Pricing of Internet Companies: Searching for

financial and non-financial value drivers

ANDREW BURNIE and SAFWAN MCHAWRAB*

* A. Burnie, Grenoble Ecole de Management S. Mchawrab, Grenoble Ecole de Management corresponding author B.P. 127 - 12, rue Pierre Sémard F - 38003 Grenoble - Cedex 01 E-mail: safwan.mchawrab@grenoble-em.com T : +33 4 76 70 60 26

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Abstract

In this paper we explore the importance of financial statements and non-financial value (volatility,

media coverage and web metrics) drivers on the pricing of Internet companies, using the relative

valuation method and, more specifically, price/sales ratio. Multiple regression analyses were run to

determine which value drivers the data supported. Our results show that financial statements could not

explain about two-thirds of variation in the price/sales ratio. Moreover, our results suggest that the

market values of profitable and unprofitable Internet companies are inherently different. In addition,

we find evidence to support payout ratio as a value driver for profitable Internet companies reflecting

maturation in the Internet industry. At the same time, we also find evidence that Media Mentions is an

explanatory factor in general for unprofitable Internet companies, and not only after an IPO. We also

find sufficient evidence to support number of visits and recent revenue growth as important value

drivers.

JEL : G320

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1. Introduction

Understanding the factors that drive the value of a particular asset is core to determining its value.

This has never been more apparent than during the process of valuing Internet companies, where a

series of mispricing mistakes has brought this sharply into focus. Examples of such pricing errors

range from the over-priced ‘Facebook Fiasco’ to Twitter being 73% under-priced, and LinkedIn

being under-priced by more than 100% on flotation.

The need for improved analytical tools to determine value is thus apparent. In high growth,

unprofitable companies which are characteristic of early phase Internet companies, valuation cannot

be based on conventional financial metrics such as discounted cash flow, and so comparisons with

similar companies have been made to produce a relative valuation (Holthausen and Zmijewski, 2012;

Roosenboom, 2012; Brahmana and Hooy, 2011).

This study explores the explanatory power of value drivers in the pricing of Internet companies. We

subdivided value drivers into those found as metrics within the financial statements of the company

and those which are more unconventional (non-financial) and may act as surrogate markers of a future

increase in value. These value drivers have been evaluated by multiple regression analyses against a

multiple of current firm value. The identification of core value drivers from financial statements

allowed estimation of the multiple. The addition of non-financial value drivers in a repeat of the

analysis permitted a more unorthodox equation to be created, which tempered financial value drivers

with the non-financial metrics that influence future growth and risk, and hence value. Profitable

companies have been compared to unprofitable companies.

This paper is organized as follows. After this introduction, Section 2 offers a brief review of the firm

valuation method, and we introduce the multiple and its potential value drivers. Section 3 discusses

the methodology, data sample and the models. In Section 4, we present and discuss our results, while

Section 5 concludes.

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2. Literature Review

2.1 Valuation method

Valuing companies has always been a complex task. It becomes more difficult in the case of Internet

companies, where choosing an appropriate valuation method is challenging. Five methods are usually

designated for valuing firms: cost approach, income approach, real options analysis, relative valuation

using multiples and the venture capital method (van de Schootbrugge and Wong, 2013).

With Internet companies, cash flows are often negative, and limited data and high uncertainty inhibit

reliable forecasting (Isimbabi, 2002). Such limitations suggest that the income approach should not be

used by itself. However, best practice should include an income approach, as it forces investors to

concretely state their assumptions and enables sensitivity analyses.

Even the most accurate income approach will tend to underestimate value due to the value of the

firm’s options – particularly its growth opportunities. Real options analysis seeks to fill this gap.

However, it is of dubious empirical validity and its price estimates are highly sensitive to difficult-to-

estimate variables (Mchawrab et al., 2015).

Relative valuation requires few explicit assumptions and is simple, fast and highly explicable,

particularly compared to the complexity of real options analysis. The multiples also incorporate an

element of the current mood of the market (Damodaran, 2012). The fact that multiples are the most

popular valuation technique underscores their importance to businesses (Brahmana and Hooy, 2011),

albeit in conjunction with other methods (Holthausen and Zmijewski, 2012), particularly the income

approach technique (Roosenboom, 2012). Relative valuation is also reasonably objective and its

reliance on high-quality databases improves its validity (Mchawrab et al., 2015). In practice, it is often

as accurate as the slower, more prudent (Fernandez, 2015a) income approach, suggested by

Roosenboom’s (2012) analysis of French IPOs, which was corroborated by Kaplan and Ruback’s

(1995) US data. Relative valuation is an essential component of the venture capital method as this

method incorporates relative valuation into its model (Sahlman, 1987). However, relative valuations

may be flawed in their assumption of semi-strong efficiency (Mchawrab et al., 2015), where the

market perfectly utilises all publicly available information (Fama, 1970). The validity of this

assumption is debatable (Shiller, 1981). Instead, there may be a systematic bias in the valuation of

Internet companies (Fernandez, 2015a) – i.e., all are under- or overvalued.

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Sousa and Pinho (2014) raised the specific question of the valuations of a cluster of Internet

companies on IPO, especially those managing social networking sites. IPO is the pivotal point at

which the market tests the value of a company because it is open to both professional and retail

investors. Facebook was valued at 99 times its earnings, higher than 99% of companies in the

Standard & Poor 500 Index, and the share price fell more than 50% in the three-month period post

IPO. Twitter shares rose 70% on their first day of trading. This underlines the mismatch between offer

prices and market valuation, with media hyperbole based on large and growing numbers of Internet

users being the foundation for valuation. It exposes the core weaknesses of finding an appropriate

multiple and a group of firms in which value has already been tested by the market. Conventionally,

companies have been clustered according to their business descriptions, and then the multiple values

analysed for each subsector. This is problematic for Internet companies as there is subsector

ambiguity, be it a B2C and B2B split (Isimbabi, 2002) or a portal and content/community firms and e-

tailer split (Trueman et al., 2000). Comparability can be enhanced by using multiple regression to

identify the key value drivers that explain the variation in multiples across sampled firms (Bhojraj and

Lee, 2002; Henschke and Homburg, 2009; Holthausen and Zmijewski, 2012; Damodaran, 2012).

These can be included when performing the relative valuation. They would act as proxies for finding

firms of similar cash flows, growth and risk, the implicit assumptions defined by Damadoran (2010)

when finding comparable firms.

Thus, defining the appropriate multiple and knowing the value drivers are essential for producing a

robust relative valuation. The clustering of the multiples might be an adjunct to traditional ways of

defining Internet companies into sectors in which the margin between different types is blurred.

2.2 Identifying the multiple

The price/sales ratio was the preferred multiple for Internet companies in the subsequent analyses for

the following reasons. Internet companies include numerous firms with negative earnings and cash

flows. This undermines the price/earnings ratio, which Liu et al. (2002) suggested was the most

appropriate multiple. The inclusion of firms with negative earnings and cash flows is key to producing

a more robust and realistic set of revenue multiples (Deng et al., 2012).

Multiples based on earnings or cash flows are unsuitable (e.g., price/earnings, price/EBITDA and

price/cash flow ratios). This is because large investments in Research & Development (R&D) and

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marketing depress earnings and cash flows but could increase market values.

Use of the price/book ratio (Van der Goot and Knauff, 2001; Trueman et al., 2000) may be distorted

by significant off balance sheet intangible assets or by accounting policy variations. Thus, it is

unreliable for Internet companies, where the main assets tend to be intangible (Athanassakos, 2007).

Hence, the preference for the price/sales ratio, which is less volatile, can be used for loss-making

firms (many Internet firms are), and is less distorted by accounting policy variations (following

Fernandez, 2015b; Sousa and Pinho, 2014; Armstrong et al., 2011; Demers and Lev, 2001).

The determinants of the price/sales ratio were derived using a stable growth dividend discount model

(Damodaran, 2012:544-5; Frykman and Tolleryd, 2010:157):

Equation 1: Determinants of the price/sales ratio using the stable growth dividend discount model.

This suggests that value drivers influence the price/sales ratio by affecting risk (the cost of equity),

expected growth, the profit margin and payout ratio.

2.3 Value Drivers

In the tech bubble crash of the late 1990s, outdated and flawed accounting practices were blamed for

providing biased information, which led to inflated pricing and fuelled the bubble (Stiglitz, 2003;

Krugman, 2004). Bahattacharya et al. (2010) challenged this by examining the failure of prediction

ability in the context of Internet IPOs. They demonstrated that sales, R&D expenses, advertising

expenses, leverage and accumulated deficits could predict Internet IPO failure risk. Simpson (2010)

criticised the analysis for omitting non-financial metrics which Amir and Lev (1996) felt were highly

relevant in high-technology industries. In response to this, Bahattacharya et al. (2010) stated that these

variables were dominated by web traffic metrics, and it was difficult to include them as they were

most relevant to business-to-consumer Internet firms, which were not sufficient in number to be

analysed in their sample.

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2.3.1 Financial Value drivers

Based on Equation 1, the key financial metrics are Profit Margin, Payout Ratio, Growth Rate and Cost

of Equity.

Profit Margin: The regression has been run using the aggregate net income variable to assess

whether it has a negative (Van der Goot and Knauff, 2001) impact on stock prices, no impact

(Rajgopal et al., 2000) or whether an increasing positive net income and increasing negative net

income has a positive (Hand, 2000) impact on stock prices. Keating et al. (2003) investigated the 45%

decline in the market value of Internet companies at the beginning of 2000. They found little evidence

that this was due to new disclosures of web traffic statistics, earnings or earnings forecasts. Changes

in research analysts’ recommendations were mixed, showing no correlates. Accounting information

correlated better with stock prices than web traffic. The price-level analyses were consistent with a

change in investors’ perceptions of selected earnings components. Keating et al. (2003) speculated

that the fall reflected investor concern over cash availability and the ability to raise money going

forward. Bartov et al. (2002) argued that, for Internet firms, a negative cash flow could be viewed as

an investment as large spends on marketing and R&D represented investment in customer base

development and brand awareness. Operating expenses have been delineated into R&D and Sales &

Marketing (S&M) expenditure to determine which had the greater influence on revenue growth.

These variables were divided by sales. Demers and Lev (2001) concluded that R&D expenditure was

a more consistently important value driver than S&M expenditure.

Payout ratio and leverage: As the Internet industry matures over time, more firms are likely to pay

out dividends and to raise debt. Thus, the importance of controlling for the payout ratio (last recorded

ordinary dividends divided by net income), and capital structure has been assessed to establish the

extent to which Internet companies have matured. Leverage was measured using ‘Net Debt Leverage’

(last recorded Net Debt divided by Market Capitalisation) and ‘Total Leverage’ (Total Debt and

Liabilities divided by Net Sales).

Growth Rate: Schwartz and Moon (2000) applied real options theory and capital budgeting

techniques. Depending on the parameters chosen, they found that the exceptionally high values of

Internet companies reported at the height of the Dotcom Bubble could be seen as rational if the

growth rate in revenues was high enough. Armstrong et al. (2011) found that higher revenue growth

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seemed to increase the price/sales ratio. Annual revenue growth rate over the past year has thus been

included, as firms with a high recent growth rate are likely to have a higher future revenue growth

rate.

Company size: Smaller companies have a higher growth potential, and so are likely to have higher

growth rates and multiple values. Armstrong et al. (2011) found evidence to support smaller firms

having a higher price/sales ratio, although this was not confirmed by Sousa and Pinho (2014).

However, Armstrong et al. (2011) used sales as a proxy for size, which is less subject to accounting

distortions than total assets. Van der Goot and Knauff (2001) similarly found sales to be a value driver

for European Internet companies. Accounting book value may poorly reflect a firm’s true size

(Isimbabi, 2002). Net sales has been used as a proxy for size.

Cash Burn: It could fuel investment and consequently, growth, or liquidity problems and thus raise

the risk of failure and cost of equity. Hence, theoretically, a high cash outflow could inflate or depress

the price/sales ratio. Demers and Lev (2001) argued that the burst of the Dotcom Bubble saw a shift in

market perception of high cash burn from good to bad. ‘Cash Burn’ will be measured using net cash

flows from operating activities (CFO) and business investments (CFI), divided by cash and short-term

investments. CFI is included, as some business models may require high investments in tangible

assets, recorded under CFI. This metric gives an indicator of how rapidly the firm is exhausting its

liquid assets (Keating et al., 2003). A second variable (labelled ‘NegCash’) has been included that

either equals zero (if cash flows are positive) or this variable. This accounts for the differential

treatment of positive and negative cash flow firms. Including these variables in the regression has

helped to illuminate whether current market perceptions have more in common with a bubble period

or its aftermath.

2.3.2 Non-financial Value Drivers

Trueman et al. (2000) argued that financial statements were of limited use when valuing Internet

stocks as there was no significant positive association between bottom-line net income and market

price. In fact, the association was actually negative. They identified Internet usage as more important

and demonstrated that unique visitors and page views were drivers of stock price. The importance of

sector variation could be seen, as for e-tailers, page views were far more important than unique

visitors, whereas for portal and content/community firms, they were equally important. Since

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Trueman et al. (2000) the variety and type of non-financial metrics available, which can be analysed

as potential value drivers, have expanded. In essence, these metrics act as a measure of future growth

and can be subdivided into the more direct measures of website traffic and more indirect measures

such as media mentions and volatility.

2.3.2.1 Website Metrics

High website traffic could be value-relevant because it means a larger potential customer base, and

consequently, higher possible future revenue growth. Rajgopal et al. (2000) just considered the

number of unique visitors, whilst Trueman et al. (2000) included page views. This ignored the need

for websites to be persistently popular, rather than just popular, to ensure high revenue growth. Thus,

Demers and Lev (2001) extended their metrics from just ‘reach’ (the ability to attract visitors) to

‘stickiness’ (the ability to retain visitors at a site) and ‘customer loyalty’ (the ability to generate repeat

visits from surfers). The bounce rate is the percentage of visitors to a particular website who navigate

away from the site after viewing only one page. This is the opposite of ‘stickiness’ and may thus be an

inverse value driver. The importance of website traffic seemed to hold, even after the Dotcom Bubble

(Demers and Lev, 2001) and with more recent data (Sousa and Pinho, 2014). Demers and Lev (2001)

found that ‘reach’ and ‘stickiness’ had a more statistically significant impact than ‘customer loyalty.’

This conflicts with Gupta et al. (2004), who argued that customer loyalty as shown by time on site

was a major value driver. However, their analysis was based on only five firms. Both Hand (2000;

2001) and Keating et al. (2003) found such web metrics to be not as important as economic

fundamentals. High website traffic may also generate high network externalities, where the adoption

of a service by one user raises the value of that service to all other users (Shapiro and Varian, 1999).

This benefit comes not directly from the number of members, n, but rather from the number of

connections they generate among each other, (n2 – n)/2. This ‘Metcalfe’s Law’ (Shapiro and Varian,

1999) suggests that price/sales ratio will be a quadratic function of the number of unique visitors

(‘reach’). The number of websites belonging to a company has also been included, as this may be a

value driver. This is because it may represent a more diverse business model which itself may act as a

value driver or it may represent a lack of focus, in which case running several websites will not

enhance value. Thus, the literature supports website traffic as the most important non-financial value

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driver. This study will assess: number of visits, time on site, page views, bounce rate and website

number.

2.3.2.2 Media Mentions

DuCharme et al. (2001a; 2001b) examined 227 US Internet firms that went public from 1988 to 1999

and measured their media exposure seven days prior to the IPO date. They observed that there was an

undervaluation, in that the price of Internet company IPOs increased almost 75% from the time of

issue until the market closed on the first day. Under-pricing of IPOs was on average 14%, and the

greater the media coverage, the greater the under-pricing. More importantly, media coverage of

Internet firms was three times higher than media coverage of non-Internet firms. This trend was

confirmed by Demers and Lewellen (2003). Cook et al. (2006) suggested that the media attracted

‘sentiment’ investors, who were willing to pay more for the stock having read or heard about it in the

media (Liu et al., 2007). Liu et al. (2009) found that one standard deviation increase in media

coverage leads to 7.86% more under-pricing of IPOs. A similar correlate has been seen for data

representing companies from Australia (Ho et al., 2001; Jens et al., 2006) and Taiwan (Jang, 2007)

but not the UK (Staikouras and Tsatsanis, 2003). Sousa and Pinho (2014) suggested that the more up

to date database Factiva (www.factiva.com) might be used to find the number of mentions of a

company in the press over the week leading up to the IPO. This database does not distinguish ‘good’

from ‘bad’ press as this can be difficult to define and is inevitably subjective. Furthermore, as most of

the firms in the sample had not been mentioned over the week preceding the IPO, the period was

expanded to three months for the purposes of this study.

2.3.2.3 Uncertainty as measured by Volatility

Equity holders face limited exposure should an Internet company fail. Due to network externalities,

there are potentially unlimited gains should the company succeed. Hence, the expected growth rate is

increasing in uncertainty (Pastor and Veronesi, 2006). The cost of equity is also increasing in riskiness

(Damodaran, 2012). Thus, the true impact of higher uncertainty on market value is unclear. Schwartz

and Moon (2000) argued that highly volatile revenue growth could explain a high market valuation, as

this suggested the pursuit of a high-risk strategy. However, calibration of their model to match the

value of Amazon.com produced an unrealistic distribution of revenue. Pastor and Veronesi (2006)

focused instead on uncertainty regarding average profitability, which produced implied volatilities

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more consistent with the volatility observed. Stock return volatility (measured using standard

deviation of daily returns based on the past three months of data1) will be used as a proxy for

uncertainty (following Pastor and Veronesi, 2006).

3. Methodology & Data

3.1 Models

The multiple used as the basis of the analyses was the price/sales ratio. This was regressed against the

value drivers previously identified by the literature review representing the factors described in

Equation 1.

Hand (2000) demonstrated that Net firms’ log-transformed market values were neatly linear in both

log-transformed book equity and log-transformed net income. Thus, a log-log model was studied,

regressing the natural logarithm of the price/sales ratio against the natural logarithms of the value

drivers. Each model tested the overall dataset and the profitable and unprofitable firms separately

(Bagnoli et al., 2001; Gama et al., 2008). For the unprofitable firms’ dataset, Log (-Net Income

Margin) replaced Log (Net Income Margin). Thus, the slope could be interpreted as the expected

percentage decrease in price/sales ratio for a 1% increase in Net Income Margin, holding the other

value drivers fixed. We have applied three different approaches. The first used only the financial

statement variables. The original version of the regression was:

log =b0+b1 log(Net Income Margin) +b2 log(Recent Growth) +b3 log(Size)

+b4 log(Cash Burn) +b5 log(NegCash) +b6 log(Payout Ratio)+b7 log(Net Debt Leverage)

+ b8 log(Total Leverage)

Equation 2: The multiple regression model used to assess value drivers derived from only financial statements.

In order to adjust our model to the fact that most of the values found for net income margin, recent

growth in revenue, cash burn and net debt leverage metrics were negative, we added a number that

ensured all values were positive before taking the logarithms. The number added was greater than the

absolute value of the lowest value for each variable.

The second regression included media mentions and volatility as follows:

1 See Infinancials glossary for more details: http://www.infinancials.com/fe-en/glossary/volatility

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log =b0+b1 log(Net Income Margin) +b2 log(Recent Growth) +b3 log(Size)

+b4 log(Cash Burn) +b5 log(NegCash) +b6 log(Payout Ratio)+b7 log(Net Debt Leverage)

+ b8 log(Total Leverage) + b9 log(Media Mentions) + b10 log(Volatility)

Equation 3: The multiple regression model used to assess value drivers, which included Media Mentions and Volatility with financial statement variables

In the third regression, we added the web metrics variables (Visits, Time on Site, Page Views, Bounce

Rate and Website number). The main goal was to measure whether combining both sets of variables

(financial statements variables and non-financial variables) would improve the explanatory power of

the model. Moreover, we had to adjust our model depending on the data extracted. The third version

of the regression is as follows:

log =b0+b1 log(Net Income Margin) +b2 log(Recent Growth) +b3 log(Size)

+b4 log(Cash Burn) +b5 log(NegCash) +b6 log(Payout Ratio)+b7 log(Net Debt Leverage)

+ b8 log(Total Leverage) + b9 log(Media Mentions) + b10 log(Volatility)

+ b11 log(Visits) + b12 log(Time On Site)

+ b13 log(Page Views) + b14 log(Bounce Rate) + b15 log(Website Number)

Equation 4: The multiple regression model used to assess value drivers derived from financial statements and non-financial statements variables.

The literature often implicitly assumes that US results will hold globally by relying on US data to

make assertions about Internet companies in general (Sousa and Pinho, 2014). Hence, a Chow Test

was used to examine this assertion, testing for structural differences in the model (including Media

Mentions and Volatility) across geographic regions. To ensure there were sufficient data, this was

applied to the global dataset.

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3.2 Robustness

Breusch-Pagan Tests were used to test for heteroskedasticity at the 5% significance level. Where a

potential heteroskedasticity problem was identified, robust standard errors were implemented.

Following Long and Ervin (2000), the more reliable HC3 standard errors were used to provide robust

standard errors instead of White’s estimator.

Robustness was further assessed by examining the impact of adding and removing value drivers from

the models. This included seeing if statistically significant slope coefficients remained upon removing

statistically insignificant variables or upon adding variables that were a potential source of

multicollinearity.

Regressions were also run, with any identified outliers being added back to the dataset to examine

their impact.

3.3 Data Sample

To study the impact of the major financial and non-financial value drivers on the pricing of Internet

companies, we constructed a global sample covering 33 countries (representing 202 companies),

instead of focusing only on the US market. A cross-section of the last recorded annual financial data

was collected on August 17, 2015, from Infinancials’ Corporate Focus Premium Database. The non-

financial data have been collected from different sources; volatility data was sourced from the

Corporate Focus Premium Database, and media mentions from the Factiva Database. Web traffic data

were sourced from SimilarWeb.com because it uses a variety of channels (rather than just panel data)

and covers more websites. SimilarWeb.com provided a metric for ‘reach’ (the number of visits) and

‘stickiness’ (time spent on site in seconds), as well as page views and bounce rate data. The metric for

‘reach’ was more informative than Alexa’s use of ranks for popularity. Using SimilarWeb.com

required knowing the websites for each company, which was derived using the Corporate Focus

Premium Database. Web traffic data could not be extracted for Asiatic and Polish companies – partly

due to data deficiencies and partly due to unacceptable data quality. Thereby, the analysis of the web

traffic variables data was limited to European (excluding Polish) and North American companies (a

total of 72 companies).

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4. Results and Discussion

4.1 Descriptive statistics

Population Minimum Maximum Mean Median Standard Deviation Skew Excess

Kurtosis Price/Sales Ratio 202 0.01 6954.19 64.19 2.29 575.15 10.62 115.25

Net Income Margin 202 -4932.00 203.68 -24.25 0.03 347.38 -14.06 196.18

Recent Growth 202 -78.64 100.00 16.11 9.63 37.97 0.58 0.46

Size 202 0.00 66001.00 785.49 65.36 4844.19 12.33 161.60

Cash Burn 202 -176.50 5.48 -1.86 -0.02 14.32 -10.57 117.80

NegCash 202 -176.50 0.00 -2.08 -0.02 14.27 -10.62 118.64

Payout Ratio 202 -6.95 58.73 0.48 0.00 4.32 12.40 162.82

Total Leverage 202 0.03 2807.00 15.37 0.47 197.47 14.09 196.71 Net Debt Leverage 202 -6.77 12.88 0.11 -0.08 1.46 4.51 37.46

Media Mentions 202 0 15249 217.91 10 1257.93 9.62 104.00

Volatility 202 5.92 1449.71 82.36 51.76 130.57 7.60 69.34

Web traffic data available for Europe (excluding Poland) and North America only:

Visits 72 500 46725754000 1124751160 293500 6113927279.71 6.48 43.27

Time On Site 72 0 2173 414.32 325.16 376.64 2.46 7.35

Page Views 72 1 34.83 6.25 4.97 5.58 3.41 13.50

Bounce Rate 72 0.07 1 0.42 0.41 0.18 0.57 0.37

Website Number 72 1 37 5.97 2.5 7.27 1.94 4.07 Table 1: Descriptive statistics

The above descriptive statistics reveal that the variables tend to have a high excess kurtosis –

suggesting a problem with extreme outliers – and a high skew – suggesting asymmetric distributions.

This supported the need for a log-log model approach (Hand, 2000) to ensure that the distributions

were closer to the assumed normal distributions required by multiple regression analysis. There were

two companies with price/sales ratios exceeding 1000: PT Indoritel Makmur Internasional Tbk,

(6954.19) and Max Sound Corp. (4340.49). The next highest ratio was 221.91 (SeaRainbow Holding

Corp.). PT Indoritel Makmur Internasional Tbk also had the highest Net Income Margin (203.68

compared with the second highest, Yahoo! Inc at 1.63) and Max Sound Corp. had the lowest (-

4932.00 compared with second lowest, Infinio Group Limited at -75.42). PT Indoritel Makmur

Internasional Tbk and Max Sound Corp. were thus considered extreme outliers, and the subsequent

analyses were run with and without their inclusion in the overall dataset (Table 2, 3, 8).

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4.2 Regressions

Results are presented in the tables below (Table 2 – 12). Several regressions were run for each of the

models presented in Section (3.1). We adjusted and reran the regression (including and excluding

outliers and variables) due to heteroscedasticity issues, to provide robust results and when the

variables were statistically insignificant. The below tables were generated using the Stargazer R

package (Hlavac, 2015):

Table 2: Regressions on the overall dataset (profitable and unprofitable firms), examining only financial statement value drivers, with the two extreme outliers removed.

We have excluded NegCash (column 2) and both NegCash and Cash Burn (column 3), due to identified potential multicollinearity issues. Statistically insignificant variables were removed in column 4.

Table 3: Regressions on the overall dataset (profitable and unprofitable firms), examining only financial statement value drivers, with the two extreme outliers removed. We have excluded the Net Income Margin.

NegCash is excluded in column 2, due to identified potential multicollinearity issues. Statistically insignificant variables have been removed in column 3. The slope coefficients of Cash Burn and NegCash were not robust to this change. Hence, they were removed in column 4.

Recent growth was statistically significant at the 1% level for all iterations of the model, and had a

positive coefficient. This suggested that an increase in the growth rate in net sales over the past year

was expected to increase the price/sales ratio, holding all other included variables constant.

The negative slope coefficient of the Net Income Margin (Table 2) suggested that lower profitability

was associated with a higher price/sales ratio, holding everything else except Cash Burn and NegCash

constant. With Net Income Margin excluded, the negative slope coefficient on Cash Burn was

significant at the 5% level (Table 3, column 1), whilst the coefficient on NegCash was not. This

suggested that the more rapidly the firm used up its liquid assets, the higher the expected price/sales

ratio, holding the other included value drivers fixed. This result was not robust to the exclusion of

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NegCash (Table 3, column 2), or to the exclusion of statistically insignificant variables (Table 3,

column 3). There was very limited evidence to support a negative association between size and the

price/sales ratio (Table 3, columns 3 and 4), holding everything else constant. None of the identified

statistically significant coefficients were robust to the inclusion of the two outliers.

We have split our dataset into two subsets: profitable (123) and unprofitable firms (78). The results of

the regressions are presented in tables 4 and 5 below.

Table 4: Regressions on profitable firms only, examining only financial statement value drivers.

One extreme outlier was removed for the regressions in columns 3-5. Statistically insignificant variables were removed in columns 2 and 4. Size remained statistically insignificant at the 5% level, and thus was then removed in column 5. Note that Net Income Margin was calculated as Log (Net Income Margin).

Table 5: Regressions on unprofitable firms only, examining only financial statement value drivers, with the extreme outlier removed.

Regressions were run without Size (column 2) and without -Net Income Margin (column 3) due to the high correlation between these variables being a possible source of multicollinearity. Statistically insignificant variables were removed from the model with -Net Income Margin included (column 2) to derive column 4. Similarly, statistically insignificant variables were removed from the Size-included model (column 3) to derive column 5. Note that -Net Income Margin was calculated as Log (-Net Income Margin).

For profitable firms (Table 4), Recent Growth was statistically significant (at the 5% level), with a

positive slope coefficient (columns 3-5) after the exclusion of the extreme outlier. The Net Income

Margin had a positive slope coefficient. This contrasted with the negative slope coefficient observed

in the overall dataset. Using the model with all statistically insignificant variables removed (column

5), this suggested that a 1% increase in the Net Income Margin was expected to increase the

price/sales ratio by 0.622%, holding all other included value drivers constant. There was a negative

slope coefficient for Cash Burn, but a positive value for NegCash. The coefficient for Cash Burn

suggested that, overall, the more cash that was generated, rather than burnt, the lower the expected

price/sales ratio, holding the other value drivers constant. However, the NegCash coefficient was also

statistically significant. NegCash was a differential slope dummy, which meant it accounted for

17

differences in the impact of a higher Cash Burn on the price/sales ratio between firms with a positive

cash flow and firms with a negative cash flow (Gujarati, 2012). Its positive sign suggested that the

effect of burning more cash on the price/sales ratio was less for firms with a negative cash flow. The

absolute value of the slope coefficient for NegCash was similar to Cash Burn across all model

iterations. This supported the idea that the effect of burning more cash was negligible in firms with a

negative cash flow. The results also suggest that a higher payout ratio was expected to increase the

price/sales ratio, holding other included value drivers fixed. Investors may prefer profits to be paid out

in dividends rather than reinvested for future growth.

The results observed for the Recent Growth slope coefficient in the combined (Tables 2-3) and

profitable firms (Table 4) only data also held for unprofitable firms (Table 5). Running the regressions

without Size for unprofitable firms (Table 5, columns 2 and 4) and without Net Income Margin (Table

5, columns 3 and 5) led to two possible, different models (Table 5, columns 4 and 5). In column 4,

Size was excluded and Net Income Margin included. This suggested that a 1% reduction in Net

Income Margin led to a 0.417% increase in expected price/sales ratio, holding Recent Growth

constant. In column 5, Net Income Margin was excluded and Size was included. This suggested that

an increase in Net Sales (Size) by 1% was associated with a 0.29% reduction in the expected

price/sales ratio, holding Recent Growth constant. Column 4 had a higher adjusted R2 (29.5%

compared with 23.9%), lower AIC (259.26 compared with 265.21), and lower BIC (268.69 compared

with 274.63). These statistics all suggest that the model in column 4 provides a better fit of the data

than the model in column 5. Hence, they supported the exclusion of Size over Net Income Margin,

i.e., column 4 being used as the final model. No statistical significance was observed for the Cash

Burn, NegCash, Payout Ratio or Size in any of the models. Removing NegCash did not affect the

statistical significance of Cash Burn’s or the other slope coefficients. The observed statistical

significance of the slope coefficients was robust to adding the extreme outlier back into the dataset.

Adding back the outlier led to Total Leverage being statistically significant at the 5% level (column

3). This significance was not observed when Net Income Margin was included, but was robust to the

removal of statistically insignificant independent variables (column 5).

When comparing the results of both subsets, we found that the more profitable a profitable firm, the

higher its expected value; in contrast, the more profitable a loss-making firm, the lower its expected

18

value (holding other value drivers constant). The statistical significance observed for Cash Burn,

NegCash and payout ratio for profitable firms was not observed for unprofitable firms. For both

subsets, Recent Growth had a positive and statistically significant slope coefficient.

Two additional variables were added (Media Mentions and Volatility) to the set of variables. Table 6

contains the results of regression against all variables, while Volatility has been excluded in Table 7,

as it was a potential source of heteroskedasticity, and because it was statistically insignificant.

Table 6: Regressions on the overall dataset, including Media Mentions, Volatility, the financial statement value drivers, and the two extreme outliers.

Statistically insignificant variables removed in column 2. Cash Burn remained statistically insignificant at the 5% level, and so was removed in column 3.

Table 7: Regressions on the overall dataset, including Media Mentions with the financial statement value drivers. The two extreme outliers were excluded. Volatility was also excluded.

Volatility was excluded because it was a potential source of heteroskedasticity, and because it was statistically insignificant. Regressions were run with Net Income Margin included, but excluding NegCash (column 2) and removing both NegCash and Cash Burn (column 3). A regression was then run with Net Income Margin excluded, but including NegCash and Cash Burn (column 4). Then NegCash was excluded from the model (column 5). These different iterations were assessed due to identified potential multicollinearity issues. Statistically insignificant variables were removed in column 6.

Including Media Mentions in the model led to Net Income Margin, Cash Burn and NegCash being

statistically insignificant across all iterations (Table 7, columns 1-6). The slope coefficient of Recent

Growth seemed robust to the inclusion of Media Mentions. It also led to the Size metric becoming a

statistically significant value driver. This suggested that a 1% increase in Net Sales (the proxy of Size)

19

was associated with an expected reduction in the price/sales ratio by 0.21%, holding Recent Growth

and Media Mentions constant. The previously unobserved statistical significance of the company’s

size could have been due to an omitted variable bias caused by the exclusion of Media Mentions. A

larger company is likely to be mentioned more in the media. The results in Table 7 suggest that a

company mentioned more in the media is likely to have a higher price/sales ratio. The positive,

statistically significant slope coefficient for Media Mentions supports the view that the more a

company was mentioned in the media over the preceding three months, the higher its price/sales ratio,

holding Recent Growth and Size fixed. The statistical significance found for Recent Growth was not

robust to the inclusion of extreme outliers (Table 6), whilst the results for Size and Media Mentions

were robust. Negative, statistically significant slope coefficients were found for the Net Income

Margin and Volatility, which were not apparent with the extreme outliers removed. Thus, these results

seem to be an artefact resulting from the inclusion of the outliers.

Again, we have split our data with the two additional variables (Media Mentions and Volatility) into

two subsets: profitable and unprofitable firms. The results are presented below.

Table 8: Regressions on profitable firms only, examining Media Mentions, Volatility and financial statement value drivers.

Extreme outlier was removed for regressions 3-4. Statistically insignificant variables were removed in columns 2 and 4. Note that Net Income Margin was calculated as Log (Net Income Margin).

Table 9: Regressions on unprofitable firms only, examining Media Mentions, Volatility and financial statement value drivers. Extreme outlier removed.

Regressions were run without Size (column 2) and without -Net Income Margin (column 3) due to the high correlation between these variables being a possible source of multicollinearity. Statistically insignificant variables were removed from the Size-included model (column 3) to derive column 4. The statistical significance of Total Leverage was not robust to the inclusion of Net Income Margin (column 5). Similarly, statistically insignificant variables were removed from the full model (column 1) to derive column 6. Note that -Net Income Margin was calculated as Log (-Net Income Margin).

20

For profitable firms (Table 8), the significance and sign of the slope coefficients for Net Income

Margin, Recent Growth, Cash Burn, NegCash and payout ratio were the same as with the profitable

firm data models using financial statement data only. Unlike in the overall dataset, Media Mentions

did not have a statistically significant slope coefficient (Table 8, column 3). The slope coefficient of

Volatility was positive and statistically significant (Table 8, columns 3-4), suggesting that a 1%

increase in the volatility of the share price increases the expected price/sales ratio by 0.49%, holding

the other included value drivers constant.

As for the unprofitable firms (Table 9), the significance and sign of the slope coefficients for Net

Income Margin and Recent Growth were the same as with the financial statement data only model.

The slope coefficient for Size was statistically significant across all model iterations, regardless of the

inclusion of the Net Income Margin. As with the financial statement model, there seemed to be an

inverse relationship between Size and the price/sales ratio, holding the other included value drivers

constant. The statistically significant, positive slope coefficient for Media Mentions and the negative

coefficient for Size suggested that the similar observations for the combined dataset could have been

artefacts of these results. The slope coefficients suggest that, for unprofitable firms, higher press

coverage increases, whilst a higher Net Sales reduces, the expected price/sales ratio, holding

everything else constant. In contrast with the positive slope coefficient in the profitable firms dataset,

the slope coefficient for volatility was negative for unprofitable firms. This suggested that a 1%

increase in the Volatility metric was expected to reduce the price/sales ratio by 0.75%, holding the

other included value drivers fixed.

Reliable web metric data were not found for the entire global dataset. Hence, the dataset was

restricted to Internet companies from Europe (excluding Poland), the US and Canada. The most

conspicuous outlier became HealthTalk Live Inc., which had a price/sales ratio of 94.90 (compared

with the next highest value of 21.28 for Facebook Inc.). There seemed to be potential multicollinearity

problems arising from high correlations between the different web metrics included: between Time

On Site and Page Views (0.80); Page Views and Bounce Rate (-0.74); Time On Site and Bounce Rate

(-0.67); Time On Site and Net Income Margin (0.66); and Visits and Website Number (0.66, and 0.65,

excluding the outlier). There was a perfect, positive correlation between Visits and the squared value

of Visits. This meant that a regression could not be run with both variables included, preventing

21

assessment of ‘Metcalfe’s Law’. Regressions were run against all the variables (Table 10). The

dataset was again split into two subsets (profitable and unprofitable firms), and the results are

presented in Table 11.

Table 1: Regressions on European (excluding Polish) and North American Internet companies, including Media Mentions, Volatility, web metrics and financial statement value drivers, with the extreme outlier excluded.

Statistically insignificant financial statement variables were removed in column 2. A stepwise regression approach led to column 3. Column 4 enabled comparison with the global data, where web metrics were excluded (Table 7). Column 5 enabled comparison with the global model that considered only financial statements value drivers (Table 2).

Table 2: Regressions on European (excluding Polish) and North American Internet companies, including Media Mentions, Volatility, web metrics and financial statement value drivers. Profitable and unprofitable examined separately.

The extreme outlier was included (in the unprofitable firms dataset), due to the small sample sizes. A stepwise regression approach led to the displayed results. For profitable firms, ‘Net Income Margin’ is Log (Net Income Margin), whilst for unprofitable firms it is Log (-Net Income Margin). Hence, the above suggests a lower Net Income Margin increases the price/sales ratio, holding everything else constant.

Including all of the potential value drivers in the model led to the slope coefficients of the web metrics

being statistically insignificant (Table 10, column 1). This was not remedied by removing statistically

insignificant financial statement variables (Table 10, column 2). Hence, a stepwise approach was used

to determine if any of the web metrics were value-relevant. Regressions were first run with Time On

Site removed. Then Time On Site was included and Page Views was removed. The highest correlation

was between these variables. In both regressions the included variable was statistically insignificant,

and so both variables were removed from the model. The same approach was then applied to Visits

and Website Number. Finally, the Bounce Rate was removed because it was statistically insignificant.

This led to a final model (Table 10, column 3), where Visits appeared to be the only statistically

22

significant value driver of the web metrics. This model suggested that a 1% increase in website visits

was expected to increase the price/sales ratio by 0.064%, holding all other included value drivers

constant. Recent Growth, Size and Media Mentions had slope coefficients with the same statistical

significance and signs as in the global dataset, and so their interpretation was similar. Unlike in the

global dataset, Net Income Margin and Volatility were statistically significant, with negative signs.

This suggested that increasing the Net Income Margin or Volatility decreased the expected price/sales

ratio (holding everything else constant). In the case of Volatility, a 1% increase was expected to

reduce the price/sales ratio by 0.549%. The difference in results between the global dataset and this

dataset could be because of the inclusion of Visits in the model, or because the dataset’s scope

changed. Removing Visits from the model (Table 10, column 4) did not alter the sign or statistical

significance of the other slope coefficients. This was consistent with the new Net Income Margin and

Volatility results being due to the changed scope of the data. Media Mentions and Volatility were also

removed from the model (Table 10, column 5) to compare financial statement models between the

global and the reduced dataset. The previously identified results still held.

There were 29 profitable and 43 unprofitable firms in the sample, compared with 15 independent

variables. Thus, there was insufficient data to meaningfully analyse each group separately. Stepwise

regressions were performed on each dataset, removing the most likely sources of multicollinearity

first. The results (Table 11) supported the robustness of the statistically significant, positive

coefficient for Recent Growth, and the robustness of the statistically significant, negative coefficient

for Volatility (for unprofitable firms).

A Chow Test was used to determine if geography had an impact on the model. Of the firms in the

global dataset, 43.56% were from East Asia2 — hence East Asian firms were compared with the rest

of the world. The two extreme outliers were removed (PT Indoritel Makmur Internasional Tbk and

Max Sound Corp.). Performing a Chow Test on the profitable firms’ dataset provided sufficient

evidence to reject the null hypothesis that both regressions were the same at the 1% significance level.

2 China, Hong Kong, Indonesia, Japan, South Korea, Philippines, Singapore, Thailand, Taiwan, Vietnam

23

The same result was found for unprofitable firms. These results suggest that Internet companies are

valued differently in East Asia, compared with the rest of the world.

4.3 Discussion

We used a global cross-sectional dataset collected on August 17, 2015. This contrasted with the late

1990s and early 2000s US Internet company data often used by the literature (Armstrong et al., 2011;

Keating et al., 2003; Demers and Lev, 2001; Hand, 2001; Hand 2000; Trueman et al., 2000; Rajgopal

et al., 2000). The implicit assumption that US results would hold globally was challenged using a

Chow Test, which suggested that the way markets value Internet companies was different in East Asia

from the rest of the world. Furthermore, changing the scope of the data from global to specifically

Europe (excluding Poland), the US and Canada led to the Net Income Margin and Volatility

becoming statistically significant. These results suggest that the way markets value Internet

companies varies across geographic domains. This provides evidence against generalising from a US-

only dataset.

Our results presented in the tables (Tables 2-11) suggest that the manner in which the market values

profitable and unprofitable Internet companies is inherently different. This provides evidence against

the approach across much of the literature (e.g., Van der Goot and Knauff, 2001; Rajgopal et al.,

2000) of running analyses on a collated dataset of profitable and unprofitable firms. The non-

robustness of the results for the overall dataset when applied to the dataset of the profitable firms

particularly suggests that the literature’s results might not be applicable to profitable firms.

Consistent with Hand (2000), the data supported share prices as increasing in an increasing positive

profit and increasing in a decreasing negative profit. Whether this was due to the importance of R&D

(Demers and Lev, 2001) and/or S&M (Hand, 2000), expenditure could not be determined given data

deficiencies.

Recent Growth had a positive slope coefficient in all regressions, except sometimes when the extreme

outliers were included in the dataset. This was consistent with much of the literature (Armstrong et al.,

2011; Schwartz and Moon, 2000; Jorion and Talmor, 2000). The reported negative association

between a firm’s size and the price/sales ratio was consistent with previous regressions run on US

24

(Armstrong et al., 2011) and European (Van der Goot and Knauff, 2001) data. However, this was not

a robust result.

Unlike with Demers and Lev (2001) there was insufficient evidence to support Cash Burn as

increasing or decreasing the price/sales ratio in the overall dataset. For profitable firms, the data was

consistent with the price/sales ratio increasing in a decreasing positive cash flow. The NegCash slope

coefficient suggested that high cash burn did not increase the price/sales ratio for firms with a

negative cash flow.

The importance of the payout ratio as a value driver for profitable firms was not previously

highlighted in the literature review. It was consistent with maturation in the Internet industry, leading

investors to be more receptive over dividend payouts rather than demanding reinvestment for future

growth.

Although the literature supported high media coverage as a positive influence on the share price,

much of the data used was based on IPOs (Liu et al., 2009; Jang, 2007; Jens et al., 2006; Ho et al.,

2001; DuCharme et al., 2001a; DuCharme et al., 2001b). Hence, the data only supported Media

Mentions as an explanatory factor for the share price soon after an IPO. This study provided evidence

to broaden the applicability of this result to Internet company share prices in general. However, unlike

in the literature, Media Mentions seemed to be a value driver only for unprofitable firms.

Pastor and Veronesi (2006) and Schwartz and Moon (2000) both argued that higher uncertainty could

increase the expected growth rate and so increase the price/sales ratio. Higher uncertainty also raises

the cost of equity, decreasing the price/sales ratio (Damodaran, 2012). For profitable firms (Table 8,

column 4), the former effect seemed dominant, as higher volatility seemed to raise valuations. For

unprofitable firms (Table 9, column 6), the latter effect seemed dominant, as higher volatility seemed

to depress valuations.

The extent to which website traffic metrics could be assessed as value drivers was limited due to data

deficiencies. However, there was sufficient evidence to support the number of visits as an important

value driver, i.e., the importance of ‘reach’. However, SimilarWeb.com’s data did not support the

importance of ‘stickiness’, measured by time spent on the site in seconds (Demers and Lev, 2001).

25

Unlike previous literature, regressions were run on just the financial statements data and with non-

financial metrics included separately, and the results compared. The inclusion of non-financial metrics

(Media Mentions and Volatility) increased the adjusted R2 from 34.1% (Table 4, column 5) to 39.3%

(Table 8, column 4) for profitable firms, and 29.5% (Table 4, column 4) to 47.7% (Table 9, column 6)

for unprofitable firms. Including non-financial metrics in the European (excluding Polish) and North

American dataset (Table 10) raised the adjusted R2 for the overall data from 16% (column 5) to

46.9% (column 3). Including non-financial metrics improved the model’s explanatory power,

particularly for unprofitable firms and for European (excluding Polish) and North American data.

Running separate regressions also demonstrated the robustness of the different results. For example,

the Net Income Margin’s coefficient was not robust to the inclusion of Media Mentions with the

overall, global dataset (Table 7, column 6). The results for profitable and unprofitable firms were

robust (except for the Size metric) to including or excluding Media Mentions and Volatility.

The literature revealed a split between those who saw the Financial Statements as a highly important

source of value drivers in a maturing Internet industry with improved corporate governance (D’Mello

and Gruskin, 2013; Bahattacharya et al., 2010; Jorion and Talmor, 2000), and those who placed

greater emphasis on non-financial metrics (Simpson, 2010; Trueman et al., 2000; Amir and Lev,

1996). The data suggested that Financial Statements could not explain about two-thirds of variation in

the price/sales ratio. Hence, although important, there seemed to be other important value drivers the

financial statements did not capture. The adjusted R2 for the European (excluding Polish) and North

American data was double that of the global dataset. These countries are likely to have improved

corporate governance legislation and enforcement over other countries, particularly developing ones.

Hence, this result was consistent with D’Mello and Gruskin’s (2013) assertion that improved

corporate governance raised the value-relevance of the financial statements.

26

5. Conclusion

This study has shown how the multiple regression analysis of value drivers can be used to develop a

more statistically reliable relative valuation technique. The approach is highly scalable, being scaled

here to a global dataset (rather than the US dataset most commonly used in the literature), and can be

updated to reflect new information.

After various regression tests, the results obtained show that financial statements could not explain

about two-thirds of the variation in the price/sales ratio. Moreover, our results suggest that the manner

in which the market values profitable and unprofitable Internet companies is inherently different. The

maturation of the Internet industry was reflected in the profitable firms’ data, with higher payout

ratios being associated with higher values. There was also sufficient evidence to support Media

Mentions as an important explanatory factor for unprofitable firms in general, rather than only at IPO.

We also found sufficient evidence to support the number of visits and recent revenue growth as

important value drivers.

With any empirical analysis there are potential limitations to the inferences that can be drawn. The

approach applied in this study, required that the Gauss-Markov Assumptions hold, including an

assumption of no confounding variables, which cannot be assured. Moreover, there were 202 firms in

the overall dataset; the size of the sample had to be reduced to 72 when including web metrics due to

data deficiencies. Hence, future work should focus on seeking ways to ameliorate some of the

disadvantages of the multiple regression approach. Deficient web metric data could be resolved by

surveying a global sample of Internet companies for the information. Sample sizes could be expanded

by considering possible proxies for the market value of Internet companies. A possibility would be the

valuations of private Internet companies implied by transactions. Expanding the sample size would

enable different regression models to be applied across geographic areas, addressing the concern that

the global model was not robust across geographic areas.

27

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