A Financial and Strategic
Analysis of Amazon.com Inc.
MBA 298: Management Project
Instructor: Dr. K.C. Chen
Student: Thomas Eble
Date: 01 December 2015
Table of Contents
1.
Introduction ............................................................................................................................. 1
2.
Amazon.com ............................................................................................................................ 2
3.
4.
5.
2.1.
Amazon’s Business Model ................................................................................................ 3
2.2.
Amazon’s Industry and Competition................................................................................. 4
The Financial Model ............................................................................................................... 6
3.1.
Introduction to Financial Modeling ................................................................................... 6
3.2.
Step-by-Step Guide to develop a Financial Model ............................................................ 8
3.2.1.
Estimating Revenues .............................................................................................. 11
3.2.2.
Estimating COGS and SG&A ................................................................................ 15
3.2.3.
Estimating Financing Costs and Income Tax Expense........................................... 22
3.2.4.
Modeling the Balance Sheet based on the Income Statement ................................ 26
3.2.5.
Estimating CapEx and Finalizing the Financial Statements ................................... 29
3.3.
Some Technical Notes on the Input Sheets ..................................................................... 33
3.4.
Some Technical Notes on the Estimate Sheets ................................................................ 34
Enterprise Value Multiples .................................................................................................. 37
4.1.
Introduction to Relative Valuation Models ..................................................................... 37
4.2.
Enterprise Value vs. Equity Value .................................................................................. 40
4.3.
Basic vs. Diluted Shares Outstanding.............................................................................. 43
4.4.
Valuation Metrics and Multiples ..................................................................................... 46
Public Comparable Company Analysis .............................................................................. 49
5.1.
Our Valuation Approaches .............................................................................................. 49
5.2.
Introduction to Public Comparable Company Analysis .................................................. 51
5.3.
Finding Comparable Companies ..................................................................................... 52
6.
7.
8.
9.
5.4.
Some Technical Notes ..................................................................................................... 53
5.5.
Looking at Amazon’s Numbers ....................................................................................... 55
Precedent Transactions Analysis ......................................................................................... 59
6.1.
Introduction to Precedent Transactions Analysis ............................................................ 59
6.2.
Finding Comparable Transactions ................................................................................... 59
6.3.
Some Technical Notes ..................................................................................................... 61
6.4.
Looking at Amazon’s Numbers ....................................................................................... 63
Cost of Equity and WACC ................................................................................................... 65
7.1.
Cost of Equity - Risk-Free Rate and Market Risk Premium ........................................... 65
7.2.
Cost of Equity - Systematic Risk (Beta) .......................................................................... 66
7.3.
WACC - Cost of Debt ..................................................................................................... 71
7.4.
WACC - Market Value Weights and Calculation ........................................................... 72
Discounted Cash Flow Analysis ........................................................................................... 73
8.1.
In Theory - The Enterprise Discounted Cash Flow Model ............................................. 73
8.2.
In Practice - Which DCF Model should be used? ........................................................... 76
8.3.
Unlevered Free Cash Flow - Operating Assumptions ..................................................... 79
8.4.
Unlevered Free Cash Flow - Calculation Methodology .................................................. 80
8.5.
Continuing Value and Present Value Calculations.......................................................... 81
8.6.
Implied Price per Share and Sensitivity Analysis ........................................................... 84
Conclusion .............................................................................................................................. 87
10. References .............................................................................................................................. 89
11. Appendices ............................................................................................................................. 94
11.1.
Tables and Charts ......................................................................................................... 95
11.2.
Financial Model Quick Steps ..................................................................................... 105
11.3.
Amazon’s Strategic Analysis ..................................................................................... 110
1.
Introduction
The intention of this paper is to analyze the stock of Amazon.com Inc., subsequently simply
called Amazon, from an investor’s point of view. In doing so, we will perform a comprehensive
financial analysis, by first building a detailed operating model to forecast Amazon’s future
financial statements, and then valuing its stock using several methods, including public company
comparables, precedent transactions, and discounted cash flow analysis. Based on this, we will
then issue a recommendation to either buy, hold, or sell the stock. However, to yield meaningful
results, any financial analysis should be accompanied by a strategic analysis, which studies a
company’s business models and competitive advantages, as well as the macroeconomic
environment, industry fundamentals, and competitors to assess the potential for future revenue
growth and profitability. Due to the limited scope of this paper, the focus lies on the financial
analysis with only a brief strategic analysis in the beginning to provide the reader with some
basic background information on Amazon. For further reference, a detailed strategic analysis is
provided in the Appendix.
Also, to enable the reader to fully understand the structure and methodology of the paper, several
key points should be mentioned beforehand. To begin with, after this introduction and a brief
strategic overview of Amazon, the paper will be split into two parts: The first part deals with
building Amazon’s financial model, whereas the second part deals with valuing Amazon’s stock,
based on the inputs from the financial model. Next, in the interest of simplification, theoretical
concepts will mainly but not exclusively be based on four carefully selected sources: Kaplan’s
(2013 and 2014) books to prepare for the Chartered Financial Analyst (CFA) designation, which
provide basic explanations and more conceptual knowledge. Damodaran’s (2012) and Koller,
Goedhart and Wessels’ (2010) books on investment valuation, which provide more detailed
explanations that are widely used by practitioners and academics. And finally, Pignataro’s (2013)
book on financial modeling, which provides a hands-on step-by-step guide to financial modeling.
Based on these theoretical concepts, we also aim to provide a practical approach to business
valuation, as all too often there is a huge gap between what theory suggests to do and what equity
1
analysts actually do in practice. To achieve this, we will analyze and compare equity research
reports for Amazon from Deutsche Bank Markets Research (2014), Jefferies Equity Research
(2014), and UBS Global Research (2014). Additionally, where appropriate, we will use a
complete MS Excel valuation spreadsheet of IBM Inc., provided by Deutsche Bank Markets
Research (2011). In the light of practical application, the models developed and utilized
throughout this report should further be viewed as first-pass models, which intend to illustrate
basic concepts instead of going down to the smallest details.
Regarding the financial data used throughout this report, it should be noted that in a professional
setting one would most likely not use public sources like Yahoo Finance or Morningstar, even
though they typically provide credible information. Instead, financial data providers like
Bloomberg, Reuters, or Factset would be used to gather more accurate, standardized, and timely
data on markets and companies in an efficient manner. Lastly, regarding the writing style of this
paper we adopted the approach that investment banks commonly use in their equity research
reports. More precisely, from a grammatical perspective they use the first-person plural to explain
their house view with phrases like “we think” or “it is our expectation”, for example.
2.
Amazon.com
This section will take a closer look at Amazon’s business model and the industries it operates in
to provide some background for the following two main parts of this report. As will be discussed
more detailed in later sections, the strategic analyses briefly summarized in this section are key
inputs for the following financial model and valuation methods. However, they are also
extremely time-consuming and require considerable experience, especially with regard to
industry fundamentals and overall business dynamics. Therefore, unless otherwise noted, this
section will mostly draw on professional reports from S&P Capital IQ (2015), Morningstar
(2015b), Value Line (2015), as well as Amazon’s (2015) latest annual report. For further
reference, a more detailed strategic analysis of Amazon is provided in the appendix of this report.
While we will primarily focus on insights from the above mentioned sources, we highly
recommend using the information contained in the appendix to complement the analysis.
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2.1.
Amazon’s Business Model
To start with, Amazon.com was founded by current CEO and Chairman Jeffrey P. Bezos in 1994,
and is among the world’s highest-grossing online retailers with $89 billion in revenue in 2014.
The company is headquartered in Seattle, Washington, and is listed on the NASDAQ Global
Select Market under the symbol “AMZN” since its initial public offering in 1997. In 1995,
Amazon opened its virtual doors to become “Earth’s Biggest Bookstore” and “Earth’s Most
Customer-Centric Company”. Starting with books, the company has since then expanded into a
broad range of product categories including consumer electronics, media, digital downloads,
shoes and apparel, health and beauty, kids and baby, home and garden, as well as auto and
industrial. Also, outside the United States, Amazon meanwhile operates in Canada, the United
Kingdom, Germany, France, Italy, Spain, Japan, China, Mexico, India, Brazil, Australia, and the
Netherlands.
Besides expanding its online retail business in terms of additional categories and geographies,
Amazon also broadened its range of products and services to not only serve consumers but also
sellers, enterprises, and content creators. Regarding consumer products, for example, the
company also manufactures electronic devices including the so-called Kindle e-readers, Fire
tablets, Fire TVs, Echo, and Fire phones. Additionally, Amazon offers a service called Amazon
Prime, which is an annual membership program that includes free shipping, streaming of movies
and TV episodes, as well as borrowing e-books for Kindle devices. Overall, the company focuses
on a vast selection, lowest prices, and outstanding customer experience by providing easy-to-use
functionality, fast and reliable fulfillment, and timely customer service. For example, key features
of Amazon’s retail websites include personalized recommendations, customer reviews, secure
payment mechanisms, detailed product information, wedding and baby registries, customer wish
lists, and content preview of many books.
On top of that, through its Merchant and Amazon Marketplace programs, the company allows
third-party sellers to offer their products on Amazon’s retail websites or on their own websites,
and to fulfill orders through Amazon’s fulfillment service. In doing so, the company earns either
fixed fees, revenue share fees, or per-unit activity fees. Similarly, the company serves enterprises
through Amazon Web Services (AWS), which offers a variety of cloud computing, database, and
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analytics services for any type or size of business. Lastly, Amazon serves authors and
independent publishers through Kindle Direct Publishing and Amazon Publishing, as well as
musicians, filmmakers, or app developers through various programs that allow them to publish
and sell content.
Amazon manages its operations primarily on a geographic basis, with its two major segments
being North America and International. As of 2014, the former accounted for 62% of net sales
(60% in 2013), whereas the latter accounted for 38% of net sales (40% in 2013). Since both
segments provide similar products and services, they are further subdivided into media products,
electronics and other general merchandise (EGM), and other products. Looking at the numbers,
media products represented 25% of net sales in 2014 (29% in 2013), electronics and other general
merchandise (EGM) composed 68% of net sales (66%), and other products accounted for 7% of
net sales (5%). Additionally, Amazon’s business is strongly affected by seasonality, which results
in higher sales volume during the fourth quarter, which ends December 31. More precisely, the
company reported 33%, 34%, and 35% of annual revenues in 4Q for the years 2012, 2013, and
2014, respectively.
2.2.
Amazon’s Industry and Competition
As stated in its latest annual report, Amazon’s (2015, p. 3) business model is based on four
principles: “Customer obsession rather than competitor focus, passion for invention, commitment
to operational excellence, and long-term thinking.” By building on these principles, Amazon has
developed a range of sustainable competitive advantages and became one of the most disruptive
forces in the retailing industry. In fact, Amazon played a major role in the structural shift away
from traditional brick-and-mortar retailing, especially in commoditized categories with low
switching costs for consumers and intense price competition. While well-known companies like
Borders, Linens ‘N Things, or Circuit City already left the industry, names like Barnes & Noble,
RadioShack, or Office Depot struggle to match up with Amazon. This trend is further intensified
by consolidation among mass merchants like Wal-Mart and direct-to-consumer sales by
companies like Apple, for example. However, Amazon still faces intense competition from a
variety of players in its rapidly evolving industries. Among them are not only physical retailers
and other online retailers, but also companies that manufacture consumer electronics, companies
4
that provide cloud computing and data storage, companies that provide e-commerce services like
website development, fulfillment, and payment processing, as well as web search engines,
comparison shopping websites, publishers, and other media companies.
To defend and further extend its market position, Amazon identifies selection, price, and
convenience as the major competitive factors for its retail business, as well as quality, speed, and
reliability, for its seller and enterprise services. In fact, Amazon’s strong brand recognition has
come to reflects exactly these attributes, which is a rare combination among retailers that
combined with its continued efforts to enhance customer experience represents one of Amazon’s
major competitive advantages. On top of that, it is the low-cost operation of its online platform
that allows the company to charge lower prices and strengthen its competitive position. More
precisely, its fulfillment and distribution network is more cost effective and also easier to scale
than having a large physical presence like traditional retailers. Additionally, Amazon has cost
advantages from current U.S. tax laws, where online retailers only have to collect sales taxes in
states where they maintain a physical presence. Another unique advantage over competitors is a
so-called network effect, which essentially is a result of Amazon’s low prices, vast selection and
user-friendly interface. These attract millions of customers, which in turn either attract other
merchants to the Amazon platform or encourage wholesalers and manufacturers selling directly
to Amazon. This effect is further intensified through additional services including Amazon
Prime, customer reviews and product recommendations, as well as an expanded selection of both
physical products and digital content.
Looking at Amazon’s growth potential, from 2010 to 2014 the company generated a constant
average growth rate (CAGR) of 32% in revenues. Other key top-line metrics, including active
users (21% CAGR from 2010 to 2014), total physical and digital units sold (36% CAGR from
2010 to 2014), and third-party units sold (46% CAGR from 2010 to 2014) continue to outpace
global e-commerce trends, which indicates that the company also gained market share. Over the
next several years, the independent market research company Forrester Research (see Section
11.3 in Appendix) predicts a 9% CAGR of e-commerce sales in the United States, growing from
$294 billion in 2014 to $414 billion in 2018. On top of that, internationally growing internet
usage provides additional opportunities for e-commerce sales abroad, especially in emerging but
also in developed markets. For example, Forrester Research estimated a 11% CAGR of European
5
e-commerce sales from 2013 to 2017, with 18% CAGR in Southern European countries like Italy
and Spain. Going forward, the macroeconomic environment should also be supportive with lower
gasoline prices likely leading to lower shipping costs, and relatively strong consumer spending
translating to growth in online retailing.
3.
3.1.
The Financial Model
Introduction to Financial Modeling
In this section we will discuss how to build a comprehensive financial model, often also called
operating model, which is the fundamental building block of investment valuation. Essentially,
the goal of financial modeling is to analyze a company’s historical performance and to make
accurate predictions of its future performance, which can subsequently be utilized in valuation
analysis. Given that one already understands the basic concepts of the three major financial
statements, which are the income statement, balance sheet, and cash flow statement, the first step
is to understand and model the flows between those three statements. Based on that, one can then
analyze the historical financial statements and use own assumption to make projections of future
financial statements (Pignataro, 2013).
Regarding the structure and complexity of a financial model, many different designs are possible.
Koller, Goedhart and Wessels (2010), for example, suggest using an explicit forecast period of 10
to 15 years, which allows a company to reach a steady state. The tradeoff behind these
considerations is simple: If the explicit forecast period is too long, it might become difficult to
forecast individual line items. On the other hand, a too short explicit forecast period either results
in significant undervaluation or requires unrealistically high long-term growth assumptions in the
steady state. Therefore, they suggest a detailed forecast, which develops complete financial
statements for the earlier years, and a simpler forecast, focusing only on the most important
variables for the remaining years.
To add even more complexity, a financial model can be based on quarterly or annual financial
statements. While most finance textbooks, like Koller, Goedhart and Wessels (2010), Kaplan
6
(2014a), or Pignataro (2013), describe financial modeling on an annual basis, practitioners often
also incorporate quarterly financial statements. Jefferies Equity Research (2014) and Deutsche
Bank Markets Research (2014), for example, use quarterly data for their historical statements and
the first two years of their explicit forecast period, and annual data for the rest of the explicit
forecast period of 10 years. The most likely explanation for this is that companies report on a
quarterly basis and therefore equity analysts also have to adjust their short-term estimates on a
quarterly basis. However, it should be noted that such quarterly estimates only make sense in the
short term, since is questionable whether it is possible to accurately predict quarterly financial
data 10 years ahead.
Besides different time horizons, there are also various approaches to forecasting individual line
items in the financial model. As shown in the following sections, cost of goods sold, for example,
can simply be modeled as a percentage of sales, or more complex by forecasting significant input
prices based on the competitive environment of input producers. Nevertheless, one always has to
weigh the benefits of a more complex approach, since a more complex financial model or more
complex estimation methods do not automatically result in better forecasts or valuations (Kaplan,
2014a). Koller, Goedhart and Wessels (2010) support this point by noting that instead of
becoming engrossed in forecasting individual line items, it is more important to focus on the
aggregate results and the proper context. For example, it makes more sense to analyze whether a
company’s future return on invested capital (ROIC) is in line with the company’s expected
competitive position, than to precisely estimate its accounts receivable 10 years from now.
As mentioned above, a basic financial model consists of the three major financial statements.
However, depending on the degree of complexity, one could add additional components to allow
for more detailed forecasts and support the flows between the major statements. For example, one
could add a separate depreciation schedule, working capital schedule, or debt schedule, and it is
also not uncommon to add even more supporting schedules to the financial model (Pignataro,
2013). To further improve the predictive ability of a financial model, one could also use a
company’s reported segment information to create separate segment forecasts. This becomes
especially important when different business or geographic segments are expected to exhibit
different revenue growth rates, cost structures, and profit margins. Finally, sensitivity or scenario
analyses could be used to result in ranges of forecasted line items instead of only using point
7
estimates. On top of that, one could also incorporate probabilities for various scenarios to model
uncertainty (Kaplan, 2014a).
Following this discussion, for the sake of simplicity, we decided to implement only a basic
financial model, which includes the three major financial statements without any additional
schedules. Regarding the time horizon and periods, we chose the explicit forecast period to be ten
years and to use quarterly data for historical statements and forecasted statements three years
ahead. For the rest of the explicit forecast period, we decided to follow the approach
recommended by Koller, Goedhart and Wessels (2010) to only forecast the most important
variables, which is also in line with the methodology used by Jefferies Equity Research (2014).
After all, as laid out introductory to this paper, our aim is to provide an approach that is as close
as possible to the approach that analysts actually use in equity research. Regarding forecasts of
individual line items, we decided to mostly use a simple approach and we chose to incorporate
segment information into the financial model to increase our predictive ability. Also, since our
primary goal is to build a practical first-pass model, we decided not to include scenarios or
probabilities in our forecasts. As a final note, while the detailed quarterly forecasts for 2015 to
2017 will be developed in subsequent sections, the assumptions from 2017 onwards will be
developed in Section 8.3.
3.2.
Step-by-Step Guide to develop a Financial Model
As mentioned introductory to the previous section, to build a financial model we first need to
model the flows between the three financial statements, and then use the historical statements
together with our assumption to make projections of future statements. Nonetheless, as Koller,
Goedhart and Wessels (2010) note, depending on the structure and complexity of the model,
many different designs are possible. Taking the equity research reports of Deutsche Bank
Markets Research (2014), Jefferies Equity Research (2014), and UBS Global Research (2014) as
an example, one can easily see that their projected income statements for Amazon are not only
based on different assumptions but are also structured a little different. This shouldn’t be
surprising, however, since it is normal for different analysts to use different models and
assumptions, even though they are all based on the same theoretical foundations. Additionally, no
8
two companies are exactly identical and hence no two financial models will look exactly the
same, since they have to be adjusted to the subject company’s fundamentals.
That being said, from a theoretical perspective any financial model is more-or-less structured as
follows (Kaplan, 2014a) 1:
1.
Estimate revenue growth and future expected revenue
2.
Estimate cost of goods sold (COGS)
3.
Estimate sales, general & administrative costs (SG&A)
4.
Estimate financing costs
5.
Estimate income tax expense
6.
Model the balance sheet based on items that flow from the income statement
7.
Estimate capital expenditures (CapEx)
8.
Complete the forecasted balance sheet and cash flow statement based on flows from
the income statement as well as flows from each other
To create a financial model for Amazon, we will follow this step-by-step guide and explain how
to develop assumptions and estimates for the individual line items as we move forward. Of all the
above steps, estimating revenue growth is most critical, especially for fast growing companies,
since almost every line item in the financial model will either directly or indirectly be affected by
revenues (Koller, Goedhart and Wessels, 2010). In general, making projections is often
considered the hardest part of creating a financial model, since forecasting a company’s future
performance requires an understanding of its revenue drivers and its cost structure, to only name
two examples. And while an equity analyst may have years of experience with a company, and
therefore a deep understanding of the business model, the competitors, and the industry it
operates in, we don’t have such insights. Hence, in the beginning we have to make fair
generalizations, but as we gain more knowledge about a company, we can refine our assumptions
to improve our predictive ability. To do so, it is critical to gather as much information as possible,
1
Author’s note: For simplicity, the methodology shown here was condensed and slightly amended compared to its
source. Most importantly, a further step to estimate cash taxes was eliminated after step 5, since a differentiation
between cash taxes and income tax expense is only relevant for valuation purposes but not for forecasting the
financial statements. Additionally, the last step was renamed to account for the fact that the balance sheet and
cash flow statement not only depend on flows from the income statement but also on flows from each other.
9
which besides researching the company, its competitors, and its industry, entails using investor
presentations, earnings calls, equity research reports, and other data providers like Bloomberg,
Thomson Reuters, or even Yahoo Finance, for example. These sources often contain valuable
information about future company performance and can therefore guide our judgment, especially
when several such sources are used in conjunction. Nevertheless, one should keep in mind that
any projection will be somewhat uncertain, since it is simply not possible to make perfectly
accurate predictions of future performance (Pignataro, 2013).
Following these general points on how to structure a financial model, a more specific point is
how to incorporate Amazon’s segment data into our financial model. More precisely, the problem
is that Amazon’s segment data and income statement data follow a slightly different structure.
Reported segment data splits total net sales, total operating expenses, and total operating earnings
into the two main segments North America and International. Thereby, operating expenses
exclude stock-based compensation and other operating expense (income). Additionally, each
segment’s net sales are further split into the sub-segments media, electronics and other general
merchandise, and other. On the other hand, reported income statement data splits total operating
expenses into its functional components, which are cost of sales, fulfillment, marketing,
technology and content, general and administrative, and other operating expense (income),
whereas stock-based compensation is partially included in each component. As a side note,
income statements data also splits total net sales into product sales and service sales, but we
neglect this detail for our analysis. Due to these inconsistencies, there are basically three ways to
set up our model:

First, the simplest approach would be to just disregard segment information and only
work with income statement data.

Second, like in the first approach, one could base all estimates on income statement data
and link the results back to the segments to reconcile them with separate estimates and a
floating variable to balance the calculation. For example, one could estimate net sales,
operating expenses, and operating earnings for the North America segment as percentages
of the respective totals, and use the international segment to balance the calculation.
Likewise, one could estimate the two sub-segments media and electronics and general
10
merchandise as percentages of each segment’s net sales, and use the third sub-segment
other to balance the calculation.

Third, and inverse to the second approach, one could base all estimates on segment data,
which means estimating revenues, operating expenses and earnings for each segment,
where revenues would further be divided into sub-segments. Then, one could link the
results to the income statement to reconcile total operating expenses with separate
estimates and one floating variable to balance the calculation. More precisely, one could
estimate cost of sales, fulfillment, marketing, and technology and content, while general
and administrative would be used to balance the calculation.
Depending on the available information, any of the three approaches is possible. The first
approach would be easiest but clearly leaves out valuable information. By contrast, the other two
approaches provide a more complete picture of Amazon’s operating dynamics, since
reconciliation (either way) can make the analyst aware of unrealistic estimates. For example, by
reconciling strong total operating earnings growth on a segment level, an analyst could realize
that none of the segments is currently able to support such growth. Similarly, reconciling
aggregated segment operating expenses into their functional components on the income statement
could reveal growth rates that are not in line with historical values. We decided to use the third
approach for several reasons: First, since we don’t know the company well enough, we lack the
insights to establish proper estimates for separate operating expense line items on the income
statement. Hence, it is safer to just estimate segment operating expenses and earnings as
percentages of individual segment revenues. Second, by looking at individual segments, we allow
for a more granular analysis, since we are able to account for different growth rates and margins
among the segments. This is more prudent than just looking at aggregated data, as new segments
often grow much faster than established, older segments. In turn, they are usually less profitable,
especially in the first couple of years. Lastly, as mentioned before, the reconciliation can point to
unreasonable analyst estimates, which is also beneficial for our analysis.
3.2.1. Estimating Revenues
Following the above step-by-step methodology, we will start our analysis with estimating
revenue. As mentioned before, a detailed analysis of revenue growth and its underlying drivers is
11
critical, since any stock’s valuation essentially builds on the forecasted revenue growth rate. In
theory, there are three approaches to forecast future revenues: The bottom-up approach builds on
company-specific data like reported segments, historical revenue growth, or new product
introductions. The top-down approach derives revenue projections from assumptions about a
macroeconomic variable, like nominal GDP growth, together with the expected relationship
between a company’s revenues and that variable. Lastly, the hybrid approach incorporates
elements of both bottom-up and top-down analysis and is most often found in practice. For
example, an analyst could estimate industry sales depending on overall GDP growth, and then
forecast a company’s revenue based on its market share in the industry (Kaplan, 2014a).
Additionally, when analyzing growth of a firm’s top-line, an understanding of the underlying
drivers like unit sales, pricing, currency, and acquisitions or divestitures is critical. For example,
revenue growth for a firm with lots of international activities may be impacted by changes in
foreign exchange rates. Moreover, organic revenue growth could be heavily distorted by
acquisitions, which is considered anorganic growth (Fiore, 2013). Additionally, on the most basic
level, revenues are driven by pricing and volume. Hence, insights into whether a company takes
initiatives to increase volumes or prices can be valuable. In this regard, it is also important to
consider outside forces, since intense competition may lead to market share losses, or force the
company to lower its prices (Pignataro, 2013). On the other hand, companies are said to have
pricing power if they are able to raise prices without an increase in input cost. Another more
detailed driver of revenue growth is related to changes in the so called sales mix. A “mix-shift” is
observed when revenue changes can be attributed to a changing proportion of high-margin
products vs. low-margin products (Fiore, 2013).
To gain more insights into prices and volumes, it is important to incorporate an analysis of the
competitive environment and a company’s competitive advantages. For example, does a company
have the capabilities and resources to compete effectively in the future? Also, does it have the
right products to retain existing customers and maybe win additional customers, or will
competitors have the better products and displace the company’s market position? Clearly, there
are more questions than answers, but the key to a good revenue forecast is a well-structured and
well-informed analysis (Koller, Goedhart and Wessels, 2010). Also, it should be noted that the
above approaches are not exhaustive, but rather intended to give an initial overview on how to
12
approach a revenue forecast. As a final point, after explaining several ways how to properly
forecast revenues, there are also many wrong ways to do it. Just to name one example, it is often
assumed that future revenue growth will equal historical revenue growth. However, making this
assumption is neither recommended nor safe, unless further research indicates that such a
relationship is actually warranted (Pignataro, 2013). To summarize, no matter which forecasting
method one uses and how much insight one has, revenue forecasts over the long-term will almost
always be imprecise due to unpredictable changes in customer preferences, technologies, and
corporate strategies. It is therefore important to constantly reevaluate whether the current forecast
is in line with industry dynamics, competitive positioning and historical growth rates (Koller,
Goedhart and Wessels, 2010).
Following this theoretical discussion, and in line with the above structural considerations, we
decided to primarily use a bottom-up approach to incorporate Amazon’s segment data into our
revenue forecast. More precisely, we estimated separate revenue growth rates for each of the
three sub-segments, which are media, electronics and other general merchandise (EGM), and
other, within the two major segments North America and International. Subsequently, we
aggregated these estimates to arrive at total segment revenues and finally at total revenue for
Amazon. However, as discussed below, to derive these revenue growth rates we also considered
various economic and industry-specific factors. Therefore, we also included elements of a topdown approach, even though we didn’t explicitly model market shares or relationships to
macroeconomic variables. Hence, our approach could also be considered somewhat hybrid.
As can be seen from Table 1, Amazon’s consolidated annual sales growth slowed down over the
past three years, with +27.1%, +21.9%, and +19.5% in 2012 , 2013, and 2014, respectively.
Excluding the negative effect of foreign exchange rates on the International segment, these
numbers compare to 29%, 24%, and 20% in 2012, 2013, and 2014, respectively. Looking at the
sales mix of Amazon’s major segments, North America increased its share from 54.7% in 2010 to
62.3% in 2014, indicating relatively higher annual revenue growth than International, which
accounted for 45.3% and 37.7% of total revenues in 2010 and 2014, respectively. Analyzing the
aggregated sub-segments in the same manner reveals that other experienced the highest annual
growth compared to prior year periods, followed by EGM and media. As a result, media’s share
13
of total revenues dropped from 43.5% in 2010 to 25.3% in 2014, whereas EGM’s and other’s
shares rose from 53.7% and 2.8% to 68.4% and 6.3%, respectively (Amazon, 2015).
Taking a closer look at segment results, North America recorded year-over-year sales growth of
30.4%, 27.9%, and 24.6% in 2012, 2013, and 2014, respectively. On the other hand, International
only recorded year-over-year sales growth of 23%, 13.9%, and 12% in 2012, 2013, and 2014,
respectively. This growth patterns were primarily driven by increased unit sales, which in turn
reflect price reductions, increased product offerings, and in-stock inventory availability, as well
as sales in faster growing categories, like EGM (Amazon, 2015).
Based on our brief business and industry analysis in Section 2, we expect Amazon’s North
America EGM sales to grow by 19%, 17%, and 15%, while we expect Amazon’s International
EGM sales to increase by 17%, 22%, and 25%, for the years 2015, 2016, and 2017, respectively.
These forecasts reflect our expectation of continued addition of new products and competitive
pricing, as well as further expansion of the Amazon Prime service. In line with historical
numbers, we expect growth in the North America segment to further slow down, due to the
already high market share resulting from rapid expansion over the previous years. By contrast,
we expect growth in the International segment to gain momentum based on an already strong
presence in the United Kingdom, Germany and Japan, as well as expansion in new countries,
most likely emerging markets.
After the recent slowdown in year-over-year media sales growth, especially in the International
segment, we forecast Amazon’s North America media sales to grow by 0.5%, 8%, and 7%,
whereas we forecast Amazon’s International media sales to increase by 0.5%, 4%, and 6%, for
the years 2015, 2016, and 2017, respectively. These forecasts are based on our expectation of
further growth in selection and attractive pricing, as well as the addition of new product
segments, like online streaming, for example. Additionally, our conservative growth estimates for
2015 reflect the significant slowdown from 2013 to 2014. However, we consider this to be more
transitory than structural. Therefore, we expect year-over-year growth in the North America
segment to return to about 7% to 8% from 2016 onwards, whereas we expect the International
segment to switch to a growth trajectory based on expansion into new markets.
14
Lastly, we expect Amazon’s North America other sales to grow by 39%, 32%, and 27%, while
we expect Amazon’s International other sales to increase by 8%, 10%, and 12%, for the years
2015, 2016, and 2017, respectively. Generally speaking, other sales include all of Amazon’s nonretail activities, like advertising and web services, for example. Nevertheless, it should be noted
that Amazon Web Services (AWS) sales are only included in the North America segment, instead
of being separated into North America and International. In this regard, our growth assumptions
reflect a growing demand for web services, online media, e-commerce, and web 2.0 in general.
Major drivers therefore are rapid growth in cloud computing, increasing broadband penetration,
more secure online payment methods, as well as an increase in social networking. Similarly, to
the other two sub-segments, we expect growth in the North America segment to further
slowdown, while we expect the International segment to slightly increase going forward.
3.2.2. Estimating COGS and SG&A
After estimating revenues, the first step in forecasting the remaining line items on the income
statement is to determine what economically drives them. While most operating line items, like
COGS or SG&A, for example, will be directly tied to revenues, others will be tied to a specific
asset. Interest income and interest expense, for example, are both non-operating line items and
are usually tied to cash and marketable securities and interest-bearing debts, respectively. The
easiest way to incorporate this into a financial model is to calculate forecast ratios based on the
historical values of the line items and their associated drivers. After estimating future forecast
ratios, one can then multiply them by the estimated drivers to arrive at a forecast values for the
individual line items. Taking COGS as an example, one would calculate the historical ratio of
COGS/revenue, then forecast this ratio going forward, and finally multiply it by the revenue
forecast to arrive at a future value for COGS (Koller, Goedhart and Wessels, 2010).
Looking at the individual line items, SG&A is usually more stable than COGS due to the higher
fixed cost component, whereas COGS is closely related to revenue. For example, expenses for
corporate headquarters or management salaries are more fixed in nature and tend to gradually
grow with the company rather than with revenues. On the other hand, expenses for marketing and
distribution are more variable in nature and hence more closely related to revenues. Research and
development (R&D), another operating expense that is sometimes reported as a separate line
15
item, is also relatively stable as a percentage of revenue. However, they may be uncorrelated to
revenues at other times, since they are eventually driven by management decisions. Depending on
the desired accuracy of analysis, an analyst could simply use historical trends of these items,
which likely indicate changing business or market conditions, or use a more elaborate approach
by also analyzing input costs and business segments. For example, even though it doesn’t apply
here, prices for commodity-type inputs like fuel can be volatile and therefore significantly affect
an airline’s COGS. Lastly, an examination of competitors’ cost structures could further aid the
analysis but should also account for differences in business models (Kaplan, 2014a).
Since changes in COGS and SG&A lead to changes in gross and operating margins, we can also
analyze a company’s cost structure from a margin perspective. To start with, gross profit margin
is defined as the ratio of gross profit to revenue, whereas gross profit is calculated as revenue
minus COGS. In general, gross margins are the most stable profitability metric, because of the
high variable cost component in COGS. Besides sales growth, this ratio is very important, as it
yields insights into a company’s strategy in terms of both pricing power and operating efficiency
(Fiore, 2013). For example, a higher gross margin may result from technologically superior
products, which allow a company to charge higher prices for its products. This is often the case
when products can be differentiated from others in terms of branding, quality, or patent
protection, and is also evidenced by above-average research expenses (Kaplan, 2013a). On the
other hand, it could also be an indication of reduced production costs through economies of scale,
since gross and operating margins tend to increase with increasing production and sales volumes.
However, an evaluation of the presence of economies of scale in an industry requires a more
detailed peer group analysis. Hence, gross and operating margins, should always be evaluated in
both a time-series and cross-sectional analysis (Kaplan, 2014a). As a side note, the above
mentioned strategies, meaning higher prices due to product differentiation vs. lower costs due to
economies of scale, are referred to as differentiation strategy and cost leadership strategy,
respectively (Hitt, Ireland, and Hoskisson, 2014).
Besides gross profit margin, operating profit margin, also called EBIT (earnings before interest
and taxes) margin, is a more comprehensive measure of profitability, since it measures how much
a company earns from its operations before considering the effects of financing and taxes. It is
calculated similar to gross margin, but also takes into account SG&A, so the formula becomes
16
revenue minus COGS and SG&A. As mentioned earlier, with increasing revenues companies
strive for margin expansion, mainly by leveraging the fixed cost components of SG&A, but (to a
lesser extent) also COGS. Additionally, operating margins yield insight into a firm’s operating
leverage, which is especially important for companies in cyclical industries. Firms with a high
proportion of fixed costs are considered to have high operating leverage and tend to see more
rapid EBIT margin expansion during up-cycles, but also more rapid contraction in down-cycles
of the economy. Hence, it could also be beneficial for analysts to track normalized gross and
operating margins for the company in question, its peers, or the industry as a whole (Fiore, 2013).
As a final point, the best way to make initial projections for COGS and SG&A expenses is to
look at historical trends, which can later be refined as necessary. For example, if COGS as a
percentage of revenue remained relatively stable over the past three years, we can safely assume
that COGS is mostly variable and grows in line with revenue going forward. However, if COGS
as a percentage of revenue was rather volatile over the past three years, further research is
warranted to understand the underlying reasons. For example, the company could have changed
its business model or taken initiatives to decrease costs. Either way, the most common methods to
making assumptions based on historical trends are as follows (Pignataro, 2013):
1. Take an average percentage of the last three years
2. Take a maximum percentage of the last three years (conservative approach)
3. Take a minimum percentage of the last three years (aggressive approach)
4. Take the last year’s percentage
5. Have the percentages steadily increase or decrease year-over-year
Nonetheless, it is important to note that these are only the most common methods. Hence, other
trends, like initially decreasing and then constant COGS as a percentage of sales, for example,
may work better based on the many influencing factors discussed above.
Following this theoretical discussion, and in line with the above structural considerations, we can
now estimate operating expenses and operating earnings on a segment level. As mentioned
above, to do this we can link both line items to revenues and either forecast operating expenses or
operating earnings. We decided to forecast operating earnings, since it is more practical to
17
analyze a company’s cost structure from a margin perspective. As a side note, to remain
consistent with the above theoretical discussion, we assume that COGS and SG&A account for
all of Amazon’s operating expenses.
As can be seen from Table 1, Amazon’s consolidated operating margin decreased from 2.7% to
2% over the past three years, with both major segments exhibiting faster growth in operating
expenses than in revenues. More specifically, operating margins in the North America segment
dropped from 4.6% in 2012 to 3.8% in 2014, while operating margins in the International
segment dropped from 0.3% in 2012 to -0.9% in 2014. Since we will take a closer look at
operating expense drivers with regard to their functional components below, we can subsequently
focus on forecasting operating earnings for each segment. Broadly speaking, following the recent
margin pressures we are conservative for 2015, but expect margins to stabilize on somewhat
normalized levels from 2016 onwards. More precisely, we expect operating margins of 3.6%,
4%, and 4% for the North America segment, and 0%, 0.7%, and 1% for the International segment
in 2015, 2016, and 2017, respectively. These forecasts reflect our expectation of continued global
growth in web services, due to rising internet usage, growing e-commerce volumes, and
companies moving their IT infrastructures to the cloud. This should benefit Amazon’s non-retail
business, especially AWS, where margins are considerably higher than in its retail business,
where margins traditionally are very thin. On top of that, we expect Amazon’s growing market
dominance, especially in the United States, to positively affect margins by increasing negotiating
power and operating efficiencies. Potential headwinds to margin expansion could come from the
“mix-shift” away from media to EGM, where margins are lower, as well as from intense
competition in the online retail space, as more and more “brick-and-mortar” retailers increase
their online presence.
After forecasting segment earnings, we can then link the aggregated results from Table 1to the
income statement in Table 2 to reconcile total operating expenses with their functional
components, which we also tied to revenues. More precisely, cost of sales, fulfillment, marketing,
and technology and content are estimated as percentages of revenues, while general and
administrative is used to balance the calculation. It should be noted, that cost of sales is listed
separately from operating expenses to allow for gross profit calculation, whereas it is listed under
operating expenses in the income statement input sheet. However, it is still considered a part of
18
Amazon’s operating expenses. Also, since we only reconcile our previous operating expense
estimate, we assume that the functional components as a percentage of revenues remain constant.
For example, we forecast fulfillment expense in 1Q 2015 to be 11.3% of revenues, which is the
same percentage value as in 1Q 2014. The only exceptions to this simplification are cost of sales,
where we follow the historical pattern, and SG&A, which is not forecasted but used to balance
the calculation. With cost of sales decreasing and all other costs held constant, it is not surprising
that SG&A, as a percentage of revenue, increases over time. In this regard, our assumptions
might be a little bit over simplistic and therefore could be further refined, but since they won’t
affect our total operating expense forecast, we are fine with that for now.
As shown in Table 2, Amazon’s cost of sales in absolute dollars increased over the past three
years, mainly as a result of increased product, digital media content, and shipping costs, which in
turn were caused by increased sales and digital offers. Nevertheless, cost of sales decreased as a
percentage of revenues, which is evidenced by increasing gross margins of 24.8%, 27.2%, and
29.5% in 2012, 2013, and 2014, respectively. This trend was primarily driven by increasing
service sales as a percentage of total sales, including third-party seller fees, digital content
subscriptions, and other non-retail activities, such as AWS (Amazon, 2015). Going forward, we
expect this trend to continue with gross margins of 70%, 69.5%, and 69% in 2015, 2016, and
2017, respectively. Taking a closer look at the other functional components, fulfillment costs
increased in both absolute and relative terms over the last three years, primarily driven by
variable costs associated with increased sales volumes, inventory levels, and sales mix, as well as
costs from expanding fulfillment capacity and payment processing costs. Similarly, marketing
costs rose since 2012 in both absolute and relative terms, mainly caused by increased spending on
online marketing channels, payroll, and television advertising. Technology and content also
increased in both absolute and relative terms over the last three years, due to increased spending
on technology infrastructure and payroll. While technology costs consist mostly of R&D
activities related to platform development for new and existing products, content costs consist
mainly of activities related to category expansion, editorial content, buying, and merchandising
selection. Lastly, SG&A exhibited a similar growth pattern in both absolute and relative terms,
which was primarily caused by increases in payroll and professional service fees (Amazon,
2015). Regarding forecasts for the above cost categories, as mentioned before, we simply kept the
historical relationships constant, except for SG&A, which balances the calculation.
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Next, as mentioned briefly with regard to the structure of our financial model in Section 3.2,
Amazon doesn’t allocate stock-based compensation and other operating expense (income) to its
segments. As a result, there is one line item called pro-forma operating expenses (income), which
is the value we forecasted on a segment level, and one line item called reported operating
expenses (income), which includes stock-based compensation and other operating expense
(income), and is in line with the income statement input sheet. This differentiation goes all the
way down to net income and earnings per share (EPS), and is also in line with practitioners, as
exemplified by Jefferies Equity Research (2014) and Deutsche Bank Markets Research (2014).
However, both expenses are still considered operating expenses and therefore we have to forecast
them based on their economic drivers. While stock-based compensation, just like most other
operating line items, is tied to revenues, we use a more elaborate approach for other operating
expense (income). Since this line item consists primarily of intangible asset amortization, we
decided to treat it as amortization expense for acquired intangible assets in our financial model 2.
This simplifies our analysis, as Amazon actually reports expected annual amortization expense of
acquired intangible assets for five years ahead. Therefore, we only have to take the annual values
from Amazon’s (2015) latest annual report and equally distribute them over the respective four
quarters.
As Table 2 shows, stock-based compensation increased over the last three years in both absolute
and relative terms, primarily due to an increase in the number of awards granted to existing and
new employees (Amazon, 2015). Going forward, like with the operating expense line items
before, we simply assume that the historical relationships with revenues remain intact.
Nonetheless, this simplification might not be warranted and therefore requires some further
research, since stock-based compensation normally increases over time due to a growing number
of employees and a rising stock price. Other operating expense (income), meaning amortization
expense for intangible assets, decreased slightly in relative terms, whereas it fluctuated in
absolute dollars over the course of the last three years. For 2015, 2016, and 2017, as explained
2
Author’s note: This treatment also fits with Amazon’s cash flow statement where net income is adjusted for noncash charges, including depreciation and amortization, stock-based compensation, and other operating expense
(income), to arrive at cash flow from operating activities. Thereby, amortization expense for acquired intangible
assets shouldn’t be confused with depreciation and amortization, which is included in operating expenses and
accounts for depreciation of property and equipment, as well as amortization of previously capitalized internal-use
software and website development (Amazon, 2015).
20
above, we simply utilize Amazon’s annual amortization expense estimates of $202 million, $185
million, and $161 million, and equally distribute them over the respective four quarters.
Lastly, while operating expenses are often aggregated into SG&A or some other categories for
financial modeling, depreciation and amortization (D&A) is usually treated as a separate line
item, since it is needed to properly forecast net PP&E, but more importantly, since it is a critical
input for valuation models. Hence, even though Amazon doesn’t report it as a separate operating
expense line item on the income statement, we can pull the historical values from the cash flow
statement, where it is included as an adjustment to reconcile net income (loss) to net cash from
operating activities. To then forecast D&A, there are basically three approaches: The simplest
approach is to forecast it as a percentage of revenue, like most other operating line items, which
is the preferred method when CapEx are smooth. On the other hand, when CapEx occur in large
amounts only every couple of years, it makes more sense to forecast D&A as a percentage of net
PP&E. The third and most complicated approach is to forecast D&A based on a depreciation
schedule, which, however, requires detailed information about the company’s assets (Koller,
Goedhart and Wessels, 2010). To keep our financial model as simple as possible, like with the
operating expense line items before, we decided to forecast D&A as a percentage of revenues.
As can be seen from Table 2, Amazon’s D&A expense increased over the past three years in both
absolute and relative terms. Following this pattern, we expect D&A to rise from 5.3% of
revenues in 2014 to 5.5% of revenues in 2014 and 2015, and to remain at this level for 2016 and
2017. As we will see in Section 3.2.5, these numbers fit with our CapEx estimates of 6% going
forward, since growth firms usually have CapEx/D&A ratios greater than one, indicating that
they are in a capital investment phase. To put this into context, ratios equal to one imply that
productive assets are replaced at the same rate as they wear out, whereas ratios lower than one
show that a company is no longer investing in productive assets. As Table 4 shows, Amazon’s
CapEx/D&A ratio normalized considerably over the past three years, dropping from 175.3% in
2012 to 103.1% in 2014. Combining our estimates for D&A and CapEx results in a slightly
higher ratio of 109.1% from 2015 onwards, which, however, is justified by Amazon’s potential
for further business expansion and revenue growth.
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3.2.3. Estimating Financing Costs and Income Tax Expense
As mentioned previously, while most operating line items are tied to revenue, non-operating lineitems are usually not. Hence, in this section we will take a closer look at the two most important
non-operating line items, which are interest income and interest expense, along with two less
important non-operating line items, which are equity-method investment activity and other
income (expense). Finally, we will also take a look at income tax expense and how to adjust it for
pro-forma items. In doing so, it is important to keep in mind that, as shown in Section 4.4, nonoperating line items are usually excluded from free cash flow calculations, which implies that
they won’t affect a company’s valuation. Instead these forecasts are merely required to build a
complete income statement, which can then be used for cash flow panning. Hence, we don’t
need as robust forecasts for non-operating line items as we did for operating line items above,
which actually do affect free cash flow calculation (Koller, Goedhart and Wessels, 2010).
With this in mind, interest income and interest expense are usually tied directly to the asset that
generates the income, or the liability that generates the expense, which typically are cash and
marketable securities and interest-bearing debts. While Kaplan (2014a) suggests using averages
of this period’s and last period’s balance sheet values, Koller, Goedhart and Wessels (2010)
recommend using only prior period’s values to simplify the financial model and avoid circularity
errors. The problem with average values is that they introduce a feedback effect, since interest
income is a function of cash, which itself depends on interest income. Additionally, analysts
should also include any planned debt issuance or retirement, as well as the maturity profile of the
company’s existing debt to improve their forecasts (Kaplan, 2014a). Besides this relatively
simple approach to forecasting interest income and expense, similar to D&A above, Pignataro
(2013) suggests building a debt schedule to better forecast interest expense and interest income.
To keep our model simple, we decided to forecast interest income based on the average of
Amazon’s cash and cash equivalents as well as marketable securities, whereas interest expense is
tied to the average of Amazon’s long-term debt. A complicating factor here is the fact that we
project quarterly instead of annual figures, while interest is usually quoted on an annual basis. For
simplicity and comparability, we therefore annualize quarterly values using simple interest, even
22
though this is not technically correct. Furthermore, annual values slightly differ from quarterly
values due to the two-period averages used as calculation bases.
As shown in Table 2, Amazon’s effective cash interest rate, calculated as interest income divided
by an average of this period’s and last period’s cash and marketable securities, remained almost
flat over the last three years. On the other hand, its effective debt interest rate, calculated as
interest expense divided by an average of this period’s and last period’s long term debt, increased
from 3.2% in 2012 to 3.7% in 2014. To forecast interest income and interest expense from 2015
to 2017, we simply take the 2014 effective cash and debt interest rates of 0.3% and 3.7%,
respectively, which results in growing interest income and flat interest expense going forward.
Next, we must estimate the two less important non-operating line items, which are other income
(expense) and equity-method investment activity. Taking a closer look at Amazon’s (2015) latest
annual report reveals that other income (expense) consists primarily of foreign currency losses
and realized gains and losses on marketable securities sales. Since these are usually random onetime events, they are very hard to predict, and it is therefore safest to set them to zero. In doing
so, it should be noted that we could somewhat overestimate earnings, as the line item was mostly
negative over the last three years. Nevertheless, due to the small amount we decided to keep it at
zero, since it would only have a minor effect anyways. The same reasoning applies to equitymethod investment activity, which is reported net of tax, meaning below Amazon’s reported pretax income.
Following the methodology from Section 3.2, the last step to complete our income statement is to
estimate income tax expense. This, however, is not an easy task, since it is influenced by a variety
of factors. To start with, one has to differentiate between the statutory tax rate, the effective tax
rate and the cash tax rate. While the statutory rate is a country’s standard tax rate, effective rates
and cash rates are calculated as income tax expense or cash taxes paid as a percentage of pre-tax
income. Thereby, income tax expense is calculated as cash taxes due plus changes in deferred tax
liabilities minus changes in deferred tax assets. The reason for this is that the difference between
income tax expense and cash taxes paid is typically recorded in deferred tax accounts on the
balance sheet (Kaplan, 2014a). Furthermore, a company’s effective tax rate is usually lower than
its statutory rate of 35% in the United States for a variety of reasons. For example, a company
23
may have lower-taxed global operations, including the use of global funding structures and
certain tax credits, or it may simply have recognized expenses on the income statement that are
not deductible for tax purposes (Pignataro, 2013).
That being said, the easiest way to forecast future income tax expense, as recommended by
Pignataro (2013), is to base projections on the historical trend of the company’s effective tax rate,
similar to how we forecasted operating expenses above. These projections can then be further
refined, for example, by using the company’s own expectation of future effective tax rates, which
they sometimes disclose in their annual reports. While it seems that this approach is also used by
practitioners, as exemplified by Deutsche Bank Markets Research (2011), Koller, Goedhart and
Wessels (2010) explicitly state to not forecast income tax expense as a percentage of pre-tax
income and instead use a more complex approach. More precisely, they separate operating and
non-operating taxes, because otherwise their calculation of free cash flow would be affected by
changes in leverage and non-operating items. However, as will be discussed in Section 8, we use
a separate calculation of operating income taxes for valuation purposes, so for now we can
simply apply the approach suggested by Pignataro (2013).
In doing so, we simply divide Amazon’s provision (benefit) for income taxes by the reported pretax income. But, as can be seen from Table 2, both input values fluctuate heavily due to
Amazon’s low profitability and complex tax treatment. As a result, the effective tax rate is also
quite volatile and sometimes can’t even be calculated due to negative reported pre-tax income.
Hence, for simplicity we estimate Amazon’s effective tax rate to be 33% going forward, which is
slightly below the marginal tax rate of 35% for U.S. corporations with taxable income above
$18.3 million, but in line with the 31% to33% estimates used by Jefferies Equity Research
(2014).
Finally, there is only one more line item left to complete Amazon’s income statement, which is
related to income tax expense. More specifically, we added a line item called tax adjustments for
non-GAAP/pro-forma items to account for the difference between pro-forma and reported
figures. The reason for this adjustment is that in the pro-forma calculation we exclude stockbased compensation and other operating expense (income), which results in higher pre-tax
income (loss). In turn, this should lead to a higher provision (benefit) for income taxes.
24
Additionally, instead of using our effective tax rate of 33% we chose a 25% adjustment, since
stock-based compensation and amortization of intangibles, which is the main component of
operating expenses (income), have a special tax treatment. This methodology is also in line with
Jefferies Equity Research (2014).
Before we move on to forecasting the balance sheet, it should be noted, that on the very bottom
of the income statement in Table 2, pro-forma and reported earnings per share (EPS) are
calculated on both a basic and diluted basis. In line with Amazon’s reporting, EPS are calculated
by simply dividing either pro-forma or reported net income by basic or diluted weighted-average
shares outstanding (Amazon, 2015). As will be discussed more detailed in Section 4.3, current
shares outstanding is an important input for our valuation analysis, while future shares
outstanding are important to calculate future EPS and book value per share (BVPS), which in
turn affect future earnings and book value multiples. To project future shares outstanding, one
has to project further share issuances or buybacks, which, as we will see in Section 3.2.5, is
usually modeled on the cash flow statement based on historical numbers or other sources of
information, like a company’s share buyback plan, for example. The dollar amount from the cash
flow statement can then be converted to the actual share count by using the current stock price, or
making reasonable assumptions about future stock price changes or discounts for buying in bulk.
This methodology is also in line with practitioners, as exemplified by Deutsche Bank Markets
Research (2011). Furthermore, one could add more detail on the diluted share adjustments,
instead of holding the difference between basic and diluted shares outstanding constant
(Pignataro, 2013).
Following this brief discussion, for simplicity, we decided to keep both basic and diluted shares
outstanding at their current levels, which is the safest approach without doing further research or
having additional information. By contrast, Jefferies Equity Research (2014) and Deutsche Bank
Markets Research (2014) both assume a growing number of shares outstanding, which could be
considered more conservative from a valuation standpoint, since it leads to lower per-share
metrics and higher forward multiples. Hence, as discussed in more detail in Section 4.1, when
comparing Amazon’s forward price/earnings multiple to a benchmark, the higher multiple would
indicate that Amazon is overvalued at its current stock price.
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3.2.4. Modeling the Balance Sheet based on the Income Statement
Following the step-by-step methodology from Section 3.2, the next step is to model the balance
sheet. It should be noted, however, that one could also model the cash flow statement first and
construct the balance sheet in the last step. As mentioned before, Pignataro (2013), for example,
uses a separate working capital schedule where he only estimates operating balance sheet
accounts to subsequently model the cash flow statement and as a final step create the complete
balance sheet. No matter which approach is used, the key is to understand that balance sheets and
cash flow statements critically depend on each other. More precisely, when modeling the
financial statements, the balance sheet either has to drive the cash flow statement, which means
the cash flow statement is created from balance sheet changes, or the cash flow statement has to
drive the balance sheet, which means the balance sheet is created from how cash is spent or
sourced. While both methods are used in practice, the latter one is theoretically preferred, since it
is more logical and results in a better picture of cash flow. Taking the balance sheet item net
PP&E as an example, with the former approach we would not know how much of the change in
the line item would be attributable to CapEx and how much would be attributable to depreciation
on the cash flow statement (Pignataro, 2013). That being said, when looking at the step-by-step
methodology from Section 3.2 again, it seems like we used the former approach, where the cash
flow statement is created from balance sheet changes. However, in fact we use a combination of
both, since we only forecast working capital accounts and goodwill directly on the balance sheet,
whereas all other balance sheet items flow from the forecasts we make on the cash flow
statement. The only exception to this rule is retained earnings, which not only flows from the
cash flow statement but also from the income statement, since it is affected by dividends issued
and net income.
Before taking a closer look at how to forecast the individual working capital accounts, we should
decide which accounts to actually include in working capital. A common definition for working
capital, sometimes also called net working capital, is current assets minus current liabilities.
Working capital is considered a measure of near-term liquidity, since positive working capital
indicates that a company is able to pay its short-term bills, whereas negative working capital
indicates liquidity problems (Kaplan, 2013a). Nonetheless, for financial modeling we use a
narrower definition of working capital, called operating working capital or non-cash working
26
capital, which is defined as non-cash current assets minus non-cash current liabilities. In contrast
to the above definition, we now exclude cash and cash equivalents like marketable securities
from current assets, since they are not considered operating items. Additionally, we exclude
short-term debt, like notes payable or the current portion of long-term debt, from current
liabilities, since they are considered financing rather than operating items (Pignataro, 2013;
Kaplan, 2014a; Kaplan 2014b). However, it should be noted that Koller, Goedhart and Wessels
(2010) provide a more complex version of operating working capital by differentiating between
working cash and excess cash, which is cash not needed to operate the business. Thereby, the
working cash balance is included in the calculation of operating working capital. For simplicity,
and to stay in line with practitioners, as exemplified by Deutsche Bank Markets Research (2011),
we chose to use the simple definition of operating working capital and therefore exclude cash and
equivalents.
As can be seen from the section title already, we can now forecast working capital line items
based on their historical relationship with income statement line items. This ensures that working
capital estimates grow in line with revenues, since most income statement line items are
somehow linked to revenues (Kaplan, 2014a). To do so, we will follow the same methodology
that we used to forecast the income statement, in that we first determine what drives the line item
economically and then calculate a forecast ratio to estimate the future value of the line item.
While revenue is typically the forecast driver for accounts receivable, COGS is typically used for
inventories and accounts payable, since, like COGS, both line items are closely related to input
prices. However, the choice of forecast driver varies greatly in practice, especially with regard to
other working capital items like accrued expenses, prepaid expenses, or deferred revenues. For
example, Koller, Goedhart and Wessels (2010) recommend using revenues to forecast accrued
expenses, while Pignataro (2013) suggests using SG&A as a forecast driver, stating that accrued
expenses are commonly related to operating expense line items. Deutsche Bank Markets
Research (2011), on the other hand, uses COGS to forecast accrued expenses. Therefore, to
choose an appropriate forecast driver, the key is to understand which income statement line item
most likely drives the working capital line item, and to do further research if the economic
relationship is unclear. Prepaid expenses, for example, could be related to either SG&A or
COGS, depending on what the company is actually prepaying. No matter which driver is
eventually used, it is important to keep in mind that when forecasting working capital we are
27
mainly concerned about trends, and since both SG&A and COGS grow at the same rate as
revenue, prepaid expenses will also grow with revenues (Pignataro, 2013). For this reason,
Koller, Goedhart and Wessels (2010) recommend projecting all working capital items based on
revenues to keep the model simple. Furthermore, they suggest using days instead of percentages
as forecast ratios, which is also common among practitioners, as exemplified by Deutsche Bank
Markets Research (2011). Thereby, forecasts are tied more closely to operations, since an
inventory forecast, for example, could more easily be adjusted when a company’s management
announces to reduce its inventory holding period by 60 days.
Following this theoretical discussion, as Table 3 shows, we decided to forecast accounts
receivable and unearned revenue based on revenue, whereas inventories, accounts payable, and
accrued expenses are forecasted as a percentage of COGS. Also, for the sake of completeness, we
calculated both the days and percentage values as forecast ratios. To actually forecast the
individual line items, we then used the common assumption that working capital as a percentage
of revenues remains constant (Kaplan, 2013a; Pignataro, 2013). Nevertheless, instead of simply
using last period’s days or percentage values as an indicator for next period’s working capital
performance, we looked at working capital performance over the last three years to account for
potential outliers. More specifically, based on this analysis, we forecasted inventories, accrued
expenses, and unearned revenue to follow a pattern where 2Q and 3Q percentage values are
somewhat higher than 1Q and 4Q percentage values. Similarly, we expect accounts payable to be
somewhat lower in 1Q compared to the other quarters, while we assume accounts receivable to
remain flat throughout the four quarters.
Lastly, as mentioned introductory to this section, with goodwill we only have to estimate one
more line item to complete our forecasted balance sheet, since all other line items flow in from
the cash flow statement. Generally speaking, a company records goodwill when the purchase
price for a business acquisition exceeds the target’s book value. However, as shown in the next
section, future acquisitions are usually not modeled explicitly. Therefore, as shown in Table 3,
we keep goodwill constant at its current level unless otherwise indicated by the company, for
example, by announcing a potential acquisition or goodwill impairment (Koller, Goedhart and
Wessels, 2010). As a final point, it should be noted that while in our analysis all other balance
sheet line items flow from the cash flow statement, this might not be the case with other
28
companies. Therefore, when balance sheet items have no related cash flow item, it is safest to
keep them constant going forward. Oftentimes this will be the case for non-operating items, like
equity investments or pension liabilities, which don’t affect our valuation anyways. Hence,
forecasts for these items are solely for the purpose of financial planning (Pignataro, 2013; Koller,
Goedhart and Wessels, 2010).
3.2.5. Estimating CapEx and Finalizing the Financial Statements
After forecasting the complete income statement and some balance sheet items, following the
methodology from Section 3.2 our last step is to estimate CapEx along with some other cash flow
statement line items to finally complete the forecasted financial statements. To briefly recap, in
financial modeling balance sheets and cash flow statements critically depend on each other.
Therefore, we previously forecasted working capital accounts and goodwill directly on the
balance sheet, while assuming that all other balance sheet items flow from the forecasts we make
on the cash flow statement. Similarly, we will now forecast CapEx and several other cash flow
line items and then complete the cash flow statement with items that flow from the forecasts we
already made on the balance sheet and the income statement. Subsequently, we are then able to
complete the balance sheet with the forecasts we made on the cash flow statement.
Besides CapEx, there are many line items on the cash flow statement that are difficult to forecast
and sometimes even difficult to define. For example, other activities under cash flow from
operations, business acquisitions, purchases (and sales) of securities, and other investing
activities under cash flow from investments, repurchases (and issuances) of shares, and other
financing activities under cash flow from financing, and finally effects of exchange rate on cash.
With some of these items, like other operating activities, it is unclear what exactly caused the
cash flow, which in turn makes it difficult to predict a future value. Additionally, some of these
items, like business acquisitions, are non-recurring in nature and therefore also difficult to predict
without further company guidance. On the other hand, items like purchases (and sales) of
securities, are not necessary for business operations but instead driven by other decisions. Such
items can be quite volatile and are also almost impossible to predict. With all these items it is
important to do further research, for example, by reading the footnotes to the financial statements
or listening to the latest conference call. However, if no further information can be found it is
29
safest to just assume a value of zero or use one of the following projection methods, which are
similar to the ones introduced in Section 3.2.2 (Pignataro, 2013):
1. Conservative (the minimum of the past three years)
2. Aggressive (the maximum of the past three years)
3. Average (the average of the past three years)
4. Last year (recent performance)
5. Repeat the cycle
6. Year-over-year growth
7. Project out as percentage of an income statement or balance sheet line item
For example, if a company records large CapEx only every third year, and smaller CapEx on the
other years, it is most prudent to use projection method #5 to continue this trend. Clearly, without
more detailed information on any one of the above mentioned line items, it is not easy to
determine which method to use. Nonetheless, it should be noted that quite often such line items
are insignificant to the overall valuation. In fact, as we will see in Section 8, CapEx has the
greatest impact on our valuation analysis. Therefore, it is important to have a high-level
understanding of what the analysis is created for to judge whether additional research is
warranted for a specific line item. In general, if a line item greatly affects the overall valuation,
more time should be spent to develop reasonable assumptions (Pignataro, 2013).
With that in mind, as shown in Table 4, other expense (income), losses (gains) on sales of
marketable securities, and deferred income taxes under cash flow from operating activities are set
to zero and held constant, which is the safest approach, since they are likely non-recurring and we
don’t have enough information available. Similarly, acquisitions and sales (purchases) of
marketable securities under cash flow from investing activities are set to zero and held constant,
since they are likely one-time events, which are not planned for several years ahead. The same
reasoning applies to excess tax benefits under cash flow from financing activities and foreign
currency effects. This treatment is also in line with practitioners, as exemplified by Jefferies
Equity Research (2014). Regarding dividends issued, common stock proceeds (repurchases),
debt issuance (repayment) and capital leases, we could have used more elaborate projection
methods like a debt schedule for debt issuance (repayment) and capital leases, or information
30
from Amazon’s share buyback plan for common stock proceeds (repurchases), for example
(Pignataro, 2013). However, since all these line items won’t affect our valuation analysis, and for
the sake of simplicity, we set all items to zero and held them constant going forward. As a side
note, Amazon has never paid dividends before, so setting this line item to zero going forward is
clearly the safest approach. The reason why all these items don’t affect our valuation analysis is
that all of them represent financing decisions, which by definition don’t affect unlevered free
cash flow, whereas levered free cash flow will only be slightly affected by changes in leverage
(Kaplan, 2014a). Unlevered free cash flow, which is the cash flow measure that we will use for
our valuation analysis, and levered free cash flow will be discussed in more detail in Section 4.4.
The last line item that needs to be estimated to complete our forecasted cash flow statement is
CapEx. As so often, there are several ways to project this line item. Koller, Goedhart and Wessels
(2010) recommend to forecast the balance sheet item net PP&E as a percentage of revenue, but
note that forecasting the cash flow statement item CapEx as a percentage of revenue is also quite
common. It should be noted, however, that solely focusing on revenue as an economic driver may
not be the most accurate forecasting method. For example, when a manufacturer with lots of
spare capacity records strong revenue growth after an economic downturn, it would be difficult to
also justify strong CapEx growth (Pignataro, 2013). Therefore, one should have a solid
understanding of a company’s operations and future plans to improve estimates of capital
requirements, for example, by separating CapEx for maintenance from CapEx for growth
(Kaplan, 2014a). That being said, to keep our model simple and to stay in line with practitioners,
as exemplified by Deutsche Bank Markets Research (2011), we will forecast CapEx as a
percentage of revenue.
As can be seen from Table 4, over the past three years Amazon’s CapEx increased in absolute
terms, whereas it decreased as a percentage of revenue from 6.2% in 2012 to 5.5% in 2014. It
should be noted, however, that the 2012 figure was heavily distorted by an acquisition of
corporate office space for $1.4 billion. Excluding this outlier and looking at the past five years
since 2010, one can see that Amazon’s CapEx steadily increased in both absolute and relative
terms, primarily due to increased investments in additional capacity for fulfillment operations as
well as increased investments in technology infrastructure like AWS (Amazon, 2015). Since
Amazon expects this trend to continue, we forecast CapEx to rise from 5.5% of revenues in 2014
31
to 6% of revenues in 2015, and to remain at this level for 2016 and 2017. As mentioned in
Section 3.2.2 already, these numbers fit with our D&A estimates of 5.5% going forward, since
growth firms usually have CapEx/D&A ratios greater than one, indicating that they invest in
productive assets in anticipation of further business expansion and revenue growth.
After estimating all necessary line items, we can now finalize the cash flow statement in Table 4
with items that flow from the forecasts we already made on the balance sheet in Table 3, meaning
the changes in operating assets and liabilities, and the income statement in Table 2, meaning
reported net income and the non-cash adjustments to reconcile net income to net cash. More
precisely, the non-cash adjustments are depreciation and amortization, stock-based compensation,
and other operating expense (income), whereas the operating balance sheet accounts are
inventories, accounts receivable, accounts payable, accrued expenses, and unearned revenue. To
briefly recap theory, using the indirect method of calculating cash flow from operating activities,
one starts with net income and adjusts for non-cash charges, gains and losses related to investing
or financing cash flows, and changes in operating working capital accounts. By adding cash flow
from investing activities, cash flow from financing activities, and exchange rate effects, which
are all forecasted directly one the cash flow statement, one eventually arrives at the change in
cash for the period. Adjusting the beginning cash balance for this figure subsequently results in
the ending cash balance (Kaplan, 2013a).
With the complete cash flow statement, we are then able to finalize the balance sheet in Table 3
with items that flow from the forecasts we made on the income statement in Table 2, and the cash
flow statement in Table 4. Starting from the top of the balance sheet we calculate this year’s cash
and cash equivalents by simply adding the same-year ending cash balance from the bottom of the
cash flow statement to last year’s cash and cash equivalents. For all other line items, the general
rule is to subtract this year’s related cash flow statement item from last-year’s balance sheet item
if the balance sheet item is an asset, and adding this year’s related cash flow statement item to
last-year’s balance sheet item if the balance sheet item is a liability. This makes logical sense,
since the value of this year’s balance sheet item is simply last year’s value adjusted for the related
cash impact. The reason for the change in signs is that liabilities are directly related to cash, in
that the liability increases if cash is positive and decreases if cash is negative, whereas assets are
inversely related to cash, in that the asset increases if cash is negative and decreases if cash is
32
positive (Pignataro, 2013). Following this methodology, on the assets-side, marketable securities
are adjusted for sales and purchases of marketable securities, as well as losses (gains) on sales of
marketable securities. Current and long-term deferred tax assets are equally adjusted for deferred
income taxes. Property and equipment is adjusted for depreciation and amortization (D&A) as
well as purchases of property and equipment (CapEx), and lastly other assets are adjusted for
other operating expense (income) and acquisitions. The reason for this last adjustment is that
other assets are primarily related to acquired intangible assets and equity investments
(Amazon.com, 2015).
On the liabilities-side, long-term debt is adjusted for debt issuance (repayment), while other longterm liabilities, which are primarily related to capital lease obligations (Amazon, 2015), are
adjusted for capital lease repayments. Treasury stock is adjusted for common stock repurchased,
whereas additional paid-in capital is adjusted for common stock proceeds and stock-based
compensation. To be perfectly precise, the adjustments to additional paid-in capital should be
split between common stock and additional paid-in capital, in that the par value should be added
to common stock, whereas the difference between issue price and par value should be added to
additional paid-in capital. Nevertheless, for simplicity we held common stock constant and only
adjusted additional paid-in capital, since the change in common stock would be negligibly small
anyways. In fact, both line items are often combined for modeling purposes (Pignataro, 2013).
Next, accumulated other comprehensive loss is adjusted for currency effects on cash and other
expense (income), which is primarily related to foreign currency losses (Amazon, 2015).
Regarding the other expense (income) adjustment to accumulated other comprehensive loss, this
is only our “best guess”, which is often the case for “other” cash flow statement items, where it is
not clear to which balance sheet line item they should be linked (Pignataro, 2013). Lastly,
retained earnings are adjusted for reported net income from the income statement and dividends
issued from the cash flow statement. As a final note, the derivation of retained earnings is also
called the principle of clean surplus accounting (Koller, Goedhart and Wessels, 2010).
3.3.
Some Technical Notes on the Input Sheets
For consistency, quarterly and annual figures are always taken from Amazon’s respective
financial statements. For example, 3Q 2013 figures are taken from the 3Q 2013 statements, even
33
though they would also be available from the 2014 statements. Therewith, potential restatements
are not taken into account and quarterly figures on the income statement and the cash flow
statement input worksheets may not always add up to annual figures in case a restatement
occurred. For example, 2010 accounts receivable on the cash flow statement differs from the sum
of 1Q to 4Q 2010 accounts receivable. Additionally, to make sure that manual inputs are entered
correctly, some line items, like net change in cash on the cash flow statement, are calculated to
easily crosscheck inputs and highlight potential wrong inputs. Similarly, on the balance sheet and
the cash flow statement input worksheets there are check-lines for assets to equal liabilities and
for this period’s beginning cash to equal last period’s ending cash, respectively.
Furthermore, recently Amazon made two major accounting changes: First, from 4Q 2014
onwards, repayments of capital and finance leases are treated as separate line items on the cash
flow statement input worksheet, whereas before they were aggregated with repayments of longterm debt in a line item called repayments of long-term debt, capital lease, and finance lease
obligations. We already adjusted the line items and formulas to account for this change. Second,
from 1Q 2015 onwards, the segment Amazon Web Services (AWS) was added to the traditional
segments North America and International on the segment inputs worksheet. However, since we
value Amazon as of January 01, 2015, we will not take this into account for our financial model
and our valuation models. Nevertheless, the financial model is already set up to handle the
additional segment, meaning that the formulas and line items are already adjusted. As a final
note, when including the 2014 AWS figures from the 2015 statements onwards, the line items
other revenues, segment operating expenses, and segment operating income of the North America
segment should be adjusted, since AWS results were included in these line items so far.
3.4.
Some Technical Notes on the Estimate Sheets
On the bottom of the income statement estimates worksheet, some valuation metrics were added
compared to the income statement input worksheet. These are necessary for the discounted cash
flow and multiples valuation in later sections and will be discussed in more detail in Section 4.4.
The first metric, adjusted EBIT, is calculated as pro-forma operating income (loss) adjusted for
other operating expense (income), which means we include amortization of intangibles but
exclude stock-based compensation as operating expenses. Similarly, adjusted EBITDA is
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calculated as pro-forma operating income (loss) adjusted for depreciation and amortization,
which means we exclude stock-based compensation, depreciation and amortization, and
amortization of intangibles. To avoid any confusion, as laid out in Section 3.2.2, we decided to
treat other operating expense (income) as expense for intangible asset amortization, which differs
from the line item depreciation and amortization. Furthermore, stock-based compensation is
excluded from EBIT and EBITDA, since it is a non-cash expense, and we calculate both
measures to be proxies for operating cash flow in order to find out how much cash Amazon’s
business operations generate. This methodology is similar to Amazon’s (2015) segment
reporting, where both stock-based compensation and other operating expense (income) are
excluded from operating expenses. Amazon justifies this by stating that both line items are also
excluded from internal operating plans and measurement of financial performance. However, the
company also notes that this non-GAAP treatment has limitations in that it distorts workforce
related expenses and therefore results in too low operating expenses. Nevertheless, we decided to
incorporate the non-GAAP measure in our financial model, which is also in line with
practitioners, as exemplified by Jefferies Equity Research (2014) and Deutsche Bank Markets
Research (2014).
Looking at the remaining two metrics, for simplicity, we calculated levered free cash flow as cash
flow from operations minus CapEx, whereas, to be perfectly precise it should also be adjusted for
net borrowing. Furthermore, tangible book value is calculated as tangible assets minus liabilities,
whereas tangible assets only exclude goodwill and neglects other intangible assets. The reason
for these simplified calculations is that both metrics are only used to complement our relative
valuation analysis, for which we will focus on multiples of revenues, earnings, and EBITDA.
Similar to the income statement worksheets, we added and rearranged some line items on the
cash flow statement estimates worksheet compared to the cash flow statement input worksheet.
More precisely, under cash flow from operating activities on the cash flow statement estimates
worksheet, unearned revenue includes additions to unearned revenue and amortization of
previously unearned revenue from the cash flow statement input worksheet. Also, the sub-total
line items funds from operations and change in net working capital were added for illustrative
purposes. Next, under cash flow from financing activities, debt issuance (repayment) includes
proceeds from long-term debt and repayments of long-term debt (and until 3Q 2014 also
35
principal repayments of finance and capital lease obligations). Similarly, capital/finance lease
repayment includes principal repayments of capital lease obligations and principal repayments of
finance lease obligations. Furthermore, the line items common stock proceeds and dividends
issued were added to allow for stock issuance and dividend payments going forward. Lastly,
excess tax benefits from stock-based compensation are listed under the cash flow from operating
and financing activities in the same amounts but with different signs. While this is similar to the
cash flow statement input worksheet, it should be noted that this is only a reclassification to
properly calculate operating and financing cash flows and hence doesn’t affect net cash.
Like with the other two financial statements before, we also changed the setup of the balance
sheet estimates worksheet compared to the balance sheet input worksheet. More specifically, on
the bottom of the balance sheet estimates worksheet, we added a section called metrics and ratios,
where we calculate various metrics, like invested capital or net debt (cash), and various ratios to
asses Amazon’s liquidity or profitability, for example. Together with some other figures, like
enterprise value or per share calculations, these metrics and ratios are then categorized and
summarized on the ratios worksheet. In fact, financial-ratio analysis is frequently used by
investors to gauge the financial performance and condition of a company. Furthermore, ratio
analysis is often complemented by common-size analysis, which we included on the common
size worksheet. To briefly recap theory, common-size financial statements are a restatement of
financial information in a standardized form. By eliminating the effects of size, common-size
financial statements allow for comparison of items over time (time-series analysis) and across
firms (cross-sectional analysis). While total assets are also often used as a base value for balance
sheets, we used total revenues for both the income statement and the balance sheet in our
analysis. To provide even more detail, we could have also calculated a common-size cash flow
statement.
That being said, both tools are very important to analyze a company’s performance and
strategies, because the derived relationships can then used to forecast financial statements to
understand how the company’s performance is likely to evolve (Kaplan, 2013a). For example,
increasing goodwill along with increasing debt levels could be an indication of anorganic revenue
growth, where an acquisition was financed with debt. Nonetheless, since this paper focuses on
building a financial model and using various valuation methods, financial-ratio analysis and
36
common size analysis are not discussed explicitly. Nevertheless, the relationships derived from
both analyses are still utilized in forecasting the financial statements.
Besides metrics and ratios, on the very bottom of the balance sheet estimates worksheet, we
added a section called advanced calculations. These calculations are based on advanced financial
modeling methods for invested capital, net operating profit less adjusted taxes (NOPLAT), and
free cash flow (FCF), as explained in Koller, Goedhart and Wessels (2010). However, they are
not within the scope of this paper and can therefore be ignored.
Lastly, on the balance sheet estimates worksheet, goodwill can’t be adjusted so far, since it is not
linked to any cash flow statement line item on the cash flow statement estimates worksheet.
Hence, if adjusted, the balance sheet wouldn’t balance anymore. To account for goodwill
impairment, one would have to add the line item goodwill impairment to the income statement
estimates worksheet and the non-cash adjustments on the cash flow statement estimates
worksheet. On the other hand, to increase goodwill after a business acquisition, one would have
to add another line item under investing activities on the cash flow statement estimates
worksheet, which would separate the difference between the purchase price and the target’s book
value from the line item acquisitions. In general, one could make the financial model more
flexible by adding further line items to any of the three financial statements. For example, similar
to goodwill impairment one could account for asset impairments by adding the respective line
items to the income statement and cash flow statement estimate worksheets and then link them to
net PP&E on the balance sheet estimate worksheet.
4.
4.1.
Enterprise Value Multiples
Introduction to Relative Valuation Models
From a theoretical perspective, there are two types of valuation models: Absolute valuation
models, which aim to estimate an asset’s intrinsic value, and relative valuation models, which
determine the value of an asset relative to the values of other assets. While discounted cash flow
models (also called present value models) and asset-based models fall into the first category,
37
price multiples and enterprise value multiples (also called multiplier models or market multiple
models) fall into the latter (Kaplan, 2014a; 2013a). For now we will focus on relative valuation
models, but discounted cash flow models will also be discussed in more detail in Section 8.
To begin with, multiples are among the most widely used tools for equity valuation, because they
are easily calculated and are not as sensitive to input parameters as discounted cash flow models.
Additionally, they reflect current market conditions, since they measure relative and not intrinsic
value, which is why multiplier models often result in valuations closer to market prices than
discounted cash flow models (Damodaran, 2012). The underlying economic argument for
multiplier models is the law of one price, which is the notion that two similar assets should sell at
the same price, or in this case, two comparable companies should have approximately the same
multiple (Kaplan, 2013a). Basically, multiples can be used in two ways. On one hand, an analyst
could use multiples to evaluate whether an asset is properly valued relative to a benchmark.
Typical benchmarks are a stock's historical average, which is considered a time series
comparison, or comparable stocks and industry averages, which is considered a cross-sectional
comparison. However, as the name implies, either way one can only establish a relative value
estimate based on comparables, often simply called “comps”, so we can only conclude that a
stock is over- or undervalued relative to the benchmark (Kaplan, 2014a; 2013a). On the other
hand, an analyst could estimate a stock’s intrinsic value by multiplying forecasted fundamentals,
like earnings per share (EPS), by an estimated multiple, like the forward P/E, for example (Bodie,
Kane and Marcus, 2014).
As briefly mentioned introductory, there are two basic types of multiplier models: Price multiples
are based on the ratio of stock price to fundamentals like earnings, revenues, book values, or cash
flow per share, whereas enterprise value multiples are typically based on the ratio of enterprise
value to either revenue or EBITDA (Kaplan, 2013a). The difference between price multiples and
enterprise value multiples is that the former uses an equity value in the numerator, while the latter
uses enterprise or firm value. Hence, to yield a proper multiple, the denominator should also be
either an equity value, like net income and book value of equity, or a firm value, like revenue and
EBITDA (Damodaran, 2012). Equity value and enterprise value, along with the proper associated
valuation metrics will be discussed in more detail in the next section. In addition, there are two
basic calculation methodologies: Multiples based on trailing or historical fundamentals and
38
multiples based on forward or leading fundamentals. Many practitioners use forward multiples
rather than trailing multiples, because the latter are often considered to only reflecting the past.
Nonetheless, one has to keep in mind that forward multiples are based on projections and are
hence prone to forecasting errors and analyst biases. For this reason, the same multiple can take
on quite different values depending on whether trailing or leading fundamentals were used to
calculate it (Kaplan, 2013a).
Furthermore, when using multiplier models for valuation purposes, it is critical that firms used as
comparables are really comparable, which may not be the case due to differences in size, risk,
growth potential, cash flows, or even the industry they operate in. Additionally, one has to judge
the applicability of multiples before using them for valuation. For example, using P/E multiples
for cyclical firms is difficult due to their sensitivity to economic conditions. Also, they may not
be comparable in an international context due to different accounting standards used to calculate
earnings per share (Kaplan, 2013a). Finally, the multiple has to be uniformly defined across the
set of comparable companies to be useful for valuation purposes, as even the simplest multiples
can be defined differently by different analysts. Taking the P/E multiple as an example again,
while the market price is mostly used in the numerator, the EPS number in the denominator could
be of the most recent fiscal year, the last four quarters, or the upcoming fiscal year, resulting in
either the current P/E, the trailing P/E, or the forward P/E, respectively. To further complicate
matters, EPS can be based on basic or diluted shares outstanding and also include or exclude
extraordinary items (Damodaran, 2012). Therefore, it would clearly be wrong to compare a
company’s current P/E multiple to another company’s forward P/E multiple, for example.
While this sounds simple, it can actually become quite problematic when valuing a company
based on the current P/E multiple, since different firms can have different fiscal years
(Damodaran, 2012). For example, if a company’s fiscal year equals the calendar year, at the
beginning of January this company’s current price would be divided by very recent earnings. On
the other hand, if a company’s fiscal year ends in March, at the beginning of January this
company’s current price would be divided by earnings from nine months ago. Clearly, in such a
case it would be more prudent to use the trailing P/E multiple, which is based on the last four
quarters of earnings. Another way to adjust for such differences is to use a technique called
calendarization, which simply means to combine quarterly with annual data to make sure that all
39
company financials are as of the same date (Pignataro, 2013). In the above example, this would
entail combining the latter company’s last financial year’s 4Q earnings with this financial year’s
1Q to 3Q earnings to make it comparable to the former company.
Finally, it is crucial to have a sense of a multiple’s typical, high, or low value relative to the
industry and the market as a whole. While analysts are mostly familiar with industry multiples,
they often don’t know how a multiple is distributed across the entire market. This, however, is an
important consideration, since stocks, no matter what industry they are in, compete for the same
investor dollars. Additionally, knowing how multiples vary across sectors can be helpful in
determining whether a sector is over- or undervalued. To judge a multiple’s value, analysts
typically use the multiple’s distributional characteristics, like average and median, standard
deviation, maximum and minimum, and also percentiles. Since averages are more prone to be
affected by outliers, the multiple’s median is often the preferred measure. For the same reason,
25th and 75th percentiles are considered more useful than maximum and minimum values of a set
of comparable companies. Lastly, to mitigate the effect of outliers, they are usually excluded
when computing statistics, or the multiple is constrained to be within a range of specific values.
P/E multiples, for example, can easily become as high as 500 or 1000 if high stock prices
coincide with very low earnings, which is most likely not representative and distorts the average
of the sample (Damodaran, 2012).
4.2.
Enterprise Value vs. Equity Value
Equity value, also often called market value (as opposed to book value), is simply a company’s
market capitalization, which is defined as shares outstanding times share price. Therefore, equity
value can be thought of the value of a company attributable to only equity holders, thereby
excluding debt holders, minority interest holders, and other obligations (Pignataro, 2013). In
contrast, enterprise value, also often called firm value, measures the value of the whole company
instead of only its equity, and can be viewed as what it would cost to acquire the entire company.
Hence, a common formula to calculate enterprise value is as follows (Kaplan, 2013a):
Enterprise Value (EV) = market value of common stock + market value of preferred stock
+ market value of debt – cash and short-term investments
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The rationale for subtracting cash and short-term investments is that while debt increases the cost
to a potential acquirer of the entire company, any liquid assets effectively lower its cost.
Furthermore, the main benefit of enterprise value over equity value is that it is capital structure
neutral, in that it eliminates the effects of leverage. Therefore, analysts often use it to compare the
value of firms with significantly different capital structures. However, it should be noted that
depending on the desired degree of complexity, various definitions of enterprise value appear in
academic literature. For example, Kaplan (2014a) also adds minority interest, but notes that for
simplicity most analysts assume a value of zero for minority interest and preferred equity,
whereas Pignataro (2013) uses a more practical definition, which also includes debt equivalents:
Enterprise Value (EV) = equity value + preferred securities + short-term and long-term
debts + current portion of long-term debts + capital lease obligations + other non-
operating liabilities (e.g. unallocated pension funds) + non-controlling interest – cash and
cash equivalents
To add even more complexity, Koller, Goedhart and Wessels (2010) provide the most
comprehensive definition and suggest adjusting debt and any other non-equity claims to convert
enterprise value into equity value. This includes short-term and long-term debt, debt equivalents,
like unfunded pension liabilities and capitalized operating leases, as well as hybrid securities, like
preferred stock, convertible debt, and employee stock options. On the other hand, practitioners, as
exemplified by Jefferies Equity Research (2014) and Deutsche Bank Markets Research (2011),
simply adjust for net debt, which is total debt adjusted for cash and equivalents, taking into
account pensions and other interest-bearing liabilities, as well as marketable securities.
Lastly, a common problem with enterprise value is that the market value of debt is often not
available, which is why analysts often either use the book value of debt or the market value of
similar bonds. Nevertheless, book value may not be a goods proxy for market value, especially if
market conditions or the company’s creditworthiness have changed significantly since the debt
was issued (Kaplan, 2013a). Another common problem with equity value is to decide which
number of shares to use in the calculation. In valuation, the above definition of equity value is
usually reversed, in that once equity value is derived it is divided by the number of shares to
arrive at a share price estimate. But no matter if one aims to value a company or simply wants to
41
calculate a company’s market capitalization, the question remains the same. On top of that, the
question extends to the computation of earnings per share (EPS) and book value per share
(BVPS), and while it may seem like an easy task, it can become quite complicated when
companies use stock options and so-called restricted stock units (RSUs) for employee
compensation. While their effect is neglectable for most firms, it can be quite substantial at
technology firms, for example, which are more heavily weighted towards options than other firms
(Damodaran, 2012). Therefore, we will take a closer look at this problem in the next section.
To summarize, the above discussion shows that even though all approaches are based on the
same theoretical foundations, the practical application can differ substantially. As we will discuss
more detailed in Section 8.2 with regard to discounted cash flow models, this is mostly due to
different definitions and degrees of complexity used in practice. However, for the sake of
simplicity, and to provide an approach that is as close as possible to what analysts actually do in
equity research, our definition of enterprise value will be most similar to the methodology used
by Pignataro (2013), as well as Jefferies Equity Research (2014) and Deutsche Bank Markets
Research (2011).
As can be seen from Table 5, in line with the previous discussion, we start our calculation of
Amazon’s enterprise value by first computing its equity value. More precisely, as will be
discussed in the following section with regard to basic vs. diluted shares outstanding, we multiply
Amazon’s year-end share prices by the weighted-average diluted shares outstanding to arrive at
equity value estimates of $151,651 million, $186,235 million, and $146,485 million for 2012,
2013, and 2014, respectively. We then subtract cash and short-term investments, and add shortterm and long-term debt, minority interest, and preferred stock to arrive at our enterprise value
estimates of $107,287 million, $176,979 million, and $137,334 million for 2012, 2013, and 2014,
respectively. Taking a closer look at the numbers, one can easily see that both equity value and
enterprise value fluctuate heavily, which is mostly caused by changes in Amazon’s stock price,
since share counts and balance sheet adjustments only changed by relatively small amounts.
Finally, it should be noted that we use the term “estimate” to highlight the fact that the above
equity and enterprise values, even though they are historical and don’t depend on any
assumptions, still depend on our chosen calculation methodology. For example, Deutsche Bank
Markets Research (2014), Jefferies Equity Research (2014), and UBS Global Research (2014) all
42
calculate different EV/revenue multiples for 2012 and 2013, even though all use the same
historical revenue figures. Hence, they must use somewhat different calculation methodologies
for enterprise value. Compared to our methodology, Jefferies Equity Research (2014), for
example, also takes into account other interest-bearing liabilities. According to Amazon’s (2015)
latest annual report, this could be justified by the fact that the line item other long-term liabilities
primarily consists of finance and capital leases, which are typically considered interest-bearing
debts. Nevertheless, for consistency and comparability, we decided to exclude other interestbearing liabilities, since we also disregard them in our public comparable company valuation to
simplify the analysis. To be perfectly precise, we should have scrutinized all of Amazon’s
liabilities and also all peer companies’ liabilities on a case-by-case basis to decide whether to
include or exclude individual line items in the calculation of enterprise value.
4.3.
Basic vs. Diluted Shares Outstanding
As mentioned in the previous section, even though it sounds simple, the question of which
number of shares to actually use for valuation purposes can be quite complicated. From a
theoretical perspective, Koller, Goedhart and Wessels (2010) suggest to use a company’s most
recent number of undiluted shares outstanding, if the full value of options and convertible debt
was already adjusted by using an option-based valuation approach when converting enterprise
value to equity value. Otherwise their effect on equity value per share would be double-counted
in the calculation process. If not, however, they suggest using a diluted number of shares, which
adds the dilutive effect of options and convertible debt to the number of undiluted shares
outstanding. Damodaran (2012) follows the same methodology, but describes several ways to
account for the dilutive effect, including an option pricing model, the treasury stock method, or
the fully diluted approach. Pignataro (2013), on the other hand, simply uses undiluted shares
outstanding and adds the dilutive effect approximated by the treasury stock method. No matter
which methodology is used, Koller, Goedhart and Wessels (2010) suggest using actual shares
outstanding instead of weighted-average shares outstanding, which are usually reported by
companies to calculate earnings per share. The reason for this is that the latter usually takes into
account the portion of the year that the newly issued shares were outstanding. Hence, both
numbers would match only if all shares would be issued at the first day of the year, whereas, with
rising share counts over the year, the weighted-average is usually lower (Kaplan, 2013a). As can
43
be seen from Amazon’s (2015) latest annual report, for example, the number of basic shares
outstanding was constantly higher than the number of weighted-average basic shares outstanding
from 2012 to 2014.
So which number of shares should we use to value Amazon? Regarding the number of stock
awards, Amazon (2015, p. 65) states that “common shares outstanding plus shares underlying
outstanding stock awards totaled 483 million” on December 31, 2014, thereof 17.4 million
restricted stock units (including granted, vested, and forfeited) and 0.4 million stock options. In
contrast, Amazon (2015) reports the actual number of common stock outstanding as of January
16, 2015 to be 464.4 million. Hence, the difference between both share counts represents the
maximum possible dilutive effect. Yet this doesn’t imply that the dilutive effect will fully
materialize. For example, the dilutive effect (on a weighted-average basis) during 2013 was only
8 million even though the maximum possible dilutive effect from outstanding stock awards and
options totaled 15.8 million at the end of 2012 (Amazon, 2015). The most likely reason for this
difference is that while restricted stock units may be granted, they may not be vested yet, or that
some options are simply not exercisable or in the money, yet.
That being said, to stay consistent with the approaches suggested by Damodaran (2012),
Pignataro (2013) and Koller, Goedhart and Wessels (2010), we would have to subtract the fair
value of the restricted stock units from enterprise value and account for stock options by either
using an option pricing model or the treasury stock method. However, due to Amazon not
reporting an exercise price in its annual report, and due to the complexity of using an option
pricing model, both approaches are technically not feasible. Therefore, and for the sake of
simplicity, we decided to use the most recently reported number of weighted-average diluted
shares outstanding as a closest proxy for our valuation purposes. Since Amazon’s weightedaverage diluted shares outstanding also include the dilutive effect of restricted stock units, we
also don’t have to separately adjust for them when converting equity value to enterprise value 3.
3
Author’s note: As stated in its most recent annual report, Amazon (2015, p. 43) includes all stock awards, i.e.,
restricted stock units and stock options, in its diluted EPS calculation: “Diluted earnings per share is calculated
using our weighted-average outstanding common shares including the dilutive effect of stock awards as
determined under the treasury stock method. In periods when we have a net loss, stock awards are excluded from
our calculation of earnings per share as their inclusion would have an ant dilutive effect."
44
Nonetheless, two problems arise from this approximation: First, as mentioned above, the number
of non-weighted shares outstanding is usually higher than the number of weighted shares
outstanding if we assume a growing share count. Hence, our estimate of non-weighted diluted
shares outstanding should be slightly above the number of weighted diluted shares outstanding.
Second, as can be seen from Amazon’s annual report excerpt in the footnote, Amazon doesn’t
include stock awards in its EPS calculation during periods with a net loss. Since Amazon actually
reported a net loss for the fiscal year 2014, the number of weighted-average basic shares of 462
million coincides with the number of weighted-average diluted shares for this period. Therefore,
as Table 5 shows, we based our estimate on the 4Q 2013 number of 472 million weightedaverage diluted shares outstanding. For consistency, we used the same methodology to derive our
share count estimates for the years 2012 and 2013, which resulted in 461 million and 467 million
weighted-average diluted shares outstanding, respectively.
As mentioned before, the share count issue also extends to the computation of earnings per share
(EPS), book value per share (BVPS), as well as our DCF analysis in Section 8. Hence, for
simplicity and consistency, we will use the same number of weighted-average diluted shares
outstanding for these calculations. Regarding EPS and BVPS, in fact equity research reports, as
exemplified by UBS Global Research (2014), Deutsche Bank Markets Research (2014), or
Jefferies Equity Research (2014), only mention them on a diluted and adjusted basis, without
further reference on what exactly was adjusted for. For this reason, we also decided to use P/E
multiples on a diluted and adjusted (pro-forma) basis for our public comparable companies
analysis in Section 5. Regarding our DCF analysis, to be consistent with the above theoretical
discussions of enterprise value and number of shares, we have to use some measure of diluted
shares outstanding to arrive at a share price estimate, since we don’t adjust for any stock awards
when converting enterprise value to equity value. This is also in line with practitioners, as
exemplified by UBS Global Research (2014), Deutsche Bank Markets Research (2014), or
Jefferies Equity Research (2014). Interestingly, all of them used different diluted share counts for
estimating Amazon’s price per share with 475 million, 477.4 million, and 470 million,
respectively. However, one also has to keep in mind that the reports are as of July 2014.
To conclude, like with enterprise value in the preceding section, the above analysis shows that
even a seemingly simple problem, like choosing a share count for valuation purposes, can
45
become very complex in practice. As mentioned previously with regard to enterprise value
calculations, this is mostly due to different definitions and degrees of complexity used in practice.
Unfortunately, the fact that we have no insight into how practitioners derive their differing
numbers of diluted shares outstanding doesn’t make things easier.
4.4.
Valuation Metrics and Multiples
Before taking a closer look at valuation multiples in the following sections, it is crucial to
understand the various fundamentals that affect the value of a multiple. In fact, the common
believe that multiplier models use fewer assumptions than discounted cash flow models, which
we mentioned as a benefit of multiplier models before, is not entirely true. To be more precise,
both methods are based on the same assumptions, but while they are explicit and stated in
discounted cash flow models, they are implicit and unstated in multiplier models. Hence, no
matter which valuation method is used, firm value is essentially a function of the capacity to
generate cash flows along with the expected growth and risk of these cash flows. In other words,
any multiple is determined by the same three variables: risk, growth, and capacity to generate
cash flows. Therefore, as a general rule, companies with higher growth, less risk, and greater cash
flow generating potential should trade at higher multiples, et vice versa (Damodaran, 2012).
As already mentioned above, the most popular metrics used in multiplier models are earnings,
revenues, book values, or cash flows. While earnings are probably the most frequently used
denominator for price multiples, EBITDA and EBIT are most common for enterprise value
multiples. To briefly recap, using firm value in the numerator requires also using a firm value like
EBITDA, or EBIT in the denominator. Regarding the way that multiples are affected by
fundamentals, both P/E multiples and EV/EBITDA multiples are positively related to cash flow
growth and inversely related to the company’s overall risk level, as determined by cost of equity
and WACC (Kaplan, 2014a). However, using an EBITDA multiple instead of an earnings
multiple has two major advantages: First, negative valuation metrics render a valuation multiple
meaningless and EBITDA is usually positive, even if earnings are not. Second, it is more useful
when comparing companies with different degrees of leverage, since EBITDA flows to both debt
and equity holders, and also when valuing capital intensive businesses with high levels of
depreciation and amortization (Kaplan, 2013a). As a final point, revenue is also often used as a
46
valuation metric for enterprise value multiples. In addition to the benefits of EV/EBITDA
multiples over P/E multiples mentioned above, EV/revenue multiples enjoy the advantages that
revenue is always positive, harder to manipulate, and less volatile than earnings. Nonetheless,
they also come with additional disadvantages, in that they do not capture differences in cost
structures and in that high revenues are not indicative of high earnings (Kaplan, 2014a).
Regarding the use of EBITDA and EBIT as valuation metrics, there are some subtle but
important differences to keep in mind. To begin with, EBITDA is often used by analysts as a
proxy for cash flow, even though it doesn’t capture capital expenditures and growth in working
capital. Also, as explained above, EBITDA is often used to normalize differences in capital
expenditures among a set of comparable companies, since it adjusted for depreciation and
amortization, which tends to rise and fall with a company’s capital expenditures (Kaplan, 2014a).
However, with regard to cash flow proximity it often makes more sense to use EBIT, since
EBITDA somewhat overstates cash flow. The reason for this is that EBITDA doesn’t account for
capital expenditures, while adding back depreciation and amortization. Similarly, EBIT doesn’t
account for capital expenditures, but at least it doesn’t add back depreciation and amortization. In
this sense, even though EBITDA is commonly used to normalize differences in capital
expenditures, EBIT should be a better cash flow proxy for companies with high capital
expenditures.
While our relative valuation of Amazon will focus on EV/EBITDA, EV/revenue and P/E
multiples, for the sake of completeness, we also decided to calculate a cash flow and a book value
multiple. More precisely, we calculate price/tangible book value and equity value/levered free
cash flow to complement our analysis, whereas the latter could be further divided by the number
of shares to result in the price/levered free cash flow multiple. Using tangible book value instead
of total book value removes intangible assets from equity, which results in a lower adjusted book
value and hence a higher multiple. The reason for this adjustment is to improve comparability
across firms, which may have grown either internally or through acquisitions. In the latter case,
goodwill and other intangible assets will show up on the balance sheet, whereas in the former
case they won’t (Kaplan, 2013a). As can be seen from Table 2, we only excluded goodwill in our
calculation, as Amazon doesn’t report other intangible assets as a separate line item, but only
47
includes them in other assets on the balance sheets. However, due to the small amount of
Amazon’s other intangibles, the effect of this simplification is negligible.
Additionally, using levered free cash flow instead of unlevered free cash flow results from the
point we made in Section 4.1 regarding consistency of multiples: Since equity value is used in the
numerator, an equity value has to be used in the denominator. In theory, depending on whether
one wants to estimate enterprise value or equity value, one has to use different cash flow
definitions and required returns (Kaplan, 2014a):
Enterprise Value = FCFF discounted at WACC
Equity Value = FCFE discounted at cost of equity
with FCFF = free cash flow to firm, often also called unlevered free cash flow (UFCF), and FCFE
= free cash flow to equity, often also called levered free cash flow (LFCF). Thereby, unlevered
free cash flow is considered cash available to all the firm’s investors, while levered free cash flow
is defined as cash available to equity holders after funding capital requirements, working capital
needs, and debt financing requirements. More precisely, a common definition for unlevered free
cash flow, which we will adopt for our valuation, is as follows (Pignataro, 2013; Kaplan, 2014a):
FCFF = EBIT x (1 – tax rate) + non-cash charges – investment in working capital – capital
expenditures.
To receive levered cash flow, this formula has to be adjusted for after-tax interest payments and
net borrowings, which is defined as new debt issues subtracted for repayments (Kaplan, 2014a):
FCFE = FCFF – Interest x (1 – tax rate) + net borrowing
FCFE = EBIT x (1 – tax rate) + non-cash charges + net borrowing – after-tax interest –
investment in working capital – capital expenditures.
However, like with the calculation of enterprise before, it should be noted that various definitions
of levered and unlevered free cash flow appear in academic literature. While we will discuss
48
different calculation methods in more detail with regard to discounted cash flow analysis in
Section 8, Table 2 shows that for now we simply calculate levered free cash flow as cash flow
from operations minus CapEx. This is also in line with Amazon’s (2015) calculation of free cash
flow as a non-GAAP financial measure, even though, to remain in line with our above definition,
we should also adjust for net borrowing.
5. Public Comparable Company Analysis
5.1.
Our Valuation Approaches
In the following sections, we will apply the first of our three chosen valuation methods, which is
a relative valuation approach, called public comparable company analysis. Public comparable
company analysis utilizes market multiples, meaning multiples based on current market prices, to
value a company. Our second valuation method, which is also a relative valuation approach,
called precedent transactions analysis, will build on the same theoretical foundations, but instead
of market multiples it utilizes purchase multiples, meaning multiples based on historical
transactions, to assess the relative value of a company. In contrast to the first two methods, our
third valuation method is an absolute valuation method, called discounted cash flow analysis,
which values a company based on its expected future cash flows. As this brief introduction
shows, each of the three methods approaches value from a different perspective and based on
different, often quite subjective variables. Therefore, if all three methods result in a similar
valuation range, from a financial perspective, we can be relatively confident in our estimate of
Amazon’s value (Pignataro, 2013).
That being said, before using any of these valuation methods it is also important to be aware of
the advantages and drawbacks of each. Compared to the other two methods, the major advantage
of public comparable company analysis is that it gives a market perspective, as it is based on the
most recent stock prices and financials of the company. But at the same time this is also one of its
major drawbacks, since a stock, an industry, or the entire market could be over- or undervalued
(Damodaran, 2012). Another disadvantage, which will be discussed in more detail below, is the
difficulty to find a set of truly comparable companies, especially if a company has a unique
49
business model. The major advantage of precedent transaction analysis is the fact that instead of
market prices, it is based on purchase prices of entire companies, which usually include a
premium. Hence, if we were interested in acquiring a company, such an analysis could help us
determining how much of a premium we would have to pay. However, by definition, this type of
analysis has several major drawbacks: First, historical transactions may be irrelevant if the
economic environment has changed in the meantime, since multiples will also have changed then.
Second, it might be difficult to find relevant transactions, especially in industries where there are
only few transactions, and third, even if there are relevant transactions, it might be difficult to
find data, especially with transactions of private companies (Pignataro, 2013). Finally, the major
advantage of discounted cash flow analysis is that it is the most technical of the three valuation
methods, since it is based on a company’s expected future cash flows. Compared to the other two
methods, which are driven by either market or historical data, this allows us to establish own
values for input variables based on our expectations about the future. Unfortunately, like with
public company comparables, at the same time this is also a major drawback of the discounted
cash flow method, since model projections could simply be wrong. Additionally, it is often
difficult to determine a proper discount rate and terminal value, which both significantly affect
the overall valuation (Pignataro, 2013).
To summarize this brief discussion, while all three valuation methods have their drawbacks, they
also have their strengths. Therefore, the key is to take advantage of each method’s strengths,
while avoiding their weaknesses in trying to find a fair value for Amazon. In doing so, it is
important to keep in mind that each valuation method offers just one possible point of view to
draw conclusions from. Hence, to facilitate better decision making, it is often helpful to
summarize the valuation results on one page. Also, since each method depends on a range of
parameters, it is best practice to expand the valuation output across a range of value estimates, as
opposed to centering on a single point estimate. While it is almost impossible to come up with a
proper single value estimate, a valuation range is often enough to suggest the future direction of
an investment. To graphically represent this valuation summary, we will use a floating bar chart,
often called a “football field” chart, where we use Amazon’s fundamentals to convert the
calculated comparable companies and comparable transactions multiples into actual stock price
estimates, which, as briefly mentioned in Section 4.1, is another way to interpret multiples.
Thereby, the value ranges are based on each multiple’s distributional characteristics, which are
50
the median, maximum and minimum values, and also percentiles. By also including Amazon’s
current stock price, we can then use this football field chart to assess Amazon’s value and to draw
a final conclusion on whether it is properly valued.
5.2.
Introduction to Public Comparable Company Analysis
In this section we will build on the theoretical foundations discussed in Section 4 by using
relative valuation to find a fair value for Amazon. More precisely, we will compare various
multiples for Amazon with benchmark values for these multiples, which are based on a set of
publicly traded comparable companies. In doing so, we will follow the general methodology
suggested by Kaplan (2014a):
1.
Select and calculate the multiple that will be used
2.
Select and calculate the benchmark, e.g., the mean or median multiple of comparable
stocks or the entire industry, or simply the stock's historical average multiple
3.
Compare the stock’s multiple to the benchmark
4.
Examine whether any observed difference between the multiples of the stock and the
benchmark are explained by the underlying determinants of the multiple, and make
appropriate valuation adjustments
If, after going through this step-by-step process, the stock’s multiple is above or below the
benchmark value, the stock is considered either overvalued, meaning the stock’s multiple is
above the benchmark, or undervalued, meaning the stock’s multiple is below the benchmark.
Nonetheless, before making this judgment, it is imperative to check whether any differences
between the multiple and the benchmark could be explained by the fundamentals that affect a
company’s multiple, as discussed in Section 4.4. To briefly recap, the three variables that affect
company value are cash flow generation, growth potential, or risk (Damodaran, 2012). Taking the
P/E multiple as an example, a stock with a lower multiple than the benchmark could either be
undervalued, or properly valued but with either lower growth potential or higher risk. In other
words, to conclude that a stock is truly under- or overvalued, we have to make sure that we
compare apples to apples (Kaplan, 2014a).
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5.3.
Finding Comparable Companies
Before we can apply this step-by-step methodology, however, we have to define a set of publicly
traded comparable companies, which is also often called a peer group. In fact, this is the most
important and most difficult part of relative valuation, since no two firms are identical, and even
firms in the same business can still differ on characteristics like risk, growth potential and cash
flows (Damodaran, 2012). Unfortunately, there is no simple and uniform way to derive a peer
group, neither in theory nor in practice. Looking at Amazon, for example, peer groups vary
widely among different analysts and data providers. More specifically, as Table 6 shows,
Jefferies Equity Research (2014) and S&P Capital IQ (2015) define completely different peer
groups, while Yahoo Finance (2015) and Morningstar (2015a), with Alibaba Group Holding Ltd.,
list at least one common competitor. The most likely reason for this variation is the fact that
Amazon has quite a unique business model, which makes it hard to find comparable businesses.
So how should we choose our set of comparable companies then? Pignataro (2013) suggests to
start with companies that are similar in size, product, and geography, whereas Damodaran (2012,
p. 650) defines a comparable company as “one with cash flows, growth potential, and risk similar
to the firm being valued”. Interestingly, both definitions have not even one common component.
However, Damodaran (2012) also notes that most analysts define comparable firms to be in the
same industry, thereby implicitly assuming that firms in the same industry have similar risk,
growth, and cash flow profiles. Unfortunately, this industry-focused approach becomes difficult
when there are very few publicly traded firms in a particular industry, or when there are large
differences among them regarding the above mentioned characteristics. For example, there may
be hundreds of computer software companies in the U.S., but there may also be huge differences
among them regarding not only products and size, but also growth, risk, and cash flows. In turn,
this shows that depending on how broad we define our industry, we will most likely also receive
a wider range of multiples for our comparable companies.
Keeping both Pignataro’s (2013) and Damodaran’s (2012) definitions in mind, and taking into
account that Amazon has quite a unique business model, as briefly discussed in Section 2.1, it is
easy to see that finding a set of comparable companies for Amazon is no easy task. After all,
there are simply no “close” comparables to Amazon, which is why even practitioners define very
52
different peer groups. For these reasons, we decided to define our peer group after the fact that, as
measured by revenue, Amazon is one of the largest online retailers in the world, and the largest
online retailer in the United States (MarketLine, 2014; Wall Street Journal, 2014). Hence, we
simply took the next three top-ranked U.S. online retailers by revenue, which are Wal-Mart
Stores Inc., Apple Inc., and Staples Inc. (Wall Street Journal, 2014). Finally, we added Ebay Inc.
and Alibaba Group Holding Ltd. to our list of comparable companies, since, due to their similar
business models, they are often considered to be the closest comparables to Amazon
(MarketLine, 2014 and 2015). Clearly, none of the above companies is perfectly comparable to
Amazon: Wal-Mart Stores Inc. and Staples Inc., for example, both have a strong online presence,
but they also operate physical stores. Furthermore, Wal-Mart Stores Inc. is primarily a grocery
retailer and Staples Inc. is an office supply chain store. Similarly, Apple has a strong online
presence with its digital sales from iTunes and the App Store, as well as its hardware sales, but in
the end Apple primarily designs and develops consumer electronics and software. Lastly, Alibaba
Group Holding Ltd. and Ebay Inc., even though they are considered to be the closest comparables
to Amazon, essentially only provide sales services via web portals, instead of actually selling
products like Amazon. Anyhow, while not perfectly comparable, all of these companies have at
least something in common with Amazon. For example, Apple Inc. also sells smartphones and
tablet computers, Staples Inc. and Wal-Mart Stores Inc. also sell products online, Ebay Inc. also
offers a shopping platform for other businesses, and Alibaba Group Holding Ltd. also offers
cloud computing services.
5.4.
Some Technical Notes
Before we take a close look at the numbers, we want to highlight some technical difficulties that
often complicate a seemingly simple comparable company analysis. To start with, a major
problem with this valuation method is data availability. In equity research or investment banking
financial data is usually readily available from data providers like Bloomberg, Reuters, or
FactSet, for example. Also, data from these sources is mostly standardized, which allows for
quick and easy cross-sectional comparisons of different companies. If such sources are not
available, however, gathering peer group data can be a very time-consuming and tedious process.
While basic data, like shares outstanding or share prices, can easily be derived from public
sources like Yahoo Finance and companies’ annual reports in a standardized way, gathering
53
financial data, like enterprise values or forecasted financials, can be problematic, since oftentimes
not all values are available from the same data source. Of course, one could simply use different
sources, but this could distort the analysis without knowledge on how the values were calculated.
For example, determining enterprise values for Amazon’s peer companies requires calculating
equity values first. As mentioned earlier, market capitalizations are simply calculated as shares
outstanding times share price, and they are usually easily available from public sources.
Nonetheless, a quick analysis shows that on the same day, Google Finance (2015) quotes
Amazon’s market capitalization to be 314.9 billion, while Morningstar (2015a) quotes 315.6
billion, and Reuters (2015) quotes 316.6 billion. As already discussed in Section 4.3, this is due
to the various share counts used in the calculation, where we decided to use the weighted-average
diluted shares outstanding. While this is only an approximation, it greatly simplifies the analysis
of our peer companies, since we can easily take the share counts from the respective annual and
quarterly reports and the share prices from Yahoo Finance (2015).
After determining equity value, the calculation of enterprise value becomes even more
complicated, since various balance sheet adjustments are necessary. Here, the two main questions
are what to adjust and where to take the data from? While we already answered the first question
in Section 4.2, the second question poses a greater challenge, since, like with equity values
above, different sources almost always provide different enterprise values. For example, as
mentioned before, Deutsche Bank Markets Research (2014), Jefferies Equity Research (2014),
and UBS Global Research (2014) all calculate different EV/revenue multiples for Amazon in
2012 and 2013, even though all use the same historical revenue figures. Again, this should not be
surprising, due to the many definitions and calculation methodologies being used in practice. To
allow for maximum standardization and comparability among our peer companies, we decided to
make only the same simple balance sheet adjustments that we made for Amazon. Furthermore,
we chose to derive all values for net debt, minorities, and preferred stock from Morningstar
(2015a), instead of the individual company reports. The reason for this is that Morningstar allows
exporting data to MS Excel, which greatly simplifies our standardization process
(“calendarization”) to Amazon’s fiscal year, which ends December 31. In this regard, we only
adjust the fiscal years of Apple Inc. and Alibaba Group Holding Ltd., even though the fiscal years
of Wal-Mart Stores Inc. and Staples Inc. also differ from Amazon by one month. To be perfectly
precise, we could have further split up their quarterly data into monthly data, as suggested by
54
Pignataro (2013). However, for the sake of simplicity, we decided to neglect this one-month
difference. Lastly, since the reports for Alibaba Group Holding Ltd. were in Chinese Yuan, we
had to adjust them to U.S. Dollars using the USD/CNY exchange rate of 6.192 as per December
31, 2014. As a final note, like with the varying market capitalizations quoted by different sources
before, Yahoo Finance (2015), for example, provides different balance sheet adjustments for
some of our peer companies than Morningstar (2015a), which again underscores the importance
of using the same data source across all companies.
The problem of data availability also extends to valuation metrics. More specifically, the main
question with revenue, EBITDA, and EPS is where to get the projections from, since public
sources almost never provide them. On top of that, metrics like EBITDA and EPS are often
adjusted by equity analysts in a variety of ways. Hence, even if we found projections somewhere
in the public domain, we would again compare apples to oranges if we would take historical
values from another source or if we would take different sources for different companies. For this
reason, we decided to take both historical and forecasted figures mostly from UBS equity
research reports 4. As briefly mentioned in Section 4.3, UBS, like most other equity research
providers, only shows adjusted values for EBITDA and EPS, and also only shows EPS on a
diluted basis. Therefore, we decided to also use these values for our analysis, even though we
don’t exactly know what items were adjusted. Nevertheless, using the same research provider for
all comparable companies increases the comparability of valuation metrics, since the same
adjustments are most likely applied across all companies. Finally, we had to standardize the
numbers of Apple Inc. and Alibaba Group Holding Ltd. again, and since the reports for Alibaba
Group Holding Ltd. were in Chinese Yuan, we had to adjust them to U.S. Dollars again.
5.5.
Looking at Amazon’s Numbers
After defining the peer group and gathering all necessary data, we can now focus on the above
step-by-step methodology to find a fair value range for Amazon. Regarding the choice of
multiples, as discussed in Section 4, depending on whether an equity value or enterprise value is
used in the nominator and depending on which valuation metric is used in the denominator, a
4
Author’s note: UBS equity research reports only provided Apple Inc.’s fiscal year numbers, while we needed
quarterly numbers to adjust for the fiscal year. Hence, for Apple Inc. we used equity research reports from Societe
General instead of UBS.
55
variety of valuation multiples is possible. However, as can be seen from Table 6, we decided to
focus on EV/EBITDA, EV/revenue, and P/E, which are the most common multiples encountered
in practice. Regarding the choice of a benchmark, taking the P/E multiple as an example, typical
benchmarks include the stock’s average historical P/E, the P/E of a competitor with similar
operating characteristics, the P/E of an equity index, and the average or median P/E of a peer
group or industry (Kaplan, 2014a). Again, as already discussed in Section 4.1on how to judge a
multiple’s value, we decided to take the medians of our chosen multiples across our set of
comparable companies as a primary benchmark, supported by the 25th and 75th percentiles and
maximum and minimum values. Finally, to get a more comprehensive picture, we decided to
utilize both forward multiples (2015E) and trailing multiples (2014).
After selecting and calculating the multiples, we can then compare them to the benchmark values
to draw conclusions about Amazon’s current valuation. To facilitate better judgment on whether
or not any difference between Amazon’s multiples and the benchmarks are explained by the
underlying determinants of the multiples, we also included EBITDA margins (as a proxy for cash
flow generation capacity), as well as revenue and EBITDA growth rates (as a proxy for future
cash flow growth) in our table of operating statistics 5.
The first thing to note when looking at Table 6 is that we excluded Alibaba Group Holding Ltd.
from our multiples analysis. With EV/revenue of 22.5x and 16.3x, EV/EBITDA of 41.7x and
31.1x, and P/E of 48.7x and 39.7x for 2014 and 2015, respectively, the company’s multiples
deviate significantly from the rest of the peer group and would therefore only distort the
analysis 6. As discussed already in Section 4.1, this could be due to different accounting
standards, since Alibaba Group Holding Ltd. is the only non-American company included in the
peer group. Also, since the company operates in different markets, its multiples most likely
reflect different economic conditions and expectations compared to the rest of the peer group.
However, taking into account Alibaba Group Holding Ltd.’s revenue and EBITDA growth rates
5
Author’s note: If necessary, beta (from the WACC worksheet) could be used as an additional proxy for risk, since
the other parameters to calculate cost of equity using the CAPM are the same for all companies. In theory, required
returns are used to measure risk. Hence, the appropriate risk measure for enterprise value multiples would be
WACC, whereas the appropriate risk measure for price multiples would be cost of equity (Damodaran, 2012).
6
Author’s note: To be perfectly precise, we should have also eliminated Alibaba Group Holding Ltd. from the
operating statistics table to not distort the distributional characteristics. However, since it doesn’t directly affect
our valuation analysis, we decided not to do so.
56
of 37.7% and 34%, respectively, combined with EBITDA margins above 50%, its high valuation
is also somewhat justified compared to the rest of the peer group. To briefly recap, no matter
which valuation method is used, firm value is a function of the capacity to generate cash flows
along with the expected growth and risk of these cash flows.
After excluding Alibaba Group Holding Ltd., the multiples seem to be narrowly distributed
across the peer companies with EV/revenue ranging from 0.5x to 3.8x, EV/EBITDA ranging
from 7.8x to 11.9x, and P/E ranging from 11.7x to 19.1x. Comparing Amazon’s multiples to
these benchmarks shows that as measured by EV/revenue, Amazon is slightly below the median,
while it is clearly above the maximum as measured by EV/EBITDA and P/E. With regard to the
P/E multiple this is not surprising, since Amazon has very low and sometimes even negative
earnings, which renders the multiple meaningless for our analysis. Looking at EV/EBITDA
instead has two major benefits: First, EBITDA is usually positive even if earnings are not, and
second, it is capital structure neutral in that it eliminates the effects of leverage. On top of that,
the multiple adjusts for depreciation and amortization, which benefits our analysis in that Staples
Inc. and Wal-Mart Stores Inc. operate physical stores and are therefore most likely more capital
intensive than pure online companies like Ebay Inc., for example.
To assess whether Amazon, with its EV/EBITDA multiples of 21x and 17.2x is actually
overvalued compared to the peer group’s maximum multiples of 11.9x and 11.6x for 2014 and
2015, respectively, we can analyze margins and growth rates again. Looking at margins, one can
easily see that Amazon’s EBITDA margins for 2015 are very close to Staples Inc. and Wal-Mart
Stores Inc., which is justified by the fact that they are all retail companies. Nevertheless,
Amazon’s revenue and EBITDA growth rates are significantly higher, which justifies its higher
EV/EBITDA multiples compared to both peer companies. Compared to Ebay Inc., Amazon
shows a higher growth potential combined with considerably lower margins, which could be an
indication that the market values future growth more heavily than current cash flow generation.
However, this conclusion doesn’t fit when also including Apple Inc., which exhibits both higher
margins and growth potential compared to Amazon, and still trades at lower EV/EBITDA
multiples. Therefore, our analysis remains somewhat inconclusive at this point and we should
take a look at EV/revenue to complement our findings so far.
57
Compared to EV/EBITDA multiples, EV/revenue multiples have the advantage that revenues are
not only always positive, but are also harder to manipulate and less volatile than earnings. Yet
they do not capture differences in cost structures and high revenues are also not indicative of high
earnings. That being said, in contrast to our assessment of EV/EBITDA, Amazon, with its
EV/revenue multiples of 1.5x and 1.3x, seems to be slightly undervalued compared to the peer
group’s median multiples of 1.9x and 1.3x for 2014 and 2015, respectively. Taking revenue
growth rates into account explains Staples Inc. and Wal-Mart Stores Inc.’s relatively low and also
Apple Inc.’s relatively high EV/revenue multiples. However, Ebay Inc. trades at the highest
EV/revenue multiple, even though both Apple Inc. and Amazon exhibit considerably higher
revenue growth rates, which, again, leaves our analysis somewhat inconclusive.
To summarize this analysis, one always has to keep in mind that multiples only reflect current
market conditions, which is a major drawback, since any stock, industry, or the entire market
could be over- or undervalued. Additionally, companies might not be truly comparable due to
differences in size, risk, growth potential, cash flows, or even the industry they operate in.
Therefore, Ebay Inc.’s high multiples, for example, might simply be the result of temporary
overvaluation, or the fact that it is not a suitable peer company for our analysis. That being said,
we conclude that Amazon is somewhat undervalued based on our EV/revenue analysis, even
though our EV/EBITDA analysis contradicts this assessment. The reason for this is that revenue
multiples are generally considered more representative for companies with strong growth and low
or even negative earnings. Such companies are often start-ups, which are in the early stage of
their life cycle, where earnings are typically distorted by heavy capital investment before
profitability increases as sales increase and investments level off. Lastly, as briefly mentioned in
Section 5.1, all of the points just discussed can also be seen from Table 11, where we converted
the multiples into stock price estimates for Amazon. Not surprisingly, due to Amazon’s relatively
low profitability, both P/E multiples and EV/EBITDA multiples suggest a significantly lower
stock price, which implies that Amazon would be strongly overvalued at its current stock price of
$310.35. On the other hand, revenue multiples indicate that Amazon might be undervalued with a
median stock price estimate of approximately $380, which, however, is accompanied by a wide
range of stock price estimates from below $200 to almost $800.
58
6.
6.1.
Precedent Transactions Analysis
Introduction to Precedent Transactions Analysis
As briefly introduced in Section 5.1, precedent transactions analysis essentially looks at what
others have paid to acquire similar companies, and then uses this information to value the focus
company. Hence, it is also a relative valuation approach, but instead of market multiples it
utilizes purchase multiples. The main difference between both is that market multiples use
current market values in the numerator, whereas purchase multiples are based on the purchase
price paid for a company (Pignataro, 2013). According to our definition, EV/EBITDA, for
example, is based on market capitalization adjusted for net debt, minority interest, and preferred
stock. In a purchase multiple, however, EV/EBITDA is be based on purchase price adjusted for
net debt, minority interest, and preferred stock. Thereby, purchase price is defined as the number
of shares times the price paid per share, which is usually significantly higher than the market
price at that time. In turn, this results in the so-called purchase premium, which we briefly
mentioned before.
Like public comparable company analysis, precedent transactions analysis builds on the
theoretical foundations discussed in Section 4. Furthermore, it follows the same step-by-step
methodology that we described for public comparable company analysis in Section 5.2, with only
a few modifications. The three main differences are as follows: First, instead of publicly traded
comparable companies, we look for historic transactions of comparable companies. Second,
instead of standardizing financials to match with our focus company, we standardize them to the
announcement date of the transaction, and third, we only use EV/revenue and EV/EBITDA
multiples.
6.2.
Finding Comparable Transactions
While it was already difficult to find public comparable companies and the necessary data, it is
even more difficult to find comparable M&A transactions with relevant data. In fact, as
mentioned in Section5.1, the scarcity of comparable transactions and relevant data are two major
59
drawbacks of precedent transactions analysis. Unfortunately, like with public comparable
company analysis, there is no simple and uniform way to find precedent transactions, but again, a
good starting point is to focus on companies that are similar with regard to industry and
financials. A further critical factor when identifying precedent transactions is timing, since
transactions that happened many years ago may not be relevant anymore in today’s market
environment. Hence, the more recent a transaction, the more useful it is for our analysis
(Pignataro, 2013).
Unlike with comparable companies, where one usually starts with a list of publicly traded
companies 7 and then narrows it down by criteria like industry, geography, and size, it is hard to
find such lists for comparable transactions. In equity research or investment banking, such a list
of deals could easily be derived from the M&A desk, or data providers like Bloomberg, Reuters,
or FactSet, for example. Nonetheless, if such sources are not available, like with public company
comparables, gathering data can become a very time-consuming and tedious process. Therefore,
the best way to start is to use a search engine like Google and, in the case of Amazon, search for
terms like “technology M&A” or “e-commerce M&A”. The top search results that come up are
typically M&A reports from consulting firms, investment banks, or other research providers.
These reports are regularly updated and usually contain information on the most recent deals,
including size, industry, geography, announcement date, and bidder, as well as other more
general information on the M&A market. Using this technique, we eventually compiled a list of
five transactions, using reports from consulting firms Pricewaterhouse Coopers (2014) and Ernst
& Young (2014), boutique investment banks Peakstone Group (2015) and Woodside Capital
Partners (2015), which specialize in M&A advisory, as well as information from financial news
websites, like Yahoo Finance (2015), Morningstar (2015a), MarketWatch (2015), or the Wall
Street Journal (2015):

Expedia Inc. acquiring Orbitz Worldwide Inc. in 2015

Priceline.com Inc. acquiring Kayak Software Corp. in 2012

Staples Inc. acquiring Office Depot Inc. in 2015
7
Author’s note: A good starting point would be the Forbes Global 2000, a list of the world’s 2000 biggest public
companies, which can be filtered by industry, country, market value, sales, profits, and assets. For more detail, see
http://www.forbes.com/global2000/list/.
60

QVC Inc. acquiring Zulily Inc. in 2015

Priceline.com Inc. acquiring OpenTable Inc. in 2014
As a side note, even though we value Amazon on January 01, 2015, three of our chosen deals
were only announced in 2015. While two of them were closed as of October 2015, the acquisition
of Office Depot Inc. by Staples Inc. was still pending. To be perfectly precise, we should have
only used deals prior to our valuation date. However, due to a lack of relevant comparable
transactions, we decided to also include deals that happened afterwards.
6.3.
Some Technical Notes
After identifying our set of comparable transactions, the next step is to gather information about
transaction values and operating metrics to calculate the valuation multiples. In doing so, like
with comparable companies before, the main problem is data availability. However, this time we
can’t simply calculate equity value and convert it to enterprise value, because we have to base our
multiples on purchase prices. More specifically, this means that we need information on the price
paid per share along with the amount of shares outstanding when the transaction happened.
Unfortunately, financial news websites 8 often only report transaction values, which can be either
equity values or enterprise values. Additionally, their deal reports are often imprecise or
incomplete with statements like “equity value is roughly $2.4bn” or “valued at EV/EBITDA of
8.5”, without mentioning specific numbers for enterprise value or EBITDA. Hence, to derive
enterprise values for our set of transactions, we often had to compile data from several sources,
including investor presentations and SEC filings, or even make own calculations (e.g. purchase
price per share x number of shares, or EV/EBITDA multiple x LTM EBITDA). As a side note,
since reports by financial news websites can easily be incorrect, it is also advisable to doublecheck the facts found on one website with facts reported by another.
The data availability problem becomes even worse with regard to revenue and EBITDA as
valuation metrics. Unlike with comparable companies, this time the metrics have to be
standardized to the announcement date of the transaction, which means that we need the last
twelve months of historical data prior to the announcement of the transaction (often called
8
Authors note: A recommended website for reports on M&A deals is http://businessvaluationtools.com/blog/.
61
“trailing twelve months” or simply “TTM”). In turn, this implies that forecasts for revenue and
EBITDA have to be twelve months ahead from this date. To simplify this matter, instead of the
announcement date, we chose the closest quarterly cutoff date prior to the announcement as a
reference point. For example, even though the acquisition of Kayak Software Corp. by
Priceline.com Inc. was announced in November 2012, we chose 3Q2012 as the cutoff date. To
then actually gather the data, like with comparable companies, the best and most consistent
results are achieved by using equity research reports, which were recent at the time when the
transaction was announced. But taking the acquisition of Kayak Software Corp. as an example
again, it is almost impossible to find an equity research report that is three years old, and contains
exactly the data that we need to properly standardize historical and forecasted revenue and
EBITDA. That being said, for three of our chosen deals we actually found equity research reports
from Deutsche Bank and CRT Capital Group, which we used to derive historical and forecasted
metrics. However, for the other two deals we have no projections of revenue or EBITDA
available, but at least we could gather historical values from the respective annual and quarterly
reports.
To summarize this discussion, due to the use of different sources for different companies, and in
some cases even the same company, there is clearly a lack of standardization across our set of
precedent transactions. This applies not only to revenue and EBITDA, but (to a lesser extent) also
to enterprise value. For example, we couldn’t find any credible data on the enterprise value of
Staples Inc.’s acquisition of Office Depot Inc., which is still pending, and therefore had to
approximate it based on the incomplete information available. Additionally, we only found
EBITDA on an adjusted basis for some transactions and on an unadjusted basis for others. Hence,
our precedent transactions analysis is somewhat more prone to calculation errors and less reliable
than our public comparable company analysis, which is something to keep in mind when drawing
conclusions on Amazon’s fair value. As a final note, depending on what data, calculation
methodology, and precision one uses, purchase or deal multiples will almost always vary among
different providers.
62
6.4.
Looking at Amazon’s Numbers
After finding comparable transactions and gathering all necessary data, we can now focus on the
step-by-step methodology from Section 5.2 again to find a fair value range for Amazon. The first
thing to note when looking at Table 7 is that we excluded OpenTable Inc.’s and Kayak Software
Corp.’s EV/trailing revenue multiples of 12.5x and 5.8x, respectively, since they are most likely
outliers compared to the other EV/trailing revenue multiples. Also, since there was no data on
forward revenues available, we didn’t have to adjust their EV/forward revenue multiples, which
results in a relatively narrow range of 0.4x to 1.7x for both trailing and forward revenue
multiples. Looking at the EBITDA multiples, it is easy to see that they vary widely with
EV/trailing EBITDA ranging from 10.2x to 46.2x, and EV/forward EBITDA ranging from 7.6x
to 27.2x. Nevertheless, while we could also normalize their ranges by eliminating extreme values,
we decided not to do so for two reasons: First, it might not be warranted to exclude data points
without further research, since company specific factors could have caused the valuation
differences. Second, it is normal for comparable transaction multiples to be more widely
dispersed compared to comparable company multiples for a variety of reasons, which we will
discuss more detailed below.
That being said, based on median EV/trailing revenue and EV/forward revenue multiples of 1.6x
and 1.5x, respectively, Amazon seems slightly undervalued with values of 1.5x and 1.3x for 2014
and 2015, respectively. This fits with our EV/revenue analysis based on comparable companies
from the previous section. What seems odd, however, is the fact that the median EV/revenue
multiples based on comparable transactions are lower than the median EV/revenue multiples
based on comparable companies. The reason for this is that a valuation based on precedent
transactions typically results in higher multiples, since an acquirer has to pay a control premium
to buy a target company. This actually becomes evident when comparing the median
EV/EBITDA multiples of both data sets, where comparable transactions multiples are in fact
higher than comparable company multiples on both a trailing and a forward basis. However,
comparing median EV/trailing EBITDA and EV/forward EBITDA multiples to Amazon directly
remains inconclusive, since, on a trailing basis, Amazon seems slightly undervalued with 21x
compared to a median of 24.2x, whereas, on a forward basis, it seems significantly overvalued
with 17.2x compared to a median of 9.6x.
63
To summarize this analysis, one has to keep in mind that, like with comparable companies, a
transaction might not be truly comparable. In addition to that, a transaction might not be relevant
anymore, since it occurred too long ago, and therefore the economic environment along with the
multiples might have changed in the meantime. Lastly, purchase multiples might be biased by a
lack of data, or a situation where the acquirer paid a significant control premium. All of these
drawbacks complicate the analysis and often lead to a wider dispersion of multiples, which can
be seen by the wide ranges of the EV/EBITDA multiples, for example. The same would have
been true for the EV/revenue multiples, if we wouldn’t have eliminated the multiples for Kayak
Software Corp. and OpenTable Inc. Therefore, given that one doesn’t intend to acquire the entire
company, comparable transactions multiples are typically only used to complement other
valuation methods by establishing a maximum value range, which is usually caused by the
control premium. Hence, if other valuation methods result in even higher value ranges, either
something is wrong, or there must be a good explanation for it. That being said, we can conclude
that, on average, Amazon is somewhat undervalued based on our comparable transactions
analysis, even though our set of comparable transaction might not be truly representative for the
reasons mentioned above.
Again, all of the points just discussed can also be seen from Table 11, where we converted the
multiples into stock price estimates for Amazon. As discussed, the EV/revenue multiples show
less variation than the EV/EBITDA multiples, since we eliminated potential outliers from our
data set. Yet both revenue multiples suggest a median stock price of approximately $330 for
Amazon, which supports our conclusion drawn from the previous comparable company analysis
that Amazon might be slightly undervalued at its current stock price of $310.35. This is also
supported by EV/trailing EBITDA, which suggests a median stock price of about $356, whereas
EV/forward EBITDA, as measured by its median, contradicts these results. On average, however,
we can conclude that based on our comparable company and comparable transactions analysis,
Amazon, at its current stock price of $310.35, might be undervalued with a more appropriate
stock price somewhere between $330 and $380.
64
7.
Cost of Equity and WACC
Before we can take a closer look at discounted cash flow analysis as our third valuation approach,
we need to discuss some of the most important input parameters of DCF models. As explained in
Section 4.4 already, unlevered free cash flows are available to all investors, whereas levered free
cash flows are only available equity investors. Hence, the weighted average cost of capital
(WACC) should be used as the appropriate discount rate for the former, whereas cost of equity
should be used as the appropriate discount rate for the latter. As can be seen from Table 8, we
will apply the following commonly used formula to calculate WACC (Koller, Goedhart and
Wessels, 2010):
𝑊𝐴𝐶𝐶 =
𝐸
𝐷
× 𝑘𝑑 × (1 − 𝑇) +
× 𝑘𝑒
𝐷+𝐸
𝐷+𝐸
where D = market value of interest-bearing debt, E = market value of equity, (D + E) = enterprise
value, T = marginal tax rate, k d = cost of debt, and k e = cost of equity. Furthermore, while there are
various models available to estimate cost of equity, we will use the capital asset pricing model
(CAPM) to simplify the analysis (Bodie, Kane and Marcus, 2014):
𝑘𝑒 = 𝑟𝑓 + 𝛽 × �𝐸(𝑟𝑚 ) − 𝑟𝑓 �
with rf = risk-free rate, β = systematic risk of the stock, and E(rm ) − rf = expected equity market
risk premium. Since cost of equity serves as an input to the above WACC formula, we decided to
first discuss the calculation of cost of equity. Subsequently, we will take a closer look at cost of
debt and the other inputs for the WACC formula to finally derive Amazon’s weighted average
cost of capital.
7.1.
Cost of Equity - Risk-Free Rate and Market Risk Premium
As the above formula shows, we need three estimates to derive Amazon’s cost of equity, which
are the risk-free rate, the market risk premium, and Amazon’s beta. To start with, the risk-free
65
rate is usually approximated by a long-term government bond yield (Bodie, Kane and Marcus,
2014). Hence, we will use the 10-year treasury yield, which is widely seen as the benchmark rate
for the U.S. market, as a starting point for our cost of equity estimate. More precisely, as of
December 31, 2014, the 10-year constant maturity treasury yield stood at 2.17% p.a. (Federal
Reserve, 2015). Nonetheless, since we use cost of equity to discount future cash flows, we also
have to take into consideration future changes in the risk-free rate. As can be read from almost
every financial newspaper lately, it is not a matter of “if” but only a matter of “when” the Federal
Open Market Committee (FOMC) will increase the federal funds rate. Therefore, we forecast at
least three interest rate hikes of roughly 0.25% for the years to come, which results in a risk-free
rate estimate of 2.92%. To put this into context, over the last decade, the highest and lowest
values for the above benchmark rate were 5.11% and 1.53%, respectively (Federal Reserve,
2015). The historical low of 1.53% was marked in 2012, with rates gradually increasing since
then, which further justifies our assumption of increasing interest rates going forward.
Next, the equity market risk premium can be derived from either historical data or forwardlooking estimates. Based on historical averages and forward-looking estimates, Koller, Goedhart
and Wessels (2010) recommend a market risk premium of 4.5% to 5.5%. Additionally,
Damodaran (2015a) 9 currently calculates an implied equity risk premium of 6.6%. A further
approach is to derive forward-looking estimates from an annual survey of practitioners and
professors about their view on the market risk premium, which results in a median estimate of 5%
for the U.S. market in 2014 (Fernandez, Linares and Fernandez Acin, 2014). After this brief
literature review, leaving us with a range of 4.5% to 6.6%, we decided to take a more
conservative approach and therefore assume an equity market risk premium of 6% for our cost of
equity calculation.
7.2.
Cost of Equity - Systematic Risk (Beta)
The third and last input value needed to calculate Amazon’s cost of capital is its beta.
Unfortunately, coming up with an estimate of beta is not as straightforward as estimating the riskfree rate or the market risk premium. To begin with, there are various approaches to derive beta,
9
Author’s note: Professor Aswath Damodaran teaches corporate finance and valuation at the Stern School of
Business at New York University and his website is widely followed by the investing community.
66
for example, by retrieving it from financial data providers like Yahoo, Morningstar, or S&P
Capital IQ. However, betas often vary considerably among these providers, due to different time
periods and calculation methodologies used. Table 8 exemplifies this with data from five
different providers, derived on the same date (except for the S&P Capital IQ report), with
Amazon’s beta varying considerably from 0.74 (Google Finance, 2015) to 1.33 (Morningstar,
2015a; Yahoo Finance, 2015). For this reason, the preferred method should be to calculate an
own estimate of beta by using either advanced statistical software like SPSS, for example, or MS
Excel, which we will use for our analysis.
To briefly recap theory, in a portfolio management context, mean-variance analysis requires
forecasts of expected returns, variances, and covariances, instead of historical data. To estimate
these values, the market model, which is essentially a linear regression of an asset’s returns
against an observable index’s returns, can be used (Kaplan, 2014d):
𝑅 𝑖 = 𝛼𝑖 + 𝛽𝑖 × 𝑅𝑀 + 𝜀𝑖
with R i = return on asset i, R M = return on market, αi = intercept (value of R i when R M equals
zero), βi = slope (systematic risk for asset i), and εi = regression error with expected value equal
to zero (firm-specific surprises, also called unique risk).
Following the methodology suggested by Koller, Goedhart and Wessels (2010), we based our
regression analysis on five years of historical monthly return data, which we derived from Yahoo
Finance (2015). With Amazon’s and the S&P 500’s monthly returns as the dependent and
independent variable, respectively, we then derived a regression equation with the intercept α =
0.0052 and slope β = 1.0856. The chart on Table 9 visualizes the estimated regression line, often
called the “line of best fit”, along with the estimated regression equation and R2. Based on these
results, we can conclude that Amazon’s “raw beta” is 1.0856, which from a practical perspective
indicates that Amazon’s monthly return is expected to change by +1.0856% for a +1% change in
the monthly return of the S&P500. From a theoretical perspective, beta represents an index to
systematic risk, since the market’s beta is 1 by definition. Hence, a beta less than 1 indicates a
more defensive asset, whereas a beta greater than 1 indicates a more aggressive asset relative to
the market (Kaplan, 2014d). For our analysis this implies that Amazon, with a beta slightly
67
greater than 1, reacts slightly more sensitive to systematic risk factors than the market, as
measured by the S&P500 Index.
However, before we can use our beta estimate to calculate Amazon’s cost of equity, we have to
conduct some numerical tests to make sure that our estimate is statistically significant. Therefore,
we must take a closer look at the MS Excel regression output of our 60 monthly return pairs until
December 31, 2014, as shown in Table 9. The first part of the regression output (regression
statistics) shows the overall goodness-of-fit measures. Most importantly, R2 measures the
percentage of total variation in the dependent variable explained by the independent variable, and
is calculated by dividing the explained variation (RSS) by the total variation (SST), using the
values from the ANOVA table. Hence, an R2 of 0.2587 means that the monthly returns of the
S&P500 explain 25.87% of the variation in the monthly returns of Amazon, which is also called
systematic risk. The remainder is called unsystematic risk and equals 74.13% in our analysis.
Generally speaking, the higher the R2 and the lower the Standard Error, the better the overall
regression analysis and the better the approximation of the actual results through the estimated
regression equation. Also, since this is a simple linear regression, Multiple R equals the
correlation coefficient between the independent and the dependent variable and is calculated by
simply taking the square root of R2 (Kaplan, 2014c).
The second part of the regression output (ANOVA table) splits the total variation in the
dependent variable (SST) into the variation explained by the independent variable (RSS) and the
unexplained variation (SSE). In general, the F-statistic is used to test the significance of the
model as a whole, by checking whether any independent variable included in the model explains
the variation in the dependent variable. However, since this is a simple linear regression there is
only one independent variable (Kaplan, 2014c). Using the parameters from the F-distribution
table, the null hypothesis can be rejected to conclude that the slope coefficient is significantly
different from 0, and that the model is statistically significant, since the F-statistic clearly exceeds
the critical value and Significance F (essentially the P-value associated with the F-statistic) is
close to zero.
The third part of the regression output (regression coefficients table) is of most interest for our
analysis, since it shows the statistical significance of the individual regression coefficients. More
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specifically, the t-statistic is used to test whether the slope coefficient of the regression, also
called beta, is significantly different from zero. Using the parameters from the Student’s Tdistribution table, the null hypothesis can be rejected to conclude that beta is significantly
different from zero, since the t-statistic clearly exceeds the critical value and the P-value is close
to zero.
Following these statistical tests, we can be reasonably confident in our beta estimate for Amazon.
Nonetheless, as mentioned introductory, mean-variance analysis requires forecasts of expected
returns, variances, and covariances, whereas the beta derived from the market model is based on
historical relationships, and therefore is not considered a good predictor of future relationships
(also called the beta instability problem). Since beta is assumed to be mean-reverting to a level of
1, a popular adjustment to improve its predictive ability and to reflect its mean-reverting nature is
the following formula (Kaplan, 2014d):
𝛽𝑖,𝑡 = 𝛼0 + 𝛼1 × 𝛽 𝑖,𝑡−1 𝑤ℎ𝑒𝑟𝑒 𝛼0 + 𝛼1 = 1
with βi,t = the beta forecast, β i,t−1 = the historical beta derived from the market model, and
α0 + α1 = adjustment factors. Essentially, this adjustment moves the “raw beta” more towards 1,
whereas larger values of α0 push it towards 1 more quickly. Thereby, the most popular values for
α0 and α1 are 1/3 and 2/3, respectively. Applying these values to Amazon’s “raw beta” of 1.0856,
the “adjusted” or “smoothed” beta for Amazon becomes 1.0574.
Even after conducting statistical tests and using the mean-reversion adjustment, the beta derived
from the market model still only provides a point estimate of Amazon’s true beta. Hence, to
further improve this estimate we can derive an unlevered industry beta, which can then be
relevered using Amazon’s target capital structure (Koller, Goedhart and Wessels, 2010). To
derive an unlevered industry beta, we can draw on our set of comparable companies from Section
5.3, and for simplicity, instead of running the market model, we can use levered betas provided
by Morningstar (2015a) and Yahoo Finance (2015) as inputs 10. Next, to unlever these betas, there
10
Author’s note: If no or only few direct comparables exist, Koller, Goedhart and Wessels (2010) suggest adjusting
the levered betas with the above smoothing approach to dampen extreme observations toward the average.
69
are two formulas available, depending on the variability of a company’s capital structure (Chen
and Jassim, 2014):
𝛽𝑢 =
𝛽𝑙
�1 + 𝐷�𝐸 �
𝛽𝑢 =
𝛽𝑙
�1 + (1 − 𝑇𝑚 ) × 𝐷�𝐸 �
with βu = unlevered beta, βl = levered beta, D = interest-bearing debt, E = market capitalization,
and Tm = marginal tax rate. The former formula is generally used for companies with a stable
capital structure, whereas the later formula is used for unstable capital structures and takes into
account the tax saving of levered companies if they are profitable. We chose to include taxes and
use the latter formula to unlever all betas, since analyzing the stability of capital structures would
have called for using the tax formula for some and the non-tax formula for other companies,
which could have distorted our analysis. Additionally, using the tax formula adjusts for the wide
dispersion among the individual tax rates, which range from 15.5% to 33%. This is also
consistent with Damodaran’s approach to derive an unlevered industry beta, which will be
discussed in the next paragraph. Regarding the formula inputs, debt and equity values were
already available from our public comparable company analysis in Section 5. For the tax rates,
instead of statutory tax rates, we calculated a 3-year average of the annual effective tax rates
excluding outliers (calculated as income taxes / pre-tax income, with values taken from the
respective 2014 annual reports). Finally, Table 8 shows the results of this analysis with a median
levered beta of 0.91 and a median unlevered beta of 0.84.
As an alternative to our calculation of unlevered industry beta, Damodaran (2015b) provides
levered and unlevered industry betas based on the same calculation methodology but a broader
set of companies. As can be seen from Table 8, instead of only using 5 comparable firms, his
analysis includes 46 firms and results in a levered and unlevered beta of 1.40 and 1.31 for the
online retail industry, respectively. In a last step, both unlevered industry betas had to be
relevered using Amazon’s capital structure and tax rate to arrive at levered betas of 0.87 and 1.36
from our comparable companies and the overall industry, respectively. Again, the formula inputs
were already available from our public comparable company analysis in Section 5.
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After estimating all input parameters for the CAPM, we were finally able to calculate Amazon’s
cost of equity based on three different betas. As can be seen from Table 8 again, using a risk-free
rate of 2.92%, an equity market risk premium of 6%, and beta estimates of 0.87, 1.36, and 1.06,
based on our comparable companies, the overall industry, and our historical beta calculation,
respectively, Amazon’s cost of equity becomes 8.12%, 11.08%, and 9.26%. Similar to our
approach for the equity risk premium, we decided to take a more conservative approach and
hence chose the highest value, meaning the cost of equity based on the overall industry of
11.08%, for calculating Amazon’s WACC in the following sections.
7.3.
WACC - Cost of Debt
As with the input parameters for our cost of equity calculation in the previous sections, there are
also several ways of estimating cost of debt. From an accounting perspective, cost of debt could
simply be estimated by dividing this year’s interest expense by the previous year’s interestbearing debts, or by an average of this year’s and the previous year’s interest-bearing debts.
However, if a company has bonds outstanding, the two preferable methods are as follows (Chen
and Jassim, 2014): If the bonds are either non-investment grade or not actively traded, meaning
that there is no observable price or yield information, cost of debt can be approximated by adding
the default spread of the respective rating-/maturity category to the yield of a maturity matching
government bond. On the other hand, if the bonds are both publicly traded and investment grade,
one can simply take the weighted average yield to maturity (YTM) as a proxy for cost of debt.
Since Amazon, with AA-/stable and Baa1/negative, is rated investment grade by Standard &
Poor’s and Moody’s, respectively, and also has actively traded bonds outstanding, the weighted
average method can be applied to estimate cost of debt (S&P Global Credit Portal, 2015;
Moody’s Analytics, 2015). In doing so, we first had to gather all necessary data, like maturity,
amount outstanding, coupon type, and yield to maturity from Morningstar (2015a). As Table 8
shows, the before-tax cost of debt could then be calculated by weighting the individual YTMs
with the relative amounts outstanding to result in a value of 3.08%. To justify this estimate, we
further compared our approximation to a market average YTM. Since Standard & Poor’s and
Moody’s ratings differ by four notches, which is quite uncommon, we assumed that Amazon was
A-rated to derive a market average YTM. At the time of writing, the market average YTMs on A71
rated U.S. corporate bonds for five and ten years were 2.29% and 3.24%, respectively, so we
could be reasonably confident in our estimate (Bonds Online, 2015).
Nevertheless, it should be noted that both our estimated YTM and the market average YTM are
as of the beginning of October 2015, while we aim to value Amazon as of January 01, 2015.
Hence, to provide for more historical context, we also included cost of debt from an accounting
perspective in our analysis. Therefore, we divided the most recent annual interest expense by the
2-year average of interest bearing debts to result in a value of 3.67%, which is slightly higher
than our previous estimate of 3.08%. Yet, as explained above, the weighted average method is
theoretically preferred, which is why we decided to use 3.08% as the before-tax cost of debt in
our calculation of WACC. In a final step, we had to adjust our cost of debt estimate for taxes,
since we need Amazon’s after-tax cost of debt to calculate WACC. Using our assumed tax rate of
33% from Section 5, we finally arrived at an after-tax cost of debt estimate of 2.06%.
7.4.
WACC - Market Value Weights and Calculation
After estimating after-tax cost of debt and cost of equity, the last step to calculate WACC is to
determine Amazon’s capital structure on a market value, not a book value basis. To understand
why market values are used instead of book values, Koller, Goedhart and Wessels (2010, p. 266)
provide an intuitive explanation by stating that “rather than reinvest in the company, management
could return capital to investors, who could reinvest elsewhere. To return capital without
changing the capital structure, management can repay debt and repurchase shares, but must do so
at their market value.”
That being said, Amazon’s market value of equity can simply be calculated by multiplying its
stock price with the number of shares outstanding. The market value of interest-bearing debt,
however, is approximated by its book value to simplify the analysis, based on the assumption that
Amazon’s debt is currently selling near par value (Bodie, Kane and Marcus, 2014). As can be
seen from Table 8, both figures are already available from our public comparable company
analysis in Section 5 and amount to $8,265 and $146,485 for debt and equity, respectively. By
plugging all the variables into the WACC formula that we provided introductory to Section 7, we
finally arrive at a weighted average cost of capital estimate of 10.60% for Amazon.
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8.
8.1.
Discounted Cash Flow Analysis
In Theory - The Enterprise Discounted Cash Flow Model
Before applying discounted cash flow analysis to complement our previous valuation methods in
finding a fair value for Amazon, we need to take a closer look at how discounted cash flow
(DCF) models actually work. In doing so, we will first focus on the so-called enterprise
discounted cash flow (EDCF) model, as suggested by Koller, Goedhart and Wessels (2010),
which is a rather complex and more detailed approach. Based on this discussion, we will then
take a closer look at alternative and also simpler approaches in the next section. As a side note,
the EDCF model is also called the free cash flow to firm (FCFF) model by Kaplan (2014a) and
Damodaran (2012), and remains a favorite of academics and practitioners.
To start with, from a theoretical standpoint any DCF model projects future cash flows, which are
then discounted at an appropriate risk-adjusted cost of capital. As defined by Koller, Goedhart
and Wessels (2010, p. 104), “The enterprise DCF model discounts free cash flow, meaning the
cash flow available to all investors - equity holders, debt holders, and any other non-equity
investors - at the weighted average cost of capital, meaning the blended cost for all investor
capital.” After deriving the value of operations, adding back non-operating assets, like marketable
securities and equity investments, results in a company’s enterprise value. The intrinsic value per
share is then calculated by subtracting debt and other non-equity claims, like unfunded pension
obligations and employee stock options, and dividing the resulting equity value by the number of
current shares outstanding (Koller, Goedhart and Wessels, 2010).
More specifically, the value of operations can be calculated as follows (Chen and Jassim, 2014):
10
𝑉𝑂 = �
𝑡=1
𝐹𝐶𝐹𝑡
𝐶𝑉10
+
(1 + 𝑊𝐴𝐶𝐶)𝑡 (1 + 𝑊𝐴𝐶𝐶)10
73
with FCFt = expected future free cash flows, WACC = weighted average cost of capital, VO =
value of operations and CV10 = continuing value in year 10. As can be seen from the two fractions
in the above formula, the EDCF model is essentially a two-stage model with an explicit forecast
period and a perpetual period, which accounts for all cash flows received after the explicit
forecast period.
Since we already discussed WACC in Section 7, we will subsequently focus on the calculation of
free cash flow in more detail. Thereby, it should be noted that we already discussed free cash
flows in Section 4.4, where we defined free cash flow to firm (FCFF) and free cash flow to equity
(FCFE). Since the EDCF model is also called FCFF model, the former definition is typically used
for discounted cash flow analysis. However, as mentioned above, Koller, Goedhart and Wessels
(2010) provide a more detailed approach and therefore also use their own definition of free cash
flow (FCF). Afterwards, we will also take a closer look at the calculation of continuing value in
year 10, which is also often called terminal value or exit value (Weston, 2002).
To begin with, Koller, Goedhart and Wessels (2010, p. 40) define free cash flow as “the cash
flow generated by the core operations of the business after deducting investments in new capital”.
Following this methodology, Chen and Jassim (2014) calculate free cash flow as follows:
(1) NOPLAT (Net Operating Profits Less Adjusted Taxes)
(2) Depreciation
(3) Gross Cash Flow = (1) + (2)
(4) Investment in Operating Working Capital
(5) Capital Expenditures
(6) Investment in Intangible Assets
(7) Increase (decrease) in Other Operating Assets
(8) Decrease (Increase) in Accumulated Comprehensive Income
(9) Gross Investment = (4) + (5) + (6) + (7) + (8)
(10) Free Cash Flow = (3) – (9)
As can be seen from this step-by-step calculation, free cash flow is based on NOPLAT, which
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Koller, Goedhart and Wessels (2010, p. 40) define as follows: “Net operating profit less adjusted
taxes (NOPLAT) represents the profits generated from the company’s core operations after
subtracting the income taxes related to the core operations”, which implies that any profits or
losses from non-operating activities and financing expenses are excluded. Hence, NOPLAT can
be considered a profit available to all investors, as opposed to net income, which is only available
to shareholders. Also, since interest is considered a financing item, NOPLAT is independent of a
firm’s capital structure. As a side note, the same reasoning applies to free cash flow, in that it
excludes cash flows related to financing and non-operating items and can therefore be considered
an after-tax cash flow to both debt and equity holders.
Again, following Koller, Goedhart and Wessels’ (2010) methodology, Chen and Jassim (2014)
calculate NOPLAT by either starting from operating earnings or reconciling from net income:
NOPLAT = EBITA – operating cash taxes
Operating cash taxes = income tax provision + tax shield on interest expense – tax on
interest income ± changes in deferred taxes
NOPLAT = Net income + increase (–decrease) in net deferred taxes + after-tax interest
expense + loss (–gain) from discontinued operations – after-tax interest received
The adjustment for operating cash taxes in the upper NOPLAT equation is necessary, because
reported taxes are calculated after interest and other non-operating items. Additionally, reported
taxes have to be adjusted for net deferred taxes to receive actual cash taxes. To calculate EBITA,
only amortization expense, instead of depreciation and amortization, is added back to operating
earnings. The reason for this is that depreciation accounts for the loss of economic value of a
company’s physical assets over time, and hence must be included as an operating expense
(Koller, Goedhart and Wessels, 2010). Finally, since the lower NOPLAT equation starts with net
income, it has to be adjusted for interest and any other non-operating items to remain consistent
with the above definition.
To complete our discussion of the EDCF model, we have to take a closer look at the calculation
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of continuing value, which, as mentioned already, accounts for all cash flows received after the
explicit forecast period. In general, any estimate of intrinsic value depends critically on
continuing value, which unfortunately is highly sensitive to even small changes in input
variables. For example, growth rates are very difficult to forecast, especially with regard to longterm growth after the explicit forecast period (Bodie, Kane and Marcus, 2014). To actually
calculate continuing value, Chen and Jassim (2014) suggest two different approaches, whereas
the more complicated approach is based on Koller, Goedhart and Wessels (2010):
𝐶𝑉10 =
𝑁𝑂𝑃𝐿𝐴𝑇10 × (1 + 𝑔) × (1 − 𝑔⁄𝑅𝑂𝑁𝐼𝐶)
𝑊𝐴𝐶𝐶 − 𝑔
with g = the long-run growth in NOPLAT, and RONIC = the long-run forecast for return on new
invested capital (RONIC). If a company is expected to exploit competitive advantages going
forward, RONIC should be estimated closer to return on invested capital (ROIC), whereas
otherwise it should be closer to WACC.
The simpler approach is to use a relative valuation method, as described in Section 4.
Nonetheless, instead of using a P/E multiple, Chen and Jassim (2014) use an EV/EBITDA
multiple, which is common among practitioners. More precisely, they use the following formula:
𝐶𝑉10 = 𝐸𝐵𝐼𝑇𝐷𝐴10 ×
whereas
8.2.
EV
EBITDA
𝐸𝑉
𝐸𝐵𝐼𝑇𝐷𝐴
should be a normalized multiple.
In Practice - Which DCF Model should be used?
After taking a closer look at the EDCF model above, this section aims to provide some context
regarding the application of DCF models in general. As mentioned before, in theory any DCF
model projects future cash flows, which are then discounted at an appropriate risk-adjusted cost
of capital. In practice, however, there is a multitude of ways how to actually project and discount
future cash flows and subsequently adjust the value of operating assets to arrive at enterprise
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value and equity value. Hence, the EDCF model, as suggested by Koller, Goedhart and Wessels
(2010), is just one application of a DCF framework to value a company.
For example, Chen and Jassim (2014) base their valuation approach on the EDCF model
suggested by Koller, Goedhart and Wessels (2010), but simplify the free cash flow calculation in
line with Weston’s (2002) academic study of the Exxon-Mobil merger. In doing so, they derive
NOPLAT as EBIT x (1 – tax rate) and subsequently only adjust for depreciation, investments in
working capital, capital expenditures, and other assets. Additionally, while Chen and Jassim
(2014) calculate continuing value by using an EV/EBITDA multiple and an adjusted constant
growth method based on NOPLAT, Weston (2002) uses a simple constant growth method based
on free cash flow. More precisely, he estimates continuing value as FCF x (1 + g) / (WACC – g).
Finally, to arrive at equity value, both add non-operating assets to the value of operations and in a
last step adjust enterprise value for debt.
To add another perspective, Pignataro (2013) and Kaplan (2014a) also start from EBIT x (1 – tax
rate), then adjust for any non-cash charges, changes in working capital and capital expenditures.
With regard to continuing value, both suggest using valuation multiples like EV/EBITDA and the
constant growth method based on free cash flow. To arrive at enterprise value, Pignataro (2013)
does not add any non-operating assets, while Kaplan (2014a) notes that technically the free cash
flows discounted at WACC result in the value of the firm’s operating assets, and that any nonoperating assets need to be added to arrive at enterprise value. However, they also note that nonoperating assets are usually very small compared to the value of operations and hence don’t affect
the analysis that much. Finally, to arrive at equity value, Kaplan (2014a) simply suggests
subtracting the market value of debt, while Pignataro (2013) adjusts enterprise value for net debt,
including capital lease obligations, and other non-equity claims, like preferred stock and minority
interest. It is interesting to note that to arrive at equity value, Pignataro (2013) adjusts for net debt
instead of gross debt. As already discussed in Section 4.2, cash and equivalents have to be
accounted for when calculating equity value from enterprise value. Nonetheless, the reason why
the above approaches didn’t make this adjustment when calculating equity value is because they
included cash and equivalents in their calculation of working capital and hence in their
calculation of the value of operations.
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Finally, as exemplified by Jefferies Equity Research (2014) and Deutsche Bank Markets
Research (2011), practitioners seem to take an even more basic approach to arrive at free cash
flow, by simply adjusting EBITDA for implied taxes on operations (calculated as EBIT x tax
rate), capital expenditures, and changes in net working capital. This is equivalent to Pignataro’s
(2013) and Kaplan’s (2014a) approach, but instead of any kind of non-cash charges they only
adjust for depreciation. Regarding continuing value, both use the constant growth method based
on free cash flow and calculate the implied EV/EBITDA multiple based on the final-year
EBITDA. Lastly, they do not add any non-operating assets, and to arrive at equity value they
simply adjust for net debt, taking into account pensions and other interest-bearing liabilities, as
well as marketable securities.
To summarize, this brief discussion shows that even though all of the above approaches are based
on the same theoretical foundations, their practical applications can differ substantially. We
assume that there are mainly two, somewhat interrelated reasons for this: First, as shown above,
various definitions for NOP(L)AT and FCF(F) appear in academic literature. For example,
Kaplan’s (2014a) and Pignataro’s (2013) definitions of free cash flow, which we provided in
Section 4.4, are quite different from Koller, Goedhart and Wessels’ (2010) definition of free cash
flow from Section 8.1. Second, and probably more important, the degree of complexity used in
implementing the DCF models in practice varies considerably. Clearly, Koller, Goedhart and
Wessels’ (2010) business valuation framework is most detailed compared to the other approaches
included in the above discussion. However, the hard part is to establish proper inputs for this
valuation framework, because eventually any intrinsic value estimate is no better than the
assumptions used to derive it (Bodie, Kane and Marcus (2014). Additionally, Koller, Goedhart
and Wessels (2010) note that due to the complexity of their valuation framework, one should
make sure not to double-count items. For example, if income from equity investments was
already included in EBITA for some reason, equity investments should not be counted as a nonoperating asset when converting the value of operations to enterprise value, even though typically
they are treated as such.
In our view, this shows that if one does not fully understand the various implications of a more
complex model, more detail will do more harm than good. For this reason, and for the sake of
simplicity, our DCF approach will be most similar to the methodology used by Pignataro (2013),
78
as well as Jefferies Equity Research (2014), and Deutsche Bank Markets Research (2011). After
all, as laid out introductory to this paper, our aim is to provide a valuation approach that is as
close as possible to the approach that analysts actually use in equity research.
8.3.
Unlevered Free Cash Flow - Operating Assumptions
Following the previous theoretical discussions, we can now apply a DCF model to estimate a fair
value range for Amazon, which allows us to further refine our previous valuation results. In doing
so, the first step is to establish the operating assumptions necessary to calculate Amazon’s
unlevered free cash flows for ten years ahead. In particular, as can be seen from the top of Table
10, we have to establish estimates for revenue growth, GAAP operating income, depreciation and
amortization, amortization of intangibles, stock-based compensation, the increase in working
capital, and capital expenditures. In doing so, we follow the same approach that we used in
Section 3 already, in that we estimate a forecast ratio for each line item, which we then multiply
by the underlying economic driver to arrive at a forecast value for the individual line item. For
simplicity and consistency, we decided to use revenues as an economic driver for all line items,
which means that we have to establish revenue based forecast ratios for GAAP operating income,
D&A, amortization of intangibles, stock-based compensation, the increase in working capital,
and CapEx. This fits with our approach in Section 3, with the exception of amortization of
intangibles and the increase in working capital. To briefly recap, we previously estimated
amortization of intangibles based on Amazon’s reported estimates of intangible asset
amortization. Also, we estimated each working capital account separately, based on either
revenues or cost of sales, instead of making assumptions about year-over-year changes on an
aggregated level.
Since we already estimated Amazon’s quarterly financial statements until 2017 in Section 3, we
can simply link the operating assumptions for the first three years from there. Nevertheless, to
calculate unlevered free cash flows for ten years ahead, we need to expand these until 2024.
Starting with revenues, as can be seen from Table 10, we expect growth to gradually slow down
from 17% in 2018 to 11% in 2024, due to market saturation in North America, as well as
diminishing contributions from International operations. However, as noted in Section 3.2.1,
these estimates might be too conservative, given unpredictable events like new product
79
introductions, technological developments, changes in customer preferences, or different
corporate strategies, for example. That being said, it is generally safest to assume declining
revenue growth rates as a company grows over time, since smaller and younger companies
typically exhibit higher revenue growth than larger and more mature companies. With regard to
operating income, we forecast Amazon’s operating margins to further increase from 1.3% in
2018 to 1.5% in 2024, based on Amazon’s diversification efforts away from online-retailing to
higher margin businesses, like AWS, for example. Additionally, we expect Amazon’s growing
market dominance and operating efficiencies to positively affect operating margins.
Next, as already mentioned in Sections 3.2.2 and 3.2.5, it makes sense to analyze and forecast
D&A in conjunction with CapEx, which together determine our estimate of Amazon’s net PP&E.
In line with our previous reasoning, we expect D&A and CapEx to remain at 5.5% and 6% to
reflect further business expansion and revenue growth. From 2020 onwards, however, we assume
D&A to equal CapEx, which means we imply that productive assets are replaced at the same rate
as they wear out. Besides D&A, the other two non-cash expenses that we need to estimate are
amortization of intangibles and stock-based compensation. Amortization of intangibles depends
mostly on Amazon’s future acquisition activities and its assumptions used to value acquired
intangibles, like useful lives, for example. Since both variables are hard to predict, we simply
keep intangible asset amortization at 0.1% of revenues going forward. On the other hand, we
expect stock-based compensation to steadily increase from 1.8% in 2018 to 2% in 2024, which
reflects our expectation of a growing number of employees, an increase in the number of stock
awards granted, and a rising stock price. The last line item to forecast is the increase in working
capital as a percentage of revenue. Again, it should be noted that we don’t estimate the actual
amount of working capital, but the year-over-year change of this amount as a percentage of
revenue. Doing so, we expect the historical trend in working capital changes to continue going
forward, which means that we expect Amazon to generate positive cash flows by further
improving its working capital management.
8.4.
Unlevered Free Cash Flow - Calculation Methodology
After establishing all necessary operating assumptions, we can now calculate Amazon’s
unlevered free cash flows (UFCF) or free cash flows to the firm (FCFF). As mentioned above, to
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arrive at unlevered free cash flow, Pignataro (2013) starts with EBIT x (1 – tax rate) and then
adjusts for non-cash charges, changes in working capital and capital expenditures. To stay
consistent with this methodology, instead of using EBIT, we start our analysis with GAAP
operating income x (1 – tax rate). The reason for this is that we already defined EBIT in Section
3.4 as GAAP operating income adjusted for stock-based compensation. Nonetheless, to properly
calculate NOPAT, stock-based compensation expense has to be included in operating income.
Next, we add back the major non-cash charges, which for Amazon include depreciation and
amortization, amortization of intangibles, and stock-based compensation. To avoid any
confusion, as discussed more detailed in Section 3.2.2, amortization of intangibles is in fact
linked to the line item other operating expense (income), since it accounts for the largest portion
of it. Finally, changes in working capital and capital expenditures are subtracted to arrive at
unlevered free cash flow.
As Table 10 shows, based on our assumptions, Amazon’s unlevered free cash flow grows
steadily from $2,670 million in 2015 to $21,413 million in 2024. More precisely, year-over-year
free cash flow growth rates vary between 12% and 19% with the exception of 2016, 2018, and
2020. In these years we decided to increase our assumptions for Amazon’s operating margins and
its working capital management, which, not surprisingly, has a significant effect on cash flow.
8.5.
Continuing Value and Present Value Calculations
To calculate continuing value, Pignataro (2013), Jefferies Equity Research (2014), and Deutsche
Bank Markets Research (2011) all use the multiple method based on EBITDA and the constant
growth method based on unlevered free cash flow. The multiple method simply applies an
estimate of EV/EBITDA to a company’s final year EBITDA, which is in line with Chen and
Jassim’s (2014) formula shown in Section 8.1. The hard part, however, is to actually choose an
appropriate multiple. For example, we could derive a multiple from comparable companies,
simply take the company’s current EV/EBITDA multiple, or take an average of the company’s
historic EV/EBITDA multiple over the past five or ten years. Thereby, an average or median
multiple is often used if the focus company is currently believed to be overvalued by the market
(Pignataro, 2013). In any case, the multiple has to be in line with the estimated earnings growth
and required rate of return of the focus company. For example, if we took the historical 10-year
81
average EV/EBITDA multiple, we implicitly assume that future earnings growth and the required
rate of return will, on average, be similar to the previous 10 years (Kaplan, 2014a).
Looking at the numbers, as can be seen from Table 10, we estimate Amazon’s terminal
EV/EBITDA multiple to be 11x, which together with Amazon’s 2024 EBITDA of $33,239
million results in a terminal value of $365,628 million. To put this into context, as Table 5 shows,
Amazon’s current EV/EBITDA multiple is 21x, whereas comparable companies’ EV/EBITDA
multiples range from 8.5x to 11.9x, as shown in Table 6. However, as discussed more detailed in
Section 5.5, we believe that Amazon’s current EV/EBITDA multiple is not representative, since
it suggests that Amazon would be significantly overvalued at its current share price of $310.35.
Therefore, we decided to derive an estimate from our set of comparable companies but instead of
simply using the median EV/EBITDA of 9.2x we chose 11x, assuming that Amazon will still be
among the top-performers of its peer group 10 years from now.
To complement our above terminal value estimate, we can now use the constant growth method
based on free cash flow. As discussed in Section 8.2, we decided to follow the approach
suggested by Pignataro (2013) and therefore we will use the following formula:
CV10 =
UFCF10 × (1 + g)
WACC − g
with g = perpetual rate of cash flow growth, UFCF10 = final year unlevered free cash flow, and
WACC = weighted average cost of capital.
However, similar to the above multiple method, the hard part is to establish proper input values,
especially with regard to the perpetual rate of cash flow growth. While it is often suggested to use
the current GDP growth rate or the rate of inflation, one has to keep in mind that we try to
estimate a perpetual rate of cash flow growth, even though the current economic environment
could be sluggish (Pignataro, 2013). Hence, Bodie, Kane and Marcus (2014) suggest using the
long-run GDP growth rate, because even if a firm is currently in a growth industry, it can’t grow
forever at a rate higher than the aggregate economy. To further improve the long-term growth
estimate, it is often beneficial to think of scenarios where the future will be different from the
82
past, for example, due to changes in the economic and competitive environment, new government
regulations, or technological advancement (Kaplan, 2014a).
As Table 10 shows, using the constant growth method based on free cash flow, we estimate
Amazon’s terminal value to equal $316,605 million. The input parameters to arrive at this
number are Amazon’s 2024 unlevered free cash flow of $21,413, Amazon’s WACC of 10.5%,
and our terminal growth assumption of 3.5%. As a side note, even though we estimated
Amazon’s WACC to be 10.6% in Section 7, we decided to round this number to the nearest 0.5%
to stay consistent with the intervals of our sensitivity analysis, which we discuss in the following
section. Regarding our terminal growth assumption of 3.5%, we clearly lack an equity analyst’s
insights into Amazon’s businesses, its industries, the overall economic environment, and other
factors that might drive future cash flow growth. Therefore we simply followed the approach
suggested by Bodie, Kane and Marcus (2014), and based our estimate on the average annual U.S.
GDP growth rate of 3.22% from 1948 to 2015 (Trading Economics, 2015). Taking into account
structural factors like growing internet usage and e-commerce sales, as well as further
technological advancements and developments in emerging markets, we decided that a slightly
higher rate of 3.5% would be a reasonable long-term growth estimate.
After determining Amazon’s continuing values, we can finally discount the unlevered free cash
flows during the explicit forecast period along with the continuing values at our WACC estimate
of 10.5% to arrive at Amazon’s enterprise value. But before doing so we have to decide what
period we want to use for discounting our cash flows. More specifically, according to Pignataro
(2013), there are two methods to determine the discount period: The end-of-year convention,
which discounts each cash flow by a full year and hence assumes that the entire cash flow is
received at the end of the year, and the mid-year convention, which discounts each cash flow by
half a year and hence assumes that the cash flow is realized evenly throughout the year. To
illustrate the difference between both methods, Table 10 shows, that for the year 2015, for
example, the normal discount factor is 1, while the mid-year discount factor is 0.5. Since it is
more realistic to assume that cash flows are received evenly throughout the year, we decided to
use the mid-year convention to discount Amazon’s unlevered free cash flows during the explicit
forecast period and also the continuing value based on the constant growth method. For
continuing value based on the multiple method, however, it makes more sense to use the end-of83
year convention, since, by applying an EV/EBITDA multiple, we essentially assume to sell the
company at the end of the terminal year.
As can be seen from Table 10 again, discounting and summing up Amazon’s unlevered free cash
flows during the explicit forecast period from 2015 to 2024 results in a present value of $61,307
million. Similarly, discounting Amazon’s continuing values of $365,628 million and $316,605
million for the multiples method and the constant growth method, respectively, results in present
values of $134,715 million and $122,625 million. Finally, adding up the present values of the
explicit forecast period and the continuing values results in our enterprise value estimates of
$196,022 million and $183,931 million. Again, it should be noted that due to the two different
methods used to calculate continuing value, we also receive two different enterprise value
estimates for Amazon. Finally, when taking a closer look at the above numbers, one can easily
see why intrinsic value estimates depend so critically on continuing values, since, in case of
Amazon, they account for 68.7% and 66.7% of enterprise value for the multiples method and the
constant growth method, respectively. Nonetheless, such results are not out of line, as it is totally
normal that continuing values account for more than 50% of a company’s enterprise value.
8.6.
Implied Price per Share and Sensitivity Analysis
In the final step of our DCF model, enterprise value has to be adjusted for net debt and other nonequity claims to arrive at equity value. Therefore, we will simply follow the methodology
explained in Section 4.2 in reverse order. Starting with enterprise value, we add cash and shortterm investments and subtract short-term and long-term debt. These are the only balance sheet
adjustments, since Amazon has neither preferred stock outstanding nor any minority interests.
Adjusting for a total of $9,151 million therefore results in implied equity values of $205,173
million and $193,082 million, which divided by the average diluted shares outstanding result in
implied per share prices of $434.69 using the multiple method, and $409.07 using the constant
growth method.
However, as mentioned several times before, these are only point estimates that are highly
sensitive to input parameters, whereas we aim to establish a value range to draw conclusions on a
fair value for Amazon. Therefore, we decided to create sensitivity tables, which transform our
84
point estimates into intrinsic value ranges, given changes in specific input parameters. More
generally, and from a theoretical perspective, sensitivity analyses show how sensitive a valuation
output is to changes in input variables. Thereby, it is beneficial to first identify the parameters
that need most consideration, since some will have more impact on the valuation results than
others. For example, any forecast of future free cash flow growth depends strongly on a firm’s
future revenue growth and anticipated changes in profit margin. In turn, these variables depend
on overall industry profitability and life cycle, as well as the firm’s competitive strategy (Kaplan,
2014a). Nonetheless, as shown in the previous section, intrinsic value estimates depend most
critically on continuing values, which in turn are highly sensitive to even small changes in their
input parameters: WACC, long-term cash flow growth, and choice of multiple (Bodie, Kane and
Marcus, 2014). For this reason, among the many value drivers in our DCF model, we chose to
analyze the impact of exactly these three variables in our sensitivity analyses.
As can be seen from the bottom of Table 10, to do so we created two sensitivity tables, where we
vary WACC on the horizontal axes of both tables, and the terminal growth rate and the terminal
EBITDA multiple on the respective vertical axes. Taking a closer look on the combination of
WACC and the terminal growth rate first, one can see that varying WACC between 7.5% and
13.5% along with the terminal growth between 0.5% and 5.5% results in intrinsic value estimates
ranging from as low as $236.01 to as high as $1,376.26. More precisely, the former estimate
results from a WACC of 13.5% combined with a terminal growth rate of 0.5%, whereas the latter
estimate results from a WACC of 7.5% combined with a terminal growth rate of 5.5%. These
results are not a surprise, however, since a higher discount rate combined with a lower terminal
growth rate should lead to a lower intrinsic value estimate, et vice versa. Similarly, varying
WACC between 7.5% and 13.5% along with the terminal EBITDA multiple between 8x and 13x
results in intrinsic value estimates ranging from as low as $289.49 to as high as $616.59. While
the former estimate results from a WACC of 13.5% combined with a terminal EBITDA multiple
of 8x, the latter estimate results from a WACC of 7.5% combined with a terminal EBITDA
multiple of 13x. Again, this is not a surprise, since a higher discount rate combined with a lower
terminal EBITDA multiple should lead to a lower intrinsic value estimate, et vice versa.
Taking Amazon’s current stock price of $310.35 into consideration puts the company in the
upper right quadrant of the WACC/terminal EBITDA table and also to the right side of the
85
WACC/terminal growth table, which indicates that at this price the company might be
undervalued. However, such a judgment presupposes that we believe in our model and, more
importantly, in the inputs to our model. For example, simply increasing WACC from 10.5% to
12.5%, while keeping the other parameters constant, would indicate that, by our WACC/terminal
growth table, Amazon would be fairly valued at its current price, whereas, by our
WACC/terminal EBITDA table, it would still be undervalued. This shows that our intrinsic value
estimate based on the constant growth method is more sensitive to changes in WACC than our
intrinsic value estimate based on the multiple method. Either way, to be confident in our model
we should think about the probability of such an increase and reevaluate the drivers of our
WACC calculation. But since we already based our WACC calculation on a relatively
conservative beta estimate and the assumption of increasing interest rates, we can be confident
our model. Similarly, our terminal EBITDA multiple of 11x could simply be too aggressive,
knowing that our range of comparables was between 8.5x and 11.9x, and given that our per share
estimate using the constant growth method is about $25 lower. In this regard, it is interesting to
note that changing our terminal EBITDA multiple, has no effect on our results based on the
constant growth method. Generally speaking, if the valuation outputs of both methods differ too
much this could be a sign of overvaluation, since the cash flows don’t support the market
estimates unless the company reports a significant increase in revenues or earnings. However,
compared to Amazon’s current EBITDA multiple of 21x, we believe that our estimate of 11x is
relatively conservative, even though it is at the upper end of our range of comparables. To
conclude this analysis, after reevaluating our model inputs, with intrinsic value estimates of
$434.69 per share using the multiple method, and $409.07 per share using the constant growth
method, both of our calculation methods suggest that at its current share price of $310.35,
Amazon might be undervalued.
Lastly, as with the previous two valuation approaches, all of the points just discussed can also be
seen from Table 11, where we summarized our valuation outputs in a so-called football field
chart. To briefly recap, from our comparable company and precedent transactions analyses, we
concluded that Amazon might be undervalued with a more appropriate stock price somewhere
between $330 and $380. Including our discounted cash flow analysis based on both the multiples
method and the constant growth method strongly supports this conclusion with a median stock
price estimate of more than $400. Hence, all three valuation methods, which are driven by either
86
market data, historical data, or our own assumptions, suggest a strong potential for Amazon’s
stock to rise above its current price of $310.35. More specifically, given the company’s
performance in the next quarters and absent any negative events, we estimate a target range of
$360 to $400 with the midpoint of $380 being our price target for Amazon. In other words, from
an equity analyst’s perspective on December 31, 2014, we recommend buying Amazon at its
current market price of $310.35.
9.
Conclusion
Since each of the three valuation methods covered in this report suggested a somewhat higher
share price for Amazon, we finally concluded that the stock was undervalued. As mentioned
introductory to Section 5.1, from a financial perspective this could be justified, since each method
approached Amazon’s value from a different perspective, and yet all three methods resulted in a
similar valuation range. Nonetheless, it could also indicate that our model inputs were simply
wrong or unrealistic. For example, as discussed in Section 6, precedent transactions analyses
frequently result in the highest valuations due to the control premium paid by the acquirer. On top
of that, our set of comparable transactions might simply not be representative for Amazon.
Similarly, comparable company analyses are often thought to better reflect market conditions and
be less prone to analyst biases. However, as explained in Section 5, multiplier models use just as
many assumptions as discounted cash flow models, except that they are implicit and unstated.
Hence, no matter which valuation method is used, firm value is essentially a function of the same
three variables: Risk, growth, and capacity to generate cash flows. In turn, this implies that both
methods should result in similar valuations for Amazon. Nevertheless, in practice analysts must
make forecasts and assumptions, which can cause intrinsic values estimates to differ. For
example, as with precedent transactions, our set of comparable companies might simply not be
representative for Amazon. Also, our judgment of Amazon’s relative value with respect to the
above three drivers of firm value could be too aggressive.
87
Additionally, the simple fact that different models use different inputs sometimes explains
valuation differences. For example, as we saw in Section 8, the two methods used to calculate
continuing value in our discounted cash flow model relied on different inputs, and in fact resulted
in different intrinsic value estimates. This shows, that in practice it is not always easy to maintain
consistent assumptions across models, or, as Bodie, Kane and Marcus (2014, p. 621) note, it
shows “that DCF valuation estimates are almost always going to be imprecise”.
To conclude, this final discussion shows that while the models used in this paper are relatively
easy to apply, the hard part is to establish proper inputs, because eventually any intrinsic value
estimate is no better than the assumptions used to derive it. Again, as Bodie, Kane and Marcus
(2014, p. 621) state, “this should not be surprising. In even a moderately efficient market, finding
profit opportunities will be more involved than analyzing Value Line data for a few hours. These
models are extremely useful to analysts, however, because they provide ballpark estimates of
intrinsic value. More than that, they force rigorous thought about underlying assumptions and
highlight the variables with the greatest impact on value and the greatest payoff to further
analysis”.
88
10. References
Amazon (2015) Amazon Form 10-K for the fiscal year ended December 31, 2014 [Internet],
Seattle, Amazon.com Inc. Available from: <http://phx.corporateir.net/phoenix.zhtml?c=97664&p=irol-sec&control_selectgroup=Annual%20Filings> [Accessed
6th October 2015].
Bodie, Z., Kane, A. and Marcus, A. (2014) Investments. 10th ed. New York. McGraw-Hill.
Bonds Online (2015) Municipal Bonds Yields, Corporate Bonds Yields, Treasury Bond
Yields, and CDs [Internet], Wilson, Bonds Online Group. Available from:
<http://www.bondsonline.com/Todays_Market/Composite_Bond_Yields.php> [Accessed 6th
October 2015].
Chen, K.C. and Jassim, A. (2014) Pedagogical-cum-Analytical Tool for Teaching Business
Valuation. Journal of Financial Management and Analysis, 26 (2).
Damodaran, A. (2012) Investment Valuation – Tools and Techniques for Determining the
Value of Any Asset. 3rd ed. Hoboken. John Wiley & Sons.
Damodaran, A. (2015a) Damodaran Online [Internet], New York, Ney York University.
Available from: <http://pages.stern.nyu.edu/~adamodar/> [Accessed 6th October 2015].
Damodaran, A. (2015b) Betas by Sectors (US) [Internet], New York, Ney York University.
Available from: <http://people.stern.nyu.edu/adamodar/New_Home_Page/datafile/Betas.html>
[Accessed 6th October 2015].
Deutsche Bank Markets Research (2011) Deutsche Bank IBM Inc. Valuation Spreadsheet,
Frankfurt, Deutsche Bank AG.
Deutsche Bank Markets Research (2014) Amazon.com: “Investment Cycle” Or “Brave New
World” – We Think A Bit of Both (July 25, 2014) [Internet], Frankfurt, Deutsche Bank AG.
Available from: <https://www.thomsononeim.com/v-hom.asp> [Accessed September 2015].
89
Ernst & Young (2014) Global Technology M&A Update, April – June 2014 [Internet],
London, Ernst & Young LLP. Available from:
<http://www.ey.com/Publication/vwLUAssets/EY-global-technology-m-and-aupdate/$FILE/EY-global-technology-m-and-a-update.pdf> [Accessed 6th October 2015].
Federal Reserve (2015) Selected Interest Rates – Historical Data [Internet], Washington,
Federal Reserve. Available from: <http://www.federalreserve.gov/releases/h15/data.htm>
[Accessed 6th October 2015].
Fernandez, P., Linares, P. and Fernandez Acin, I. (2014) Market Risk Premium Used in 82
Countries in 2014: A Survey with 8,228 Answers. Working paper. IESE Business School.
Fiore, A. (2013) CFA Institute Industry Guides – The Machinery Industry. Charlottesville.
CFA Institute.
Google Finance (2015) Google Finance: Stock Market Quotes, News, and More [Internet],
Mountain View, Google Inc. Available from: <https://www.google.com/finance> [Accessed 6th
October 2015].
Hitt, A. Ireland, R. and Hoskisson, R. (2014) Strategic Management: Competitiveness and
Globalization. 11th ed. Stamford. Cengage Learning.
Jefferies Equity Research (2014) Amazon.com: AMZN Keeps Investing for Growth, Further
Delaying Profitability (July 25, 2014) [Internet], New York, Jefferies Group LLC. Available
from: <https://www.thomsononeim.com/v-hom.asp> [Accessed September 2015].
Kaplan (2013a) SchweserNotes 2013 CFA Level I Book 3: Financial Reporting and Analysis.
New York, Kaplan.
Kaplan (2013b) SchweserNotes 2013 CFA Level I Book 4: Corporate Finance, Portfolio
Management, and Equity Investments. New York, Kaplan.
Kaplan (2014a) SchweserNotes 2014 CFA Level II Book 3: Equity Investments. New York,
Kaplan.
Kaplan (2014b) SchweserNotes 2014 CFA Level II Book 2: Financial Reporting and
Analysis and Corporate Finance. New York, Kaplan.
90
Kaplan (2014c) SchweserNotes 2014 CFA Level II Book 1: Ethical and Professional
Standards, Quantitative Methods, and Economics. New York, Kaplan.
Kaplan (2014d) SchweserNotes 2014 CFA Level II Book 5: Derivatives and Portfolio
Management. New York, Kaplan.
Koller, T., Goedhart, M. and Wessels, D. (2010) Valuation: Measuring and Managing the
Value of Companies. 5th ed. Hoboken. John Wiley & Sons.
MarketLine (2014) Company Profile – Amazon.com, Inc. [Internet], London, Progressive
Digital Media Ltd. Available from:
<http://web.b.ebscohost.com/ehost/search/advanced?sid=d5d36a43-a7bd-4c29-b7416306af77366c%40sessionmgr115&vid=0&hid=110> [Accessed 6th October 2015].
MarketLine (2015) Industry Profile – Global Online Retail [Internet], London, Progressive
Digital Media Ltd. Available from:
<http://web.b.ebscohost.com/ehost/search/advanced?sid=d5d36a43-a7bd-4c29-b7416306af77366c%40sessionmgr115&vid=0&hid=110> [Accessed 6th October 2015].
MarketWatch (2015) Market Watch – Stock Market Quotes, Business News, Financial News
[Internet], Sunnyvale, Yahoo! Inc. Available from: <http://www.marketwatch.com/> [Accessed
6th October 2015].
Moody’s Analytics (2015) Amazon.com Inc. Credit Rating [Internet], New York, Moody’s
Investors Service Inc. Available from: <https://www.moodys.com/credit-ratings/AmazoncomInc-credit-rating-600042665?lang=de&cy=ger> [Accessed 6th October 2015].
Morningstar (2015a) Morningstar – Independent Investment Research [Internet], Chicago,
Morningstar Inc. Available from: <http://www.morningstar.com/> [Accessed 6th October 2015].
Morningstar (2015b) Amazon.com Inc – Stock Analysis [Internet], Chicago, Morningstar Inc.
Available from: <http://library.morningstar.com/> [Accessed 6th October 2015].
Peakstone Group (2015) E-Commerce M&A Update September 2015 [Internet], Chicago,
Peakstone Securities LLC. Available from: <http://www.peakstonegroup.com/wp-
91
content/uploads/2015/09/Peakstone-E-Commerce-Insights-September-2015.pdf> [Accessed 6th
October 2015].
Pignataro, P. (2013) Financial Modeling & Valuation – A Practical Guide to Investment
Banking and Private Equity. 1st ed. Hoboken. John Wiley & Sons.
Pricewaterhouse Coopers (2014) US Technology Deal Insights - Analysis and trends in US
Technology 2015 [Internet], New York, PricewaterhouseCoopers LLP. Available from:
<https://www.pwc.com/us/en/transaction-services/publications/assets/pwc-us-technology-dealinsights-2014.pdf> [Accessed 6th October 2015].
Reuters (2015) Reuters.com – Business and Financial News [Internet], London, Reuters Ltd.
Available from: <http://www.reuters.com/> [Accessed 6th October 2015].
S&P Capital IQ (2015) Amazon Inc Company Profile [Internet], New York, Standard & Poor's
Financial Services LLC. Available from:
<https://www.netadvantage.standardandpoors.com/login/NASApp/NetAdvantage/NET/login/logi
n.jsp> [Accessed 6th October 2015].
S&P Global Credit Portal (2015) Amazon.com Inc. [Internet], New York, Standard & Poor’s
Financial Services LLC. Available from:
<https://www.globalcreditportal.com/ratingsdirect/entityPage.do?entityId=540732> [Accessed
6th October 2015].
Trading Economics (2015) United States GDP Annual Growth Rate. [Internet], New York,
Trading Economics. Available from: <http://www.tradingeconomics.com/united-states/gdpgrowth-annual> [Accessed 6th October 2015].
UBS Global Research (2014) Amazon.com: Trading Profits for Revenues (Part 2) (July 24,
2015) [Internet], Zurich, UBS AG. Available from: <https://www.thomsononeim.com/vhom.asp> [Accessed September 2015].
Value Line (2015) Amazon Inc [Internet], New York, Value Line Inc. Available from:
<http://www.valueline.com/> [Accessed 6th October 2015].
92
Wall Street Journal (2014) Wal-Mart Notches Web Win against Rival Amazon [Internet],
New York, Dow Jones & Company, Inc. Available from:
<http://www.wsj.com/articles/SB10001424052702303417104579544080308837964> [Accessed
6th October 2015].
Wall Street Journal (2015) The Wall Street Journal: Business, Financial, and Economic News
[Internet], New York, Dow Jones & Company, Inc. Available from: <http://www.wsj.com/>
[Accessed 6th October 2015].
Weston, J. (2002) The Exxon-Mobil Merger: An Archetype. Journal of Applied Finance, 7
(26).
Woodside Capital Partners (2015) Global Technology M&A Trends and Analysis – 1H 2015
[Internet], London, Woodside Capital Partners Inc. Available from:
<www.woodsidecap.com/wp-content/uploads/2015/07/WCP-Global-Technology-MA-ReportFinal.pdf> [Accessed 6th October 2015].
Yahoo Finance (2015) Yahoo Finance – Business Finance, Stock Market, Quotes, News
[Internet], Sunnyvale, Yahoo! Inc. Available from: <http://finance.yahoo.com/> [Accessed 6th
October 2015].
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11. Appendices
11.1 Tables and Charts
11.2 Financial Model Quick Steps
11.3 Amazon’s Strategic Analysis
94
11.1. Tables and Charts
Table 1 – Segment Estimates* 11
Table 2 – Income Statement Estimates*
Table 3 – Balance Sheet Estimates*
Table 4 – Cash Flow Statement Estimates*
Table 5 – Enterprise Value & Overview
Table 6 – Public Company Comparables
Table 7 – Precedent Transactions
Table 8 – Weighted Average Cost of Capital
Table 9 – Beta Estimation
Table 10 – Discounted Cash Flows
Table 11 – Valuation Summary
*
Author’s note: For illustrative purposes, Tables 1 to 4 have been narrowed in that the historical values from 2010
to 2014 are not included. It is therefore highly recommended to work with the provided MS Excel spreadsheet.
95
Table 1 – Segment Estimates
Segment Estimates
($ in Millions, Except Per Share Amounts in Dollars and Share Counts in Thousands)
1Q
2Q
3Q
4Q
2015
1Q
2Q
3Q
4Q
2016
1Q
2Q
3Q
4Q
2017
CONSOLIDATED
Total consolidated net sales
% yoy growth
22,584
14.4%
22,224
14.9%
23,705
15.2%
33,835 102,348
15.4%
15.0%
26,376
16.8%
25,998
17.0%
27,735
17.0%
39,577 119,687
17.0%
16.9%
30,845
16.9%
30,419
17.0%
32,450
17.0%
46,263 139,977
16.9%
17.0%
Total operating expenses
% yoy growth
22,087
14.8%
21,719
14.7%
23,162
11.8%
33,042 100,010
16.8%
14.7%
25,658
16.2%
25,273
16.4%
26,957
16.4%
38,450 116,339
16.4%
16.3%
29,979
16.8%
29,546
16.9%
31,513
16.9%
44,910 135,947
16.8%
16.9%
497
-0.9%
2.2%
506
25.1%
2.3%
543
n/a
2.3%
792
-23.7%
2.3%
2,338
29.3%
2.3%
718
44.4%
2.7%
725
43.4%
2.8%
778
43.2%
2.8%
1,128
42.3%
2.8%
3,348
43.2%
2.8%
867
20.7%
2.8%
873
20.4%
2.9%
936
20.4%
2.9%
1,354
20.0%
2.9%
4,030
20.4%
2.9%
NORTH AMERICA
Media
% yoy growth
% of segment revenue
Electronics and other general merchandise
% yoy growth
% of segment revenue
Other
% yoy growth
% of segment revenue
Total North America net sales
% yoy growth
% of total revenue
2,825
0.0%
20.4%
9,317
19.0%
67.4%
1,674
39.0%
12.1%
13,815
16.5%
61.2%
2,464
0.0%
17.5%
9,956
19.0%
70.9%
1,624
39.0%
11.6%
14,043
17.0%
63.2%
2,761
1.0%
18.3%
10,464
19.0%
69.4%
1,863
39.0%
12.3%
15,088
17.3%
63.6%
3,579
1.0%
16.3%
16,100
19.0%
73.2%
2,327
39.0%
10.6%
22,006
17.4%
65.0%
11,630
0.5%
17.9%
45,835
19.0%
70.6%
7,487
39.0%
11.5%
64,952
17.1%
63.5%
3,051
8.0%
18.9%
10,900
17.0%
67.5%
2,209
32.0%
13.7%
16,160
17.0%
61.3%
2,661
8.0%
16.2%
11,648
17.0%
70.8%
2,143
32.0%
13.0%
16,452
17.2%
63.3%
2,982
8.0%
16.9%
12,242
17.0%
69.2%
2,459
32.0%
13.9%
17,683
17.2%
63.8%
3,866
8.0%
15.0%
18,836
17.0%
73.1%
3,071
32.0%
11.9%
25,774
17.1%
65.1%
12,560
8.0%
16.5%
53,627
17.0%
70.5%
9,882
32.0%
13.0%
76,070
17.1%
63.6%
3,265
7.0%
17.5%
12,535
15.0%
67.4%
2,806
27.0%
15.1%
18,605
15.1%
60.3%
2,847
7.0%
15.0%
13,395
15.0%
70.6%
2,722
27.0%
14.4%
18,964
15.3%
62.3%
3,191
7.0%
15.6%
14,079
15.0%
69.0%
3,122
27.0%
15.3%
20,392
15.3%
62.8%
4,136
7.0%
13.9%
21,662
15.0%
72.9%
3,901
27.0%
13.1%
29,699
15.2%
64.2%
13,439
7.0%
15.3%
61,671
15.0%
70.4%
12,550
27.0%
14.3%
87,661
15.2%
62.6%
Segment operating expenses
% yoy growth
% of segment revenue
13,318
17.9%
96.4%
13,538
17.1%
96.4%
14,544
13.8%
96.4%
21,214
19.7%
96.4%
62,613
17.3%
96.4%
15,514
16.5%
96.0%
15,794
16.7%
96.0%
16,976
16.7%
96.0%
24,743
16.6%
96.0%
73,027
16.6%
96.0%
17,861
15.1%
96.0%
18,206
15.3%
96.0%
19,577
15.3%
96.0%
28,511
15.2%
96.0%
84,155
15.2%
96.0%
Segment operating income
% yoy growth
% of segment revenue
497
-11.5%
3.6%
506
15.4%
3.6%
543
517.2%
3.6%
792
-22.2%
3.6%
2,338
11.0%
3.6%
646
30.0%
4.0%
658
30.2%
4.0%
707
30.2%
4.0%
1,031
30.1%
4.0%
3,043
30.1%
4.0%
744
15.1%
4.0%
759
15.3%
4.0%
816
15.3%
4.0%
1,188
15.2%
4.0%
3,506
15.2%
4.0%
2,642
0.0%
30.1%
6,070
17.0%
69.2%
57
8.0%
0.7%
8,769
11.2%
38.8%
2,380
0.0%
29.1%
5,747
17.0%
70.2%
54
8.0%
0.7%
8,181
11.4%
36.8%
2,535
1.0%
29.4%
6,037
17.0%
70.1%
45
8.0%
0.5%
8617.66
11.7%
36.4%
3,440
1.0%
29.1%
8,318
17.0%
70.3%
71
8.0%
0.6%
11828.9
11.8%
35.0%
10,997
0.5%
29.4%
26,172
17.0%
70.0%
228
8.0%
0.6%
37,397
11.6%
36.5%
2,748
4.0%
26.9%
7,405
22.0%
72.5%
63
10.0%
0.6%
10216
16.5%
38.7%
2,475
4.0%
25.9%
7,011
22.0%
73.4%
59
10.0%
0.6%
9545.99
16.7%
36.7%
2,637
4.0%
26.2%
7,365
22.0%
73.3%
50
10.0%
0.5%
10051.8
16.6%
36.2%
3,578
4.0%
25.9%
10,147
22.0%
73.5%
78
10.0%
0.6%
13803.5
16.7%
34.9%
11,437
4.0%
26.2%
31,930
22.0%
73.2%
251
10.0%
0.6%
43,617
16.6%
36.4%
2,913
6.0%
23.8%
9,257
25.0%
75.6%
71
12.0%
0.6%
12239.7
19.8%
39.7%
2,624
6.0%
22.9%
8,764
25.0%
76.5%
67
12.0%
0.6%
11454.5
20.0%
37.7%
2,795
6.0%
23.2%
9,207
25.0%
76.4%
56
12.0%
0.5%
12057.3
20.0%
37.2%
3,792
6.0%
22.9%
12,684
25.0%
76.6%
88
12.0%
0.5%
16564.4
20.0%
35.8%
12,123
6.0%
23.2%
39,912
25.0%
76.3%
281
12.0%
0.5%
52,316
19.9%
37.4%
8,769
10.4%
100.0%
8,181
10.9%
100.0%
8,618
8.6%
100.0%
11,829
12.0%
100.0%
37,397
10.6%
100.0%
10,144
15.7%
99.3%
9,479
15.9%
99.3%
9,981
15.8%
99.3%
13,707
15.9%
99.3%
43,312
15.8%
99.3%
12,117
19.4%
99.0%
11,340
19.6%
99.0%
11,937
19.6%
99.0%
16,399
19.6%
99.0%
51,793
19.6%
99.0%
0
-100.0%
0.0%
0
-100.0%
0.0%
0
-100.0%
0.0%
0
-100.0%
0.0%
0
-100.0%
0.0%
72
n/a
0.7%
67
n/a
0.7%
70
n/a
0.7%
97
n/a
0.7%
305
n/a
0.7%
122
71.2%
1.0%
115
71.4%
1.0%
121
71.4%
1.0%
166
71.4%
1.0%
523
71.3%
1.0%
0
0.0%
0.0%
0
0.0%
0.0%
0
0.0%
0.0%
0
0.0%
0.0%
0
n/a
0.0%
0
0.0%
0.0%
0
0.0%
0.0%
0
0.0%
0.0%
0
0.0%
0.0%
0
n/a
0.0%
0
0.0%
0.0%
0
0.0%
0.0%
0
0.0%
0.0%
0
0.0%
0.0%
0
n/a
0.0%
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
n/a
0
n/a
0.0%
0
n/a
0.0%
0
n/a
0.0%
0
n/a
0.0%
0
n/a
n/a
0
n/a
0.0%
0
n/a
0.0%
0
n/a
0.0%
0
n/a
0.0%
0
n/a
n/a
0
n/a
0.0%
0
n/a
0.0%
0
n/a
0.0%
0
n/a
0.0%
0
n/a
n/a
61.2%
38.8%
n/a
100.0%
63.2%
36.8%
n/a
100.0%
63.6%
36.4%
n/a
100.0%
65.0%
35.0%
n/a
100.0%
63.5%
36.5%
n/a
100.0%
61.3%
38.7%
n/a
100.0%
63.3%
36.7%
n/a
100.0%
63.8%
36.2%
n/a
100.0%
65.1%
34.9%
n/a
100.0%
63.6%
36.4%
n/a
100.0%
60.3%
39.7%
n/a
100.0%
62.3%
37.7%
n/a
100.0%
62.8%
37.2%
n/a
100.0%
64.2%
35.8%
n/a
100.0%
62.6%
37.4%
n/a
100.0%
24.2%
68.1%
7.7%
100.0%
21.8%
70.7%
7.5%
100.0%
22.3%
69.6%
8.0%
100.0%
20.7%
72.2%
7.1%
100.0%
22.1%
70.4%
7.5%
100.0%
22.0%
69.4%
8.6%
100.0%
19.8%
71.8%
8.5%
100.0%
20.3%
70.7%
9.0%
100.0%
18.8%
73.2%
8.0%
100.0%
20.0%
71.5%
8.5%
100.0%
20.0%
70.6%
9.3%
100.0%
18.0%
72.8%
9.2%
100.0%
18.4%
71.8%
9.8%
100.0%
17.1%
74.2%
8.6%
100.0%
18.3%
72.6%
9.2%
100.0%
0.0%
18.2%
37.7%
14.4%
0.0%
18.3%
37.7%
14.9%
1.0%
18.3%
38.1%
15.2%
1.0%
18.3%
37.8%
15.4%
0.5%
18.3%
37.8%
15.0%
6.1%
19.0%
31.3%
16.8%
6.0%
18.8%
31.3%
17.0%
6.1%
18.8%
31.5%
17.0%
6.0%
18.7%
31.3%
17.0%
6.1%
18.8%
31.4%
16.9%
6.5%
19.0%
26.6%
16.9%
6.5%
18.8%
26.6%
17.0%
6.5%
18.8%
26.7%
17.0%
6.5%
18.5%
26.6%
16.9%
6.5%
18.7%
26.6%
17.0%
YOY SEGMENT OPERATING INCOME GROWTH (DECLINE)
North America
(11.5%)
15.4%
517.2% (22.2%)
11.0%
International
(100.0%) (100.0%) (100.0%) (100.0%) (100.0%)
AWS
n/a
n/a
n/a
n/a
n/a
Total operating income
(0.9%)
25.1%
n/a (23.7%)
29.3%
30.0%
n/a
n/a
44.4%
30.2%
n/a
n/a
43.4%
30.2%
n/a
n/a
43.2%
30.1%
n/a
n/a
42.3%
30.1%
n/a
n/a
43.2%
15.1%
71.2%
n/a
20.7%
15.3%
71.4%
n/a
20.4%
15.3%
71.4%
n/a
20.4%
15.2%
71.4%
n/a
20.0%
15.2%
71.3%
n/a
20.4%
Total operating income
% yoy growth
% of total revenue
INTERNATIONAL
Media
% yoy growth
% of segment revenue
Electronics and other general merchandise
% yoy growth
% of segment revenue
Other
% yoy growth
% of segment revenue
Total International net sales
% yoy growth
% of total revenue
Segment operating expenses
% yoy growth
% of segment revenue
Segment operating income (loss)
% yoy growth
% of segment revenue
AWS
Total AWS net sales
% yoy growth
% of total revenue
Segment operating expenses
% yoy growth
% of segment revenue
Segment operating income (loss)
% yoy growth
% of segment revenue
SUPPLEMENTAL NET SALES INFORMATION
NET SALES MIX
North America
International
AWS
TRADITIONAL NET SALES MIX
Media
Electronics and other general merchandise
Other
TRADITIONAL YOY NET SALES GROWTH (DECLINE)
Media
Electronics and other general merchandise
Other
Total Traditional net sales
96
Table 2 – Income Statement Estimates
Income Statement Estimates
($ in Millions, Except Per Share Amounts in Dollars and Share Counts in Thousands)
1Q
2Q
3Q
NET SALES
North America net sales
International net sales
AWS net sales
Total net sales
% yoy growth
Cost of Sales
% of revenue
Total gross profit
% of revenue
4Q
2015
1Q
2Q
3Q
22,006
64,952
11,829
37,397
0
0
33,835 102,348
15.4%
15.0%
23,684
71,644
70.0%
70.0%
10,150
30,704
30.0%
30.0%
16,160
10,216
0
26,376
16.8%
18,332
69.5%
8,045
30.5%
16,452
9,546
0
25,998
17.0%
18,069
69.5%
7,929
30.5%
17,683
10,052
0
27,735
17.0%
19,276
69.5%
8,459
30.5%
2,988
11.3%
1,126
4.3%
2,434
9.2%
779
3.0%
7,327
429
1.6%
46
0.2%
7,802
3,062
11.8%
1,225
4.7%
2,715
10.4%
202
0.8%
7,205
526
2.0%
46
0.2%
7,776
3,437
12.4%
1,295
4.7%
2,991
10.8%
(41)
-0.1%
7,682
508
1.8%
46
0.2%
8,236
4,490
11.3%
2,013
5.1%
3,251
8.2%
1,190
3.0%
10,943
551
1.4%
46
0.1%
11,540
718
725
44.4%
43.4%
2.7%
2.8%
243
153
205.0% 2565.5%
0.9%
0.6%
778
43.2%
2.8%
223
282.6%
0.8%
13,815
8,769
0
22,584
14.4%
15,809
70.0%
6,775
30.0%
14,043
8,181
0
22,224
14.9%
15,557
70.0%
6,667
30.0%
15,088
8,618
0
23,705
15.2%
16,594
70.0%
7,112
30.0%
OPERATING EXPENSES
Fulfillment
% of revenue
Marketing
% of revenue
Technology and content
% of revenue
General and administrative
% of revenue
Pro-forma operating expenses
Stock-based compensation
% of revenue
Other operating expense (income), net
% of revenue
Reported operating expenses
2,558
11.3%
964
4.3%
2,084
9.2%
671
3.0%
6,278
367
1.6%
51
0.2%
6,696
2,618
11.8%
1,047
4.7%
2,321
10.4%
176
0.8%
6,162
449
2.0%
51
0.2%
6,661
2,937
12.4%
1,107
4.7%
2,556
10.8%
(32)
-0.1%
6,568
434
1.8%
51
0.2%
7,053
3,838
11.3%
1,721
5.1%
2,779
8.2%
1,020
3.0%
9,358
471
1.4%
51
0.1%
9,879
11,951
11.7%
4,840
4.7%
9,741
9.5%
1,834
1.8%
28,366
1,722
1.7%
202
0.2%
30,290
Pro-forma operating income (loss)
% yoy growth
% of revenue
Reported GAAP operating income (loss)
% yoy growth
% of revenue
497
-0.9%
2.2%
80
-45.5%
0.4%
506
25.1%
2.3%
6
n/a
0.0%
543
n/a
2.3%
58
n/a
0.2%
792
-23.7%
2.3%
271
-54.1%
0.8%
2,338
29.3%
2.3%
415
131.7%
0.4%
NON-OPERATING EXPENSES
Interest income
Effective cash interest rate %
Interest expense
Effective debt interest rate %
Other income (expense), net
% of revenue
Total non-operating income (expense)
11
0.30%
(76)
3.7%
0
0.0%
(65)
10
0.30%
(76)
3.7%
0
0.0%
(67)
11
0.30%
(76)
3.7%
0
0.0%
(66)
13
0.30%
(76)
3.7%
0
0.0%
(63)
44
0.2%
(306)
3.7%
0
0.0%
(261)
Pro-forma pre-tax income (loss)
Reported GAAP pre-tax income (loss)
432
14
439
(61)
477
(7)
729
208
5
33.0%
104
0
0.0%
(20)
33.0%
125
0
0.0%
(2)
33.0%
121
0
0.0%
68
33.0%
130
0
0.0%
323
-13.9%
1.4%
10
-91.2%
0.0%
334
77.4%
1.5%
(41)
-67.5%
-0.2%
359
n/a
1.5%
(5)
-98.9%
0.0%
530
-3.5%
1.6%
139
-35.0%
0.4%
464
472
464
472
464
472
464
472
464
472
0.70
-14.7%
0.68
-14.6%
0.72
76.3%
0.71
73.3%
0.77
n/a
0.76
n/a
1.14
-3.5%
1.12
-3.5%
0.02
-91.3%
0.02
-91.3%
(0.09)
-67.7%
(0.09)
-68.3%
(0.01)
-98.9%
(0.01)
-98.9%
Adjusted EBIT
% yoy growth
% of revenue
447
-4.3%
2.0%
455
21.0%
2.0%
Depreciation and amortization
% of revenue
1,242
5.5%
Adjusted EBITDA
% yoy growth
% of revenue
Provision (benefit) for income taxes
Effective tax rate %
Tax adjustments for non-GAAP/pro-forma items
Equity-method investment activity, net of tax
% of revenue
Pro-forma net income (loss)
% yoy growth
% of revenue
Reported GAAP net income (loss)
% yoy growth
% of revenue
Weighted average shares outstanding:
Basic
Diluted
Pro-forma EPS
Basic
% yoy growth
Diluted
% yoy growth
Reported GAAP EPS
Basic
% yoy growth
Diluted
% yoy growth
4Q
2016
1Q
2Q
3Q
4Q
2017
25,774
76,070
13,803
43,617
0
0
39,577 119,687
17.0%
16.9%
27,506
83,182
69.5%
69.5%
12,071
36,504
30.5%
30.5%
18,605
12,240
0
30,845
16.9%
21,283
69.0%
9,562
31.0%
18,964
11,454
0
30,419
17.0%
20,989
69.0%
9,430
31.0%
20,392
12,057
0
32,450
17.0%
22,390
69.0%
10,059
31.0%
13,976
11.7%
5,660
4.7%
11,391
9.5%
2,129
1.8%
33,156
2,013
1.7%
185
0.2%
35,355
3,494
11.3%
1,317
4.3%
2,847
9.2%
1,038
3.4%
8,695
502
1.6%
40
0.1%
9,237
3,583
11.8%
1,433
4.7%
3,177
10.4%
364
1.2%
8,557
615
2.0%
40
0.1%
9,212
4,021
12.4%
1,515
4.7%
3,499
10.8%
88
0.3%
9,123
594
1.8%
40
0.1%
9,758
5,248
11.3%
2,354
5.1%
3,800
8.2%
1,586
3.4%
12,988
644
1.4%
40
0.1%
13,672
16,346
11.7%
6,619
4.7%
13,323
9.5%
3,076
2.2%
39,363
2,355
1.7%
161
0.1%
41,879
1,128
42.3%
2.8%
531
95.8%
1.3%
3,348
43.2%
2.8%
1,150
177.3%
1.0%
867
20.7%
2.8%
325
33.8%
1.1%
873
20.4%
2.9%
218
42.4%
0.7%
936
20.4%
2.9%
302
35.0%
0.9%
1,354
20.0%
2.9%
670
26.2%
1.4%
4,030
20.4%
2.9%
1,514
31.7%
1.1%
55
0.2%
(306)
3.7%
0
0.0%
(251)
16
0.30%
(76)
3.7%
0
0.0%
(60)
15
0.30%
(76)
3.7%
0
0.0%
(61)
17
0.30%
(76)
3.7%
0
0.0%
(60)
20
0.30%
(76)
3.7%
0
0.0%
(57)
68
0.2%
(306)
3.7%
0
0.0%
(238)
29,699
87,661
16,564
52,316
0
0
46,263 139,977
16.9%
17.0%
31,922
96,584
69.0%
69.0%
14,342
43,393
31.0%
31.0%
13
0.30%
(76)
3.7%
0
0.0%
(63)
12
0.30%
(76)
3.7%
0
0.0%
(64)
13
0.30%
(76)
3.7%
0
0.0%
(63)
16
0.30%
(76)
3.7%
0
0.0%
(60)
2,077
153
655
179
661
89
715
160
1,067
470
3,097
899
806
265
812
157
876
242
1,297
613
3,791
1,276
51
33.0%
481
0
0.0%
59
33.0%
119
0
0.0%
29
33.0%
143
0
0.0%
53
33.0%
139
0
0.0%
155
33.0%
149
0
0.0%
297
33.0%
550
0
0.0%
87
33.0%
135
0
0.0%
52
33.0%
164
0
0.0%
80
33.0%
159
0
0.0%
202
33.0%
171
0
0.0%
421
33.0%
629
0
0.0%
1,545
477
57.1%
47.6%
1.5%
1.8%
103
120
n/a 1161.8%
0.1%
0.5%
488
46.2%
1.9%
59
n/a
0.2%
523
45.9%
1.9%
107
n/a
0.4%
763
43.9%
1.9%
315
126.6%
0.8%
2,251
45.6%
1.9%
602
486.3%
0.5%
584
22.5%
1.9%
177
47.5%
0.6%
596
22.1%
2.0%
105
76.6%
0.3%
638
22.0%
2.0%
162
50.9%
0.5%
924
21.1%
2.0%
411
30.3%
0.9%
2,742
21.8%
2.0%
855
42.0%
0.6%
464
472
464
472
464
472
464
472
464
472
464
472
464
472
464
472
464
472
464
472
3.33
56.4%
3.27
53.8%
1.03
47.6%
1.01
47.6%
1.05
46.2%
1.03
46.2%
1.13
45.9%
1.11
45.9%
1.64
43.9%
1.62
43.9%
4.85
45.6%
4.77
45.6%
1.26
22.5%
1.24
22.5%
1.29
22.1%
1.26
22.1%
1.37
22.0%
1.35
22.0%
1.99
21.1%
1.96
21.1%
5.91
21.8%
5.81
21.8%
0.30
-35.0%
0.29
-35.0%
0.22
n/a
0.22
n/a
0.26
1161.8%
0.25
1161.8%
0.13
n/a
0.13
n/a
0.23
n/a
0.23
n/a
0.68
126.6%
0.67
126.6%
1.30
486.3%
1.28
486.3%
0.38
47.5%
0.38
47.5%
0.23
76.6%
0.22
76.6%
0.35
50.9%
0.34
50.9%
0.89
30.3%
0.87
30.3%
1.84
42.0%
1.81
42.0%
493
n/a
2.1%
742
-25.8%
2.2%
2,136
27.5%
2.1%
672
50.3%
2.5%
679
49.1%
2.6%
731
48.5%
2.6%
1,081
45.8%
2.7%
3,163
48.1%
2.6%
826
23.0%
2.7%
833
22.7%
2.7%
896
22.5%
2.8%
1,313
21.5%
2.8%
3,869
22.3%
2.8%
1,222
5.5%
1,304
5.5%
1,861
5.5%
5,629
5.5%
1,451
5.5%
1,430
5.5%
1,525
5.5%
2,177
5.5%
6,583
5.5%
1,696
5.5%
1,673
5.5%
1,785
5.5%
2,544
5.5%
7,699
5.5%
1,739
15.0%
7.7%
1,728
14.2%
7.8%
1,847
66.2%
7.8%
2,653
9.8%
7.8%
7,967
21.5%
7.8%
2,169
24.7%
8.2%
2,155
24.7%
8.3%
2,303
24.7%
8.3%
3,304
24.5%
8.3%
9,931
24.6%
8.3%
2,563
18.2%
8.3%
2,546
18.2%
8.4%
2,721
18.1%
8.4%
3,898
18.0%
8.4%
11,728
18.1%
8.4%
(5,327)
(11.29)
1,590
3.37
1,013
2.15
5,219
11.06
2,494
5.28
(4,822)
(10.22)
1,964
4.16
1,280
2.71
6,186
13.11
4,608
9.76
(5,568)
(11.80)
2,318
4.91
1,512
3.20
7,182
15.22
5,444
11.53
7,799
16.52
8,207
17.39
8,636
18.30
9,246
19.59
9,246
19.59
9,795
20.75
10,380
21.99
10,996
23.30
11,861
25.13
11,861
25.13
12,540
26.57
13,260
28.09
14,017
29.70
15,071
31.93
15,071
31.93
VALUATION METRICS
(Levered) free cash flow
Per share (diluted)
(Tangible) book value
Per share (diluted)
97
Table 3 – Balance Sheet Estimates
Balance Sheet Estimates
($ in Millions, Except Per Share Amounts in Dollars and Share Counts
1Q
ASSETS
Current assets:
Cash and cash equivalents
9,230
Marketable securities
2,859
Inventories
6,956
% of cost of sales
11.0%
Inventory days
39.6
Accounts receivable, net and other
4,517
% of revenue
5.0%
Accounts receivable days
18.0
Deferred tax assets
0
Total current assets
23,562
Property and equipment, net
17,080
Deferred tax assets
0
Goodwill
3,319
Other assets
2,842
Total assets
46,802
LIABILITIES AND STOCKHOLDERS’ EQUITY
Current liabilities:
Accounts payable
% of cost of sales
Accounts payable days
Accrued expenses and other
% of cost of sales
Unearned revenue
% of revenue
Total current liabilities
Long-term debt
Other long-term liabilities
Total liabilities
Stockholders’ equity:
Common stock
Treasury stock, at cost
Additional paid-in capital
Accumulated other comprehensive loss
Retained earnings
Total stockholders’ equity
Total liabilities and stockholders’ equity
Check
in Thousands)
2Q
3Q
4Q
2015
1Q
2Q
3Q
4Q
2016
1Q
2Q
3Q
4Q
2017
10,820
2,859
7,778
12.5%
45.0
4,445
5.0%
18.0
0
25,902
17,191
0
3,319
2,791
49,203
11,833
2,859
8,297
12.5%
45.0
4,741
5.0%
18.0
0
27,730
17,310
0
3,319
2,741
51098.7
17,051
2,859
9,474
10.0%
36.0
6,767
5.0%
18.0
0
36,151
17,479
0
3,319
2,690
59638.8
17,051
2,859
9,474
13.2%
47.6
6,767
9.4%
34.0
0
36,151
17,479
0
3,319
2,690
59,639
12,230
2,859
8,066
11.0%
39.6
5,275
5.0%
18.0
0
28,430
17,611
0
3,319
2,644
52,003
14,193
2,859
9,034
12.5%
45.0
5,200
5.0%
18.0
0
31,286
17,741
0
3,319
2,598
54,944
15,474
2,859
9,638
12.5%
45.0
5,547
5.0%
18.0
0
33,518
17,879
0
3,319
2,551
57,267
21,660
2,859
11,002
10.0%
36.0
7,915
5.0%
18.0
0
43,437
18,077
0
3,319
2,505
67,338
21,660
2,859
11,002
13.2%
47.6
7,915
9.5%
34.3
0
43,437
18,077
0
3,319
2,505
67,338
16,091
2,859
9,365
11.0%
39.6
6,169
5.0%
18.0
0
34,484
18,231
0
3,319
2,465
58,499
18,409
2,859
10,494
12.5%
45.0
6,084
5.0%
18.0
0
37,846
18,383
0
3,319
2,425
61,973
19,921
2,859
11,195
12.5%
45.0
6,490
5.0%
18.0
0
40,465
18,546
0
3,319
2,384
64,714
27,103
2,859
12,769
10.0%
36.0
9,253
5.0%
18.0
0
51,984
18,777
0
3,319
2,344
76,424
27,103
2,859
12,769
13.2%
47.6
9,253
9.6%
34.5
0
51,984
18,777
0
3,319
2,344
76,424
11,699
18.5%
66.6
6,956
11.0%
1,355
1.5%
20,010
8,265
7,410
35,685
12,445
20.0%
72.0
7,778
12.5%
1,778
2.0%
22,002
8,265
7,410
37,677
13,275
20.0%
72.0
8,297
12.5%
1,896
2.0%
23,468
8,265
7,410
39143.2
18,947
20.0%
72.0
10,421
11.0%
2,030
1.5%
31,399
8,265
7,410
47073.6
18,947
26.4%
95.2
10,421
14.5%
2,030
2.8%
31,399
8,265
7,410
47,074
13,565
18.5%
66.6
8,066
11.0%
1,583
1.5%
23,214
8,265
7,410
38,889
14,455
20.0%
72.0
9,034
12.5%
2,080
2.0%
25,569
8,265
7,410
41,244
15,421
20.0%
72.0
9,638
12.5%
2,219
2.0%
27,278
8,265
7,410
42,953
22,005
20.0%
72.0
12,103
11.0%
2,375
1.5%
36,482
8,265
7,410
52,157
22,005
26.5%
95.2
12,103
14.5%
2,375
2.9%
36,482
8,265
7,410
52,157
15,750
18.5%
66.6
9,365
11.0%
1,851
1.5%
26,965
8,265
7,410
42,640
16,791
20.0%
72.0
10,494
12.5%
2,433
2.0%
29,719
8,265
7,410
45,394
17,912
20.0%
72.0
11,195
12.5%
2,596
2.0%
31,703
8,265
7,410
47,378
25,537
20.0%
72.0
14,046
11.0%
2,776
1.5%
42,359
8,265
7,410
58,034
25,537
26.4%
95.2
14,046
14.5%
2,776
2.9%
42,359
8,265
7,410
58,034
5
(1,837)
11,502
(511)
1,959
11,118
46,802
5
(1,837)
11,952
(511)
1,918
11,526
49,203
5
(1,837)
12,386
(511)
1,913
11,955
51,099
5
(1,837)
12,857
(511)
2,052
12,565
59,639
5
(1,837)
12,857
(511)
2,052
12,565
59,639
5
(1,837)
13,285
(511)
2,172
13,114
52,003
5
(1,837)
13,811
(511)
2,231
13,699
54,944
5
(1,837)
14,319
(511)
2,339
14,315
57,267
5
(1,837)
14,870
(511)
2,654
15,180
67,338
5
(1,837)
14,870
(511)
2,654
15,180
67,338
5
(1,837)
15,371
(511)
2,831
15,859
58,499
5
(1,837)
15,986
(511)
2,936
16,579
61,973
5
(1,837)
16,581
(511)
3,098
17,336
64,714
5
(1,837)
17,224
(511)
3,509
18,390
76,424
5
(1,837)
17,224
(511)
3,509
18,390
76,424
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
METRICS & RATIOS
Net debt (cash)
Operating working capital
Total capital
Invested capital (simple, net debt + equity)
Invested capital (complex, exc goodwill/intangibles)
Invested capital (complex, inc goodwill/intangibles)
NOPAT (simple, operating income x (1 - tax rate)
NOPLAT (complex)
(3,824)
693
7,294
14,704
17,773
23,934
53
87
(5,414)
1,041
6,112
13,522
18,232
24,342
4
38
(6,427)
1,402
5,529
12,939
18,712
24,771
39
73
(11,645)
1,893
920
8,330
19,372
25,381
182
215
(11,645)
1,893
920
8,330
19,372
25,381
278
413
(6,824)
2,357
6,291
13,701
19,968
25,930
163
194
(8,787)
2,858
4,912
12,322
20,599
26,515
103
134
(10,068)
3,381
4,247
11,657
21,261
27,131
150
181
(16,254)
4,095
(1,073)
6,337
22,172
27,996
356
387
(16,254)
4,095
(1,073)
6,337
22,172
27,996
770
894
(10,685)
4,660
5,174
12,584
22,892
28,675
218
245
(13,003)
5,268
3,576
10,986
23,652
29,395
146
173
(14,515)
5,903
2,821
10,231
24,448
30,152
202
229
(21,697)
6,766
(3,307)
4,103
25,543
31,206
449
476
(21,697)
6,766
(3,307)
4,103
25,543
31,206
1,014
1,122
0.5 x
32.6 x
0.5 x
21.3 x
0.5 x
16.9 x
0.6 x
17.9 x
1.7 x
54.1 x
0.5 x
11.2 x
0.5 x
9.1 x
0.5 x
8.2 x
0.6 x
9.7 x
1.8 x
29.2 x
0.5 x
6.6 x
0.5 x
5.8 x
0.5 x
5.5 x
0.6 x
6.8 x
1.8 x
20.7 x
1.2 x
0.6 x
(9.0)
1.2 x
0.6 x
(9.0)
1.2 x
0.6 x
(9.0)
1.2 x
0.6 x
(18.0)
1.2 x
0.6 x
(13.6)
1.2 x
0.6 x
(9.0)
1.2 x
0.7 x
(9.0)
1.2 x
0.7 x
(9.0)
1.2 x
0.7 x
(18.0)
1.2 x
0.7 x
(13.4)
1.3 x
0.7 x
(9.0)
1.3 x
0.7 x
(9.0)
1.3 x
0.7 x
(9.0)
1.2 x
0.7 x
(18.0)
1.2 x
0.7 x
(13.1)
(2.2 x)
(34.4%)
NM
(3.1 x)
(47.0%)
NM
(3.5 x)
(53.8%)
NM
(4.4 x)
(92.7%)
NM
(1.5 x)
(92.7%)
NM
(3.1 x)
(52.0%)
NM
(4.1 x)
(64.1%)
NM
(4.4 x)
(4.9 x)
(70.3%) (107.1%)
NM
NM
(1.6 x)
(107.1%)
NM
(4.2 x)
(67.4%)
NM
(5.1 x)
(78.4%)
NM
(5.3 x)
(5.6 x)
(83.7%) (118.0%)
NM
NM
(1.8 x)
(118.0%)
NM
EBITDA (adjusted) / net interest
EBIT (adjusted) / net interest
26.6 x
6.8 x
25.9 x
6.8 x
28.1 x
7.5 x
41.8 x
11.7 x
30.5 x
8.2 x
34.2 x
10.6 x
33.5 x
10.5 x
36.5 x
11.6 x
54.7 x
17.9 x
39.5 x
12.6 x
42.6 x
13.7 x
41.5 x
13.6 x
45.4 x
15.0 x
68.8 x
23.2 x
49.3 x
16.2 x
DuPont Analysis (GAAP) & Profitability
Net income / pre-tax income (tax burden)
x Pre-tax income / operating income (interest burden)
x Operating income / revenue (operating margin)
x Revenue / total assets (asset turnover)
= ROA (GAAP)
x Total assets / total equity (financial leverage)
= ROE (GAAP)
67.0%
67.0%
17.9% #########
0.4%
0.0%
0.5 x
0.5 x
0.0%
(0.1%)
4.2 x
4.3 x
0.1%
(0.4%)
67.0%
(12.7%)
0.2%
0.5 x
(0.0%)
4.3 x
(0.0%)
67.0%
76.6%
0.8%
0.6 x
0.2%
4.7 x
1.1%
67.0%
37.0%
0.4%
1.7 x
0.2%
4.7 x
0.8%
67.0%
73.9%
0.9%
0.5 x
0.2%
4.0 x
0.9%
67.0%
57.9%
0.6%
0.5 x
0.1%
4.0 x
0.4%
67.0%
71.7%
0.8%
0.5 x
0.2%
4.0 x
0.7%
67.0%
88.6%
1.3%
0.6 x
0.5%
4.4 x
2.1%
67.0%
78.1%
1.0%
1.8 x
0.9%
4.4 x
4.0%
67.0%
81.5%
1.1%
0.5 x
0.3%
3.7 x
1.1%
67.0%
71.8%
0.7%
0.5 x
0.2%
3.7 x
0.6%
67.0%
80.1%
0.9%
0.5 x
0.3%
3.7 x
0.9%
67.0%
91.5%
1.4%
0.6 x
0.5%
4.2 x
2.2%
67.0%
84.3%
1.1%
1.8 x
1.1%
4.2 x
4.6%
0.7%
3.0%
0.3%
0.9%
4.2%
2.2%
2.6%
12.3%
3.3%
0.9%
3.6%
1.2%
0.9%
3.6%
0.8%
0.9%
3.7%
1.3%
1.1%
5.0%
5.6%
3.3%
14.8%
12.2%
1.0%
3.7%
1.7%
1.0%
3.6%
1.3%
1.0%
3.7%
2.0%
1.2%
5.0%
10.9%
3.6%
14.9%
24.7%
Activity (Operating Efficienty) & Liquidity
Total Asset turnover
Working capital turnover
Current ratio
Cash ratio
Cash conversion cycle
Leverage (Capital Structure) & Coverage
Net debt / EBITDA (adjusted)
Net debt / total equity
Net debt / total capital
ROA (adjusted)
ROE (adjusted)
ROIC (simple)
0.7%
2.9%
0.4%
0.7%
2.9%
0.0%
98
Table 4 – Cash Flow Statement Estimates
Cash Flow Estimates
($ in Millions, Except Per Share Amounts in Dollars and Share Counts in Thousands)
1Q
2Q
3Q
OPERATING ACTIVITIES
Net income (loss)
10
(41)
(5)
4Q
2015
1Q
2Q
3Q
4Q
2016
1Q
2Q
3Q
4Q
2017
139
103
120
59
107
315
602
177
105
162
411
855
Adjustments to reconcile net income (loss) to net cash from operating activities:
Depreciation of property and equipment, including inte 1,242
1,222
1,304
Stock-based compensation
367
449
434
Other operating expense (income), net
51
51
51
Losses (gains) on sales of marketable securities, net
0
0
0
Other expense (income), net
0
0
0
Deferred income taxes
0
0
0
Excess tax benefits from stock-based compensation
0
0
0
Funds from operations
1,669
1,681
1,784
1,861
471
51
0
0
0
0
2,521
5,629
1,722
202
0
0
0
0
7,655
1,451
429
46
0
0
0
0
2,046
1,430
526
46
0
0
0
0
2,061
1,525
508
46
0
0
0
0
2,187
2,177
551
46
0
0
0
0
3,089
6,583
2,013
185
0
0
0
0
9,383
1,696
502
40
0
0
0
0
2,416
1,673
615
40
0
0
0
0
2,433
1,785
594
40
0
0
0
0
2,581
2,544
644
40
0
0
0
0
3,639
7,699
2,355
161
0
0
0
0
11,069
Changes in operating assets and liabilities:
Inventories
Accounts receivable, net and other
Accounts payable
Accrued expenses and other
Unearned revenue
Change in net working capital
Net cash provided by (used in) operating activities
1,343
1,095
(4,760)
(2,851)
(468)
(5,641)
(3,972)
(822)
72
747
822
423
1,242
2,923
(518)
(296)
829
518
118
652
2,435
(1,177)
(2,026)
5,672
2,124
134
4,728
7,249
(1,175)
(1,155)
2,488
614
207
980
8,635
1,408
1,492
(5,382)
(2,355)
(447)
(5,285)
(3,239)
(968)
76
890
968
497
1,462
3,524
(604)
(347)
966
604
139
757
2,944
(1,364)
(2,368)
6,584
2,465
156
5,472
8,561
(1,529)
(1,148)
3,057
1,682
345
2,406
11,789
1,638
1,746
(6,255)
(2,738)
(524)
(6,133)
(3,717)
(1,130)
85
1,042
1,130
583
1,710
4,143
(701)
(406)
1,121
701
162
877
3,459
(1,574)
(2,763)
7,625
2,850
180
6,319
9,958
(1,766)
(1,337)
3,533
1,943
401
2,773
13,842
INVESTING ACTIVITIES
Purchases of property and equipment, including intern (1,355)
% of revenue
6.00%
% of depreciation & amortization
109.1%
Acquisitions, net of cash acquired, and other
0
Sales and maturities of marketable securities and othe
0
Purchases of marketable securities and other investme
0
Net cash provided by (used in) investing activities
(1,355)
(1,333)
6.00%
109.1%
0
0
0
(1,333)
(1,422)
6.00%
109.1%
0
0
0
(1,422)
(2,030)
6.00%
109.1%
0
0
0
(2,030)
(6,141)
6.00%
109.1%
0
0
0
(6,141)
(1,583)
6.00%
109.1%
0
0
0
(1,583)
(1,560)
6.00%
109.1%
0
0
0
(1,560)
(1,664)
6.00%
109.1%
0
0
0
(1,664)
(2,375)
6.00%
109.1%
0
0
0
(2,375)
(7,181)
6.00%
109.1%
0
0
0
(7,181)
(1,851)
6.00%
109.1%
0
0
0
(1,851)
(1,825)
6.00%
109.1%
0
0
0
(1,825)
(1,947)
6.00%
109.1%
0
0
0
(1,947)
(2,776)
6.00%
109.1%
0
0
0
(2,776)
(8,399)
6.00%
109.1%
0
0
0
(8,399)
FINANCING ACTIVITIES
Excess tax benefits from stock-based compensation
Dividends issued
Common stock proceeds
Common stock repurchased
Debt issuance (repayment)
Capital/finance lease repayment
Net cash provided by (used in) financing activities
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Foreign-currency effect on cash and cash equivalents
Net increase (decrease) in cash and cash equivalents
0
(5,327)
0
1,590
0
1,013
0
5,219
0
2,494
0
(4,822)
0
1,964
0
1,280
0
6,186
0
4,608
0
(5,568)
0
2,318
0
1,512
0
7,182
0
5,444
Beginning Cash and Cash Equivalents
Ending Cash and Cash Equivalents
14,557
9,230
9,230
10,820
10,820
11,833
11,833
17,051
14,557
17,051
17,051
12,230
12,230
14,193
14,193
15,474
15,474
21,660
17,051
21,660
21,660
16,091
16,091
18,409
18,409
19,921
19,921
27,103
21,660
27,103
Table 5 – Enterprise Value & Overview
Valuation Overview & EV Multiples - Amazon.com Inc.
($ in Millions, Except Per Share Amounts in Dollars and Share Counts in Thousands)
Amazon.com Inc. - Assumptions & Valuation Overview
Valuation Date:
Ticker:
Company Name:
Share Price (closing, non-adjusted):
Diluted Shares Outstanding (average):
--> Yahoo finance/10K on 31 Dec of year:
$
250.87 $
461,000
2012
01.01.2015
AMZN
Amazon.com Inc.
398.79 $
310.35
467,000
472,000
2013
2014
Last Historical Year:
Fiscal Year Ends:
Days in Year:
Share Units:
Tax Rate:
Blue indicates hard-coded numbers.
Black indicates formulas.
Green indicates links from other worksheets.
(Only applies to valuation worksheets)
2014
Dec 31
360
1000
33%
Amazon.com Inc. - Enterprise Value and Equity Value
Enterprise Value Calculation
FY2012
FY2013
FY2014
Diluted Equity Value (Market Cap):
$
115,651
$ 186,235
$
146,485
Less: Cash & Short-Term Investments
Plus: Short-Term & Long-Term Debt
Plus: Minority Interest
Plus: Preferred Stock
Enterprise Value:
$
$
$
$
$
11,448
3,084
107,287
$ 12,447
$
3,191
$
$
$ 176,979
$
$
$
$
$
17,416
8,265
137,334
EV / Revenue
EV / EBIT
EV / EBITDA
EqV / (L)FCF
P / E (adjusted, diluted)
P / E (GAAP, diluted)
P / B (tangible, diluted)
FY2012
1.8 x
71.1 x
28.0 x
292.8 x
161.2 x
NM
20.1 x
Valuation Multiples
FY2013
FY2014
2.4 x
1.5 x
94.2 x
81.9 x
33.7 x
21.0 x
91.7 x
153.3 x
676.8 x
26.2 x
75.1 x
145.8 x
NM
19.3 x
FY2015E
1.3 x
64.3 x
17.2 x
FY2016E
1.1 x
43.4 x
13.8 x
FY2017E
1.0 x
35.5 x
11.7 x
58.7 x
94.8 x
1426.3 x
15.8 x
31.8 x
65.1 x
243.3 x
12.3 x
26.9 x
53.4 x
171.4 x
9.7 x
99
Table 6 – Public Company Comparables
Public Company Comparables - Amazon.com Inc.
($ in Millions, Except Per Share Amounts in Dollars and Share Counts in Thousands)
Comparable Companies - World's Largest Online Retailers by Revenue
Valuation Metrics
Comparable Companies - Operati
Capitalization
Projected Projected
Share
Equity
Enterprise
Revenue
EBITDA (adj.)
Diluted EPS (adj.) Revenue EBITDA
Company Name
Price
Value
Value
2014
2015E
2014
2015E
2014 2015E Growth Growth
eBay Inc.
$ 56.21 $ 70,937 $ 68,466 $ 17,902 $ 19,099 $ 5,742 $ 5,911 $ 2.95 $ 3.08
6.7%
2.9%
Staples Inc.
17.61
11,790
12,287
22,690
22,500
1,387
1,371
0.95
0.94
(0.8%)
(1.2%)
Wal-Mart Stores Inc.
85.90
278,574
316,927
487,061
499,966
37,148
39,046
5.00
5.22
2.6%
5.1%
Apple Inc.
109.33
642,970
646,910
199,800
238,232
67,663
82,721
7.42
9.33
19.2%
22.3%
Alibaba Group
103.60
268,117
257,767
11,462
15,787
6,186
8,287
2.13
2.61
37.7%
34.0%
Maximum
75th Percentile
Median
25th Percentile
Minimum
$ 109.33
103.60
$ 85.90
56.21
17.61
$ 642,970
278,574
$ 268,117
70,937
11,790
$ 646,910
316,927
$ 257,767
68,466
12,287
Amazon.com Inc.
$ 310.35
$ 146,485
$ 137,334
Comparable Companies - Valuatio
Capitalization
Share
Equity
Enterprise
Company Name
Price
Value
Value
eBay Inc.
$ 56.21 $ 70,937 $ 68,466
Staples Inc.
17.61
11,790
12,287
Wal-Mart Stores Inc.
85.90
278,574
316,927
Apple Inc.
109.33
642,970
646,910
Alibaba Group
103.60
268,117
257,767
$ 487,061
199,800
$ 22,690
17,902
11,462
$ 499,966
238,232
$ 22,500
19,099
15,787
$ 67,663
37,148
$ 6,186
5,742
1,387
$ 82,721
39,046
$ 8,287
5,911
1,371
$ 7.42
5.00
$ 2.95
2.13
0.95
$ 9.33
5.22
$ 3.08
2.61
0.94
37.7%
19.2%
6.7%
2.6%
(0.8%)
$88,988
$102,348
$6,555
$7,967
$2.13
$3.27
15.0%
Enterprise Value /
Enterprise Value /
EBITDA
Revenue
2014
2015E
2014
2015E
3.8 x
3.6 x
11.9 x
11.6 x
0.5 x
0.5 x
8.9 x
9.0 x
0.7 x
0.6 x
8.5 x
8.1 x
3.2 x
2.7 x
9.6 x
7.8 x
$ 109.33
103.60
$ 85.90
56.21
17.61
$ 642,970
278,574
$ 268,117
70,937
11,790
$ 646,910
316,927
$ 257,767
68,466
12,287
3.8 x
3.4 x
1.9 x
0.6 x
0.5 x
3.6 x
2.9 x
1.7 x
0.6 x
0.5 x
11.9 x
10.2 x
9.2 x
8.8 x
8.5 x
11.6 x
9.6 x
8.5 x
8.0 x
7.8 x
19.1 x
18.7 x
17.9 x
16.6 x
14.7 x
18.7 x
18.4 x
17.4 x
15.3 x
11.7 x
Amazon.com Inc.
$ 310.35
$ 146,485
$ 137,334
1.5 x
1.3 x
21.0 x
17.2 x 145.8 x
94.8 x
34.0%
22.3%
5.1%
2.9%
(1.2%)
54.0%
33.9%
32.1%
7.6%
6.1%
52.5%
34.7%
30.9%
7.8%
6.1%
21.5%
7.4%
7.8%
Amazon.com Inc. - Possible Peer Groups
Jefferies S&P Capital IQ Yahoo finance
eBay
Expedia
Alibaba Group
Groupon Priceline
Apple
Cimpress NetFlix
Shutterfly Overstock.com
Blue Nile 1-800-FLOWERS
Morningstar
Alibaba Group
eBay
AutoZone
O'Reilly Automotive
Rakuten Inc. ADR
Sector / Industry
US TechnolConsumer Discr Catalog & Mail Consumer Cyclical
Internet Internet Retail
Speciality Retail
Sources (accessed 06 Oct 2015):
Jefferies and S&P Capital IQ: Amazon stock reports
Yahoo fina https://finance.yahoo.com/q/co?s=AMZN
Morningst http://www.morningstar.com/stocks/XNAS/AMZN/quote.html
With 22.5x and 16.3x EV/Revenue, 41.7x and
31.1x EV/EBITDA, and 48.7xand 39.7x P/E
most likely outliers (usually excluded from the
analysis); taken out here since it distorts the
valuation summary chart (even though peer
group is very small already) --> to include
values again, just copy formula from above!
P / E Multiple
2014 2015E
19.1 x 18.3 x
18.5 x 18.7 x
17.2 x 16.5 x
14.7 x 11.7 x
Maximum
75th Percentile
Median
25th Percentile
Minimum
EBITDA Margin
2014
2015E
32.1% 30.9%
6.1%
6.1%
7.6%
7.8%
33.9% 34.7%
54.0% 52.5%
Table 7 – Precedent Transactions
Precedent Transactions - Amazon.com Inc.
($ in Millions, Except Per Share Amounts in Dollars and Share Counts in Thousands)
E-Commerce M&A Transactions Over $1 Billion with US-Based Sellers Since 1/1/2012
Amazon.com Inc. - Comparable M&A Transactions
Acquirer Name
Expedia Inc.
Priceline.com Inc.
Staples Inc.
QVC Inc.
Priceline.com Inc.
Target Name
Orbitz Worldwide Inc.
Kayak Software Corp.
Office Depot Inc.
Zulily Inc.
OpenTable Inc.
Maximum
75th Percentile
Median
25th Percentile
Minimum
Operating Metrics
Announcement
Equity
Date
Value
02/2015 $
1,328
11/2012
1,800
02/2015
6,300
08/2015
2,400
06/2014
2,600
$
$
6,300
2,600
2,400
1,800
1,328
Enterprise
Value
$
1,600
1,650
5,950
2,086
2,381
Trailing
Revenue
$
932
283
16,157
1,281
190
Forward
Trailing
Revenue
EBITDA
$
978 $
156
NA
68
15,780
565
1,428
45
NA
64
Forward
EBITDA
$
166
NA
778
77
NA
$
$
$
$
$
5,950
2,381
2,086
1,650
1,600
$
16,157
1,281
932
283
190
$
15,780
8,604
1,428
1,203
978
$
$
565
156
68
64
45
$
778
472
166
121
77
EV /
Trailing
Revenue
1.7 x
0.4 x
1.6 x
1.7 x
1.7 x
1.6 x
1.0 x
0.4 x
Valuation Multiples
EV /
EV /
Forward
Trailing
Revenue
EBITDA
1.6 x
10.2 x
NA
24.2 x
0.4 x
10.5 x
1.5 x
46.2 x
NA
37.4 x
1.6 x
1.5 x
1.5 x
0.9 x
0.4 x
46.2 x
37.4 x
24.2 x
10.5 x
10.2 x
EV /
Forward
EBITDA
9.6 x
NA
7.6 x
27.2 x
NA
27.2 x
18.4 x
9.6 x
8.6 x
7.6 x
With 5.8x and 12.5x most likely outliers (usually excluded
from the analysis); taken out here since it distorts the
valuation summary chart (even though peer group is very
small already) --> to include value again, just copy formula
from above!
100
Table 8 – Weighted Average Cost of Capital
WACC Analysis - Amazon.com Inc.
($ in Millions, Except Per Share Amounts in Dollars and Share Counts in Thousands)
Discount Rate Calculation - Assumptions
Risk-Free Rate (10y Treasury as of 12/31/2014):
Equity Risk Premium:
Cost of Debt (after taxes):
Cost of Equity:
Source:
2.92%
6.00%
2.06%
11.08%
http://www.federalreserve.gov/releases/h15/data.htm
Comparable Companies - Levered Betas and Unlevered Beta Calculation
Levered
Equity
Unlevered
Name
Beta
Debt
Value
Tax Rate
Beta
eBay Inc.
0.91 $ 7,627 $ 70,937
16.5%
0.84
Staples Inc.
1.40
1,116 $ 11,790
33.0%
1.32
Wal-Mart Stores Inc.
0.82
47,488 $ 278,574
32.0%
0.73
Apple Inc.
1.09
36,403 $ 642,970
26.0%
1.05
Alibaba Group Holding Ltd.
11,180 $ 268,117
15.5%
--> Unlevered beta = Levered beta / (1 + (1 - tax rate) * D/E)
Source: Morningstar/Yahoo finance (as of 06 Oct 2015)
Median
0.91
Overall Industry - Levered and Unlevered Beta
Number Levered
Industry
of Firms
Beta
Retail (Online)
46
1.4
D/E Ratio
0.0752
0.84
Unlevered
Tax Rate
Beta
9.38%
1.31
--> Unlevered beta = Levered beta / (1 + (1 - tax rate) * D/E)
Comparable Companies
Overall Industry
Historical Beta Calculation
0.84
1.31
$8,265
$8,265
Equity
Value
$ 146,485
$ 146,485
Tax Rate
Amazon.com Inc. - Cost of Debt Calculation
Maturity
Name
Coupon %
Date
Amazon Com
0.65
11/27/2015
Amazon Com
1.20
11/29/2017
Amazon Com
2.60
05/12/2019
Amazon Com
3.30
05/12/2021
Amazon Com
2.50
11/29/2022
Amazon Com
3.80
05/12/2024
Amazon Com
4.80
05/12/2034
Amazon Com
4.95
05/12/2044
Source (accessed 06 Oct 2015):
Coupon Type
(Fix/Float)
Fixed
Fixed
Fixed
Fixed
Fixed
Fixed
Fixed
Fixed
Weighted
YTM %
0.054
0.143
0.249
0.312
0.410
0.479
0.628
0.802
9,000
3.077
http://quicktake.morningstar.com/StockNet/bonds.aspx?Symbol=AMZN&Country=USA
Amount $m
750
1,000
1,000
1,000
1,250
1,250
1,250
1,500
Price
100
99.8
101.5
102.9
97
102.8
103.6
102.3
Yield to
Maturity %
0.65
1.29
2.24
2.81
2.95
3.45
4.52
4.81
Cost of Debt based on bonds outstanding (before taxes)
Cost of Debt based on balance sheet (before taxes)
--> most recent annual interest expense /2-year average interest-bearing debt
3.08%
3.67%
Cost of Debt based on bonds outstanding (after taxes)
Cost of Debt based on balance sheet (after taxes)
2.06%
2.46%
Which Cost of Debt should be used for calculation of WACC?
Source (data as of Jan 2015, accessed 06 Oct 2015):
http://people.stern.nyu.edu/adamodar/New_Home_Page/datafile/Betas.html
List of firms included in industry:
http://www.stern.nyu.edu/~adamodar/pc/datasets/indname.xls
Amazon.com Inc. - Levered Beta & WACC Calculation
Unlevered
Beta
Debt
Amazon.com Inc. - Levered Betas for Comparison to Calculated Values
Data Provider
Levered Beta
Date
Data Source
S&P Capital IQ Report
0.96
09/26/2015 http://www.netadvantage.standardandpoors.com
Yahoo Finance
1.33
06/10/2015 http://finance.yahoo.com/q?s=AMZN
Morningstar
1.33
06/10/2015 http://www.morningstar.com/stocks/XNAS/AMZN/quote.html
Google Finance
0.74
06/10/2015 https://www.google.com/finance?cid=660463
Reuters
0.75
06/10/2015 http://www.reuters.com/finance/stocks/overview?symbol=AMZN.OQ
Balance Sheet Bonds Outstan
Bonds Outstanding
Levered
Beta
33%
33%
0.87
1.36
1.06
--> Levered beta = Unlevered beta * (1 + (1 - tax rate) * (D/E)
Cost of Equity Based on Comparables: Comparables
Cost of Equity Based on Overall Industry:
Overall Industry
Cost of Equity Based on Historical Beta: Historical
Which Cost of Equity should be used for calculation of WACC?
8.12%
11.08%
9.26%
Overall Industry
--> (WACC = ke x %-equity + kd x (1-tax rate) x %-debt + kps x %-preferred stock)
Amazon.com Inc. - WACC
10.60%
101
Table 9 – Beta Estimation
Beta Estimation - Amazon.com Inc.
($ in Millions, Except Per Share Amounts in Dollars and Share Counts in Thousands)
Amazon.com Inc. - Input Data for Regression Model
Date Amazon.com
S&P500 R(stock)(market)
01.12.2009
134.52
1115.10
04.01.2010
125.41
1073.87 -6.77% -3.70%
01.02.2010
118.40
1104.49 -5.59% 2.85%
01.03.2010
135.77
1169.43 14.67% 5.88%
01.04.2010
137.10
1186.69 0.98% 1.48%
03.05.2010
125.46
1089.41 -8.49% -8.20%
01.06.2010
109.26
1030.71 -12.91% -5.39%
01.07.2010
117.89
1101.60 7.90% 6.88%
02.08.2010
124.83
1049.33 5.89% -4.74%
01.09.2010
157.06
1141.20 25.82% 8.76%
01.10.2010
165.23
1183.26 5.20% 3.69%
01.11.2010
175.40
1180.55 6.16% -0.23%
01.12.2010
180.00
1257.64 2.62% 6.53%
03.01.2011
169.64
1286.12 -5.76% 2.26%
01.02.2011
173.29
1327.22 2.15% 3.20%
01.03.2011
180.13
1325.83 3.95% -0.10%
01.04.2011
195.81
1363.61 8.70% 2.85%
02.05.2011
196.69
1345.20 0.45% -1.35%
01.06.2011
204.49
1320.64 3.97% -1.83%
01.07.2011
222.52
1292.28 8.82% -2.15%
01.08.2011
215.23
1218.89 -3.28% -5.68%
01.09.2011
216.23
1131.42 0.46% -7.18%
03.10.2011
213.51
1253.30 -1.26% 10.77%
01.11.2011
192.29
1246.96 -9.94% -0.51%
01.12.2011
173.10
1257.60 -9.98% 0.85%
03.01.2012
194.44
1312.41 12.33% 4.36%
01.02.2012
179.69
1365.68 -7.59% 4.06%
01.03.2012
202.51
1408.47 12.70% 3.13%
02.04.2012
231.90
1397.91 14.51% -0.75%
01.05.2012
212.91
1310.33 -8.19% -6.27%
01.06.2012
228.35
1362.16 7.25% 3.96%
02.07.2012
233.30
1379.32 2.17% 1.26%
01.08.2012
248.27
1406.58 6.42% 1.98%
04.09.2012
254.32
1440.67 2.44% 2.42%
01.10.2012
232.89
1412.16 -8.43% -1.98%
01.11.2012
252.05
1416.18 8.23% 0.28%
03.12.2012
250.87
1426.19 -0.47% 0.71%
02.01.2013
265.50
1498.11 5.83% 5.04%
01.02.2013
264.27
1514.68 -0.46% 1.11%
01.03.2013
266.49
1569.19 0.84% 3.60%
01.04.2013
253.81
1597.57 -4.76% 1.81%
01.05.2013
269.20
1630.74 6.06% 2.08%
03.06.2013
277.69
1606.28 3.15% -1.50%
01.07.2013
301.22
1685.73 8.47% 4.95%
01.08.2013
280.98
1632.97 -6.72% -3.13%
03.09.2013
312.64
1681.55 11.27% 2.97%
01.10.2013
364.03
1756.54 16.44% 4.46%
01.11.2013
393.62
1805.81 8.13% 2.80%
02.12.2013
398.79
1848.36 1.31% 2.36%
02.01.2014
358.69
1782.59 -10.06% -3.56%
03.02.2014
362.10
1859.45 0.95% 4.31%
03.03.2014
336.37
1872.34 -7.11% 0.69%
01.04.2014
304.13
1883.95 -9.58% 0.62%
01.05.2014
312.55
1923.57 2.77% 2.10%
02.06.2014
324.78
1960.23 3.91% 1.91%
01.07.2014
312.99
1930.67 -3.63% -1.51%
01.08.2014
339.04
2003.37 8.32% 3.77%
02.09.2014
322.44
1972.29 -4.90% -1.55%
01.10.2014
305.46
2018.05 -5.27% 2.32%
03.11.2014
338.64
2067.56 10.86% 2.45%
01.12.2014
310.35
2058.90 -8.35% -0.42%
Annualized St 27.78% 13.01%
Correlation coefficient 0.5086
RISK ANALYSIS
Systematic Risk = beta² x StD (market)² 0.0200
Unsystematic Risk
0.0572
Total Risk = StD (stock)² = Variance
0.0771
Systematic Risk (%) = R Square
Unsystematic Risk (%)
Total Risk (%)
0.2587
0.7413
1.0000
BETA SMOOTHING
Beta derived from regression analysis
Beta adjusted for mean-reversion
1.0856
1.0574
Data Source:
Yahoo finance - use closing price, not adj. closing price (adjusted for dividends and splits)
http://finance.yahoo.com/q/hp?s=^GSPC&a=11&b=1&c=2009&d=11&e=31&f=2014&g=m
http://finance.yahoo.com/q/hp?s=AMZN&a=11&b=01&c=2009&d=11&e=31&f=2014&g=m
Instructions
1.) Dowload 61 most recent monthly closing prices to valuation date
2.) Paste them in the table on the left (= 60 pairs of historical monthly returns)
3.) Sort the data ascending (i.e. from far back to most recent)
4.) Set up Excel for Data Analysis: Excel Options --> Add Ins --> Manage --> Go --> Analysis ToolPak --> Ok
(Data Analysis should then appear on the Data Tab)
5.) Under Data Analysis choose Regression: Input Y Range: stock returns, Input X Range: market returns
6.) Choose Outpout Range: Mark Cell H46 and click Ok
Blue indicates hard-coded numbers.
Black indicates formulas.
Green indicates link from raw regression output.
(Only applies to this worksheet)
Estimated Regression Equation for AMZN vs. S&P500
30.00%
R(stock)
25.00%
20.00%
y = 1.0856x + 0.0052
R² = 0.2587
15.00%
10.00%
5.00%
-10.00%
R(market)
0.00%
0.00%
-5.00%
-5.00%
5.00%
10.00%
15.00%
-10.00%
-15.00%
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.508603295
0.258677312
0.245895886
0.06962755
60
ANOVA
df
SS
1 0.098116324
58 0.281183751
59 0.379300075
MS
0.098116
0.004848
F
Significance F
20.23853354 3.33749E-05
Coefficients
Standard Error
0.00520084 0.00937024
1.085632102 0.241319911
t Stat
0.555038
4.498726
P-value
Lower 95%
0.581002994 -0.013555733
3.33749E-05 0.602577821
Regression
Residual
Total
Intercept
X Variable 1
REGRESSION STATISTICS
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression (RSS) - explained
Residual (SSE) - unexplained
Total (SST) - explained + unexplained
REGRESSION COEFFICIENTS TABLE
Intercept
X Variable 1
F-TEST DATA (F-Distribution)
0.508603295
0.258677312
0.245895886
0.06962755
60
df
df numerator (k)
df denominator (n-k-1)
Significance level (α)
Upper critical F-value
H0: ALL slope coefficients = 0
Upper 95%
Lower 95.0% Upper 95.0%
0.02395741 -0.013555733 0.02395741
1.56868638 0.602577821 1.56868638
T-TEST DATA (Student's T-Distribution)
1 Degrees of freedom (n-2)
58
0.05 Significance level (α)
4.007 Critical t-value (two-tailed)
H0: SINGLE slope coefficient = 0
SS
1 0.098116324
58 0.281183751
59 0.379300075
MS
0.098116
0.004848
F Stat
Significance F
20.23853354 3.33749E-05
Coefficients
Standard Error
0.00520084 0.00937024
1.085632102 0.241319911
t Stat
0.555038
4.498726
P-value
Lower 95%
0.581002994 -0.013555733
3.33749E-05 0.602577821
58
0.05
2.0017
Upper 95%
Lower 95.0% Upper 95.0%
0.02395741 -0.013555733 0.02395741
1.56868638 0.602577821 1.56868638
--> Adjusted Beta = 0.33 + 0.67 x Raw Beta
102
Table 10 – Discounted Cash Flows
Discounted Cash Flow Analysis - Amazon.com Inc.
($ in Millions, Except Per Share Amounts in Dollars and Share Counts in Thousands)
Amazon.com Inc. - Operating Assumptions
FY2014
FY2015
FY2016
FY2017
FY2018
FY2019
FY2020
FY2021
FY2022
FY2023
19.5%
15.0%
16.9%
17.0%
17.0%
16.0%
15.0%
14.0%
13.0%
12.0%
11.0%
GAAP Operating Income % Revenue
Depreciation & Amortization % Revenue
Amortization of Intangibles % Revenue
Stock-Based Compensation % Revenue
0.2%
5.3%
0.1%
1.7%
0.4%
5.5%
0.2%
1.7%
1.0%
5.5%
0.2%
1.7%
1.1%
5.5%
0.1%
1.7%
1.3%
5.5%
0.1%
1.8%
1.3%
5.5%
0.1%
1.8%
1.5%
6.0%
0.1%
1.8%
1.5%
6.0%
0.1%
1.9%
1.5%
6.0%
0.1%
1.9%
1.5%
6.0%
0.1%
1.9%
1.5%
6.0%
0.1%
2.0%
Increase in Working Capital % Revenue
CapEx % Revenue:
1.1%
5.5%
1.0%
6.0%
2.0%
6.0%
2.0%
6.0%
2.5%
6.0%
2.5%
6.0%
2.7%
6.0%
2.7%
6.0%
3.0%
6.0%
3.0%
6.0%
3.0%
6.0%
Revenue Growth %
Amazon.com Inc. - DCF Assumptions & Output
Yes
No
Use Multiples Method?
Yes
WACC:
10.60%
Discount Rate:
10.50%
FY2024
Terminal EBITDA Multiple:
Terminal Growth Rate:
Terminal Value:
11.0 x
3.5%
$ 365,628
PV of Terminal Value:
Sum of PV of Cash Flows:
Enterprise Value:
$ 134,715
$ 61,307
$ 196,022
Amazon.com Inc. - Cash Flow Projections
FY2014
Revenue:
EBITDA:
GAAP Operating Income:
FY2015
FY2016
FY2017
FY2018
FY2019
FY2020
FY2021
FY2022
FY2023
FY2024
Less: Taxes
88,988 102,348 119,687 139,977 163,773 189,977 218,473 249,060 281,437 315,210 349,883
$6,422
$7,765
$9,746 $11,567 $14,084 $16,338 $20,318 $23,412 $26,455 $29,630 $33,239
$ 179 $ 415 $ 1,150 $ 1,514 $ 2,129 $ 2,470 $ 3,277 $ 3,736 $ 4,222 $ 4,728 $ 5,248
7.22%
7.59%
8.14%
8.26%
8.60%
8.60%
9.30%
9.40%
9.40%
9.40%
9.50%
($59)
($137)
($379)
($500)
($703)
($815) ($1,081) ($1,233) ($1,393) ($1,560) ($1,732)
Plus: Depreciation & Amortization
Plus: Amortization of Intangibles
Plus: Stock-Based Compensation
$ 4,746
$ 133
$1,497
Less: Increase in Working Capital:
Less: Capital Expenditures
$ 6,583
$ 185
$2,013
$ 7,699
$ 161
$2,355
$ 9,008
$ 188
$2,948
$ 10,449
$ 219
$3,420
$ 13,108
$ 251
$3,933
$ 14,944
$ 286
$4,732
$ 16,886
$ 324
$5,347
$ 18,913
$ 363
$5,989
$ 20,993
$ 402
$6,998
$980
($6,141)
$2,406
($7,181)
$2,773
($8,399)
$2,670
$2,540
$4,777
$4,112
$5,603
$4,365
$7,838
$5,526
$9,092
$5,802
$12,278
$7,090
$14,246
$7,445
$16,943
$8,012
$18,976
$8,121
$21,413
$8,293
1.000
0.500
2.000
1.500
3.000
2.500
4.000
3.500
5.000
4.500
6.000
5.500
7.000
6.500
8.000
7.500
9.000
8.500
10.000
9.500
78.9%
17.3%
39.9%
16.0%
35.0%
16.0%
18.9%
12.0%
12.8%
Amazon.com Inc. - Net Present Value Sensitivity - Terminal Growth Rates
Discount Rate
#####
7.5%
8.5%
9.5%
10.5%
11.5%
0.5% $ 548.25 $ 506.72 $ 469.00 $ 434.69 $ 403.46
12.5%
$ 375.00
13.5%
$ 349.04
$ 375.00
$ 349.04
Unlevered Free Cash Flow
Present Value of Free Cash Flow
Normal Discount Period:
Mid-Year Discount:
Terminal Growth
Rate
Free Cash Flow Growth Rate:
1.5% $
548.25
$ 506.72
$ 469.00
$ 434.69
$ 403.46
$4,094
$4,749
$5,899
$6,725
$8,443
$9,456 $10,496
($9,826) ($11,399) ($13,108) ($14,944) ($16,886) ($18,913) ($20,993)
2.5% $
548.25
$ 506.72
$ 469.00
$ 434.69
$ 403.46
$ 375.00
$ 349.04
3.5% $
548.25
$ 506.72
$ 469.00
$ 434.69
$ 403.46
$ 375.00
$ 349.04
4.5% $
548.25
$ 506.72
$ 469.00
$ 434.69
$ 403.46
$ 375.00
$ 349.04
5.5% $
548.25
$ 506.72
$ 469.00
$ 434.69
$ 403.46
$ 375.00
$ 349.04
9.0 x $ 479.92
$ 444.43
68.7%
$ 196,022
$ 17,416
$ 8,265
$
$
$ 9,151
$ 205,173
Implied Price Per Share:
$ 434.69
For calculation of data tables, either hit "F9" on
keyboard to recalculate the worksheet or make
sure to activate the automatic calculation of data
tables in Excel options; Office Button --> Excel
Options --> Formulas --> Calculation Options -->
Workbook Calculation --> Set to "Automatic"
(could slow down the entire workbook
considerably!)
Amazon.com Inc. - Net Present Value Sensitivity - Terminal EBITDA Multiples
Discount Rate
$ 434.69
7.5%
8.5%
9.5%
10.5%
11.5%
8.0 x $ 445.75 $ 413.28 $ 383.75 $ 356.85 $ 332.32
Terminal EBITDA
Multiple
$974
($4,892)
$ 5,629
$ 202
$1,722
Terminal Value % EV:
Enterprise Value:
Plus: Cash & Short-Term Investments
Less: Short-Term & Long-Term Debt
Less: Minority Interest
Less: Preferred Stock
Balance Sheet Adjustment:
Implied Equity Value:
$ 412.16
$
382.80
$
356.04
12.5%
$ 309.94
13.5%
$ 289.49
$ 331.63
$ 309.34
10.0 x $ 514.08
$ 475.58
$ 440.58
$
408.74
$
379.75
$ 353.31
$ 329.19
11.0 x $ 548.25
$ 506.72
$ 469.00
$
434.69
$
403.46
$ 375.00
$ 349.04
12.0 x $ 582.42
$ 537.87
$ 497.41
$
460.64
$
427.17
$ 396.69
$ 368.89
13.0 x $ 616.59
$ 569.01
$ 525.83
$
486.58
$
450.88
$ 418.37
$ 388.74
103
Table 11 – Valuation Summary
Valuation Summary & Graph - Amazon.com Inc.
($ in Millions, Except Per Share Amounts in Dollars and Share Counts in Thousands)
Amazon.com Inc. - Valuation Statistics
Amazon.com Inc. - Range of Valuation Multiples / Premiums
25th
Minimum Pecentile
Multiple Multiple
Methodology Name
Median
Multiple
75th
Pecentile
Multiple
Amazon.com Inc. - Implied Per Share Value Range
Applicable
Maximum Amazon.com Inc. Minimum
Multiple
Figure
Multiple
25th
Pecentile
Multiple
75th
Pecentile
Multiple
Median
Multiple
Maximum
Multiple
Public Company Comparables:
2014 EV / Revenue:
2015E EV / Revenue:
2014 EV / EBITDA:
2015E EV / EBITDA:
2014 P / E:
2015E P / E:
0.5 x
0.5 x
8.5 x
7.8 x
14.7 x
11.7 x
0.6 x
0.6 x
8.8 x
8.0 x
16.6 x
15.3 x
1.9 x
1.7 x
9.2 x
8.5 x
17.9 x
17.4 x
3.4 x
2.9 x
10.2 x
9.6 x
18.7 x
18.4 x
3.8 x
3.6 x
11.9 x
11.6 x
19.1 x
18.7 x
$88,988
$102,348
$6,555
$7,967
$2.13
$3.27
$
121.48
137.80
137.87
151.40
31.37
38.37
$
136.92
152.08
141.27
155.15
35.27
50.00
$
385.94
382.52
147.29
163.53
38.02
56.81
$
657.47
655.33
160.37
181.72
39.74
60.15
$
740.43
796.71
184.98
214.91
40.56
61.34
Precedent Transactions:
Trailing EV / Revenue:
Forward EV / Revenue:
Trailing EV / EBITDA:
Forward EV / EBITDA:
0.4 x
0.4 x
10.2 x
7.6 x
1.0 x
0.9 x
10.5 x
8.6 x
1.6 x
1.5 x
24.2 x
9.6 x
1.7 x
1.5 x
37.4 x
18.4 x
1.7 x
1.6 x
46.2 x
27.2 x
$88,988
$102,348
$6,555
$7,967
$
88.82
101.15
161.73
148.48
$
207.57
218.62
165.64
165.33
$
326.31
336.08
355.99
182.19
$
334.66
355.07
538.92
330.63
$
343.02
374.06
660.74
479.07
$
331.63
$
379.75
$
434.69
$
497.41
$
569.01
Discounted Cash Flow Analysis:
8.5-12.5% Discount Rate, 9-13x Terminal Multiple:
Amazon.com Share Price on
12/31/2014: $310.35
DCF Analysis
8.5-12.5% Discount Rate, 9-13x Terminal Multiple:
Precedent Transactions
Forward EV / EBITDA:
Trailing EV / EBITDA:
Forward EV / Revenue:
Min to 25th
Trailing EV / Revenue:
25th to Median
Median to 75th
Public Company Comps
75th to Max
2015E P / E:
2014 P / E:
2015E EV / EBITDA:
2014 EV / EBITDA:
2015E EV / Revenue:
2014 EV / Revenue:
$0
$200
$400
$600
$800
$1000
$1200
104
11.2. Financial Model Quick Steps
Contents
1. Create Segment Estimates (blue highlighted values)
2. Add Income Statement Links
3. Create Income Statement Estimates (blue highlighted values)
4. Create Balance Sheet Estimates (blue highlighted values)
5. Create Cash Flow Statement Estimates (blue highlighted values)
6. Add Cash Flow Statement Links
7. Add Balance Sheet Links
1. Create Segment Estimates (blue highlighted values)

Revenue: %-growth rate per sub-segment.

Segment operating expenses/income: % of revenue per segment.
2. Add Income Statement Links

North American net sales: flows in from Segment Estimates.

International net sales: flows in from Segment Estimates.

AWS net sales: flows in from Segment Estimates.

Pro-forma operating income (loss): flows in from Segment Estimates.
3. Create Income Statement Estimates (blue highlighted values)

Reconciliation of (aggregated) segment operating expenses: Cost of
sales/Fulfillment/Marketing/Technology and content  % of revenue (General and
administrative is used as the balancing variable).

Stock-based compensation: % of revenue.

Other operating expense (income), net: forecasted annual values taken from most
recent annual report and equally distributed over four quarters.

Depreciation and amortization: % of revenue.

Interest income: % of average cash and cash equivalents and marketable securities.

Interest expense: % of average long-term debt balances.
105

Other income (expense), net: safest to set to zero, since they are one-time events
(random, not planned 5y ahead)  maybe use % of revenue formula and keep flat to
not constantly underestimate earnings).

Equity-method investment activity, net of tax: safest to set to zero, since they are onetime events (random, not planned 5y ahead)  maybe use % of revenue formula and
keep flat to not constantly underestimate earnings).

Provision (benefit) for income taxes: usually % of pre-tax income  simply set to
33% in this case, since % of pre-tax income doesn’t work for Amazon.

Tax adjustments for non-GAAP/pro-forma items: Amazon specific line item 
similar to taxes we chose a 25% adjustment.

Basic/diluted (weighted average) shares outstanding: assumed constant for simplicity.
4. Create Balance Sheet Estimates (blue highlighted values)

Inventories: % of cost of sales.

Accounts receivable, net and other: % of revenue.

Goodwill: assumed constant unless there is impairment.

Accounts payable: % of cost of sales.

Accrued expenses and other: % of cost of sales.

Unearned revenue: % of revenue.
5. Create Cash Flow Statement Estimates (blue highlighted values)
Net cash provided by (used in) operating activities (CFO)

Losses (gains) on sales of marketable securities: safest to set to zero and hold
constant, since we don't have enough info.

Other expense (income), net: safest to set to zero and hold constant, since we don't
have enough info.

Deferred income taxes: safest to set to zero and hold constant, since we don't have
enough info.
Net cash provided by (used in) investing activities (CFI)

Purchases of PP&E: % of revenue.
106

Acquisitions, net of cash acquired, and other: safest to set to zero, since they are onetime events (random, not planned 5y ahead).

Sales and maturities of marketable securities and other investments: safest to set to
zero, since they are one-time events (random, not planned 5y ahead).

Purchases of marketable securities and other investments: safest to set to zero, since
they are one-time events (random, not planned 5y ahead).
Net cash provided by (used in) financing activities (CFF)

Excess tax benefits from stock-based compensation: safest to set to zero, since they
are one-time events (random, not planned 5y ahead).

Dividends issued: safest to set to zero, since they are one-time events (random, not
planned 5y ahead)  Amazon has never paid dividends; usually estimated in line with
historic trend.

Common stock proceeds: safest to set to zero, since they are one-time events (random,
not planned 5y ahead).

Common stock repurchased: safest to set to zero, since they are one-time events
(random, not planned 5y ahead)  not entirely true, since repurchases are typically
planned ahead in a buyback schedule.

Debt issuance (repayment): safest to set to zero, since they are one-time events
(random, not planned 5y ahead)  not entirely true, since debt has fixed maturity
dates and companies often indicate if they simply retire or refinance their debt.

Capital/finance lease repayment: safest to set to zero, since they are one-time events
(random, not planned 5y ahead).

Foreign-currency effect on cash and cash equivalents: safest to set to zero, since they
are one-time events (random, not planned 5y ahead).
6. Add Cash Flow Statement Links
Adjustments to reconcile net income (loss) to net cash from operating activities

Net income (loss): flows from income statement.

Depreciation and amortization: flows from income statement.

Stock-based compensation: flows from income statement.
107

Other operating expense (income), net: flows from income statement.

Excess tax benefits from stock-based compensation: linked to same line item under
CFF with different sign (reclassification to properly calculate CFO and CFF; doesn't
affect net cash).
Changes in operating assets and liabilities

Inventories: flows in from balance sheet.

Accounts receivable, net and other: flows in from balance sheet.

Accounts payable: flows in from balance sheet.

Accrued expenses and other: flows in from balance sheet.

Unearned revenue: flows in from balance sheet.
7. Add Balance Sheet Links
Assets

Cash and cash equivalents: flows in from bottom of the cash flow statement (ending
cash balance).

Marketable securities: flows in from CFI (sales and purchases of marketable
securities) and CFO in this case (losses/gains on sales of marketable securities) 
often assumed to remain constant.

Deferred tax assets: flows in from CFO (deferred income taxes), split between longterm and short-term deferred tax assets in this case  often assumed to remain
constant.

Property and equipment, net: flows in from CFO (depreciation and amortization) and
CFI (purchases of property and equipment).

Other assets: flows in from CFO (other operating expense/income) and CFI
(acquisitions) in this case  often assumed to remain constant (safer approach,
especially with long-term other assets that are not part of the operating process,
whereas short-term other assets could also be linked to revenues).
Liabilities

Long-term debt: flows in from CFF (debt issuance/repayment).
108

Other long-term liabilities: flows in from CFF (capital lease repayments) in this case
 often assumed to remain constant (safer approach, especially with long-term other
liabilities that are not part of the operating process).

Treasury stock, at cost: flows in from CFF (common stock repurchased).

Additional paid-in capital: flows from CFO (stock-based compensation) and CFF
(common stock proceeds) if additional shares are issued.

Accumulated other comprehensive loss: flows in from bottom of the cash flow
statement (currency effects) and CFO (other expense/income) in this case should
technically only include CFF items that are not reflected on the balance sheet, yet).

Retained earnings: flows from the income statement (reported net income) and CFF
(dividends issued) if dividends are paid.
109
11.3. Amazon’s Strategic Analysis
Contents
1. Environmental Analysis (Notation: T = Threat; O = Opportunity)
2. Industry Analysis (Porter’s Five Forces)
3. Internal Analysis - Amazon’s Competitive Advantage(s)
4. Do Amazon’s Competitive Advantages match its Environment?
5. Is Amazon a Strategically Competitive Company?
6. Do Amazon’s Financials support this Strategic Analysis?
The following analysis focuses mainly on Amazon’s core business, which is e-commerce, since a
comprehensive analysis of all business segments (e.g. Amazon Web Services, Amazon Kindle,
Amazon Fire Phone) would also involve analysis of the consumer electronics industry as well as
the information storage and computing industry, among others, which all have different
competitive drivers. Also, it would require a more differentiated internal analysis, which would
go far beyond the scope of this paper.
1. Environmental Analysis (Notation: T = Threat; O = Opportunity)
An environmental analysis explores dimensions in broader society that influence an industry and the firms
within it:

Demographics: e.g. population size, age structure, geographic distribution, ethnic mix, income
distribution.

Economics: e.g. interest rates, inflation rates, GDP growth, FX rates, overall state of the economy.

Sociocultural: e.g. cultural values, lifestyle changes, workforce diversity, society’s attitudes,
product preferences.

Political/legal: e.g. antitrust laws, taxation laws, deregulation, lobbying, embargos.

Global: e.g. critical global markets that are changing, important political events, relevant new
global markets.

Technological: e.g. product innovations, new ICT, R&D expenditures, government subsidies, new
knowledge.
(Hitt, Ireland, and Hoskisson, 2014)
110
Demographics

Development of disposable income/consumer spending (also influenced by economic
factors) Increase/decrease in potential customers [O and T].

Growth of 'online' households/population using internet Increase in internet users [O].
Economics

Growth of the 'online-shopping'/e-commerce sector (influenced by political and social
factors) Increase in market share compared to brick-and-mortar retailers [O].
Sociocultural

Shifts in buyer behavior (e.g. rather buy online than in stores) Increase in potential
customers [O].

Increase in social networking Rise of new marketing channels [O].
Political/legal

Governments promote e-commerce through ICT (“Information and Communication
Technology”) investments Increase in internet users [O].

National/international regulations in e-commerce and data protection law, especially in
the European Union (i.e. EU Electronic Commerce Directive; e.g. Google was banned in
China) Could negatively affect growth in potential future markets [T].
Global

Growing demand in emerging markets (strong GDP growth in China/India vs.
Europe/US) Development of potential future markets [O].

Global technological inequality limits business to countries with high internet penetration
Could negatively affect the development of potential future markets [T].
Technological

ICT development for more and faster internet access (e.g. mobile phones, TV, broadband,
4G) Increased frequency/new sources of internet usage [O].

Improvements in internet security (also influenced by legal factors) Increase in internet
users [O].
111
Key takeaways:

The general environment shows an increasing and attractive market to be exploited.

It is most positively influenced by sociocultural and technological factors.

Amazon’s strategy should comply with legal regulations and global technological
inequalities.
The bankruptcy of Borders Group Inc. is a paramount example of how the external
environment affects industries and firms. Borders relied on its large storefronts, which were
conveniently located for customers to visit. However, technology rapidly changed customers’
shopping patterns. By relying on the internet, Amazon changed the competitive landscape and
it is possible that Borders’ core competencies eventually became its core rigidities.
2. Industry Analysis (Porter’s Five Forces)
The following analysis is focused on the structure of the e-commerce industry in general. More
specifically, it looks at the five forces that characterize the industry and its profitability through
the distribution of value-added among participants, suppliers and buyers. Also, there will also be
a focus on how these forces affect Amazon in particular.
The five forces that affect the ability of all firms to operate profitably within a given industry as well as
factors influencing each of the five forces are as follows:

Threat of new entrants: economies of scale, product/brand differentiation, capital requirements,
switching cost, government policy, cost disadvantage independent of scale, access to distribution
channels, expected retaliation.

Bargaining power of suppliers: large and few/concentration, no substitutes, many small individual
buyers, goods critical to buyer, high switching costs, threat to integrate forward.

Bargaining power of buyers: large and few/concentration, significant portion of sales, low
switching costs, threat to integrate backward.

Threat of substitutes: few switching costs, lower substitute price, equal substitute quality and
performance, low differentiation.

Rivalry among firms: slow industry growth, numerous equally balanced/fragmented industry, high
fixed costs, lack of differentiation, low switching costs, high strategic stakes, high exit barriers.
(Hitt, Ireland, and Hoskisson, 2014)
112
Threat of new entrants  LOW

High capital investment needed to establish an efficient supply chain and logistics
network, even though the initial investment to enter the industry is quite low (only
shopping-website needed).

Strong brand differentiation, since established online retailers’ brands like Amazon create
trust and loyalty.

Large global online retailers like Amazon buy in bulk and are therefore able to offer lower
prices (i.e. economies of scale), which is a major competitive advantage, as there is much
choice online.

Experience in e-commerce yields advantages in terms of technology and customer loyalty
(e.g. Amazon's first mover advantage in e-commerce and its high level of customer
satisfaction).
Bargaining power of suppliers  VERY LOW

In terms of business models, online retailers mostly need to source server capacity. Since
only few companies offer the technology needed by large online retailers like Amazon,
suppliers could have some bargaining power (high supplier concentration combined with
high switching costs once a server infrastructure is established). However, depending on
size (e.g. Amazon) this power could be mitigated.

In terms of products, suppliers have virtually no bargaining leverage against large global
online retailers like Amazon, since they often are the largest purchasers for them.
However, this could be different for smaller players in the industry and also for some
special brand-name products. A contributing factor here is the declining supplier
concentration, since in general more suppliers are available due to global shipping.
Bargaining power of buyers  VERY LOW
The bargaining power of buyers against large global online retailers like Amazon is
virtually non-existent, since customers are mostly individuals (low buyer concentration).
However, this is mitigated to some degree by low switching costs for customers due to a
large variety of online shops and price comparison websites.
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Threat of substitutes  MEDIUM
In terms of the e-commerce business model there are two major threats:

Physical stores (major advantage: review and touch the product before buying it).

Mail order (not as popular anymore).
Rivalry among existing firms  HIGH

The industry is highly fragmented, since brick-and-mortar retailers, department stores as
well as specialty stores operate their own shopping websites (besides direct competitors
like Alibaba or Ebay).

High fixed costs (distribution centers) and storage costs (wide product inventory for fast
deliveries).

Increased opportunities for smaller retailers targeting niche markets, as search engines
like Google become more popular for online shopping (combined with low switching
costs).

Strong growth of the e-commerce industry mitigates competition to some degree.
Competitor Environment in E-commerce:
As mentioned before, Amazon has several business segments and hence competes against a
variety of companies in different markets. When solely looking at the e-commerce business,
Amazon most directly competes with other online retailers like Alibaba (China), Zalando
(Europe) or Rakuten (Japan). However, Ebay (auctions) and Google (search engine), even though
they have a different business model, are also strong competitors in the online environment.
Zappos could also be considered a direct competitor, but was acquired by Amazon in 2009.
Additionally, almost all brick-and-mortar retailers (especially Wal-Mart, Target, or Barns &
Noble) and also department stores (Macy’s, for example) or specialty stores (Staples, for
example) are operating sophisticated shopping websites. Taken together, this competitor analysis
shows the high degree of fragmentation and hence the intense competition in the e-commerce
industry.
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3. Internal Analysis - Amazon’s Competitive Advantage(s)
Following the discussion of Amazon’s external environment, the following section will cover an
internal analysis of the company. More precisely internal analysis aims to discover a firm’s core
competencies, which it uses to create superior value for customers. These core competencies, by
definition, are sources of competitive advantages. While the analysis of resources includes
threshold resources (e.g. capable IT-technology) and unique resources (e.g. Jeff Bezos as a
strategic leader), the analysis of capabilities focuses merely on unique capabilities and hence
potential core competencies. Since a firm’s intentions regarding value-creation also guide its
choice of business-level strategy, Amazon’s business-level strategy will also be examined.
In theory, firms create value by innovatively bundling and leveraging their resources to form capabilities
and core competencies. Resources by themselves typically are not competitive advantages; in fact, resources
create value when the firm uses them to form capabilities, some of which become core competencies, and
hopefully competitive advantages. Because of the relationship among resources, capabilities, and core
competencies, we also discuss the value chain and examine four criteria firms use to determine if their
capabilities are core competencies and, as such, sources of competitive advantage.
When using their core competencies to create superior value for customers (1. “do better”, i.e. create more
value than competitors ) that competitors cannot duplicate (2. “do what others can’t”), firms have
established a competitive advantage. The stronger the core competencies, the higher the value created.
In theory, firms create value by innovatively bundling and leveraging their resources to form capabilities
and core competencies. Resources by themselves typically are not competitive advantages; in fact, resources
create value when the firm uses them to form capabilities, some of which become core competencies, and
hopefully competitive advantages. Because of the relationship among resources, capabilities, and core
competencies, we also discuss the value chain and examine four criteria firms use to determine if their
capabilities are core competencies and, as such, sources of competitive advantage.
(Hitt, Ireland, and Hoskisson, 2014)
115
A business-level strategy is an integrated set of commitments and actions the firm uses to gain a competitive
advantage by exploiting core competencies in specific product markets. What the firm intends regarding
value creation affects its choice of business-level strategy. Value is created by a product’s low cost, by its
differentiated features, or by a combination of low cost and high differentiation. A business-level strategy is
effective only when it is grounded in exploiting the firm’s capabilities and core competencies to gain a
competitive advantage in specific product markets.
•
Cost-leadership strategy: An integrated set of actions taken to produce goods or services with
features that are acceptable/competitive to customers at the lowest cost, relative to that of
competitors.
•
Differentiation strategy: An integrated set of actions taken to produce goods or services at an
acceptable/competitive cost that customers perceive as being different in ways that are
important/valuable to them.
(Hitt, Ireland, and Hoskisson, 2014)
The value chain allows a firm to understand the parts of its operations that create value and those that do
not. The firm earns above-average returns only when the value it creates is greater than the costs incurred
to create that value (thereby resulting in a profit margin).Ultimately, creating superior value for customers
is the source of above-average returns.
The value chain separates a business into a series of value-generating activities to better understand the
activities through which a firm develops a competitive advantage and creates shareholder value. The goal of
these activities is to offer customers a level of value that exceeds the cost of the activities, so that the amount
that the customer is willing to pay for a product exceeds the cost of the activities in the value chain. Hence,
the value chain is a useful analysis tool for defining a firm’s core competencies and the activities in which it
can pursue a competitive advantage through either low cost (“squeezing them out of the value-adding
activities”) or differentiation (“focus on activities associated with core competencies to perform better than
competitors”). Evaluating a firm’s capability to execute its value chain activities and support functions is
challenging. Just as identifying and assessing the value of a firm’s resources and capabilities requires
judgment it is equally necessary when using value chain analysis because no obviously correct model or rule
is universally available to help in the process.
(Hitt, Ireland, and Hoskisson, 2014)
116
Amazon’s Resources

Capable IT-technology and internet as sole distribution channel.

State-of-the-art warehouses and distribution centers for fast deliveries.

Unique patent-protected software/shopping platform (e.g. '1-click'-shopping feature).

Range of product categories, large inventories and different geographic markets.

Long-established and powerful brand/image (#1 global online shopping/service).

Jeff Bezos as an outstanding strategic leader.
Amazon’s Capabilities (Notation: BOLD = Competitive Advantages)

Efficient supply-chain and logistics management.

Excellent CRM (e.g. website personalization, customer service/-satisfaction, corporate
culture/vision).

Customer innovation and technological development (new features and tools to improve
shopping experience, e.g. 'Look Inside the Book' or Amazon Web Services (AWS)).

Effective inventory control through customer-data collection to anticipate their needs.

E-commerce experience (first-mover advantage: customer loyalty, market leader,
economies of scale/scope).
Value Chain Analysis and VRIN Criteria:
Using value chain analysis to discover sustainable competitive advantages, it becomes obvious
that Amazon has several core competencies, which serve as sources of competitive advantage and
create superior value for customers on both the direct and the indirect level. Amazon’s excellent
CRM and e-commerce experience (both are highlighted above) clearly create unique value for
customers and also meet the VRIN criteria for sustainable competitive advantages in that both
core competencies are highly valuable, rare, inimitable and non-substitutable. The other core
competencies, however, may not last forever if not constantly improved and hence may only
yield a temporary competitive advantage. For example, Amazon’s efficient SCM and its control
of inventories through customer-data collection are not unique (e.g. Wal-Mart also possess this
capabilities) and can also be more easily imitated than Amazon’s e-commerce experience.
117
Amazon’s capabilities mapped on the Value Chain Model:
Effective control of
inventories through
customer-data collection
Customer innovation &
technological development
E-commerce experience
State-of-the-art CRM
Efficient supply-chain and logistics management
Amazon’s Business-Level Strategy:
In theory, firms using the integrated cost leadership/differentiation strategy strive to provide
customers with low-cost products and valued differentiated features. The following analysis
shows that Amazon pursues this kind of strategy:
 Differentiation: As Amazon is in the e-commerce business, differentiation can only be
achieved through offered services to customers as there is no differentiation among products.
Amazon clearly provides differentiated features with its excellent customer service, its patentprotected shopping software and its fast deliveries. Also, it continues to innovate and enhance
customer experience through innovation and technology development. In the end, shopping at
Amazon is not only about the product, it is about the shopping experience.
 Cost leadership: At the same time, Amazon provides customers with low-cost products due to
economies of scale and scope. Also, using the Internet as a distribution channel allows
Amazon to sell at prices that are lower than those of brick-and-mortar competitors.
Additionally, Amazon’s scalable business model allows it to grow sales whereas costs are
118
largely fixed. In fact, it appears that Amazon is the low-cost leader in the industry, which is
valuable when dealing with rivals because they then hesitate to compete on the basis of price.
This low-cost position is also supported by Amazon’s efficient supply-chain management and
its effective control of inventories.
4. Do Amazon’s Competitive Advantages match its Environment?
To answer this question, a traditional SWOT analysis will be used as a tool to summarize the
above internal and external analysis (in less detail) and also to introduce some new points, which
were not mentioned before (e.g. Amazon’s scalable business model is clearly a strength, but
difficult to address within the resources/capabilities framework; also weaknesses were not
covered in the above analysis).
Matching what a firm can do (a function of its resources, capabilities, and core competencies in the internal
organization) with what it might do (a function of opportunities and threats in the external environment) is a
process that yields insights the firm requires to select its strategies. In other words, a firm’s core
competencies, integrated with an understanding of the conditions in the external environment, should drive
the selection of strategies.
(Hitt, Ireland, and Hoskisson, 2014)
Key takeaways:
The SWOT analysis clearly shows Amazon’s strong market position within e-commerce, which
is achieved through brand, size, technology, customer loyalty, partnerships, and lowest prices.
Therewith, Amazon is perfectly positioned to exploit the opportunities and reduce the threats in
its external environment. For example, Amazon’s scalable business model and experience allow
it to enter new markets quickly to increase revenues and market share. Also, its strong brand,
patented technology and economies of scale and scope allow Amazon to strengthen entry barriers
and deal with intense competition within the industry. Of course, some threats (e.g. laws or
regulations) and weaknesses (e.g. the lack of physical presence) remain. However, these are
either hard to address (in case of physical stores) or almost impossible to control at all (in case of
laws and regulation).
119
Regarding the lack of physical presence, in 2015 it was rumored that Amazon might buy the
struggling electronics retailer RadioShack. While this major strategic move would contain lots of
risks, it would wipe out some of Amazon’s major weaknesses. For example, Amazon could
finally showcase its products and services to customers in “physical” stores and employees could
educate potential customers similar to what happens at Apple stores. With local stores Amazon
could also improve its distribution services by offering pick-ups and storage opportunities.
SWOT Model applied to Amazon:
Strengths
Powerful brand and strong e-commerce experience
Customer innovation and technological development
Economies of scale and scope and low-price strategy (Amazon
is the world’s largest online retailer)
Efficient supply-chain and excellent CRM
Partnerships and licensing (e.g. Amazon Payments/Webstore,
Fulfillment by Amazon)
Scalable business model (revenue growth in new markets
without much costs, as software exists already)
Diversification (product categories and geographical markets)
Skilled workforce (besides tax reasons Jeff Bezos founded
Amazon in Seattle, WA, because of the large pool of software
engineers there [Microsoft and technical universities])
Opportunities
Growing e-commerce sector and consumer spending 
opportunity to increase market share
Global increase in internet-users/frequency of usage 
potential future markets and customers
Become a technology provider  license software to
companies looking for e-retailing proficiency
Further strengthen barriers to entry (brand/image, patented
technology, experience, partnerships)
Further exploit cost advantage through economies of scope
and scale
Expansion through acquisitions (e.g. Twitch acquisition)
Weaknesses
No physical presence/stores (high degree of market dependence)
Low price strategy (threat of cheaper deals of competitors)
Delivery time vs. obtain directly in stores (also the “free delivery”
strategy is very expensive due to rising logistics costs)
Threats
Laws and regulations with negative impact  dependency on
internet leads to risk
Strong competition (e.g. eBay or Alibaba); also “physical” stores go
online (e.g. Barnes & Noble)
Threat of substitution from “physical” retailing industry (review,
touch and feel products)
Changing product demand combined with strong seasonality  risk
of obsolete inventory
120
5. Is Amazon a Strategically Competitive Company?
Strategic competitiveness and above-average returns result only when core competencies (identified by
studying the internal organization) are matched with opportunities (determined by studying the firm’s
external environment).
(Hitt, Ireland, and Hoskisson, 2014)
To be a strategically competitive company, Amazon must have successfully implemented a
value-creating strategy (i.e. exploit core competencies and gain competitive advantage) to create
superior value for its customers and hence earn above-average returns. According to this
definition, the above discussion of core competencies, value creation, and competitive advantage
clearly shows that Amazon is a strategically competitive company. Hence, the following section
will focus on the indicators that suggest that Amazon will be able to stay strategically competitive
in the future:
 First, Amazon has developed a unique position in the e-commerce industry. More specifically,
Amazon is almost a synonym for e-retailing and with its size and market dominance Amazon
clearly is the industry leader. As mentioned before, even though it is difficult to find reliable
market share data, Amazon is the world’s largest online retailer with sales totaling $74.5bn in
2013 compared to $8.6bn of Alibaba and estimated online-sales of $9bn for Wal-Mart, for
example (margins will be discussed in the next section). Also, recent predictions show that ecommerce sales will grow more than any other segment of the retail industry in the years to
come. While this will probably attract even more competitors, it also suggests a bright future
for Amazon, since market growth means less intense rivalry (at least in theory) and Amazon
could further exploit its dominance. Regarding growth rates (see charts below), for the United
States eMarketer predicts a CAGR of 12.8% until 2018, while Forester Research projects a
CAGR of 10.1% for Europe until 2017. Developing markets will also play a major role in ecommerce going forward. For example, the Chinese e-commerce market (the largest national
e-commerce market) grew by 65% in 2013 compared to 23% in North America and 17% in
Europe.
121
 Second, Amazon was a first-mover in e-commerce and shaped the industry from its
beginnings. Jeff Bezos (Founder and CEO) claims that one of the major reasons for his firm’s
leadership position is innovation and experimentation. Over the years, Amazon was able to
maintain this position and today Amazon still remains the industry standard and shapes the
industry. This is clearly evidenced by Amazon’s high investments in future growth. In 3Q
2014, Amazon reported earnings with a net loss of $437mn or 95 cents per share (vs. -$41mn
or -9 cents in 3Q 2013), which was mainly due to high investments not only in technology and
retail activities but also in categories as diverse as book publishing, same-day grocery delivery
(“Amazon Fresh”) and video game live-streaming ($970mn acquisition of Twitch). All these
investments will support Amazon’s growth for years to come.
 Third, Amazon enjoys a very high customer loyalty through its excellent customer relationship
management and also the shopping experience it offers to customers. More specifically,
Amazon’s corporate culture is all about creating superior customer value (and hence aboveaverage returns) with the following mission statement: “Amazon.com strives to be Earth's
most customer-centric company where people can find and discover virtually anything they
want to buy online. By giving customers more of what they want - low prices, vast selection,
and convenience - Amazon.com continues to grow and evolve as a world-class e-commerce
platform.” Another indicator of Amazon’s customer centric vision is Jeff Bezos’ 1997 letter to
shareholders, which is attached to every annual report since then. This letter contains two key
topics: It is all about the long term and it is all about increasing customer value. These two
statements serve as a paramount example of strategic thinking. Also, in a recent interview with
Forbes magazine, Bezos said that he isn’t focused on the optics of the next quarter. Instead he
is planting seeds today to wait for them to grow into trees and he is focused on what is good
for customers.
122
E-Commerce Market Projections:
Sources: http://www.internetretailer.com/trends/sales/us-e-commerce-sales-2013-2017/ and
http://www.internetretailer.com/trends/sales/european-e-commerce-forecast-2013-2017/
Sources: http://www.internetretailer.com/trends/sales/china-leads-way-e-commerce-growth/ and
http://www.internetretailer.com/trends/sales/how-china-and-us-compare-e-commerce-2010-2013/
123
6. Do Amazon’s Financials support this Strategic Analysis?
Ultimately stock market investors are interested in only two figures: revenues and margins. More
precisely, they are not interested in the status quo, but want to see revenue (“top line”) growth
and margin stability going forward, which when both goals are achieved leads to earnings
(“bottom line”) growth and eventually to rising stock prices. Since Amazon is a publicly traded
company this notion forms the basis for the following financial analysis.
The analysis over the four-year period from 2010 to 2013 shows a tremendous top-line growth
with a revenue CAGR of 29.6%, whereas net income deteriorated during the same period. When
looking at financial ratios, the results regarding efficiency measures (ROA, ROE, and ROIC) and
also profitability measures (gross, operating and net margin) are quite similar. While both,
efficiency and profitability measures (except gross margin) were on average decreasing since
2010, they saw their lows in 2012 with a slight improve in 2013. The reason for that lies in the
constant increase in revenues, assets, long-term liabilities and equity, while operating and net
income actually decreased. Interestingly, at the same time gross profit and gross margin
increased, which clearly implies Amazon’s economies of scale in COGS. The decreasing
operating and net income figures can be attributed to an over-proportional increase in operating
expenses (compared to revenue growth) as well as an increase in non-operating expenses (mainly
interest expense, equity investments and other). However, these cost increases are no major cause
for concern, since they show Amazon’s continued effort to expand its business through high
investment activity, which is also evidenced by increasing CAPEX.
After all, Amazon still generates positive free cash flow to provide for debt service/repayment
and dividend payments (even though Amazon never paid a dividend so far). Another mitigating
factor for Amazon’s very weak earnings is its constant increase in operating cash flow as well as
its constantly increasing cash conversion rate (operating cash flow/operating income), even
though its cash flow-to-sales ratio slightly declined since 2010. Furthermore, one has to consider
that Amazon is a technology firm and as such can be categorized as a growth stock (vs. a value
stock), which usually trade at higher P/E ratios due to their low earnings.
Altogether, the above analysis shows that Amazon is a financially solid company, which has
successfully formulated and implemented a value-creating strategy in the past and the indicators
discussed in the preceding section show that it will also be able to do so in the future. Hence,
124
despite its weak profitability, Amazon has strong potential and from the standpoint of an
investment analyst I would recommend to buy the stock (not considering the current
economic/stock market environment).
Amazon’s Financial Analysis
30%
1,600
1,400
25%
1,200
20%
1,000
800
15%
600
10%
400
5%
200
0%
0
-200
2010
Operating Income
2011
Net Income
2012
Operating Margin (%)
2013
Net Margin (%)
-5%
Gross Margin (%)
6,000
12%
5,000
10%
4,000
8%
3,000
6%
2,000
4%
1,000
2%
0
0%
2010
OCF
2011
CAPEX
FCFF (OCF - CAPEX)
2012
CAPEX/Sales (%)
2013
OCF/Sales (%)
125
20%
16%
12%
8%
4%
0%
2010
2011
2012
2013
-4%
ROE (%)
ROA (%)
ROIC (%)
Notations and formulas used in the above diagrams:







ROIC (return on invested capital) = NOPAT/Invested Capital
NOPAT (net operating profit after tax) = EBIT x (1-tax rate)
Invested Capital = Book Value of Long-term Debt + Book Value of Equity
ROA (return on assets) = Net Income/Total Assets
ROE (return on equity) = Net Income/Total Equity
FCFF (free cash flow to firm) = OCF (operating cash flow) – CAPEX (capital expenditures)
(only approximated due to missing information about cash interest)
Gross Margin = Gross Profit/Total Revenues
Operating Margin = EBIT/Total Revenues
Net Margin = Net Income/Total Revenues
126
Income Statement
Revenues
% change
COGS
% change
Gross profit
% change
OPEX (excl. COGS)
% change
Operating Income
% change
Pretax Income
Income Taxes
% tax rate
Net Income
% change
2010
34,204
26,561
7,643
6,237
1,406
1,497
352
23.5%
1,152
-
2011
48,077
40.6%
37,288
40.4%
10,789
41.2%
9,927
59.2%
862
-38.7%
934
291
31.2%
631
-45.2%
2012
61,093
27.1%
45,971
23.3%
15,122
40.2%
14,446
45.5%
676
-21.6%
544
428
78.7%
(39)
n/a
2013
74,452
21.9%
54,181
17.9%
20,271
34.0%
19,526
35.2%
745
10.2%
506
161
31.8%
274
n/a
Balance Sheet
Total Current Assets
Total Assets
Total Current Liabilites
Total Liabilities
Total Equity
2010
13,747
18,797
10,372
11,933
6,864
2011
17,490
25,278
14,896
17,521
7,757
2012
21,296
32,555
19,002
24,363
8,192
2013
24,625
40,159
22,980
30,413
9,746
Cash Flow Statement
OCF
CAPEX
CAPEX incl. acquisitions
2010
3,495
979
1,331
2011
3,903
1,811
2,516
2012
4,180
3,785
4,530
2013
5,475
3,444
3,756
Financial Ratios
ROE (%)
ROA (%)
Gross Margin (%)
Operating Margin (%)
Net Margin (%)
ROIC (%)
CAPEX/Sales (%)
OCF/Sales (%)
FCFF (OCF - CAPEX)
Cash Conversion Rate
2010
16.8%
6.1%
22.3%
4.1%
3.4%
12.8%
3.9%
10.2%
2,516
249%
2011
8.1%
2.5%
22.4%
1.8%
1.3%
5.7%
5.2%
8.1%
2,092
453%
2012
-0.5%
-0.1%
24.8%
1.1%
-0.1%
1.3%
7.4%
6.8%
395
618%
2013
2.8%
0.7%
27.2%
1.0%
0.4%
3.9%
5.0%
7.4%
2,031
735%
ROIC Calculation
Cash and Equivalents
Marketable Securities
Ling-term debt
Short-term debt
2010
3,777
4,985
1,561
-
2011
5,269
4,307
2,625
-
2012
8,084
3,364
3,084
-
2013
8,658
3,789
3,191
-
IC = BV LTD + BV EQ
8,425
10,382
11,276
12,937
NOPAT = EBIT * (1 - tax %)
1,075
593
144
508
127