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Executive Summary
This thesis is motivated by the challenges of contemporary finance in appraising
companies according to their underlying, intrinsic value. Scholars and practitioners within
the field of financial analysis have searched in futility for an efficient and accurate valuation
technique; a single, magic wand that could estimate companies’ intrinsic value accurately.
Instead, several competing contemporary valuation methods occupy the literature today.
The purpose of the thesis is to find the intrinsic value of Vestas. To do so, this thesis will
evaluate and discuss contemporary valuation models in order to choose the most
appropriate models. Vestas is of particular interest being a Danish market leader within a
major growth industry. Under the current momentum that wind power enjoys, it is
relevant to examine if the intrinsic value of the company deviates from the current market
price.
In order to answer the thesis statement, the environmental, competitive, and internal
situation of Vestas will be addressed. The strategic analyses reveal the profitable prospects
for the industry, but also disclose an enormous dependence on political support. Vestas
also faces an increasingly tougher competition. A historical financial analysis reveals that
Vestas’ has increased profitability, mainly due to low working capital and an improved
gross margin.
By synthesising the strategic and financial analyses, the intrinsic value of Vestas is then
derived under three different scenarios. The estimates are then examined via a sensitivity
analysis, which is supplemented by a simulation and a peer group analysis.
The thesis finds an intrinsic value estimate of 53.91 EUR but also note significant,
theoretical issues within the employed financial theory. This thesis therefore stresses that
estimates, share price targets, buy/sell recommendations, and so forth should be treated
extremely critically and one must not be seduced by precise formulas with imprecise
assumptions (Graham 1974).
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The concept of future prospects and particularly of continued growth in the future invites the
application of formulas out of higher mathematics to establish
the present value of the favored issue.
But the combination of precise formulas with highly imprecise assumptions can be used
to establish, or rather justify, practically any value one wishes.
Benjamin Graham 1973, “The Intelligent Investor”, 4th ed.,
Harper and Row, New York, pp. 315-316.
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Brief Table of Contents
EXECUTIVE SUMMARY
I
PART I – THESIS INTRODUCTION
1
PART II - CRITICAL REVIEW OF VALUATION MODELS
5
PART III – STRATEGIC ANALYSIS OF VESTAS
21
3.
COMPANY PROFILE
21
4.
EXTERNAL STRATEGIC ANALYSIS
22
5.
INTERNAL STRATEGIC ANALYSIS
36
PART IV – FINANCIAL STATEMENT ANALYSIS OF VESTAS
42
6.
42
HISTORICAL FINANCIAL ANALYSIS
PART V – VALUATION OF VESTAS
48
7.
ESTIMATING THE COST OF CAPITAL
48
8.
VALUATION
54
9.
CONCLUSION
69
10.
BIBLIOGRAPHY
71
11.
APPENDICES
77
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COMPREHENSIVE TABLE OF CONTENTS
EXECUTIVE SUMMARY
I
PART I – THESIS INTRODUCTION
1
1.1.
INTRODUCTION
1
1.2.
THESIS STATEMENT
1
1.3.
SOURCES AND LITERATURE
2
1.4.
DELIMITATION
3
PART II - CRITICAL REVIEW OF VALUATION MODELS
5
2.1.
INTRODUCING THE VALUATION CONUNDRUM
5
2.2.
THE TECHNICAL VERSUS FUNDAMENTAL CONUNDRUM
5
2.3.
FOUR CATEGORIES OF MODELS WITHIN FUNDAMENTAL VALUATION
7
2.4.
THEORETICAL FOUNDATION
7
2.4.1.
Asset-based Valuation Models
7
2.4.2.
Multiple Valuation Models
8
2.4.3.
Accounting-based Valuation Models
10
2.4.3.1.
Dividend Discount Models
11
2.4.3.2.
Cash-Flow Valuation Models
12
2.4.3.3.
Economic Profit Valuation Models
12
2.4.3.4.
Residual Income Valuation Models
13
2.4.3.5.
Abnormal Earnings Growth Valuation Models
14
2.4.4.
Real Options Valuation
15
2.5.
CONCLUDING DISCUSSION OF THE FOUR GROUPS OF VALUATION MODELS
16
2.6.
THE AUTHOR’S SYNTHESISING CONCLUSION
18
2.7.
SUMMARISING THE CRITICAL REVIEW OF VALUATION MODELS
19
2.8.
APPLICABILITY OF VALUATION MODELS TO A VALUATION OF VESTAS
19
PART III – STRATEGIC ANALYSIS OF VESTAS
21
3.
COMPANY PROFILE
21
4.
EXTERNAL STRATEGIC ANALYSIS
22
4.1.
ENVIRONMENTAL ANALYSIS – USING THE PESTED FRAMEWORK
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INDUSTRY ANALYSIS - USING PORTER’S FIVE FORCES FRAMEWORK
4.2.
4.2.1.
Foundation - Static Analysis
26
26
4.2.1.1.
Threat of substitutes
27
4.2.1.2.
Rivalry among competing firms
29
4.2.1.3.
Threat of new entrants
30
4.2.1.4.
Bargaining power of customers
30
4.2.1.5.
Bargaining power of suppliers
30
4.2.2.
COMPETITOR AND DEMAND ANALYSIS – A WORLD MARKET UPDATE FROM VESTAS’ PERSPECTIVE.
4.3.
5.
Putting Porter into Perspective – Dynamic Analysis
INTERNAL STRATEGIC ANALYSIS
VALUE CHAIN ANALYSIS – USING BARNEY’S VRIO FRAMEWORK
5.1.
31
32
36
36
5.1.1.
Primary Activities
37
5.1.2.
Supportive Activities
39
5.2.
SUMMARISING SWOT TABLE
40
PART IV – FINANCIAL STATEMENT ANALYSIS OF VESTAS
42
6.
42
HISTORICAL FINANCIAL ANALYSIS
6.1.
INTRODUCTION
42
6.2.
GROWTH
43
6.3.
ROIC
44
6.4.
CREDIT RISK
46
PART V – VALUATION OF VESTAS
48
7.
48
ESTIMATING THE COST OF CAPITAL
7.1.
INTRODUCTION
48
7.2.
ESTIMATING THE COST OF EQUITY
48
7.3.
ESTIMATING BETA
50
7.4.
WACC
52
8.
8.1.
VALUATION
INTRODUCTION
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8.2.
FORECAST-DRIVERS AND CONSTANT VARIABLES
54
8.3.
BASE, BEARISH, AND BULLISH SCENARIO-ANALYSES
56
8.3.1.
Base case scenario
56
8.3.2.
Bearish scenario
58
8.3.3.
Bullish scenario
60
8.3.4.
Conclusion on scenario analyses
61
8.4.
SENSITIVITY ANALYSIS
61
8.5.
SIMULATION OF FORECAST-DRIVERS
64
8.6.
PEER GROUP ANALYSIS
65
8.7.
COMPARING PRICE TARGETS AND ESTIMATES.
67
9.
CONCLUSION
10.
BIBLIOGRAPHY
69
71
10.1.
ACADEMIC BOOKS AND ARTICLES
71
10.2.
REPORTS AND OTHER SOURCES
75
NEWS PAPER ARTICLES
76
11.
77
APPENDICES
11.1.
MARKET PREMIUM CALCULATIONS
77
11.2.
MARKET WEIGHT REPORT CSE 15/04-09
78
11.3.
LUNDHOLM’S CREDIT RISK FRAMEWORK
79
11.4.
GUIDE TO THE VALUATION MODEL
80
11.5.
BASE SCENARIO – VALUATION SUMMARY
81
11.6.
BEARISH SCENARIO – VALUATION SUMMARY
82
11.7.
BULLISH SCENARIO – VALUATION SUMMARY
83
11.8.
CONSTANTLY HELD VARIABLES IN THE FORECAST
84
11.9.
PRICE TARGETS FROM LEADING INVESTMENT BANKS
86
11.10.
THREE PEER GROUP ANALYSES: WIND, SOLAR, INDUSTRIALS
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LIST OF FIGURES
Figure 1: Displaying the two main groups of valuation approaches. ............................................... 5
Figure 2: Displaying the four main categories of valuation models. ................................................ 7
Figure 3: PESTED-analysis - Overview....................................................................................................... 23
Figure 4: Impact of PTC expiration on annual installation of wind capacity on US market. . 24
Figure 5: Self-reinforcing relation: Government support, Demand, and Cost reductions. .... 24
Figure 6: Industry Analysis using the Five Forces Framework – Overview ................................ 27
Figure 7 In-depth presentation of Threat of Substitutes analysis................................................... 28
Figure 8: Relative energy prices for different energy sources. ......................................................... 29
Figure 9: Five Forces Framework in a time-oriented perspective .................................................. 31
Figure 10: BTM Consult’s figure showing the top ten suppliers’ market share and presence
in 2008. ................................................................................................................................................................... 33
Figure 11: The leading suppliers in the top 10 markets in 2008. .................................................... 34
Figure 12: Value chain activities as well as their competitive and financial implications ... 37
Figure 13 Summarising SWOT-analysis .................................................................................................... 41
Figure 14: Historical growth rates – Geographical segments ........................................................... 43
Figure 15: Decomposing ROIC-tree for Vestas based on 2005-2008 annual reports.............. 44
Figure 16 Expense groups in Vestas in percent of total revenue 2005-2008 ............................. 45
Figure 17: Vestas’ Credit Risk in the Lundholm Framework ............................................................ 47
Figure 18: Estimated market premiums in recent research - S&P 500......................................... 49
Figure 19 Rolling 2-year beta regressions against the MSCI World index. .................................. 51
Figure 20 Historical and future decomposition of Vestas’ WACC. .................................................. 53
Figure 21 Overview of forecast drivers in the different valuation periods ................................. 55
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Figure 22 Development in growth, ROIC, EBITA and gross margin in the base case scenario
................................................................................................................................................................................... 57
Figure 23: Development of EP and FCF in the detailed forecast period. ...................................... 58
Figure 24 Development in growth, ROIC, EBITA and gross margin in the bearish scenario 59
Figure 25 Development in growth, ROIC, EBITA and gross margin in the bullish scenario . 60
Figure 26: The three scenario share price estimates and the weighted share price estimate.
................................................................................................................................................................................... 61
Figure 27: Sensitivity Analysis – Effect on share price, table and graph ...................................... 63
Figure 28: Histogram of simulated share price estimates.................................................................. 64
Figure 29 Peer Group analysis of Vestas against Wind Energy Peers ........................................... 66
Figure 30 Overview of the price targets in EUR from analysts and this thesis. ......................... 67
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Part I – Thesis Introduction
1.1. Introduction
Central within renewable energy is wind power. Despite Denmark’s small size and lack of
heavy industrial tradition, the global market leader within the field is Vestas Wind Systems
A/S (Vestas) Danish (BTM 2008: 25). The company is the third largest Danish company
measured on their 10.2 % market weight (Bloomberg: 14/04-09).
The main motivation behind this thesis the challenges of contemporary finance in
appraising companies according to their underlying, intrinsic value (Penman 2007: vi)
Vestas’ position is within a growth industry entailed by huge potentials but also major risks
(BTM 2008: vi). Under the current momentum that wind energy enjoys, it is relevant to
examine if the intrinsic value of the company deviates from the current market price.
This paper is also motivated by the abundance of competing, contemporary valuation
methods and their different, theoretical foundations. Although residual income, abnormal
earnings, and real options models are prominent within academia, practitioners often
revert back to multiples and discounted cash flow models (Barker 1999). Therefore,
contemporary valuation models will be evaluated and discussed in order to provide a
thorough overview of the valuation models.
1.2. Thesis statement
The purpose of this paper is to find the intrinsic value of Vestas, which is of particular
interest being a Danish world market leader within a major growth industry. This paper
will answer the thesis statement:
What is the estimated, intrinsic value of a Vestas’ share in a stand-alone case?
In order to answer the thesis statement, the following issues will be addressed.
Contemporary valuation models and their theoretical backgrounds will be examined. Their
differences, advantages and disadvantages, as well as their usage by practitioners will also
be critically reviewed. The review will result in a selection of appropriate models for the
valuation of Vestas.
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In an external strategic analysis, the external factors that can affect the profitability of wind
energy will be discussed. The competitive situation of Vestas will also be addressed. An
internal strategic analysis shall discuss and clarify Vestas’ competitive advantages.
A historical financial analysis will reveal both past and current drivers of Vestas’
profitability.
Prior to the valuation, the problematic, theoretical issues concerning cost of capital will be
addressed in order to estimate Vestas’ cost of capital. Synthesising the strategic and
financial analyses will then enable a forecast of the pro-forma budgets. Using the cost of
capital, the intrinsic value of Vestas will be derived under base, bearish, and bullish
scenarios. The value estimates’ sensitivity to changes in key variables will be examined via
a sensitivity analysis, which will be supplemented by a simulation and a peer group
analysis.
1.3. Sources and literature
The paper is based on both qualitative and quantitative data. The strategic analyses are
primarily based on qualitative, secondary data from market reports, journals, news papers,
but also Vestas’ own publications and has therefore been treated very critically. The same
applies to the financial analysis, which is mainly based on quantitative, secondary data
from Vestas’ annual reports. Even though Vestas’ reports have been verified by publicly
chartered accountants, all the material from Vestas is expected to be of a subjective
character and displaying Vestas in the best possible light within the limitations of the law
(Clatworthy 2005: 63-77).
One of the most significant providers of market information within wind energy is Danish
BTM Consult. BTM Consult, however, was founded in 1986 by two former Vestas
employees and maintains a strong cooperation with Vestas (Jensen 2003: 237). For
instance, Vestas also recognises BTM as their market information provider (AR 2008:17).
Therefore, special attention has been paid to any bias in the provided information from
BTM.
A profound literature research was conducted in order to form the theoretical basis. Most
of the relevant literature has been incorporated in the making of this thesis, although
additional texts could always have been included.
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1.4. Delimitation
It is necessary to make certain delimitations in order to keep the thesis focused on finding
the intrinsic value of Vestas on a stand-alone basis.
The thesis could have been based on other scenarios. For instance, Vestas could be
acquired, for instance, by its’ competitor, American GE Wind. This would require profound
knowledge of the acquirer, possible synergy effects, and a new required rate of return
(Koller 2007: 447-52). Doing so would ultimately change the focus of this thesis.
The thesis has been performed from an external, long term investor’s perspective without
any access to information from internal compartments of Vestas.
The different valuation models will be reviewed and discussed in order to have an
overview of the different theoretical models, as opposed to using any arbitrary model.
However, to maintain focus, there will be no empirical studies of the models’ accuracy or
practitioners’ usage of these models. The discussion will be based on previous studies. The
financial markets are also assumed to be efficient according to Fama’s Efficient Market
Hypothesis, as there will be no in-depth discussion of this topic (Fama 1970). The thesis’
focus within the finance area also means that the accounting standards and techniques of
Vestas and shall remain largely uncommented.
The terms “intrinsic value of Vestas” and “intrinsic value of Vestas’ share” can obviously be
used interchangeably, as it is just a question of dividing by number of shares. Still, focus
will be on the latter.
The strategic analyses could easily have been extended in size, but the limited space has
been spent in other areas. Therefore, the strategic analyses will be concise and summarised
in figures. Nevertheless, all major findings have been included and the analyses still
provide a comprehensive picture.
This thesis has been written using British English. Also, inclusive first person pronouns,
“we”, will also be employed to include the reader in the thesis for the purpose of linguistic
variation even though there is a single author.
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The annual report for 2008 was published February 11, 2009. The next quarterly report is
publicised April 28, just 3 days before the hand-in of this thesis. Therefore, April 14 has
been chosen as the cut-off date.
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Part II - Critical review of valuation models
2.1. Introducing the valuation conundrum
People working within the field of financial analysis have for decades been searching for an
efficient and accurate valuation technique, the magic wand that can estimate companies’
intrinsic values accurately (Gentry 2003: 3). Academics have sought after the wand in
scientific endeavour, whereas professionals and investors have sought after competitive
advantages and comparatively superior returns. Both groups have been unable to develop
a universally applicable and accurate technique although various different models have
been put forward (Gentry 2003: 3-4).
Ideally, the balance sheet of a company would precisely depict the current value of the firm
and thus be traded at a P/B ratio of 1.0. As Penman notes, no such perfect balance sheet
exists, hence the need for valuation techniques in the first place (Penman 2007: 199).
Being able to valuate a firm accurately can make the difference between financial success
or failure, both for investors, acquiring firms, and so forth. The purpose of this part of the
thesis is to compare and evaluate all major valuation techniques in order to choose the
most appropriate for a valuation of Vestas.
2.2. The technical versus fundamental conundrum
Generally, we can divide valuation into two supra-groups, technical and fundamental
analysis techniques.
Figure 1: Displaying the two main groups of valuation approaches.
Based on Clatworthy 2005: 47.
By far the most dominating analysis technique is fundamental analysis (Clatworthy 2005:
47). Two of the original main proponents behind the fundamentalist’s approach are
Graham and Dodd (Gentry 2003: 3). The definition given by Penman is that “fundamental
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analysis is the method of analyzing information, forecasting payoffs from that information,
and arriving at a valuation based on those forecasts… instead of market prices” (Penman
2007: 84 & 73). This is supported by Bauman (Bauman 1996: 1).
Within the fundamentalist approach, buying a share is not just buying an arbitrary piece of
paper; it means that the investor is buying a piece of a business. Therefore, Penman states
“If you are going to buy a business, know the business” (Penman 2007:14). Consequently, the
starting point in the fundamentalist’s valuation process is a thorough strategic analysis of
the firm. A comprehensive understanding of the firm’s economic factors is necessary before
using valuation models (Penman 2007: 16-17).
Contrary to the fundamentalist’s approach, technical analysts believe that share prices are
determined entirely by supply and demand in the stock market (Clatworthy 1996: 50).
Technical analysis involves studying the historical actions of the market instead of studying
the firm, its goods or its surrounding market (Edwards 2001). Several scholars have
confirmed technical analysis’ predictive powers (Canegrati 2008, Bettman 2009). Irwin
also conducted a meta-study of 96 studies regarding technical analysis and concluded that
56 studies yielded positive results in favour of the technique. However, Irwin also stresses
that there are serious, problematic issues with data snooping within these studies (Irwin
2007). Bettman suggests a triangulation of fundamental and technical analysis to
synthesise the strengths of the two methods into one superior approach (Bettman 2009).
Bettman’s suggestion is interesting, but further studies are needed within the area to
validate the findings.
In conclusion, for the matter of this thesis we regard technical analysis as a tool most
beneficial to day traders and the like, with no or very little benefits to long-term investors.
Fundamentalist investors are often very dismissive of technical analysis. As the American
investment guru Warren Buffett expresses “If past history was all there was to the game, the
richest people would be librarians”.1 The focus in this thesis will be entirely on
fundamentalist techniques. Fundamental investors focus on long-term profitability and
ignore short-term volatility in stock prices (Penman 2007: 19).
1
http://www.guardian.co.uk/world/2008/apr/20/usa.subprimecrisis
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2.3. Four categories of models within fundamental valuation
Within the fundamental valuation approach, scholars put forward the following four
categories of models.
Figure 2: Displaying the four main categories of valuation models.
Based on Plenborg 2000: 4 and Damodaran 2006: 9.
With such a wide range of contemporary valuation models, it is essential to have an
overview and critically scrutinise the models prior to any usage (Penman 2007: 18-20).
Penman believes a good valuation model has three main ingredients: good thinking, good
application and a good balance between costs and benefits (Penman 2007: 21). Plenborg
proposes four criteria: the model’s precision, realistic assumptions, usability, and a fair
value that is easy to understand (Plenborg 2000: 2-3). Their tenets will form the basis for
the assessment of the models, which will follow in the next section, starting with assetbased valuation models.
2.4. Theoretical foundation
2.4.1.
Asset-based Valuation Models
Asset-based valuation models values all assets of the firm at an estimated fair value, which
can easily be expressed algebraically. Total firm value V firm equals market value of assets
MV ( A) .
V firm  MV ( A)
Based on Penman 2007: 82.
Although the model may seem simple, it can be time-consuming to assess all assets of
especially larger firms. Penman also points out several theoretical weaknesses. Market
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values may be unavailable for illiquid assets. Even if market values are available, they may
not be correct measures for the intrinsic value of the specific assets in a particular going
concern. Finally, the value of brand assets and firm synergies are notoriously difficult to
estimate (Penman 2007: 82).
The models are, however, applicable to smaller firms with a high value of inherent goodwill
such as restaurants and so forth. The models also apply to valuation of asset-heavy
companies, often within primary industries, as most of the assets in these firms are
tangible goods with relatively precise market values. Asset-based valuation is also of
relevance for firms that are soon expected to be liquidated (Penman 2007: 82-83). Still, the
models have a limited scope for usage and are therefore only discussed briefly.
2.4.2.
Multiple Valuation Models
In the 1930’s, Graham and Dodd argued that analysing firms’ key ratios could reveal
investment opportunities (Gentry 2003). Multiple-based valuation today forms a part of
most text-books within valuation (Koller 2005, Damodaran 2006, Penman 2007).
The reason for the multiples’ popularity is their apparent simplicity (Koller 2005: 362).
Multiples are simply a market price variable, often stock price, divided by one of the firm’s
value driver; earnings, book value or sales to name a few. Algebraically, this is expressed as
follows. Vx is the price for the firm that is being valuated. V y is the value driver for the firm
that is being compared,  is the multiple on the value driver, and  is the pricing error
(Plenborg 2000: 18).
Vx   * V y  
Based on Liu 2002, and Plenborg 2000.
Investors can quickly compare firms’ multiples based on the market values for comparable
firms within the same industry, the so-called peer group. Multiples are mainly used to
compare publicly traded firms across industries. The models can also be applied to
privately held firms. In acquisitions or IPOs of private firms, investors can use multiples
from existing publicly-traded firms to estimate the firm’s potential market value. As a
complement to more complex valuation techniques, practitioners regularly present
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valuations partially based on multiples, such as the price to earnings multiple (Koller 2005:
61).
The necessary steps in the model appear simple. First, comparable firms, the peer group,
are identified. Next, the relevant value multiples are chosen. Finally, the multiples are then
aggregated into single numbers by choosing a weighing of the peer group figures and
applied to the corresponding value driver of the firm being valued (Penman 2007: 76.)
Multiple valuation models do not require detailed multi-year forecasts concerning complex
numbers such as profitability, growth, and risk. The belief is that the market is efficient in
pricing these.
The models’ simplicity is, however, deceiving. The selection of “truly” relevant value
drivers and identifying the “truly” comparable peer group is highly problematic. Deciding
how to calculate the firm multiples and how to weigh the combined, peer group multiples
also cause much problems (Schreiner 2007: 2-4).
During the research for this thesis, the amount of literature dealing with multiple-based
models is immensely less than literature dealing with accounting-based models.
Paradoxically, the most frequently used models in real life receive the least attention from
scholars (Schreiner 2007: V).
Most importantly, by basing its foundation on market prices, multiple-based models
assume that the markets are efficient in their pricing (Fama 1970). This is a highly
debatable topic according to fundamentalist investors, and one could discuss if the method
is within the fundamental approach in the first place, although “pure” fundamentalist
valuation without referring to market prices at some point is difficult (Penman 2007: 7682).
Researchers differ in their view on multiples. Penman dismisses multiples referring to the
tenet that a valuation of the intrinsic value can under no circumstance be based on existing
market prices (Penman 2007: 77). However, both Koller and especially Damodaran
approve of the method as a valid and almost necessary sanity check to any accountingbased valuation and also as a source of insight into the value drivers of the firm (Koller
2005: 378 and Damodaran 2005). Koller and Schreiner agree on the superiority of
forwarded multiples, that is, estimation of for instance forwarded earnings, instead of using
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historical data. However, they also disagree, as Koller argues heavily in favour for using
adjusted enterprise value multiples to avoid biases from capital structure and nonoperating profit and losses (Koller 2005:366) This makes sense logically, but Schreiner’s
empirical data indicate that equity value multiples are superior (Schreiner 2007: 100).
There is obviously no consensus within area. To conclude this discussion, we note that
stand-alone multiple evaluation can rarely be used except in special cases such as in
acquisitions or IPOs of private firms. Some scholars also approve it as a quick complement
to other models. Koller is the strongest proponent, arguing that multiple valuation should
receive as much attention, as the model it supports (Koller 2005:380) Penman and
Plenborg, however, oppose multiple valuation and remain very critical of the model
(Penman 2007: 77-78, Plenborg 2000: 24-26).
2.4.3.
Accounting-based Valuation Models
Prior to dealing with the different forms of accounting-based valuation, a few issues that
apply to all models within this category will be addressed. All models assume some form of
discount factor based on the cost of capital. To estimate these unobservable costs of capital
or equity2, asset pricing models can translate firm risk into expected returns (Penman
2007: 471-76).
The most commonly used model is Sharpe’s one-factor CAPM model (Sharpe 1964). Two
common alternatives, Fama-French’s three-factor model and the arbitrage pricing theory
models shall not be used, as Penman points out, even the one-factor CAPM is demanding…we
really don’t know what the cost of capital for most firms is” (Penman 2007: 114-16). The
focus in this thesis will therefore be on Sharpe’s CAPM model.
The CAPM model claims that the expected rate of return on a security E(ri) is equal to the
risk-free rate rf plus the security’s beta i times the expected return of the market E(rm)
subtracted by the risk-free rate rf. Beta is a measure of the firm’s correlation with the
market and thus the only firm specific measure in the model.
E(ri) = rf + i [E(rm) – rf]
2
The choice naturally depends on whether the model presents firm or equity value, respectively.
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Source: Koller 2005:294.
Especially beta and the market risk premium are highly difficult to estimate (Koller 2005:
298). The risk-free rate is relatively easy to assess, as ten-year government bonds can be
used as a proxy. For more on this topic, see section 7.2 Estimating the Cost of Equity, page
48.
The brief introduction to cost of capital shows the huge uncertainty in any estimate of the
cost of capital or equity. Unfortunately, the estimates play an integral part of all the models
as the discount factor in the denominator, as we will see briefly. The issue constitutes one
of the major weaknesses in all the accounting-based models (Penman 2007: 476).
All the models expect for the Discount dividend model also contain problem in the
estimation of their terminal value. The idea is that growth tends to mean-revert in the long
period (Plenborg 2009: 7). In order to maintain focus on the differences of the models and
for the sake of simplicity, infinite valuation periods are assumed for all the following
accounting-based models.
2.4.3.1. Dividend Discount Models
The accounting-based valuation models all build on Williams’ Dividend discount model
from 1938 which was extended with the classic Gordon Growth Model (Gentry 2003: 3).
The discounted dividend model (DDM) is as follows.

Veq  
t 1
div
(1  re ) t
Based on Plenborg 2000: 6.
The model provides the equity value of the firm by discounting all future dividends by the
required return on equity of the investors. This is the basic idea in all the following
accounting-based models. The model seems simple and intuitively correct, as shareholders
focus on dividends, at least in the short term (Penman 2007:122). The weakness of the
dividend model is precisely its focus on dividends; it ignores value creation in the firm
(Plenborg 2000: 6). Its main applicability is to firms in stable industries with stable
earnings and fixed payout ratio (Penman 2007:122). In practical terms, such conditions
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apply to few firms, which partly explain why the model is uncommonly used by
practitioners.
2.4.3.2. Cash-Flow Valuation Models
Various types of cash flow models exist. Koller presents enterprise, capital and equity cash
flow models, but the focus will here be on the enterprise discounted cash flow model (DCF)
(Koller 2005:102). DCF provides the total value of a firm and its operations from an
enterprise or entity perspective, whereas DDM focuses on the equity perspective. In the
DCF, free cash flows to both equity and debt holders are discounted by a weighted average
cost of capital (WACC).

V firm  
t 1
FCFF
(1  WACC ) t
Based on Plenborg 2000: 7
The model is popular and appears logical as cash flows as physical entities that are easy to
think about (Penman 2007: 127). Another strong point is the model’s incorporation of tax
shield effects by using WACC (Koller 2005:111). Despite its popularity amongst
practitioners, the model has serious theoretical flaws. It assumes that all liquidity after
payments of dividends is invested in projects returning a rate of return equal to the cost of
capital. Historically, some firms tend to “hog” excess liquidity for unprofitable projects
instead of paying it out or buying back shares (Plenborg 2000:9). Penman also points out
the highly problematic issue that necessary, sound investments are treated as a loss of
value, at least in the short term. In real life, investments are necessary for firms’ survival,
even if the returns may lie years ahead. Long forecast horizons are therefore needed which
is another weakness of the model (Penman 2007: 127). Penman finds the model applicable
primarily to firms with stable cash flows or constantly growing cash flows, which are
rather unrealistic assumptions (Penman 2007:127). Koller disagrees with Penman and
maintains his primary foundation in the DCF framework (Koller 2005: 101-02)
2.4.3.3. Economic Profit Valuation Models
This category includes various valuation models that originate as far back as the 19th
century (Penman 2007: 186). The models were revived in the 1990’s as several
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consultancies and accounting firms started using the framework. Examples include
McKinsey’s Economic Profit model, Stern Stewart’s Economic Value Added model, or
KPMG’s Value Added model (Penman 2007: 89). Here, focus will be on McKinsey’s EP
model. Via algebra, the DCF model can be reformulated to the following formula.

V firm  ICt * 
ROIC t  WACC  * ICt 1
(1  WACC ) t
t 1
Based on Plenborg 2000: 9
The model shows that a firm is only able to provide an economic profit if its return on
invested capital (ROIC) exceeds its costs of capital, WACC. Otherwise, firm value equals
invested capital (ICt). Like DCF, EP is also made from the firm perspective.
Whereas DCF treats even sound investments as declining cash flow, EP provides a better
insight into the actual economic performance of the firm. Thus, both DCF and EP models
provide relevant and valuable information, but in different ways (Koller 2007: 116). The
advantages and disadvantages of EP models will be discussed in the following section
concerning RI models, which are the equity counterpart of EP models.
2.4.3.4. Residual Income Valuation Models
The recent popularity of the residual income model (RI) is attributed to the analytical work
of Ohlson (Ohlson 1995). In the same way DCF is the enterprise equivalent of the equitybased DDM, EP is the enterprise equivalent of the equity-based RI model. (Plenborg 2000:
10). The formula reflects the difference.

REq t  re  * Eqt 1
t 1
(1  re ) t
Veq  Eqt * 
Based on Plenborg 2000: 10
The model shows, that a firm is only able to provide residual income, if the rate of return on
its equity (REq) exceeds its required return on equity (re). Otherwise equity value equals
equity (Eqt).
The more accounting-based EP and RI models assume clean surplus accounting (CSA) in
the accounts, which are not required in the more flow-based CF models. CSA requires that
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all income and expenses are placed on the income statement and not on the statement of
equity (Plenborg 2000: 11). The most common examples of breaches of CSA include
unrealised gains and losses on securities, derivatives, and foreign currency translations
(Penman 2007: 269).
If CSA is fulfilled RI and EP can be used across different GAAPs. If not, it can have dramatic
consequences. As an example, Daimler-Benz’ 1993 annual reports showed a profit of 168
mDEM in Germany but a loss of 1 bnUSD on NYSE (Plenborg 2009: 5). If the assumption of
CSA can be maintained, however, EP and RI models can like DCF models be applied across
different types of GAAP and accounting methods (Plenborg 2000:11).
EP and RI models possess several advantages according to Penman. Highlights include the
focus on the value drivers; profitability and growth in investments. It forecasts a more
graspable term, earnings, instead of cash flow which may fluctuate depending on the level
of investments (Penman 2007: 175). Analysts and businesses also typically forecast
earnings not cash flows. This also makes it easier to validate RI and EP forecasts with the
following years’ income statement. It is harder to validate CF forecasts, as investments may
alter the picture dramatically.
In RI and EP framework more value is also recognised in the short term, which enables
shorter forecast horizons. RI and EP models are more robust to value-destroying
investments that may grow earnings but at rates less than the required rate of return
(Penman 2007: 176). The RI and EP framework is also more robust against earnings
creating by accounting manipulation. If earnings are increased via accounting
manipulation, it will be offset exactly by the lower book value (Penman 2007: 178).
The models, however, requires a profound understanding of accrual accounting and relies
on accounting numbers, which necessitates an accounting analysis (Penman 2007:175,
Plenborg 2000:15).
2.4.3.5. Abnormal Earnings Growth Valuation Models
The Abnormal Earnings Growth model (AEG) is the latest newcomer among the
accounting-based models and originates from the 1995 Ohlson framework (Ohlson 1995,
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Penman 2007:206). The model provides the value of the equity by discounting accountingbased earnings with the cost of equity.

Veq  
t 1
Earnt
(1  re ) t
Based on Plenborg 2000: 12.
The model is intuitively simple, as it also focuses on earnings, which is what many
investors focus on and it is also indifferent to different GAAPs (Penman 2007:213).
Contrary to EP and RI models though, AEG models do not provide any insight into the
drivers of growth, particularly the balance sheet. It also requires an understanding of cumdividend earnings which equals the sum of earnings of the firm and earnings of paid-out
dividends (Penman 2007: 213, Plenborg 2000: 13).
2.4.4.
Real Options Valuation
Finally, real options have received much attention in recent years’ academic literature
within valuation. A real option is the business’ option to make or abandon a capital
investment in a business project. Real options are options based on a “real”, tangible asset
and have many similarities within financial options. For instance, the exercise and share
prices from financial options are equal to, respectively, present value of the fixed costs over
the lifetime of the investment and present value of cash flows expected from the
investment (Leslie 1998).
Significant theorists within the field are Trigeorgis (1993, 2005) and Copeland et al.
(2001). They put forward that real options can play an important role in the net present
value capital budgeting processes that many firms use to choose among projects. The main
claim is that traditional net present value budget analyses (NPV) are unable to incorporate
the future value of options in a capital budgeting analysis (Trigeorgis 2005 & Block 2007:
255). On future projects, firms may ignore their option to cancel a project. A budget
analysis may then inaccurately provide a negative NPV, which then causes the project to be
discarded. However, by including the option value inherent in the project, the project may
have a positive NPV.
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Still, practitioners within equity valuation continue to use accounting-based models. The
effects of real options are therefore often ignored in contemporary equity valuation
(Atauallah 2009: 57). This can lead to seriously flawed investment decisions, as highly
profitable investments can be overlooked.
For valuation of projects on a business level, Trigeoris formulates the “new” NPV equation
as follows.
S ( NPV ) P( NPV ) ROV ( FV  SV )
The strategic S(NPV) equals the findings of a traditional P(NPV) plus the option value
ROV(FV+SV), which is the sum of the flexibility value and strategic value inherent in any
project (Trigeoris 2005: 32).
As an alternative, McKinsey & Company proposes finding the strategic NPV using a fourstage process. The idea is that the value of the real options should be added to the value of
the firm provided by a DCF model (Trigoris 2005, Ataullah 2005: 58, and Ashton 2003:
424).
Therefore, the DCF analysis is first applied. Then uncertainty in the project is modelled by
identifying cash flows in different scenarios. Next, the inherent flexibility in the
management’s different options is then modelled into a decision tree, which can also be
estimated using real option valuation (Leslie 2000).
As a technique, it is mostly relevant for oil companies in for natural-resource discovery or
for IT-companies in technology-related investments and so forth. As several scholars point
out, real options are also ideal for medical and biotech companies for example (Block 2007:
255). In these cases, the existing real options framework justifies the relatively high
complexity and uncertainty inherent in real options models. For other types of firms, the
model is rarely applicable.
2.5. Concluding discussion of the four groups of valuation models
The first section will briefly discuss the usage of the models among practitioners. Some
researchers find that multiples valuation is the most frequently used method by
practitioners and accounting-based valuation only second. Barker, for instance, states that
the P/E ratio the most important driver and FCF only secondary (Barker 1999). This is
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backed up by Demirakos, who finds that the multiple P/E is present in 90% of analysts’
reports, whereas DCF only is employed in 40% of the reports (Demirakos 2004: 230).
However, more recent research contradicts these claims. For example, Imam finds that DCF
has grown in importance among practitioners and is now, although the study focuses on
UK analysts (Imam 2008). Block finds that only 14 % of American firms use real options
within in their budget analysis, indicating the low dispersion of the method among
practitioners (Block 2007: 10).
The empirical findings are somewhat in line with the discussions in the previous,
theoretical sections. Here, the main conclusion was that accounting-based models stood
strongest, while they could benefit from multiples providing a sanity check.
Given that all the accounting-based models are based on the same underlying DDM, the
choice of model should not matter, as all models should provide the same result. In real life,
however, the RI and EP (hereafter just RI) models are less sensitive to changes in the
budget variables. The DCF model first dissolves the accounting figures and then rebuilds
them into cash flows. The DCF model therefore has to estimate both “normal” and residual
income, which increases the sensitivity to changes in variables. Residual income is the sole
focus of RI models. Several studies around the millennium proposed that RI outperforms
DCF in term of predictability and estimate accuracy, and RI were therefore recommended.
(Penman 1998, Olsson 2000, Plenborg 2000: 15). The claim was contested by Lundholm in
2001, as he argued that the whole valuation literature comparing the accuracy of DCF and
RI was misguided. He stresses that the theory prescribes that both models provide
identical valuations, if implemented correctly (Lundholm 2001). Penman acknowledges
Lundholm’s findings, but claims that practice is the final arbiter of competing models
(Penman 2001). In other words, the theorists within the field are very dispersed in their
views.
Generally, there is a somewhat established consensus that the merits of asset-based and
real options valuation are only applicable to special cases (Koller 2005:131, Penman
2007:83, Plenborg 2009:16). Little focus is currently given to asset-based and cash flow
based models in academia. Koller focuses on using both DCF and EP, stressing that DCF is
less sensitive to accounting principles (Koller 2005: 101). Penman is in favour of RI models
and criticises AEG models intensely (Plenborg 2000, Penman 2007). Ohlson, one of the
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main proponents of AEG, still maintains its merits and is supported by other scholars
(Jorgensen 2005, Ohlson 2005).
2.6.The author’s synthesising conclusion
Summarising the different views provides the following conclusions. Asset-based and real
options models can be useful but only under special circumstances. Multiples valuation can
provide a quick and dirty sanity check, but also has severe methodological and practical
issues, which are only exacerbated by the model’s heavy reliance on market’s pricing of the
peers. The accounting-based models stand strongest, and although DCF is most commonly
used by practitioners, it receives little attention within academia. Focus is definitely on the
RI versus AEG model discussion.
For general purposes, the RI and EP models are the most preferred model by the author.
They are easy to explain to readers outside the finance area, as it avoids the rather complex
term of cum-dividend earnings (Penman 2007: 213). Whereas the RI model is strong in
telling the story of a firm’s economic performance, DCF reveals the development of cash
flows. Therefore, for general purpose valuations, a combination of the RI or EP model with
a DCF model is a theoretically strong. It includes their respective advantages, although it
also adds complexity.
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2.7.Summarising the Critical review of Valuation Models
The following figure summarises the previous, theoretical discussion. Enterprise-based EP
and equity-based RI have been put together, as they share most of their advantages and
disadvantages, although the models are not completely similar.
Figure 3: Summarising the critical review of valuation models.
Category
Model name
Asset-based Replacement
Advantages
Focus on assets
Disadvantages
Cannot capture brand value
Cannot capture synergies etc.
Pricing all assets takes time
Useful for
Asset-heavy companies
Small businesses with goodwill
Liquidation
Focus on assets
No "market" liquidation values
Pricing all assets takes time
Firms soon to be liquidated
Multiples
Various
Relatively quick
No proforma budgets
Assumes market pricing
Difficult to choose peers
Difficult to choose variables
Challenging to weigh variables
Private firms going public
Private firms as target firms
Sanity check to other models
Accounting
-based
DDM
Simple, only dividends
Reliance on cost of equity
No focus on value creation
Affected by pay-out ratio
Stable firms with fixed pay-out ratios
DCF
CF is easy to think about
Incorporates tax shield effects
Indifferent to different GAAPs
Reliance on WACC
Terminal value calculation
Assumes reinvestment at WACC
Investments = value-destruction
Firms with stable CF
Firms with constantly growing CF
EP & RI
Focus on economic perf.
Earnings -> easier to forecast
Forecasts can be validated
Shorter time horizons
If CSA, indifferent to GAAPs
Robust against earnings
manipulation by accounting
Robust against earnings
manipulation by investments
Reliance on WACC / cost of eq.
Terminal value calculation
Requires CSA
Understanding of accrual acc.
Requires accounting analysis
All other firms
AEG
Shorter time horizons
Investors "buy" earnings
Indifferent to different GAAPs
Reliance on cost of equity
Terminal value calculation
Complex cum-div. earnings
Requires earnings analysis
No insight in value drivers
Unrecommendable
Ignores earnings drivers and
balance sheet
RO
Captures value of options
Complex
Requires replicable portfolio
Rarely applicable
Oil companies in resource discovery
Medical and biotech companies
Other special cases
Real
Options
2.8. Applicability of valuation models to a valuation of Vestas
The choice of valuation models for Vestas will now be discussed based on the previous,
theoretical discussions.
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Vestas is neither an asset-heavy company nor a very likely candidate to go bankrupt, as will
be demonstrated in section 6.4 Credit Risk page 46. Therefore, the asset-based models are
irrelevant for the remainder of the thesis.
Applying a real options model to Vestas would include replicating the portfolio of Vestas
and its projects (Koller 2005: 131). Modelling the portfolio is possible, but would pose
severe, time-consuming difficulties for a large, industrial firm like Vestas. Penman points
out that a model must have a good balance between cost and benefit (Penman 2007: 21).
Subsequently, real options models are not suitable for Vestas, and will not be included in
the valuation.
As mentioned in the discussion of the models, accounting-based models stand the strongest
theoretically. The EP models appear to have the most advantages and their main strength
lies in their indication of the firm’s economic performance, whereas a DCF model focuses
on the cash flows. The two models shall be used in combination, partly for validation, but
mainly because they provide different but valuable information. (Koller 2005: 116) The
models’ results will of course be thoroughly scrutinised using scenario and sensitivity
analyses, and a simulation of the value estimates. Still, multiples will also be applied as a
useful sanity check. Especially forwarded multiples models are highlighted as providing a
useful sanity checks to the value found via other models. In conclusion, the valuation will
employ DCF, EP and Multiple valuation models.
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Part III – Strategic Analysis of Vestas
The purpose of the strategic analysis part is to identify and discuss both external and
internal factors which will then enable an evaluation of Vestas’ future growth and profit
potentials (Penman 2007: 14). The analysis will have a top-down approach. The external
factors will be analysed first and followed by an analysis of Vestas’ internal factors. The
strategic analyses will be summarised in a SWOT analysis, which will provide an overview
of the analyses.
3. Company profile
Vestas, the largest supplier of wind turbines in the world3, focuses its principal activities on
the product development, manufacturing, turnkey delivery and maintenance of wind
turbine installations. The company’s sale of wind turbines is by far its main activity and
constituted 92 % of its 2008 revenue as sale of services constituted the remaining 8 % (AR
2008: 62).
Vestas was originally a hydraulic crane manufacturer which entered the wind turbine
market in the oil crisis of the late 1970s. The wind energy market expanded and Vestas
entered the US market, in which they invested heavily. When tax advantages on wind
turbine investments were removed in the US in 1986, Vestas was forced to sell off all nonwind -related assets and re-emerge as Vestas Wind Systems A/S. In the 1990s, Vestas
bought Danish Wind Technology A/S. Germany, Denmark and Spain emerged as the major
European markets. Vestas then went public on the Copenhagen Stock Exchange in 1998. In
2001, Vestas sold its 40 % stake in Gamesa Eólica, a Spanish joint-venture from 1994. In
2004, the Danish rival, NEG Micon, was then acquired.
3
Still the largest, one might add, as Vestas has been losing market share in recent years. The runner-up,
American GE Wind, is only 1.2 pct. points from Vestas’ 19.8 % market share. (BTM 2008: 24) For more on this
topic, refer to section 4.3 Competitor and Demand Analysis – A World Market Update from Vestas’
perspective., p. 32
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In 2008, Vestas supplied 5581 MW of wind turbine capacity, a 19.8 % market share (BTM
2008: 24). Vestas reports sales in more than 40 countries and maintains production
facilities in Denmark, Germany, Italy, India, the UK, Spain, Norway, Sweden and China.
Vestas’ core competence is within the larger >1.5 MW segment of wind turbines, which
constituted 92 % of Vestas’ total supplied MW in 2008 (BTM 2008: 32). 80.4 % of global
supply in MW in 2008 was within the segment. The company, via its ambitious “10 by
2010” plan, targets to increase its production capacity to 10,000MW by the end of 2010
(AR 2008: 16).
4. External Strategic Analysis
4.1. Environmental Analysis – Using the PESTED Framework
Vestas’ profitability, growth potential and value are highly dependent on external factors,
which will be analysed using a PESTED model adapted to Vestas based on Grant and
Ireland (Grant 2008: 66 & Ireland 2007: 35). The model differs from the more traditional
PESTEL, as the political and legislative segments have been combined into one segment.
Oppositely, the demographic and socio-cultural segments are split into two separate
segments. The summary will precede the actual discussion to provide an initial overview.
All the segments, their underlying elements, and the conclusions are summarised in the
figure below. In the figure, the sign of the underlying element as well as its importance
have been indicated by the coloured cells to the right.
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Figure 3: PESTED-analysis - Overview
Generel Segment
Political/Leg.
Underlying element
Kyoto-protocol
Change in EU/US politcal attitude towards the environment
US goal to reduce dependence on foreign oil
REFIT in EU and PTC in the US
Dependence on politicians' subsidies. Without, WE is expensive
Effect Magnitude
+2
+3
+1
+3
-3
Economic
Current financial crisis --> Fewer developers can find finance
Dependence on the state of the market
WE Short time-span --> Short-term interest --> Higher risks
Dependence on prices of raw materials - notably steel
Global business --> Currency exposures
-3
-2
-1
-1
-1
Socio-cultural
Changed attitude towards the enviroment
Increased focus on ethical issues
Neighbors to wind mills and animal activists
+2
+1
-1
Technological
Development of IT
Potential in +5 MW offshore wind energy
Wind research is expected to lower $/kw cost
Development of alternative, cost-efficient renewable energy
Economies of scale expected to lower $/kw cost
+1
+1
+2
-1
+1
Demographic
Population growth in countries with high energy consumption
(China/US)increasing need for energy globally
Constantly
+1
+2
Environmental
Climate debate dominated by enviromentalists (No "Lomborg")
Recent studies indicate melting poles
Environmental legislation enforced - Renewable quotas
+2
+1
+2
Adapted by the author to fit Vestas. (Model synthesises Grant 2008: 66 & Ireland 2007: 35)
As the analysis indicates, wind energy enjoys a massive political support at the moment
and especially the European REFIT and the American PTC, economic incentive schemes
have boosted the industry. However, the political support is also the industry’s Achilles’
heel. If the political winds suddenly change, Vestas and its competitors may have to
reconfigure their businesses like in 1986, when the US tax rebates were suddenly changed,
as mentioned in section 3. Company profile, page 21. To illustrate just how sensitive wind
energy still is to changes in the political support, the following graph depicts the
development on the American market in recent years where the PTC scheme expired three
times.
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Figure 4: Impact of PTC expiration on annual installation of wind capacity on US market.
Source: American Wind Energy Association.
The existing political support has a positive, self-reinforcing effect, which is crucial to the
wind industry for the time being. The major threat is obviously if political support
disappears before wind energy is competitive on an unsubsidised basis.
Figure 5: Self-reinforcing relation: Government support, Demand, and Cost reductions.
Source: Hoogwijk 2008, REN 21.
In the economic segment, there are many negative elements and the current crisis is
obviously the most urgent issue. Developers have a hard time finding the finance for their
projects, as Vestas’ CEO Ditlev Engel also acknowledges (AR 2008:05). The consequences of
the crisis are exacerbated by the 1-2 years of typical lead time depending on the project’s
size. That is, the full consequences of the crisis will not be revealed before the end of 2009,
possibly 2010.4
4
Refer to http://www.vestas.com/en/investor/announcements/company-announcements.aspx
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The change in the socio-cultural attitude towards the environment can be regarded as the
reason for the political change. Still, the socio-cultural change is given a lower importance
in the summary. The reason is the obvious fact that no policies are implemented before a
political majority in favour for “wind-energy” friendly policies. The socio-cultural change is
still important and is assigned a magnitude of +2.
The technological segment also brings positive prospects for Vestas. The future potential of
off-shore wind farms can solve both the lack of available land and also improve efficiency,
as wind speeds are typically higher offshore than onshore. The wind is also more stable
offshore, as hills and other land obstacles can create turbulent winds for onshore wind
turbines. Still, the current off-shore price €/kw is much higher due to the increased
installation and maintenance costs. Ditlev Engel also acknowledges that the off-shore will
not play a major part in the short-term (AR 2008:04). Developments in the technological
segment could also paralyse the whole industry, if for instance the solar industry was to
achieve an enormous, technological breakthrough which would lower the solar power’s
€/kw price dramatically. The element is only given a magnitude of -1, as the probability of
such a rapid development in the short and medium term is relatively low. These issues will
be discussed much further in section 4.2.1.1 Threat of substitutes, page 27.
The demographic segment is also interesting. The energy consumption and population
growth in the two key markets US and China will impact the wind energy industry in years
to come. The countries’ growing need for energy has positive prospects for Vestas. (BTM
2007: 71)
Finally, the environmental segment includes many positive elements for Vestas. Recent
research is more often in favour of an environmentalist view against global warming,
which is believed to be caused by fossil fuels among other things. The issue still remains
highly debatable. In Denmark, the debate was temporarily bi-polar due to the “sceptical
environmentalist” Bjørn Lomborg. His school of thought occupies very little space in the
for practical examples on lead times within wind energy.
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public debate now. 5 Vestas benefits from a public debate dominated by environmentalists,
as the movement obviously is in favour of renewable energy.
In conclusion, the external analysis of Vestas’ environment shows a somewhat optimistic
prospect for Vestas. There are, however, also some potentially very damaging elements. In
the short term, the paradox is that while the political climate has not been much better, the
financial climate could not be much worse, as customers struggle to find raise finance (AR
2008: 04-05) In the longer run, the biggest threat is if political support disappears before
wind energy is competitive on an unsubsidised basis. A major, technological breakthrough
within other energy sources, especially solar energy, could pose a serious threat.
4.2.Industry Analysis - Using Porter’s Five Forces Framework
4.2.1.
Foundation - Static Analysis
For the later valuation, it is essential to evaluate the industry’s attractiveness, intensity of
competition and level of future profitability. The chosen theoretical framework is Porter’s
Five Forces Framework (Porter 1985). The following figure provides a concise overview of
my analysis of the wind turbine industry using the Five Forces Framework. The first
column displays the type of competitive force dealt with. The second column provides the
overall conclusion on the level of the threat. The third and fourth columns deal with the
underlying elements in depth. The final column indicates the effect for Vestas in which a
short, green bar is positive and a long, red bar indicates a negative outlook. The five forces
will now be dealt with separately.
5
http://www.lomborg-errors.dk/ Biologist Kåre Fog scientifically disproves Lomborg’s claims.
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Figure 6: Industry Analysis using the Five Forces Framework – Overview
Competitive Force
Substitutes
Overall
Medium
Underlying element
Nuclear power
Low price on fossil fuels
Other renewable energy sources
Customers' propensity to substitute
Wind energy price relative to "broad" substitutes
Level of threat
Low-Medium
Medium-high
Medium
Low-Medium
High
Rivals
Medium
Relatively few global competitors
High growth industry
Excess capacity and high exit-barriers
Different target segments - Low/High MW
Dominant national competitor in major markets
Ability to copy patents and products
Low-Medium
Low
High
Low-Medium
Medium-high
Low
Entrants
Low-Medium Economies of Scale
High capital intensity
High R&D costs
High product differentiation
Low-Medium
Medium
Low-Medium
Low-Medium
Suppliers
Medium-High Few suppliers
Suppliers' ability to downstream integrate
Suppliers' business outside wind energy
Medium-high
Low
Medium-high
Buyers
Medium
Medium-high
Low-Medium
Low-Medium
Fewer, bigger suppliers
Operational reliability is imperative
Buyers' ability to upstream integrate
Effect for Vestas
Adapted by the author to fit Vestas. (Theory is based on Grant 2008: 72 and Porter:1980)
4.2.1.1. Threat of substitutes
Threat of substitutes in the broad sense includes all sources of electricity including the
cheaper, more traditional substitutes, such as nuclear power and fossil fuels. In the narrow
sense, however, only sources of renewable electricity are true substitutes. The following
figure provides an overview.
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Figure 7 In-depth presentation of Threat of Substitutes analysis
Energy Type
Fossil
Pros
+ Cheap - if no environmental taxes
+ Cheap to install
-
Cons
Carbondioxide
Other pollutants
Dependence on foreign states - Middle East
Potentially facing heavy taxes
Nuclear
+ No carbondioxide
+ Long time span of 40 years
+ Cheap running costs
-
Massive installation costs
Dependence on uranium import and prices
Waste management problems
Security problems - Terrorist threat
Expensive to shut down plant
Wind
+
+
+
+
No carbondioxide
Quick Installation
"Unlimited supply"
No dependence of supplying countries
-
Requires available, windy area
Volatility requires advanced electricity net
Dependent on political/financial support
Solar
(thermal)
+
+
+
+
+
+
No carbondioxide
Decreasing costs
Less volatility than wind production
"Unlimited supply"
No dependence of supplying countries
Largest renewable technical potential
-
Requires lot of space
Less developed than wind
Dependent on political/financial support
Requires available, sunny area
Seasonal fluctuations in production
Hydro
+ No carbondioxide
+ Relatively cheap within renewables
-
New locations are very limited
Large installation costs
Often disrupts wild life and agriculture
Requires massive political support
Biomass
+ No carbondioxide
+ Cheap setup costs
-
Large production requires huge area
Drives food prices up
Ethical issue - Starvation in poor countries
Fossils emit CO2 and other pollutants, whereas nuclear power has a waste management
problem in addition to the ever-present post 9/11 “terrorist threat”. That is, fear of
terrorists either bombing nuclear plants or using “dirty” nuclear bombs (Congress Report
2005) Biomass’ impact on global food prices poses an ethical issue in the Western world,
especially its consequences for the world’s poorest. Solar power has a huge technical
potential, but is currently very expensive (REN 21:40). As the following figure shows, other
types of renewable energy are currently less price-competitive than wind power.6 Hydro
6
The figure is based on Australian data from 2006, but it is the author’s judgment that it is somewhat
representative of the global situation today.
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power is cheap, but poses no severe threat to wind energy due to the very limited locations
available for new sites (USGS 2009).7
Figure 8: Relative energy prices for different energy sources.
Levelised costs for energy sources
Solar Photovoltaics
Geothermal
Biomass
Solar thermal
Wind power (Optimal)
Small Hydro power
Nuclear
Gas: combined cycle
Coal
120
89
88
85
55
55
40
37
28
0
20
40
60
80
100
120
140
Cost (AUD/MWh)
Source: Graham 2006.
On one hand, wind energy is the cheapest and most feasible renewable energy source at the
present. On the other hand, the wind industry is very dependent on subsidies and feed-in
tariffs, as mentioned in the PESTED analysis. Should the politicians on a global scale
suddenly change their mind and erase all supportive schemes overnight, the industry could
face serious difficulties. In terms of unsubsidised prices, traditional energy sources such as
fossils and nuclear power are still much cheaper. Therefore, the threat is overall of a
medium level.
4.2.1.2. Rivalry among competing firms
The threat from rivals is deemed to be of medium threat and consists of opposing forces.
Few truly global competitors exist, especially in the growing >1,5 MW segment of wind
turbines (BTM 2007: 27 & 32). On the other hand, the exit-barriers are high, due to the
huge investments necessary for wind turbine production. Participants could be forced to
continue despite neutral or negative economic performance, potentially making
competition fiercer. Especially Siemens Wind Power and GE Wind could stay in business
for an elongated time since the major industrial conglomerates behind them could choose
7
The US government-run U.S. Geological Survey points out “…most of the good spots to locate hydro plants
have already been taken.” http://ga.water.usgs.gov/edu/wuhy.html
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to supply them with capital injections. The competition is also anticipated to be fierce in
the near future with the entrance of several Chinese players.
4.2.1.3. Threat of new entrants
Next, the threat from new entrants is overall estimated to be of a low to medium level.
There are large economies of scale in the production of wind turbines. Being a successful
wind turbine manufacturer requires investments in factories, and R&D departments, often
in several countries (BTM 08:V). Finally, there are several different types of wind turbines
depending on the speed and type of wind, creating a diversified product portfolio which is
harder for new entrants to copy.
These observations, however, mainly apply to the case of “truly” new entrants, that is,
companies inexperienced within the energy sector. Traditional, large oil companies have a
completely different starting point, as their cash flows from fossil fuels could provide the
necessary financial funds. Even if a renewable energy subsidiary should provide negative
cash flow initially, it may be regarded as a necessary long term investment for many of the
large oil companies. However, some traditional oil companies claim they will no longer
invest in renewable energy (Guardian 17/04-09). It is difficult to validate such press
releases, as they could be made for strategic reasons. Still, this circumstance raises the level
of threat to the overall low to medium level.
4.2.1.4. Bargaining power of customers
In recent years, buyers of wind turbines tend to be less and less private and professional
developers and more often large utilities (BTM 2008: 33). These experienced, professional
customers buy much larger quantities and possess much more bargaining power. Still, at
least so far, the market has been a seller’s market, and therefore, the force is still within the
low to medium range.
4.2.1.5. Bargaining power of suppliers
The bargaining power of suppliers is medium-high, There are relatively few suppliers in
the industry, for instance one out of three wind turbines currently in operation is fitted
with wings from Danish LM Glasfiber, making the supplier a key player in the industry
(BTM 2008:114). Hansen Transmissions, now acquired by Suzlon, accounts for 30 % of the
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world supply of gearboxes, which further adds to the monopolistic situation within some of
the components (BTM 2008:115) Also, these suppliers often have major business areas
outside wind turbines granting them more bargaining power. However, the suppliers are
often unable to integrate downstream, that is, acquire wind turbines manufacturers or
manufacture turbines themselves, which does lower their bargaining power slightly.
4.2.2.
Putting Porter into Perspective – Dynamic Analysis
In order to make Porter’s framework more dynamic, a time-orientation analysis will
supplement the previous analysis. The analysis will deal with both short term profitability
and long term profitability.
Figure 9: Five Forces Framework in a time-oriented perspective
Competitive Force
Rivals
Substitutes
Entrants
Suppliers
Buyers
Overall profitability
Short-term
2009-2013
Medium
Medium
Low-Medium
Medium-High
Low-Medium
Medium
Long-Term
2014-2023
Change
Medium-high Negative
Low-Medium Positive
Low-Medium Neutral
Low-Medium Positive
Medium
Negative
Medium-High Slightly positive
As the figure shows, Vestas is already within an attractive industry. Wind energy will
maintain its potential if the technological developments within the different renewable
energy sources are assumed to be evenly distributed. As wind becomes more price
competitive, the threat of substitutes should decrease in particular from the currently
cheap fossil energy sources. The bargaining power of suppliers should also decrease. More
upstream integration is anticipated. This could be either via acquisitions or for certain
components via organic growth. Also, lack of key components should be a thing of the past,
as suppliers have expanded their capacities.8
8
A reference to the crisis of 2006, in which shortage of crucial components hit the industry (Business.dk
20/03/07)
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On the other hand, the bargaining power of buyers is expected to increase, as professional
customers from large utilities buy large quantities, which naturally gives them more
bargaining power. The intensity of rivalry will also increase, as Vestas has previously
enjoyed some sort of first mover advantage, which will decrease due to the entrance of
professional conglomerates such as GE Wind and Siemens. Still, Vestas is within a fairly
profitable industry with a positive outlook.
Vestas’ CEO Ditlev Engels’ is, as expected, far more optimistic, as he states “Honestly it is
difficult to identify an industry with a better outlook. Modern energy is the future.” (Vestas SH
2009/1: 2).
4.3. Competitor and Demand Analysis – A World Market Update from
Vestas’ perspective.
As a supplement to the previous analyses, a competitor and market analysis will follow.
The following figure from 2008 shows the top ten suppliers’ market share on the vertical
axis and their market presence in number of countries above 50 MW on the horizontal axis.
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Figure 10: BTM Consult’s figure showing the top ten suppliers’ market share and presence in
2008.
Source: BTM 2008: 28
As mentioned in 1.3 Sources and literature, page 2, information from BTM needs to be paid
special attention. Besides the different ways of measuring market share, one could also
question BTM’s >50MW criteria. Other criteria could display different pictures of the
competitive situation. Regardless, the figure gives us a good idea of Vestas’ unique position
with its presence in several, major markets. The uniqueness is also illustrated in the
following figure, which shows the leading suppliers in the top 10 key markets in 2008.
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Figure 11: The leading suppliers in the top 10 markets in 2008.
Source: Based on BTM 2008:29
The figure shows that in all of the five biggest markets major, national players dominate,
which works against Danish-based Vestas. However, Vestas is among the top three
suppliers in all of the 10 major markets expect Portugal and China. None of its competitors
can boast such a global presence. Globally, there only three large-scale manufacturers of
off-shore wind parks, Vestas being one of them.
The figure also eminently shows the relative magnitude in MW of the two major markets,
US and China. Based on installed capacity9, the two markets represented an astounding 52
% of global demand (BTM 2008:19). China alone experienced a growth rate of 92 % in
2008 (BTM 2008:13). As Vestas “only” grew by 30 %, Vestas lost market share in this
market as well. Vestas has relatively impressive growth rates, but the market is simply
growing so fast, that they cannot maintain their territory.
The Sino-American markets will in all likeliness continue their dominance. The valuation
will partly be based on 3 geographical drivers, Europe, Americas, and Asia. Obviously, US
and China will constitute major, underlying components of their respective growth drivers.
Fortunately for Vestas, the firm is already constructing production plants in both markets,
9
BTM provides both numbers for supplied and installed capacity. The supplied capacity was 111 % of
recorded installation. They differ as some turbines are in transit or under construction. For more on these
methodological issues, refer to BTM 2008: 28.
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as well as major R&D centres in Houston, Boston and Colorado (AR 2008: 20). In other
words, the company has increased its presence and capacity in these strategic markets in
anticipation of the growth (AR 2008: 10).
Vestas risks excess capacity and the consequent costs in case of a major drop in demand,
which may be the case currently. In anticipation of growth in demand, Vestas strives to
maintain all employees, at least in the present (AR 2008: 26). It is yet too difficult how
much the firm will be impacted by the global recession, but the quarterly report for Q1 and
CEO Engel’s guidance for 2009 will be strong indications of Vestas’ situation.
To some extent, Vestas’ falling market share is also a result of the prioritising of Vestas’
executive management. Vestas has since 2005 prioritised EBIT-margin, and net working
capital as the two most important financial goals (AR 2008:16). The third most important
goal, market share, was replaced by revenue in 2008, as the original goal of a 35 % market
share in 2008 was conveniently dropped (AR 2008:24) The actual figure was much lower,
19.8 % (BTM 2008: 24). To some extent, Vestas’ focus on profitability has sacrificed its
superior market leadership. A mere 2 years ago, Vestas enjoyed a 28.2 % market share
(BTM 2008:29). On the other hand, Vestas has become profitable since its 2005 annual
report.
In conclusion, this brief walkthrough of Vestas and its competitive situation has displayed
its uniqueness as a global player and the difficulties in that national players dominate the
five major markets. It also showed the Sino-American dominance on the demand side of
global wind energy.
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5. Internal Strategic Analysis
5.1. Value Chain Analysis – Using Barney’s VRIO framework
The purpose of this section is to identify the competitive advantages of Vestas. The analysis
will be conducted using Barney’s synthesis of his own VRIO analysis with Porter’s classic
Value Chain framework (Barney 2002, Porter 1985: 33-61). The synthesised model is
efficient in valuating organisational strengths and weaknesses. The level of competitive
advantages within each value-creating activity will be judged according to Barney’s four
criteria behind the VRIO abbreviation. Activities can be valuable, rare, costly to imitate and
exploited by the organisation (Barney 2002: 150-62).
In order to maintain competitive parity, an activity has to be valuable, if the company is to
exploit it. If the activity is also rare, exploitation may grant the firm a temporary advantage
over competitors. If the activity is also costly to imitate, competitors are deterred from
pursuing it. If the company’s strategies and policies enable the firm to exploit the activity,
the company has a sustained competitive advantage (Barney 2002: 150-51).
It is worth noting that Barney strongly recommends building the resource-based analysis
on intra-organisational information. However, obtaining and analysing such data is outside
the scope of this report, so the analysis will be conducted using external data as an
approximation for intra-organisational information (Barney 2002: 161-62). In the analysis,
all information from Vestas has been regarded as particularly biased and treated very
critically. The following figure presents an overview of the findings, and subsequently, the
activities will be discussed further in depth.
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Figure 12: Value chain activities as well as their competitive and financial implications
Costly to
Value Chain Activity Valuable Rare Imitate
Primary
Logistics

Production


Marketing and Sales


Support

Supporting
Infrastructure
HRM
R&D






Exploited by Competitive
Organisation Implications




Financial Implications
Parity
Temporary advantage
Temporary advantage
Parity
Neutral
Positive
Positive
Neutral
Temporary advantage
Sustained advantage
Sustained advantage
Positive
Superior
Superior
The theoretical foundation is based on Barney (Barney 2002: 150-51).
Value Chain as proposed by Barney , procurement has been ignored.( Barney 2002: 137)
5.1.1.
Primary Activities
Within logistics, Vestas has implemented a Six Sigma System with its suppliers as one of the
most important tools among Vestas’ quality improvement initiatives (AR 2008: 018). Six
Sigma is fundamentally about identifying and eliminating causes of defects and errors in
business processes, notably manufacturing (Zu 2008) While some authors believe Six
Sigma to be just traditional quality management in a new packaging (Clifford 2001), others
have found empirical data proving the benefits of the Six Sigma approach. (Pande 2000). A
discussion of the theory is beyond the scope of this thesis, but Vestas’ initiative is
considered positive.
Secondly, Vestas’ “sourcing excellence”, one of Vestas 12 must-win battles, recommends
suppliers to set up business in the US and China. Vestas and its suppliers will in this way be
ready for the expected growth within these two key markets. (AR 2008: 018), Finally,
Vestas’ policy of having two suppliers of all items should also decrease potential
bottlenecks (AR 2008:23). Outbound logistics is fairly complicated in the industry. Vestas
has established several long-term relationships with transportation providers, which is
valuable, but not rare. All the above-mentioned initiatives are certainly valuable, but also
fairly common within the industry. Within logistics Vestas has presently no comparative
advantage, only competitive parity.
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The production activity is different. Vestas is in the process of building factories in Spain,
China and US (AR 2009: 21). Having production facilities in these major markets should
give Vestas an advantage, although the advantage of in-sourcing production is debated
within the industry (Finansnyheder: 20/03-09).10 Furthermore, Vestas has experienced
fewer failures recently (AR 2008: 59). Finally, another one of the 12 must-win-battles,
“Production Excellence”, has increased the already strong output by 4,5 % from 2007 to
2008 underlining Vestas’ production capability (AR 2008: 18). The relatively high
productivity, the recent initiatives in Vestas’ pipeline, and “global” production facilities are
very valuable. The latter especially is relatively rare in an industry where some of Vestas’
competitors are mainly national players. They can, however, be imitated and especially the
Chinese manufacturers Goldwind and Sinotel have a cost advantage due to the low Chinese
wages. Thus, Vestas currently enjoys a temporary competitive advantage.
The sales and marketing activity has recently participated in the design of the wind
turbines. The intention is to create turbines that satisfy customer needs based on the input
from the sales people (Inside 01/09: 6). Also a New Group Marketing and Customer insight
Business unit has been established recently in order to maintain continuous dialogues with
customers (Inside 01/09: 33). Being present throughout the world with established sales
offices and business units is also certainly valuable and fairly rare, but can be imitated.
Vestas benefits from a temporary, competitive advantage within this field.
The support activity has recently been strengthened tremendously by the creation of the
business unit “Vestas Spare parts and Repair” (AR 2008:29). The unit will among other
things be occupied with Vestas’ 24 hour monitoring of more than 11,500 wind turbines
globally (AR 2008: 19). These steps are more than necessary, after Vestas has experienced
several negative incidents with turbines malfunctioning and wings falling off (Ingeniøren
29/01-08). Vestas’ support function is valuable, but has underperformed in the past.
Currently, the firm barely obtains a comparative parity within the activity, although this
may change in the long term.
10
Some professionals say Vestas should outsource production of towers & gearboxes. (Finansnyheder:
20/03-09)
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5.1.2.
Supportive Activities
The infrastructure of Vestas is relatively strong. Its infrastructural strength lies in the
organisation of several different business units within all relevant geographical areas, (AR
2008: 23-24). This strong foundation is only supplemented with new business units such
as the repair business unit and Vestas China (Shareholder information 01/09: 5). The
company has a temporary competitive advantage, which may however be replicated.
Within human resource management, the firm has a relatively strong employer brand and a
strong graduate programme, which may attract above-average skilled employees in the
near future (AR 2008: 5)11. The initiative “People before Megawatt” entails education of
existing employees (AR 2008: 19). The huge e-learning programmes within all levels and
units of Vestas should both upgrade the skills of employees and improve the employee
retention rate further (Inside 01/09: 13). The programmes are supplemented by 11 worldwide training centres (Inside 01/09: 36). Finally, in a time where absence is increasing in
some industries, Vestas enjoys a low total absence due to illness of 2.5 % compared to the
average of 3.9 % in Danish industries (AR 2008: 100). On the negative side, the financial
crisis may force Vestas to lay off employees, even though they intend not to (AR 2008: 10,
18). A massive lay-off will of not be beneficial to the HRM activity. Also, 74 % of Vestas’
employees work within the Euro zone, so Vestas probably also has relatively high wage
expenses (AR 2008: 24)
In conclusion, Vestas’ history as one of the earliest wind turbine manufacturers and sole
Danish survivor has created a unique organisation. Vestas possesses a sustained
competitive advantage.
In terms of research & development, Vestas has recently opened the industry’s largest R&D
centre in Aarhus with another R&D centre in Singapore. This is in addition to its already
existing R&D centre in Colorado, US (AR 2008: 21). These expansions are equivalent to an
increase by 58 % or 1345 employees from 2007 to 2008. These R&D centres will be
accompanied by coming R&D centres in the UK and in the energy capital of US, Houston
(AR 2008: 21). Vestas also formed a strategic alliance with Boeing to collaborate on
11
Some observers find the wind energy industry to be inept in recruitment (Epn 07/04-09)
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technology research, although Vestas R&D director points out that it will take years for
specific results to amount (Finansnyheder 17/03-09). There is no doubt that Vestas invests
heavily in R&D and the capabilities Vestas has built up within this activity are valuable, rare
and costly to imitate. Thus, Vestas clearly has a sustained competitive advantage.
In conclusion, the VRIO-based value chain analysis has visibly identified Vestas’ areas of
competitive advantage. Its core competencies lies within its talent-attracting HRM and
global, extensive R&D. Clearly, both are sustained competitive advantages.
5.2. Summarising SWOT table
A typical SWOT-analysis is used as an independent, analytical tool, and enjoys much
popularity among practitioners but less in the academic world. Several authors point out
the weaknesses of SWOT as an analytical tool (Barney 2007: 11). Especially Grant is critical
in saying that:
“…[A]rbitrary classification of external factors into opportunities and threats and internal
factors into strength and weaknesses is less important than a careful identification of these
external and internal factors followed by an appraisal of their implications.” (Grant 2008: 13)
While their observations are duly noted, the factors were identified and discussed
rigorously in the strategic analyses. In the following the SWOT framework shall be used to
summarise the previous strategic analyses.
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Figure 13 Summarising SWOT-analysis
Strengths
Weaknesses

Global production and sales

Relatively high wage expenses.

Strong presence in key growth markets

Weak support - turbines crashing

New SBU’s: China and Repair Centre

Decreasing market share

Strong HRM

Industry’s strongest R&D investments

Still market leader

One of 3 firms with off-shore capabilities
Opportunities
Threats

Strong political support

Dependence on political support

Kyoto Protocol, REFIT & PTC

Short-term: Current financial crisis'

Changed social attitude

Dependence on state of the market

Wind research can lower €/kw

Threat from other renewables

Ever increasing need for energy

New entrants: major industrial/energy firms

Environmental legislation

National dominance in key growth markets

High growth industry

“Broad” substitutes cheaper than wind
energy
This will conclude the strategic analysis for now, as we move on to the historical financial
analysis.
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Part IV – Financial Statement Analysis of Vestas
6. Historical Financial Analysis
6.1.Introduction
In this section, the historical performance of Vestas will be analysed. This critical section
will together with the strategic section create the basis for the drivers of the valuation
model.
The areas of focus will be on growth and return on invested capital, ROIC. Growth and ROIC
are the most important parameters to examine in relation to forecasting future
performance (Penman 2007:371, Koller 2005: 61). The analysis shall cover all annual
reports from 2004, which is after the NEC Micon acquisition and thus more representative
of today’s Vestas. No comparison between Vestas and its competitors has been made, as
Vestas has few competitors that are truly similar, as previously mentioned in section 4.3
Competitor and Demand Analysis, page 32. The focus will be on analysing the trends within
Vestas’ key ratios. In line with the prescriptions provided by Penman and Koller, all the
statements of Vestas have been reformulated. They are available on the enclosed Excel
Sheet – see Vestas Model Base case scenario.xls.
The reformulation separates operating from financing activities. Investors put money into
a business based on the profitability of the operating activities of the firm. The financing
activities only act as a supportive activity and should therefore be excluded in any
valuation of a firm’s operational performance (Penman 2007: 244-249). A good example is
the much renowned story of how German auto-maker Porsche in 2008 made 80% of its 9
bnEUR profits on financial derivatives linked to rival Volkswagen (Wall Street Journal
08/10-08). Obviously, Porsche’s ability to make profits from its operations, producing cars,
did not increase. The story exemplifies precisely why operating and financing activities
need to be separated.
As mentioned in section 1.4 Delimitation, page 3, the accounting standards and techniques
of Vestas and so forth shall remain uncommented. However, note that minor corrections
have been made. As an example, some financial leases are included under property, but
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they are so insignificant that their effect is ignored (AR 2008: 68). Also, it is necessary to
comment on currency effects that could change the growth figures dramatically. The
functional currency of Danish Vestas is still DKK. The presentation currency in their annual
reports and consequently also in this thesis is EUR (AR 08:52). The Danish Krone’s strong
tie to the Euro does not terminate currency risks completely, as 40 % of Vestas’ income still
originates from outside the euro-zone, while 80 % of the costs are within the euro-zone
(AR 2008: 24). However, Vestas hedges contracts by means of forwarding contracts and
also hedges any additional, exposed net cash flows (AR 2008: 24). Therefore, currency
effects are ignored assuming that Vestas’ currency risk exposure is somewhat insignificant
(AR 2008: 87-88).
6.2. Growth
Growth is as mentioned one of the most important drivers for value-creation. As profit
margins are mean-reverting to some extent, it is evident that long-term growth in revenue
is essential for value-creation (Koller 2005: 189). The next section shall therefore deal with
the revenue growth in Vestas.
As the following figures displays, Vestas has experienced impressive growth rates in the
2004-2008 period with a compounded annual growth rate of 21 % (CAGR). Decomposing
the growth in geographical areas reveals that Americas, hereunder the US, provided the
highest growth. Asia however delivered a somewhat more sluggish growth while Europe
supplied the biggest increase in terms of absolute figures.
Figure 14: Historical growth rates – Geographical segments
Growth in Vestas' operating revenues
2005
2006
2007
2008
CAGR
Europe
45,7% 0,2%
Americas
221,9% -5,1%
Asia/Oceania -13,8% 25,8%
20,5%
67,3%
-1,6%
37,0%
10,2%
6,8%
19,3%
41,3%
7,9%
Total
26,1%
24,2%
20,6%
mEUR
4.000
Geographic segments
Vestas' revenue Geographic segments
3.000
4.000
2.000
3.500
Growth rates in (EUR)
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7,6%
1.000
3.000
0
2.500
2.0002004 2005 2006 2007 2008
mEUR
51,6%
Europe
North and South America
Asia and Oceania
1.500
1.000
500
0
43
2004
Europe
2005
2006
North and South America
2
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The growth rates are remarkable, but are also expected within a growth industry such as
wind energy. Even more so during a time of massive political and economical support, as
discussed in the strategic analysis. Also note, that Vestas’ relative growth is lower than the
market, since Vestas has lost market share in recent years. However, Vestas has focused on
profitability rather than market share, as the next section will display.
The three geographical areas, Europe, Americas, and Asia, are also selected as the main
forecast drivers. The decomposition of the historical growth rates of these areas enables
me to estimate more precise, future growth rates for these drivers in the valuation.
Although Penman suggests a very in-depth analysis of growth and sustainable earnings, the
above-mentioned analysis shall suffice (Penman 2007: 409-437).
6.3. ROIC
ROIC is one of the best value drivers in an evaluation of a firm’s ability to create value for
its shareholders (Koller 2005: 183). As discussed in the review of valuation models, a firm’s
ROIC has to exceed WACC for it to make any economic profit; otherwise the firm is
breaking even or even destroying value.
The strength of ROIC is also its focus on the profitability of operating activities, rather than
the more traditional ROE and ROA that mix up financing and operating activities (Penman
2007: 378). ROIC measures the operating profitability of capital employed as it is net
operating profit less adjusted taxes (NOPLAT) divided by invested capital (IC) (Koller
2005: 182).
ROIC
NOPLAT
IC
The invested capital can be calculated exclusive and inclusive of goodwill. Obviously,
excluding goodwill measures profitability on the physical assets held. Including goodwill is
superior in measuring the historical performance for the company’s shareholders (Koller
2005: 183). Management must create value for the firm with any acquisitions and
consequent goodwill. If ROIC does not exceed WACC, the firm is destroying value for the
shareholders. Focus will therefore be on ROIC inclusive goodwill. The sources of the recent
improvements in Vestas’ ROIC can be found by decomposing the ROIC in a ROIC-tree.
Figure 15: Decomposing ROIC-tree for Vestas based on 2005-2008 annual reports
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Vestas has improved its ROIC significantly within recent years. Although the cash rate has
some impact, the development is mainly caused by a significant increase in Vestas’
operating margin, here defined as the EBITA-margin. S,G,A,R&D12 expenses have increased
somewhat, but have been offset by the lowered depreciation rate. Sales expenses have
increased due to the increase in the global sales forces, which reflects Vestas global effort.
The main driver behind the change in operating margin is the change in gross margin.
Vestas has simply been better at reducing the cost of sales, as illustrated in the following
figure.
Figure 16 Expense groups in Vestas in percent of total revenue 2005-2008
12
The cryptic abbreviation represents sales, general, administrative, and research & development costs.
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Expenses in % of revenue
Revenue in each year = 100%
1,8%
100%
2,3%
3,9%
96%
4,9%
2,4%
5,5%
2,0%
5,7%
86%
82%
79%
2006
2007
2008
R&D
Sales, Gen. &
Adm. Exp.
Cost of sales
0%
2005
Vestas has successfully reduced the cost of materials used. Obviously, the drop in raw
material price in 2008 has been beneficial, as a major part of the costs of a wind turbine is
raw materials. Steel alone can amount to 15 % of the total costs. Still, CEO Ditlev Engel has
had an increased focus on the EBIT margin since his takeover from former CEO Svend
Siggård in 2005, and the efforts have had positive results. (AR 08:16).
Returning to the ROIC-tree, it is obvious that the strengthened ROIC-margin is also carried
forward by an increase in average capital turnover. While fixed assets have not been
employed more effectively, net working capital has decreased rapidly, which is a positive
trend. The main reason is prepayments from customers, which partially outweighs the
effect of capital employed in assets and trade payables (AR 2008:10).
Most factors are believed to maintain their positive trend, except for the working capital
ratio which cannot be expected to maintain its current low level (AR 2008: 26). Although
Koller has documented a somewhat mean-reverting process in the ROIC for high
performers, Vestas will probably be able to maintain its high ROIC, at least in the coming
years (Koller 2005:148). This assumes that the financial crisis will only have short,
temporary effects and that the strong, political support is maintained. Overall, the historical
financial analysis indicates a strong performance. Both high growth rates and higher
efficiency in form of increasing EBIT-margins have been accomplished.
6.4.Credit Risk
As the last step in the historical financial analysis of Vestas, the inherent credit risk in
Vestas will be touched upon. Even CEO Engel acknowledges its impact on Vestas (AR 2008:
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5). The theoretical foundation will be the framework provided by Falkenstein in Lundholm,
in which the credit risk is based on 5 key ratios13 (Lundholm 2007: 115). Within the
different ratios, industrial firms are grouped into 10 deciles. The firms are then assigned
historical default probabilities according to their individual deciles. Refer to appendix 11.3
Lundholm’s Credit Risk Framework, page 79.
The following table shows that Vestas is an unlikely candidate to default within the near
future. Its default probability is remarkably below the historical average of 5 percent for
industrial firms. This is both including and excluding the outlying quick ratio. (Lundholm
2007: 112).
Figure 17: Vestas’ Credit Risk in the Lundholm Framework
2004 2005 2006 2007 2008 Decile (1st is best)
Default prob.
NI/Assets
-4% -8% 1%
5% 10%
2
2%
Liabilities/Assets 60% 69% 65% 65% 63%
6
5%
EBIT/Int. exp. -128% -370% 489% 2953% 7422%
1
1%
Sales growth
52% 8% 26% 24%
4
3%
Quick Ratio
60% 47% 54% 56% 34%
10
9%
Avg. excl. Quick ratio
2,8%
Avg. incl. Quick ratio
4%
The decreasing quick ratio is caused by an increase in its denominator, current liabilities..
The increase in current liabilities is mainly due to increases in prepayments and trade
payables (AR 2008:10). Vestas may be unable to pay back these trade payables that are all
due within a year if sufficient cash in-flows are not attained (AR 2008:87).
On the other hand, Vestas enjoys an enormous EBIT/Int. exp. ratio, so they seem
improbable to default on their loans. Overall, Vestas’ inherent credit risk is not significant
at the present time. With a solid foundation in the financial and strategic analyses, it is now
possible to move on the valuation part of this thesis.
13
A sixth ratio, inventory holding time, has been ignored, as this is not applicable to Vestas.
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Part V – Valuation of Vestas
7. Estimating the Cost of Capital
7.1. Introduction
The purpose of this section is to find the weighted average capital cost (WACC) of Vestas,
which serves as the discount factor in the later DCF and RI valuations. Both are made from
the enterprise perspective, hence the need for the full cost of capital and not just cost of
equity (Plenborg, 2000: 6-11).
The WACC is the approximation for the average opportunity cost of the all investors. The
WACC treats the different types of investors according to the source of capital – debt,
equity, convertible debt and various other sources (Penman 2007: 473-75). As the WACC is
a weighted model which takes the tax shield effect of debt into consideration, it can be used
as a discount factor while ignoring debt. D/V and (E/V) equal the target level of
debt(equity) to market-based enterprise values. rd and (re) equal cost of debt (equity),
while Tm equals the firm’s marginal tax rate. (Koller 2005: 292)
D
E
WACC * rd * (1  Tm ) * * re
V
V
7.2.Estimating the Cost of Equity
The WACC requires an estimate for cost of equity re. Estimating the cost of equity poses a
difficult issue within financial theory. Penman points out, "we really don’t know what the
cost of capital for most firms is” (Penman 2007: 114-16). The CAPM model claims re is equal
to the risk-free rate rf plus the security’s beta i times the expected return of the market
E(rm) subtracted by the risk-free rate rf. Beta is a measure of the firm’s correlation with the
market and thus the only firm specific measure in the model.
E(ri) = rf + i [E(rm) – rf]
Source: Koller 2005:294.
Some scholars are dismissive of CAPM models. Penman points out the circularity in
traditional CAPM models as they use market-weights instead of intrinsic value-weights.
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That is, market-prices are used in the estimation of equity cost of capital, which are then
use to find underlying value (Penman 2007:693). Instead, Penman suggests assessing risk
by conducting analysis of the determinants of fundamental risks. Such an analysis is
outside the scope of this thesis. Therefore, the school of thought behind Koller will be
adopted and a traditional CAPM shall be employed. As previously mentioned in the
discussion of the valuation models, the thesis is based on the CAPM model as the best
available option in lack of an empirically valid asset pricing model.
In the CAPM, especially beta and the market risk premium are highly difficult to estimate
(Koller 2005: 298). The risk-free rate is relatively easy to assess, as ten-year government
bonds can be used as a proxy. Vestas has a majority of estimated 60 % of international
shareholders in 2008 (AR 2008: 119). Therefore, US bonds are used, but alternatively
Eurobonds could also be used. Final option is Danish government bonds, although their
relatively strong ties to Eurobonds may call for simply using these bonds instead.
To find the market risk premium, the American S&P 500 index has been employed. The
main reasons are that total return index data is easily available back until 198814 and
Vestas’ majority of international shareholders. However, choosing among major indices has
little importance, as they are strongly correlated (Koller 2005: 310). The following table
presents the author’s results and compares them to the results of the main researchers in
the field. For the calculations refer to appendix 11.1 Market Premium calculations, page 77.
Figure 18: Estimated market premiums in recent research - S&P 500
Aritmetic average
Geometrical average
My estimate Arnott Dimson
4,8%
2,4%
5,0%
3,0%
3,0%
-
Welch Shiller
6,0%
6,0%
4,3%
-
Wright
7,0%
5.5%
Ibbotson
9,4%
7,1%
Based on Arnott (2002), Dimson (2003), Welch (2000), Shiller (2000), Smithers (2003), and Ibbotson
(2006).
As the table shows, there are significant differences in the estimates of the market risk
premium. Wright proposes that arithmetic averages are conceptually superior, but less
14
Strictly speaking, one can find the total return from 1963 by adding dividends to the price index return, but
for the purpose of this assignment, 20 year-old data from 1988 is deemed sufficient.
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stable than geometrical averages (Wright 2004:3). Still, geometrical averages, which are
compounded annualised returns, appear more logical. Wright suggests using geometrical
averages as the basis and then adjusting to the arithmetic average to some extent (Wright
2004:4). In Wright’s study, the confidence intervals of the estimations spans over a range
as much as four percentage points (Wright 2004:4) Obviously, this imprecision can have
dramatic consequences in the valuation processes, as different premiums lead to different
discount rates and ultimately different intrinsic values. Market premium’s importance shall
be illustrated later on in the valuation by the means of a sensitivity analysis.
Paradoxically, the recent drop in the stock markets leads to the of lower historical risk
premiums. Koller suggests that the problem can be solved by including long time series in
the regressions (Koller 2005: 303). But as Arnott points out:
“It is dangerous to shape future expectations based on extrapolating these lofty historical
returns....” “the observed excess return and the prospective risk premium, two fundamentally
different concepts that, unfortunately, carry the same label—risk premium.” (Arnott
2002:80&64).
We are using historical market data as input in a fundamental analysis trying to estimate
future risk premiums. These are not always strongly correlated and we are also violating
Penman’s fundamentalist tenet about avoiding using market prices in a valuation of
underlying value (Penman 2007: 19) In lack of stronger theoretical foundation, we have to
continue regardless of the inherent imprecision. The estimated risk premium of in the
author’s study in all likeliness is affected by the recent drops in the market. As current
investors are assumed to be increasingly risk-averse, the underlying prospective risk
premium must have risen as well. The long-term value of the risk premium is therefore
estimated to be 5.5 %.
7.3. Estimating beta
In estimations of betas, scholars frequently debate two common issues with regard to datachoice, the frequency of observations and the time necessary for good approximations.
Koller suggests rolling five-year monthly data, but also notes that 2 year weekly data is
used by the information service provider Bloomberg (Koller 2005: 308). Using daily data
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entails more noise and would also cause a challenge with regards to holidays.15 The
author’s studies are based on 2-year weekly data.
The figure below displays the equity betas for Vestas and its two closest competitors
Gamesa and REpower on rolling two-year basis. The equity betas have been estimated by
using total return on the stocks regressed on the MSCI World Index. It is beneficial to use a
large market index such as MSCI World, and not the local Danish OMX20 index. Vestas is
one of the most dominant stocks in the OMX with a massive 10,2 % market weight as of
April 15. Using this index would create a huge bias, as Vestas’ own stock price is too highly
correlated with the OMX20 index (Koller 2005: 310). See appendix 11.2 Market Weight
Report CSE 15/04-09, page 78.
The Dresdner Kleinwort Wasserstein’s index of renewable energy firms has been included,
as an indication of the renewable sector’s risks. The weighted-average of a group of firms is
of course less volatile than a single firm (Koller 2005:311). As the index’ beta is above 1,
the whole sector is evidently more sensitive to the state of the market - as previously
discussed in the strategic analyses.
Figure 19 Rolling 2-year beta regressions against the MSCI World index.
Evidently, beta fluctuates remarkably across time, as betas can be highly volatile (Penman
2007: 113-16) Like market premiums, different betas can alter valuations significantly.
15
Different countries have different holidays, and therefore the MSCI is updated on days where Vestas is not
traded, but this could be avoided by matching the days of the stock and index.
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Vestas has not changed its business model significantly within the last years and is
assumed to continue their present business model. Therefore, it can be assumed that
historical data can provide an estimate for the future beta. Calculating Vestas’ beta on a
2001-09 period returns a beta of 1.68 with an R2 of 24 %. However, in ’03-04 Vestas’
merger with former competitor NEC Micon evidently changed Vestas’ beta temporarily. On
a 2006-09 period, the regression results in beta of 1.86 and a R2 of 49 %. Again, we see that
while the formulas may be highly precise, the assumptions and the end result are relatively
imprecise. In lack of better theoretical foundation, we continue by estimating a beta of 1.6
for Vestas.
7.4. WACC
Vestas has in recent years had a solvency ratio of roughly 30-35 %. The firm pursues to
maintain a solvency ratio of at least 40 % on a book value basis (AR 2008:92). It is feasible
to use ten-year US government bonds as a proxy for the risk-free rate due to the 60 %
majority of international shareholders in Vestas (AR 2008: 119 & Koller 2005:296).
Furthermore, Vestas informs that their weighted interest rate is 6.0 % in 2008 (AR 2008:
77). The future interest rate is estimated to be maintained in the 6 % region, but still 50
basis points higher, as the borrowing can be more expensive for corporations in the
current market.
By using the WACC formula previously presented on page 48, it is possible to calculate the
past and future WACC. The underlying data behind the risk-free rate beta, return on debt,
and marginal tax rates are found in the annual reports, and thus represent the values on
31/12 of the relevant year.
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Figure 20 Historical and future decomposition of Vestas’ WACC.
2004
Rf
2005
4,29%
4,37%
2006
4,67%
2007
2008
4,21%
2,18%
Forward WACC
3,9%
Beta
1,51
1,44
2,01
1,63
1,98
1,60
Risk Premium
5,5%
5,5%
5,5%
5,5%
5,5%
5,5%
Re
12,58%
12,29%
15,72%
13,17%
13,04%
12,74%
EqV / MV
74,3%
81,8%
95,7%
98,3%
97,7%
97,7%
Rd
3,10%
4,50%
4,90%
6,30%
6,00%
6,50%
DV / MV
25,7%
18,2%
4,3%
1,7%
2,3%
2,3%
30%
30%
28%
28%
28%
28%
9,90%
10,62%
15,20%
13,03%
12,85%
12,56%
Marginal tax rate
WACC
In order to calculate the future WACC, the previously estimated beta of 1.6 has been
employed. Even though Vestas has a target capital structure, one could argue in favour of
using changing WACC values over the years, as the market value of equity/debt ratio is
unlikely to remain constant. However, one can also regard the found WACC as an average
of the different, theoretically correct WACC’s (Lundholm 2007:203). Alternatively, one
could calculate different WACCs or in severe cases, use the adjust present value model, as
Koller suggests (Koller 2005: 326) However, the added complexity is likely to exceed the
potential benefits, as Lundholm points out (Lundholm 2007:204). In conclusion, the
estimated WACC of 12.56 % shall be used as a constant in the valuation.
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8. Valuation
8.1. Introduction
Now that the WACC is also estimated, the two main parts, strategic analysis and historical
financial analysis come together in this section and form the basis for the valuation of
Vestas.
First, the choice of forecast-drivers and variables held constant will discussed. In order to
give a more nuanced picture of Vestas’ situation, the valuation will be conducted under
three scenarios; base, bearish and bullish scenarios. These will be largely based on the
conducted strategic and financial analyses.
Naturally, the base scenario is the most probable, but the two more extreme scenarios have
also been developed to give investors a detailed picture of how differently things may
develop for Vestas. In other words, the scenarios depict the downside and upside of the
share. All three scenarios include both Discounted Cash Flow and Economic Profit models.
Next, a sensitivity analysis and a simulation on a selection of the variables will follow,
which eminently shows the investor the inherent uncertainty in the valuation. In the
sensitivity analysis, it will be clearly depicted how sensitive the valuation is to isolated
changes in the variables. The simulation of key variables will indicate the overall effect of
variable deviations on the share price. Finally, Vestas will be compared to its peers via a
peer group analysis, which is based on the market’s pricing of shares
Note that the chosen cut-off date for the valuation is April 14, 2009, and that the valuation
value naturally differs from a valuation conducted on January 1, 2009. However, the issue
has been tried to be solved via a mid-year correction factor, which takes into account how
much of the year has already passed.
8.2. Forecast-drivers and constant variables
For all three scenarios, the valuation will be conducted in using 3-stage models, including a
short-term, long-term and continuing value periods. The most relevant and practically
feasible forecast-drivers differ in the three valuation periods. Forecast-drivers are
variables that are feasible to forecast. Variables that are insignificant or difficult to forecast
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shall be held constant. The variables held constant in all three scenarios are commented on
in section 11.8 Constantly held variables in the forecast, page 84, where their values are
also available. For most of the constantly-held variables, historical figures have formed the
basis.
Three important variables shall be commented on here. The WACC is unlikely to remain the
same in the 3 scenarios, but it is challenging to find sound, supportive arguments for
different betas and thus different WACC’s in the strategic scenarios. As mentioned
previously, there is much insecurity attached to the WACC in the first place. The WACC has
therefore been maintained at the same level in all three scenarios. For all three scenarios,
ROIC and growth are expected to decrease as new entrants will eventually be attracted as
discussed in the strategic analyses.
The following figure shows the different forecasts drivers.
Figure 21 Overview of forecast drivers in the different valuation periods
Period
Year
Revenue Growth Europe
Revenue Growth Americas
Revenue Growth Asia
Gross Margin
Total Revenue Growth
EBITA-Margin
Detailed forecast period Continuing Value
Short-term Long-term
2009-2013 2014-2023 > 2024






RONIC
Growth in NOPLAT


In the short-term, the chosen main drivers are revenue growth forecasts that have been
divided into 3 geographical zones. This enables detailed forecasts, which are naturally
based on the previous strategic analyses. China and the US will constitute a major part in
the growth drivers from Asia and Americas, respectively.
Obviously, one could have forecasted separately on the on- and off-shore segments, but the
off-shore segment only constitutes 5 % of the market forecast for the period 2009-2013
(BTM 2008:61) Alternatively, one could also try to forecast on country level. The increased
complexity would go against Penman’s tenet of maintaining “a good balance between costs
and benefits” (Penman 2007: 21).
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In the long-term, revenue growth and EBITA-margin were chosen, although EBITDA or
EBIT margin could work as well. For the continuing value, RONIC and growth on NOPLAT
are the obvious drivers (Koller 2005: 273-75). RONIC, return on newly invested capital, is
equal to ROIC in 2023. The growth rate in the terminal period is an estimated 3.5 %. This
may seem high, but will be dealt with in the sensitivity analysis. Internally, Vestas uses a
2.5 % long-term growth rate, but acknowledges that it is well below the expected growth
rate (AR 2008:62)
Finally, Vestas’ own forecasts have been noted, but the forecasts of this thesis are based on
an independent basis. As an example, Vestas’ 7,200 mEUR forecast for 2009 is deemed
improbable and such subjective considerations permeate the models.
8.3. Base, Bearish, and Bullish scenario-analyses
The following three subsections will present the main findings from the three scenario
analyses. The main focus is on the short term. However, the models are printed in hardcopy on page 81 to 83 and also available electronically on the attached CD-rom. A guide to
the models is available in the appendices as well, on page 79.
8.3.1.
Base case scenario
The strategic analysis showed the unique and strong position Vestas enjoys in the current
market. Combining this with the strong indications from the financial analysis of Vestas, it
is probable that Vestas will be able to maintain its high growth and increasing profitability.
In the base case scenario, Vestas is forecasted to experience a moderate growth rate of 8 %
in 2009. Especially the Americas will show very little growth due to the difficulties for
customers to raise finance. Asia and Europe will still have a modest growth, but Vestas will
be 500 mEUR short from reaching their current guidance of 7200 mEUR revenue. The
current low, raw material prices will enable a slightly increasing gross margin.
In 2010, however, growth will start to increase, mainly picking up in the Americas. In 20112012, Asia and Americas will be the main growth drivers, but also Europe will show some
growth as a consequence of the EU’s binding commitments to reduce carbon dioxide
emissions. In 2013 the current PTC runs out in the US, which constitutes a major part of
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Vestas’ business in the Americas (AR 2008: 25).16 It is difficult to forecast the political
development in the field. The PTC is expected to be followed by a similar supportive
system. Some disruptions in the market are anticipated, however, which is why the growth
is comparatively less in this year. China will constitute a major growth driver in the AsianPacific region especially from 2011 and onwards.
Figure 22 Development in growth, ROIC, EBITA and gross margin in the base case scenario
Drivers in 2009-2013 - Base case scenario
2009
2010
2011
2012
2013
75
mEUR
45%
35%
55
Revenue Growth
Europe
North and South America
Asia and Oceania
Total Revenue Growth
15%
0%
15%
11%
20%
20%
20%
20%
25%
30%
30%
27%
25%
30%
30%
27%
25% 35
10% 15
30%
22% (5)
Gross margin
ROIC
22%
22%
24%
24%
24% (25)
18%
15%
23%
27%
29%
25%
15%
5%
2005
2009
2013
2017
2021
-5%
Revenue - Left axis
-15%
EBITA margin - Right axis
ROIC - Right axis
WACC - Right axis
The current, comparatively high ROIC cannot be maintained in the short term. However,
after 2010, the ROIC will yet again improve before reaching its maximum in 2013-14. The
forecasts in this scenario provide an estimated share price of 52.77 EUR.
For the base case scenario alone, the development of EP and DCF in the detailed forecast
period shall be commented on. The development for the bearish and bullish scenarios are
available in the appendices and on the enclose CD.
16
The US constituted 78 % of Vestas’ sales in Americas and 24 % of total sales.
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mEUR
Figure 23: Development of EP and FCF in the detailed forecast period.
2.500
2.000
1.500
1.000
500
0
(500)
Development in FCF and PV(FCF)
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Free Cash Flow
PV of FCF
mEUR
Development in EP and PV(EP)
1.400
1.200
1.000
800
600
400
200
0
2009
2010
2011
2012
2013
2014
2015
EP
2016
2017
2018
2019
2020
2021
2022
2023
PV of EP
The models both provide the same value estimate, as the theory prescribes. Their graphs,
however, differ as they tell different stories. In the DCF model, it is obvious that 2009
provides a negative cash flow. This is at least partially due to the large capital investments.
The DCF cash flows decrease more slowly than the EP and needs a longer time horizons as
discussed in the theory. The EP models clearly indicates that the next, two years will be less
profitable but then a significant amount of the forecasted economic profit is earned in the
2011-2015 period. As discussed under the theory of the valuation models, DCF and EP
obviously tell different, but valuable stories. For more on the model, refer to appendix 11.5
Base Scenario – Valuation Summary, page 81.
8.3.2.
Bearish scenario
In the bearish scenario, Vestas is forecasted to experience a very modest growth in 2009.
The Americas will in fact decrease in terms of total annual installed MW, due to especially a
drop on the US market as well as intensified competition. Further market share will be lost
to GE Wind. Asia and Europe will still have some growth. Vestas will be very far from their
current guidance of 7200 mEUR revenue. The estimate is in the 6200 mEUR range, a minor
improvement from 2008. The reason why the revenue does not decrease even further is
due to the nature of the wind energy business. Revenue is only recognised upon complete
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delivery for supply-only17 contracts, whereas revenue from turn-key18 projects are
recognised as the individual turbines are delivered (AR 2008: 54). This means that Vestas
will have significant revenue in 2009 based on contracts from 2007-08, and therefore the
full effect will not be seen until 2010.
Despite the current low raw material prices, the gross margin will be under pressure and
decrease. In 2010, growth will start to increase, mainly picking up in the Americas. In
2011-2012, all three regions will show some growth. In 2013, the end of the current PTC in
the US will cause a significant contraction of the American market.
Figure 24 Development in growth, ROIC, EBITA and
gross margin in the bearish scenario
75
2010
2011
2012
2013
Gross margin
ROIC
25%
35
15%
15
Revenue Growth
Europe
North and South America
Asia and Oceania
Total Revenue Growth
35%
55
Drivers in 2009-2013 - Bearish scenario
2009
45%
mEUR
10%
-10%
5%
4%
15%
20%
10%
15%
15%
20%
15%
16%
15%
20%
15%
16%
15%
-20%
15%
13%
16%
16%
18%
18%
19%
8%
5%
10%
11%
13%
(5)
(25)
5%
2005
2009
2013
2017
Revenue - Left axis
ROIC - Right axis
2021
-5%
-15%
EBITA margin - Right axis
WACC - Right axis
After five unprofitable years due to relatively large capital expenditures and excess
capacity, ROIC will again exceed WACC, but only modestly throughout the long-term
period. The forecasts in this scenario provide an estimated share price of 17.89 EUR. For
more
17
on
the
model,
refer
to
appendix
11.6
Contracts, where Vestas “just” has to deliver the wind turbine. The customer is then responsible for
maintenance and so forth (AR 2008: 54). This type of contract is less risky for Vestas, as operating risk is
transferred to the customer. The share of supply-only contracts has been increasing (AR 2008:24).
18
Contracts, in which Vestas has to deliver the wind turbine and maintain service and reparations in typically
5 years (AR 2008: 54). Vestas is responsible until the “key has been turned”, hence the name.
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Bearish Scenario – Valuation Summary, page 82.
8.3.3.
Bullish scenario
In the bullish scenario, Vestas is forecasted to experience a significant growth rate of 22 %.
From all regions, but especially the Americas, orders will flow in heavily in Q3-Q4 2009. A
precondition for such a development is that developers and utilities will find it easier to
raise finance. A significant part of the income will be recognised in 2009, as excess capacity
will enable quick execution of orders.
Vestas’ forecasted revenue will be in the 7400 mEUR range, thereby exceeding the current
guidance of 7200 mEUR revenue. The current low raw material prices will enable
continuingly increasing gross margins. In 2011-2013, Asia and Americas especially will
show impressive growth, carried forward by China and the US as mentioned in the
competitor and market analysis. The EU’s binding commitments will also drive the
European market forward as discussed in the PESTED analysis. In 2013, the current PTC
will be followed by equivalent, or even reinforced legislation, which will enable Vestas to
maintain significant growth in the US.
Figure 25 Development in growth, ROIC, EBITA and gross margin in the bullish scenario
Drivers in 2009-2013 - Bullish scenario
2009
2010
2011
2012
2013
Europe
North and South America
Asia and Oceania
Total Revenue Growth
25%
15%
20%
22%
25%
25%
35%
26%
25%
40%
35%
30%
30%
40%
35%
33%
30%
40%
35%
34%
Gross margin
ROIC
24%
24%
26%
26%
26%
22%
21%
31%
36%
41%
Revenue Growth
mEUR
75
45%
35%
55
25%
35
15%
15
(5)
5%
2005
2009
2013
(25)
The current, comparatively high ROIC will
Revenue - Left axis
ROIC - Right axis
2017
2021
-5%
-15%
EBITA margin - Right axis
WACC - Right axis
decrease in 2009-2010 due to a low level of orders. Also, Vestas will have significant capital
expenditures. However, the benefits of these expenditures will be reaped in the 2012-2016
period, where Vestas will experience a considerable ROIC. This scenario assumes that no
competing renewable energy receives a breakthrough and that political support remains
strong, as discussed in the PESTED-analysis. The forecasts in this scenario provide an
estimated share price of 99.06 EUR. For more on the model, refer to appendix 11.7 Bullish
Scenario – Valuation Summary, page 83.
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8.3.4.
Conclusion on scenario analyses
The bearish and bullish scenarios are intentionally extreme and deemed equally probable.
Therefore, they will both weight 10 %, whereas the base case weighs 80 % in the final
estimate. Using the three scenario estimates, the weighted share price estimate is 53.91
EUR.
In conclusion, we see Vestas does enjoy a unique position within a unique growth industry,
which is in line with the strategic analyses. The conclusion on the valuation part is that the
estimated target price of 53.91 seems reasonable, although it was also evident that
apparently precise valuations are in fact very imprecise and very sensitive to slight
adjustments, and therefore the conclusion is made with certain reservations.
Obviously, the two extreme scenarios exemplify how uncertain intrinsic value estimates
are even for the same firm in different scenarios.
Figure 26: The three scenario share price estimates and the weighted share price estimate.
Share price
Weights of the scenarios
10%
125
10%
99,06 €
EUR
100
75
50
25
53,91 €
52,77 €
17,89 €
0
Bearish
80%
Bearish
Base
Bullish
Base
Bullish
Different Scenarios
Weighted
average
The high level of uncertainty arises even without adjusting for possible variations in the
variables. This will be further examined in the following section.
8.4. Sensitivity Analysis
Just how sensitive the estimates are to even slight changes in the variables will be
displayed in the following. The sensitivity analysis will be based on the base case scenario
which will be used as a proxy for the weighted average. The analysis shall reveal the effect
of an isolated, marginal change in selected variables, that is, while all other variables are
held constant. In the section concerning estimation of the cost of capital, it was noted
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several times, that the calculations were very uncertain. Therefore, the effect of these
estimates on the share price estimate is extraordinarily interesting (Plenborg 09:4).
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Figure 27: Sensitivity Analysis – Effect on share price, table and graph
Change in %-points
-1
+1
+2
Revenue Growth ('09-'23)
-17%
-9%
10%
21%
EBITA margin ('09-'23)
-32%
-16%
16%
32%
Growth in NOPLAT (≥2024)
-1%
0%
3%
6%
ROIC (≥2024)
-4%
-2%
0%
1%
Market Risk Premium
70%
27%
-19%
-33%
WACC
37%
16%
-13%
-23%
Change in beta
-0,5
-0,25
+0,25
+0,50
56%
23%
-17%
-29%
Sensitivity Analysis
80%
Effect in % on share price
-2
60%
40%
20%
0%
-20%
-40%
-2
-1
0
+1
+2
Change in %-points
Beta
Revenue Growth ('09-'23)
EBITA margin ('09-'23)
Growth in NOPLAT (≥2024)
ROIC (≥2024)
Market Risk Premium
WACC
Beta
Note that beta varies with ±0,5 in the graph - not in percentage points like the other variables.
As the table and graph show, the share price is affected tremendously by changes in all
variables, except for growth in NOPLAT and ROIC in the terminal period. The percentage of
value arising from the terminal period is in all three scenarios less than 50 %. The reasons
are the relatively long detailed forecast period of 15 years and that the forecasts indicate
that a comparatively significant amount of Vestas’ future, economic profit will be earned in
detailed forecast period. Therefore, the model is fairly insensitive to change in the terminal
value drivers, ROIC and NOPLAT. Revenue and EBITA margin, however, have a great effect
on the share price. Unsurprisingly, however, the share price is most sensitive to the
estimate market risk premium, beta and WACC (Penman 2007: 113-16).
The sensitivity analysis has accurately displayed by how much the intrinsic value varies
due to even slight changes in variables. This is one of the inherent weaknesses of the
accounting-based models, as discussed in the review of the models. The estimated share
price is extremely sensitive to changes especially in the cost of capital estimates, which are
essential in the accounting-based models. The models’ basic foundation is built on the
ability to discount using the cost of capital.
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8.5. Simulation of forecast-drivers
The previous sensitivity analysis displayed the sensitivity of isolated, marginal changes in
the drivers. The following simulation of the forecast-drivers will show the combined effect
of variation in the drivers. It is also based of the base case scenario model.
For each driver a simulation interval has been defined in each year. The interval defines the
upper and lower boundaries of the simulation and has subjectively been defined as the
maximum and minimum realistic boundary after the author’s judgment. The share price
estimate is then simulated 1000 times. The model is enclosed on the CD as “Vestas Model Simulation model.xlsm”.
In order to force some realism into the model and avoid very profitable years to be
followed by very unprofitable years, a random number is drawn for revenue growth and
the EBITA margin. The number then affects the following year’s development and so forth.
Having simulated the value, the model then comes up with an estimate for the share price.
The median of the 1000 simulations is 50.79 EUR with the arithmetic mean close by at
50.33 EUR. Although the figures are close to the 53.91 EUR weighted estimate, the
following graph reveals how much the simulated estimates vary.
Figure 28: Histogram of simulated share price estimates
Histogram - Distribution of simulated share price
No. of simulations
35
30
25
20
15
10
5
71
68
65
62
59
56
53
50
47
44
41
38
35
32
0
Simulated share price in EUR
The histogram shows that the values vary significantly ranging from 34 to 69 EUR. The
histogram is of course affected by the width of the chosen simulation interval, but still
shows the potential variation in the estimated share price. In their different ways, the
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scenario analyses, the sensitivity analysis, and the simulation model all display the
uncertainty in the intrinsic value estimates.
8.6. Peer Group Analysis
Finally, a peer group analysis shall be conducted in line with the conclusion from section
2.4.2 Multiple, page 8. Here, it was found that a peer group provides a useful sanity check of
estimates found via other models, although it breaks Penman’s fundamentalist tenet about
avoiding market based prices (Penman 2007: 19). Another problem with the approach was
the difficulty in finding “true” peers and “true” multiples.
In Vestas’ case, there is no other true, global peer to Vestas, as previously mentioned. In
terms of market share, GE Wind is the closest peer, but as with Siemens Wind, both firms
are only parts of conglomerates making comparisons troublesome. Therefore, the only
available peers are Spanish Gamesa, German Nordex and REpower. REpower is owned19 by
Indian Suzlon, which is also included.
In the analysis, the multiples of the chosen peers shall be examined and compared to
Vestas. In the following figure, PE and EV/EBIT(DA) multiples are used for comparison. EVmultiples’ strength lie in that they are unaffected by capital structure making comparison
more correct (Koller 2005:366). Furthermore, scholars agree on the superiority of
forwarded multiples. (Koller 2005, Liu 2002, Schreiner 2007). The focus is therefore on
multiples for 2009-2010, although historical comparisons are also feasible. The multiples
are based on the median of analysts’ forecasted multiples.
19
http://www.wwindea.org/home/index.php?option=com_content&task=view&id=175&Itemid=40
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Figure 29 Peer Group analysis of Vestas against Wind Energy Peers
Peer Group - Wind Energy
Author's estimates
Company namc
Price
(USD)
MV
PE
PE EV/EBITDA EV/EBITDA EV/EBIT EV/EBIT EV/EBIT EV/EBIT
(mUSD) (2009) (2010)
(2009)
(2010)
(2009)
(2010) (2009)
VESTAS WINDSYSTEMS
58,4
10.806,3
18
14
9
8
12
10 15
GAMESA CORPN.TEGC.
17,4
4.233,3
19
13
8
6
14
11
NORDEX
16,5
1.299,3
17
22
10
10
12
13
121,2
1.089,9
19
17
8
7
10
7
1,3
1.983,8
8
11
6
6
7
7
Arit. average excl. Vestas
16
16
8
7
11
10
Arit. average excl. Vestas and Suzlon
19
17
9
8
12
10
MV-weighted average
16
14
8
7
12
10
Vestas premium: Arit. average
10%
-8%
17%
3%
7%
-2%
Same excl. Suzlon
-6%
-17%
8%
-3%
-4%
-9%
7%
1%
19%
9%
-1%
-3%
Fair price: Arit. avg. excl. Suzlon
40
35
46
41
41
39
Fair price: MV-weighted
45
43
51
47
42
41
REPOWER SYSTEMS
SUZLON ENERGY
Vestas premium: MV-weighted
VWS price
(2010)
15
43
Source: Thomson Financials Datastream
The market assesses Vestas differently depending on how the multiple is calculated. One
can also discuss whether or the not the slightly outlying Suzlon should be included. One
could argue that outliers that alters the estimates significantly should be excluded from a
statistical point of view. Obviously, this issue shows the problem with multiple valuation, as
it is easy to manipulate the result by choosing the exact firms and multiples that give the
desired result (Penman 2007: 78).
The conclusions from the strategic analyses suggested that Vestas might be traded at a
premium given its unique position as a global wind turbine manufacturer. The figure,
however, gives the ambivalent results that the share is traded at both premiums and
discounts, depending on multiple.
The estimates from the base case scenario have also been included in the upper right
corner, which are somewhat comparable to the consensus, especially in 2009. Multiples
have also been conducted with Vestas among solar energy manufacturers and also with
industrials such as GE, Siemens, and Caterpillar. Vestas does trade to a significant premium
compared to the traditional industrials, indicating the market’s belief in wind energy as a
growth industry. Vestas also trades a significant premium in comparison to solar energy
manufacturers. This underlines wind energy’s strong position within renewable energy,
which is in line with the conclusions from the PESTED analysis. For more on the additional
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multiple analyses refer to appendix 11.10 Three peer group analyses: Wind, Solar,
Industrials, page 87.
8.7. Comparing price targets and estimates.
The following figure shows the share price targets from leading investment banks in
comparison with the author’s estimated intrinsic value.
Figure 30 Overview of the price targets in EUR from analysts and this thesis.
Price targets in EUR from leading investment banks
120
100
EUR
80
60
40
20
0
Apr-08
Jun-08
Jul-08
Sep-08
Price target in EUR
Oct-08
Dec-08
Feb-09
Mar-09
May-09
Author's estimated intrinsic value
Source: www.kurstarget.dk – Vestas. See appendix 11.9, page 86.
Coincidentally, the estimated intrinsic value is somewhat comparable with the consensus
target prices. But in reality, the figures cannot be compared. The analyst is paid to come up
with typically 12-month target prices. In most cases that means that intrinsic value are
estimated similarly to this thesis, but are adjusted to reflect market moods, news flow, and
other short-term perspectives. Rather than estimating the intrinsic value and ignoring any
noise in the market, the analyst should normally be more interested in being able to predict
the share price in 12 months. Doing so provides food on the table, and suits on his body.
In a perfect world according to fundamentalists, one could imagine that all investors would
ignore such short-term fluctuations and conduct solid, fundamentalist valuations as the
sole basis for investment decisions. Share price fluctuations, “noise”, could only come from
different estimates, not market mood or other short-term perspectives (Penman 2007: 9).
In this fundamentalist world, bubbles would (almost) not exist and the difference between
market prices and intrinsic values would theoretically be much smaller (Penman 2007: 67). Market price will converge towards underlying value over time under the
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fundamentalist approach. As Bhojraj states, “current share price is like a noisy proxy for the
true but unobservable intrinsic value” (Bhojraj 2002).
However, reality is that in contemporary finance, markets do not necessarily act according
to theory and the same applies to valuation models and so forth. Therefore, the author
stresses the need for treating the theory and models very critically.
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9. Conclusion
The purpose of this paper was to find the intrinsic value of Vestas in a stand-alone case. To
do so, fundamental valuation models and their theoretical backgrounds were first
discussed and evaluated in the search for the most appropriate theoretical frameworks.
Asset-based and real options models were found to be useful mostly under special
circumstances. Multiples valuation could provide quick and dirty sanity checks, but also
had severe methodological and practical issues. For general purposes, the RI and EP
models were the most preferred models by the author, as they are strong in telling the
story of the firm’s economic performance. DCF is good in telling the development of cash
flows. Therefore, a combination of the RI or EP model with a DCF model is theoretically
strong. For this thesis, a DCF and EP combination was chosen, supplemented by a multiple
analysis. Having chosen the models, focus was again on Vestas.
Overall, the future looks somewhat bright for Vestas. The current political support is a
essential for the industry, but also show the Achilles’ heel of the industry. In the longer run,
the biggest threat would be if the support disappears before wind energy is competitive on
an unsubsidised basis. The industry analysis indicated an attractive industry. The main
negative aspects, however, include the bargaining power of buyers and the intensity of
rivalry which are both expected to increase. The historical financial analysis revealed that
Vestas has increased profitability mainly due to a drop in working capital and improved
gross margin.
Prior to the valuation, the problematic, theoretical issues concerning cost of capital were
addressed in order to estimate Vestas’ cost of capital. Synthesising the strategic and
financial analyses then enabled a forecast of the pro-forma budgets. Using the cost of
capital, the intrinsic value of Vestas was derived under three different scenarios.
Vestas is a company within a profitable industry with good growth opportunities. Much of
the growth, however, is already incorporated in the stock. The thesis’ intrinsic value
estimate of 53.91 EUR was an attempt to include all the mentioned observations and
discussions into a single figure. As the sensitivity analysis and simulation indicated the
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estimate may still be far from the true intrinsic value, but represents the author’s best
estimate.
An interesting part of this thesis was the continuous, general discussions about the severe,
theoretical flaws in contemporary finance; in the financial theories, valuation models, and
so forth. Estimates, share price targets, buy/sell recommendations and so forth should
therefore be treated extremely critically and one must not be seduced by precise formulas
with imprecise assumptions (Graham 1974).
…[P]recise formulas with highly imprecise assumptions can be used to establish, or rather
justify, practically any value one wishes.(Graham 1974)
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10. Bibliography
10.1.
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Clifford, L. 2001.,“Why you can safely ignore Six Sigma” Fortune, Vol. 143, No. 2, p. 140
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Ireland, R. et al. 2007,” The Management of Strategy” 8th ed., South-Western Cengage
Learning, Canada.
Irwin, S. et al. 2007, “What Do We Know About the Profitability of Technical Analysis?”
Journal of Economic Surveys, 21, No. 4, pp. 786-826, Blackwell Publishing Oxford.
Jensen, Ib Konrad 2003, “Mænd i modvind – Et dansk industrieventyr”, Børsens Forlag,
Denmark.
Jorgensen, Bjorn et. al. 2005, “An Empirical Assessment of the Valuation Accuracy of The
Abnormal Earnings Growth Valuation Model”, Vol. 6, no. 10, presented by Korea University.
Koller, Tim et al. 2005, “Valuation: Measuring and Managing the Value of Companies, 4th
ed., John Wiley and Sons.
Leslie, K. J. et al. 1998, “The Real Power of Real Options” Corporate Finance, lss. 158, p. 13
Lundholm, Russell 2007, “Equity Valuation and Analysis” McGraw-Hill, New York.
Lundholm, Russell 2001, “On comparing residual income and discounted cash flow models
of equity valuation: A response to Penman”, Contemporary Accounting Research, Vol. 18, no.
4, pp. 692-712.
Ohlson, James A. 1995, “Earnings, Book Values, and Dividends in Equity Valuation”,
Contemporary Accounting Research, Vol. 11, No. 2, pp. 661-687.
Ohlson, James A. 2005, “On Accounting-Based Valuation Formulae”, Review of Accounting
Studies, Vol. 10, pp. 323–347,
Pande, P.S. et al. 2002. ”The Six Sigma way, Team Field book: An Implementation Guide for
Process Improvement Teams”. McGraw-Hill, New York.
Penman, Stephen 2001, “On comparing cash flow and accrual accounting models for use in
equity valuation: A response to Lundholm and O’Keefe”, Contemporary Accounting
Research, Vol. 18, no. 4, pp. 681-689.
Penman, Stephen 2007, “Financial Statement Analysis and Security Valuation”, McGrawHill, Singapore.
Plenborg, Thomas, 2009, “Værdiansættelse af virksomheder”, Børsens Håndbøger, no. 2.
Porter, Michael 1985,"Competitive Strategy", The Free Press, New York.
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Schreiner, Andreas 2007, “Equity Valuation Using Multiples: An Empirical Investigation”
University of St. Gallen, DBA Dissertation.
Sharpe, W.F. 1964, “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions
of Risk!, Journal of Finance, Vol. 19, pp. 425-442.
Shiller R. 2000, “Measuring bubble expectations and investor confidence”. Journal of
Psychology and Markets, Vol. 1, No. 1.
Trigeorgis, L. 1993, “Topics in real options and applications” Financial Management, vol.
22(3), pp. 202–223.
Trigeorgis, L. 2005, “Making use of real options simple: An overview and applications
in flexible/modular decision making” The Engineering Economist, vol. 50(1), pp. 25–
53.
Welch I. 2000, “Views of financial economists on the equity premium and on professional
controversies” Journal of Business, Vol. 73, No. 4.
Wright, Stephen et al. 2003, “A Study into Certain Aspects of the Cost of Capital for
Regulated Utilities in the U.K.”, On behalf of Smithers & Co Ltd, commissioned by U.K.
regulators.
Zu, Xingxing et al. 2008: “The evolving theory of quality management: The role of Six
Sigma”, Journal of Operations Management, Vol. 26, pp. 630-650.
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10.2.
Reports and other sources
“Acquisition of REpower by Suzlon is important step in international cooperation”:
http://www.wwindea.org/home/index.php?option=com_content&task=view&id=175&Ite
mid=40
Biologist Kåre Fog scientifically disproves Lomborg’s claims, page 26.
http://www.lomborg-errors.dk/
BTM Consult Aps 2008, “BTM World Market Update 2007”, A. Rasmussens Bogtrykkeri,
Ringkoebing, Denmark.
Kurstarget: Showing the share price estimates of analysts concerning Danish firms.
www.kurstarget.dk
USGS 2009. US government-run U.S. Geological Survey points out “…most of the good spots
to locate hydro plants have already been taken.”
http://ga.water.usgs.gov/edu/wuhy.html
Vestas SH, Shareholder information 2009/1, 12 page publication.
Available at http://www.vestas.com/en/investor/shareholder/shareholder-information
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News paper articles
Business.dk 20/03/07: “Også i år vil Vestas mangle komponenter”.
http://www.business.dk/article/20070320/borsnyt/103201015/
Børsen 11/03-09: “Vestas and Boeing to collaborate on technology research projects”
http://borsen.dk/?treeid=2614&id=80302&urlpart_firm=vestas_wind_systems
Børsen, 6/11-08: ”Vestas/CEO: Kunder ønsker ikke større møller”
http://borsen.dk/?treeid=2613&id=55466&urlpart_firm=vestas_wind_systemsA
Børsen, 22/04-09: ”Rival vil slå Engel i 2010”
http://borsen.dk/investor/nyhed/155904/
Epn, 07/04-09: ”Vindbranchen er dårlig til rekruttering”
http://epn.dk/brancher/energi/alternativ/article1657373.ece
Finansnyheder, 20/03-09: ”Vestas: Vindekspert forstår ikke tårnfabrik i USA”
http://www.finansnyheder.dk/news/shownewsstory.aspx?storyid=10213859
Finansnyheder, 17/03-09: “Vestas/R&D-chef: År til resultater af Boeing-samarbejde”
http://www.finansnyheder.dk/news/shownewsstory.aspx?storyid=10207806
Guardian 17/03-09: “Shell dumps wind, solar and hydro power in favour of biofuels”.
http://www.guardian.co.uk/business/2009/mar/17/royaldutchshell-energy
Guardian 20/04-08: Warren Buffett quote, page 6:
http://www.guardian.co.uk/world/2008/apr/20/usa.subprimecrisis
Ingeniøren, 29/01-08: ”Vinge rev sig løs fra Vestas-mølle og fløj 40 meter væk”
http://ing.dk/artikel/85123-vinge-rev-sig-loes-fra-vestas-moelle-og-floej-40-meter-vaek
Wall Street Journal 08/10-08: “As rivals stall, Porsche engineers a financial windfall”.
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11. Appendices
11.1.
Start
End
Frequency
Name
Code
CURRENCY
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Market Premium calculations
1989 Start
1989
Aritmethical avg.
4,84%
2008 End
2008
Geometrical avg.
2,96%
Y
Frequency Y
S&P 500 COMPOSITEName
- TOT RETURN
US TREASURY
IND (~U$)
,
CONSTANT MATURITIES 10 YR - MIDDLE RATE
S&PCOMP(RI)~U$ Code
Y74758
U$
CURRENCY
Return on investment - Payment of interest = Excess
288,12
1988
9,17
379,41
1989
7,93
1,32
1,08
1,24
367,63
1990
8,15
0,97
1,08
0,89
479,63
1991
6,86
1,30
1,07
1,24
516,18
1992
6,7
1,08
1,07
1,01
567,09
1993
5,77
1,10
1,06
1,04
575,7
1994
7,81
1,02
1,08
0,94
792,04
1995
5,64
1,38
1,06
1,32
973,9
1996
6,34
1,23
1,06
1,17
1298,82
1997
5,74
1,33
1,06
1,28
1670,01
1998
4,7
1,29
1,05
1,24
2002,11
1999
6,41
1,20
1,06
1,13
1837,36
2000
5,1
0,92
1,05
0,87
1618,98
2001
5,17
0,88
1,05
0,83
1261,18
2002
3,92
0,78
1,04
0,74
1622,94
2003
4,21
1,29
1,04
1,24
1784,96
2004
4,29
1,10
1,04
1,06
1887,93
2005
4,37
1,06
1,04
1,01
2186,13
2006
4,67
1,16
1,05
1,11
2306,23
2007
4,21
1,05
1,04
1,01
1452,95
2008
2,18
0,63
1,02
0,61
Source: Datastream 13/03-09
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11.2.
Ticker
KFX
MAERSKB DC Equity
MAERSKA DC Equity
CARLB DC Equity
COLOB DC Equity
DNORD DC Equity
DCO DC Equity
DANSKE DC Equity
DSV DC Equity
FLS DC Equity
GEN DC Equity
LUN DC Equity
NKT DC Equity
NDA DC Equity
NOVOB DC Equity
NZYMB DC Equity
SYDB DC Equity
TOP DC Equity
TRYG DC Equity
VWS DC Equity
WDH DC Equity
Market Weight Report CSE 15/04-09
Name
A P Moller - Maersk A/S
A P Moller - Maersk A/S
Carlsberg A/S
Coloplast A/S
D/S Norden
Danisco A/S
Danske Bank A/S
DSV A/S
FLSmidth & Co A/S
Genmab A/S
H Lundbeck A/S
NKT Holding A/S
Nordea Bank AB
Novo Nordisk A/S
Novozymes A/S
Sydbank A/S
Topdanmark A/S
TrygVesta AS
Vestas Wind Systems A/S
William Demant Holding
% Weight
Shares
Last Price
in the Index in the Index
11,5
2,2
28300,0
11,3
2,2
27800,0
5,6
118,9
252,5
2,8
42,4
353,0
1,4
44,6
166,0
1,5
48,9
165,0
7,3
698,8
56,3
1,7
190,2
47,2
1,7
53,2
169,5
1,9
44,9
233,5
3,7
196,9
100,8
0,5
23,7
123,3
3,1
457,7
36,0
23,1
526,5
237,3
3,9
54,3
386,0
1,1
67,5
85,3
1,8
16,7
586,0
3,5
68,0
276,5
10,2
185,2
297,5
2,6
59,0
238,8
Source: Bloomberg, 15/04-09 13:34
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11.3.
Lundholm’s Credit Risk Framework
Based on their different ratios, industrial firms are grouped into 10 deciles. The firms are
then assigned historical default probabilities according to their deciles. The historical,
average sum is 5 %. (Lundholm 2007)
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11.4.
Guide to the valuation model
The following guide will hopefully make it easier to understand the model. After several
months of work the model may seems simple, but the author has recognises the difficulties
in understanding others’ model.
The two dark blue sheets to the left are the input sheet in which the balance sheet and
income statement data has been typed, which result in the third sheet, the historical
statements.
The fourth sheet, the regular blue WACC sheet, depicts both historical and future
calculations of the WACC.
The turquoise fifth and sixth sheets are also input sheets. In the Driver sheet, the shortterm and long-term drivers are typed in. In the Forecast Driver sheet, various, necessary
information is typed in, but the sheet also contains financial statement.
The seventh sheet, Results, is, as the name indicates, the results of both the historical and
future forecasts.
The final four green sheets all contain “end” information. The eight sheet, Valuation
summary, summarises the valuation, and calculates the share values. This is one the most
important sheet in the model. The ninth sheet, Output, contains, the ROIC tree and various
other information, whereas the tenth sheet, Table, only includes a minor table of the
development in ROIC and EBITA and so forth in the detailed forecast period. The final
sheet, Credit risk, is based on Lundholm’s framework and calculated the credit risk of
Vestas based on 5 key accounting ratios.
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11.5.
Base Scenario – Valuation Summary
Vestas - Base Case
EUR
Value of Operations: DCF approach
Free Cash
Discount
Year
Flow
Factor
2009
(228)
2010
451
2011
493
2012
642
2013
822
2014
850
2015
1.053
2016
1.097
2017
1.271
2018
1.456
2019
1.422
2020
1.606
2021
1.738
2022
1.797
2023
1.991
Cont. Value
22.369
Cash paid out 1/1-2009 to 15/04-2009
Operating Value
0,888
0,789
0,700
0,622
0,552
0,490
0,435
0,387
0,343
0,305
0,271
0,240
0,213
0,190
0,168
0,168
16
Continuing value-%
Detailed forecast period value-%
Mid -Year Adjustment Factor
Operating Value (Adjusted)
PV
of FCF
(202)
356
345
399
454
417
458
424
436
444
385
386
371
341
335
3.766
9.115
41,3%
58,7%
1,098
10.006
Value of Operations: Economic Profit
Economic
Discount
Year
Profit
Factor
PV
of EP
Operating Value
Excess Mkt Securities
10.006
41
2009
210
2010
108
2011
458
2012
670
2013
865
2014
935
2015
911
2016
861
2017
972
2018
1.082
2019
961
2020
1.046
2021
1.145
2022
1.087
2023
1.153
Cont. Value
10.524
Cash paid out 1/1-2009 to 15/04-2009
Present Value of Economic Profit
Invested Capital (incl. goodwill)
187
85
321
416
477
458
397
333
334
330
260
251
245
206
194
1.772
6.265
2.850
Financial Investments
Excess Pension Assets
26
0
0,888
0,789
0,700
0,622
0,552
0,490
0,435
0,387
0,343
0,305
0,271
0,240
0,213
0,190
0,168
0,168
Operating Value
Mid -Year Adjustment Factor
Operating Value (Adjusted)
Value of Equity
Enterprise Value
Equity Value
9.115
1,098
10.006
Comparison of key ratios
10.073
(123)
(162)
(2)
0
0
0
0
0
(13)
9.773
Debt
Capitalized Operating Leases
Retirement Related Liability
Preferred Stock
Minority Interest
Long-Term Operating Provision
Restructuring Provision
Future Stock Options
Stock options
No. shares (millions)
Reduction of share capital from 1/1-2009-15/04-09
No. shares (millions) on 15/04-2009
Value per Share
185
0
185
52,77
Latest year -High
Latest year -Low
Value Difference - High
Value Difference - Low
93,97
24,16
-43,8%
118,4%
Evaluation of entry and exit multiples
From:
To:
2005
2008
Revenue growth (CAG)
Adjusted EBITA growth (CAG)
NOPLAT growth (CAG)
Invested capital growth (CAG)
Averages
2009
2013
2014
2018
2019
2023
2008
26,4%
-275,3%
-241,2%
13,9%
21,3%
22,3%
18,1%
13,7%
12,4%
7,2%
7,5%
10,0%
7,6%
4,2%
4,2%
6,3%
Operating Value
Adj. EBIT/Revenues
2024
10.006
Excess Mkt Securities
Financial Investments
22.369
41
26
4,4%
11,7%
10,6%
8,8%
Enterprise Value
10.073
22.369
Revenues/Invested Capital (pre-Goodwill)
ROIC (after tax, pre-Goodwill)
ROIC (after tax, including Goodwill)
Average Economic Profit
7,7
48,2%
12,9%
(17)
4,7
40,9%
23,9%
462
4,9
38,5%
26,9%
952
4,6
30,2%
23,4%
1.078
Revenue
Adjusted EBITA
NOPLAT
6.035
733
637
43.379
3.253
2.407
Cash Tax Rate
WACC
6,9%
13,0%
26,0%
12,6%
26,0%
12,6%
26,0%
12,6%
1,7
13,7
15,8
0,5
6,9
9,3
=
22.9 %
Growth in NOPLAT
=
3.5 %
mEUR
RONIC in the continuing value
2.500
2.000
1.500
1.000
500
0
(500)
Enterprise / Revenue
Enterprise / Adjusted EBITA
Enterprise / NOPLAT
41,3
%
58,7
%
Development in FCF and PV(FCF)
Continuing value-%
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Free Cash Flow
Detailed forecast period
value-%
PV of FCF
mEUR
Development in EP and PV(EP)
1.400
1.200
1.000
800
600
400
200
0
2009
2010
2011
2012
2013
2014
EP
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2015
2016
2017
2018
2019
2020
2021
2022
2023
PV of EP
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11.6.
Bearish Scenario – Valuation Summary
Vestas - Base Case
EUR
Value of Operations: DCF approach
Free Cash
Discount
Year
Flow
Factor
2009
(463)
2010
98
2011
60
2012
79
2013
191
2014
377
2015
479
2016
526
2017
578
2018
665
2019
652
2020
670
2021
720
2022
688
2023
787
Cont. Value
9.593
Cash paid out 1/1-2009 to 15/04-2009
Operating Value
0,888
0,789
0,700
0,622
0,552
0,490
0,435
0,387
0,343
0,305
0,271
0,240
0,213
0,190
0,168
0,168
16
Continuing value-%
Detailed forecast period value-%
Mid -Year Adjustment Factor
Operating Value (Adjusted)
PV
of FCF
(411)
77
42
49
106
185
209
203
198
203
176
161
154
130
132
1.615
3.230
50,0%
50,0%
1,098
3.545
Value of Operations: Economic Profit
Economic
Discount
Year
Profit
Factor
PV
of EP
Operating Value
Excess Mkt Securities
2009
(101)
2010
(261)
2011
(77)
2012
(49)
2013
37
2014
143
2015
164
2016
195
2017
229
2018
258
2019
224
2020
172
2021
197
2022
139
2023
152
Cont. Value
1.011
Cash paid out 1/1-2009 to 15/04-2009
Present Value of Economic Profit
Invested Capital (incl. goodwill)
(90)
(206)
(54)
(30)
20
70
71
75
79
79
61
41
42
26
26
170
380
2.850
Financial Investments
Excess Pension Assets
0,888
0,789
0,700
0,622
0,552
0,490
0,435
0,387
0,343
0,305
0,271
0,240
0,213
0,190
0,168
0,168
Operating Value
Mid -Year Adjustment Factor
Operating Value (Adjusted)
Value of Equity
3.230
1,098
3.545
Comparison of key ratios
3.545
41
26
0
Enterprise Value
3.613
(123)
(162)
(2)
0
0
0
0
0
(13)
3.313
Debt
Capitalized Operating Leases
Retirement Related Liability
Preferred Stock
Minority Interest
Long-Term Operating Provision
Restructuring Provision
Future Stock Options
Stock options
Equity Value
No. shares (millions)
Reduction of share capital from 1/1-2009-15/04-09
No. shares (millions) on 15/04-2009
185
0
185
17,89
Value per Share
Latest year -High
Latest year -Low
Value Difference - High
Value Difference - Low
93,97
24,16
-81,0%
-26,0%
Evaluation of entry and exit multiples
From:
To:
2005
2008
Revenue growth (CAG)
Adjusted EBITA growth (CAG)
NOPLAT growth (CAG)
Invested capital growth (CAG)
Adj. EBIT/Revenues
Averages
2009
2013
2014
2018
2019
2023
26,4%
-275,3%
-241,2%
13,9%
12,8%
1,4%
-1,6%
10,8%
8,8%
11,3%
12,1%
6,7%
7,0%
2,6%
2,6%
5,5%
Operating Value
2008
2024
3.545
Excess Mkt Securities
Financial Investments
9.593
41
26
4,4%
5,5%
8,0%
6,9%
Enterprise Value
3.613
9.593
Revenues/Invested Capital (pre-Goodwill)
ROIC (after tax, pre-Goodwill)
ROIC (after tax, including Goodwill)
Average Economic Profit
7,7
48,2%
12,9%
(17)
4,1
17,5%
9,9%
(90)
4,1
25,5%
16,2%
198
4,0
21,6%
15,1%
177
Revenue
Adjusted EBITA
NOPLAT
6.035
733
637
24.837
1.490
1.153
Cash Tax Rate
WACC
6,9%
13,0%
22,6%
12,6%
22,6%
12,6%
22,6%
12,6%
0,6
4,9
5,7
0,4
6,4
8,3
=
14.5 %
Growth in NOPLAT
=
3.5 %
mEUR
RONIC in the continuing value
1.000
800
600
400
200
0
(200)
(400)
(600)
Enterprise / Revenue
Enterprise / Adjusted EBITA
Enterprise / NOPLAT
50,0%
Development in FCF and PV(FCF)
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
50,0%
Continuing value-%
Detailed forecast period value-%
Free Cash Flow
PV of FCF
Development in EP and PV(EP)
300
mEUR
200
100
0
(100)
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
(200)
(300)
EP
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11.7.
Bullish Scenario – Valuation Summary
Vestas - Base Case
EUR
Value of Operations: DCF approach
Free Cash
Discount
Year
Flow
Factor
PV
of FCF
2009
(177)
2010
646
2011
758
2012
1.019
2013
1.382
2014
1.458
2015
1.926
2016
2.179
2017
2.604
2018
3.052
2019
3.016
2020
3.078
2021
2.912
2022
3.267
2023
3.010
Cont. Value
42.296
Cash paid out 1/1-2009 to 15/04-2009
Operating Value
(157)
509
531
633
763
715
838
843
894
930
817
740
622
619
507
7.120
16.925
0,888
0,789
0,700
0,622
0,552
0,490
0,435
0,387
0,343
0,305
0,271
0,240
0,213
0,190
0,168
0,168
16
Continuing value-%
Detailed forecast period value-%
Mid -Year Adjustment Factor
Operating Value (Adjusted)
Value of Operations: Economic Profit
Economic
Discount
Year
Profit
Factor
2009
376
2010
330
2011
799
2012
1.185
2013
1.694
2014
2.187
2015
2.090
2016
2.135
2017
2.423
2018
2.692
2019
2.635
2020
2.456
2021
2.249
2022
2.431
2023
2.121
Cont. Value
24.879
Cash paid out 1/1-2009 to 15/04-2009
Present Value of Economic Profit
Invested Capital (incl. goodwill)
42,1%
57,9%
1,098
18.579
Operating Value
Mid -Year Adjustment Factor
Operating Value (Adjusted)
Comparison of key ratios
From:
To:
Adj. EBIT/Revenues
334
260
559
737
935
1.072
910
826
832
821
713
590
480
461
357
4.188
14.075
2.850
16.925
1,098
18.579
Operating Value
Excess Mkt Securities
18.579
41
Financial Investments
Excess Pension Assets
26
0
Enterprise Value
18.646
(123)
(162)
(2)
0
0
0
0
0
(13)
18.346
Debt
Capitalized Operating Leases
Retirement Related Liability
Preferred Stock
Minority Interest
Long-Term Operating Provision
Restructuring Provision
Future Stock Options
Stock options
Equity Value
No. shares (millions)
Reduction of share capital from 1/1-2009-15/04-09
No. shares (millions) on 15/04-2009
185
0
185
99,06
Value per Share
Latest year -High
Latest year -Low
Value Difference - High
Value Difference - Low
93,97
24,16
5,4%
310,0%
Evaluation of entry and exit multiples
2005
2008
Revenue growth (CAG)
Adjusted EBITA growth (CAG)
NOPLAT growth (CAG)
Invested capital growth (CAG)
0,888
0,789
0,700
0,622
0,552
0,490
0,435
0,387
0,343
0,305
0,271
0,240
0,213
0,190
0,168
0,168
Value of Equity
PV
of EP
Averages
2009
2013
2014
2018
2019
2023
2008
26,4%
-275,3%
-241,2%
13,9%
29,0%
34,5%
29,8%
16,6%
16,5%
11,4%
11,6%
14,2%
9,0%
0,5%
0,5%
7,9%
Operating Value
2024
18.579
Excess Mkt Securities
Financial Investments
42.296
41
26
4,4%
13,9%
12,8%
9,4%
Enterprise Value
18.646
42.296
Revenues/Invested Capital (pre-Goodwill)
ROIC (after tax, pre-Goodwill)
ROIC (after tax, including Goodwill)
Average Economic Profit
7,7
48,2%
12,9%
(17)
5,3
54,6%
32,9%
877
5,6
52,8%
39,9%
2.305
5,1
35,5%
29,8%
2.378
Revenue
Adjusted EBITA
NOPLAT
6.035
733
637
75.994
6.080
4.463
Cash Tax Rate
WACC
6,9%
13,0%
26,6%
12,6%
26,6%
12,6%
26,6%
12,6%
3,1
25,4
29,3
0,6
7,0
9,5
RONIC in the continuing value
=
25.7 %
Growth in NOPLAT
=
3.5 %
Enterprise / Revenue
Enterprise / Adjusted EBITA
Enterprise / NOPLAT
42,1%
Development in FCF and PV(FCF)
4.000
57,9%
mEUR
3.000
2.000
1.000
Continuing value-%
0
(1.000)
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Free Cash Flow
Detailed forecast period value-%
PV of FCF
Development in EP and PV(EP)
1.400
1.200
mEUR
1.000
800
600
400
200
0
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
EP
Bachelor Thesis
© Emad Sharghbin 2009
PV of EP
83
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11.8.
Constantly held variables in the forecast
This appendix provides an overview of all variables held constant in all the scenarios and
the simulations. It refers to the sheet “Forecast Driver” in the 3 scenario models. The list is
made according to how the sheet is divided. For an overview, click on the boxed “1” which
is in row with the letters of the columns in Excel. This will collapse all the different sections.
Clicking on the plusses will expand the sections.
OPERATIONS:
S,G&Adm. costs:
Average of last 3 years to reflect increasing trend.
R&D
R &D is more and more important, and one could argue that it
would receive increasing funding. The variables is held constant at
3,5 % of revenue, which is above the past funding of 2-2,5 % of
revenue.
Working Capital:
All variables are based on the average of the last 5 years. However,
Accounts receivable, Accounts payable, and other current
liabilities have all grown significantly within recent years,
therefore these are only based on last 3 years’ average.
BALANCE SHEET ITEMS:
Capex
Vestas’ guidance for 2009 includes 1200 mEUR on investments in
non-current assets, of which 1000 is in tangibles, both excl. dep.
However, the company has said that they will lower capex
depending on Q1, so capex is forecasted around 900 inclusive of
depreciation. Capex for 2010 and 2013 have both been put at 3 %
of revenue, while 2011-2012 is assigned 5 %. A slow down is
anticipated in 2010, that will be followed by an expansion in 20112012, which will be stabilised again in 2013. Of course, in the 3
different scenarios, capex will be different, but it is outside the
scope of this report to estimate exactly how much additional capex
a bullish scenario necessitates and so forth.
Bachelor Thesis
© Emad Sharghbin 2009
84
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Goodwill:
Goodwill is expected not to be amortised, and also, Vestas is not
expected to acquire any firms. Hence goodwill is kept at 0.
OFF-BALANCE SHEET ITEMS:
Intangibles:
Additions and amortisations are both put at last 3 years average.
Op. leases:
Calculated b y finding CAGR for 2004-2008 then using it each year.
Interest rate is same as interest rate on debt.
Options:
Value of Options Outstanding is put at 12,615 mEUR according to
AR 2008: 82.
Preferred stock:
0, as there is none in Vestas.
PROVISIONS & NON-OP- P&L:
Long-term debt rate:
As mentioned in the section about WACC, it is estimated to be
around 6,5 % forward-going.
TAX:
Marginal, effective tax: Vestas’ own guidance forecasts 28 % corporate tax (AR 2008: 11).
Although it is possible to estimate effective tax rate based on in
which countries Vestas makes its profits, the added complexity
outweighs the benefits.
Deferred tax:
Average of last 5 years.
LONG TERM & CV DRIVERS:
Cash tax rate:
Average of last 5 years.
Closing Net PPE & Other IC:
Average of last 3 years to reflect decreasing trends.
Cumulative GW & Intangibles:
Written up with average of historical mean in 200408, which was 45 mEUR.
Bachelor Thesis
© Emad Sharghbin 2009
85
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11.9.
Price targets from leading investment banks
Date
Price target in DKK
23-04-2009
435
22-04-2009
200
07-04-2009
260
27-03-2009
240
25-03-2009
238
12-03-2009
223
10-03-2009
270
09-03-2009
240
26-02-2009
260
13-02-2009
525
12-02-2009
345
12-02-2009
380
10-02-2009
272
05-02-2009
425
30-01-2009
499
23-01-2009
330
22-01-2009
414
28-10-2008
250
15-10-2008
380
02-10-2008
590
20-08-2008
810
19-08-2008
750
14-08-2008
750
11-07-2008
850
11-07-2008
545
04-07-2008
850
24-06-2008
650
07-06-2008
540
21-05-2008
675
Price target in EUR
58
27
35
32
32
30
36
32
35
70
46
51
37
57
67
44
56
34
51
79
109
101
101
114
73
114
87
72
91
Bank
Goldman Sachs
Credit Suisse
JPMorgan
Standard & Poors´s
Norddeutsche L.
DZ Bank
Deutshe Bank
Morgan Stanley
Society General
Danske Bank
ING
Jyske Bank
Royal Bank of Scotland
Nordea
Jefferies
Citigroup
Piper Jaffray
Merrill Lynch
Cheuvreux
UBS
Handelsbanken
Alm. Brand Henton
Dresdner Kleinwort
Kaupthing/FIH
Dansk Aktie Analyse
HSBC
Gudme Raaschou
Carnegie
ABG Sundal Collier
Price targets in EUR from leading investment
banks
120
100
80
60
40
20
0
Feb-08
Jun-08
Sep-08
Dec-08
Price target in EUR
Bachelor Thesis
© Emad Sharghbin 2009
Mar-09
Jul-09
Author's estimate
86
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11.10.
Three peer group analyses: Wind, Solar, Industrials
Available on the enclosed CD – Peergroup_Vestas.xls.
Peer Group - Wind Energy
Author's estimates
EV/EBITDA EV/EBIT EV/EBIT EV/EBIT
Price
MV
PE
PE
EV/EBITDA
(USD)
(mUSD)
(2009)
(2010)
(2009)
(2010)
(2009)
VESTAS WINDSYSTEMS
58,4
10.806,3
18
14
9
8
12
10 14
GAMESA CORPN.TEGC.
17,4
4.233,3
19
13
8
6
14
11
Company namc
(2010) (2009)
16,5
1.299,3
17
22
10
10
12
13
121,2
1.089,9
19
17
8
7
10
7
1,3
1.983,8
8
11
6
6
7
7
Arit. average excl. Vestas
16
16
8
7
11
10
Arit. average excl. Vestas and Suzlon
19
17
9
8
12
10
MV-weighted average
16
14
8
7
12
10
Vestas premium cf. arit. average
10%
-8%
17%
3%
7%
-2%
Vestas premium cf. arit. avg. excl. Suzlon
-6%
-17%
8%
-3%
-4%
-9%
7%
1%
19%
9%
-1%
-3%
-6%
-17%
8%
-3%
-4%
-9%
Price according to MV-multiples
45
43
51
47
42
41
Price according to avg. excl. Suzlon
40
35
46
41
41
39
NORDEX
REPOWER SYSTEMS
SUZLON ENERGY
Premium MV-weighted
Premium/discount ex. Suzlon
VWS price on 27/04-09:
EV/EBIT
(2010)
13
43
Source: Thomson Financials Datastream
EV/EBITDA EV/EBIT EV/EBIT
Price
MV
PE
PE
EV/EBITDA
(USD)
(mUSD)
(2009)
(2010)
(2009)
(2010)
(2009)
(2010)
VESTAS WINDSYSTEMS
58,4
10.806,3
18
14
9
8
12
10
SOLARWORLD
25,6
2.864,7
14
11
6
5
8
7
3,2
37,2
3
2
2
1
3
1
SOLON
12,3
154,1
7
5
9
7
12
10
PHOENIX SOLAR
41,5
277,7
10
8
6
5
6
5
9
7
6
5
7
6
14
11
6
5
8
7
105%
118%
58%
66%
58%
67%
Solar energy
SUNWAYS
Arithmetic average excl. Vestas
MV-weighted average
Vestas premium cf. arithmetic average
EV/EBITDA EV/EBIT EV/EBIT
Price
MV
PE
PE
EV/EBITDA
(USD)
(mUSD)
(2009)
(2010)
(2009)
(2010)
(2009)
(2010)
VESTAS WINDSYSTEMS
58,4
10.806,3
18
14
9
8
12
10
GENERAL ELECTRIC
12,1 127.886,6
12
13
22
21
41
42
SIEMENS
65,1
59.531,4
10
10
6
6
8
9
ABB 'R'
15,0
34.778,9
13
16
7
8
8
10
CATERPILLAR
33,6
20.229,3
26
21
17
16
38
30
ALSTOM
62,3
17.919,6
12
11
7
6
9
8
20
Industrials
15
14
12
11
21
Arithmetic average excl. Vestas, GE, and Caterpillar 12
12
7
7
8
9
13
13
15
14
27
27
18%
0%
-21%
-34%
-44%
-52%
15%
Caterpillar
Vestas premium cf. arithmetic average excl. GE and47%
42%
13%
38%
8%
Arithmetic average excl. Vestas
MV-weighted average
Vestas premium cf. arithmetic average
Source: Thomson Financials Datastream.
Bachelor Thesis
© Emad Sharghbin 2009
87