, Company Valuation, Multiple Valuation Method, Multiples, Internet, Start-up, CCV, Comparable Company, High growth
Quote paper
Leonard Rasche (Author), 2013, Valuing Internet Start-ups, Munich, GRIN Verlag, https://www.grin.com/document/264878

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