Decrease the interest payments for regulated companies

(1)

H.P.Panman and M.V.Kremer

Abstract

This paper constructs a model to decrease the interest payments of regulated companies. The study focuses on regulated companies in the gas and electric industry and specifically on the N.V. Nederlandse Gasunie. In order to decrease the interest payments, regulated companies can alter debt maturity and credit rating. To determine how to improve the combination of debt maturity and credit rating, we use a regression analysis and Data Envelopment Analysis. In Data Envelopment Analysis the interest coverage is used as a proxy for efficiency of the interest payments. Debt maturity and credit rating are the inputs for the Data Envelopment Analysis. From the Data Envelopment Analysis, we conclude that the Gasunie is performing above average, however it has opportunities to decrease the interest payments. The results of the regression analysis suggest that the Gasunie can best decrease their interest payments by matching asset and debt maturity or lower the gearing ratio. Descriptive statistics show us that companies in the peer group of Gasunie have a significant longer debt maturity compared with the companies in the control group.

(2)

Decrease the interest payments for regulated

companies

Master thesis

Msc Business Administration

Specialization Finance, profile Corporate Finance

Groningen, 2010

Student Names:

M.V. Kremer

Student Number: S1535404

H.P.Panman

Student Number: S1555367

Supervision Gasunie:

A. Huijsen, E.F.W. Vasbinder

(3)

PREFACE

We would like to thank Arjan Huijsen and Ernst Vasbinder from Gasunie for their guidance and critical comments. From the University of Groningen we would like to show our gratitude to Auke Plantinga for making this internship possible, his critical remarks and support.

(4)

1. INTRODUCTION

In this study we examine the interest payments of regulated companies in the gas and electric infrastructure industry. The aim of the study is to provide an advice to decrease interest payments looking at the main variables influencing the interest rate.

This study focuses on the Gasunie, the gas infrastructure owner and developer of the Netherlands. Gasunie is a regulated company which restricts the financing options for new investments. For example, equity financing is rare, because the state is the only shareholder of the Gasunie (Gasunie Annual Report 2007, P 118). There are two main factors the Gasunie and other regulated companies can influence to change their interest payments.

First, there is a trade-off between longer and shorter debt maturity. Shorter debt maturity is generally associated with lower interest payments and longer debt maturity with higher interest payments (www.bloomberg.com/markets/rates). However, taking a shorter debt maturity than the maturity of the investment will increase the refinancing risk. This is the risk that a company cannot acquire new debt for the remaining maturity of the investment.

The second trade-off is between low and high credit rating. Projects with more risk are generally associated with a higher expected profit and a lower credit rating. Projects with less risk are associated with lower expected profits and a higher credit rating. A lower credit rating causes the interest payments to rise and vice versa. Influencing the credit rating is difficult, because external companies determine the current credit rating (Koller et al, 2005).

In theory the interest payments can be decreased with a shorter debt maturity and/or higher credit rating. However the trade-off in debt maturity and credit rating shows that lowering the interest payments can have negative consequences for the company. We perform a study for both trade-offs with the following research question:

How can regulated firms decrease their interest payments looking at the trade-off of debt maturity and the trade-off for credit rating?

(7)

2. LITERATURE REVIEW

In the second section we will discuss the main variables that influence the interest payments. For the complete overview we start with general capital structure theory and regulation theory. In appendix table 1 we present a summary of the most important articles used in this paper.

2.1 Capital Structure Theory

Modigliani and Miller’s (1958) proof about capital structure irrelevance in perfect market condition is a common starting point for all corporate financing studies. The theory concludes that the value of a company cannot be influenced by changing the composition of debt and equity. In a following research by Modigliani and Miller (1963), they revise the first statement and conclude that when taxes are taken into account, the valuation of a firm can be influenced. The reason for this revision is that as interest payments on debt are tax-deductible, financing with debt creates value. This new statement of Modigliani and Miller would imply that a company should use 100% debt financing. Kraus and Litzenberger (1973) disagree with the 100% debt statement of Modigliani and Miller and suggest that the tax shield is to be traded off against bankruptcy costs, so the capital structure determination is no longer a zero sum game. The revised capital structure irrelevance proposition makes the amount of debt and equity influence the valuation of a company. The research by Modigliani and Miller (1958, 1963) and Kraus and Litzenberger (1973) assumes that the interests of the manager and shareholders are perfectly aligned. This assumption is dropped by Jensen and Meckling (1976), who state that corporate managers are pursuing private benefits like higher salaries or job security. The interest aligned between manager shareholder and debt holder is explained further in this section with the debt overhang problem. For the research it is important to take into account that valuation of a company can differ due to tax shields.

2.2 Regulation

Regulated companies are described by Koller et al (2005) as: “a company which is regulated by a government institution in order to maximize the value for the society”. The regulation process is needed, because otherwise these companies could exert great market power due to their market position (natural/technical), or regulation is needed to ensure the safety and investments of these companies.

(8)

leverage is possible due to their stable cash flows. Finally, Barclay (1995) finds that the capital structure of regulated companies consists of relatively more long term debt than short term debt when compared to companies that are not regulated. The possible reason for this longer debt maturity is presented by Myers (1977), who states that regulated companies have less direct influence on future investment decisions, so to prevent refinancing problems the maturity of the investments is matched with debt.

Taggart (1985) links the main regulation theories with capital structure using formula 2.3. (2.3)

Formula (2.3) is structured in the following way. At the beginning of the period, the firm chooses capital stock (K) which it finances with combinations of Debt (D) and/or Equity (E). For a given capital stock the financing decision is fully specified once D has been chosen. When the firm is unregulated it also has to choose output price of its product (p). Then it tries to maximize the market value (V). In theory, regulated firms are only allowed to choose K and D in order to maximize V. However, Taggart (1985) suggests that the choice of K and D influences the regulators choice of p. This is shown in the next two theories on regulation and capital structure.

The first discussion by Taggart (1981) on regulated firms and capital structure is based upon the market failure theory1. Taggart finds a positive link between the amount of debt (D) and the market price regulators choose (p). The relationship between debt and price implies that a higher amount of debt results in a higher regulatory price setting. The positive relation is caused by regulated firms increasing the amount of debt (D) and thereby increasing the probability of default. The risk-averse regulators attempt to minimize the probability of default by setting higher output prices (p). Higher output prices imply more income for the regulated firms (V). Note that the theory implies 100% debt financing. Fraja and Stones (2004) conclude that 100% debt financing is not socially optimal, because too much debt implies more financial distress and more variability in the prices paid by the consumers. The price variability can be explained by the following example: when a firm increases leverage to reduce the cost of capital, the regulatory price can be reduced. But if an exogenous shock

1

(9)

occurs, the amount of equity used as buffer is reduced because of the earlier increase in leverage. When the equity capital falls below a certain level, the price is raised to create a new buffer.

The second theory on regulation and capital structure is constructed by Dasgupta and Nanda (1993). Dasgupta and Nanda construct a regulation model in which a higher percentage of debt implies a lower dividable surplus to either consumers or producers due to the higher probability of default. Price setting is influenced by bankruptcy risk. As debt increases, the regulator can only decrease the risk of bankruptcy by increasing the price. This creates a disadvantage for the consumers who pay for this trade-off with a higher price. Dasgupta and Nanda (1993) conclude that capital structure is strongly related to the political economy2. The model provides insights in how companies have incentives to raise debt when the political force is more pro consumer, in order to prevent regulators from enlarging the gross surplus of consumers. This theory assumes that regulated companies can determine their own leverage. Cambini et al (2009) adds that regulators can also choose to act more pro producers to stimulate competition or to stimulate investments by setting a higher output price. In this section, the regulation theories in combination with capital structure are discussed. The most important factors for the empirical research are the trade-off between price regulation and debt level. Higher debt levels are associated with a higher regulatory price (Taggart, 1981). This is in contrast with Fraja and Stones (2004) who find that after a certain level, the higher debt level will result in a lower regulatory price. The statement of Kraus and Litzenberger (1973) that the capital structure puzzle is a trade-off between the tax shield and bankruptcy costs is an important factor for the research part.

2.3 Debt Maturity

The effect of the debt maturity on capital structure can be described by the yield curve. The yield curve describes the relationship between the interest rate and the remaining time to maturity. In appendix figure 1 an example of a common yield curve is presented. The interest rate of a yield curve is influenced by many factors; future expected inflation and economic factors among others. Often the yield curve is positive, which implies that the yield will rise as maturity lengthens a year. The

2

(10)

positive yield is caused by several factors, which include expected inflation, risk free rate and future uncertainties. A risk premium has to be paid for these uncertainties.

Myers (1977) presents the first discussion on why some firms borrow more than others, why some borrow with short-term and others with long-term instruments. The answer on this question is presented in the following example: Myers suggests that a company consists of both assets in place and future growth opportunities. The future growth opportunities have lower collateral value and are subject to the debt overhang problem. In the case of debt overhang, all the future earnings are subject to payments to existing debt holders before payments to equity holders. This implies that the firm cannot issue new debt because the default risk becomes too high. Besides this, shareholders are less likely to issue new stock, because the payments to existing debt holders are significantly large. Thus the firm does not accept all positive net present value investments, because it cannot be financed.

The main conclusion by Myers (1977) is that leverage is positively related with assets-in-place. This is because assets in place have higher collateral value and reduce agency costs associated with free cash flow. The agency costs of free cash flow are reduced, because debt payments help to reduce the agency cost of free cash flow by reducing the cash flow available for spending at the discretion of the manager. These and other related theories have led the profession to conclude that firms should use relatively more debt to finance assets in place and relatively more equity to finance growth opportunities (Hovakimian, Opler, and Titman 2001).

The article of Myers (1977) concludes that the difference between short-term debt and long-term debt is a result of companies trying to match their asset life time with corresponding debt. There are more recent articles that examine different hypotheses influencing debt maturity. Stohs and Mauer (1996) consider four different hypotheses to influence the debt maturity: underinvestment problem, signaling, matching and taxes. The four hypotheses are tested in a variety of articles explained in the next section.

(11)

growth opportunity. The reason is that short term debt holders usually have less influence on investment opportunities compared to long term debt holders.

The second hypothesis affecting debt maturity is the signaling hypothesis. This hypothesis implies that firms choose the maturity of debt to signal project quality. Flannery (1986) argues as follows: firms are indifferent about the composition of debt when the insiders of the firm and the market investors possess the same information about the future company prospects. However, when the firm insiders are better informed than outside investors, then the firm will choose the most overvalued securities. Barclay and Smith (1995) argue that the valuation of long- term debt is more sensitive to changes in firm valuation of short term debt. So when a firm is mispriced, in both situations debt is mispriced but the mispricing is larger for long-term debt. In a situation where the bond market cannot distinguish between low quality (overvalued) firms and high quality (undervalued) firms, high quality firms prefer the less underpriced short term debt. Low quality firms will prefer the more overpriced long term debt. In equilibrium high quality firms will issue more short term debt and lower quality firms will issue more long term debt.

The third hypothesis by Myers (1977) is the matching principle, matching the maturities from assets and debt reduces the costs of financial distress. The firm should match the maturity of its liabilities with the maturity of the assets. When debt has a shorter maturity than assets, there may be a shortage in cash when the debt is due. When debt has a longer maturity than the cash flow of the project, debt has to be paid off after the latest income of the project.

The last hypothesis influencing debt maturity is the tax shield hypothesis from Kane, Marcus and Mcdonald (1985). The tax shield hypothesis states that the firm makes a trade-off between the tax shield of debt and the (indirect) bankruptcy costs. There are three different reasons for this trade-off. First, the optimal debt maturity increases when the distribution costs of issuing securities increases, so the refinancing costs are spread over a longer period. Second, when the tax advantage decreases, the maturity increases so the remaining tax advantage will exceed the distribution costs. Third, optimal debt maturity increases as firm value volatility decreases, because the firm does not have to rebalance its capital structure as often in order to moderate expected bankruptcy cost (Stohs and Mauer, 1996). Kane et al. (1985) argue that the optimal debt maturity of a firm increases as the tax advantage decreases because the firm wants to ensure that the remaining tax advantage of debt is not less than amortized costs associated with the issuance of new securities.

(12)

(1991) analyzes the debt maturity for firms with private information. The optimal debt maturity for firms is a trade-off between short term debt maturity to improve credit rating and liquidity risk. Short-term maturity is usually seen as a signal for good news, because it shows that the firm expects to be successful in refinancing the investment in the future. Short-term debt matures before the cash flows arrive from an investment, so it has to be refinanced at future credit rating terms. However, the long-term debt has a maturity matching the timing of cash flows. The short-term debt gives more control to lenders because the borrower can only pay off the old debt by issuing new debt. When the borrower cannot pay the debt, lenders have the right to liquidate or take control. This is why good firms sometimes choose to issue long term debt to prevent the risk of not being able to find refinancing in the future, even if the firm expects more favorable news than average. For firms with low ratings the refinancing risk outweighs the information effect. This is why there is a minimum rating so that the benefits of short term debt are worth the refinancing risk. Very low rated firms however, may have no other choice than short term debt, despite the high refinancing risk and possibility of losing control to lenders. So, there are two different types of short term borrowers. First the lowest rated firms who have no choice, and the high rated firms who use the short-term financing to take advantage of the arrival of information. Firms in between these two categories rely mostly on long-term debt.

The article of Diamond (1991) also presents another interesting result: The credit rating of a company is negatively related to its leverage. In the same time, the rating on a firm’s debt is negatively related to the maturity of the debt. This implies that a highly leveraged firm usually has a longer debt maturity, because of the firm’s credit rating.

Barclay and Smith (1995) find that publicly traded firms have a clear relation between debt maturity and bond rating. They confirm the expectations raised by Diamond, namely that firms with a higher credit rating have more short-term debt when compared to companies with lower credit rating. This result also confirms the signaling hypothesis earlier described in paragraph 2.3.1 where firms signal their quality through debt maturity.

(13)

Stohs and Mauer (1996) also find a non-monotonic relation between the credit rating of a company and the debt maturity. Firms that have a high credit rating or a very low credit rating have a shorter average debt maturity than other firms in their research. Also, the type of debt issued differs by credit rating. Firms with the highest credit rating (AAA and AA) tend to rely most on directly placed debt, such as debentures and commercial paper. Firms with intermediate credit rating (A, BBB and BB) are active in public debt markets, however they issue more bank debt (notes, bank loans, revolving credit, and promissory notes) compared to other credit rated companies. Stohs and Mauer also find that companies without a credit rating use little long term debt, and rely mostly on bank debt.

Finally, the article by Hackethal and Jensen (2006) confirm the non-monotonic relationship between credit rating and the debt maturity as described in the article of Stohs and Mauer (1996). However, they find another result in companies without a credit rating and for companies with a medium credit rating. Unrated companies tend to have a longer maturity than predicted in the previous studies. The firms with a medium credit rating do not have an increasing maturity as predicted in the previous studies. The deviation is due to the fact that the article uses a different proxy for the credit rating. They also use accounting-based and market figures to predict the risk.

Barclay (1995) finds that regulated firms, large firms and firms with few growth opportunities have more long-term debt in their capital structure. Regulated firms have more long-term debt financing because regulated firms have less possibilities to make responsible decisions on future investments as managers in an unregulated firm. This reduction in responsibility reduces the incentive of short term debt. Barclay also finds a strong correlation between the debt maturity and firm size. Larger firms tend to have a significantly longer debt maturity. In Appendix Figure 2 the bond rating is plotted against debt maturity.

(14)

3. METHODOLOGY

3.1 Debt Maturity

The first variable that influences the interest payments of a company is the debt maturity. Usually, the relation between debt maturity and interest costs is positive (Bloomerg). This relationship is presented in Figure 3.1.

Figure 3.1: The expected relationship between interest rate and debt maturity

In teres t Yi el d Lo w H ig h Short Long Maturity Source: www.bloomberg.com/markets/rates

The trade-off for the shorter debt maturity and lower interest payments is the higher refinancing risk. The refinancing risk is the possibility that a company cannot acquire new debt for the remaining maturity of the investment (Koller et al, 2005). The relation between the refinancing risk and the debt maturity is presented in figure 3.2.

Figure 3.2: The expected relationship between refinancing risk and debt maturity

Refi n an ci n g Ri sk Lo w H ig h Short Long Debt Maturity

Source: Koller et al, 2005

(15)

as debt with a maturity of more than one year. The long-term debt as ratio of total debt is also used in Antoniou et al. (2006) and Stohs et al. (1996). This ratio is most applicable for this study because of data availability.

Table 3.1

Factors in literature expected to influence debt maturity

Factor Measurement Expected relation

Underinvestment problem Capital expenditure/Assets Positive

Signaling Quality (EBIT/Assets) Negative

Matching Fixed Assets/Depreciation Positive

Tax shield Tax Expense/Profit for Tax Negative

Underinvestment is the ratio of capital expenditures divided by total assets. Signaling is the ratio of earnings before interest divided by total assets. Matching is the ratio of fixed assets divided by depreciation. Tax Shield is the ratio of tax expense divided by profit before tax.

Underinvestment problem

We expect the underinvestment problem has a positive relationship with debt maturity (Myers, 1977). The underinvestment problem occurs when shareholders may prefer not to invest in positive net present value growth opportunities when a large portion of the value created goes to the payment of existing debt holders, rather than to the shareholders. The underinvestment problem can be solved by issuing short term debt that matures before the exercise date of the growth opportunities. A company that has more growth opportunities should issue more short-term debt. Stowe and Xing (2005) argue that the growth opportunities can be measured by taking the capital expenditures as percentage of the total assets.

Signaling Hypothesis

We expect the relation between signaling and debt maturity to be negative. Flannery (1986) states that in equilibrium, the market cannot distinguish between high quality and low quality firms. High quality firms signal their quality by issuing short-term debt that has a higher refinancing risk. Cai et al. (2007) argue the quality of a firm can be measured by the ratio of EBIT divided by assets.

Matching principle

(16)

Tax shield

We expect debt maturity to be inversely related to the tax shield. Kane et al (1985) argues that the firm wants to ensure that the remaining tax advantage of debt is not less than the amortized costs associated with the issuance of new securities. The tax shield ratio is measured by dividing the total tax expense with earnings before taxes.

The four hypotheses and the control variables lead to the regression equation as presented in formula (3.1). We added more variables because Ozkan (2002) argues there are several other variables that influence debt maturity. For example Ozkan argues profitability measures like profit margin influence debt maturity. We added the natural logarithm of total assets as a control variable, because we expect that a larger company has better access to the capital market.

Formula (3.1) Regression equation debt maturity:

3.2 Credit Rating

(17)

Table 3.2

Determinants founded in literature expected to influence credit rating Factor Measurement

Net working Capital/Sales Standardized Gearing Equity/Total Assets Net worth/Total Assets Standardized Sales/Net Worth

Long Term Debt/Total Assets Sales/Total Assets

Net income/Net Worth Standardized Long term Debt/Net Worth Standardized Net income/Total Assets Standardized control variables

Total Assets Log. Total Assets Total Debt Log. Total Debt

Net Working Capital Log. Net Working Capital Sales Log. Sales

We summarize the determinants influencing credit rating from Pinches and Mingo (1973). We use the determinants in the credit rating regression in formula 3.2

We use equation (3.2) for the regression to determine the most important determinants of credit rating. We expect a positive relationship between firm quality and credit rating so we add the signaling hypothesis as a control variable (Cai et al., 2007)

Formula (3.2) regression equation credit rating:

3.3 Data Envelopment Analysis

(18)

2005). Next we explain how we use the Data Envelopment Analysis (DEA) to determine how to make the trade-offs.

Figure 3.3: The expected relationship between credit rating and debt maturity on interest rate Debt Maturity

Short (Year < 1) Long (Year > 1)

High (AAA)

Credit rating

Low (D)

Farrell (1957) defines the first model of the modern efficiency measurement models. Farrell finds that efficiency is composed of two components, namely technical efficiency (ability of a firm to obtain maximal output form a given set of inputs) and allocative efficiency (ability of a firm to use inputs in efficient proportions). The first measure for relative firm efficiency using inputs and outputs is in formula (3.3). (3.3) Where:

(19)

The Data Envelopment Analysis (DEA) is introduced by Charnes, Cooper and Rhodes (1978) and is based upon formula 3.3. DEA is used to determine the efficiency of companies compared to each other based on several inputs and outputs (Cooper et al, 2006). DEA method consists of a non-parametric linear programming technique that allows relative efficiently or productivity to be defined in terms of inputs and outputs (Avkiran, 1999). The main ability of DEA is to find potential improvements for inefficient units and identify branches to benchmark.

In the original formula (3.3) the most common difficulty is finding a common set of weights to determine relative efficiency. Charnes et al. proposes that companies may value input and outputs weights differently. This is why a linear model is introduced in which companies adopt their most efficient weights. To maximize the efficiency ( ) of company (J) algebraic model in formula (3.4) needs to be solved:

(3.4)

Subject to:

Where

We use an example to explain the methodology and interpretation of DEA. In table 3.3 the sales (output), employees (input) and marketing (input) are provided for five major supermarkets in the Netherlands. For example table 3.3 shows that AH produces three units of sales using six employees and six units of marketing. In our study we use credit rating and debt maturity as input and interest payment measure as output variable.

Table 3.3

DEA Example Supermarkets

Company Sales in € (o) Employees # (i1) Marketing in € (i2) i1/o i2/o

C1000 (1) 1 2 5 2 5

Edah (2) 2 2 4 1 2

AH (3) 3 6 6 2 2

Spar (4) 1 3 2 3 2

Aldi (5) 2 6 2 3 1

(20)

To explain formula (3.4) we use the data from table 3.3 as an example. The efficiency of C1000 is obtained with use of the following model:

Subject to:

………for remaining companies

The solution yields a value for which is the current efficiency of C1000. If then C1000 is most efficient relative to the others, but if is less than 1 other companies are more efficient, even when the weights are chosen to maximize the efficiency of C1000. The solution of the model is provided in table 3.4 and plotted in figure 3.4. The table and figure show that Edah and Aldi are the efficient companies, because Edah and Aldi are on the Efficient Frontier. The companies above the frontier line are less efficient. The graph and table illustrate that AH can improve by mimicking Edah and Aldi. Currently, AH is performing for 83% efficient. In order to improve the efficiency, AH should incorporate a portfolio of 100% of Edah and 50% of Aldi, so the company moves to the efficient frontier.

Table 3.4

Solution to Supermarkets DEA: Companies to mimic in percentage

Company Efficiency C1000 Edah AH Spar Aldi IS_Emp. IS_Mar. OS_Sales C1000 0.5 - 0.5 - - - - 0.5 -

Edah 1 - 1 - - - - AH 0.83 - 1 - - 0.5 - - - Spar 0.71 - 0.21 - - 0.29 - - - Aldi 1 - - - - 1 - - -

(21)

Figure 3.4: DEA example

The Data Envelopment Analysis provides a better inside in the trade-off between credit rating and debt maturity for decreasing the interest rate. The strength of DEA primarily depends upon the peer group. This is because the most efficient companies are always based on the selected peers. DEA has several strengths which are useful in this research. First, it is possible to handle multiple inputs and outputs and it does not require any assumptions or relation between inputs to outputs. Second, companies are directly compared with each other. A limitation of the DEA is that it can cause noise because DEA uses the extreme point method. Outliers in the peer group can cause differences in the results of the DEA. The principal disadvantage of DEA is that it assumes to be free of measurement error, while there can be deviation in the results (Mester, 1996). The final limitation is that efficiency is related to the other companies in the sample. Possible other companies outside the sample might have been more efficient.

The DEA in this research is performed on three variables: debt maturity and credit rating as input variable and the logarithm of interest coverage as output variable. We use the logarithm of interest coverage in order to decrease the differences between the companies in the peer group. The DEA determines how companies can decrease the existing input variables while keeping the output variable constant. For this reason, the debt maturity variable (long term debt/total debt) is altered to short term debt/total debt, because minimizing short term debt and credit rating has a negative impact on interest. When the interest payment decreases, the total interest coverage increases. This is explained in figure 3.5. As table 3.5 shows, company B is more efficient than company A, because

(22)

they have lower inputs (credit rating and debt maturity) in obtaining the same output (interest coverage) as company A.

Figure 3.5: Relationship Debt Maturity and Credit Rating on Interest Coverage.

Table 3.5 Efficiency comparison

Credit Rating Short term debt/Total debt Interest coverage

Company A 7 0.30 1.5

Company B 6 0.20 1.5

The content of the graph is just as an example and no realistic data. Credit Rating is presented as a proxy, where 10=AAA and 1=D. Interest coverage is presented as the ratio between EBIT/Interest paid.

We use the interest coverage ratio as output variable in order to determine how regulated companies can decrease interest payments.

The first reason we use interest coverage is that the measure scales the interest payments, so the companies are better comparable.

The second reason we use interest coverage is explained by figure (3.5). The numerator of interest coverage is made up of the earnings before interest and taxes. We assume the average tax rate is equal for all companies in this study. This means that the numerator only changes because of differences in earnings and interest. The companies in the peer group are comparable in profitability, investment opportunities and risk level. For this reason we can assume the earnings only differ because of differences in size. We assume the denominator, interest payments, will only change because of size, debt maturity and credit rating. When the taxes are equal and the interest payments grow at the same rate as the EBIT the interest coverage stays equal. This is why we conclude that interest coverage mainly changes because of differences in debt maturity and credit rating.

(3.5)

(23)

than those of company A, but interest payments which are eight times larger than those of company C. Following our previous reasoning, the interest coverage of company C is larger due the differences in the debt maturity and/or credit rating of the companies.

The use of the DEA in our study is illustrated in table 3.6. In this example, company C seems the best performing company because of the highest interest coverage. The inputs of the analysis, debt maturity and credit rating, are good for company C. Company C has the shortest debt maturity and the highest credit rating, thus the cheapest debt in general. Following the relatively short-term debt maturity and high credit rating company C should have a higher interest coverage than companies A and B. Contrary to expectations, the Data Envelopment Analysis shows that company B is the most efficient company. Company B has a lower credit rating and longer debt maturity than company C, which should lead to higher interest payments. The results of the DEA imply that company B is more efficient in using the credit rating and debt maturity to acquire the resulting interest coverage.

Table 3.6

Example Interest Coverage Measurement

Company A B C Earnings+Taxes 400 4000 4000 Interest payments 100 1000 800 EBIT 500 5000 4800 Interest Coverage 5 5 6 Debt Maturity 0.5 0.4 0.6

Credit Rating AA A AAA

Efficiency (DEA) 88,60% 100% 96%

(24)

4. DATA SECTION

4.1 Data section

In order to perform the Data Envelopment analysis, a peer group is composed, consisting of regulated companies in the electricity distribution and gas distribution industry. To analyze the differences between regulated and non-regulated companies a control group is formed. The control group contains companies with revenues comparable to the peer group.

The data for the peer and control group are collected from the Amadeus Database of Thompson. The Amadeus Database collects annual data on the top 250,000 companies in 41 European countries on the balance sheets, profit and loss statements and credit scores. For additional information about the peer group, annual reports are used. In appendix table 6 the variables used in this research are summarized.

To construct a representative peer group for the Gasunie it is important to get a better understanding of the company and the industry. The Gasunie is founded in 1963. Its main activities are maintaining and developing the infrastructure and transportation of natural gas in North Western Europe. Gasunie is one of the largest owners of high pressure natural gas pipeline networks in Europe, with more than 15,000 kilometers of pipelines. In July 2004 the first step was taken to liberalize the Dutch natural gas market. A new fully independent company called Gas Transport Service (GTS) was created. GTS is the national transmission operator and is responsible for the management, operation and development of the natural gas transport system in the Netherlands. The second step in the liberalization process of the natural gas market was in July 2005, when GasTerra was created as an independent company trading in natural gas. GasTerra operates on the European energy market and acts primarily on the Dutch natural gas market. In short, it can be said that the Dutch natural gas market is split up into three segments (Annual report Gasunie, 2007):

Upstream; production of natural gas (NAM)

Midstream; natural gas infrastructure (Gasunie), natural gas trading (GastTerra) and natural gas transport (GTS)

Downstream; delivering natural gas to consumers (e.g. Nuon, Essent)

(25)

The companies in this study are found using three databases listed in table 4.1. Table 4.1

Databases reviewed for peer group selection

Publisher Year Content of database No. of companies

European Union* 2008 Contracting entities in the sector of transport or distribution of natural gas or heat.

253

European Union* 2008 Contracting entities in the sectors of production, transport or distribution of electricity

246

EPSU** 2005 EU Gas and Electricity Directives ≈400

*Source: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:349:0001:01:EN:HTML **Source: http://www.psiru.org/reports/2005-10-E-EUDirective.pdf

The companies from these databases are selected using the filters below:

The companies in the peer group are in the natural gas or electricity industry within the European Union. Filter one is applied due to the specific regulation on gas and electric companies within the European Union.

The core business of the companies in the peer group is maintaining and developing the natural gas/electricity infrastructure. The second filter is applied due to significant differences in the balance sheet when a company is also involved in natural gas/electricity sales.

The companies in the peer group have to be the owner of the natural gas/electricity infrastructure. The third filter implies the companies have the assets (natural gas/electricity infrastructure) on their balance sheet. This filter is applied because natural gas and electricity distribution companies are not always the sole owner of the assets on the balance sheet. This difference can cause significant changes on the balance sheet and credit rating.

(26)

The companies left using these four filters on the databases are presented in table 4.2. Table 4.2

Composition of the Peer Group

Company name _Country _{Main Activity} _{Total Assets in €}

Rte Edf Transport France Electricity 12,992,674

E.On Sverige _Sweden _Electricity _10,905,204

National Grid Gas United Kingdom Gas 20,840,750

Edp Distribuiçao _Portugal _Electricity _6,260,371

Snam Rete Gas Italy Gas 10,700,000

Enagas _Spain _Gas _4,308,670

Gas Natural Distribucion Spain Gas 2,547,755

Grtgaz _France _Gas _5,728,909

N.V. Nederlandse Gasunie Netherlands Gas 6,917,300

Terna _Italy _Electricity _6,290,600

Red Electrica Spain Electricity 5,315,024

Scotia Gas Networks Limited _{United Kingdom} _Gas _4,940,561

Elia Belgium Electricity 3,977,900

Fluxys _Belgium _Gas _2,069,650

Spp Distribucia Slovakia Gas 2,601,558

Northern Gas Networks _{United Kingdom} _Gas _1,646,097

Societatea Nationala De Transport Gaze Natural

Romania Gas 721,864

We obtain the data from the Amadeus database and annual reports of each specific company.

To compare the peer group with the market a control group is constructed with non-regulated companies within the European Union. The control group consists of a sample of 10,000 companies from the 242,784 companies available in the Amadeus Database. The sample is gathered on revenue size comparable with the companies in the peer group. Thereafter, the control group is adjusted for the availability of data for the required variables. After the availability selection the control group consists of a total of 8,221 companies.

4.2 Descriptive Statistics

In table 4.3 we summarize the descriptive statistics of the peer group and control group. The tables are separated for the dependent variables from regression equation 3.1 (debt maturity) and regression equation 3.2 (credit rating). Table 4.3 show that the average debt maturity for the companies in the peer group is 0.73. The credit rating is on average 6.14. This implies that the average credit is somewhat below BB, as AAA=10 and D=1.

(27)

of 0.27. This implies that the debt maturity of the peer group is significantly higher than the control group with a 1% confidence level.

The credit rating of the peer group is comparable with the control group, because both the peer group and the control group have an average credit rating of about 6.15. The standard deviation of the peer group is 0.87 and the standard deviation of the control group is 1.02.

Table 4.3

Descriptive statistics Peer group and control group

Variables Mean Median Standard deviation Peer Control Peer Control Peer Control Peer Group dependent variable regression 1

Debt Maturity 0.73 0.30 0.75 0.23 0.16 0.27 (LT/Total Debt)

Peer Group independent variables: debt maturity

Underinvestment (capex/TA) 0.04 0.02 0.058 0.03 0.106 6.23 Signaling (EBIT/TA) 0.07 0.08 0.08 0.06 0.04 0.88 Matching (Fixed A/Depr) 27.2 134 26.54 10 8.88 2720 Tax Shield (Tax/Profit BT) 0.26 0.33 0.28 0.28 0.16 4.59 Peer Group dependent variable regression 2

Credit Rating (AAA=10,D=1) 6.14 6.15 6.13 6.04 0.87 1.02 Peer Group independent variables: Credit Rating

Log Total Assets _6.68 _5.55 _6.73 _5.42 _0.36 _0.66 Networking Capital/Sales 0.18 0.09 0.19 0.06 0.34 0.87 Net Worth/Assets _0.40 _0.29 _0.35 _0.27 _0.22 _0.23 Sales/Net Worth 0.79 65 0.67 5 0.43 1059 Net Income/Sales _0.16 _0.04 _0.18 _0.02 _0.11 _0.24 Log Debt 6.42 5.37 6.47 5.25 0.46 0.67 LT Debt / Total Assets _0.44 _0.22 _0.48 _0.15 _0.20 _0.22 Sales / Total Assets 0.27 3.19 0.21 1.56 0.13 61.85 Net Income/Net Worth _-0.09 _0.63 _0.08 _0.14 _0.82 _15.60 Log Net Working Capital 11 9.32 10.75 9.30 1.05 1.60 Log Sales _6.07 _5.72 _6.12 _5.94 _0.35 _0.51 LT Debt / Net worth 1.57 12.32 1.37 0.48 1.45 627.62 Net Income / Total Assets _0.04 _0.05 _0.04 _0.03 _0.03 _0.72

(28)

When the debt maturity and credit rating are plotted in figure 4.1 for the peer group, this results in a small negative relationship between credit rating and debt maturity. This means that companies with a higher credit rating usually choose for a shorter debt maturity, which confirms the results from the literature review. However the relationship between debt maturity and credit rating is not significant. N.V Gasunie y = -0.698x + 6.651 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 0.00 0.20 0.40 0.60 0.80 1.00 C red it R at in g (1 0=A A A , 0 =D )

(29)

5. RESULTS

In this section we present the results following the research described in the methodology. The Data Envelopment analysis is used to determine how efficient the companies in the peer group are with the current credit rating and debt maturity on interest coverage. We use the results from the regression analysis to find how changes in variables will affect the debt maturity and/or credit rating. 5.1 Regression

In order to find the variables that influence debt maturity and credit rating we use the following approach. First, we perform a regression analysis with all variables from regression equation 4.1 and 4.2. This is in order to find the variables that influence debt maturity and credit rating. Second, we perform a bivariate correlation analysis to determine the interdependence of the variables in the regression. Third, using the results from the bivariate correlation and the first regression we select the most appropriate variables to include in the final regression. This regression should be without multicollinearity and confirmed by the literature. The results from the bivariate correlation and first regression on peer group debt maturity, control group debt maturity, peer group credit rating and control group credit rating are respectively presented in appendix table 7, 8, 9 and 10. Next, we discuss the results of the final regression.

In the debt maturity regression for the peer group we include the variables matching and Sales/total assets. We summarize the results in table 5.1. Following the bivariate correlation these variables have a high correlation with debt maturity. Both variables are confirmed by the literature. The sales/total assets variable measures the profitability of a company, when a company is more profitable it has a shorter debt maturity (Stowe and Xing, 2005). The second variable we include is matching, based on the matching hypothesis; this reflects the degree to which companies in the peer group match the maturity of the assets with the maturity of the debt (Myers, 1977). We do not include the long term debt/total assets variable in the regression because this variable causes high multicollinearity with matching in the final regression.

Table 5.1

Regression debt maturity on peer group

Standarized Beta Significance Tolerance VIF

(Constant) 0.00

Matching (Fixed Assets/Depreciation) 0.43 0.05 0.52 1.91 Sales / Total Assets -0.48 0.03 0.52 1.91 The variables not significant or that cause multicolinearity are removed from the

(30)

In the debt maturity regression for the control we include the variables matching, log assets and the profit. We summarize the results in table 5.2. Following the bivariate correlation analysis, we include the matching hypothesis (Myers, 1977). Secondly, the variable log assets is significantly correlated with debt maturity. This is confirmed by Barclay and Smith (1995) who argue that large firms tend to have a significantly higher debt maturity. The high correlation between debt maturity and profit margin is not predicted by our literature. We exclude the variable long term debt/total assets because this variable causes high multicollinearity with matching.

Table 5.2

Regression debt maturity on control group

Standarized Beta Significance Tolerance VIF

(Constant) 0.00

Matching (Fixed Assets / Depreciation) 0.02 0.02 1.00 1.00 Log Assets 0.36 0.00 0.96 1.04 Profit margin (%) 0.04 0.00 0.96 1.04 The variables not significant or that cause multicolinearity are removed from the presentation of the regression outcome from debt maturity on the control group. The regression analysis has been conducted on a 10% confidence level. Tolerance is 1-R2 and should be larger then 0.20. If tolerance is >0.20 multicolinearity could be present. VIF is the variance inflation factor, a VIF > 4 indicates multicolinearity.

In the credit rating regression for the peer group we include the variables signaling, gearing, sales/net worth and log debt. We summarize the results in table 5.3. Except for signaling all variables included in the regression are confirmed by Pinches and Mingo (1973). We find a high correlation of credit rating with gearing, sales/net worth and log debt. We include the signaling hypothesis because this measures the quality of a firm. Following Flannery (1986) higher quality firms are associated with a higher credit rating. We do not include other variables because this causes multicollinearity with one of the included variables.

Table 5.3

Regression credit rating on peer group

Standarized Beta Significance Tolerance VIF (Constant) 0.00

Signalling (EBIT/Assets) 0.26 0.02 0.93 1.08 Gearing (%) -0.46 0.00 0.77 1.29 Sales/Net Worth -0.38 0.00 0.83 1.20 Log Debt -0.40 0.00 0.92 1.09 The variables not significant or that cause multicolinearity are removed from the presentation of the regression outcome from credit rating on the peer group. The regression analysis has been conducted on a 10% confidence level. Tolerance is 1-R2 and should be larger then 0.20. If tolerance is >0.20

(31)

In the credit rating regression for the control group we include the variables signaling, log assets, net worth/assets, gearing and long term debt/total assets. We summarize the results in table 5.4. The bivariate correlation confirms the results from Pinches and Mingo (1973) for log assets, net worth/assets, gearing and long term debt/total assets. The signaling hypothesis is the only variable not confirmed by Pinches and Mingo that significantly influences the credit rating. The signaling hypothesis (the quality of a firm) influences the credit rating for both the control and peer group.

Table 5.4

Regression credit rating on control group

Standarized Beta Significance Tolerance VIF (Constant) 0.00

Signalling (EBIT/Assets) 0.05 0.00 0.99 1.01 Log Assets -0.10 0.00 0.86 1.16 Net Worth/Assets 0.68 0.00 0.82 1.22 Gearing (%) -0.10 0.00 0.80 1.24 LT Debt / Total Assets 0.03 0.00 0.78 1.28 The variables not significant are removed from the presentation of the regression outcome from debt maturity on the peer group. The regression analysis has been conducted on a 10% confidence level. Beta is the standardized beta. Significance should be larger then 5%. Tolerance is 1-R2 and should be larger then 0.20. If tolerance is >0.20 multicolinearity could be present. VIF is the variance inflation factor, a VIF > 4 indicates multicolinearity.

Looking at the results there are several similarities and differences. The matching hypothesis influences the debt maturity for both the peer group and the control group. However the matching hypothesis has a higher influence for the peer group. The companies in the peer group more often match the asset maturity with debt maturity. Myers (1977) argues this is because matching reduces the costs associated with financial distress.

Comparing the credit rating regression of the peer group and the control group, the similarities are the significant influence of signaling and gearing. It is interesting to note that the signaling hypothesis is the only influencing variable not discussed by Pinches and Mingo (1973). Signaling represents the quality of a firm, the effect of signaling is higher for the companies in the peer group. The gearing variable has a stronger influence on the peer group, where higher leveraged firms result in lower credit ratings.

(32)

value assets in place so more collateral is available for the debt. At last, the differences in debt maturity can be explained by Myers (1984) who predicts that debt ratios vary from industry to industry due to differences in asset risk, asset type and requirements for external funds.

5.2 Data Envelopment Analysis

We present the results from the DEA for the peer group in table 5.5. The second column presents the actual efficiency of the combination of inputs/outputs with respect to other companies in the DEA. For example Rte Edf Transport has an 83.40% efficient combination of the inputs, debt maturity and credit rating, compared to the most efficient companies in the analysis (Scotia and SPP). The third, fourth and fifth column present the possible improvements a company can make, in order to become more efficient. The fifth column, logarithm interest coverage, is kept constant except for Elia and Edp Distribuiçao. The reason for the change in interest coverage is that the DEA implies that Elia and Edp Distribuiçao have a slack in the combination of output factors. This means that looking at Elia and Edp Distribuiçao current inputs they could have got a higher output.

Gasunie 0 1 2 3 4 5 6 7 8 9 10 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 C red it R at in g (A A A =1 0, D =0 )

Debt Maturity (Long Term Debt / Total Debt)

Figure 5.1 Peer Group and Control Group on Credit Rating and Debt

Maturity

(33)

Table 5.5

Data Envelopment Analysis

Efficiency Improvements (Input) Efficiency Constant (Output)

Company name Efficiency Credit Rating ST/Total Debt Log. Interest

Coverage Rte Edf Transport 83.40% 6.08 to 5.07 0.34 to 0.10 0.47 to 0.47 E.On Sverige 91.70% 5.86 to 5.38 0.27 to 0.10 0.63 to 0.63 National Grid Gas 79.80% 6.13 to 4.89 0.21 to 0.09 0.37 to 0.37 Edp Distribuiçao 84.70% 5.08 to 4.30 0.62 to 0.08 0.04 to 0.06 Snam Rete Gas 85.10% 6.4 to 5.41 0.27 to 0.10 0.65 to 0.65

Enagas 96.70% 5.93 to 5.73 0.25 to 0.11 0.82 to 0.82

Gas Natural Distribucion 92.80% 7.05 to 6.54 0.69 to 0.12 1.25 to 1.25

Grtgaz 82.40% 6.57 to 5.41 0.18 to 0.10 0.65 to 0.65

N.V. Nederlandse Gasunie 89.90% 6.94 to 6.24 0.31 to 0.12 1.09 to 1.09

Terna 82.10% 6.20 to 5.09 0.27 to 0.10 0.48 to 0.48

Red Electrica 92% 5.82 to 5.36 0.22 to 0.10 0.62 to 0.62 Scotia Gas Networks

Limited

100% 4.30 to 4.30 0.08 to 0.08 0.06 to 0.06

Elia 73% 5.89 to 4.30 0.11 to 0.08 0.02 to 0.06

Fluxys 68.60% 6.30 to 4.32 0.14 to 0.08 0.07 to 0.07

Spp Distribucia 100% 8.01 to 8.01 0.15 to 0.15 2.03 to 2.03 Northern Gas Networks 89.60% 4.95 to 4.43 0.18 to 0.08 0.13 to 0.13 Soc. Nat. Trans. Gaze

Natural

97.60% 6.90 to 6.73 0.30 to 0.13 1.35 to 1.35

Input variable: Debt Maturity and Credit Rating, Output variable: Log interest coverage. The efficiency is measured using formula (3.3) the DEA Analysis. The efficiency is relative to the other companies in the analysis. Credit Rating is a proxy, AAA=10, D=1. The DEA is executed using the input based model. The inputs are minimized keeping the output constant. The credit rating and st/total debt column present first the original numbers followed by the improvements. The output column log. Interest is kept constant except if slack is involved..

5.3 Results Interpreted.

The results from the DEA show the efficiency of the companies, with respect to other companies in the analysis. The efficiency is measured on how inputs could be used more efficiently, or, in other words, how a company could have the same interest coverage with lower inputs. The lower inputs results in a lower credit rating or longer debt maturity. Normally, this implies higher interest payments. The current efficiency of the Gasunie is 89.90% when compared to the top performing peers. This implies that the Gasunie could have lower interest payments with the current level of credit rating and debt maturity.

(34)

debt maturity when compared to the peer group. The interest coverage for Scotia is rather low however. With the combination of debt maturity and credit rating this still is an efficient financing combination. SPP has the highest credit rating and a shorter debt maturity than the average debt maturity of the peer group. This combination gives the highest interest coverage for SPP, following the credit rating and debt maturity this is a predictable outcome.

Table 5.6

Lambdas weights (peers)

Company name _{Scotia Gas Networks} Spp Distribucia

Rte Edf Transport _0.792 _0.208

E.On Sverige 0.711 0.289

National Grid Gas _0.843 _0.157

Edp Distribuiçao 1 0

Snam Rete Gas _0.701 _0.299

Enagas 0.614 0.386

Gas Natural Distribucion _0.396 _0.604

Grtgaz 0.701 0.299

N.V. Nederlandse Gasunie _0.477 _0.523

Terna 0.787 0.213

Red Electrica _0.716 _0.284

Scotia Gas Networks Limited 1 0

Elia ₁ ₀

Fluxys 0.995 0.005

Spp Distribucia ₀ ₁

Northern Gas Networks 0.964 0.036

Soc. Nat. Tran. Gaze Natural _0.345 _0.655

Lambdas weights present the company’s position on the efficient frontier. To become more efficient a company should mimic this % of Scotia and SPP in their Debt Maturity and Credit Rating strategy.

In table 5.7 we present the current situation of the most important determinants of debt maturity and credit rating. Using the DEA we find Scotia and SPP are the most efficient companies, in table 5.7 we compare Scotia and SPP with the Gasunie.

(35)

The second variable to influence interest payments is credit rating. The Gasunie has compared to the efficient peers an intermediate credit rating, but compared to the total peer group an above average credit rating. There are four significant variables from the regression that influence the credit rating for the peer group companies as presented in table 5.7. The variable signalling hypothesis shows that the Gasunie has the highest signalling compared to the efficient peers. This implies the Gasunie is a higher quality firm (Flannery, 1986) with respect to the efficient peers. The gearing variable is higher than SPP and lower than Scotia, this is equal with the log debt variable. Log debt and gearing imply both that the SPP is relatively more financed by equity than the Gasunie which results in the highest credit rating. Overall Scotia has the most risky business strategy, with the lowest signalling ratio and highest gearing ratio/log debt which results in the lowest credit rating. The short debt maturity and long asset maturity also contributes in the more risky business strategy of Scotia. For SPP the signalling hypothesis is between the Gasunie and Scotia and the gearing variable is the lowest, which results in the highest credit rating. When taking all results into account we conclude that the Gasunie is performing above average, however it can decrease interest payments when it decreases gearing/log debt or decrease the debt maturity.

Table 5.7

Compare the credit rating and debt maturity determinants

Gasunie Scotia SPP

Debt Maturity 0.31 0.08 0.15

Sales / Total Assets 0.19 0.18 0.16

Matching (Fixed Assets / Depreciation) 33 41 35

Credit Rating 6.94 4.30 8.01

Signaling (EBIT/Assets) 0.09 0.03 0.05

Gearing (%) 23.22 41.23 15.10

Sales/Net Worth 0.24 1.15 0.19

Log Debt 6.17 6.63 5.59

Log Interest coverage 1.09 0.06 2.03

(36)

6. CONCLUSION

In this study we try to minimize the interest payments for regulated companies, specifically the N.V. Nederlandse Gasunie, while taking into account the trade-offs of debt maturity and credit rating. The research question is: How can regulated firms decrease their interest payments looking at the

trade-off of debt maturity and the trade-trade-off for credit rating?

We find in the literature that regulated companies can decrease interest payments by altering debt maturity (Myers, 1977) and credit rating (Diamond, 1991). We construct a peer group comparable with the Gasunie and compare this with a control group. We use the Data Envelopment Analysis (DEA) in combination with a regression analysis to find how companies can decrease interest payments. A regression analysis is performed to find the most important determinants of debt maturity and credit rating. We choose the most important inputs for the regression analysis on the basis of the literature. We perform the DEA to find how the Gasunie can reduce the interest payments compared with the peer group.

(37)

7. BIBLIOGRAPHY

Amadeus Database, 2009, Bureau van Dijk Inc., made available by the University of Groningen

Antonios, A., Guney, Y., Paudyal, K., 2006, The determinants of debt maturity structure: Evidence from France, Germandy and the UK, European Financial Management 12, pp. 161-194

Avkiran, N. K., 1999, The evidence on efficiency gains: The role of mergers and the benefits to the public, Journal of Banking and Finance 23, pp. 991-1013

Barclay, M. J., Smith, C.W., 1995, The Maturity Structure of Corporate Debt, The Journal of Finance 1, pp. 609-631

Bloomberg Terminal, 2009, Bloomberg L.P. by N.V. Nederlandse Gasunie.

Bradley, M., Jarrell, G.A., Kim, E.H., 1984, On the existence of an optimal capital structure: Theory and evidence, Journal of Finance 39, pp. 857-878

Cai, K., Fairchild, R., Guney, Y., 2007, Debt Maturity of Chinese Companies, Pacific-Basin Finance

Journal 16, pp 268-297

Cambini, C., Rondi, L., Spiegel, Y., 2009, Wholesale Regulation and Capital Structure in European Telecommunications Industry, Not published yet, pp. 1-16

Charnes, A., Cooper, W. W., Rhodes, E., 1978, Measuring the efficiency of decision making units,

European Journal of Operational Research 2, pp. 429-444

Cooper, W. W., Seiford, L. M., Tone, K., 2006, Data Envelopment Analysis, Springer Science and

Business Media LLC Second Edition

Dasgupta, S., Nanda, V., 1993, Bargaining and Brinkmanship: Capital structure choice by regulated firms, International Journal of Industrial Organization, pp. 475-497

Diamond, D. W., 1991, Debt Maturity Structure and Liquidity Risk, The Quarterly Journal of

Economics, pp. 709-737

Farell, M. J., 1957, The measurement of productive efficiency, Journal of the Royal Statistical Society 120, pp 253-290

Flannery, M. J., 1986, Asymmetric Information and Risky Debt Maturity Choice, The Journal of

(38)

Fraja, G., Stones, S., 2004, Risk and Capital Structure in the Regulated Firm, Journal of regulatory

Economics, pp. 69-84

Gasunie, 2007, N.V. Nederlandse Gasunie Annual Report for 2007. Netherlands, Groningen.

Guedes, J., Opler, T., 1996, The determinants of the maturity of corporate debt issues, The Journal of

Finance 51, pp. 1809-1833

Hackethal, A., Jansen, C., 2006, New empirical evidence on the debt maturity choice and the role of credit risk, working paper.

Hovakimian, A., Opler, T., Titman, S., 2001, The Debt-Equity Choice, The Journal of Financial and

Quantitative Analysis 1, pp. 1-24

Jensen, M. C., Meckling, W. H., 1976, A Theory of the Firm: Governance, Residual claims and Organizational Forms, Journal of Financial Economics 3, pp. 305-360

Kane, A., Marcus, A. J., McDonald, R. L., 1985, Debt Policy and the Rate of Return Premium to Leverage, The Journal of Financial and Quantitative Analysis 20, pp. 479-499

Koller, T., Goedhart, M., Wessels, D., 2005. Valuation: Measuring and Managing the Value of Companies, Fourth Edition. McKinsey & Company.

Kraus, A., Litzenberger R. H., 1973, A State-Preference Model of Optimal Financial Leverage, The Journal of Finance 28, pp. 911-922

Mester, L. J., 1996, A study of bank efficiency taking into account risk-preferences, Journal of Banking

and Finance 20, pp. 1025-1045

Modigliani, F., Miller, M. H., 1958, The Cost of Capital, Corporation Finance and the Theory of Investment, The American Economic Review 3, pp. 261-297

Modigliani, F., Miller, M. H., 1963, Corporate income taxes and the cost of capital: A correction, The

American Economic Review, pp. 433-443

Myers, S. C., 1977, Determinants of corporate borrowing, Journal of Financial Economics, pp. 147-175 Myers, S. C., 1984, The Capital Structure Puzzle, The Journal of Finance 3, pp. 575-592

Ozkan, A., 2002, The determinants of corporate debt maturity: evidence from UK firms, Applied

(39)

Peltzman, S., 1976, Toward a More General Theory of Regulation, The Journal of Law and Economics 19, pp. 211-240

Pinches, G.E., Mingo, K.A., 1973, A Multivariate Analysis of Industrial Bond Ratings, The Journal of

Finance 18, pp. 1-18

Stohs, M. H., Mauer, D. C., 1996, The Determinants of Corporate Debt Maturity Structure, The

Journal of Business, pp. 279-312

Stowe, J. D., Xing, X., 2006, Can growth opportunities explain the diversification discount?, The

Journal of Corporate Finance 12, pp. 783-796

Taggart, R. A. Jr., 1981, Rate-of-Return Regulation and Utility Capital Structure Decision, The Journal

of Finance 36, pp. 383-393

Taggart, R. A. Jr., 1985, Effects of Regulation on Utility Financing: Theory and Evidence, The Journal of

Industrial Economics 33, pp. 257-276

Thomas, S., 2005, The European Union gas and electricity directives, EPSU - European Commission Websites

Database regulated companies in the European Union, ECB, 2008, http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:349:0001:01:EN:HTML

(40)

8. APPENDICES

Appendix Figure 1: Yield Curve

Appendix Figure 2: Debt Maturity vs. Bond Rating

(41)

Appendix Table 1: Summary of the articles used in the research

Theory

Author Agency costs Signaling Matching Taxes Notations

Barclay and Smith - Little evidence - No evidence Support for contracting cost hypothesis, firms with more

growth options have less long term debt. Large and regulated firms more long term debt.

Stohs and Mauer Moderate support

Support for signaling hypothesis

Strong support Modest support

Mitchell (1993) Use bond maturity to monitor (monitoring)

Weakly support the signaling hypothesis.

- No support fort tax trade

off hypothesis

Flannery (1986) - Influences debt maturity

(but depends on

information asymmetry)

- - Signaling is the reason why matching time doesn’t

always work

Myers (1977) - - Matching debt and

asset maturity optimize firm value