debt levels and share price - a sensitivity analysis on...

Debt Levels and Share Price - a Sensitivity Analysis on Vestas

Debt Levels and Share Price - a Sensitivity Analysis on Vestas

Author: Lavinia Andrei

MSc. Finance and International Business

Advisor: Otto Friedrichsen

April 2011

Aarhus School of Business, Aarhus University

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 companys 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-debt-related 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 companys share price will be.

Key words: capital structure, static trade-off, dynamic trade-off, valuation, sensitivity analysis, Vestas.

Contents

5List of Figures

6List of Tables

7Chapter 1. Introduction

81.1. Problem Statement

91.2. Limitations

101.3. Methodology

111.4. Structure of the Thesis

12Chapter 2. Overview of Capital Structure Theories

122.1. Does Capital Structure Matter?

122.1.1. The M&M Theories

132.1.2. Subsequent Studies

152.2. The Trade-Off Theory

172.2.1. Determinants of Capital Structure

202.2.2. The Static Trade-Off Model

212.2.3. The Dynamic Trade-Off Model

252.3. The Pecking Order Theory

252.3.1. Information Asymmetry Considerations

262.3.2 Agency Costs Considerations

262.4. Market Timing Theory

28Chapter 3. Business Strategy Analysis

283.1. External Analysis

293.2. Porters 5 Forces Model

293.3. Competitor analysis

293.4. Internal Analysis

293.4.1. Strategy statements

303.4.2. Product and Service Mix

303.4.3. Business segments

313.5. SWOT Analysis

33Chapter 4. Analysing Historical Performance

334.1. Reorganisation of Financial Statements

334.1.1. Treatment of Accounts, Assumptions and Estimations

364.1.2. Results of Reorganisation

374.2. Credit Health

374.3. Stock Market Performance

39Chapter 5. Base case scenario valuation

395.1. Scenario description

395.2. Forecasting performance

395.2.1. Revenue growth

405.2.2. Cost of capital

425.2.3. Other inputs

435.2.4. Continuing value

435.3. Valuation result

445.4. Critique

45Chapter 6. Sensitivity Analysis

456.1. Target capital structure

466.1.1. The Static Trade-off Target Debt Level

486.1.2. The Dynamic Trade-off Target Debt Level and Adjustment Speed

496.1.3. Simulation Assumptions and Method

516.1.4. Results

546.1.5. Discussion and Critique

586.1.6. Discussion on the Adjustment Speed

596.2. The Cost of Debt

596.2.1. Types of Debt - Discussion

616.2.2. Sensitivity Analysis Assumptions and Method

626.2.3. Results and Discussion

626.3. Other Debt-Related and Non-debt-related Variables


646.3.2. Results


656.4. Simulations of All Variables


666.4.2. Results


69Chapter 7. Conclusions

72Bibliography

77Annexes

77A1. Market Definition, Size and Growth

77A1.1. Market Definition

78A1.2. Market Size

79A1.3. From Present to Future - Market Growth

82A2. PESTEL Analysis

82A2.1. Political and Legal Factors

82A2.2. Economic Factors

84A2.3. Socio-cultural Factors

85A2.4. Technological Factors

86A2.5. Environmental Factors

86A2.6. General Degree of Turbulence in the Industry Environment

87A3. Competitor Analysis

87A3.1. The top 4

88A3.2. Competition trends

90A4. Porters 5 Forces Model

90A4.1. Bargaining Power of Buyers

91A4.2. Bargaining Power of Suppliers

93A4.3. Threat of New Entrants

94A4.4. Threat of Substitute Products

96A4.5. Competitive Rivalry within the Industry

97A5. Internal Analysis

97A5.1. Corporate Vision, Mission and Strategy

98A5.2. Product and Service Mix

99A5.3. Geographic & Business Segments

100A5.4. Business Model

103A6. SWOT Analysis

103A6.1. Strengths

105A6.2. Weaknesses

105A6.3. Opportunities

107A6.4. Threats

110A7. Reorganisation of Financial Statements

110A7.1. Invested Capital

111A7.2. NOPLAT

112A7.3. Free Cash Flow

112A7.4. Return on Invested Capital

113A7.5. Revenue Growth

113A8. Interest Coverage

114A9. Historical Analysis Results

114A9.1. Income Statement

115A9.2. Balance Sheet

116A9.3. Cash Flow Statement

117A9.4. NOPLAT

117A9.5. Invested Capital

118A9.6. Free Cash Flow

119A9.7. Financial Ratios

120A10. Base Case Scenario Valuation Inputs

120A10.1. Detailed forecast

121A10.2. Key Driver Forecast

List of Figures

Figure 1: The Static Trade-off Theory16

Figure 2: Degree of Turbulence in the Industry28

Figure 3: Porters 5 Forces29

Figure 4: Vestas Revenue31

Figure 5: SWOT Analysis32

Figure 6: ROIC Tree36

Figure 7: Stock Market Performance37

Figure 8: Valuation result43

Figure 9: The Static Trade-off Model47

Figure 10: Simulation #1 Share Price Histogram53






Figure 16: Share Price Sensitivity in the Static Trade-off Case62

Figure 17: Share Price Sensitivity in the Dynamic Trade-off Case62

List of Tables

Table 1 Determinants of Capital Structure18

Table 2: Main Valuation Inputs43

Table 3: Target Capital Structure Percentage Changes Results51

Table 4: Target Capital Structure Simulation Results52

Table 5: Simulation results of other debt and non-debt related variables64

Table 6: Link between the target debt level and the cost of debt66

Table 7: Share-price sensitivity when all analysed variables are simulated66

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 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 companys 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.

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.

1.3. Methodology

The valuation of Vestas is built upon information gathered from the companys 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 Porters 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 companys 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 outsiders 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 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.

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 dont 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. This proposition represents the cornerstone of modern corporate finance and also the basis for the static trade-off and the pecking order theories.

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 companys 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 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 years time and a staggering 491% during the 24-year research period.

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 Millers 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 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 dont 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 state-preference 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 firm. Furthermore, it is shown that the market value of a 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.

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 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 bonds. Myers brings forth the idea that Millers 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.

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. 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 theories:

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.

Determinant (relation to CS)

Reasoning

Studies where it appears - refer to bibliography (relation found; model used: static (S) / dynamic (D))

Costs of financial distress (-)

High costs of financial distress (which might include bankruptcy costs and agency costs of debt) are related to low debt levels.

Bradley et al., (1984), (-; S).

Collateral value of assets / Tangibility (+)

If assets can be used as collateral, the firm will prefer to issue debt secured with assets with known values, rather than to issue other types of securities that will be undervalued because the market has less information. Costs of information asymmetry are thus avoided.

Titman and Wessels, (1988), (insignificant; S); Rajan and Zingales, (1995), (+; S); Flannery and Rangan, (2006), (+; D); Chang et al., (2009), (-&+; S); Talberg 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 that result from depreciation and investment tax credits replace the benefits of leverage.

Bradley et al., (1984), (-; S); Kim and Sorensen, (1986), (-; S); Titman and Wessels, (1988), (insignificant; S); 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-book ratio/ Investment opportunities (-)

Growth opportunities are assets that cannot be collateralised and used to secure debt. They are also connected to the suboptimal investment problem, because of the inclination of equity-controlled firms to expropriate wealth from bondholders. The more growth opportunities the firm has, the more likely it is for it to engage in this behaviour, which poses an agency problem. Growth can be measured by the market-to-book ratio. Firms tend to issue stocks if their market-to-book ratio is high, so there will be less debt in that case. It is also considered a proxy for investment opportunities.

Myers, (1977), (-; S); Kim and Sorensen, (1986), (-; S); Titman and Wessels, (1988), (insignificant; S); Rajan and Zingales, (1995), (-; S); Flannery and Rangan, (2006), (insignificant; 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).

Uniqueness (-)

If a company that sells a unique good or service goes bankrupt, its customers, employees and suppliers will suffer high costs. These firms would keep their debt levels low in order to prevent liquidation.

Titman and Wessels, (1988), (-; S); Chang et al., (2009), (-; S).

Industry classification (+/-)

Debt levels vary by industry. For example, debt levels are low for firms in industries with products requiring customized spare parts and servicing (and for which liquidation is costly).

Titman and Wessels, (1988), (significant; S); Bradley et al., (1984), (significant; S); Flannery and Rangan, (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 have an incentive to use more debt. Moreover, the more diversified a company is, the higher its debt capacity will be, since it can borrow at more favourable terms.

Titman and Wessels, (1988), (-; S); Kim and Sorensen, (1986), (insignificant; S); Rajan and Zingales, (1995), (+; S); Talberg et al., (2008), (-; S); Clark et al., (2009), (+; D).

Volatility / Operating risk (-)

If a companys earnings are very volatile, it would be excessively risky for them to hold a lot of debt. Operating risk might be one reason for high earnings volatility. A company that has a high level of operating risk may not be able to sustain high financial risk at the same time and will prefer to keep debt levels low.

Bradley et al., (1984), (-; S); Kim and Sorensen, (1986), (+; S); Titman and Wessels, (1988), (insignificant; S); Antoniou et al., (2008), (insignificant; D); Chang et al., (2009), (-&+; S).

Profitability (-)

If a company can generate funds internally, it has a smaller financing deficit and will use less debt.

Titman and Wessels, (1988), (insignificant; S); Rajan 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 effective (+)

A high effective tax rate would prompt companies to use more debt, in order to fully reap the advantages of the debt tax shields.

Kim and Sorensen, (1986), (-; S); Antoniou et al., (2008), (+; D); Byoun, (2008), (-; D).

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 issues.

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 on their customers, employees and suppliers in case of liquidation have less leverage. 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 Wessels model and find statistically significant results for all the determinants. However, they have excluded size from their model, invoking goodness-of-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 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 firm-specific 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 costs.

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 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-overs. 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 managers 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.

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%. 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.

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 couldnt, 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; 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 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 Wurglers 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 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.

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 worldwide.

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). 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 worlds cumulative installed wind power capacity under three different scenarios. In the most pessimistic scenario the reference one production of energy from the installed capacity will cover 4.9 5.6% of the worlds 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.

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 medium-strength 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.

3.2. Porters 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. 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 worlds 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:

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 year.

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 companys 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 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.

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 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.

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. Whats 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 companys 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 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 companys pension-related 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:

1

=

+

1

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. Damodarans 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 2012.

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 companys gross cash flow is always positive and mostly offset by the increases in working capital and operating leases, as well as capital expenditures.

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 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. Damodarans 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 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 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 shareholders expectations. It performs best compared to the other companies, for the entire period. During the 5 years, the companys book debt-to-value ratio decreased from 33% to 9.1%. It seems that, contrary to M&Ms 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.

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 industrys 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 2011.

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. Whats more, Vestas own expectations for 2010, as presented in the Management Report of the previous year, 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 MW, 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. Therefore, in time, Vestas will find itself in a slight disadvantageous position because of the fact that its 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 Damodarans 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 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%.

In order to estimate Vestas beta, a regression of the companys 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 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 companys 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 companys management: The proportion of equity in relation to the Group's future capital structure is expected to continue to be high. 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.

Detailed Forecast

Key driver forecast

CV

Year

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020-2024

Revenue Growth

-9%

60%

12%

10%

10%

9%

8%

8%

7%

7%

6%

6%

COGS/Rev

84%

84%

84%

84%

84%

Adj EBITA margin

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%

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.

5.3. Valuation result

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%. 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 have been a sound business decision, but the reports of closing down facilities on the Isle of Wight in the UK, as 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.

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 companys 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).

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 MIMIC model instead of the SEM) 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:

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 (target capital structure) and X is a vector of causes of . 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).

The representation shows that the latent variable, namely capital structure () 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:

*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 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.

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

debt levels and share price - a sensitivity analysis on...

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