Download Debt Levels and Share Price - a Sensitivity Analysis on Vestas

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Debt Levels and Share Price a Sensitivity Analysis on Vestas
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Author: Lavinia Andrei
MSc. Finance and International Business
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Advisor: Otto Friedrichsen
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April 2011
Aarhus School of Business, Aarhus University
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Acknowledgements
I would like to express my appreciation and thankfulness to my supervisor for
his guidance and to my friends for their support.
Abstract
Capital structure is one of the areas of corporate finance which has long been
under the scrutiny of theorists and researchers. This paper aims to take a hands-on
approach and to look at how the level of debt affects share prices in the case of Vestas.
Firstly, a plain vanilla valuation of the company is performed, yielding a share price of
EUR 30.03. The company’s optimal capital structure is thereafter determined, by
employing the two sub-frameworks of the trade-off theory: static and dynamic. The
results point out that Vestas is currently either around optimum debt levels (in the
dynamic trade-off case), or below them (in the static trade-off case). An extensive
sensitivity analysis of share prices dependent upon debt levels is built into the valuation
model, thus determining how the share price will fluctuate when the debt level changes.
In addition to target debt levels, the sensitivity analysis further looks at the effects of
other debt-related variables (cost of debt and marginal tax rate), as well as non-debtrelated ones (the risk free rate and the return on the market portfolio). Given the way the
valuation model is designed, the larger the effect that a variable has on the weighted
average cost of capital, the larger its impact on the company’s share price will be.
Key words: capital structure, static trade-off, dynamic trade-off, valuation,
sensitivity analysis, Vestas.
Contents
List of Figures ................................................................................................................... 5
List of Tables .................................................................................................................... 6
Chapter 1. Introduction ................................................................................................. 7
1.1. Problem Statement..................................................................................................... 8
1.2. Limitations ................................................................................................................. 9
1.3. Methodology............................................................................................................ 10
1.4. Structure of the Thesis ............................................................................................. 11
Chapter 2. Overview of Capital Structure Theories ................................................. 12
2.1. Does Capital Structure Matter? ............................................................................ 12
2.1.1. The M&M Theories ...................................................................................... 12
2.1.2. Subsequent Studies........................................................................................ 13
2.2. The Trade-Off Theory.......................................................................................... 15
2.2.1. Determinants of Capital Structure ................................................................. 17
2.2.2. The Static Trade-Off Model .......................................................................... 20
2.2.3. The Dynamic Trade-Off Model .................................................................... 21
2.3. The Pecking Order Theory .................................................................................. 25
2.3.1. Information Asymmetry Considerations ....................................................... 25
2.3.2 Agency Costs Considerations......................................................................... 26
2.4. Market Timing Theory......................................................................................... 26
Chapter 3. Business Strategy Analysis ....................................................................... 28
3.1. External Analysis ................................................................................................. 28
3.2. Porter’s 5 Forces Model ....................................................................................... 29
3.3. Competitor analysis ............................................................................................. 29
3.4. Internal Analysis .................................................................................................. 29
3.4.1. Strategy statements........................................................................................ 29
3.4.2. Product and Service Mix ............................................................................... 30
3.4.3. Business segments ......................................................................................... 30
3.5. SWOT Analysis ................................................................................................... 31
Chapter 4. Analysing Historical Performance .......................................................... 33
4.1. Reorganisation of Financial Statements .............................................................. 33
4.1.1. Treatment of Accounts, Assumptions and Estimations ................................ 33
1
4.1.2. Results of Reorganisation ............................................................................. 36
4.2. Credit Health ........................................................................................................ 37
4.3. Stock Market Performance .................................................................................. 37
Chapter 5. Base case scenario valuation..................................................................... 39
5.1. Scenario description ............................................................................................. 39
5.2. Forecasting performance ...................................................................................... 39
5.2.1. Revenue growth............................................................................................. 39
5.2.2. Cost of capital................................................................................................ 40
5.2.3. Other inputs ................................................................................................... 42
5.2.4. Continuing value ........................................................................................... 43
5.3. Valuation result .................................................................................................... 43
5.4. Critique ................................................................................................................ 44
Chapter 6. Sensitivity Analysis .................................................................................... 45
6.1. Target capital structure ........................................................................................ 45
6.1.1. The Static Trade-off Target Debt Level ........................................................ 46
6.1.2. The Dynamic Trade-off Target Debt Level and Adjustment Speed ............. 48
6.1.3. Simulation Assumptions and Method ........................................................... 49
6.1.4. Results ........................................................................................................... 51
6.1.5. Discussion and Critique ................................................................................ 54
6.1.6. Discussion on the Adjustment Speed ............................................................ 58
6.2. The Cost of Debt .................................................................................................. 59
6.2.1. Types of Debt - Discussion ........................................................................... 59
6.2.2. Sensitivity Analysis Assumptions and Method ............................................. 61
6.2.3. Results and Discussion .................................................................................. 62
6.3. Other Debt-Related and Non-debt-related Variables ........................................... 62
6.3.1. Simulation Assumptions and Method ........................................................... 63
6.3.2. Results ........................................................................................................... 64
6.3.3. Discussion and Critique ................................................................................ 64
6.4. Simulations of All Variables ................................................................................ 65
6.4.1. Simulation Assumptions and Method ........................................................... 65
6.4.2. Results ........................................................................................................... 66
6.4.3. Discussion and Critique ................................................................................ 67
Chapter 7. Conclusions ................................................................................................ 69
2
Bibliography ................................................................................................................... 72
Annexes .......................................................................................................................... 77
A1. Market Definition, Size and Growth .................................................................... 77
A1.1. Market Definition .......................................................................................... 77
A1.2. Market Size.................................................................................................... 78
A1.3. From Present to Future - Market Growth ...................................................... 79
A2. PESTEL Analysis ................................................................................................ 82
A2.1. Political and Legal Factors ............................................................................ 82
A2.2. Economic Factors .......................................................................................... 82
A2.3. Socio-cultural Factors.................................................................................... 84
A2.4. Technological Factors ................................................................................... 85
A2.5. Environmental Factors .................................................................................. 86
A2.6. General Degree of Turbulence in the Industry Environment ........................ 86
A3. Competitor Analysis ............................................................................................ 87
A3.1. The top 4 ........................................................................................................ 87
A3.2. Competition trends ........................................................................................ 88
A4. Porter’s 5 Forces Model ....................................................................................... 90
A4.1. Bargaining Power of Buyers ......................................................................... 90
A4.2. Bargaining Power of Suppliers...................................................................... 91
A4.3. Threat of New Entrants ................................................................................. 93
A4.4. Threat of Substitute Products ........................................................................ 94
A4.5. Competitive Rivalry within the Industry ....................................................... 96
A5. Internal Analysis .................................................................................................. 97
A5.1. Corporate Vision, Mission and Strategy ....................................................... 97
A5.2. Product and Service Mix ............................................................................... 98
A5.3. Geographic & Business Segments ................................................................ 99
A5.4. Business Model ........................................................................................... 100
A6. SWOT Analysis ................................................................................................. 103
A6.1. Strengths ...................................................................................................... 103
A6.2. Weaknesses ................................................................................................. 105
A6.3. Opportunities ............................................................................................... 105
A6.4. Threats ......................................................................................................... 107
A7. Reorganisation of Financial Statements ............................................................. 110
3
A7.1. Invested Capital ........................................................................................... 110
A7.2. NOPLAT ..................................................................................................... 111
A7.3. Free Cash Flow ............................................................................................ 112
A7.4. Return on Invested Capital .......................................................................... 112
A7.5. Revenue Growth .......................................................................................... 113
A8. Interest Coverage ............................................................................................... 113
A9. Historical Analysis Results ................................................................................ 114
A9.1. Income Statement ........................................................................................ 114
A9.2. Balance Sheet .............................................................................................. 115
A9.3. Cash Flow Statement ................................................................................... 116
A9.4. NOPLAT ..................................................................................................... 117
A9.5. Invested Capital ........................................................................................... 117
A9.6. Free Cash Flow ............................................................................................ 118
A9.7. Financial Ratios ........................................................................................... 119
A10. Base Case Scenario Valuation Inputs .............................................................. 120
A10.1. Detailed forecast ........................................................................................ 120
A10.2. Key Driver Forecast .................................................................................. 121
4
List of Figures
Figure 1: The Static Trade-off Theory ........................................................................... 16
Figure 2: Degree of Turbulence in the Industry ..............................................................28
Figure 3: Porter’s 5 Forces ..............................................................................................29
Figure 4: Vestas’ Revenue .............................................................................................. 31
Figure 5: SWOT Analysis ..............................................................................................32
Figure 6: ROIC Tree ........................................................................................................36
Figure 7: Stock Market Performance...............................................................................37
Figure 8: Valuation result ................................................................................................43
Figure 9: The Static Trade-off Model..............................................................................47
Figure 10: Simulation #1 Share Price Histogram ............................................................53
Figure 11: Simulation #2 Share Price Histogram ............................................................53
Figure 12: Simulation #3 Share Price Histogram ............................................................53
Figure 13: Simulation #4 Share Price Histogram ............................................................53
Figure 14: Simulation #5 Share Price Histogram ............................................................54
Figure 15: Simulation #6 Share Price Histogram ............................................................54
Figure 16: Share Price Sensitivity in the Static Trade-off Case ......................................62
Figure 17: Share Price Sensitivity in the Dynamic Trade-off Case ................................62
5
List of Tables
Table 1 – Determinants of Capital Structure .................................................................. 18
Table 2: Main Valuation Inputs .......................................................................................43
Table 3: Target Capital Structure Percentage Changes Results ......................................51
Table 4: Target Capital Structure Simulation Results .................................................... 52
Table 5: Simulation results of other debt and non-debt related variables .......................64
Table 6: Link between the target debt level and the cost of debt ....................................66
Table 7: Share-price sensitivity when all analysed variables are simulated....................66
6
Chapter 1. Introduction
Companies constantly strive to maximise their share price, both through their
investment choices and through their financing ones. This paper looks at the latter in an
attempt to shed some light upon how the share price would be affected by changes in
the capital structure of the firm.
In their seminal work, Miller and Modigliani (1958) posit that in a perfect
market, the capital structure of the company is irrelevant and therefore, has no influence
on the value of the company. However, their theory was based on numerous and quite
restrictive assumptions which make their conclusions work on paper more than off it. In
the real world, markets are far from perfect, transaction costs exist, and there are agency
costs of debt and equity. Those and other facts have somewhat cast a shadow on the
capital structure irrelevance principle.
If the capital structure actually does matter, then what would be the optimal
debt-to-equity level? The static trade-off theory affirms that firms select their capital
structure by systematically trading off the advantages of debt financing against its costs.
The optimal capital structure is thus reached by choosing a debt level that maximises
firm value. A cross-sectional study by Titman and Wessels, (1988) links debt levels to
costs of financial distress and bankruptcy, but also acknowledges the influence of tax
shields and the fact that debt reduces suboptimal investment. Overall, the results are
found to be inconclusive. In a more recent paper, Chang et al., (2009) build on the
research of Titman and Wessels, (1988) and, by improving the model, find statistically
significant results for all the determinants.
In spite of the aforementioned advantages, many large companies like Microsoft,
Vestas, or a considerable number of pharmaceutical companies choose to keep their
debt levels extremely low, a decision which is in conflict with the propositions of the
static trade-off theory.
The target adjustment model, a more dynamic one, posits that firms gradually
adjust their capital structure towards a target level which shifts over time, being a
function of various endogenous and exogenous factors. Clark et al., (2009) try to
determine whether firms actually do adjust toward a target capital structure and, by
studying 26,395 firms from 40 countries, they find evidence supporting the dynamic
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trade-off theory for capital structure. They also study the speed of adjustment over their
large sample of data and find differences between developed and developing countries.
The company which is used to illustrate the effects of leverage on share price is
Vestas. All throughout its history, Vestas has been known to have a low to very low
debt-to-equity ratio. It might have been due to the desire to stay safe of default risks,
which might have seemed high in this new „green” industry. Or it might have been due
to the impossibility to access external funds at an acceptable cost for various reasons.
However, the company has been growing steadily and is now looking to diversify its
capital structure, slowly starting to take on more debt. On 15 March 2010, it announced
the successful placing of a 600m Eurobond. The transaction was received very well by
the European investors and the book was more than three times oversubscribed. How
will adding more debt influence them? Does the type of debt which they chose to issue
matter?
1.1. Problem Statement
The present thesis takes a balance sheet approach to corporate valuation,
studying how the share price is affected by changes in the structure of the statement of
financial position. The aim of the paper is to analyse how changing the level of a
company’s leverage from the current one to the optimal one might affect its overall
share value.
This paper will tackle the following issues:
1) Calculate the price of Vestas’ shares given the current level of debt (which
includes the EUR 600 m Eurobond) by employing a plain vanilla valuation.
2) Determine the optimal capital structure under the static trade-off theory and
calculate the new share price of Vestas under that capital structure.
3) Determine the optimal capital structure under the target adjustment model
and calculate the new share price of Vestas under the hypothesised capital
structure. A discussion of the adjustment speed from the viewpoint of the
target adjustment model will also be undertaken.
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4) Given that the optimal capital structure involves higher debt levels, discuss
which type of debt would be most appropriate for Vestas by looking at
possible advantages and disadvantages for Vestas and also by comparing the
potential effects that the debt instruments might have on the cost of debt of
the company.
5) Perform a sensitivity analysis of the share price. The main exogenous
variable will be the level of debt. Simulation will be used to determine to
which extent it affects the share price. Moreover, the sensitivity analysis will
also look at different debt-related variables (cost of debt, marginal tax rate),
as well as non debt-related variables that affect the cost of capital (the risk
free rate and the return on the market portfolio).
1.2. Limitations
Firstly, it should be mentioned that the valuation and the discussions in the paper
are all undergone from the point of view of an external party that is not privy to inside
information from Vestas. The analysis is solely based on public information from
Vestas and various other external sources. The information taken into account is dated
up until 1 September 2010. Therefore, any information – immaterial or material enough
to possibly alter the valuation results - which was made public by the company or other
sources after 1 September has not been considered.
Moreover, in order to further limit the extent of the analysis, some issues which
might otherwise affect the valuation or the effect of optimal capital structure on share
prices have not been taken into consideration. These issues are as follows:
 personal taxes for debt and equity;
 the effect of inflation on tax gains from leverage;
 adjustment costs of changing the financing part of the balance sheet
(changes are assumed to be costless);
 financial distress costs – the analysis does not look at what happens to the
share price if the amount of leverage becomes higher than the optimum.
9
1.3. Methodology
The valuation of Vestas is built upon information gathered from the company’s
financial statements over the past 10 years, as well as various other sources, such as
competitors’ financial statements, industry reports and reports on financial markets. A
number of frameworks and models – both theoretical and empirical - have also been
used in order to answer the issues at hand in this thesis. They are all briefly outlined
below.
Firstly, for the pre-valuation documentation, a strategic business analysis has
been conducted by using frameworks such as PESTEL (Annex A2), competitor analysis
(Annex A3) and Porter’s 5 forces (Annex A4). An internal analysis of the company is
also performed (Annex A5). Thereafter, the most important facts have been summed up
and presented as an overview in the SWOT analysis (Annex A6).
Secondly, the valuation per-se employs two different methods that complement
each other: the enterprise discounted cash flow method and the economic profit method.
They provide the same result, but give different insights into the valuation. The reason
for choosing these two frameworks is that they do not include the effects of the
company’s capital structure in the valuation and focus solely on Vestas’ operating
performance.
Thirdly and lastly, the sensitivity of the share price to different debt levels will
be analysed. To do this, the following steps will be undertaken:
1) Target debt levels will be calculated based on the insight provided by two
capital structure theories: the static trade-off theory and the target adjustment
or dynamic trade-off model. For the former theory, the model used is the one
described by Chang et al., (2009), while for the latter, the model by Clark et
al., (2009).
2) Given that the valuation is conducted from an outsider’s perspective, the
target debt level is assumed to be a random variable with a normal
distribution and a mean equal to the debt levels calculated in point 1. A
simulation of possible debt target levels will be run. Because of the
assumption that the company is a going concern, the simulation results will
be limited to include only outcomes up to a certain level. Any higher
outcome could potentially mean that the firm has entered financial distress,
and therefore the going concern assumption would not be valid. Estimating
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the results of financial distress is beyond the scope of this thesis, which is
why simulation outcomes have been capped and are not allowed to be higher
than a level considered adequate and beyond which financial distress costs
seriously come into play.
3) The share price will be calculated.
4) After running an appropriate number of simulations and repeating steps 2-4,
the mean and standard deviation of the share prices will be calculated.
1.4. Structure of the Thesis
The thesis continues in the following manner: chapter 2 provides a literature
review of capital structure theories and studies, chapter 3 tackles a business strategy
analysis, the next section looks at Vestas’ historical performance, followed by the base
case valuation in chapter 5 and the sensitivity analysis in chapter 6. Conclusions are
presented in the last section – chapter 7.
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Chapter 2. Overview of Capital Structure Theories
Although vastly explored, the issue of capital structure is still largely not
clarified. A plethora of studies have been trying to answer questions like „what
influences the choice of capital structure and to which extent?”, but results have been
either inconclusive or antithetical. Like in a Picasso painting, a multitude of shapes and
colors seem to fit harmoniously with each other, but taken as a whole, they don’t make
complete sense and the overall picture can be interpreted and re-interpreted in numerous
ways. Different points of view representing different capital structure theories give birth
to different interpretations of the Picasso painting and hence, different answers to the
capital structure issues at hand.
Modigliani and Miller, (1958) argue that capital structure is irrelevant under
stringent conditions. However, reality cannot be bound within those conditions and
therefore, with the acknowledgement of that fact, three major theories – trade-off
theory, pecking order theory, market timing theory - have come to light, trying to
explain the whats, whys and hows. This section starts off with an overview of studies
trying to determine if capital structure does matter in real life and what effect it has on
the valuation of a company. If capital structure does matter, how do we determine the
optimal level of debt? Answers to this question are presented from the point of view of
all three major theories, but the focus is cast on the trade-off theory, which is central to
the sensitivity analysis part of the paper.
2.1. Does Capital Structure Matter?
2.1.1. The M&M Theories
The first attempt to create a theory linking capital structure to firm value was
undergone by Modigliani and Miller, (1958) and further reviewed and corrected in
Modigliani and Miller, (1959) and Modigliani and Miller, (1963). Their Proposition I or
„Irrelevance Proposition” states that „the average cost of capital to any firm is
completely independent of its capital structure and is equal to a pure equity stream of its
class”1. This proposition represents the cornerstone of modern corporate finance and
also the basis for the static trade-off and the pecking order theories.
1
See Miller and Modigliani (1958), p. 268.
12
There are, in fact, two distinct kinds of irrelevance propositions. The first type
is the result of market mechanisms acting against arbitrage opportunities, as developed
by Stiglitz, (1969). He shows that the proposition holds true even under less stringent
assumptions. The second type is connected to multiple equilibria, as primarily
developed by Miller, (1977), who shows that the proposition holds even when assuming
that interest payments are deductible in their entirety when calculating income taxes.
Subsequent research has focused on dis-proving the theory by using arguments
connected to tax advantages, financial distress costs, agency issues, transaction costs,
adverse selection and other issues. Contrasting theories have been put forward.
However, covering all the range of counter-arguments is not within the scope of the
paper. A comprehensive overview of developments to the date of the study
is provided by Harris and Raviv, (1991).
2.1.2. Subsequent Studies
The conclusions drawn from later analyses on the effects of capital structure
changes on firm value are somewhat diverging with respect to the irrelevance
proposition. To this date, there is no generally-accepted black-or-white answer as to
whether it does hold in real life or not.
Pinegar and Lease, (1986) investigate the effect of corporate structure changes
that do not have any tax-related impact, such as exchange from preferred to common
stock. Their hypothesis is that these exchanges still have an impact on share value
because of signaling or agency costs considerations. They find that the market value of
equity increases after announcements of this kind. Therefore, the signaling hypothesis
put forth by Leland and Pyle, (1977) is proven to influence the reaction of the market,
even though the exchanges do not affect the tax status of the issuing company in any
way.
Eckbo, (1986) analyses 723 debt offerings and tries to determine what kind of
effects they have on share prices. Theory predicts that increasing the level of debt would
have a positive impact on the valuation of the company’s shares. However, his results
do not support that affirmation. Regardless of whether straight or convertible debt is
issued, no strong positive relation is found between the offerings and the returns on the
market. Apart from a small subsample of public utility offerings, all other offerings
analysed resulted in zero abnormal returns. However, a drawback in this type of study is
that it does not account for whether the changes in capital structure are made on a short
13
or long term. Only the latter are postulated to have valuation effects, and thus, mixing
the two categories might be the reason behind the inconclusive results.
Similarly to Eckbo, (1986), Eldomiaty, (2002) categorises companies
according to systematic risk levels, but only creates 3 groups: low, medium and high
risk. His research presents two main findings: firstly, that firms seem to be exhibiting
target capital behavior throughout all three risk groups and secondly, that long term –
and not short term - debt and market value are positively related. He also analyses the
effect of different other factors across the three groups and finds different outcomes
within each group. The determinants that affect market values for all three risk groups
are target debt ratio, liquidity position and interest rate. He concludes that capital structure
has a more poignant effect if the risk level is higher.
Muradoglu and Sivaprasad, (2006) take an investment approach to the issue
and forecast abnormal returns for an investor on portfolios of debt for different classes
of risk. They group 792 companies into 9 categories based on their 4-digit industry
classification codes and further rank them according to how much debt they have
outstanding. They then attempt to determine whether cumulative abnormal returns of
the stock are related to the level of debt. The results from their analysis show that
generally, abnormal returns decline when debt levels go down. They find that if
leverage were used as a trading strategy and an investor were to invest in the lowest
leverage firms with an average debt burden of 0.23%, the investor would be able to earn
a cumulative abnormal return of 6.28% in one year’s time and a staggering 491% during
the 24-year research period2.
Carpentier, (2006) specifically looks at the long-run effects of changes in
capital structure on firm value on the French market. Her paper is one of the first to
actually suggest a direct test for the irrelevance proposition. She uses a sample of 243
French companies in a time period of 10 years between 1987 and 1996. She finds that
both the increases and the decreases in debt levels are determining both positive and
negative effects on firm value. Hence, she cannot reject Modigliani and Miller’s capital
structure irrelevance proposition.
Event study literature also touches upon the issue of market reactions to
announcements of capital structure changes (i.e. announcements of equity or long-term
debt). Spiess and Affleck-Graves, (1995), as well as Loughran and Ritter, (1995) find
2
See Muradoglu and Sivaprasad (2006), p. 17
14
negative reactions of 30% to 50% in the 5 years time-frame after the equity
announcement. This is in line with the signaling argument stating that firms only issue
equity if they know that their shares are overpriced. As a result of this fact, rational
investors adjust their perceptions of the stock.
By using more fine-tuned statistical tests for the 5-year time period following
the announcement, Dichev and Piotroski, (1999) find that straight debt issues do not
present mean abnormal returns. They also find that firms which issue convertible debt
underperform the market by as much as 50% to 70% in the same time period, the
percentage being proportional to the amount of debt issued.
In several studies, Graham also finds that capital structure matters, by
extensively researching marginal tax rates and the tax benefits of debt. In Graham,
(1996a) and Graham, (1996b), he develops an innovative method of calculating
marginal tax rates by using filed tax reports of companies. In Graham, (2000), he
estimates the value that a company leaves on the table by being too conservative and not
exercising the full benefits of debt and finds that the average firm could have as much as
double the amount of debt before the marginal tax benefits begin to decline and they
would be able to reap additional gross tax benefits of 15% of firm value.
2.2. The Trade-Off Theory
The trade-off theory was born as a result of adding taxes to the irrelevance
proposition in Modigliani and Miller, (1963). In this hypothetical instance of the world,
where only taxes matter, there is a tax advantage that results from using debt, due to the
fact that interest paid on debt is tax deductible. Therefore, firms have an incentive to use
debt as a financing tool. But why don’t they use debt to entirely finance the company?
In one of the classic articles of the trade-off literature - Kraus and Litzenberger,
(1973) - the tax advantages of debt are offset by the costs of bankruptcy. In a statepreference framework, the firm either earns enough money to cover its debt obligations
and thus, benefit from the tax advantages of debt, or cannot do so, therefore becoming
insolvent and incurring bankruptcy penalties. The optimal capital structure is
determined by finding the level of debt, such that the resulting division of states (i.e.
those where the firm is solvent versus those where the firm is bankrupt) yields the
maximum market value of the firm3. Furthermore, it is shown that the market value of a
3
See Kraus and Litzenberger (1973), p. 912.
15
levered firm is the unlevered market value, plus the corporate tax rate times the market
value of the firm's debt, less the complement of the corporate tax rate multiplied by the
present value of bankruptcy costs.4
The
same
trade-off
is
commented upon in the famous paper
by
Myers,
(1984),
“The
Capital
Structure Puzzle” and depicted in
Figure 1. There are three elements
brought into question by Myers, who
focuses on issues that are often
overlooked in literature. Firstly, he
acknowledges
Figure 1: The Static Trade-off Theory
that
the
costs
of
adjustment might keep the company
from being at the optimal level of debt.
If these more than offset the advantages of using leverage, then the firms will postpone
adjusting to optimal debt levels. However, these costs are rarely taken into
consideration in models. Secondly, Myers discusses debt and taxes. He applauds the
contribution of Miller Merton, (1977), who proves that personal income taxes play a
role in determining the optimal debt level in a company. A taxable investor will not be
interested in bonds if personal taxes on interest income from debt is under the rate of
interest on bonds5. Myers brings forth the idea that Miller’s explanation hinges upon the
marginal tax rate. Once one takes into account the fact that not all firms face the same
marginal tax rate, the explanation crumbles. Lastly, Myers looks at the costs of financial
distress, which include more than the classical bankruptcy costs. They include subtler
issues such as agency, moral hazard, monitoring and contracting costs, which are more
difficult to quantify – and usually are not quantified in models - , but still have an
impact on capital structure.6
The trade-off theory can be divided into two strands of literature, one dealing
with static trade-off models and the other with dynamic trade-off ones. The former
postulates that firms cannot be anywhere but at the solution: the optimal level of debt.
4
See Kraus and Litzenberger (1973), p. 915.
5
See Miller (1977), p. 268.
6
See Myers (1984), p. 579-580.
16
The latter acknowledges that firms can move away from the target, because of
disturbances, and they constantly adjust their debt levels to reach the optimal level. This
last type of model allows for the possibility to calculate the speed of adjustment – how
long it takes companies that have moved away from the target to get back to the optimal
level. Thus, the model is also called the target-adjustment model. Frank and Goyal,
(2007) provide two definitions pinpointing the two sub-types of theories7:
„Definition 1. A firm is said to follow the static trade-off theory if the firm's
leverage is determined by a single period trade-off between the tax benefits of debt and
the deadweight costs of bankruptcy.
Definition 2. A firm is said to exhibit target adjustment behavior if the firm has
a target level of leverage and if deviations from that target are gradually removed over
time.”
2.2.1. Determinants of Capital Structure
Theoretical and empirical research has pointed out various factors that influence
the optimal level of capital structure. A brief overview of studies is presented in the
table on the next page, comprising both static and dynamic studies of capital structure.
Determinants of capital structure are presented – along with the reasoning behind their
influence -, in a non-exhaustive manner. Nor are the studies presented in the rightmost
column the only ones which look at those specific determinants.
The sign in between brackets documents how the determinant is supposed to
affect capital structure (negatively or positively), based on theoretical reasoning. The
results of the cross-sectional results are presented in the rightmost column.
7
See Frank and Goyal (2007), p. 7.
17
Table 1 – Determinants of Capital Structure
Determinant (relation Reasoning
Studies where it appears - refer to bibliography (relation
to CS)
found; model used: static (S) / dynamic (D))
Costs
of
financial High costs of financial distress (which might include bankruptcy costs and Bradley et al., (1984), (-; S).
distress (-)
Collateral
agency costs of debt) are related to low debt levels.
value
of If assets can be used as collateral, the firm will prefer to issue debt secured with
assets / Tangibility (+)
Titman and Wessels, (1988), (insignificant; S); Rajan
assets with known values, rather than to issue other types of securities that will
and Zingales, (1995), (+; S); Flannery and Rangan,
be undervalued because the market has less information. Costs of information
(2006), (+; D); Chang et al., (2009), (-&+; S); Talberg
asymmetry are thus avoided.
et al., (2008), (+; S); Antoniou et al., (2008), (+; D);
Byoun,
(2008),
(+;
D);
Clark
et
al.,
(2009),
(insignificant, D).
Non-debt tax shield (-)
Firms with large non debt tax shields use less debt because the tax deductions Bradley et al., (1984), (-; S); Kim and Sorensen, (1986),
that result from depreciation and investment tax credits replace the benefits of
(-; S); Titman and Wessels, (1988), (insignificant; S);
leverage.
Flannery and Rangan, (2006), (insignificant; D);
Antoniou et al., (2008), (+; D); Chang et al., (2009), (&+; S); Clark et al., (2009), (insignificant; D).
Growth/Market-to-
Growth opportunities are assets that cannot be collateralised and used to secure Myers, (1977), (-; S); Kim and Sorensen, (1986), (-; S);
book ratio/ Investment debt. They are also connected to the suboptimal investment problem, because of
opportunities (-)
Titman and Wessels, (1988), (insignificant; S); Rajan
the inclination of equity-controlled firms to expropriate wealth from and Zingales, (1995), (-; S); Flannery and Rangan,
bondholders. The more growth opportunities the firm has, the more likely it is for (2006), (insignificant; D); Talberg et al., (2008), (-; S);
it to engage in this behaviour, which poses an agency problem. Growth can be Antoniou et al., (2008), (-; D); Byoun, (2008), (-; D);
measured by the market-to-book ratio. Firms tend to issue stocks if their market- Chang et al., (2009), (-&+; S); Clark et al., (2009),
to-book ratio is high, so there will be less debt in that case. It is also considered a
proxy for investment opportunities.
18
(insignificant; D).
Uniqueness (-)
If a company that sells a unique good or service goes bankrupt, its customers, Titman and Wessels, (1988), (-; S); Chang et al., (2009),
employees and suppliers will suffer high costs. These firms would keep their (-; S).
debt levels low in order to prevent liquidation.
Industry classification Debt levels vary by industry. For example, debt levels are low for firms in
Titman and Wessels, (1988), (significant; S); Bradley et
(+/-)
industries with products requiring customized spare parts and servicing (and for
al., (1984), (significant; S); Flannery and Rangan,
which liquidation is costly).
(2006), (significant; D); Byoun, (2008), (+, D); Chang et
al., (2009), (significant; S).
Size / Diversification Large companies have a lower cost of debt and lower bankruptcy costs, so they Titman and Wessels, (1988), (-; S); Kim and Sorensen,
(+)
have an incentive to use more debt. Moreover, the more diversified a company is, (1986), (insignificant; S); Rajan and Zingales, (1995),
the higher its debt capacity will be, since it can borrow at more favourable terms.
(+; S); Talberg et al., (2008), (-; S); Clark et al., (2009),
(+; D).
Volatility / Operating If a company’s earnings are very volatile, it would be excessively risky for them Bradley et al., (1984), (-; S); Kim and Sorensen, (1986),
risk (-)
to hold a lot of debt. Operating risk might be one reason for high earnings
(+; S); Titman and Wessels, (1988), (insignificant; S);
volatility. A company that has a high level of operating risk may not be able to
Antoniou et al., (2008), (insignificant; D); Chang et al.,
sustain high financial risk at the same time and will prefer to keep debt levels (2009), (-&+; S).
low.
Profitability (-)
If a company can generate funds internally, it has a smaller financing deficit and
Titman and Wessels, (1988), (insignificant; S); Rajan
will use less debt.
and Zingales, (1995), (-; S); Flannery and Rangan,
(2006), (-; D); Talberg et al., (2008), (-; S); Antoniou et
al., (2008), (-; D); Byoun, (2008), (-; D); Chang et al.,
(2009), (-&+; S); Clark et al., (2009), (insignificant; D).
Tax rate: marginal or A high effective tax rate would prompt companies to use more debt, in order to Kim and Sorensen, (1986), (-; S); Antoniou et al.,
effective (+)
(2008), (+; D); Byoun, (2008), (-; D).
fully reap the advantages of the debt tax shields.
19
2.2.2. The Static Trade-Off Model
A textbook static trade-off model is presented by Bradley et al., (1984), who
use cross-sectional, firm specific data to test for the existence of an optimal level of
debt. They find that debt ratios are inversely related to costs of financial distress, (which
take into account bankruptcy and agency costs), to the level of non-debt tax shields, and
to the variability of firm value (if the costs of financial distress are significant). They
also find that industry dummy variables explain 54% of the variation in leverage ratios.
Kim and Sorensen, (1986), test the effect of business risk, growth rate and the
size of the firm on leverage ratios. Their findings show that high growth firms use less
debt, high risk firms use more debt, and firm size is uncorrelated. Similarly to Myers,
(1977), these effects are opposite to what theory might suggest. The study also
examines the impact of agency costs by classifying the 168 firms in two equal groups:
one of companies with a high and another with a low degree of inside ownership. Tests
prove that a higher degree of ownership does in fact translate into a higher degree of
debt, and therefore agency costs are important in determining the optimal capital
structure. Several explanations based on agency costs are provided. The agency costs of
equity explanation states that firms with heavy insider ownership would prefer to use
debt in order to avoid the costs of equity that result from consuming perquisites. The
agency costs of debt explanation states that firms with high inside ownership have a
lower agency cost of debt, which is why lenders might prefer to lend to them. High
inside ownership imposes closer control, and therefore, standard provisions and
covenants are more effective. Moreover, creditors might regard high inside ownership
firms as more likely to negotiate suboptimal investment issues8.
Titman and Wessels, (1988), analyse the theoretical determinants of capital
structure by employing different measures of long/short-term or convertible debt instead
of a single aggregate one. They focus on 8 determinants: the collateral value of assets,
non-debt tax shields, growth, uniqueness of the line of business, industry classification,
size, volatility and profitability, but find overall inconclusive results. However, they do
report some interesting findings which are in line with real life practices. They find that
debt levels are inversely related to uniqueness because firms that can impose high costs
8
See Kim and Sorensen (1986), p. 141.
20
on their customers, employees and suppliers in case of liquidation have less leverage 9.
They also find that short term ratios are negatively related to firm size, a result that
stems from the practice of small companies of not issuing long term debt because of the
high costs they would have to incur. All the other determinants have an insignificant
effect on capital structure. In a more recent work, Chang et al., (2009) expand on
Titman and Wessel’s model and find statistically significant results for all the
determinants. However, they have excluded size from their model, invoking goodnessof-fit criteria.
2.2.3. The Dynamic Trade-Off Model
2.2.3.1. Development of the Theory
This strand of theory presents multiple variations by focusing on various
elements. Some dynamic trade off research develops partial adjustment models which
include a large part of the determinants presented in Table 1 (Flannery and Rangan,
(2006); Byoun, (2008); Clark et al., (2009) – the results are also presented in the table).
Other papers have a more specific focus: Kane et al., (1984) on taxes; Harris and Raviv,
(1990) on the informational role of debt; Zwiebel, (1996) on managerial entrenchment;
Liu, (2009) on the historical market-to-book ratio; and Goldstein et al., (2010) on EBIT.
The first models developed did not include transaction costs. Brennan and
Schwartz, (1984) build an equilibrium variation model for a hypothetical firm for which
both the investment policy and the financing policy are endogenously decided upon.
The feasibility set for the decisions is determined by the investment opportunities, the
capital market equilibrium, as well as the bond indentures that the company is restricted
by. The analysis brings to light three main issues that impact upon the financing
decision and the optimal amount of leverage. These are the design of the bond
indenture, the initial capital structure, and the choice of capital structure given the
current debt levels of the company. Kane et al., (1984) have a different approach and
look at the traditional tax advantages-bankruptcy costs trade off in an options valuation
model that incorporates bankruptcy costs and corporate, as well as personal taxes. Their
simulation analysis points out that if the tax advantage is small, then the cost of being
very far away from the optimum is small as well. This result is in line with the observed
wide range of debt levels that companies have. Kane et al. also acknowledge that there
9
See Titman and Wessels (1988), p. 17.
21
might be other considerations driving debt levels (like agency costs or moral hazard)
that they have not looked at. These models predict somewhat higher target debt levels
because, since transaction costs are inexistent, there is nothing in the way of firms
adjusting to the optimal debt levels and reaping the full advantages of debt.
Fischer et al., (1989) develop the model of Kane et al., (1984) and include
transaction costs in a dynamic model where capital structure depends on a set of firmspecific characteristics. Their model gives rise to a hypothesis about the type of firms
that go through a wide range of debt levels. These firms have a low effective corporate
tax rate, a high variance of underlying asset value, a small asset base (i.e. they are small
companies) and low bankruptcy costs10.
One of the more recent researches including transaction costs is the model of
Hennessy and Whited, (2005). They attempt to shed some light on the facts that had
remained unexplained to that date by the static trade-off models, for example the inverse
relation between profitability and size on the one hand and debt levels on the other. Like
in Brennan and Schwartz, (1984), their investment and financing decisions are
endogenous, but in the current case, the decisions are joint. The model also comprises
graduate income taxes, personal taxes on interest income and on dividends, financial
distress costs, as well as equity flotation costs. They find that there is no target leverage.
Furthermore, they prove that past debt levels affect current ones. Apart from path
dependency, leverage also presents hysteresis (i.e. the effects of a current decision are
made apparent with a certain delay in time). Another piece of research on path
dependency which points out that historical market-to-book ratios affect current ones is
by Liu, (2009). Results show that there is a partial adjustment model at work and there
are strong relations between past market-to-book ratios and current debt levels.
A strand of literature looks away from firm-specific characteristics and tries to
provide explanations for the level of debt based on other factors. Harris and Raviv,
(1990) propose a model based on the signaling and the disciplining role of debt. The
signaling argument states that investors “read” the signals of the firm and eliminate
uncertainty about the quality of the firm. The disciplining argument states that debt can
be used as an instrument which exerts pressure on managers to perform efficiently and
prevents them from empire building. This is accomplished through the fact that debt
gives creditors the opportunity to compel the firm to go into liquidation. The trade-off
10
See Fischer et al. (1989), p. 33.
22
between these advantages and disadvantages leads to an optimal capital structure.
However, if applied in practice, the model possesses some drawbacks related to
difficulties in analysing the “signals” and learning from them.
Zwiebel, (1996) analyses the dynamics of capital structure and takes an agency
costs approach by looking at debt under managerial entrenchment. His model also plays
upon the disciplining role of debt, like in Harris and Raviv, (1990). The difference is
that here, the manager himself chooses to use debt as a commitment device to forgo
value-decreasing investments and through that, preventing potential take-over’s. The
cost that balances the aforementioned benefit is that too much debt makes its effects less
stringent and managers might still chose to undertake empire-building projects. The
authors outline numerous various implications of the model which are in line with the
classical free cash flow models (for example, that growth opportunities and profitability
are negatively related to debt levels), as well as some that differentiate their model from
free cash flow models (for example, a strong point of this model is that the benefits and
costs that are traded off have the same source: the use of debt as manager’s commitment
to be efficient).
Antoniou et al., (2008) analyse the determinants of capital structure in different
settings: capital market - oriented economies (UK and US) and bank-oriented ones
(Germany, France and Japan). The results related to firm characteristics and leverage
based on the entire sample are presented in Table 1. Moreover, they conclude that the
characteristics of the legal system and the market conditions affect leverage levels. A
higher rule of law pushes firms to keep their debt levels at a low. In markets where bank
– ownership is accustomed, debt levels are higher because the company can be rescued
in case of bankruptcy by the shareholding bank.
Of the most recent studies, Sabiwalsky, (2010) proposes a nonlinear model
based on the classic tax shields - bankruptcy costs trade-off; here, the target debt level is
not static, but changing and chosen such as to maximise the difference between the debt
tax shield and the costs of insolvency. He finds that size is a major determinant of the
explanatory power of the model (24%, 16% and respectively 11% of the variation of
debt adjustments of medium, small and large samples). Thus, he concludes that the
trade-off model explains the choices of medium sized firms best.
23
2.2.3.2. Results on the Adjustment Speed
Jalilvand and Harris, (1984) analyse a sample of US firms and their financing
decisions in order to determine what variables influence the firms’ adjustment speeds.
They find that size, interest rate and stock price levels all have significant effects. Sjoo,
(1996) finds that the most influencing macroeconomic variables on the Swedish market
are adjustment processes of domestic price levels, interest rates and export prices.
Drobetz and Wanzenried, (2006) use a sample of 90 Swiss firms and find that size is
inversely related to adjustment speed, while the availability of economic prospects and
term spread are positively related.
With regard to the speed of adjustment itself, research has failed to come to a
common conclusion. Earlier research, as well as conventional wisdom predicts
adjustment speeds between 8% and 15%11. Later studies prognosticate somewhat higher
speeds. Huang and Ritter, (2009) find speeds of 17% per year for book leverage
(closing the gap in 3.7 years) and 23.2% for market leverage (closing the gap in 2.6
years). Flannery and Rangan, (2006) find that an average firm closes in to its target with
around one third of the difference each year. Byoun, (2008) splits firms into 4 different
categories, according to whether they have a financial deficit or surplus and if they are
above or below target levels. The group with the highest adjustment speed is the
financial surplus – above target (30%), followed by financial deficit – below target
(20%), financial surplus – below target (5%) and finally financial deficit – above target
(2%). Antoniou et al., (2008) investigate market-oriented versus bank-oriented
economies. Their results point out that French firms are fastest to adjust, followed by
firms in the US, UK, Germany and Japan. Clark et al., (2009) compare the adjustment
speed between developed and developing countries and find that in the former, speed is
independent of legal and institutional factors, while in the latter, it is the reverse, with
tax variables being highly significant. As expected, firms adjust slower in developing
countries. The mean around the world is 30.5%, and varies between 17% and 44.1%.
Similarly, Cook and Tang, (2010) examine the relations between macroeconomic
conditions and capital structure adjustment speed and find comparable results with
Clark. Better macroeconomic conditions relate to higher speeds.
11
See Flannery and Rangan (2005), p. 481.
24
2.3. The Pecking Order Theory
The pecking order theory was born out of the desire to answer some questions
that the trade-off theory couldn’t, for example, why it is that in reality large firms use
less debt than predicted.
The basic reasoning behind the pecking order theory is summed up in either of
the following terms, depending on which explanation of firm behavior we choose to
buy: “information asymmetry”, developed by Myers and Majluf, (1984), or “agency
costs”, developed by Jensen, (1986). When looking to finance projects, firms follow a
pecking order: they prefer internal to external financing and debt to equity if they must
use external sources. In a pre-pecking order article, Stiglitz, (1973) reaches the same
conclusion of financing sources preference based on tax arguments. He takes into
account corporate taxes and personal taxes on interest and dividends.
Does the pecking order theory actually relate to what happens in the corporate
world? Shyam-Sunder and Myers, (1999) tested the pecking order theory against a static
trade-off model and found that the former has much more explanatory power of the
time-series variation of debt ratios. Conversely, Frank and Goyal, (2003) pinpointed
that, “net equity issues track the financing deficit more closely than do net debt
issues”12; this contradicts the pecking order theory, indicating that it does not entirely
explain corporate behavior.
2.3.1. Information Asymmetry Considerations
The argument of Myers and Majluf, (1984) and Myers, (1984), states that firms
have more information regarding their operations and worth than outside investors have.
This is why, whenever firms are in need of funds, they exhibit a preference for internal
funds, and when using external funds, they prefer to issue debt rather than equity, for
fear that their shares will be underpriced by the less knowledgeable market. Sometimes,
they might even prefer not to take positive-NPV investments, should these be financed
with equity.
Heaton, (2002) underpins the term “managerial optimism” as being the
behavior of managers who believe their firm is undervalued. They use a simple model
trading off the benefits of refraining from undertaking bad investments because of the
high perceived cost of financing and the costs of passing up positive-NPV projects for
12
See Frank and Goyal (2003), p.217.
25
the same reason. They point out that managers who behave that way follow a pecking
order. However, the difference between the benefits and costs tends to vary by firm.
In order to overcome information asymmetry barriers, firms use signaling to let
the investors know the true value of their shares (Leland and Pyle, (1977)). Cadsby et
al., (1990) demonstrated with the use of game theory – a Nash equilibrium model – that
good firms will always use signals when these are available, with the intention to
distinguish themselves in the eyes of the investors.
2.3.2 Agency Costs Considerations
Jensen, (1986), comments upon the fact that the use of debt minimizes agency
costs related to managers. The managers are assumed to act in their own best interests,
which may not coincide with those of the shareholders. If a company produces a
substantial amount of free cash flow (cash flow over the amount required to fund the
entire array of positive NPV projects), conflicts might arise between shareholders and
managers as to the payout methods of the free cash flow. Shareholders will prefer
dividends, but managers might want to use the funds for empire building or over-using
perks. By using debt, managers commit to making periodic payouts, thus limiting
squandering of company funds. Jensen also states that debt is better than announcing a
permanent increase in dividends, because the latter is not binding and it can always be
undone by managers.
2.4. Market Timing Theory
Baker and Wurgler, (2002) document companies’ habit to time the market
when attempting to raise funds. Firms decide whether to issue stocks or debt based on
the market-to-book ratio. If the market-to-book ratio is high (i.e. shares are overvalued),
companies will prefer to issue stock and thus, raise funds in a cheap way. Otherwise,
they will use debt. Therefore, firms’ current capital structure is a result of past decisions
and efforts to time the market. Baker and Wurgler’s main finding is that low leverage
firms are those which issued shares when their market-to-book was high, while high
leverage firms are those which issued debt when their market-to-book were low.
Kayhan and Titman, (2007) also examine the relation between firms’ histories
and their current capital structure and find that past returns on the stock market explain
current debt levels. Their results show that firms reduce their leverage if they raise
26
capital in years when stock prices are high. Moreover, firms appear to be more likely to
issue stock after an increase in prices on the equity capital market.
27
Chapter 3. Business Strategy Analysis
3.1. External Analysis
Vestas is competing in the wind turbine manufacturing industry. One way to
define this industry could be as the one “dealing with the research and development,
manufacture, construction, sale, and maintenance of wind turbines for residential,
commercial or industrial purposes”.
The modern wind industry is only about 3 decades old. Over the years, it has
grown considerably, with record double-digit growth rates sometimes going close to
50% per year, as was the case in 2008-2009. At the end of 2009, the cumulative market
size had reached a total of 158,505 installed MW worldwide13.
As for the future, industry growth rates are expected to increase in the next 5
years, but at a slower pace, as quantified by The Global Wind Energy Council
(GWEC)14. The annual installed capacity growth for 2010 is only expected to amount to
6.6%, considerably less if compared to 41.3% in 2009. The annual installed capacity
will slowly rise with each year, while the growth of cumulative installed capacity is
characterized by a decreasing trend.
GWEC has estimated the growth of the world’s cumulative installed wind
power capacity under three different
Figure 2: Degree of
scenarios. In the most pessimistic
Turbulence
–
scenario
the
reference
one
–
in
the
Industry
production of energy from the installed
capacity will cover 4.9 – 5.6% of the
world’s total energy demand by 2030.
Under
the
two
more
optimistic
scenarios – moderate and advanced –
wind energy will cover 15 - 17.5% and
18.8 - 21.8%, respectively.
Source: collected information
13
See 34) Global Wind Energy Council 2009. Global Wind 2009 Report. Brussels: Global
Wind Energy Council. p. 12.
14
See 34) Ibid., p. 17.
28
The PESTEL analysis provides a picture of the degree of turbulence in the
industry, depicted in Figure 2 on the previous page. The political, legal and economic
factors are the ones with the highest impact, while technological factors have a mediumstrength effect. Socio-cultural and environmental issues come last in terms of
ramifications.
For details, see Annex A1. Market Definition, Size and Growth, as well as
Annex A2. PESTEL Analysis.
Figure 3: Porter’s 5 Forces
3.2. Porter’s 5 Forces Model
A more in depth analysis of the 5
forces affecting the industry, as well as a
discussion
of
the
general
degree
of
turbulence in the industry is presented in
Annex A4.
3.3. Competitor analysis
At the moment, there are a total of
52 wind turbine manufacturers worldwide.
Source: collected information
Competition within the industry is quite
fierce, with the top 10 companies having 78.7% market share and each being very close
to the other in terms of market share. Vestas is the world’s leading manufacturer, with a
12.5% share, surpassing the second runner up - GE Wind Energy - by only 0.1%. The
most considerable ascension of 2009 was the growth of the Chinese manufacturers, 3 of
which made it in the top 10 for the first time.
The two most notable trends in the market are the gradual shift from
oligopolistic towards monopolistic competition and the rise of the Chinese
manufacturing companies, detailed in Annex A3. Competitor Analysis.
3.4. Internal Analysis
This section is a summary of the more extensive analysis in Annex A5. Internal
Analysis.
3.4.1. Strategy statements
Vestas’ strategy statements are as follows:
29
 vision – “Wind, oil and gas”;
 mission – “Failure is not an option”;
 strategy – “Number 1 in modern energy”;
 values / principles – “Cost of energy”, “Business case certainty”, “Easy
to work with”.
Vestas has proven that they are sticking to their vision and mission. Throughout
time, they have shown their resilience. Despite the financial crisis that brought despair
to many industries, Vestas managed to achieve record revenue and EBIT (revenue was
9.96% higher than the previous year, while EBIT was 28.14% higher). Another example
of their determination is the fact that they invested EUR 160m in building a tower plant
in Colorado, US. They proved to be committed to the US market, even though they did
not receive any order on the US market that year15.
The strategy of the company explains how they aim to accomplish their vision.
In this sense, Vestas wants to be “Number 1 in modern energy”, not only in terms of
market share, but also in terms of safety standards, performance of power plants,
customer satisfaction and green production. So far, they have managed to achieve that.
3.4.2. Product and Service Mix
Vestas produces 9 types of onshore turbines ranging from 850 kW to 3 MW
and 2 types of offshore ones, both with a nameplate capacity of 3 MW. The company is
currently developing a 6MW offshore turbine
As for the company’s service mix, Vestas has the following areas of focus:
installation, maintenance and repair. The main support functions that enable it to serve
customers are the Performance & Diagnostics Centre and the Vestas Spare Parts &
Repair.
Vestas expects the same growth for the demand of its services, as for its
products.
3.4.3. Business segments
Vestas is operating in 3 geographic segments covering the entire world
(Europe, Americas and Asia/Pacific). The company has production plants, sales and
15
Information retrieved from Ditlev Engel interview: 77) Rose, C. 2010. Ditlev Engel on
Charlie Rose. New York.
30
service units and R&D functions in all of them.
As expected, Europe is the largest, both in
Figure 4: Vestas’ Revenue
terms of revenue, and in terms of number of
people employed.
Historically, Vestas has had a more
than steady revenue stream, and has always
managed to improve its yearly sales figures. It
registered a record growth rate of 55% in 2004.
At the other extreme was the growth 2006,
which amounted to only 7.6%. Figure 4 depicts
Source: data from financial reports
revenue evolution from 2001 to 2009.
3.5. SWOT Analysis
This section presents the SWOT analysis for Vestas, based on the previously
presented information. Figure 5 depicted on the next page sums up the analysis, which
is presented in full in Annex A6. SWOT Analysis.
31
Figure 5: SWOT Analysis
Source: collected information
32
Chapter 4. Analysing Historical Performance
4.1. Reorganisation of Financial Statements
Vestas’ historical financial statements from 2000 and up to 2009, inclusively,
have been reorganized for valuation purposes and the effects of non-operating accounts
have been singled out and separated from operating ones, since only the latter are useful
in order to calculate the worth of the company through this valuation model.
4.1.1. Treatment of Accounts, Assumptions and Estimations
There are some issues that are worth highlighting before going deeper into the
historical analysis.
Firstly, a mention of the changes in reporting of financial statements should be
given attention to. In 2000, Vestas went through a share split. Hence, the valuation
spreadsheet presents the figures adjusted for the split. What’s more, there was a
transition from GAAP to IFRS which took place in 2005. This entailed changing the
treatment of goodwill related to business combinations, which was previously
amortised, changing income-recognition criteria and the treatment of prepaid service,
reclassifying deferred tax assets as non-current assets, and lastly, recognising deferred
tax liabilities, pensions and similar liabilities in current and non-current liabilities
instead of provisions.
Secondly, since holding large cash reserves does not bring the company high
returns, operating cash was assumed to be no higher than 2% of operating revenues.
The extra amount was deemed excess marketable securities. The amount was not
considered in valuing the company operations and was added back after the operating
value was calculated.
Thirdly, taxes are an issue that was given consideration due to the international
scope of the company. The marginal tax rate is not explicit and has to be calculated. In
attempting to estimate the company’s marginal tax rate, certain assumptions had to be
enforced due to insufficient information and the use of proxies has been resorted to.
Graham, (1996b) looks at the most appropriate proxies for this rate. His
research shows that the best proxy is a simulated tax rate that he develops.
Unfortunately, it is beyond our ability to calculate it, because it uses information from
tax filings, which we do not have access to. The second best would be a trichotomous
33
variable equal to the top statutory rate if both taxable income and net operating loss
carryforwards are positive; half of the top statutory rate if either is positive and the other
is 0 and 0 otherwise. We have chosen to start from there and make a few necessary
adjustments.
Previous to the group reorganization from 2004, Vestas had subsidiaries in 9
different countries, as well as minority interest of 49% in an Indian associate company.
Thus, their revenues were taxed at different rates. Damodaran, (1994) states that the
appropriate marginal tax rate for companies which operate in multiple tax locales is the
average of the different marginal tax rates, weighted by the operating income of each
locale. His suggestion was also taken into account.
Given all the arguments presented above, the marginal tax rate for 2000 – 3003
was a trichotomous variable equal to:

if both taxable income and net operating loss carryforwards - the average
of the statutory tax rates of the countries where subsidiaries were set up,
weighted by operating income of each;
 if either taxable income or net operating loss carryforwards are zero and
the other is positive – half of the aforementioned average;
 0, otherwise.
In the event that the company had sales in a country where no subsidiary was
established and no information regarding where the revenue was registered, an
additional assumption was taken on. All sales in Europe (excluding the Nordic Region)
were taxed in Germany, all those in the Americas was taxed in the US, all those in
Asia/Pacific were taxed by the Indian associate company, and the rest in Denmark.
Starting from 2004, the Vestas Group was reorganized in business units
focusing on sales or production and therefore, all income is taxed in Denmark. With this
in mind, the Danish statutory tax rate was used instead of the weighted average from
above.
The choices presented above are completely arbitrary and thus, to compensate
for the assumptions taken on, the issue has been subjected to simulation in Section 6.3.
in order to analyse the sensitivity of the share price to the marginal tax rate.
Vestas disclosed that it had both defined benefit and defined contribution
pension plans. Since only the former are relevant for valuation purposes, we looked into
whether Vestas recorded any plan assets or liabilities. The company’s pension-related
34
liabilities were larger than plan assets, which is why Vestas recorded a retirement –
related liability of EUR 2 mil. The amount was subtracted from the value of operations
in order to find equity value.
Vestas disclosed deferred tax assets of EUR 110 mil, which was treated as an
equity equivalent, meaning that NOPLAT has been adjusted to account for the yearly
change in the account and investor funds were also reconciliated by adding the same
amount.
Based on information from the footnotes in the annual reports, Vestas’
provisions have been split into income smoothing provisions and warranties provisions.
The former fall into the category of equity equivalents and are treated similarly to
deferred taxes, while the latter are treated as other non-interest-bearing liabilities. The
amounts are subtracted from revenues to compute EBITA and the associated reserve is
netted against operating assets.
Operating leases were valued based on the rental expense, using the following
formula:
𝐴𝑠𝑠𝑒𝑡 𝑣𝑎𝑙𝑢𝑒𝑡−1 =
𝑅𝑒𝑛𝑡𝑎𝑙 𝑒𝑥𝑝𝑒𝑛𝑠𝑒𝑡
1
𝑘𝑑 +
𝐴𝑠𝑠𝑒𝑡 𝑙𝑖𝑓𝑒
Rental expense on operating leases is only disclosed starting from 2005, when
Vestas adopted the IFRS. For the first 5 years of the historical analysis, rental expenses
have been estimated using Prof. Damodaran’s spreadsheet (Damodaran, (2007)).
Thus, operating leases totaled EUR 636 mil at the end of 2009. In the valuation
spreadsheet, the value of leases is subtracted from net PPE and added back later on to
determine Invested Capital.
Finally, employee stock options were valued using Black – Scholes, but, given
the very large difference between the high strike price and the current low spot price of
the shares, the total value of the options does not have a great impact on the valuation
end result. The inputs into the calculation are based on information presented in the
2009 annual report under share-based incentive programme for 2007 to 2009 and for
2010 to 201216.
16
See 93) Vestas 2009. Annual Report 2009. Randers: Vestas Wind Systems A/S., p. 34
35
4.1.2. Results of Reorganisation
Invested Capital increased more than tenfold over the 10 year period, from
EUR 416 mil to EUR 4,611 mil, as can be observed in Annex A6.1. Invested Capital.
As suspected, the largest proportion is attributed to investments in property, plant and
equipment materialized in production facilities, followed by goodwill and intangibles.
The evolution of NOPLAT - total income generated from operations available
to Vestas’ investors - is depicted in Annex A6.2. NOPLAT. The company had a
negative NOPLAT in 2004 and 2005, when it recorded losses; yet, having gotten back
on track, it managed to improve its NOPLAT considerably up to a total of EUR 767 mil
in 2009, compared to EUR 188 mil from the beginning of the historical period.
As Vestas is currently in the growth stage of its life-cycle, it reinvests its cash
flow back into the company in order to fuel growth, which can be clearly observed in
the results of the free cash flow calculation, shown in Annex A6.3. Free Cash Flow.
With the exception of the year 2005, the company’s gross cash flow is always positive
and mostly offset by the increases in working capital and operating leases, as well as
capital expenditures.
ROIC
Figure 6: ROIC Tree
31.6%
Pre-tax ROIC
Cash tax rate
44%
28.2%
Operating margin
Average capital turns
16.1%
2.73
Gross margin
Operating working capital/Revenue
23.6%
14%
SGA expenses
Fixed assets / Revenue
-6.8%
18.8%
Depreciation / Revenue
Other assets / Revenue
-2.4%
3.8%
Other operating expenses / Revenue
-0.6%
Other adjustments / Revenue
Source: author’s calculations
2.3%
Return on Invested Capital is detailed in Annex A6.4. Return on Invested
Capital. In order to put the figures into perspective and find out what the drivers of this
return are, the ROIC tree depicted in Figure 6 was developed, with ratios computed for
36
the last year of the historical analysis period. The tree unveils the fact that the impetus
for Vestas’ ROIC is primarily given by the level of optimization and capital efficiency,
portrayed by the high average capital turns. A turnover of 2.71 means that with EUR 1
invested in working capital, the company managed to generate EUR 2.71 in revenue.
Revenue growth is also an important input of the valuation. Historically, it has
been fluctuating greatly from one year to the next, with no clear trend pointing in a
certain direction. The largest change was in 2004, when Vestas had a 55% revenue
growth, whereas 2 years later, the smallest growth rate was recorded, a modest 7.6%.
The evolution over the historical period is shown in Annex A6.5. Revenue Growth.
4.2. Credit Health
We have already posited that Vestas uses little debt. Just exactly how little is
highlighted by calculating interest coverage. Interest coverage ratings were calculated
relative to EBIT, EBITDA and EBITDAR, the last of which takes into account rental
expenses for operating leases from the financial report footnotes. All ratios have
reached a peak value in 2008 (16.7, 20.1, and 20.6 respectively) and fell briskly the
following year mainly because of the growth in rental expenses which was at a much
greater pace than the growth in EBIT. The results are portrayed in Annex A7. Interest
Coverage.
Vestas’ debt is not rated. However, with the help of Prof. Damodaran’s
spreadsheet (Damodaran, (2007)), synthetic ratings estimations were performed. The
results were situated between D (in 2003 and 2004, because of a red bottom line) and
AAA (maintained over the past 3 years).
4.3. Stock Market Performance
Since the hypothesis investigated in the
Figure 7: Stock Market Performance
thesis links debt levels with share prices, it
might be insightful to look at historical share
prices. Figure 5 depicts 3 different measures of
Total Return to Shareholders. A comparison to
the other 3 major competitors mentioned in
Chapter 3. Competitor Analysis is not possible,
since GE Wind Energy is part of the
multinational conglomerate GE which makes
Source: author’s calculations
37
available share prices irrelevant for comparison. As for Sinovel and Enercom, they are
not traded on the stock market. However, 3 other listed competitors in the top 10 were
included: REPower, Gamesa and Suzlon. Siemens was not taken into account because
as a group, it is very diversified and the figures would be indicative of the entire group
and not of Siemens Wind Power itself.
Over the past 5 years, Vestas has greatly exceeded shareholder’s expectations.
It performs best compared to the other companies, for the entire period. During the 5
years, the company’s book debt-to-value ratio decreased from 33% to 9.1%. It seems
that, contrary to M&M’s theory, market value in this case was inversely proportional to
debt levels. The only thing that could explain this relationship and still be in accordance
with theory would be that Vestas’ target debt level is low and somewhere around the
current levels. This shall be further investigated in Chapter 6 – Sensitivity Analysis.
38
Chapter 5. Base case scenario valuation
5.1. Scenario description
The base case scenario represents the foundation of the valuation and of the
simulation analysis, as this is the point from which the investigation will develop. In
this scenario, the industry is assumed to grow at a moderate pace and there will be no
shocks which might affect its development. Current regulations and policies will still be
in place and will continue to propel the industry forward. Incumbents will continue to
receive subsidies and tax credits. Imposed country-specific renewable energy targets are
also assumed to be successfully implemented. In time, the industry’s characteristics will
gradually start shifting towards those of a maturing trade. Increased competition will
drive prices and market shares downwards. This is expected, given the current
competition trends within the industry (Annex A3.2. Competition Trends).
Vestas’ financial position will have a slight drop in 2010, but will stabilize
thereafter. The reason for the drop is mostly represented by a decline in income, as
detailed in the first and second quarter reports of 2010. Major expected orders from
several countries did not materialize yet and will be recognized as income during
201117.
5.2. Forecasting performance
5.2.1. Revenue growth
As we have seen when comparing the evolution of revenue growth in time with
that of major macroeconomic factors in Annex A2.2. Economic Factors, there is a weak
correlation between them. One reason behind that might be the fact that the industry is
still in a growing stage and it is fuelled by numerous factors, like the global concern for
a sustainable future and the political environment. Moreover, the fact that Vestas was
one of the pioneers in the field gave the company a competitive advantage. Therefore,
when forecasting future rates, we have chosen to base our assumptions on past
performance of the company, more than on external factors. What’s more, Vestas’ own
17
See 95) Vestas 2010b. Interim Financial Report, Second Quarter 2010. Randers: Vestas., p. 1
39
expectations for 2010, as presented in the Management Report of the previous year18, as
well as the first two interim financial reports of 2010 are also taken into account. The
last annual report mentions a revenue of EUR 7 bil in 2010, but expectations have been
adjusted downward due to sales which failed to materialise. As for 2011 revenues, these
are expected to be of record amount, since the firm and unconditional order intake in the
first half of 2010 totals 4289 MW19, almost as much as in 2009 as a whole (4759 MW).
Thus, revenue growth rates start off at -9%, are expected to peak at 55% in 2011 and
then start dwindling, finally reaching a level of 6% for the continuing value period. The
reason for the decline can partly be attributed to the aforementioned industry changes
and partly to the fact that Vestas does not rely mostly on serving its home country
market, as major competitors are doing. Vestas sold only 57 MW in Denmark over the
whole of 2009, compared to 3569 MW sold by GE Wind Energy in the US 20. Therefore,
in time, Vestas will find itself in a slight disadvantageous position because of the fact
that it’s playing an away game.
5.2.2. Cost of capital
In order to estimate the cost of capital, the weighted average formula was used:
Vestas’ debt is not traded, thus not rated. Without being able to rely on the
market for information regarding yields to maturity, the cost of debt was approximated
using Professor Damodaran’s estimation spreadsheet (Damodaran, (2007)). The inputs
into the spreadsheet (level of debt, rental expense and interest expense) were the
forecasts of 2014, the last year of the detailed forecast, and lead to a cost of debt of
3.65%. However, analysts opine that the yield-to-worst for Vestas Eurobonds is 4.37%
(Andersen, (2010)). Since the 2009 debt level is of EUR 339 mil, and the debt levels
have increased considerably after the bond issue, the estimated cost of debt was adjusted
See the section „Outlook for 2010”, 93) Vestas 2009. Annual Report 2009. Randers: Vestas
18
Wind Systems A/S., p. 27.
19
94) Vestas 2010a. Interim Financial Report, First Quarter 2010. Randers: Vestas.; 95) Vestas
2010b. Interim Financial Report, Second Quarter 2010. Randers: Vestas.
20
33) Glader, P. 2010. G.E. Leads U.S. Wind Market but Faces More Competition. Available:
http://online.wsj.com/article/SB10001424052702303720604575170500339244626.html
January 2011].
40
[Accessed
27
upwards with 46 percentage points to 4.11%; this adjustment is the average of the yield
to worst and the predicted cost of capital, weighted by the proportion of the Eurobond
versus existing debt.
The after-tax cost of debt was used in the valuation. A marginal tax rate of
25% was employed in the adjustment, as this is the expected statutory rate of Denmark
for the foreseeable future (see Section 4.1.1. Treatment of Accounts, Assumptions and
Estimations for the reasoning of this particular choice of rate).
As for the cost of equity, the CAPM model was utilised:
The proxy for the risk free rate was a 10 year government bond yield. Koller
et al., (2005) suggest that the German Eurobond is the best choice when valuing
European firms. Taking that suggestion into account, the risk free rate used equals
2.9%21.
In order to estimate Vestas’ beta, a regression of the company’s stock returns
on the S&P500 market index return was conducted:
A period of 3 years of daily data was used, as recommended by Daves et al.,
(2000) and lead to a beta of 1.42, meaning that Vestas’ stock moves in the same
direction as the market, but with more variation. This might explain the delayed effect
of the financial crisis: in the midst of the crisis, revenues were growing steadily (as
shown in Figure A8 – Capacity Growth and Macroeconomic Variables), while at
present, when economies are recovering, the company is expecting low revenues.
To account for the fact that betas are mean reverting, the Bloomberg
Smoothing Mechanism was employed. The adjusted beta is:
As for the return of the market portfolio, given that it cannot be estimated
per se, a market index was used as a proxy. Koller et al., (2005) state that the most
commonly used proxy is the S&P500. It is also the longest trading index. Since all the
major indexes are highly correlated with each other, the S&P500 was deemed as an
21
6)
Bloomberg.
2011.
Government
Bonds
[Online].
http://www.bloomberg.com/markets/rates-bonds/government-bonds/germany/
2011].
41
Bloomberg.
[Accessed
Available:
6
January
appropriate proxy. Monthly returns dating back to January 1950 were used. The
arithmetic average was annualized and lead to an Rm of 8.47%. Therefore, the resulting
market risk premium is of 5.57%, which is in line with Koller et al., (2005), who also
perform some estimations and obtain a market risk premium of 5.5%.
The company’s capital structure also influences its cost of capital. Koller et al.,
(2005) opine that target debt levels should be used in the forecast. Since historical debt
levels have been fluctuating greatly, no inferences were based on previous debt levels.
In order to determine the target, we have firstly taken into account the declaration of the
company’s management: „The proportion of equity in relation to the Group's future
capital structure is expected to continue to be high”22. After including the EUR 600 mil
Eurobond, the debt levels increased to 24.52%. This is considered to be the target level
of debt. Book levels were employed in calculations, since the debt is not traded and
since the company does not find itself in a position of financial distress. However, it
should be acknowledged that the current level of interest rates might cause differences
between the market and book values of debt. After factoring in off-balance sheet debt –
the value of operating leases – the debt target decreases to 17.05%.
After plugging in all the estimations, the resulting cost of capital for the
forecast period equals 8.3%.
5.2.3. Other inputs
As far as the other rates and inputs are concerned, the reasoning behind them
starts off by taking into account company expectations for 2010, which are rather
unflattering for Vestas. Their evolution in time loosely follows a curved shape, where
they improve, stagnate shortly and thereafter slightly worsen. The guidelines provided
by annual reports for the year 2010 include:
-
an EBIT margin of 5-6%;
-
a NWC of 15% of annual revenue at year-end;
-
investments in net property, plant and equipment of EUR 250 mil;
-
investments in intangible assets of EUR 350 mil;
-
a fall in warranty provisions of 3%.
To sum up, the major inputs are shown in Table 2 below. All other figures are
presented in Annex A9. Base case scenario valuation inputs.
22
93) Vestas 2009. Annual Report 2009. Randers: Vestas Wind Systems A/S., p. 87,
42
Table 2: Main Valuation Inputs
Detailed Forecast
Key driver forecast
CV
2020-
Year
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Revenue Growth
-9%
60%
12%
10%
10%
9%
8%
8%
7%
7%
6%
6%
COGS/Rev
84%
84%
84%
84%
84%
6.4%
6.8%
7.2%
7.2%
7.2%
7.2%
7.2%
7.2%
7.2%
7.2%
7.2%
7.2%
ROIC
8.3%
13.6%
13.8%
13.9%
14%
14%
14%
14%
13.9%
13.9%
13.8%
13.8%
WACC
8.3%
8.3%
8.3%
8.3%
8.3%
8.3%
8.3%
8.3%
8.3%
8.3%
8.3%
8.3%
Adj EBITA
margin
2024
5.2.4. Continuing value
The inputs that dictate the continuing value amount were chosen based on the
fact that Vestas is a growing company in a young industry, and therefore, the continuing
value should represent a considerable portion of the valuation amount. A ROIC of 9%
and a growth in NOPLAT of 6% were forecasted, both slightly below the rates in the
last year of the key driver forecast. Moreover, the ROIC value is also based on a piece
of research conducted by Koller et al., (2005), in which they found that median ROICs
across several industries from 1963 to 2003 was 9%. They also found that although
these differ by industry (industries that rely on patents and brands have ROICs above
median, while utilities – below), they gradually regress towards the median. As a result
of these inputs, continuing value amounts to 79.9% of total operating value.
Figure 8: Valuation result
5.3. Valuation result
Value of Equity
The results of the valuation are presented in the
table to the right. The end result of EUR 30.03 is slightly
higher than the current market valuation, more exactly
by 18.41%23. The reason behind the difference is the
belief that considerable negative media coverage has
affected share prices. The fact that Vestas decided to
relocate production facilities to cheaper regions might
Operating Value
Excess Mkt Securities
Financial Investments
Excess Pension Assets
Enterprise Value
Debt
Capitalized Operating Leases
Retirement Related Liability
Preferred Stock
Minority Interest
Long-Term Operating Provision
Restructuring Provision
Future Stock Options
Stock options
have been a sound business decision, but the reports of
Equity Value
closing down facilities on the Isle of Wight in the UK, as
No. shares (thousands)
Value per Share
Source: valuation spreadsheet
23
Given the price of EUR 25.36 on 31 January 2011.
43
7.099
355
12
0
7.466
(351)
(980)
(2)
0
0
0
0
(16)
(0,022)
6.117
204
30,03
well as in Scandinavia has taken its toll on the share price on the market.
However, to account for the effects of various variables on the valuation,
investigations were conducted in Chapter 6 – Sensitivity analysis.
5.4. Critique
Vestas’ historical evolution over the decade between 2000 and 2009 is
fluctuating considerably. The valuation assumes that the company stabilises by the end
of the key driver forecast, but, since we are dealing with a very volatile industry, this
might not be the case. At this point in time, it is hard to predict at what level the rates
will stabilise and because of the sensitivity of the valuation model, even slight
differences have a considerable impact on the valuation.
44
Chapter 6. Sensitivity Analysis
This chapter represents an attempt to translate hypotheses and expectations into
numbers. More precisely, it represents an attempt to quantify how sensitive Vestas’
share price is in respect to various factors. Firstly, the effects of different target capital
structures will be measured, followed by a section looking at the cost of debt and
finally, a few debt-related and non-debt-related variables that affect the cost of capital:
the marginal tax rate, the risk-free rate and the return on the market portfolio. The
concluding section of the chapter presents an overview of all the simulation results.
6.1. Target capital structure
In their 2009 annual report, Vestas mention that they plan to keep debt levels
low, but do not give any indications as to how low. The company’s 2010 Eurobond
brings debt to book value up to 24.52%, based on the 2010 company forecast evolution.
Myers and Majluf, (1984) only posited the existence of the target debt levels,
but did not indicate how one could calculate them. They name what the costs and
benefits of debt are, but they do not attempt to measure them precisely. From all of
those, subsequent literature that tries to quantify costs or benefits mainly deals with tax
advantages (Graham and Lemmon, (1998), Graham, (2003) and Graham, (2003)), and
bankruptcy costs (Warner, (1977)). All the other types of costs and benefits largely
remain elusive. In research, particularly empirical studies, the most popular method
employed is the usage of proxies as independent variables utilised to regress target
levels on.
In an attempt to quantify Vestas’ target debt level, various estimations were
performed. An inverse approach to that described above was used, namely that the
coefficients resulting from statistical regressions were used to calculate Vestas’ target
capital structure. Hence, as with the majority of trade-off literature, costs and benefits
were not calculated directly, but the use of proxies and regression models lead to the
uncovering of target leverage levels.
Two different models were used, one representative for the static trade-off
theory, and the other for the dynamic trade-off theory, respectively that of Chang et al.,
(2009) and of Clark et al., (2009).
45
6.1.1. The Static Trade-off Target Debt Level
The most important reasons behind deciding to use this particular model were
the following:
 the model is based on the seminal work of Titman and Wessels, (1988).
In addition, the more recent model presents improvements (refined
indicators, the use of the MIMIC24 model instead of the SEM25) which
bring about greater accuracy and result significance;
 unlike a regular regression model, this type of model highlights the exact
relationship
between
the
unobservable
attributes
(meaning
the
immeasurable determinants of capital structure) and the observable
variables (meaning the proxies used to replace the determinants);
 the comprehensiveness of the model is another advantage. It includes
proxies for growth, profitability, collateral value of assets, non-debt tax
shields, uniqueness, volatility, and industry;
 the large sample size is a sign that results obtained through this model
could be representative for Vestas as well (13,887 firm-year observations
stretching over 16 years and covering 351 industries).
 with the exception of size – which, compared to the older model, was not
taken into account because of goodness of fit criteria – all other
determinants of capital structure were statistically significant.
The precursor of this model is the structural equation model of Titman and
Wessels, (1988) comprising of 15 determinants of capital structure:
24
Multiple Indicators and Multiple Causes Model, a reduced form of SEM.
25
Structural Equation Modeling.
46
Chang et al., (2009) use a MIMIC model in their analysis. The restricted
formula for the model is:
where Y is a vector of indicators of the latent variable η (target capital
structure) and X is a vector of causes of η. ξ denotes disturbance. The diagram below
shows the relationship between the different vectors, which is explanatory for both the
model of Titman and Wessels, (1988) and also that of Chang et al., (2009).
Figure 9: The Static Trade-off Model
X1
y1
ε1
y2
ε2
y3
ε3
η
X2
X3
ξ
Source: Chang et al., (2009)
The representation shows that the latent variable, namely capital structure (η) is
dictated by a series of causes (X1, X2, X3), which are in fact the determinants of capital
structure. These are then measured by corresponding y variables.
By using the vector of causes, X, two different equations were set up and the
target debt levels for both long term and short term debt were calculated, as presented
below:
47
*The independent variables in the two equations respectively stand for: RD/S: R&D/Sales; CE/TA: Capital
Expenditure/Total Assets; GTA: Percentage Change in Total Assets; MBA: Market-to-Book Assets; MBE: Market-to-Book Equity;
RD/TA: R&D/Total Assets; NDT/TA: Non-Debt Tax Shields/Total Assets; ITC/TA: Investment Tax Credits/Total Assets; Dep/TA:
Depreciation Expense/ Total Assets; IGP/TA: Investments & Gross Property, Plant and Equipment/Total Assets; OI/TA: Operating
Income/Total Assets; OI/S: Operating Income/Sales; STDGOI: Standard Deviation of Percentage Change in Operating Income;
CV(ROI): Coefficient of Variation of ROI; CV(ROE): Coefficient of Variation of ROE; CV(OITA): Coefficient of Variation of
Operating Income/Total Assets; IND: industry – two-category dummy variable.
The overall debt-to-book value is 39.73%, where book value is adjusted to
include off balance sheet items (reserve for income smoothing provisions and leased
assets). Compared to the base case scenario, which only includes the Eurobond, this
target level is 1521 percentage points higher.
By using the base case scenario valuation and plugging in this debt level into
the WACC, the new share price equals EUR 63.47 – more than the double of the
previous EUR 30.03 or a change of 111.36%.
6.1.2. The Dynamic Trade-off Target Debt Level and Adjustment
Speed
The reasons for which the model by Clark et al., (2009) was thought to be
appropriate were the following:
 this model, too, presents highly significant results from a statistical point of
view;
 the authors also perform regressions on subsamples based on country
considerations. Therefore, the regression which was used for Vestas is based
on the results that Clark et al., (2009) present for Denmark. This is thought
to yield results that are more insightful and closer to the actual truth.
Clark et al., (2009) use a partial adjustment model to test whether firms move
towards a target and, if yes, at which adjustment speed they do so. Their model is
presented below:
where MDR is the market-to-debt ratio and λ is the adjustment speed.
In order to estimate the future market-to-debt ratio, the following model is
used:
where Xi,t is the vector of firm characteristics used to predict the debt levels of next
period and Fi is the vector of firm fixed effects.
48
Vestas’ target capital structure was calculated based on its own firm specific
characteristics and using the regression below:
*The independent variables in the regression respectively stand for: MDR1: Market-to-Debt ratio equal to (Long-Term
Debt + Current Liabilities)/(Total Assets - Book Equity + Market Equity; EBIT/TA: EBIT/Total Assets; MB: Market-to-Book
Ratio; ln(TA): natural logarithm of Total Assets; RDDUM: dummy variable equal to 1 if there was an R&D expense and 0 otherwise;
RD/TA: R&D/Total Assets; DEP/TA: Depreciation Expense/Total Assets. The “L” stands for one-period lagged variables.
From this model, the resulting debt-to-book value ratio is of 22.73%, which
yields a share value of EUR 28.01, lower that the EUR 30.03 by 6.73%. The difference
is much smaller than the previous case due to the fact that the debt ratio change which
has an impact on WACC is also smaller than before.
It should be noted that the level of debt estimated using this model is very close
to the target debt level used in the forecast period, which includes the Eurobond. The
former is only 1.79% lower than the latter. A natural conclusion would be that perhaps
there is some reason for issuing a Eurobond of EUR 600 million. A suspicion that
Vestas is already close to target levels was already raised in Section 4.3. Stock Market
Performance.
Even though it might seem that the target debt level is somewhat low, we must
bear in mind that certain traits which are characteristic to Vestas and are documented in
literature are connected to low debt levels. Kim and Sorensen, (1986) document the fact
that high growth firms use less debt. Uniqueness is also a determinant negatively related
to leverage, which Titman and Wessels, (1988), as well as Chang et al., (2009),
determine with the use of R&D expenditures. Vestas does not report its R&D expenses,
but it does boast having the largest R&D department in the industry, employing 2,980
people at the end of 2009. This might be indicative of a high degree of uniqueness.
6.1.3. Simulation Assumptions and Method
In order to find how sensitive the share price is, the first step was to analyse the
change in equity price that would be produced if the target debt level is increased and
decreased. Changes in increments of 1% are induced, up to +/- 10% compared to the
49
forecasted target debt level of the base case. The percentage changes in share price
compared to the base case are portrayed in the next section.
The next step involves a total of 6 main simulations. These were adjusted
during further simulations, to provide feasible results. Each simulation was run 5000
times and all the outcomes are presented in the next section.
The first simulation assumes that the target capital structure of Vestas is the
one calculated using the static trade-off model of Chang et al., (2009). However, since
in real life, the target capital structure is not an unflinching number, but the realization
of a random variable, a distribution was set up as the basis for the simulation. The target
capital structure was assumed to have a normal distribution, the mean of which was the
previously calculated target value (39.73%), while the standard deviation was calculated
using the formula:
which yielded a deviation of 86.08%. Since the deviation is that large, in some
instances, the simulation can return a negative value or a value above 100%, both of
which are economically impossible. We have thus restricted the simulation in two ways.
Firstly, to only take into account positive figures. If the simulated value is negative, we
assumed that the company will pay off all of its debt. Secondly, the target level has been
capped at 65%. All the simulation results that are above will revert to 65%26, which is
considered a reasonable approximation of the level beyond which financial distress
costs might start to come into play. It should be acknowledged that these assumptions
regarding standard deviation and the financial distress threshold are largely arbitrary,
due to lack of data. Moreover, they are restrictive and influence the results to a
considerable extent.
By simulating target cap structure it is possible to get a WACC lower than the
growth rate for continuing value, which leads to value destruction because continuing
value is negative. It is also possible to get a WACC higher than ROIC, which also leads
to value destruction. Vestas however stands on solid ground, and therefore, WACC
should be in between the two rates. Thus, a conditional simulation is performed and the
26
All other listed top 10 manufacturers (Gamesa, Suzlon, Siemens as a whole, REPower) have
debt levels between 56% and 72%.
50
cost of capital’s variation was restricted to the interval defined by the growth rate at the
lower limit and the ROIC at the upper one. This is considered in line with reality
because Vestas is a young company in a growing industry, and therefore, most of the
enterprise value should be in the continuing value. This was the second simulation.
The third simulation is similar to the first, but uses the results of the dynamic
trade-off model as the mean for the target debt level simulation. The rest of the
assumptions and methodology are identical. To calculate the standard deviation, a
similar formula was used as in the previous case, yielding a deviation of 9.1%. The
conditional simulation – the fourth simulation - is also performed, but the results do
not differ much.
The fifth simulation connects the static and dynamic trade-off model results
and assumes that the target level lies in between the two. A uniform distribution where
the lower and upper limits of the range are the debt levels calculated through the
dynamic and respectively static trade-off model is set up. A secondary conditional
simulation is run in this case as well, being the sixth simulation and the last of the
capital structure simulations.
6.1.4. Results
Table 3: Target Capital Structure Percentage Changes Results
Debt Level
Change
-5%
Table 3 presents the results
Share Price
(EUR)
24,85
% Change from
Best Case
-17,25%
-4%
25,78
-14,17%
analysis. A percentage change of
-3%
26,75
-10,92%
+1% has a slightly bigger impact
-2%
27,78
-7,48%
-1%
28,87
-3,85%
Base Case
1%
30,03
31,26
0,00%
4,09%
2%
32,57
8,44%
results suggest that as the debt moves
3%
33,96
13,08%
further away from the base case
4%
35,45
18,04%
5%
37,04
23,35%
of
the
preliminary
simulation
than a change of -1%, but in the same
direction predicted by theory: less
debt leads to a lower share price. The
target, the impact becomes smaller
and smaller, meaning that it is not
directly proportional with the debt level percentage change.
The table below, Table 4, lists the results of the base case scenario and of all
the debt target level simulations, which are further commented upon in this section.
51
Table 4: Target Capital Structure Simulation Results
Simulation
Reference
Base Case
#1
#2
#3
#4
#5
#6
Target Capital Structure*
WACC
Share Price
24.52%
34.8% (29%)
34.3% (28.9%)
22.8% (9.1%)
22.7% (9%)
31.2% (4.9%)
31.3% (4.9%)
8.3%
7.6% (2.2%)
7.5% (1.4%)
8.4% (0.6%)
8.4% (0.5%)
7.8% (0.3%)
7.8% (0.3%)
EUR 30.03
EUR 42.50 (EUR 469)
EUR 63.61 (EUR 597)
EUR 31.06 (EUR 14.53)
EUR 31.72 (EUR 20.56)
EUR 41.10 (EUR 9.57)
EUR 41.24 (EUR 9.61)
* Target capital structure is measured as debt to total book value, where the latter is adjusted to include off balance sheet items.
() The numbers in between parentheses represent standard deviations.
The first simulation (#1) results in a mean share value of EUR 42.5. However,
in 2081 instances, the share was worthless due to the fact that the cost of capital was
either larger that the ROIC of the continuing value, or less than the growth.
The first simulation was upgraded and the conditional simulation (#2) run
next restricted the cost of capital in between the two other rates. This time, the mean
share value was EUR 63.61 - much higher due to the fact that the share price was never
allowed to be worthless. The price didn’t fall below EUR 4.56 (the result of simulations
where the cost of capital is the lowest and is equal to the growth rate in NOPLAT).
Given that the highest historical price ever recorded for Vestas was of EUR 92.79, this
result – even though very far from the current economic reality - is not completely
impossible.
The histograms in Figures 10 and 11 depict the share price resulted from the
first two simulations. In both histograms, most of the results are clustered around the
lower values, with fewer and fewer results scattered towards the higher prices. In Figure
10, the leftmost column portrays the results when the share was worthless, and the next
one, the results when all the debt was paid off (the target capital structure was all
equity). In the latter case, share value is EUR 13.20.
Instead of allowing share value to be 0 due to an inappropriate cost of capital, a
condition was set so that the WACC is in between the ROIC and growth rate of the
continuing value. Thus, the minimum share value is EUR 4.65. In Figure 11, the
leftmost column represents all the instances when the share price was EUR 4.65. The
next column portrays 1946 cases out of which 1905 correspond to a no debt capital
structure and a cost of WACC of 9%. The resulting share price in those 1905 cases was
EUR 20.5.
52
Figure 10: Simulation #1 Share Price Histogram
Source: based on simulation results
Figure 11: Simulation #2 Share Price Histogram
Source: based on simulation results
The results of the third simulation (#3) imply a share value of EUR 31.06.
This is much closer to the EUR 30.03 from the forecasted base case due to the fact that
the target debt levels and the standard deviation are much lower in this case than in the
former. In this case, there are no instances of the share having the null value.
Next, the conditional simulation (#4) where the variation of the cost of capital
is limited between ROIC and growth is also performed, but the results do not differ
much. This time, the mean value of the share price is EUR 31.72. The smaller
difference between the two results can also be attributed to the more restricted variation
of the target capital structure.
The following histograms (Figures 12 and 13) portray the share price results
for simulations #3 and #4. The shape resembles the bell shape of a skewed distribution
more than in the previous two cases due to the fact that the standard deviation of the
target capital structure is much smaller and therefore, there are no abnormal results and
no restrictions that would distort the shape of the results’ distribution too much. In the
third simulation, the maximum debt level is 52.2%, while in the fourth, it is 58.2%.
Figure 12: Simulation #3 Histogram of Results
Source: based on simulation results
Figure 13: Simulation #4 Histogram of Results
Source: based on simulation results
53
As for the last unconditional simulation (#5), which entails a uniform
distribution, the results were – as expected – in between those found above: the average
share price was EUR 41.26. The fact that the variation interval is quite small (from
22.74% to 39.73%) and that the share price was always positive (the minimum recorded
value was EUR 27.78) are the causes for such a high result. In the last conditional
simulation (#6), the result is almost identical: EUR 41.24.
As expected, the histograms showing the share prices (Figure 14 and 15 below)
are much more even in shape, compared to all the previous cases. This is naturally due
to the type of distribution chosen for the target capital simulation.
Figure 14: Simulation #5 Histogram of Results
Figure 15: Simulation #6 Histogram of Results
Source: based on simulation results
Source: based on simulation results
In conclusion, the results show that the higher the debt level, the lower the cost
of capital, and the higher the equity value; these findings are in line with theory and
with expectations, given the conditioned debt target levels to be less than a potential
financial distress threshold. However, it is important to note the sensitivity of the model,
as can be seen from the very high share price particularly in simulation #2, but also in
simulation #1. Hence, the variation in results might only be particular to how the model
is built, and not necessarily solely to company and market characteristics.
6.1.5. Discussion and Critique
Oddly enough, a brief historical walk-through of debt levels and stock value
shows figures that are contrary to theoretical predictions (and also to the results of the
simulation): Vestas’ share price and its degree of indebtedness seem to be inversely
related. The maximum share price of Vestas over the 12 years when it has been listed is
EUR 92.79. The price was registered in August 2008, the same year in which the
company also had the lowest ever debt levels, the debt to book total cap ratio being as
54
little as 5.8%. Perhaps at that point in time, there were other, more potent factors driving
share prices. Or perhaps it was a sort of pre-financial crisis bubble (share prices fell as
low as EUR 32.23 in November the same year). At the opposite spectrum, the share
price was lowest from 2002 to 2005, when leverage levels were historically the highest
(varying between 26.7% and 33.4% book debt to value). In the latter case, the equity
value was influenced considerably by not meeting market expectations over the 4 year
period. Vestas continually failed to meet beginning-of-the-year forecasted targets during
that time frame. Moreover, the NEG Micon combination in 2004 also led to a
downward adjustment of prices.
Given the facts portrayed above, why is it that the calculated target levels
could be reasonable estimations? The following results are sample means extracted
from cross sectional studies that aim to test whether the target capital structure exists.
Bradley et al., (1984) find that, depending on the industry, debt levels can be between
9.1% (drugs and cosmetics industry) and 58.3% (airline industry). Kim and Sorensen,
(1986) find that the mean debt ratio for firms with a low degree of inside ownership27 is
32%, coupled with a standard deviation of 16%. Clark et al., (2009) document an
average debt-to-value of 42.8% for their Danish sub-sample, with a corresponding
standard deviation of 20%. Liu, (2009) analyse a sample with a mean book debt to value
of 47.5% (standard deviation of 21.5%) and a market debt to value of 39.5% (standard
deviation 24.8%). Therefore, the estimated target levels for Vestas fall within the
reported interval or within maximum one standard deviation from the mean of each of
the results presented above.
However, the values calculated for Vestas fall towards the lower end of the
debt intervals from previous studies. This can be explained by a few company
characteristics which are documented in literature as to have a negative impact on debt
levels. One of these characteristics is company size. Brennan and Schwartz, (1984), as
well as Hennessy and Whited, (2005) and Graham, (2000) document the inverse
relationship between size and the level of debt. A second characteristic is growth
opportunities, documented by Myers, (1977), Kim and Sorensen, (1986), Rajan and
Zingales, (1995), Talberg et al., (2008), Antoniou et al., (2008) and Byoun, (2008). A
27
In 2009, Vestas’ board of directors and executive management owned a total of 0.07% of
overall number of shares.
55
third factor that leads to lower debt levels is that the model disregards transaction costs.
Kane et al., (1984) make this observation with regard to the effects of transactions costs.
Another observation which should be noted concerns the current debt levels of
listed industry competitors in the top 10: Gamesa, Suzlon, and REPower. Siemens has
not been considered because the debt levels in their annual report refer to the group and
not Siemens Wind Power. Koller et al., (2005) state that in order to calculate target debt
levels, it is important to also look at peers. The observed capital structures range from
56% to 72%, without taking into account off balance-sheet items. Why, then, would
Vestas’ target be so much lower? One answer would be that the industry is now in the
growing stage and all the incumbents are trying to get a foothold in the market and
accumulate as much share of it as possible. That means investing heavily, which
requires rising considerable funding in very little time – a strategy employed by all
competitors. It is likely that companies will revert to their target levels – which are
much lower – as the industry matures. Vestas might have lower debt levels because of
being ahead in the adjustment towards optimum. Another argument would be that
Vestas has been one of the pioneers in the market and thus, has had time to grow in the
past – which Vestas did mostly using internally generated funds, but also debt. The
high-leverage period is a thing of the past for Vestas and took place from 2002-2005, as
the historical analysis shows. For a debt-conservative company such as Vestas, their
debt levels peaked at 33.4% in 2005.
An additional issue which might raise question marks is the high standard
deviation in the static trade-off case. The reasons why this result came about are the
fact that the number of observations used to calculate the deviation was quite small (10
in total) and that the difference between the target and actual debt levels was high in all
of the cases.
Looking at where peers stand might point towards a conclusion with regards to
the plausibility of the standard deviation. The book debt-to assets ratio was calculated
for the other listed peers in the top 10 (Gamesa, Suzlon, and REPower). It turns out that
they all have lower standard deviations28 compared to Vestas, due to the fact that their
debt levels are higher and thus, closer to the optimum. Gamesa comes quite close to
Vestas’ standard deviation (80.24% for the former, compared to 86.08% for the latter),
but in the Spanish competitor’s case, the high standard deviation is a result of very high
28
Gamesa has a standard deviation of 80.24%, Suzlon of 41.62% and REPower of 55.94%.
56
debt levels, contrary to Vestas’ financing position. Due to the discrepancies between
industry incumbents it is difficult to draw a conclusion solely based on comparisons.
However, if we look at the literature, the high standard deviation is in line with
previous research. In Chang et al., (2009) the study on which the estimation of the static
trade-off target debt level is based, the sample analysed has a mean long term debt to
market value ratio of 44% and a standard deviation of 194%. The results for Vestas are
much lower. Therefore, the standard deviation reported by Chang et al., (2009) sheds a
more favourable light on Vestas’ 86.08% result.
The next issue of concern is the actual valuation model which the simulation
has been built into. The two frameworks used in the model (discounted cash flows and
economic value) focus mostly on Vestas’ operating performance, and therefore, they
only include the effects of the company’s capital structure to a very limited extent. And
since the simulations make use of the valuation model, the sensitivity analysis is bound
and connected to how the model is built.
The spreadsheet doesn’t account for factors and market forces outside the
company: bubbles, fads and trends (like the IT bubble, for example) – it is a plain
vanilla valuation sheet that – if used to make economic decisions - should only be
regarded as a point of view in a more complex analysis. It presents a very simplified
view compared to reality.
Apart from the feature that market and industry forces – which clearly affect
prices – are not built in, other - more technical - drawbacks include for example the fact
that the model is completely insensitive to changes in the balance sheet structure, and
particularly important for the present issue, to the debt-equity mix. The only link
between debt levels and share prices is through the debt to value ratio in the WACC,
and not through the cost of debt. The latter is computed separately, and is an invariable
input into the model. Therefore, it can be argued that the effect of debt levels is only
superficial. Section 6.4. tries to reconcile this drawback by modifying the spreadsheet so
that the amount of leverage has an effect on the cost of debt.
An additional and final comment to the structure of the valuation model is that
it works with accounting statements and does not actually try to compute the specific
costs and benefits of debt, which are so vastly talked about (Kraus and Litzenberger,
(1973); Miller, (1977); Myers, (1984); Frank and Goyal, (2007)). Even though they
57
might have given a more panoramic view upon the link with stock prices, an exact
estimation of these costs and benefits remains elusive in this model.
6.1.6. Discussion on the Adjustment Speed
As for the adjustment speed, it would be difficult to accurately calculate how
fast Vestas would reach target optimal debt levels. Previous studies fail to reach a
generally accepted conclusion related to the adjustment speed, which ranges from 8%
(Flannery and Rangan, (2006)) to 23.2% (Huang and Ritter, (2009)).
Clark et al., (2009) report adjustment speeds for numerous developed or
developing countries around the world. In order to be consistent with the model chosen
for the dynamic trade-off target level calculation, an adjustment speed can be deducted
from the study’s results with respect to Denmark, yielding a much higher level than the
previously mentioned 23.2%. Vestas should have a speed of 57.2%, meaning that the
company should reach its target level in less than 2 years. We will assume that this is an
appropriate estimation, for the sake of consistency with the model used to calculate the
optimal amount of leverage.
In Vestas’ case, the historical evolution of debt levels is not linear, thus not
growing year by year, but fluctuating considerably: if the dynamic trade-off estimation
of 22.7% is considered to be the target, then the observation of historical fluctuations is
puzzling. Debt levels can be as high as 33% one year (as was the case in 2005) and as
low as 12% the next. The company has managed to come close to the projected target
level only in 2010, after issuing the Eurobond, when the company reached a debt-tovalue ratio of 24.52%.
Why, then, was the company so slow to reach its optimal target level? Possibly
because the company has been slowed down by the turmoil in the global economy,
which acted as a disturbance and determined the firm to substantially deviate from the
optimum. This hypothesis is supported by the company’s historical debt-to-value ratios,
which were quite high before the financial crisis and decreased considerably as the
crisis unfolded, thus reaching a level below optimum, instead of leveling off at 22.7%.
Another explanation could be connected to industry risk. The wind industry is
generally viewed as a risky one. If Vestas’ management considers this to be accurate
and is risk averse, then the slow adjustment speed might be a conscious decision made
by management and aiming to keep the company at bay from too many debt-related
58
costs. This explanation might also be linked to why debt levels fell so abruptly during
the financial crisis, which might have been a measure to reduce exposure to risk.
6.2. The Cost of Debt
Based on the results of the previous section, the conclusion to be drawn is that it
increasing debt levels would be beneficial for Vestas’ share price. In the introduction,
one question posed was whether the type of debt which Vestas would chose to issue
matters. The answer is yes, because each debt instrument has different characteristics
and therefore, influences the cost of debt in a particular way. What, then, would be the
appropriate types of debt for the company? This section does not aim to be an
exhaustive presentation of all debt instruments, but slightly touches upon the advantages
and disadvantages of some of the most popular debt financing methods, especially in
relation to the previous discussion. What’s more, no attempt has been made to quantify
these advantages and disadvantages.
6.2.1. Types of Debt - Discussion
A good option would be securitisation – the issue of secured debt like
mortgage bonds. Vestas holds numerous fixed assets that could easily be used as
collateral. In fact, studies show that the higher the proportion of property plant and
equipment, the more debt a company should have (Rajan and Zingales, (1995);
Flannery and Rangan, (2006); Talberg et al., (2008); Antoniou et al., (2008); Byoun,
(2008)). In 2009, the ratio of fixed to total assets (measured in book values) held by
Vestas was 22.7%. Peers’ ratios range between 16.69% for REPower, to 26.06% for
Gamesa, to 58.32% for Suzlon. Gamesa comes closest to Vestas, but unlike the Danish
turbine manufacturer, the Spanish one has a debt to assets ratio of 60.58%, much higher
than Vestas’ 24.52%29. It might be, therefore, that Gamesa is truly putting their assets to
use as collateral.
Advantages of the mortgage bond include lower costs for borrowing, an effect
of smaller yields. Due to the fact that the bond is secured by assets, investors require a
smaller interest that matches the lower bond risk.
Another advantage is that the cost of debt will not soar after issuing this type of
debt, due to the low yield and implied risk. This has a beneficial effect on the share
29
Vestas’ ratio includes the Eurobond and also takes into account lased assets, unlike
Gamesa’s ratio, which is calculated with “on balance sheet” items.
59
capital, because it will not trigger a large increase in the cost of capital, thus reducing
the value of stock.
Moreover, mortgage bonds allow the company to make use of otherwise
illiquid assets – property, plant and equipment – and turn them into a very liquid means
of raising funds.
Effects on the cost of debt are also an incentive to use this instrument: it
implies less chances for the cost of debt to increase very much, since the yield is smaller
than in other cases, which means that the cost of capital would not increase very much
either and the share price would not decrease by a considerable amount
However, there are disadvantages as well. The fact that these instruments
require lower interest payments compared to other types of debt – or even none at all –
is one of them. A consequence of this is that the company does not benefit from tax
shields as much as they would with straight debt, for example.
Another potential option for Vestas could be to issue convertible bonds hybrid instruments between debt and equity.
Advantages include lower fixed cost for borrowing due to the lower yield. The
option to convert the instrument into stock at the time of maturity is valuable in itself
and compensates the investor for the lower interest.
The effects on the cost of debt are similar to the mortgage bonds presented
above and thus represent an advantage for using convertible bonds as well.
A third advantage would be incurred if Vestas includes a call protection option.
This feature would entitle the issuer with the right to call the bond before term and
therefore, compel the holder to convert the bond into stock at a date prior to maturity. It
is a useful feature if company earnings, along with share prices, are forecasted to rise
considerably, rendering the conversion to stock at maturity unprofitable for the
company. However, calling the bond would mean giving up the tax advantages of the
bond, so the trade-off between current tax shields and future profitability should be
considered when deciding to call the bond before maturity.
And finally, just as in the previous case, these types of instruments can be
called before maturity, if the company pays off the entire mortgage in advance.
One of the main disadvantages would be that the company benefits from less
tax shields, as in the case of mortgage bonds.
60
Another disadvantage would be dilution of EPS, which takes place at the time
of conversion and is certainly undesired by current shareholders. In addition to that,
there is also the threat of diluting control, in the event that a large part of the debt issued
is bought by one single investor.
A third debt instrument that Vestas could use is the zero-coupon bonds or
strips.
Much as in the cases of the previous instruments, advantages include less
monthly costs, since the bond pays no coupons, and they can be called before maturity.
Moreover, even though the company doesn’t pay coupons, it still records
interest expense, which will be paid back in one lump sum at maturity or when the bond
is called. Therefore, Vestas would benefit from tax advantages in this case as well.
The effect on the cost of debt would also not be of considerable impact, if the
company does not cross the financial distress threshold.
Disadvantages would include the fact that these are the most volatile
instruments and their swings are closely related to fluctuations in interest rates. At
present times, interest rates have been known to change dramatically which would
prompt investors to be reluctant to buy these bonds because of possible future
fluctuations. Attractiveness of the instrument should thus carefully be considered before
the issue.
In conclusion, each debt instrument has its advantages and disadvantages for
Vestas. A black and white decision cannot be made and the pluses and minuses of each
have to be traded off and analysed from the viewpoint of the company’s financial
strategy in order to decide which would be appropriate. What’s more, the decision of
whether to subordinate new debt to the current Eurobond or to issue it at the same
seniority will also take its toll on the cost of debt and the share price.
6.2.2. Sensitivity Analysis Assumptions and Method
Since assumptions regarding the inputs in a potential simulation of the cost of
capital would be very much like tossing a coin, the sensitivity analysis in this section
aims to portray the effects of changes of the base case scenario cost of debt in
increments of 0.1% up to a difference of +/-0.5% from the previously forecasted figure
of 4.1%.
In order to calculate the cost of capital, the assumption that the company will
increase the amount of debt to the target level is applied to the calculation. Thus, two
61
sets of results for sensitivity analyses will be presented. The first assumes that the target
debt level is of 39.73%, as the static trade-off model predicted. The other assumes
22.7%, based on the dynamic trade-off model.
6.2.3. Results and Discussion
The two graphs below (Figures 16 and 17) depict the results of the simulation.
As the cost of debt increases, so does the cost of capital. The results for the static tradeoff framework show that a change of +/-0.1% in the cost of debt triggers a change of
+/0.03% in the cost of capital, leading to a difference of -/+2.7% in share prices
compared to the base case. As for the case where the dynamic trade-off target level was
used, a change of +/-0.1% in the cost of debt causes the cost of capital to modify by +/0.02% and the share price by -/+0.9%.
Figure 16: Share Price Sensitivity in the Static
Figure 17: Share Price Sensitivity in the
Trade-off Case
Dynamic Trade-off Case
Source: author’s calculations
Source: author’s calculations
As would be expected, the share price drops when the cost of capital increases.
The decrease is in smaller and smaller increments each time. The graphs also point out
that the change is more dramatic in the static trade-off model due to the fact that the
higher percentage of debt makes the cost of debt have a larger impact on the cost of
capital.
6.3. Other Debt-Related and Non-debt-related Variables
This section will focus on the effects of both debt related variables (marginal
tax rate), as well as non-debt related variables that affect the cost of capital (the risk free
rate and the return on the market portfolio).
62
6.3.1. Simulation Assumptions and Method
Four different simulations are performed in this last section of the sensitivity
analysis. In the first three only one of the three variables is allowed to fluctuate, while in
the last one, all three are simulated. Results are presented in the following section.
Antoniou et al., (2008) documents a positive relationship between the tax rate
and amount of debt the company uses, with the intention to make the most of tax
shields. The marginal tax rate in the base case scenario is assumed to be the Danish
statutory tax rate, 25%. However, it is very difficult to calculate the marginal tax rate
precisely with the limited information at hand. Graham develops a method of
calculating marginal tax rates from company tax files (Graham, (1996a); Graham,
(1996b); Graham, (2000); Graham, (2003); Graham and Lemmon, (1998)). He recalculates the amount of debt owed by changing the amount of interest deductions and
hence tax shields, from which he derives the marginal tax rate. To put results into
perspective and not let the base case 25% estimation constrain the results, a simulation
was performed under the assumption that the variable has a normal distribution, with
values clustering closer to the assumed mean of 25%. The standard deviation is 10.8%.
The latter is based on the results of Plesko, (2003). He uses the same trichotomous
variable as we have used to estimate the marginal tax rate and reports the 10.8% as the
standard deviation of his sample.
In the second simulation, the variable under the microscope is the risk free rate.
We have also assumed this variable to have a normal distribution, with a mean of 2.9%
(the estimation for the forecast period) and a standard deviation of 1.26%. The latter
was estimated from historical yields of the German Eurobond with a 10 year maturity
from January 1993 to January 2011, as extracted from the ECB database.
The third variable, the return on the market portfolio, is evaluated in the next
simulation. The assumptions in this case are that it has a normal distribution with a
mean of 8.5%, as in the base case forecast period, and a standard deviation of 4.2%. The
deviation was calculated as the standard deviation of the S&P500 returns from 1950 to
2010.
Because of the high standard deviation, it is possible for the return on the
market to be lower than the risk free rate, which, in some cases, could result in a
negative cost of debt. Therefore, the return on the market is not allowed to fall below
the risk free rate. Another effect of the high standard deviation is that the resulting cost
63
of capital could be so high that the value of operations is below the claims on the
company’s cash flow (debt, leases, stock options and retirement related liabilities),
which makes the share worthless.
The last simulation allows all three variables to fluctuate based on the
assumptions outlined above.
6.3.2. Results
The table below presents the mean and standard deviation assumptions,
alongside the results of all three simulations. For each of the variables, the simulation
was run 5000 times.
Table 5: Simulation results of other debt and non-debt related variables
Variable
Base Case
Tm
Rf
Rm
All
Mean
St. Dev.
25%
2.9%
8.5%
10.8%
1.26%
4.2%
WACC
8.3%
8.3% (0.1%)
8.3% (0.3%)
8.4% (3.7%)
8.3% (3.7%)
Share price
EUR 30.03
EUR 30.13 (EUR 1.87)
EUR 30.64 (EUR 4.71)
EUR 48.51 (EUR 422.63)
EUR 81.86 (EUR 1115)
The numbers in between parentheses represent standard deviations.
Results show that the marginal tax rate and the risk free rate do not influence
the cost of capital and share price to a very large extent. The results of the simulation
are quite close to those of the base case scenario. However, the return on the market
portfolio has a much greater impact. The mean WACC is only 0.01% higher than the
base case, but the standard deviation of 3.7% yields a much higher average share price
and standard deviation. The valuation model that the simulation is built into is very
sensitive to changes in the cost of capital, which is why the third simulation yields these
results.
The last simulation is the one with the highest resulting share price, due to the
fact that all variables are allowed to fluctuate. Hence, there is a great variation of results
from the base case, resulting in a EUR 81.86 share price and a corresponding standard
deviation of EUR 1115.
6.3.3. Discussion and Critique
Graham, (1996a) states that the fact that marginal tax rates are never
explicitly calculated, and instead always replaced by various proxies is in fact the reason
why most research fails to find tax as an important consideration in the choice of capital
64
structure. The fact that this analysis was grounded on no explicit calculation, but only
on simulation, might bias the results. We find that the marginal tax rate does not have a
material impact on the valuation, just as Graham had posited.
In addition to the above, it can be argued that the standard deviation used for
the simulation might not be representative in the case of Vestas, since it was extracted
from previous research and is based on the sample used in that research. However, the
restriction needed to be imposed due to the inexistence of more suitable options to be
used in the estimation.
The valuation seems to be slightly more sensitive to the risk free rate, perhaps
due to the fact that the standard deviation of the simulated variable is much higher than
in for the other variables.
The return on the market portfolio seems to have the highest impact on the
valuation. Because of the restrictions imposed on the simulation, 1654 cases of a share
price of EUR 0 were recorded. In all of these cases, the cost of capital was very high,
and by discounting the company’s cash flows, their present value was below that of the
claims on the cash flows.
6.4. Simulations of All Variables
The main purpose of the previous simulations was to try to isolate the effect a
certain variable had on the share price and then attempt to analyse it. This final section
takes a different approach and performs all-encompassing simulations, which allow all
the variables previously studied in this section to fluctuate.
6.4.1. Simulation Assumptions and Method
Two final simulations are performed in an attempt to link all the previous
simulations together and determine what the cumulative effect on the share price might
be. These last two simulations differ from each other in that the first uses the static
trade-off target level and standard deviation, while the second uses the dynamic-tradeoff results.
In both cases, the debt target level fluctuation is restricted to the interval [0%;
65%], based on the same reasoning as in Section 6.1.3.
65
Moreover, based on the target level simulated in each case, the valuation
spreadsheet has been modified to link the debt level to the cost of debt, a link which was
previously inexistent. Therefore, a higher debt level now gives rise to a higher cost of
debt. Since the exact relationship between the two variables is dependent on many
factors which are outside the scope of this study (like determining the exact mix of debt
instruments issued or their seniority), an arbitrary variation in the cost of capital of 1%
for each 10% points of debt level
Table 6: Link between the target debt level and the cost of debt
Target Debt Level*
Between...
0%
5%
15%
25%
35%
45%
55%
65%
And...
4,99%
14,99%
24,99%
34,99%
44,99%
54,99%
64,99%
65%
Cost of
debt
0,00%
2,20%
3,20%
4,20%
5,20%
6,20%
7,20%
8,20%
* Target debt level is measured as debt to total book value, where
the latter is adjusted to include off balance sheet items.
variation, compared to the forecasted
debt level in the base case scenario and
in the same direction as the leverage
variation is considered a reasonable
assumption. Starting from the base case
scenario inputs, a debt level of 25%
corresponds to a cost of debt of 4.2%.
Table 6 illustrates exactly how the two
variables are tied together. A Vlookup
Excel function was used to operate the
simulation.
The risk free rate, the return on the market portfolio and the marginal tax rate
all fluctuate independently, based on the same assumptions as in Section 6.3.
6.4.2. Results
Table 7 below compares the results of these last two simulations with those of
conditional simulations #2 and #4 from Section 6.1.
Table 7: Share-price sensitivity when all analysed variables are simulated
Variable
Static Trade-off Case
Simulation #2
New simulation (#2’)
34% (29.2%)
Debt 34.3% (28.9%)
Target
Level*
Cost Of Debt
WACC
Share Price
4.2%
4.3% (3.6%)
7.5% (1.4%)
7.7% (1.3%)
EUR
63.61 EUR 76.30 (EUR 543)
(EUR 597)
Dynamic Trade-off Case
Simulation #4 New simulation (#4’)
22.7% (9%)
23% (4.9%)
4.2%
3.3% (0.6%)
8.4% (0.5%)
7.1% (0.8%)
EUR
31.72 EUR 80.34 (EUR 166.61)
(EUR 20.56)
* Target debt level is measured as debt to total book value, where the latter is adjusted to include off balance sheet items.
() The numbers in between parentheses represent standard deviations.
66
Given the fact that the target debt levels were simulated based on the same
requirements, it was expected to obtain similar results, as has in fact happened in both
the static and the dynamic trade-off case. As in Section 6.1.4., the lower result in the
dynamic trade-off case is attributed to a lower distribution mean and a lower variance
than the static trade-off case.
With regards to the cost of debt, it was also lower in the dynamic trade-off
case. This is a result of linking the cost of debt to the target debt level and more
specifically, of the assumptions taken on for the type of distribution that the target debt
level variable fits into. For simulation #4’, 68.2% of the results are within one standard
deviation from the mean target debt level, or specifically within the interval [18.1%;
27.9%], which translates into a cost of debt of 3.2%; 4.2% or 5.2% as leverage
increases. For simulation #2’, the statistical interval would be [-46.35%; 125.81%],
which is restricted to [0%; 65%] for a more reasonable result, from an economical point
of view. This means that the cost of debt in the latter case can take on any value from
0% to 8.2%, which might result in a higher average value, as well as a higher standard
deviation.
Results for the cost of capital pull together the effects of all the simulated
variables. Since the risk free rate, marginal tax rate and return on the market portfolio
exercise the same influence on the WACC, it is safe to conclude that the lower value in
simulation #4’ also stems from the lower mean value and standard deviation of the
dynamic trade-off target capital structure. The model was proven to be quite sensitive to
changes in WACC, which is why the very large difference between equity prices in
simulation #4 and #4’ can also be attributed to the 1.3% difference in WACC.
As we have seen in Section 6.2., the higher the cost of debt, the lower the
share price. The same relation between variables can be observed in Table 7:
simulation #2’ has a higher average cost of capital and a lower average share price than
simulation #4’.
6.4.3. Discussion and Critique
Since the main difference compared to the previous simulations is related to
debt level and cost, the main critique is to the same issues. Even though these last
simulations seek to mirror economic reality and simulate all the variables at the same
time, they still have some drawbacks. In an attempt to make the actual state of things
easier to grasp, the cost of debt is only assumed to be influenced by the level of debt.
67
Since leverage is below the financial distress threshold, the relation between debt and its
cost is assumed to be direct and proportional. It might be, however, that in the real
world, the relation would be an exponential one and would most likely also be
influenced by other factors like the types of debt issued, their maturity, new debt
seniority and whether or not debt is subject to any covenants. An endeavour to scratch
the surface of the enumerated factors has been attempted in Section 6.2.1. Types of
debt, but, because of the complexity of information needed, the valuation model was not
modified to include these factors.
68
Chapter 7. Conclusions
The aim of this thesis was to present a sensitivity analysis of the share price of
Vestas with regards to the optimal debt level, as well as various other variables, some
debt-related (cost of debt and marginal tax rate), and others non-debt-related (risk free
rate and return on the market portfolio).
The starting point of the analysis was a base case scenario valuation which
yielded a share price of EUR 30.03. The financing side of the balance sheet comprised
of 24.52% debt – which included the EUR 600 mil Eurobond - with a cost estimated at
4.1%. The marginal tax rate was 25%, the risk free rate – 2.9%, and the return on the
market portfolio – 8.3%.
The optimal capital structure was then determined. Two different models were
employed in this respect. The first is the static trade-off model which was derived from
the research of Chang et al., (2009) and yielded a target of 39.73%. By plugging in this
result in the base case scenario, the share price would be EUR 63.47, more than double
than before.
The second model is representative of the dynamic trade-off theory and was
extracted from Clark et al., (2009). This model yielded a target debt level of 22.73% and
a share price of EUR 28.01, both slightly below the base case. Judging by these figures,
Vestas is almost at optimum leverage, but it took the company a long time to reach its
optimum. Clark et al., (2009) posit that the adjustment speed to target leverage for
Danish companies is less than 2 years. Vestas’ more gradual adjustment was probably
caused by the turbulence in the global economy and an attempt to limit default risk
exposure, as the company is an incumbent in a new and somewhat risky industry.
If the optimal debt level is the one calculated through the static trade-off
model, then Vestas needs to further increase debt levels. A brief discussion of
appropriate debt instruments pointed out that appropriate choices would be the less
risky instruments which would not prompt the cost of debt to increase considerably and
thus, affect share prices in a negative way. Such instruments could be secured debt
(mortgage bonds), convertible debt or zero-coupon bonds.
The sensitivity analysis follows and 5 simulation variables were scrutinized.
With regards to the target debt levels, a change of +1% has a slightly bigger impact than
a change of -1%, but in the same direction predicted by theory: less debt leads to a
69
lower share price. Share prices range from EUR 31.06 to EUR 63.61, given the
assumptions characterising each simulation and whether the static or the dynamic tradeoff target was used.
Results point out that, as the cost of debt increases, so does the cost of capital
and that the share price drops when the cost of capital increases. The decrease is in
smaller and smaller increments each time. The results from the static trade-off
framework show that a change of +/-0.1% in the cost of debt leads to a +/-0.03% change
in the cost of capital, and further on to a difference of -/+2.7% in share prices compared
to the base case. In the dynamic trade-off case, a change of +/-0.1% in the cost of debt
causes the cost of capital to modify by +/-0.02% and the share price by approximately /+0.9%.
The next variables analysed were the marginal tax rate, the risk free rate and
the return on the market portfolio, which were simulated within the base case scenario.
The respective average results were EUR 30.13, EUR 30.64 and EUR 48.51. The very
high standard deviation of the return on the market portfolio leads to the largest
difference from the base case result. If all the variables are simulated simultaneously,
the resulting share price is as high as EUR 81.86. In combination, the effects of all the
variables on the equity value build up considerably.
The last two simulations integrate all the variables and also link the debt levels
with the cost of debt. Within the static trade-off framework, the cost of debt was higher
than in the dynamic trade-off state, and the resulting share prices were of EUR 76.30,
and EUR 80.34, respectively.
Even though all these results are higher than the current share price of Vestas,
they are still below the maximum share price in the company history: EUR 92.79.
Assumptions are one of the factors that affect the results to a large extent.
Another factor is the valuation model. Since the analysis was performed within the
valuation spreadsheet, the way it is built greatly impacts on the sensitivity analysis
results. Valuation focuses on how much company operations are worth, and hence, the
only way the simulation variables affect share prices is through the cost of capital. The
valuation model is in fact extremely sensitive to changes in the cost of capital and
therefore, the larger the impact on the cost of capital, the larger the effect on share price.
Therefore, given the constraints outlined above, the conclusion is that debt
levels do influence share prices and that Vestas is either close to, or below the optimum
70
amount of leverage, depending on the framework considered. By managing the
financing of its operations in a way that would keep the cost of capital as low as
possible, the company can increase its share price considerably.
However, there are also factors outside the grasp of the company, which affect
the final outcome to a very large extent: the highest result of the individual one-variable
simulations was the effect of the return on the market portfolio. Therefore, external
variables should not be neglected in the determination of the optimal capital structure.
71
Bibliography
1) Acher, J. 2010. China Became Top Wind Power Market in 2009 [Online].
Copenhagen:
Reuters.
Available:
http://www.reuters.com/article/idUSTRE62S12620100329
[Accessed
19
November 2010 2010].
2) Andersen, A. H. 2010. Significant Downgrade by Vestas. Silkeborg: Jyske Bank.
3) Antoniou, A., Guney, Y. & Paudyal, K. 2008. The Determinants of Capital Structure:
Capital Market-Oriented Versus Bank-Oriented Institutions. Journal of
Financial and Quantitative Analysis, 43, 59-59.
4) Archer, C. L. & Jacobson, M. Z. 2005. Evaluation of Global Wind Power. Journal of
Geophysical Research, 110.
5) Baker, M. & Wurgler, J. 2002. Market Timing and Capital Structure. The Journal of
Finance, 57, 1-32.
6) Bloomberg. 2011. Government Bonds [Online]. Bloomberg. Available:
http://www.bloomberg.com/markets/rates-bonds/government-bonds/germany/
[Accessed 6 January 2011].
7) Bradley, M., Jarrell, G. A. & Kim, E. H. 1984. On the Existence of an Optimal
Capital Structure: Theory and Evidence/Discussion. The Journal of Finance, 39,
857-878.
8) Brennan, M. J. & Schwartz, E. S. 1984. Optimal Financial Policy and Firm
Valuation/Discussion. The Journal of Finance, 39, 593-607.
9) Byoun, S. 2008. How and When Do Firms Adjust Their Capital Structures toward
Targets? The Journal of Finance, 63, 3069-3096.
10) Cadsby, C. B., Frank, M. & Maksimovic, V. 1990. Pooling, Separating, and
Semiseparating Equilibria in Financial Markets: Some Experimental Evidence.
The Review of Financial Studies, 3, 315-342.
11) Carpentier, C. 2006. The Valuation Effects of Long-Term Changes in Capital
Structure. International Journal of Managerial Finance, 2, 4-18.
12) Chang, C., Lee, A. C. & Lee, C. F. 2009. Determinants of Capital Structure Choice:
A Structural Equation Modeling Approach. Quarterly Review of Economics and
Finance, 49, 197-213.
13) Clark, B. J., Francis, B. B. & Iftekhar, H. 2009. Do Firms Adjust toward Target
Capital Structures? Some International Evidence. SSRN eLibrary. SSRN.
14) Cook, D. O. & Tang, T. 2010. Macroeconomic Conditions and Capital Structure
Adjustment Speed. Journal of Corporate Finance, 16, 73-87.
15) Damodaran, A. 1994. Damodaran on Valuation : Security Analysis for Investment
and Corporate Finance, New York, Wiley.
16) Damodaran, A. 2007. Ratings.Xls.
17) Daves, P. R., Ehrhardt, M. C. & Kunkel, R. A. 2000. Estimating Systematic Risk:
The Choice of Return Interval and Estimation Period Journal of Financial and
Strategic Decisions 13, 7 - 13.
18) Dichev, I. D. & Piotroski, J. D. 1999. The Performance of Long-Run Stock Returns
Following Issues of Public and Private Debt. Journal of Business Finance &
Accounting, 26, 1103-1132.
19) Drobetz, W. & Wanzenried, G. 2006. What Determines the Speed of Adjustment to
the Target Capital Structure? Applied Financial Economics, 16, 941-941.
72
20) Eckbo, B. E. 1986. Valuation Effects of Corporate Debt Offerings. Journal of
Financial Economics, 15, 119-151.
21) Efiong, A. & Crispin, A. 2007. Wind Turbine Manufacturers; Here Comes Pricing
Power. Merrill Lynch.
22) Eldomiaty, T. I. 2002. Dynamics of Firm’s Market Value, Capital Structure and
Risk. New Zealand Finance Colloquium Archives.
23) Emerging Energy Research 2009. Global Wind Turbine Markets and Strategies
2009 - 2020. Emerging Energy Research.
24) Emerging Energy Research 2010. Wind Turbine Component Supply Chain
Strategies: 2010 - 2025. Cambridge, MA, USA: Emerging Energy Research.
25) European Wind Energy Association 2009. Wind Energy, the Facts. Brussels:
European Wind Energy Association.
26) European Wind Energy Association 2010. Wind Energy Factsheets. Brussels:
European Wind Energy Association.
27) Financial Times and Harris Interactive 2010. Energy Sources Poll. New York:
Financial Times/Harris Interactive.
28) First Solar 2010. First Solar Corporate Overview 2010. First Solar, Inc.
29) Fischer, E. O., Heinkel, R. & Zechner, J. 1989. Dynamic Capital Structure Choice:
Theory and Tests. The Journal of Finance, 44, 19-40.
30) Flannery, M. J. & Rangan, K. P. 2006. Partial Adjustment toward Target Capital
Structures. Journal of Financial Economics, 79, 469-506.
31) Frank, M. Z. & Goyal, V. K. 2003. Testing the Pecking Order Theory of Capital
Structure. Journal of Financial Economics, 67, 217-248.
32) Frank, M. Z. & Goyal, V. K. 2007. Trade-Off and Pecking Order Theories of Debt.
SSRN eLibrary. SSRN.
33) Glader, P. 2010. G.E. Leads U.S. Wind Market but Faces More Competition.
Available:
http://online.wsj.com/article/SB100014240527023037206045751705003392446
26.html [Accessed 27 January 2011].
34) Global Wind Energy Council 2009. Global Wind 2009 Report. Brussels: Global
Wind Energy Council.
35) Global Wind Energy Council 2010. Global Wind Energy Outlook 2010. Brussels:
Global Wind Energy Council.
36) Goldstein, R., Ju, N. & Leland, H. 2010. An Ebit-Based Model of Dynamic Capital
Structure, Forthcoming.
37) Graham, J. R. 1996a. Debt and the Marginal Tax Rate. Journal of Financial
Economics, 41, 41-74.
38) Graham, J. R. 1996b. Proxies for the Corporate Marginal Tax Rate. Journal of
Financial Economics, 42, 187-222.
39) Graham, J. R. 2000. How Big Are the Tax Benefits of Debt? Journal of Finance,
55, 1901-1941.
40) Graham, J. R. 2003. Taxes and Corporate Finance: A Review. The Review of
Financial Studies, 16, 1075-1129.
41) Graham, J. R. & Lemmon, M. L. 1998. Measuring Corporate Tax Rates and Tax
Incentives: A New Approach. Journal of Applied Corporate Finance, 11, 54-65.
42) Harris, M. & Raviv, A. 1990. Capital Structure and the Informational Role of Debt.
The Journal of Finance, 45, 321-349.
43) Harris, M. & Raviv, A. 1991. The Theory of Capital Structure. The Journal of
Finance, 46, 297-356.
73
44) Heaton, J. B. 2002. Managerial Optimism and Corporate Finance. Financial
Management, 31, 33-45.
45) Hennessy, C. A. & Whited, T. M. 2005. Debt Dynamics. The Journal of Finance,
60, 1129-1165.
46) Huang, R. & Ritter, J. R. 2009. Testing Theories of Capital Structure and
Estimating the Speed of Adjustment. Journal of Financial and Quantitative
Analysis, 44, 237-271.
47) Jalilvand, A. & Harris, R. S. 1984. Corporate Behavior in Adjusting to Capital
Structure and Dividend Targets: An Econometric Study. THE JOURNAL OF
FINANCE, 39, 127-146.
48) Jensen, M. C. 1986. Agency Costs of Free Cash Flow, Corporate Finance, and
Takeovers. American Economic Review, 76, 323-329.
49) Junfeng, L., Pengfei, S. & Hu, G. 2010. China Wind Power Outlook 2010. Chinese
Renewable Energy Association and Global Wind Energy Council.
50) Kane, A., Marcus, A. J. & Mcdonald, R. L. 1984. How Big Is the Tax Advantage to
Debt? The Journal of Finance, 39, 841-853.
51) Kayhan, A. & Titman, S. 2007. Firms' Histories and Their Capital Structures.
Journal of Financial Economics, 83, 1-32.
52) Kenitzer, S. 2007. The Power of Multiples: Connecting Wind Farms Can Make a
More Reliable - and Cheaper - Power Source [Online]. American
Meteorological
Society.
Available:
http://www.eurekalert.org/pub_releases/2007-11/ams-tpo112107.php [Accessed
19 November 2010 2010].
53) Kim, W. S. & Sorensen, E. H. 1986. Evidence on the Impact of the Agency Costs of
Debt on Corporate Debt Policy. Journal of Financial and Quantitative Analysis,
21, 131-144.
54) Koller, T., Goedhart, M. & Wessels, D. 2005. Valuation, John Wiley & Sons.
55) Kraus, A. & Litzenberger, R. H. 1973. A State-Preference Model of Optimal
Financial Leverage. The Journal of Finance, 28, 911-922.
56) Leland, H. E. & Pyle, D. H. 1977. Informational Asymmetries, Financial Structure,
and Financial Intermediation. Journal of Finance, 32, 371-388.
57) Liu, L. X. 2009. Historical Market-to-Book in a Partial Adjustment Model of
Leverage. Journal of Corporate Finance, 15, 602-612.
58) Lopez, M. R. 2009. Global Market Share in Wind Turbine Manufacturers Unveiled
[Online]. Available: http://www.ecoseed.org/en/wind-energy/article/8-windenergy/833-global-market-share-in-wind-turbine-manufacturers-unveiled
[Accessed 19 November 2010].
59) Loughran, T. & Ritter, J. R. 1995. The New Issues Puzzle. Journal of Finance, 50,
23-51.
60) Miller Merton, H. 1977. Debt and Taxes. THE JOURNAL OF FINANCE, 32, 261261.
61) Miller, M. H. 1977. Debt and Taxes. The Journal of Finance, 32, 261-275.
62) Modigliani, F. & Miller, M. H. 1958. The Cost of Capital, Corporation Finance and
the Theory of Investment. American Economic Review, 48, 261-298.
63) Modigliani, F. & Miller, M. H. 1959. The Cost of Capital, Corporation Finance, and
the Theory of Investment: Reply. American Economic Review, 49, 655-670.
64) Modigliani, F. & Miller, M. H. 1963. Corporate Income Taxes and the Cost of
Capital: A Correction. American Economic Review, 53, 433-444.
65) Muradoglu, G. & Sivaprasad, S. 2006. Capital Structure and Firm Value: An
Empirical Analysis of Abnormal Returns. DocStoc eLibrary.
74
66) Myers, S. C. 1977. Determinants of Corporate Borrowing. Journal of Financial
Economics, 5, 147-175.
67) Myers, S. C. 1984. The Capital Structure Puzzle. The Journal of Finance, 39, 575.
68) Myers, S. C. & Majluf, N. S. 1984. Corporate Financing and Investment Decisions
When Firms Have Information That Investors Do Not Have. Journal of
Financial Economics, 13, 187-221.
69) Office for National Statistics. 2010. Domestic Energy Consumption Per Household:
By
Final
Use
[Online].
Available:
http://www.statistics.gov.uk/STATBASE/ssdataset.asp?vlnk=7287 [Accessed 19
November 2010].
70) Pinegar, M. J. & Lease, R. C. 1986. The Impact of Preferred-for-Common
Exchange Offers on Firm Value. THE JOURNAL OF FINANCE, 41, 795-815.
71) Plesko, G. A. 2003. An Evaluation of Alternative Measures of Corporate Tax Rates.
Journal of Accounting and Economics, 35, 201-226.
72) Quilter, J. 2010a. Horns Rev I Offshore Wind Farm Is Shut Down. Available:
http://www.windpowermonthly.com/news/1007703/Horns-Rev-I-offshore-windfarm-shut-down/ [Accessed 6 December 2010].
73) Quilter, J. 2010b. Investigators Still Reviewing Horns Rev I Shutdown. Wind Power
Monthly
[Online].
Available:
http://www.windpowermonthly.com/news/1010108/Investigators-reviewingHorns-Rev-I-shutdown/ [Accessed 6 December 2010].
74) Rajan, R. G. & Zingales, L. 1995. What Do We Know About Capital Structure?
Some Evidence from International Data. The Journal of Finance, 50, 14211460.
75) Renewable Energy Policy Network for the 21st Century 2010. Renewables 2010 Global Status Report. Paris: Renewable Energy Policy Network for the 21st
Century.
76) Renewable Uk 2010. Wind Energy Generation Costs. London: Renewable UK.
77) Rose, C. 2010. Ditlev Engel on Charlie Rose. New York.
78) Sabiwalsky, R. 2010. Nonlinear Modelling of Target Leverage with Latent
Determinant Variables - New Evidence on the Trade-Off Theory. Review of
Financial Economics, 19, 137-150.
79) Schmitz, S. 2010. Wind Turbine Supply: Comparing Us & Eu Markets.
Infrastructure Journal.
80) Shyam-Sunder, L. & Myers, S. C. 1999. Testing Static Tradeoff against Pecking
Order Models of Capital Structure. Journal of Financial Economics, 51, 219244.
81) Sjoo, B. 1996. Dynamic Adjustment and Long-Run Economic Stability. Economic
Modelling, 13, 427-463.
82) Sovacool, B. K. 2009. Contextualizing Avian Mortality: A Preliminary Appraisal of
Bird and Bat Fatalities from Wind, Fossil-Fuel, and Nuclear Electricity. Energy
Policy, 37, 2241-2248.
83) Spiess, D. K. & Affleck-Graves, J. 1995. Underperformance in Long-Run Stock
Returns Following Seasoned Equity Offerings. Journal of Financial Economics,
38, 243-268.
84) Stiglitz, J. E. 1969. A Re-Examination of the Modigliani-Miller Theorem. American
Economic Review, 59, 784-794.
85) Stiglitz, J. E. 1973. Taxation, Corporate Financial Policy, and the Cost of Capital.
Journal of Public Economics, 2, 1-34.
75
86) Talberg, M., Winge, C., Frydenberg, S. & Westgaard, S. 2008. Capital Structure
across Industries. International Journal of the Economics of Business, 15, 181200.
87) The Financial Forecast Centre 2010a. Crude Oil Price Forecast. In: Crude Oil Prices
- Past Trend, P. V. F. P. (ed.).
88) The Financial Forecast Centre 2010b. Usd to Eur Forecast. In: Us Dollar to Euro
Currency Exchange Rate - Past Trend, P. V. F. P. (ed.).
89) The Wind Power 2010. Offshore Wind Farms. www.thewindpower.net.
90) Titman, S. & Wessels, R. 1988. The Determinants of Capital Structure Choice. The
Journal of Finance, 43, 1-19.
91) United Nations Environment Programme & Bloomberg New Energy Finance 2010.
Global Trends in Sustainable Energy Investment 2010. Paris: United Nations
Environment Programme.
92) Vestas. 2007. It All Starts with a Loyal Supplier. Available:
http://www.vestas.com/en/media/article-display.aspx?action=3&NewsID=1706
[Accessed 5 December 2010].
93) Vestas 2009. Annual Report 2009. Randers: Vestas Wind Systems A/S.
94) Vestas 2010a. Interim Financial Report, First Quarter 2010. Randers: Vestas.
95) Vestas 2010b. Interim Financial Report, Second Quarter 2010. Randers: Vestas.
96) Vestas Company Announcement. 2008. V90-3.0 Mw Offshore Wind Turbine Back
on the Market Again [Online]. Randers: Vestas Wind Systems A/S Available:
http://www.vestas.com/files//Filer/EN/Investor/Company_announcements/2008/
080218-MFKUK-11.pdf [Accessed 6 December 2010].
97) Warner, J. B. 1977. Bankruptcy Costs: Some Evidence. Journal of Finance, 32,
337-347.
98) Windfacts Table 3.1: Design Choices of Leading Manufacturers.
99) Wiser, R. & Bolinger, M. 2010. 2009 Wind Technologies Market Report. US
Department of Energy.
100) World Wind Energy Association 2009. World Wind Energy Report 2009.
101) Zwiebel, J. 1996. Dynamic Capital Structure under Managerial Entrenchment. The
American Economic Review, 86, 1197-1215.
76
Annexes
A1. Market Definition, Size and Growth
A1.1. Market Definition
Vestas sums up its company vision in three words: “Wind, oil and gas”. They
see themselves on a par with all other energy producers, and thus, they see themselves
as part of the global energy industry. However, that is a vision for the future.
On a general level, Vestas is part of the energy industry. However, at present,
the wind power industry itself is still in its infancy and therefore exhibits completely
different characteristics, when compared to the other, more mature energy industries,
like coal and gas energy or nuclear power. The set-up costs for producing energy from
wind are quite high. Therefore, a characteristic of the wind energy market is that it is
highly subsidized. This way, industry incumbents are provided with an incentive to
invest in and develop wind energy parks. Moreover, subsidies allow them to compete in
terms of prices charged for the energy produced with producers that use fossil fuels. In
time, however, after breaking even with the setting up costs, wind energy is cheap.
Therefore, cost efficiency considerations related to the fact that using wind is free,
should also be taken into account. Another characteristic is industry growth at an
incredible pace, in contrast to conventional energy industries, which show only slightly
increasing growth rates, at best. Finally, environmental considerations are probably
the most obvious characteristics separating the wind industry from the established
energy industries, since wind is a clean energy source.
Given the differences between the industries, it would be inappropriate to place
Vestas in the big picture they see themselves in, mainly because they are not competing
with the large, established energy producers at the moment. Therefore, we can infer that
they are operating in the wind energy industry and not the larger and all-encompassing
energy industry. Moreover, they are not in the business of producing energy themselves,
but producing the tools used for making energy and then selling them. Hence, the
industry Vestas is directly competing in can be further limited to the wind turbine
manufacturing industry.
77
One way to define the wind turbine manufacturing industry could be as “the
industry dealing with the research and development, manufacture, construction, sale,
and maintenance of wind turbines for residential, commercial or industrial purposes”.
A1.2. Market Size
The first large scale, electricity-producing windmill was built in 1888 in
Cleveland, Ohio, but the wind turbine industry started off almost one century later, in
the 70s. The main motivation for the boom was most likely to have been the oil crises
which resulted from the Arab Oil Embargo in 1973 and the drop in Iran’s oil production
in 1979. Both made oil prices soar and hence, increased focus was targeted towards
finding alternative energy solutions. Danish manufacturers pioneered the industry.
In terms of the market size, it will not be restricted to a certain country or
region, since Vestas competes neither nationally, nor within a certain geographic area,
but worldwide. It ships its products mainly in Europe, but also exports in America and
Asia. Its three greatest competitors (based on market shares) are from the US, China and
Germany.
Over the years, the industry has grown considerably, with record double-digit
growth rates sometimes going close to 50% per year, as was the case in 2008-2009. The
evolution over the past years is portrayed in figures 1 and 2 presented below. The
graphs depict the annual and respectively cumulative installed capacity, both worldwide
and for Vestas. At the end of 2009, cumulative market size had reached a total of
158,505 installed MW worldwide30.
Figure A2
Figure A1
Source: Data from Global Wind Energy Council, (2009)
30
Source: Data from Global Wind Energy Council, (2009)
See 34) Global Wind Energy Council 2009. Global Wind 2009 Report. Brussels: Global
Wind Energy Council. p. 12.
78
Current industry size can be expressed in a series of key figures, as presented
in Table A1 below. The measurements are all from the end of 2009.
Table A1: Industry size indicators
Market Size Indicator
Installed capacity
Energy production from installed capacity
World consumption covered by wind power
Market worth for turbine installations
Number of employees worldwide
Estimate
158,505 MW
163 TWh31
2%32
EUR 45 bn33
627,92734
A1.3. From Present to Future - Market Growth
2009 was a record year, with cumulative worldwide capacity growing more
than ever, by as much as 32%. The global concern for a sustainable future fuelled
growth in the industry. As Figure A3 shows, growth trends and strategies were specific
to geographic areas. In the more established geographical markets (i.e. Europe), the
tendency was towards relocating to cheaper production sites, in order to meet the
production demands of an industry on its way to globalisation. The younger markets
like Asia and USA started ramping up harder to close the gap between them and
Europe. There were also major markets with huge potential (i.e. Canada, Brazil)
entering the initial build-up stage in full swing.
Figure A3
Source: Emerging Energy Research, (2009)
31
See 34) Ibid., p. 13.
32
See 100) World Wind Energy Association 2009. World Wind Energy Report 2009., p. 5.
33
See 34) Global Wind Energy Council 2009. Global Wind 2009 Report. Brussels: Global
Wind Energy Council., p. 8.
34
See 35) Global Wind Energy Council 2010. Global Wind Energy Outlook 2010. Brussels:
Global Wind Energy Council., p. 11.
79
At the end of 2009, the financial crisis struck the wind energy industry as well,
having a delayed effect. Even though the same overall increasing trends in Figure A3
apply, growth has slowed down. The crisis brought about lower prices for coal and gas,
which made wind too expensive and too risky in comparison. Moreover, difficulties in
raising finance and the considerable increase in equity requirements for starting wind
farm projects seriously affected industry growth. The failure of the 2009 Copenhagen
Climate Conference participants to come to an agreement regarding CO2 prices and
target requirements that would act as successors to the Kyoto Protocol further decayed
the chances that growth rates would remain at the same level as the high historical ones.
GWEC estimated the level of annual installed capacity at the end of the year to be only
6.41% higher than 2009, incomparable to the 47.31% registered growth from 2008 to
2009.
The market is currently facing difficult times. 2010 has also been a challenging
year, with the crisis still taking its toll. GWEC (Global Wind Energy Council, (2010))
estimates that the growth in installed capacity in 2010 will range from 26,753 MW in
the most pessimistic but most likely scenario, to 43,152 MW in the most optimistic and
least likely one. If we compare the figure to the 38,343 MW growth registered in 2009
we realize how sensitive the industry is to changes in the economy.
As for the future, industry growth rates are expected to increase in the next 5
years, but at a slower pace, as quantified by GWEC35. The evolution is depicted in the
figure to the right. We can clearly see that market trends are ascending. However,
growth rates are below the record levels of
before the financial crisis. Annual installed
Figure A4
capacity growth for 2010 is only expected
to amount to 6.6%, considerably less if
compared to 41.3% in 2009.
Annual
installed capacity will slowly rise with
each year, while the growth of cumulative
installed capacity is characterized by a
decreasing trend. Comparisons between
Source: Global Wind Energy Council, (2009)
35
See 34) Global Wind Energy Council 2009. Global Wind 2009 Report. Brussels: Global
Wind Energy Council., p. 17.
80
Figure A5
Figure A6
Source: Data from Global Wind Energy Council, (2009)
Source: Data from Global Wind Energy Council, (2009)
the trends and growth rate trends are portrayed in Figures A5 and A6.
Even though the market seems to be slowing down at present, market potential
is enormous and there would be room for meteoric growth. Archer and Jacobson, (2005)
estimate that even if only approx. 20% of the world’s wind power would be captured, it
could satisfy 100% of the world’s energy demand for all purposes (6995–10177 MTOE)
and over seven times the world’s electricity needs (1.6–1.8 TW). The question is
whether industry and economic factors will allow producers to tap into this enormous
potential for development.
Figure A7
GWEC has estimated
the growth of the world’s
cumulative installed wind power
capacity under three different
scenarios. The time span is as
far out into the future as 2030
(see Figure A7). In the most
pessimistic
scenario
–
the
reference one – production of
energy
from
the
installed
capacity will cover 4.9 – 5.6%
Source: Global Wind Energy Council, (2010)
of the world’s total energy demand by 2030. Under the two more optimistic scenarios –
moderate and advanced – wind energy will cover 15 - 17.5% and 18.8 - 21.8%,
respectively. Production of windmills will not only cover installation of new windmills,
but also replacement of older and less efficient turbines that become obsolete.
81
A2. PESTEL Analysis
A2.1. Political and Legal Factors
Political issues have a high impact on the wind turbine industry. Governments
that adhered to the Kyoto Protocol have committed themselves to lowering greenhouse
gasses (GHG). As part of the package of measures that they have taken to achieve this,
they set mandatory renewable energy targets (RETs) for electricity retailers, who must
comply with the requirement of producing part of the supplied electricity from
renewable energy sources. Hence, RETs might be considered a driving force for the
turbine manufacturing industry because the higher the targets, the higher the demand for
turbines and the larger the wind industry - as a whole - becomes. At the beginning of
2010, 85 countries – out of which all 27 EU countries - have RETs. The target average
for EU is 20% by 202036. Currently, in the US and Canada, there are no national RETs,
but some of the states have imposed local targets.
83 countries worldwide37 also have power generation promotion policies,
such as feed-in tariffs, renewable portfolio standards, capital subsidies or grants,
investment tax credits, sales tax or VAT exemptions etc., which provide incentives for
the use of renewable energy sources.
Figure A8
A2.2. Economic Factors
When comparing the growth in installed
capacity
(both
annual
and
cumulative)
with
macroeconomic variables, we notice that the
evolution of the wind industry is not highly
correlated to the state of the economy. By examining
the graph to the right, we could conclude that there is
a
weak
correlation
to
the
macroeconomic
indicators presented in Table A2 on the next page. It
seems that the effects of the financial crisis have
been combated by political efforts and financial
Source: Data from World Bank
36
st
See 75) Renewable Energy Policy Network for the 21 Century 2010. Renewables 2010 -
Global Status Report. Paris: Renewable Energy Policy Network for the 21 st Century., p.35.
37
See 75) Ibid., p. 37.
82
back-up that governments provided and by the fact that the worldwide green trend
makes the industry very attractive. In fact, the real story is somewhat different. There
was a rather delayed effect of the crisis. The protective regulatory measures were
efficient enough to minimize the effects of the economic slump, but not to eradicate
them. Growth rates have slowed down, but they are still extremely high compared to the
other indices.
.
Table A2: Capacity
Growth and Macroeconomic Variables
Year
Annual installed capacity growth
Cumulative capacity growth
Inflation, GDP deflator
USA real interest rate
UK real interest rate
GDP growth
2005
40,50%
24,09%
4,70%
2,80%
2,60%
3,60%
2006
32,21%
25,32%
5,50%
4,60%
1,80%
4%
2007
30,31%
26,72%
5,70%
5,10%
2,60%
3,90%
2008
31,03%
28,20%
8,40%
2,90%
1,60%
1,70%
2009
47,31%
31,76%
2,70%
1,50%
-0,70%
-1,90%
Source: Data from World Bank
In the beginning of 2009, at the height of the financial crisis, the wind turbine
industry was still registering record growth rates. However, things took a different turn
and, by the end of 2010, it became clear that the industry would not reach projections
for the year.
Apart from the classical effects which were affecting all industries (low credit
availability, high interest rates, collapse of equity markets), the crisis impacted on
the industry in more specific ways as well. The average debt versus equity mix
changed from 10% required equity to 25-30%38. Coupled with the impossibility to
access funds via equity markets, it made matters worse. Moreover, in USA, the number
of investors shrank to almost none. The loss of “tax equity” investors – usually large
companies who were investing in renewable energy projects for the sake of tax
advantages – had considerable impact on the industry across the Atlantic Ocean.
Because of the crisis, a large number of investing companies recorded losses and hence,
tax shields became useless. With the loss of tax advantages, investment in the wind
industry rapidly declined.
Combating measures were taken to insure that investment in the industry will
not come to a halt. Governments worldwide committed a total of EUR 139 billion as
38
See 25) European Wind Energy Association 2009. Wind Energy, the Facts. Brussels:
European Wind Energy Association., „Project Financing – Traditional Methods” section
83
‘green stimulus’39, consisting of grants, cash for research and development,
contribution to grid developments and financial assistance for projects. More and better
support policies were taken on. Institutional investors and companies with strong
balance sheets which had available funds continued to invest in the industry, breaching
the financing gap. As a result, by the end of 2009, the industry had bounced back.
Overall, the drop in total investments was only 6% less than the previous year40.
Another economic factor that influences the industry is the price of other
energy sources. The economic crisis brought about even lower prices for oil and fossil
fuels, which made electricity producers continue to use traditional energy plants and
think twice before investing in more expensive and riskier wind power projects.
A2.3. Socio-cultural Factors
The green tend is in full swing. Concepts like “sustainability”, “environmental
awareness” and “eco-friendly” are on everyone’s lips, from average citizens, to
companies, to governments. And this socio-cultural trend has seeped into every area of
life. The mundane is changing and examples of that fact are abundant. People are
driving more hybrid cars and living in eco-friendly houses. As a result of signing the
Kyoto Protocol, companies started trading their CO2 emissions. The Dow Jones
Sustainability Indexes were launched in 1999 to track the most sustainable companies
worldwide. And that is not even the tip of the iceberg. The bottom line is that everyone
is involved. People are concerned about the future of the planet and there is a general
appeal for action to be taken.
Wind energy is reaping a lot of benefits from this “green trend” because it has
a lot to offer in return. That is precisely why it has become widely popular throughout
society, and its popularity ratings are on the rise. To give just a few examples, a poll
conducted by the Financial Times and Harris Interactive, (2010) in USA and 5 other
European countries find an overwhelmingly large number of respondents favoring wind
energy. In the US, 87% of respondents favour a large increase in the number of wind
farms (with 50% being strongly in favour of the matter). The results for the other
39
See 91) United Nations Environment Programme & Bloomberg New Energy Finance 2010.
Global Trends in Sustainable Energy Investment 2010. Paris: United Nations Environment Programme.,
p. 5.
40
See 34) Global Wind Energy Council 2009. Global Wind 2009 Report. Brussels: Global
Wind Energy Council., p. 6.
84
countries are: Great Britain – 82% (38%), France – 77% (33%), Italy – 87% (49%),
Spain - 90% (53%), and Germany – 82% (40%).
A2.4. Technological Factors
Competition within the industry is a driving force of turbine technology
improvements and product development. Companies have kept the established threeblade, gear-driven Danish design, but the size of the turbines and their capacity has
increased considerably. The industry standard is now the 1.5 MW turbine, but the race
for bigger and more powerful turbines is fierce. In 2007, Enercon installed the world’s
largest turbine – E112 - which totaled 6 MW. In 2008, the company broke its own
record and erected the E126 model. The latter had a nameplate capacity of 6 MW, but
technical revisions showed it can produce more than 7 MW. In the beginning of 2010,
the Norwegian company Enova announced they are developing an offshore 10 MW
turbine, which will be able to produce enough electricity to supply 3845 average UK
homes41.
In order to cope with the increase in capacity and furthermore, to permit the
large-scale integration of wind energy sources, an improvement in transmission capacity
needs to take place. The US is investing heavily in the development of a smart grid,
which makes use of digital technology and involves a two-way communication between
the retailer and the end user. This way, the retailer can better control energy savings.
Another development is the super grid, which refers either to large
international electrical grids, or grids of superior performance. Kenitzer, (2007) argues
that if wind farms are connected to each other and to the main international transmission
grid, differences in wind speed at various locations can be smoothed out and there will
be a more or less constant supply from wind power to the electrical network.
Unfortunately, access to electrical grids is one of the threats the industry is
fighting against. There are numerous remote areas that have good wind but are too far
from the electrical grids to merit investment at the moment. According to a factsheet of
the European Wind Energy Association, (2010), electricity infrastructure is ageing
rapidly and the EU needs to build 43% of the total capacity it currently has, just to
41
Domestic
Data valid for 2001 consumption, retreived from 69) Office for National Statistics. 2010.
Energy
Consumption
Per
Household:
By
Final
Use
[Online].
Available:
http://www.statistics.gov.uk/STATBASE/ssdataset.asp?vlnk=7287 [Accessed 19 November 2010].
85
replace the old plants and keep pace with the increasing demand. A need for a transEuropean power grid is becoming clearer and clearer. Onshore and offshore electrical
parks should be connected to national networks in order to prevent future power supply
shortages. In order to do that, power lines that are decades old need to be replaced by
new “electricity highways” that can cope with the increase in transmission capacity
and can connect new power sources to the upgraded European grid.
A2.5. Environmental Factors
Apart from issues such as visual intrusion and noise, which can be solved by
building more offshore than onshore farms, it has also been argued that wind parks
cause damage to the ecosystems they are built in. Onshore parks cause damage to birds
and bats while offshore ones, to fish and other marine wildlife. However, Sovacool,
(2009) has looked into the matter of avian mortality and has found that wind farms are
responsible for only 0.3 – 0.4 fatalities per GWh. Fossil fuels, on the other hand are
found to be much more harmful, causing as much as 5.2 fatalities per GWh.
To put the mind of those who uphold such arguments at ease, park developers
such as Dong Energy for example, have complied to undergoing environmental
monitoring programs to make sure no damage is done to wildlife.
A2.6. General Degree of Turbulence in the Industry Environment
Figure A9 depicts the
relative power of each of the
Figure A9: Degree of
Turbulence
factors
mentioned
in
the
in
the
Industry
PESTEL analysis. As it points
out, the industry is shaped by
two
primary
factors
with
opposite effects: political and
legal on the one hand and
economic
on
the
other.
Positive measures imposed by
political rule are employed in
order to compensate for the
mostly negative effects of the
Source: collected information
economic crisis.
86
The imprint of socio-cultural factors is less visible. These factors might be the
underlying explanation as to why the industry took off in the first place, but in the
present state, they are of less significance and no major shifts which might affect the
industry are expected to stem from these factors. Therefore, they will cause little
disturbance to the current state of things.
The technological factors represent a threat that the industry is currently
dealing with, mainly in terms of grid connectivity. These factors cause medium
disturbances in the industry because they pose a menace to the development of wind
parks.
The last category – environmental factors - mainly comprises criticism towards
the industry. Fear of damage to the environment has been alleviated by recent research,
while solutions for the other environmental threats – noise and visual intrusion – can be
minimized through building offshore wind parks. Therefore, the category in discussion
only imposes a minor print on the shape of the industry, as skeptics will always exist.
A3. Competitor Analysis
At the moment, there are a
Figure A10
total of 52 wind turbine manufacturers
worldwide. Competition within the
industry is quite fierce, with the top 10
companies having 78.7% market share
and each being very close to the other
in terms of market share, as the figure
to the right depicts, in rounded figures.
Vestas
is
the
world’s
leading
Source: Data from Acher, (2010)
manufacturer, with a 12.5% share, surpassing the second runner up - GE Wind Energy by only 0.1%. The most considerable ascension of 2009 was the growth of the Chinese
manufacturers, 3 of which made it in the top 10 for the first time.
A3.1. The top 4
The Danish manufacturer Vestas is at the helm of the industry and has been the
long-standing leader. However, Vestas has seen its market share decay over the past
years because of increased competitiveness. Vestas, unlike all the other manufacturers,
is not relying on its home market to fuel company growth and is exporting almost all of
87
its products. In 2009, Vestas delivered 57 MW in Denmark, compared to a total of
4,707 outside of their home country. With 98.85% out of total delivered MW exported
worldwide, Vestas is facing local competition on all of the markets it is shipping its
turbines to.
The second runner-up is the US-based GE Wind Energy, with a total share of
12.4%. The company started off under the name of “Zond” in 1980 and was
subsequently acquired by Enron and then by GE during Enron’s bankruptcy
proceedings. It is currently the market leader in the US, which is the country with the
highest cumulative amount of wind production in the world, as of the end of 200942.
Therefore, it represents a serious threat for Vestas in the race for industry leadership.
The Chinese company Sinovel comes in third, with 9.2% of the market. The
boom of the Chinese suppliers – lead by Sinovel - is truly impressive, China being the
country with the highest amount of capacity installed in 200912. The major part of it was
manufactured and erected by Sinovel (3,510 MW43 out of 26,010 MW12).
The fourth-largest market share worldwide is held by the German Enercon.
The company also holds the largest market share of their home market – currently third
in the world based on cumulative installed capacity12. Enercon, set up in 1984,
differentiates itself through the use of a more efficient direct drive or gearless wind
turbine, combined with an annular generator. Most other wind turbines use a gearbox.
The advantages of Enercon’s technology include improved controllability.
A3.2. Competition trends
Table A3 on the following page shows the two competition trends which are
currently shaping the industry: the shift from oligopolistic towards monopolistic
competition and the increasing importance of the Chinese manufacturers.
42
See 100) World Wind Energy Association 2009. World Wind Energy Report 2009., p. 16.
43
According to the company website: http://www.sinovel.com/Companyoverview2.html.
88
Table A3: Competition
Company
Vestas (Denmark)
GE Wind Energy (United States)
Sinovel (China)
Enercon (Germany)
Goldwind (China)
Gamesa (Spain)
Dongfang (China)
Suzlon (India)
Siemens Wind Power (Denmark/Germany)
Repower (Germany)
Acciona Windpower (Spain)
Nordex
2009
12.5%
12.4%
9.2%
8.5%
7.2%
6.7%
6.5%
6.4%
5.9%
3.4%
-
200844
19%
18%
5%
9%
4%
11%
4%
7%
7%
3%
4%
4%
200745
22.8%
16.6%
3.4%
14%
4.2%
15.4%
10.5%
7.1%
4.4%
3.4%
2009 rank
1
2
3
4
5
6
7
8
9
10
-
2008 rank15
1
2
9
4
8
3
11
5
6
12
7
10
2007 rank16
1
2
10
4
8
3
5
6
7
9
A3.2.1. The trend towards monopolistic competition
The four-firm concentration ratio for the year 2009 totals 42.6% and shows that
the industry is at the border between oligopolistic and monopolistic. In comparison, the
ratios for 2008 and 2007 are 57% and 68.8%, respectively. Vestas, who had the lion’s
share in 2007, decreased by as much as 10.3% in just 2 years and is seriously threatened
by GE. Clearly, the industry structure is shifting because of competition forces at play.
As the number of players on the market will continue to increase, competition will
evolve into a monopolistic one.
An effect of this shift is the fact that the market has transformed from a seller’s
to a buyer’s market. Prices are now starting to follow the law of demand and the
industry is becoming more and more globalised. The target of governments worldwide
is to turn wind power into a commodity, and not a resource out of the reach of less
developed countries.
A3.2.2. The Chinese Wave
As we can observe from Table A3, in 2007, there were only 2 Chinese
companies in the top 10, and both of them are in the last quarter of the classification.
One of the companies was Sinovel, who was on the last position of the list with 3.4%
and the other was Goldwind, at number 8. In just two year’s time, Sinovel surpassed its
co-national rival and entered the top 3, taking the bronze medal with 9.2% market share.
44
See 58) Lopez, M. R. 2009. Global Market Share in Wind Turbine Manufacturers Unveiled
[Online]. Available: http://www.ecoseed.org/en/wind-energy/article/8-wind-energy/833-global-marketshare-in-wind-turbine-manufacturers-unveiled [Accessed 19 November 2010].
45
See 98) Windfacts Table 3.1: Design Choices of Leading Manufacturers.
89
By 2009, Goldwind also dropped to number 5 and, interestingly enough, one additional
company made its way into the top: Dongfang, currently on the 7th position.
Overall, China is the country with the highest market share (22.9%), followed
by Germany (17.8%, if the sales of multi-national Siemens are included). In 2009,
China has increased its cumulative capacity by more than 100% 46. Junfeng et al., (2010)
found that domestic turbine manufacturers now cover 70% of the market in China and
are beginning to look more and more towards export markets.
The underlying boost for this growth was given by the commitment made at
the Copenhagen Climate Conference by the Chinese government to supply as much as
15% of the country’s electricity demand through renewable energy. This requires an
unparalleled addition of capacity. Junfeng et al., (2010) produce three scenarios stating
that wind energy capacity will reach 150 GW, 200 GW or 230 GW by 2020. Given that
China has a total of 25.8 GW currently installed, the increase to the projected levels
would truly be unprecedented, even in the case that the pessimistic scenario comes true.
A4. Porter’s 5 Forces Model
A4.1. Bargaining Power of Buyers
The main buyers in the industry are large independent power providers (IPPs)
and utilities companies, according to a Merrill Lynch report (Efiong and Crispin,
(2007)).
The number of turbine buyers is still relatively small and purchases are in large
quantities. However, given the growing interest in wind energy, the number of buyers
is increasing rapidly worldwide. Therefore, one of the shifts that the industry is
currently going through is the transformation from a seller’s to a buyer’s market. As a
result, individual buyers have increasingly less power, but taken together, buyers can
exert more pressure for lowering prices. On the whole, their bargaining power is low to
medium.
It is worth mentioning that the trend is actually driven by political will, rather
than an actual interest in supplying energy from wind. The enlargement of the buyer
base is dictated by the degree of government support. For example, there is a higher
demand of turbines in Germany than in Russia, Argentina or South Africa (Global Wind
46
See 49) Junfeng, L., Pengfei, S. & Hu, G. 2010. China Wind Power Outlook 2010. Chinese
Renewable Energy Association and Global Wind Energy Council.
90
Energy Council, (2010)), even though the latter countries have better wind resources.
The determining factor for higher demand is a favourable political climate. As
mentioned before, an increasing number of countries is setting targets and promoting
support policies. A beneficial policy landscape determines an increase in the
bargaining power of buyers.
The bargaining power is also influenced by the high switching costs involved.
These include installation, maintenance and later on, decommissioning and replacing
older turbines. Once a turbine manufacturer made a sale, the high switching costs lock
buyers down, therefore determining a low bargaining power.
The threat of backward integration should also be considered. From this
perspective, bargaining power is low. Most of the customers in the industry are electric
utilities companies and independent power producers. Integrating backwards and
acquiring turbine manufacturers is not a trend because it would involve very high
amounts of funds. The only case in the industry where backward integration took place
was GE, who acquired Zond in 1980 from Enron, during bankruptcy proceedings.
Therefore, the price of acquisition was under market value. Over the subsequent three
decades, there were no other cases of backward integration. Developing an in-house
turbine manufacturer is also not very plausible because of the major knowledge
requirements mainly in terms of design, and engineering.
On the whole, buyers presently have a low to medium bargaining power,
which is, however, expected to strengthen in the future.
A4.2. Bargaining Power of Suppliers
As Figure A11 on the next page shows, the supply chain is highly
fragmented because of the complex technical structure of the turbine. The first three
supply chain categories represent bottlenecks in the supply chain, depicted in the
figure by key pinch points. These are the suppliers with the highest bargaining power,
due to the high concentration of the supply of important parts to a few companies.
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Figure A11: Supply chain
Source: Emerging Energy Research, (2010)
However, the industry trend is towards vertical integration. Pressure is
exerted on the turbine manufacturers to simultaneously lower costs and develop better
turbines, which in turn exerts the same pressure on the suppliers. Hence, turbine
manufacturers find themselves dependent upon their key suppliers.
In 2006 and 2007, the industry was facing shortages in various components,
such as blades, generators and bearings (Wiser and Bolinger, (2010)). The industry was
severely affected because of this lack of production capacity. However, due to an
increasing demand, it is expected that the number of suppliers will steadily increase.
Integration trends are depicted in Figure A12 on the next page. Blades and
towers are being produced in-house more and more. For example, Vestas, Suzlon,
Nordex and Gamesa all have their own blade manufacturing facilities in China. As for
towers, any company located in countries with an existing steel industry will find it
convenient and cost-effective to produce domestically.
Generators and gearboxes are moving closer to the turbine manufacturers by
setting up regional manufacturing facilities, in order to reduce costs and time-todelivery. Wiser and Bolinger, (2010) find the same results for the American market,
where a higher number of equipment pieces are being produced domestically in recent
years.
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Figure A12: Component suppliers’ strategy overview
Source:Emerging Energy Research, (2010)
Given the characteristics of the supply chain, we can infer than suppliers have
a moderate to high bargaining power, but, given the vertical integration trends and the
expectation that there will be more suppliers in the future, their bargaining power is
expected to diminish.
A4.3. Threat of New Entrants
The threat of new entrants is dependent upon industry entry and exit barriers.
The most important barriers to entry in the wind turbine industry include capital
requirements, patents, know-how and economies of scale.
As one might suspect, capital requirements for
Figure A13: Cost breakdown
of a 2 MW turbine
establishing a manufacturing plant are huge, in the millions of
EUR and in double and even triple digit numbers. The
requirements for building wind farms are large as well. The
cost of a 2MW wind turbine installed in Europe in 2006 was as
much as to EUR 1,227. 76% of that cost is represented by the
turbine itself (see Figure A13). According to the European
Wind Energy Association, (2009), typical deal financing for a
wind farm project is 70-75% loan and 25-30% equity. In all, it
amounts to a high investment, making the industry less
accessible to new entrants.
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Source:
European
Wind
Energy Association, (2009)
If acquiring financing is deemed possible, the next barriers to overcome are to
acquire know how (since the industry is highly technical) and more importantly, to
make sure there are no infringements to existing patents. The best known case was
Enercon’s denial to export products to the US because of a patent that Kenetech
registered before Enercon could do so. Thus, Kenetech prohibited the German
company’s access to the market until 2010 (Schmitz, (2010)).
If a company manages to surpass the obstacles outlined above, it will still have
major difficulties competing with the more established producers, who benefit from
high economies of scale.
Barriers to exit are determined by the high costs involved and the relatively
long payback period. This might demotivate companies to enter the industry.
Despite numerous and generally high barriers, governments extensively use
policies in order to encourage investments in the industry. High demand for wind power
also attracts investors in the industry.
In conclusion, the fact that the industry is considered attractive, there is a
medium to high threat of new entrants, despite the medium to high entry and exit
barriers. It is expected to intensify.
A4.4. Threat of Substitute Products
Substitutes for wind power might
Figure A14: Carbon footprint of energy sources
include power from fossil fuels (gas, oil,
coal), nuclear fusion, as well as other
renewable sources (solar photo-voltaic
cells (PV), waves and rivers, biomass). The
question to address is whether they are true
substitutes.
By analyzing the economics of
Source: First Solar, (2010)
wind and comparing it with other sources, we can find some answers to the question
posed. Out of all energy sources, wind is the least harmful to the environment. Its
carbon footprint is exactly 75 times smaller than that of coal (see Figure A14). It is also
the most cost-effective. Onshore wind is the second least expensive power source, after
gas, in terms of capital outlay. Their slightly higher operating and maintenance costs are
offset by the fact that wind is free, unlike other fuels (see Table A4).
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Table A4: Costs used to derive energy
Technology
Gas
Coal
Nuclear
Onshore wind
Offshore wind
Capital cost
(EUR/kW)
770
1,955
2,370-3,555
1,540-1,896
2,963-4,266
O&M cost
(EUR/kW/yr)
O&M cost
(EUR/MWh)
4.7
8.3
10.6-13
Fuel cost (2009 avg, EUR/MWh)
16.6
8.9
7.7
63
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Source: figures in GBP from Renewable UK, (2010), converted to EUR at GBP 1.185/EUR
The cost curve has a downward slope with the speed of the wind, as shown in
Figure A15. The graph depicts two types of wind turbines: a GBP 1,300/kW or EUR
1,540 and a GBP 1,600/kW or EUR 1,893. If set up in areas with sufficient wind
(moderate to fresh breeze on the Beaufort scale), turbines can generate cheaper
electricity than coal and nuclear energy. Naturally, the bigger the turbine, the cheaper
the electricity becomes (Figure A16).
Figure A15: Generation costs of onshore wind and
other power sources
Figure A16: Generation costs of offshore wind
sources
Source: Renewable UK, (2010)
Source: Renewable UK, (2010)
However, the future evolution of prices is entirely another matter. Wiser and
Bolinger, (2010) reports that 2009 saw a sharp drop in the price of wholesale electricity,
largely driven by natural gas prices. The discovery of gas deposits lowered expectations
of an increase in the price of natural gas. On the wind turbine market, prices are starting
to ease, but they remain overall high by historical standards. Therefore, it is safe to say
that fossil fuels remain a very attractive substitute, cost-wise.
Governments have a decisive word to say with regards to the future. Pressure is
exerted on governments to further pave the way for renewables. Whether they will, and
to what extent, is going to determine how high the threat of substitute energy sources
will be.
In conclusion, the threat of substitute products is medium-to-high, but given
the advantages of wind power, it is expected to slowly decrease.
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A4.5. Competitive Rivalry within the Industry
As discussed in Annex A3 – Competitor Analysis, rivalry within the industry is
quite strong. Hence, we can label this force as having a high impact.
The fact that the industry is quite concentrated is an effect of the medium to
high entry barriers. What’s more, the industry is becoming more and more
consolidated. The big fish are eating the small fish in an attempt to improve market
share. In the US alone, 11 acquisitions were announced in 2007, 5 transactions in 2008
and 6 in 2009, according to Wiser and Bolinger, (2010).
Companies’ market share is determined more by how much they can
produce, rather than issues such as product or brand differentiation, product positioning
or company strategy. All the producers are continuously striving for technological
improvements that would allow them to erect higher and more powerful turbines.
Moreover, they are all relocating production facilities in cheaper counties (China, US
and Spain) in an attempt to keep costs as low as possible. Another thing they have in
common is that they mostly focus on their home markets (all except for Vestas, who is
exporting throughout the world). The fact that they are all following the same pattern
makes rivalry more intense. In business strategy terms, industry competition is turning
the ocean red.
Added pressure from increasing prices of raw materials (e.g. steel) and
components’ shortages (such as in 2008) intensifies rivalry.
Moreover, the large, established producers, such as Vestas and GE are
watching their market shares shrink because of fast growing companies, such as the
Chinese ones, which have newly entered in the top 10. However, apart from Vestas, all
the other companies are largely relying on their home market and exporting very little.
So far, very few direct conflicts have been registered (for example, the Kenetech Enercon patent dispute in the US which ended with preventing Enercon from entering
the American market).
Given all the issues briefly outlined above, competitive rivalry within the
industry is medium and the trend is towards intensifying.
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A5. Internal Analysis
A5.1. Corporate Vision, Mission and Strategy
Vestas’ strategy statements are as follows:
 vision – “Wind, oil and gas”;
 mission – “Failure is not an option”;
 strategy – “Number 1 in modern energy”;
 values / principles – “Cost of energy”, “Business case certainty”, “Easy
to work with”.
The company vision provides the target and long term focus of development.
Vestas strives to create a world in which wind is as important a power source as oil and
gas. They expect that by 2020, a total of 10% of the world’s energy supply will come
from wind47.
In a strategic management sense, the mission puts into words a shorter term
perspective. For Vestas - “Failure is not an option”, meaning that they will pave the way
for wind generated electricity, while improving company efficiency. They are
committed to a Triple15 target (an EBIT margin of 15% on a revenue of EUR 15bn by
2015), to which failure does not apply. If they grow, wind grows as well, so they aim to
see wind on the same level with the traditional energy sources, because they have not
only a business to defend, but a planet.
Vestas has proven that they are sticking to their vision and mission. Throughout
time, they have shown their resilience. Despite the financial crisis that brought despair
to many industries, Vestas managed to achieve record revenue and EBIT (revenue was
9.96% higher than the previous year, while EBIT was 28.14% higher). Another example
of their determination is the fact that they invested EUR 160m in building a tower plant
in Colorado, US. They proved to be committed to the US market, even though they did
not receive any order on the US market that year48.
The strategy of the company explains how they aim to accomplish their vision.
In this sense, Vestas wants to be “Number 1 in modern energy”, not only in terms of
market share, but also in terms of safety standards, performance of power plants,
47
48
See 93) Vestas 2009. Annual Report 2009. Randers: Vestas Wind Systems A/S., p. 15.
Information retrieved from Ditlev Engel interview: 77) Rose, C. 2010. Ditlev Engel on
Charlie Rose. New York.
97
customer satisfaction and green production. So far, they have managed to achieve that.
At the moment, they have the lion’s share of the worldwide market: 12.5%. In terms of
safety, the number of accidents in 2009 is 42.6% smaller than in 2008 and is at a record
low. When it comes to performance, Vestas is constantly undertaking measures of
prevention for power plant optimization through maintenance and monitoring of their
turbines. Customer satisfaction is measured yearly by Vestas through the customer
loyalty index, which went up to 64 in 2009 from 52 in the previous year. As for green
production, 49% of the energy they used in 2009 was renewable and 85% of their
electricity consumption was also from renewable sources, the latter being a record
achievement.
In conclusion, Vestas’ strategy statements are not just a marketing trick and the
company truly lives up to them and by them.
A5.2. Product and Service Mix
Vestas produces 9 types of onshore turbines ranging from 850 kW to 3 MW
and 2 types of offshore ones, both with a nameplate capacity of 3 MW. The company is
currently developing a 6MW offshore turbine, which will determine the cost of energy
to plummet, compared to any other energy sources. The company wants to increase the
share of offshore turbines it installed, which is currently only at 2%.
Throughout the year 2009, Vestas produced and shipped 3,320 wind turbines
totaling 6,131 MW in aggregate capacity, compared to 6,160 MW and 3,250 wind
turbines in 2008.
As for the company’s service mix, Vestas has the following areas of focus:
installation, maintenance and repair. The main support functions that enable it to serve
customers are the Performance & Diagnostics Centre and the Vestas Spare Parts &
Repair. The former is responsible for monitoring more that 15,500 turbines worldwide,
accounting for 26,600 MW, or 69% of Vestas’ total installed capacity. The latter
supports the company’s service units and is responsible for supplying spare parts and
repairs of key components worldwide. Vestas expects the same growth for the demand
of its services, as for its products.
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A5.3. Geographic & Business Segments
Vestas is operating in 3 geographic
Figure A17: Distribution of employees at the end
segments covering the entire world (Europe,
of 2009 by geographic and business segments
Americas and Asia/Pacific). The company has
production plants, sales and service units and R&D
functions in all of them.
As expected, Europe is the largest, both in
terms of revenue, and in terms of number of people
employed (see Figures A17 and A18). It accounts
for 68% of both its number of employees and its
revenue. The Americas hold 11% of the number of
employees and 21% of revenue, while Asia/Pacific
Source: Vestas, (2009)
Figure A18: Distribution of 2009 revenue by
geographic segments (mEUR)
21% and 11%, respectively.
Compared to the previous year, Vestas
had 0.5% less employees and there was a shift from
Europe to the Americas and Asia/Pacific, most
notably in the production units. As for revenue, the
distribution was 60% from Europe, 26% from the
Source: Vestas, (2009)
Americas and 14% from Asia/Pacific. This clearly
points out once again that at the moment, Europe is the driving force in the industry, as
well as the most powerful market, since it is also the oldest one.
Figure A19: Segment financials 2009
Figure A20: Segment financials 2008
Source: Vestas, (2009)
Source: Vestas, (2009)
The same conclusion is also drawn by looking at each segment’s financials
(Figures A19 and A20). Global revenues grew by 15%, Europe’s sales units accounting
for exactly 10% out of the 15% increase (two thirds). Americas and Asia/Pacific sales
units decreased, and the rest was contributed by the production facilities.
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Historically, Vestas has had more than a steady
Figure A21
revenue stream, and has always managed to improve its
yearly sales figures. It has had record growth in 2004,
registering a growth of 55%. At the other extreme is the
growth was from 2006 when they recorded a 7.6% rate.
Figure A21 depicts revenue evolution from 2002 to 2009.
A5.4. Business Model
Source: data from financial reports
Figure A22: The business model
Source: StrategyLab
A5.4.1. Infrastructure
Vestas’ core capabilities lie in the technology they use, their green production
process and their corporate culture, which is committed to the concept of “green”.
Their partner network includes suppliers, with which the company works
very closely, to the point that some of the employees are even located with the suppliers
and assist them in developing customised parts specific to the needs of Vestas. To some
extent, governments worldwide are also in the partner network, since politics are fueling
growth in the industry.
The value configuration of the company is a traditional, self-owned value
chain with more than 1,000 partners worldwide. The company is not outsourcing any of
their primary or support activities. They are only buying components from their
suppliers and are producing their own blades, nacelles and towers in their dedicated
production facilities. Distribution is also made through their own channels, serving each
of the main geographic segments.
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A5.4.2. Offer
What Vestas offers is a mix of one particular product and a bundle of support
services. The product is represented by an entire green energy system: the turbine. The
company does not separate the three main components (blades, nacelle and tower), but
sees them as a unitary system. The additional services offered are installation,
maintenance and repair.
A5.4.3. Customers
The target customers of Vestas are energy park developers or independent
power providers, therefore, large companies such as Dong. However, Vestas is also
servicing smaller companies with its more petite, but versatile 850 kW turbines: V52
and V60. Vestas reports that their revenue of 2009 came from 201 customers, 27 less
than the previous year49. The company also aims for larger customers in the future, as
mentioned in their last annual report.
The relationship with customers is characterized by direct contact. It is a
close, long-term relationship. Because of the fact that sales are made directly by Vestas,
who also provides service and support for the windmills, the company is always in
touch with its clients. Vestas is continually improving its customers’ satisfaction,
measured by the customer loyalty index. In 2009, it reached a record of 64.
In order to reach its customers, Vestas uses its own direct distribution
channels. It has set up sales departments in all three geographic segments in which it
operates. Europe brings in the largest amount of revenue, followed by the Americas and
Asia/Pacific.
A5.4.4. Finance
Cost structure is divided between raw materials and consumables, direct
labour, indirect expenses such as salaries, depreciation of production facilities, rental
and lease expenses, as well as provisions for losses. The total cost of sales for 2009
amounted to EUR 5,195m, but the cost breakdown is not reported by the company.
Revenue streams, as depicted in Figures A18 – A20 in Section A5.3., come
from three major geographical segments that span the world and in each, from
production and sales of turbines, as well as from services rendered to customers.
49
See 93) Vestas 2009. Annual Report 2009. Randers: Vestas Wind Systems A/S., p. 16.
101
Historically, the company has managed to increase its revenues yearly, as
depicted in Figure A21 in Section A5.3.
In conclusion, Vestas has a sound business model, which was tried and tested
for almost a quarter of a century and has overcome even the most difficult
developments, such as the financial crisis of recent years.
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A6. SWOT Analysis
The present SWOT analysis represents an overview of all the issues presented
in Annexes A1-A5.
A6.1. Strengths
Vestas’ strengths mainly fall within 3 large categories: company strategy,
finance and lastly, product and production.
A6.1.1. Company strategy
Vestas was the first major turbine manufacturer. Later on, Vestas became the
first turbine manufacturer to ever set up wind turbines offshore. The company installed
its first turbines off the shores of Sweden in 1990. The company has thus benefitted
from a first mover advantage. Vestas is still the company with the highest amount of
installed and operating MW, both worldwide and also offshore (1313.2 MW, rated by
nameplate capacity, compared to 1083.1 MW installed by Siemens, according to The
Wind Power, (2010)). Vestas has also supplied turbines for the largest offshore park in
the world – Thanet in Great Britain, a sign that they want to make the most of the
benefits of being a first mover.
The fact that Vestas is currently the world market leader is thus a result of the
first mover advantage. Clearly, this is one of the company’s biggest strengths, but that
does not protect them from the threat of competition. Even though their market shares
have been decreasing because of competition and other factors, they are committed to
remaining “No.1 in modern energy”, as they mention in their strategy statements.
In keeping with the trend of making wind energy more mainstream, another
opportunity might be the fact that Vestas is positioning itself as a „modern energy
producer”, not a „wind energy producer”. It shifted to another much larger and much
more profitable industry.
A6.1.2. Finance
The fact that they have always given customers a reliable product translated
into a steady financial growth over the years and continuously improved earnings.
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A6.1.3. Product and Production Process
The strong relationship with suppliers is another strength of the company.
Vestas realized the importance of a strong connection with the suppliers during the
supplies shortage, when the company’s bottom line turned red. Therefore, the company
has taken some measures to prevent repercussions like these from re-occurring in the
future. In autumn 2007, it has rolled out a Supplier Loyalty programme in which 110 of
Vestas’ biggest suppliers are part of (Press Release Vestas, (2007)). In recent years, it
has relocated some of its employees at its suppliers. Moreover, some key suppliers have
become more closely involved in the development process at Vestas, with the intention
to reduce costs and improve overall quality (Vestas, (2009)). Their in-house
manufacturing helps them keep costs under control, while still delivering leading
products.
One of the strengths of Vestas is the fact that it is in the forefront of
technological advances. Vestas has the largest R&D department, with offices in all of
their market segments (Europe, America and Asia). The R&D department is continually
making innovating improvements to existing products and bringing to market bigger
better windmills. At the moment, they are working on the 6 MW offshore turbine,
which is currently one of the segments where companies are competing fiercely in. No
other company has put a 6MW model on the market, but several companies have
announced coming models (Siemens, Gamesa and Enercom). Enercom is currently
testing the 6 MW E-126 onshore in Germany.
Diversity of employees might also be an advantage because it brings together
a lot of different ways of thinking. Innovation might be easier in a diverse
environment. They employ Lean in their production processes and are also aiming to
reach 6sigma by the end of 2010.
The company has a great deal of production flexibility. It operates
manufacturing plants in Denmark, Germany, India, Italy, Britain, Spain, Sweden,
Norway, Australia, China, and the United States.
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A6.2. Weaknesses
A6.2.1. Production process
On the weaknesses side, it is fair to mention the overall high costs of supply
parts and manufacturing that lead to a high price of the windmill. To these, even higher
grid connection and maintenance costs are added, especially for offshore parks.
A6.2.2. Company image
The pressure to reduce the overall high costs lead the company to close down
some of its production facilities and relocate to cheaper sights. They have closed
down operations in the UK (Isle of Wight) in July 2009, laying off a total of 624 people
on Isle of Wight and in Southampton. Protests from a group of ex-employees followed
soon after the announcement.
A6.2.3. Product weaknesses
Vestas has experienced various operational problems with its turbines, which
might shed unfavourable light on the quality of the turbines. For example, the V90
offshore turbine of 3 MW was withdrawn from the market for over one year due to
problems with its gearbox. It was re-released for offshore use on 1 May 2008 (Vestas
Company Announcement, (2008)). In June 2010, part of the largest offshore park in
Denmark, Horns Rev 1 was shut down due to a malfunction in its transformer station
(Quilter, (2010a)). Previously, the same park was affected by a design fault in the
towers, causing the turbines to slide (Quilter, (2010b)).
A6.3. Opportunities
A6.3.1. The Regulatory Environment
Opportunities encompass first of all the environmental trend, which is
supported through regulations, agreements and treaties.
The Kyoto Protocol is an international treaty signed in 1997, and which
entered into force in 2005. It currently binds a number of countries worldwide to targets
set for the reduction of greenhouse gasses. The Protocol was open for signing between
16 March 1998 and 15 March 1999 and totaled 84 signatory states that also ratified the
Protocol. Currently, the number is 194 (193 states and one regional economic
integration organization – the EU). Wind energy is one of the most vastly employed
105
solutions to help countries reach those targets, due to its advantages, discussed in Annex
3.4. Threat of Substitute Products.
Either in the attempt to reach targets they have committed to, or for other
reasons, governments are trying to boost this industry by employing many different
types of “green stimulus” etc. In the USA – currently the number one country by
installed capacity50 - the industry boom of 2009 was largely due to the American
Recovery and Reinvestment Act (ARRA), enacted by the Congress in February 2009.
The bill provides production or investment tax credits, as well as tax credits for new
manufacturing facilities. It also provides the framework for a USD 6 billion loan
guarantee program, which was particularly beneficial during the economic downturn. In
the second largest country by installed capacity20, China, a wind feed-in tariff system
helps increase the profitability of both offshore and onshore wind farms. The tariff
encourages developing larger wind parks by linking wind park size directly to the tariff
rate: the bigger the park, the more generous the rate. Other countries, currently not in
the top 10 of World Wind Energy Association, (2009), are ramping up and trying to
reach higher green energy levels. Australia passed a “20% by 2020” law in August 2009
and Japan set a national target to reduce greenhouse gasses by 25% until 2020.
Moreover, there are other political measures that are not directly connected to
the wind industry, but that might positively affect the industry. For example, in July
2008, the EU Parliament voted for extending the Emissions Trading Scheme to the
Aviation Industry after 2012. After 2012, countries might start investing more in wind
energy because they are aware that they have to balance the greenhouse gasses
emissions from aircrafts with using more renewable energy, to avoid buying extra
carbon credits.
A6.3.2. Recent industry evolutions
Backward integration might be a way to consolidate the business in the more
distant future. A number of competitors are already backward integrated, so there is a
trend in the industry towards larger, more diversified companies. Siemens, Suzlon, GE
Energy and Gamesa have all incorporated gearbox producers. Vestas already produces
nacelles and blades and towers and might want to integrate some of its key suppliers as
well, in order to secure a steady flow of parts. At the moment, Vestas’ strategy is only
50
See 100) World Wind Energy Association 2009. World Wind Energy Report 2009., p. 8
106
to fortify the relationship with suppliers through loyalty programmes and other
measures, but shifts in competition structure might affect Vestas’ future strategy.
Rising oil prices are advantageous for wind energy, because they make wind
and other renewable energy sources more appealing. 2009 was an all-time-low record
year because of the financial crisis. 2010 saw crude oil prices peak in May and then
plummet and stay low until September. However, they’ve started to go up again and the
current quotes are around EUR 66.72 per barrel51. The Financial Forecast Centre,
(2010a) estimated the future rising trend, forecasting prices to rise to around EUR 75
per barrel as early as April 2011.
A6.4. Threats
A6.4.1. Government Regulations
Firstly, the industry’s dependence on government regulations might turn out
to be a two edged sword. First of all, there is the threat that government aid will be
reduced, which will affect all renewables, not only wind.
Moreover, Vestas faces a lot of uncertainty about the future of the US market.
The USA lacks a binding national target for the share of renewables in electricity
production, since it has not ratified the Kyoto Protocol. There are a number of states that
have self-imposed targets, but many voices have called for a national renewable energy
standard (RES).
Lastly, “green stimulus” does not target wind energy in particular, but
renewable energy in general. The reason why wind energy took off compared to the
other green energy sources is that the installation and maintenance costs to energy
output ratio is the most advantageous. If some other technology is developed that would
have a better cost-output ratio, there will be a massive shift within the renewable energy
industry towards that new technology, to the detriment of turbine manufacturers.
Therefore, wind energy might be “number one”, but its pedestal is somewhat frail.
A6.4.2. International Agreements
The Kyoto Protocol provided stimulus for the industry by setting 2012
renewable energy targets. However, there have been no agreements regarding targets
after 2012. Negotiations of country representatives participating in COP14 in Poznan,
51
Yahoo Finance quote from 9 December 2010.
107
Poland and COP15 in Copenhagen, Denmark, have failed to result in renewed targets.
In December 2010, all eyes fall on the COP16 Summit in Cancun, Mexico.
A6.4.3. High Pressure to Keep Costs Low
Costs are a major issue because these are an important factor determining
competitiveness. Turbine manufacturers are under a twofold pressure when it comes to
costs. They need to keep their own costs low, while producing better turbines which
minimise the costs of generating electricity.
Keeping costs low is also starting to be dictated by market forces. There is a
transition from a seller’s to a buyer’s market. Therefore, it will no longer be the
producers who are setting the prices, but the customers, which implies that producers
must become more cost effective and perhaps even try to differentiate their products in
the future.
Dependence on government regulations is also a threat from the cost
perspective, because it might prevent companies from becoming more cost efficient,
knowing that subsidies will work for them, for example. With the increasing pressure to
keep prices low, less subsidies will mean that Vestas has to become more efficient in its
production in order to match the offers of its new competitors.
A6.4.4. Economic environment risks
The low dollar is negatively affecting Vestas’ bottom line, especially since the
Americas are the second largest geographical segment. The Financial Forecast Centre,
(2010b) estimates that the USD to EUR exchange rate will fall to as little as 1.25 by
March 2011, from a current average rate for December of 1.406.
A6.4.5. Industry risks
There is an intense competition among turbine producers to secure
suppliers.
Demand for wind turbines increased much rapidly than forecasted by
suppliers, who have lagged behind and are trying to ramp up capacity to reach demand
from turbine manufacturers, especially on the generator front. Vestas’ competitors are
trying to secure the supply of parts and raw materials, which induced a trend toward
vertical integration in the industry. Therefore, turbine manufacturers scramble to acquire
some of their key suppliers or to get them to enter exclusive arrangements.
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Another threat is the fact that there are numerous substitute products,
contrasted in Annex A3.4. Threat of Substitute Products.
Yet another factor with a negative impact is the fact that there is limited access
to electricity grids around the world. Current grids are close to maximum transmission
capacity and an improved super-grid structure connecting “electricity highways” is
being called for.
109
A7. Reorganisation of Financial Statements
A7.1. Invested Capital
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- -----------------
Invested Capital
Operating Working Capital
226
534
659
627
900
538
181
548
1.316
Net Property Plant and Equipment
115
164
281
321
469
466
490
638
1.030
1.461
Other Assets Net of Other Liabs
8
5
5
4
9
5
11
13
25
16
Less: Short Term Warranties Provision
0
(42)
(51)
(58)
(104)
(138)
(139)
(154)
(128)
(111)
Less: Long Term Warranties Provision
(43)
(21)
(35)
(39)
(64)
(83)
(66)
(107)
(85)
(82)
69
71
52
159
53
57
185
126
235
636
Value of Operating Leases
Op. Invested Capital (excl.Goodwill)
(9)
375
710
912
1.014
1.264
845
662
508
1.625
3.236
Goodwill & Intangibles
6
5
35
59
449
477
478
507
644
812
Cumulative Written Off & Amortized
1
2
3
5
22
52
85
114
146
206
382
717
951
1.078
1.735
1.375
1.225
1.129
2.415
4.254
Excess Marketable Securities
11
0
0
0
141
55
368
667
41
355
Investments
22
1
1
1
3
12
12
1
1
1
Non-operating Assets
0
0
0
0
0
0
0
0
0
0
Retirement Related Assets
0
0
0
0
0
0
0
0
0
0
Total Investor Funds
416
718
951
1.078
1.880
1.442
1.604
1.797
2.457
4.611
Total Common Equity & Pref. Stock
3.364
Op. Invested Capital (incl.Goodwill)
215
528
596
613
1.251
962
1.262
1.516
1.955
Cum Goodwill Written Off & Amortized
1
2
3
5
22
52
85
114
146
206
Deferred Income Taxes
0
(11)
(10)
(15)
(75)
(140)
(162)
(154)
(63)
(110)
14
21
0
0
0
0
0
0
0
0
0
0
0
6
7
8
21
39
50
40
Dividends Payable
Short Term Income Smotthing Provision
Long Term Income Smoothing Provision
11
26
44
61
38
9
36
3
9
121
241
566
634
670
1.243
891
1.242
1.518
2.097
3.621
Minority Interest
0
0
0
0
0
0
0
0
0
0
Restructuring Provisions
0
0
0
0
0
0
0
0
0
0
Long-term operating Provision
0
0
0
0
0
0
0
0
0
0
Retirement-Related Liabilities
0
0
0
1
1
2
3
2
2
2
106
81
266
248
582
492
174
150
123
351
Adjusted Equity
Interest Bearing Debt
Value of Operating Leases
Total Investor Funds
69
71
52
159
53
57
185
126
235
636
416
718
951
1.078
1.880
1.442
1.604
1.797
2.457
4.611
110
A7.2. NOPLAT
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- -----------------
NOPLAT
Reported EBITA
134
75
76
8
(85)
218
472
700
916
Adj for Operating Leases
3
2
5
2
2
9
8
14
50
Adj for Non-operating component of pension expense
0
0
0
0
(0)
1
1
(1)
0
Add: Interest associated with Long-term operating Provision
0
0
0
0
0
0
0
0
0
Add: Increase in Short Term Income Smoothing Provision
0
0
6
1
0
13
18
11
(10)
Add: Increase in Long Term Income Smoothing Provision
Adjusted EBITA
15
19
17
(23)
(29)
28
(33)
6
112
152
95
103
(12)
(113)
268
466
730
1.068
(154)
(195)
Taxes on EBITA
47
(21)
(30)
5
(39)
(59)
Change in Deferred Taxes
(11)
1
(5)
(60)
(65)
(22)
NOPLAT
188
75
68
(66)
(217)
(254)
8
91
(47)
187
319
626
767
Taxes on EBIT
Prov for Inc Taxes
50
15
18
(12)
33
50
152
203
230
Tax Shield on Interest Exp
6
7
11
8
7
14
5
5
16
Tax Shield on Operating Lease Interest
1
1
2
0
0
3
2
4
13
Tax Shield on Non-operating component of pension expense
0
0
0
0
(0)
0
0
(0)
0
Tax Shield on Interest associated with Long-term operating Provision
0
0
0
0
0
0
0
0
0
(2)
(1)
(1)
(2)
(1)
(3)
(5)
(17)
(4)
Tax on Interest Income
Tax on Non-operating Income
(102)
0
(1)
0
0
(4)
0
0
(47)
21
30
(5)
39
59
154
195
254
Net Income
322
45
36
(39)
(192)
111
291
511
616
Add: Increase in Deferred Taxes
(11)
1
(5)
(60)
(65)
(22)
8
91
(47)
Add: Increase in Short Term Income Smoothing Provision
0
0
6
1
0
13
18
11
(10)
Add: Increase in Long Term Income Smoothing Provision
15
19
17
(23)
(29)
28
(33)
6
112
Add: Goodwill Amortization
1
1
2
17
30
33
29
32
60
Add: Extraordinary Items
0
0
0
0
0
0
0
0
(37)
0
Taxes on EBIT
(0)
Reconciliation to Net Income
Add: Special Items After Tax
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
184
65
55
(102)
(255)
162
313
651
694
Add: Interest Exp. After Tax
9
10
13
43
42
36
14
15
47
Add: Interest Exp. On Long-term operating Provision
0
0
0
0
0
0
0
0
0
Add: Interest Exp. on Op. Leases
2
1
3
1
2
7
6
11
38
Add: Minority Interest
Adjusted Net Income
Add: Interest Exp. on Non-operating component of pension expense
Income Available to Investors
Add: Restructuring Charges
Less: Interest Income After-Tax
Less: Non-operating Income After Tax
NOPLAT
(143)
0
0
0
0
194
76
71
(58)
(0)
1
1
(212)
205
334
(1)
676
0
778
0
0
0
0
0
0
0
0
0
(2)
(2)
(1)
(9)
(5)
(8)
(14)
(50)
(11)
(4)
188
0
(1)
0
75
68
(66)
111
0
(217)
(11)
187
0
0
319
626
(1)
767
A7.3. Free Cash Flow
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- -----------------
Free Cash Flow
NOPLAT
188
75
68
(66)
(217)
187
319
626
767
35
57
74
128
93
91
109
103
158
223
131
142
62
(124)
277
428
729
925
(308)
(125)
33
(273)
362
357
190
(556)
(768)
(72)
(118)
(86)
(90)
(107)
(160)
(265)
(521)
(606)
Incr in other operating assets/liabilities
(8)
(56)
(27)
(190)
20
39
6
14
26
Incr in Short Term Warranties Provisions
42
8
8
45
35
1
15
(26)
(17)
Incr in Long Term Warranties Provisions
(21)
14
25
19
(17)
41
(22)
(3)
(2)
19
(107)
106
(3)
(128)
58
(109)
(401)
Depreciation
Gross Cash Flow
Increase in Working Capital
Capital Expenditures
Inv in Operating Leases
4
Gross Investment
(370)
(259)
(175)
(378)
326
92
46
(1.220)
(1.769)
Free Cash Flow Excl. Goodwill
(147)
(127)
(34)
(316)
202
369
474
(491)
(844)
(32)
(25)
(407)
(58)
(33)
(59)
(169)
(228)
(159)
(59)
(724)
143
336
416
(660)
(1.072)
Investment in Goodwill and Intangibles
0
Free Cash Flow Incl. Goodwill
(147)
AT Interest Income
2
2
1
11
(0)
0
Foreign Exchange Translation
0
(4)
(Incr)/Decr Retirement Related Assets
0
0
169
Restructuring Cash Flow
Extraordinary items
(Incr)/Decr Excess Mkt Sec
Non-operating Cash Flow
Cash Flow Available to Investors
14
50
11
(141)
9
87
5
(313)
8
(299)
625
(314)
(5)
(2)
4
(2)
(10)
(43)
0
0
0
0
0
0
0
0
0
1
(3)
(9)
12
11
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
37
36
(161)
(61)
(861)
231
41
131
(28)
(1.338)
AT Interest Expense
9
10
13
43
42
36
14
15
47
Interest on Operating Leases
2
1
3
1
2
7
6
11
38
Interest on Nonoperating Component of Pension Expense
0
0
0
0
(0)
1
1
(1)
0
Interest on Long-term Operating Provision
0
0
0
0
0
0
0
0
Financing Flow
0
Decr/(Incr) in Debt
25
(185)
18
(334)
90
318
24
27
(228)
Decr/(Incr) in Operating Leases
(109)
(401)
(2)
19
(107)
106
(3)
(128)
58
Decr/(Incr) in Retirement Rel. Liab
0
0
(1)
0
(1)
(1)
1
0
0
Decr/(Incr) in Long-term Operating Provision
0
0
0
0
0
0
0
0
0
Payments to Minorities
0
0
0
1
0
0
0
0
0
Common Dividends
7
42
11
0
0
0
0
0
0
Preferred Dividends
0
0
0
0
0
0
0
0
0
Decr/(Incr) in Preferred
0
0
0
0
0
0
0
0
Decr/(Incr) in Share Capital
(5)
(48)
3
(678)
102
(191)
27
29
Total Financing Flow
36
(161)
(61)
(861)
231
41
131
(28)
0
(793)
(1.338)
A7.4. Return on Invested Capital
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- -----------------
Return on Invested Capital (BY)
Net PPE / Revenues
9,0%
11,7%
17,0%
12,5%
13,1%
12,1%
10,1%
10,6%
15,5%
Working Capital / Revenues
17,6%
38,3%
39,9%
24,5%
25,1%
14,0%
3,7%
-0,1%
8,3%
Net Other Assets / Revenues
2,7%
0,8%
-1,7%
2,6%
-2,9%
-4,1%
-0,2%
-2,0%
0,7%
3,4
2,0
1,8
2,5
2,8
4,6
7,3
11,9
Rev. / Inv. Capital (pre-Goodwill)
Pre-Tax ROIC
40,4%
13,4%
11,3%
-1,1%
-8,9%
31,8%
70,3%
Cash Tax Rate
-24,0%
21,5%
33,7%
-472,4%
-91,9%
30,5%
50,1%
10,6%
7,5%
-6,5%
-17,2%
22,1%
1,7
2,4
After-Tax ROIC (pre-Goodwill)
Rev. / Inv. Capital (incl. Goodwill)
After-Tax ROIC (incl. Goodwill)
3,3
1,9
2,1
2,8
4,1
143,8%
65,7%
31,4%
14,2%
28,2%
48,2%
123,4%
47,2%
4,0
5,3
2,7
49,2%
10,4%
7,2%
-6,2%
-12,5%
13,6%
26,1%
55,5%
31,8%
Net PPE / Revenues
10,9%
16,0%
18,2%
15,4%
13,1%
12,4%
11,6%
13,8%
18,8%
Working Capital / Revenues
29,7%
42,8%
38,9%
29,8%
20,1%
9,3%
1,8%
4,5%
14,0%
Net Other Assets / Revenues
1,8%
-0,6%
1,1%
-0,8%
-3,7%
-2,2%
-1,3%
-0,6%
3,8%
2,4
1,7
1,7
2,2
3,4
5,1
8,3
5,7
Return on Invested Cap (Avg)
Rev. / Inv. Capital (pre-Goodwill)
2,7
Pre-Tax ROIC
28,0%
11,8%
10,7%
-1,0%
-10,7%
35,6%
79,6%
68,5%
44,0%
After-Tax ROIC (pre-Goodwill)
34,7%
9,2%
7,1%
-5,8%
-20,6%
24,7%
54,6%
58,7%
31,6%
After-Tax ROIC (incl. Goodwill)
34,2%
9,0%
6,8%
-4,7%
-14,0%
14,3%
27,1%
35,3%
23,0%
Average ROE
86,6%
8,0%
5,9%
-4,2%
-17,3%
10,0%
21,0%
29,4%
23,2%
112
A7.5. Revenue Growth
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- -----------------
Growth Rates
Revenue Growth Rate
47,6%
8,9%
18,5%
55,0%
39,9%
7,6%
26,1%
24,2%
10,0%
Adjusted EBITA Growth Rate
NA
-37,1%
8,3%
-111,2%
875,5%
-337,4%
73,5%
56,8%
46,3%
NOPLAT Growth Rate
NA
-60,1%
-8,5%
-196,8%
227,1%
-186,0%
71,3%
96,1%
22,5%
87,5%
32,6%
13,4%
61,0%
-20,8%
-10,9%
-7,9%
114,0%
76,1%
320,5%
-86,0%
-21,1%
-210,1%
388,5%
-157,9%
162,4%
75,6%
20,5%
Invested Capital Growth Rate
Net Income Growth Rate
A8. Interest Coverage
Year
EBIT
EBITDA
EBITDAR
Interest
Rental Expense
Interest & Rental Expense
EBIT/Interest & Rental Expense
EBITDA/Interest & Rental Expense
EBITDAR/Interest & Rental Expense
2000
93,1
115,8
122,7
2001
132,6
168,6
175,5
2002
73,7
131,3
136,8
2003
74,2
149,2
168,9
2004
-9,1
136
148,3
2005
-115,7
7,5
20,2
2006
184,9
308,3
330,1
2007
443
581
592,0
2008
668
803
823,0
2009
856
1074
1123,0
7,2
6,9
14,1
15
6,9
21,9
16,6
5,5
22,1
24,2
19,7
43,9
50,9
12,3
63,2
48,4
12,7
61,1
50,1
21,8
71,9
19
11
30,0
20
20
40,0
62
49
111,0
6,6
8,2
8,7
6,1
7,7
8,0
3,3
5,9
6,2
1,7
3,4
3,9
-0,1
2,2
2,3
-1,9
0,1
0,3
2,6
4,3
4,6
14,8
19,4
19,7
16,7
20,1
20,6
7,7
9,7
10,1
113
A9. Historical Analysis Results
A9.1. Income Statement
EUR
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ------------------
Income Statement
Revenues
Other Operating Revenues
868
1.281
1.395
1.653
2.561
3.583
3.854
4.861
6.035
0
0
0
0
0
0
0
0
0
6.636
0
(717)
(1.065)
(1.200)
(1.432)
(2.317)
(3.375)
(3.325)
(3.974)
(4.767)
(5.073)
Selling, Gen & Admin Expenses
(35)
(47)
(64)
(71)
(108)
(128)
(165)
(240)
(375)
(449)
Depreciation Expense
(22)
(35)
(57)
(74)
(128)
(93)
(91)
(109)
(103)
(158)
0
0
0
0
0
(73)
(56)
(66)
(90)
(40)
Reported EBITA
94
134
75
76
8
(85)
218
472
700
916
Amortization of Goodwill
(1)
(1)
(1)
(2)
(17)
0
0
0
0
0
0
0
0
0
0
(30)
(33)
(29)
(32)
(60)
Reported EBIT
93
133
74
74
(9)
(116)
185
443
668
856
Non-Oper Income
25
19
0
1
(0)
(0)
16
0
0
1
3
4
3
3
10
6
11
19
66
14
Cost of Goods Sold
Other Oper Expense
Intangibles Amort. (Excl. Goodwill)
Interest Income
(7)
(15)
(17)
(24)
(51)
(48)
(50)
(19)
(20)
(62)
Restructuring Charges
0
0
0
0
0
0
0
0
0
0
Special Items
3
246
0
0
0
0
0
0
0
0
Earnings Before Taxes
117
386
60
54
(50)
(158)
161
443
714
809
Income Taxes
(40)
(50)
(15)
(18)
12
(33)
(50)
(152)
(203)
(230)
0
0
0
0
(1)
0
0
0
0
0
77
337
45
36
(39)
111
291
511
579
Interest Expense
Minority Interest
Income Before Extraordinary Items
Extraordinary Items (After Tax)
Net Income
Preference dividends
Earnings for common shareholders
0
0
0
0
0
77
337
45
36
(39)
0
0
0
0
0
77
337
45
36
(39)
(192)
0
(192)
0
(192)
0
0
0
37
111
291
511
616
0
0
0
0
111
291
511
616
0
0
0
0
0
0
0
0
0
77
337
45
36
(39)
(192)
111
291
511
616
Earnings per share (EUR)
730,86
3.211,46
430,42
339,40
(280,08)
(1.094,84)
606,93
1.571,24
2.759,12
3.115,47
Earnings per share - fully diluted (EUR)
730,86
3.211,46
430,42
339,40
(280,08)
(1.094,84)
606,93
1.571,24
2.759,12
3.115,47
Common dividends
Retained profit
114
0
A9.2. Balance Sheet
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ------------------
Balance Sheet
Operating Cash
17
21
21
20
51
72
77
97
121
133
Excess Marketable Securities
11
0
0
0
141
55
368
667
41
355
Accounts Receivable
163
338
692
777
1.290
621
711
660
938
525
Inventories
247
402
223
193
436
698
881
1.107
1.612
1.663
Other Current Assets
0
0
0
0
0
540
466
452
833
1.359
Total Current Assets
439
760
937
990
1.918
1.985
2.502
2.983
3.545
4.035
Net Property Plant and Equipment
115
164
281
321
469
466
490
638
1.030
1.461
Goodwill
3
2
1
11
308
322
320
320
320
320
Other Intangible Assets
3
3
34
48
141
156
158
187
324
492
Other Operating Assets
8
5
5
4
9
5
11
13
25
16
22
1
1
1
3
12
12
1
1
1
Deferred tax asset
0
11
10
15
75
140
162
154
63
110
Other Non-operating Assets
0
0
0
0
0
0
0
0
0
0
Retirement Related Assets
0
0
0
0
0
0
0
0
0
0
591
945
1.269
1.390
2.924
3.085
3.654
4.296
5.308
6.435
65
38
153
142
110
51
11
25
109
12
106
123
148
212
404
520
808
889
1.030
1.062
145
Investments
Total Assets
Short term debt
Accounts Payable
Tax payable
Dividends payable
Short Term Income Smoothing Provisions
Short Term Warranties Provisions
1
8
6
5
17
50
33
73
42
14
21
0
0
0
0
0
0
0
0
0
0
0
6
7
8
21
39
50
40
0
42
51
58
104
138
139
154
128
111
Other Current Liabilities
96
95
124
146
456
822
1.114
1.363
1.884
1.157
Total Current Liabilities
281
327
481
570
1.098
1.589
2.125
2.543
3.243
2.527
Balancing Debt
0
0
0
0
0
0
0
0
0
0
Long Term Debt
41
43
113
106
472
441
163
125
14
339
Deferred Income Taxes
0
0
0
0
0
0
0
0
0
0
Other Operating Liabilities
0
0
0
0
0
0
0
0
0
0
Restructuring Provisions
0
0
0
0
0
0
0
0
0
0
Long Term Income Smoothing Provisions
11
26
44
61
38
9
36
3
9
121
Long Term Warranties Provisions
43
21
35
39
64
83
66
107
85
82
Long-term operating Provisions
0
0
0
0
0
0
0
0
0
0
Retirement Related Liabilities
0
0
0
1
1
2
3
2
2
2
Minority Interest
0
0
0
0
0
0
0
0
0
0
Preferred Stock
0
0
0
0
0
0
0
0
0
0
Total Common Equity
215
528
596
613
1.251
962
1.262
1.516
1.955
3.364
Total Liabs and Equity
591
945
1.269
1.390
2.924
3.085
3.654
4.296
5.308
6.435
115
A9.3. Cash Flow Statement
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ------------------
Traditional Cash flow
Reported EBITA
Depreciation
EBITDA
Less investment in working capital
Foreign exchange translation effects
Operating cashflow
Less tax paid
134
75
76
8
(85)
218
472
700
35
57
74
128
93
91
109
103
158
169
131
149
136
7
308
581
803
1.074
(316)
(123)
33
(286)
329
375
149
(525)
(2)
4
(10)
(43)
0
(4)
(5)
(2)
916
(871)
0
(147)
5
178
(151)
341
681
720
235
203
(53)
(17)
(24)
(36)
(65)
(90)
(104)
(143)
(174)
(72)
(118)
(160)
(265)
(521)
(606)
Capital investments
Less capex
Less investment
Less goodwill & intangibles acquired
Short Term Warranties Provision
Long Term Warranties Provision
Less other operating assets/liabilities
Total capital investments
(86)
(90)
(107)
22
0
0
(3)
(9)
1
11
0
(32)
(25)
(407)
(58)
(33)
(59)
(169)
(228)
42
(21)
8
14
8
4
45
25
35
19
1
(17)
15
41
(26)
(22)
(17)
(3)
(8)
(56)
(27)
(190)
20
39
(38)
(184)
(126)
(621)
(100)
(169)
6
(250)
0
0
14
26
(724)
(828)
Finance
Interest Income
4
3
3
10
6
11
19
66
14
Interest Expense
(15)
(17)
(24)
(51)
(48)
(50)
(19)
(20)
(62)
Debt raised/repaid
(25)
185
(18)
334
(90)
(318)
(24)
(27)
228
Total finance payments
(36)
171
(39)
294
(133)
(357)
(24)
19
180
1
Plus non-operating income &expense
Non-Oper Income
19
0
1
(0)
(0)
16
0
0
246
0
0
0
0
0
0
0
0
Extraordinary items
0
0
0
0
0
0
0
0
37
Investment in non-operating assets
0
0
0
0
0
0
0
0
0
Retirement Related Assets
0
0
0
0
0
0
0
0
0
265
0
1
(0)
(0)
16
0
0
38
Restructuring charges
0
0
0
0
0
0
0
0
0
Short Term Income smoothing Provision
0
0
6
1
0
13
18
11
(10)
Long Term Income Smoothing Provision
Special items
Total non-operating items
Less payments from reserves and to minorities
15
19
17
(23)
(29)
28
(33)
6
112
Long-term operating Provision
0
0
0
0
0
0
0
0
0
Retirement Related Liabilities
0
0
1
0
1
1
(1)
0
0
Minority interest
0
0
0
(1)
0
0
0
0
0
15
19
24
(23)
(28)
42
(16)
17
102
Less pref dividends paid
0
0
0
0
0
0
0
0
0
Prefs issued/(redeemed)
0
0
0
0
0
0
0
0
0
Less dividends paid
7
(21)
0
0
0
0
0
0
Equity raised/repaid
(24)
27
(14)
678
(102)
191
(27)
(29)
793
Total equity cash payments
(17)
6
(14)
678
(102)
191
(27)
(29)
793
Net Cashflow
(11)
0
(0)
141
(87)
313
299
(625)
314
Total payments to reserves/minorities
Equity
116
0
A9.4. NOPLAT
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ------------------
NOPLAT
Reported EBITA
134
75
76
8
(85)
218
472
700
916
Adj for Operating Leases
3
2
5
2
2
9
8
14
50
Adj for Non-operating component of pension expense
0
0
0
0
(0)
1
1
(1)
0
Add: Interest associated with Long-term operating Provision
0
0
0
0
0
0
0
0
0
Add: Increase in Short Term Income Smoothing Provision
0
0
6
1
0
13
18
11
(10)
Add: Increase in Long Term Income Smoothing Provision
Adjusted EBITA
Taxes on EBITA
15
19
17
(23)
(29)
28
(33)
6
112
152
95
103
(12)
(113)
268
466
730
1.068
(154)
(195)
53
(21)
(30)
5
(39)
(59)
Change in Deferred Taxes
(11)
1
(5)
(60)
(65)
(22)
NOPLAT
194
75
68
(66)
(217)
187
(254)
8
91
(47)
319
626
767
A9.5. Invested Capital
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ------------------
Invested Capital
Operating Working Capital
226
534
659
627
900
538
181
548
1.316
Net Property Plant and Equipment
115
164
281
321
469
466
490
638
1.030
1.461
Other Assets Net of Other Liabs
8
5
5
4
9
5
11
13
25
16
Less: Short Term Warranties Provision
0
(42)
(51)
(58)
(104)
(138)
(139)
(154)
(128)
(111)
Less: Long Term Warranties Provision
(43)
(21)
(35)
(39)
(64)
(83)
(66)
(107)
(85)
(82)
69
71
52
159
53
57
185
126
235
636
375
710
912
1.014
1.264
845
662
508
1.625
3.236
Goodwill & Intangibles
6
5
35
59
449
477
478
507
644
812
Cumulative Written Off & Amortized
1
2
3
5
22
52
85
114
146
206
382
717
951
1.078
1.735
1.375
1.225
1.129
2.415
4.254
Excess Marketable Securities
11
0
0
0
141
55
368
667
41
355
Investments
22
1
1
1
3
12
12
1
1
1
Non-operating Assets
0
0
0
0
0
0
0
0
0
0
Retirement Related Assets
0
0
0
0
0
0
0
0
0
0
Total Investor Funds
416
718
951
1.078
1.880
1.442
1.604
1.797
2.457
4.611
Total Common Equity & Pref. Stock
3.364
Value of Operating Leases
Op. Invested Capital (excl.Goodwill)
Op. Invested Capital (incl.Goodwill)
(9)
215
528
596
613
1.251
962
1.262
1.516
1.955
Cum Goodwill Written Off & Amortized
1
2
3
5
22
52
85
114
146
206
Deferred Income Taxes
0
(11)
(10)
(15)
(75)
(140)
(162)
(154)
(63)
(110)
14
21
0
0
0
0
0
0
0
0
0
0
0
6
7
8
21
39
50
40
Dividends Payable
Short Term Income Smotthing Provision
Long Term Income Smoothing Provision
11
26
44
61
38
9
36
3
9
121
241
566
634
670
1.243
891
1.242
1.518
2.097
3.621
Minority Interest
0
0
0
0
0
0
0
0
0
0
Restructuring Provisions
0
0
0
0
0
0
0
0
0
0
Long-term operating Provision
0
0
0
0
0
0
0
0
0
0
Retirement-Related Liabilities
0
0
0
1
1
2
3
2
2
2
106
81
266
248
582
492
174
150
123
351
Adjusted Equity
Interest Bearing Debt
Value of Operating Leases
Total Investor Funds
69
71
52
159
53
57
185
126
235
636
416
718
951
1.078
1.880
1.442
1.604
1.797
2.457
4.611
117
A9.6. Free Cash Flow
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ------------------
Free Cash Flow
NOPLAT
194
75
68
(66)
(217)
187
319
626
767
35
57
74
128
93
91
109
103
158
229
131
142
62
(124)
277
428
729
925
(308)
(125)
33
(273)
362
357
190
(556)
(768)
(72)
(118)
(86)
(90)
(107)
(160)
(265)
(521)
(606)
Incr in other operating assets/liabilities
(8)
(56)
(27)
(190)
20
39
6
14
26
Incr in Short Term Warranties Provisions
42
8
8
45
35
1
15
(26)
(17)
Incr in Long Term Warranties Provisions
(21)
14
25
19
(17)
41
(22)
(3)
(2)
19
(107)
106
(3)
(128)
58
(109)
(401)
Depreciation
Gross Cash Flow
Increase in Working Capital
Capital Expenditures
Inv in Operating Leases
4
Gross Investment
(370)
(259)
(175)
(378)
326
92
46
(1.220)
(1.769)
Free Cash Flow Excl. Goodwill
(141)
(127)
(34)
(316)
202
369
474
(491)
(844)
(32)
(25)
(407)
(58)
(33)
(59)
(169)
(228)
(159)
(59)
(724)
143
336
416
(660)
(1.072)
Investment in Goodwill and Intangibles
Free Cash Flow Incl. Goodwill
AT Interest Income
0
(141)
2
2
1
11
(0)
0
Foreign Exchange Translation
0
(4)
(Incr)/Decr Retirement Related Assets
0
0
178
Restructuring Cash Flow
Extraordinary items
(Incr)/Decr Excess Mkt Sec
Non-operating Cash Flow
Cash Flow Available to Investors
14
50
11
(141)
87
(313)
(299)
625
(314)
(5)
(2)
4
(2)
(10)
(43)
0
0
0
0
0
0
0
0
0
1
(3)
(9)
12
11
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
37
231
41
131
51
(161)
(61)
118
9
(861)
5
8
(28)
(1.338)
A9.7. Financial Ratios
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ----------------- ------------------
Ratios
Adjusted EBITA / Revenues
Cost of Goods Sold / Revenues
SGA costs / Revenue
EBITDA / Revenue
Depreciation / Revenues
Reported EBITA / Revenues
82,6%
83,2%
86,0%
86,7%
90,5%
94,2%
86,3%
81,8%
79,0%
4,0%
3,7%
4,6%
4,3%
4,2%
3,6%
4,3%
4,9%
6,2%
6,8%
13,3%
13,2%
9,4%
9,0%
5,3%
2,2%
9,5%
13,3%
14,8%
16,8%
2,5%
2,7%
4,1%
4,4%
5,0%
2,6%
2,4%
2,2%
1,7%
2,4%
10,9%
10,4%
5,4%
4,6%
0,3%
-2,4%
5,6%
9,7%
11,6%
13,8%
Adjustments to EBITA / Revenues
Adjusted EBITA / Revenues
76,4%
1,4%
1,5%
1,7%
-0,8%
-0,8%
1,3%
-0,1%
0,5%
2,3%
11,8%
6,8%
6,3%
-0,5%
-3,2%
7,0%
9,6%
12,1%
16,1%
Return on Invested Capital (BY)
Net PPE / Revenues
9,0%
11,7%
17,0%
12,5%
13,1%
12,1%
10,1%
10,6%
15,5%
Working Capital / Revenues
17,6%
38,3%
39,9%
24,5%
25,1%
14,0%
3,7%
-0,1%
8,3%
Net Other Assets / Revenues
2,7%
0,8%
-1,7%
2,6%
-2,9%
-4,1%
-0,2%
-2,0%
0,7%
3,4
2,0
1,8
2,5
2,8
4,6
7,3
11,9
Rev. / Inv. Capital (pre-Goodwill)
Pre-Tax ROIC
40,4%
13,4%
11,3%
-1,1%
-8,9%
31,8%
70,3%
Cash Tax Rate
-28,0%
21,5%
33,7%
-472,4%
-91,9%
30,5%
51,7%
10,6%
7,5%
-6,5%
-17,2%
22,1%
1,7
2,4
After-Tax ROIC (pre-Goodwill)
Rev. / Inv. Capital (incl. Goodwill)
3,3
After-Tax ROIC (incl. Goodwill)
1,9
2,1
2,8
4,1
143,8%
65,7%
31,4%
14,2%
28,2%
48,2%
123,4%
47,2%
4,0
5,3
2,7
50,7%
10,4%
7,2%
-6,2%
-12,5%
13,6%
26,1%
55,5%
31,8%
Net PPE / Revenues
10,9%
16,0%
18,2%
15,4%
13,1%
12,4%
11,6%
13,8%
18,8%
Working Capital / Revenues
29,7%
42,8%
38,9%
29,8%
20,1%
9,3%
1,8%
4,5%
14,0%
Net Other Assets / Revenues
1,8%
-0,6%
1,1%
-0,8%
-3,7%
-2,2%
-1,3%
-0,6%
3,8%
2,4
1,7
1,7
2,2
3,4
5,1
8,3
5,7
Return on Invested Cap (Avg)
Rev. / Inv. Capital (pre-Goodwill)
2,7
Pre-Tax ROIC
28,0%
11,8%
10,7%
-1,0%
-10,7%
35,6%
79,6%
68,5%
44,0%
After-Tax ROIC (pre-Goodwill)
35,8%
9,2%
7,1%
-5,8%
-20,6%
24,7%
54,6%
58,7%
31,6%
After-Tax ROIC (incl. Goodwill)
35,3%
9,0%
6,8%
-4,7%
-14,0%
14,3%
27,1%
35,3%
23,0%
Average ROE
90,6%
8,0%
5,9%
-4,2%
-17,3%
10,0%
21,0%
29,4%
23,2%
Growth Rates
Revenue Growth Rate
47,6%
8,9%
18,5%
55,0%
39,9%
7,6%
26,1%
24,2%
10,0%
Adjusted EBITA Growth Rate
NA
-37,1%
8,3%
-111,2%
875,5%
-337,4%
73,5%
56,8%
46,3%
NOPLAT Growth Rate
NA
-61,4%
-8,5%
-196,8%
227,1%
-186,0%
71,3%
96,1%
22,5%
87,5%
32,6%
13,4%
61,0%
-20,8%
-10,9%
-7,9%
114,0%
76,1%
339,9%
-86,6%
-21,1%
-210,1%
388,5%
-157,9%
162,4%
75,6%
20,5%
Gross Investment Rate
161,5%
197,0%
123,6%
613,8%
262,5%
-33,2%
-10,7%
167,3%
191,3%
Net Investment / NOPLAT
172,6%
270,2%
148,9%
-376,9%
193,0%
-98,0%
-48,5%
178,4%
210,1%
Invested Capital Growth Rate
Net Income Growth Rate
Investment Rates (excl. Goodwill)
Financing
EBIT/Interest Payable
8,8
4,4
3,1
(0,2)
(2,4)
3,7
23,3
33,4
13,8
Adjusted EBITA/Interest payable
10,1
5,7
4,3
(0,2)
(2,3)
5,4
24,5
36,5
17,2
Cash Coverage (Gross CF / Interest)
15,3
7,9
5,9
1,2
(2,6)
5,5
22,5
36,5
14,9
Debt / Total Cap (Book)
Debt / Total Cap (Market)
31,9%
12,7%
29,3%
26,7%
31,0%
33,4%
11,7%
8,8%
5,8%
9,1%
1,7%
2,4%
20,5%
14,7%
26,9%
16,7%
2,8%
1,1%
1,6%
3,8%
Valuation indicators
Mkt Val Op Inv Cap/ BV Op Inv Cap
4,8
1,4
1,7
1,2
2,8
8,1
25,0
4,3
2,6
Market / Book (incl. Cum Goodwill)
4,7
1,4
1,7
1,2
2,1
4,8
11,8
3,3
2,2
22,3
13,2
16,6
(134,6)
(21,1)
19,9
27,3
9,7
7,8
Mkt val Op Inv Cap / Adj EBITA
Enterprise value / EBITA
67,3
25,0
17,0
21,9
(217,6)
(24,8)
31,0
29,8
11,4
10,1
Price Earnings Ratio
80,7
9,6
21,8
38,6
(39,3)
(12,7)
53,4
47,1
14,8
14,1
119
A10. Base Case Scenario Valuation Inputs
A10.1. Detailed forecast
EUR
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
--------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ---------------
Operations
P&L
Operating Revenue: % Growth
Operating revenues
868
47,6%
1.281
8,9%
1.395
18,5%
1.653
55,0%
2.561
39,9%
3.583
7,6%
3.854
26,1%
4.861
24,2%
6.035
10,0%
6.636
0
NA
0
NA
0
NA
0
NA
0
NA
0
NA
0
NA
0
NA
0
NA
0
83%
(717)
83,2%
(1.065)
86,0%
(1.200)
86,7%
(1.432)
90,5%
(2.317)
94,2%
(3.375)
86,3%
(3.325)
81,8%
(3.974)
79,0%
(4.767)
76,4%
(5.073)
84,0%
(5.073)
84,0%
(8.116)
84,0%
(8.928)
84,0%
(9.820)
84,0%
(10.803)
4%
(35)
3,7%
(47)
4,6%
(64)
4,3%
(71)
4,2%
(108)
3,6%
(128)
4,3%
(165)
4,9%
(240)
6,2%
(375)
6,8%
(449)
6,0%
(362)
6,0%
(580)
6,0%
(638)
6,0%
(701)
6,0%
(772)
0%
0
0,0%
0
0,0%
0
0,0%
0
0,0%
0
2,0%
(73)
1,5%
(56)
1,4%
(66)
1,5%
(90)
0,6%
(40)
1,0%
(60)
1,0%
(97)
1,0%
(106)
1,0%
(117)
1,0%
(129)
2%
17
1,6%
21
1,5%
21
1,2%
20
2,0%
51
2,0%
72
2,0%
77
2,0%
97
2,0%
121
2,0%
133
2,0%
121
2,0%
193
2,0%
213
2,0%
234
2,0%
257
Inventories: % Revenue
Inventories
29%
247
31,4%
402
16,0%
223
11,7%
193
17,0%
436
19,5%
698
22,8%
881
22,8%
1.107
26,7%
1.612
25,1%
1.663
22,0%
1.329
22,0%
2.126
22,0%
2.338
22,0%
2.572
22,0%
2.829
Acc Rec: % Revenues
Accounts receivable
19%
163
26,4%
338
49,6%
692
47,0%
777
50,4%
1.290
17,3%
621
18,4%
711
13,6%
660
15,5%
938
7,9%
525
8,0%
483
8,0%
773
8,0%
850
8,0%
935
8,0%
1.029
Acc. Pay: % Revenues
Accounts payable
12%
106
9,6%
123
10,6%
148
12,9%
212
15,8%
404
14,5%
520
21,0%
808
18,3%
889
17,1%
1.030
16,0%
1.062
16,0%
966
16,0%
1.546
16,0%
1.701
16,0%
1.871
16,0%
2.058
0%
0
0,0%
0
0,0%
0
0,0%
0
0,0%
0
15,1%
540
12,1%
466
9,3%
452
13,8%
833
20,5%
1.359
15,0%
906
15,0%
1.449
15,0%
1.594
15,0%
1.754
15,0%
1.929
OCL: % Revenues
Other current liabilities
11%
96
7,4%
95
8,9%
124
8,8%
146
17,8%
456
23,0%
822
28,9%
1.114
28,0%
1.363
31,2%
1.884
17,4%
1.157
16,0%
966
16,0%
1.546
16,0%
1.701
16,0%
1.871
16,0%
2.058
Total operating working capital
WC increase/(decrease)
WC: % Revenues
227
542
316
42,4%
665
123
47,7%
632
(33)
38,2%
917
286
35,8%
588
(329)
16,4%
213
(375)
5,5%
64
(149)
1,3%
590
525
9,8%
1.461
871
22,0%
906
(555)
15,0%
1.449
543
15,0%
1.594
145
15,0%
1.753
159
15,0%
1.929
175
15,0%
Other Revenue: % Growth
Other revenues
COGS: % Revenue
Cost of Goods Sold
SGA: % Revenue
SGA
Other Op Exp: % Revenue
Other Operating Expense
-9,0%
6.039
0
60,0%
9.662
0
10,0%
10.628
0
10,0%
11.691
0
10,0%
12.860
0
Working capital
Op Cash: % Revenue
Operating cash
OCA: % Revenues
Other current assets
26,1%
120
A10.2. Key Driver Forecast
EUR
Revenue growth
Revenue
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
20
--------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------9,0%
8,0%
8,0%
7,0%
7,0%
7,0%
6,0%
6,0%
6,0%
6,0%
6,0
14.018
15.139
16.350
17.495
18.719
20.030
21.231
22.505
23.856
25.287
26.80
Adjusted EBITA margin
Adjusted EBITA
7,2%
1.007
7,2%
1.088
7,2%
1.175
7,2%
1.257
7,2%
1.345
7,2%
1.439
7,2%
1.525
7,2%
1.617
7,2%
1.714
7,2%
1.817
7,2
1.92
Cash tax rate
NOPLAT
29,6%
709
29,6%
766
29,6%
827
29,6%
885
29,6%
947
29,6%
1.013
29,6%
1.074
29,6%
1.138
29,6%
1.207
29,6%
1.279
29,6
1.35
Closing Net PPE as % Revenues
Net PPE
13,7%
1.920
13,7%
2.074
13,7%
2.240
13,7%
2.397
13,7%
2.565
13,7%
2.744
13,7%
2.909
13,7%
3.083
13,7%
3.268
13,7%
3.464
13,7
3.67
Other Invested Capital as % Revenues
Other Invested Capital
Invested Capital (pre-Goodwill)
23,9%
3.355
5.275
23,9%
3.623
5.697
23,9%
3.913
6.153
23,9%
4.187
6.583
23,9%
4.480
7.044
23,9%
4.793
7.537
23,9%
5.081
7.990
23,9%
5.386
8.469
23,9%
5.709
8.977
23,9%
6.052
9.516
23,9
6.41
10.08
Cumulative Goodwill
Invested Capital
Net Investment
2.768
8.043
436
2.768
8.465
422
2.768
8.921
456
2.768
9.352
431
2.768
9.813
461
121
2.768
10.306
493
2.768
10.758
452
2.768
11.237
479
2.768
11.745
508
2.768
12.284
539
2.76
12.85
57