a study of swedish public companies

Capital structure and firm performance – A study of Swedish public companies

Bachelor’s thesis, Business Administration Accounting Spring 2014 Supervisor: Johan Åkesson Authors: Richard Dumont, Robert Svensson

Bachelor’s thesis Business Administration, Handelshögskolan Göteborgs Universitet, Accounting, Spring 14 Authors: Richard Dumont and Robert Svensson Supervisor: Johan Åkesson

Title: Capital structure and firm performance – A study of Swedish public companies Background and problem: Developments in capital structure during the last 30 years have resulted in a number of capital structure theories. At the same time, and in spite of all research on the topic, capital structure policies were one of the reasons for many company problems when the financial crisis hit in 2008. It is therefore interesting to look at how capital structure has evolved in the last decade as well as to test the functional relationship between capital structure and firm performance on a large scale. Purpose: To map and explain the development of capital structure and firm performance in Swedish companies during the last decade. Limitations: The thesis will only focus on companies listed on the Swedish stock exchange and with yearly sales amounting to at least SEK 10m. Method: A large-scale quantitative cross-sectional study including some 300 Swedish companies and 8 years of financial statements data. Relationships have been tested with a multiple regression model and developments of financial data have been tracked and compared over an 8-year period. Results and conclusions: There is a negative relationship between debt-to-equity and return on equity for Swedish firms during 2005-2012. Companies can thereby increase their return on equity by decreasing their debt-to-equity levels. Further research: A study of optimal capital structure for Swedish firms, using the latest developments in capital structure theory and using similar data as in this study. Key words: Regression model, capital structure, return on equity

Table of Contents

1. Introduction.......................................................................................................................11.1 Purpose.......................................................................................................................................2

2. Literaturereview..............................................................................................................32.1Trade‐offtheory...........................................................................................................................32.2Peckingordertheory.................................................................................................................52.3Financialcrisis..............................................................................................................................5

3. Methodology.......................................................................................................................73.1Methodologyfordescribingdevelopmentsinfirmperformanceandcapitalstructure................................................................................................................................................73.1.1Useofmeasures...................................................................................................................................73.1.2.Definitionofmeasures.....................................................................................................................83.1.2.1Returnonassets............................................................................................................................................83.1.2.2Returnonequity...........................................................................................................................................93.1.2.3Debt‐to‐equityratio.....................................................................................................................................93.1.2.4Averageinterestondebt.........................................................................................................................10

3.2Methodologyforlinearregressionanalysis...................................................................113.2.1Descriptionofcontrolvariables.................................................................................................123.2.2Hypothesisdevelopment..............................................................................................................133.2.3Multicollinearity...............................................................................................................................143.2.4Goodness‐of‐fit..................................................................................................................................143.2.5Causalityversuscorrelation........................................................................................................15

3.3 Datacollection.......................................................................................................................16

4. Results...............................................................................................................................174.1Variabledevelopments..........................................................................................................174.2Regressionanalysis.................................................................................................................20

5.Analysis.................................................................................................................................225.1Discussionoffinancialratios...............................................................................................225.2Analysisofregressionresults..............................................................................................23

6.Conclusions.........................................................................................................................26

7.References...........................................................................................................................27

Appendix...................................................................................................................................30

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1. Introduction Capital structure is an important issue from a financial standpoint because it is linked to the firm’s ability to meet the objectives of its stakeholders (Simerly & Li, 2000). Modigliani and Miller (1958) argued that in a perfect market, the value of a firm is unaffected by how it is financed. When imperfections of real markets are taken into account, however, capital structure can have a substantial impact on firm performance (Abor, 2005; Denis 2012). Modigliani and Miller (1963) found that higher leverage leads to increased performance due to tax benefits. Developments in capital structure research during the last 30 years have resulted in a number of capital structure theories that predicts somewhat contradictory results (Baker & Martin, 2011). For instance, trade-off theory suggests a positive relationship between firm performance and leverage (Margaritis & Psillaki, 2010), whereas the pecking-order theory predicts a negative relationship (Baker & Martin, 2011). Additionally, research has shown that the effect of leverage on firm performance might depend on the specific environment of the firm (Simerly & Li, 2000). Because of the inconsistent theories that exist within the field it is important to provide empirical results that can help validate or disprove these theories. Bertmar and Molin (1977) carried out a comprehensive study during the period 1966 to 1972 to map the developments and relationships between a set of financial measures and capital structure. They found that the level of debt financing increased in Swedish firms during the studied period. Perhaps Bertmar and Molin’s (1977) analysis of financial performance and capital structure of Swedish companies is the most widely known field study in this area. Apart from this, not much research has been done on capital structure and firm performance among Swedish companies. It is therefore interesting to update some of the research and compare and analyze the result in light of the new important developments in capital structure research (Denis, 2012). In addition, the financial crisis during 2008 and 2009 also makes it interesting to look at the developments of capital structure. This is because many problems that companies experienced were created specifically from capital structure policies (Baker & Martin, 2011). Financial economists have critically evaluated capital structure theory as a result (Baker & Martin, 2011). The years before, during and after the financial crisis thereby provides a good period for studying the relationship between firm performance and capital structure in different economic environments, which has been found to have an effect on the relationship (Margaritis & Psillaki, 2010).

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The present thesis aims to study firm performance and capital structure in a similar manner as Bertmar and Molin (1977). This will be done by answering the following research questions:

How have ROE and ROA developed between 2005 and 2012? How has average interest on debt developed between 2005 and 2012? How has debt-to-equity ratio developed between 2005 and 2012? How do capital structure choices influence firm performance?

1.1 Purpose The objective of the present study is twofold. The first objective is to quantitatively describe developments of firm performance and capital structure before, during, and after the financial crisis 2005 to 2012. Secondly the objective is to find how capital structure choices have influenced firm performance during the period and to understand if the relationship is different during the boom, crisis years and the following recession.

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2. Literature review This section primarily reviews literature connected to capital structure and the relationship between capital structure and firm performance. Moreover, the financial crisis is described with regards to causes and possible effects, on the economy and businesses in general and on capital structure in particular. The studies of capital structure are usually said to have started with Modigliani and Miller (1958). They described how capital structure affects a company’s value in a perfect economy. In that case, companies get an increase in profitability from the higher leverage that exactly corresponds to the rise in discount rate due to the higher risk, thus a company’s valuation would be unaffected by its capital structure. However, Modigliani and Miller (1963) later concluded that, in practice, capital structure does affect company valuation since higher leverage gives greater benefits from tax shielding. Kraus and Litzenberger (1973) expanded on the concept of tax shields when they introduced the trade-off theory and the notion of an optimal capital structure. This theory, along with another important capital structure theory; the pecking order theory (Myers and Majluf, 1984), will be further reviewed in sections 2.1 and 2.2 respectively. In an attempt to explain the relation between firm profitability and capital structure, Johansson and Runsten (2005) presented the following formula:

Equation 1.

In the formula, IR represents the average interest of a company’s loans, TL/E is the total liabilities-to-equity ratio, ROA is return on assets and ROE is return on equity. Important to note is that a company can increase their ROE by changing their capital structure. As long as the return on assets is higher than the average interest rate, the ROE will increase with higher leverage. Several studies have been done on the relationship between firm performance and capital structure (see for example Simerly & Li, 2000; Margaritis & Psillaki, 2010). Bertmar and Molin (1977) studied capital structure and profitability in Swedish companies from 1966 to 1972. They found that debt-to-equity ratio and return on equity have a negative correlation.

2.1 Trade-off theory In contrast to dividend, interest payments on debt reduces a company’s taxable income. At the same time, debt increases the likelihood of bankruptcy for a company. According to the trade-off theory, capital structure reflects the trade-off between tax-benefits and expected costs of bankruptcy. (Kraus & Litzenberger, 1973)

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Equation 2. The firm’s value in trade-off theory

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2

1

The firm’s choice of leverage is then determined by maximizing V in the equation above. In the model, R is a random cash flow of a firm. T denotes the constant corporate tax rate and D is the required debt payments. The first-order condition with respect to D is:

Equation 3.

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A number of relationships can be derived from the formulas and can hence be used to explain capital structure decisions by the companies studied (Baker & Martin, 2011). When the tax increases in the equation, the debt should also increase since higher tax will give higher tax advantages. There should also be a positive relationship between debt and profitability because expected bankruptcy costs and tax shields are more valuable for profitable firms. There is, however, mixed empirical results for the relationships predicted by the formula (Baker & Martin, 2011). One of the reasons that capital structure research has been generating a variety of different results ever since the work of Modigliani & Miller (1958; 1963) is the complexity of measuring tax benefits (Graham, 2000). The reason for the complexity has been mainly data problems and the complex tax codes (Graham, 2000). In addition, quantifying the interest taxation effects and understanding the bankruptcy process and financial distress is also prominent issues (Graham, 2000). Graham (2000) developed a new measure of tax benefit that included the entire tax benefit function. As a result, it is concluded that the typical US firm can double tax benefits by issuing debt until the marginal tax benefit begins to decline (Graham, 2000). It has been a central issue in financial research that firms are consistently having lower debt levels than what is predicted as optimal levels by the trade-off theory (Ju et al., 2005). The traditional literature on optimal capital structure using the trade-off theory usually only includes bankruptcy costs and tax shields (Ju et al., 2005). The problem with these studies is that they are static and hence do not incorporate the rights of bondholders to force firms in to bankruptcy (Ju et al., 2005). As a result, few traditional studies have not yet provided compelling response to what the optimal value-maximizing capital structure is, Ju et al. (2005) being among the exceptions. Ju et al. (2005) suggests that the traditional research with a trade-off theory perspective usually suggests that firms are overleveraged. That is, firms would benefit from reducing their debt-to-equity ratio. When managers make capital structure decisions with the

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objective to maximize firm performance, however, Ju et al. (2005) finds empirical evidence that the median firm in Standard & Poor’s Compustat database is underleveraged and hence would benefit from increasing their debt-to-equity ratio. One major problem with the trade-off theory is that it assumes market efficiency and symmetric information (Baker & Wurgler, 2002). The decision to issue new equity depends on the stock market performance. The market timing theory suggests that companies are issuing debt depending on the business cycle. When the general economy is in bad condition, firms will not issue equity. When the economy is booming, however, equity issuance is high. That is, during good times, the debt-to-equity ratio should decrease (Baker & Martin, 2011).

2.2 Pecking order theory The pecking order theory was popularized by Myers (1984) and Myers and Majluf (1984) and is based on asymmetric information between firms and investors (Baker & Martin, 2011; Frank & Goyal, 2008). The principle of pecking order theory is that equity is a less preferred way to finance a firm because investors believe that managers will only issue new equity when the equity is overvalued. Specifically, managers of a firm are supposed to use a pecking list when they need to finance their operations (Graham & Harvey, 2001). As a result, investors will pay a lower value for the issued equity (Myers & Majluf, 1984). Empirical evidence also suggests that issuance of new equity result in stock price reductions (Baker & Martin, 2011). The pecking-order theory does not suggest a specific debt ratio as optimal, but instead seek external financing only when there are insufficient internal funds (Graham & Harvey, 2001). And when they do seek external financing, they always prefer debt over issuing new equity (Myers, 1984). The pecking order theory suggests a negative correlation between debt and profitability (Baker & Martin, 2011). This is because high-quality firms tend to use internal funds for financing its operations, whereas low-quality firms have to seek external financing, usually debt, in absence of profits. There is also a correlation between the level of asymmetric information and the incentive to issue new equity (Baker & Martin, 2011). Tong and Green (2005) set up a study to test whether the trade-off theory or the pecking order theory best predicted the performance of Chinese companies. They found statistically significant support for the pecking order theory over the trade-off theory. Specifically, they found that there was a negative correlation between debt and profitability.

2.3 Financial crisis The financial crisis started in 2007 in the United States before spreading to Europe in the middle of 2008 (Crotty, 2009). It caused indexes on several global stock markets, including the U.S and Sweden, to fall by over 50% and is considered the worst crisis since the Great Depression in the 1930s (Crotty, 2009). One of the main reasons for the

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crisis was the loose monetary policy employed by the Federal Reserve. For a couple of years before the crisis, the federal funds interest rate was set well below the level historical experience would suggest given the situation (Taylor, 2009). This led to a large amount of mortgage sales and a housing boom, which subsequently resulted in a bust and thereafter a bank run that gave many financial institutions liquidity problems (Crotty, 2009). Cornett et al. (2011) conclude that the liquidity crisis that many banks experienced led to a decrease of credit supply, thus making it more expensive for firm’s to borrow money. New lending volumes in the US fell with 47% during the fourth quarter of 2008 to a level 79% lower than during the credit peak in 2007 (Ivashina & Scharfstein, 2010). The effect of this is highlighted in a study by Campello et al. (2010), where a majority of the surveyed companies were said to be affected by the worsened access to credit markets. Affected firms were restricted in terms of investments and a majority of them were forced to cancel or turn down interesting projects, resulting in hindered economic growth (Campello et al., 2010). Most studies have focused on the effects of the decreased credit supply (see for example Popov & Udell, 2012; Chor & Manova, 2012) but apart from this, the crisis also brought about lower demand for goods and increased risk. In an empirical study, Kahle and Shulz (2011) find that net equity issuance starts to decrease before net debt issuance and remains on a low level during the crisis. This finding implies that credit supply may not be the dominating factor behind firms’ restrained financial and investment policies. Reasons for this would be the fact that the increased risk results in a higher cost of equity and that the expected cash flows as well as investment opportunities decrease (Kahle and Shulz, 2011). Altogether, the drop in credit supply resulting in higher interest rates and the regression of capital demand mean that lending volumes decreased even more than during a normal recession (Ivashina & Scharfstein, 2010).

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3. Methodology The methodological approach for the present study is divided into two main parts. The source data is extracted from the same database and the same definitions apply to the whole study. The first part of the method presents the approach taken to answer the first part of the study; describing how the financial ratios have developed during the studied period. This section includes definitions of the financial ratios and motivations of the various choices taken in terms of P&L and balance sheet items. The second part of the section presents the selected research approach for the regression analysis used to study the relationship between debt-to-equity and return on equity. Lastly, this section also includes a description of how the data were collected. The objective of this study is to map and describe the development of key financial ratios and the relationship between return on equity and debt-to-equity. Therefore, results and analysis are separated into different sections in order to be able to emphasize the empirical findings of the study, rather than the analysis.

3.1 Methodology for describing developments in firm performance and capital structure This section will describe the methodology that was used in order to fulfill the first part of the research objective, describing the developments in company performance and capital structure for Swedish companies. Firstly, the measures that have been used are presented along with explanations as to why they are relevant. Secondly, the chosen measures are defined in more detail.

3.1.1 Use of measures When evaluating a company’s performance, Johansson and Runsten (2005) argue that net income is useless as a measure. Standing alone, it does not give information about whether a company generates any return to its shareholders, which is necessary in order to ensure the company’s survival. Net income is instead meaningful first when it is put in relation to the capital that was required in order to produce the profit. Based on this discussion, a company’s performance will in this study be measured by using the financial ratio return on equity after taxes (ROE). According to Johansson and Runsten (2005), this is the most important financial ratio from a shareholders’ perspective since it can easily be compared to the market cost of capital. The measure is calculated as the percentage of the net income compared to shareholder equity. Return on equity is influenced by firms’ decisions regarding capital structure. A performance measure free from the influence of financial decisions, return on assets (ROA), has also been used in this study to assess companies’ performance. While return on equity can be made to look good by utilizing high leverage, return on assets considers both debt and equity and is thus not affected by the leverage. However, return on assets is a more industry-dependent variable and does not offer the same comparability as return on equity (Hawawini et. al, 2003). Altogether, by utilizing both the described measures

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this report aims to present a clear picture of Swedish companies’ performance during the studied period. For describing the development in capital structure for Swedish companies, the debt-to-equity (D/E) ratio will be calculated. Also included as a variable for this study is the companies’ average interest on debt. In the literature review 2.3, it was mentioned how low interest rates were one of the drivers for the financial crisis and therefore it is interesting to see how this number have changed over the studied period. Furthermore, there is a probable correlation between average interest on debt and the debt-to-equity ratio. For example, Bertmar and Molin (1977) found that average interest on debt and debt-to-equity ratio have a positive correlation. Therefore, the average interest measure may help to explain the development in capital structure. The results are presented with the median and one aggregate number for each measure. The aggregate number is the result of all companies’ numbers weighted together, e.g. the combined earnings before interest expenses divided by the combined average total assets give the aggregate number for ROA. Thus, larger companies will contribute to a greater extent to this number. The median does not take any company specific variables into account but is simply the middle number in a sample arranged from lowest to highest value. Because of the characteristics of the data, the median rather than the average was used (Bertmar & Molin, 1977). Several smaller companies have numbers that can be considered outliers and would skew the result if using the average. The aim of the median is to show the value for the “typical” company in the data set. Data is gathered from companies’ yearly income statements and balance sheets. The income statement shows the sum of a company’s revenues and expenses over a period, usually a calendar year. The balance sheet summarizes a firm’s assets, equity and liabilities at a certain point in time, usually the end of a calendar year. Usually, return on assets and the average interest on debt is calculated based on an average of the opening and closing balances (Johansson & Runsten, 2005). This approach will also be used in this study and in order to be consistent, all numbers taken from balance sheets will represent averages.

3.1.2. Definition of measures The four financial ratios are defined more closely in this subsection. Each financial ratio will be broken down in its component parts, in order to increase the transparency and motivate the choices of the study.

3.1.2.1 Return on assets The first performance measure, return on assets is defined the following way:

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Equation 4.

For the calculations, the definition from Bertmar and Molin (1977) has been applied. Here, earnings before interest expenses (named EBI) consist of the following entries in the income statement: profit/loss after financial expenses (RR1), extraordinary income (E1), extraordinary expenses (EE), group contribution (E2), external interest costs (IC1) and interest expenses to group companies (IC2).

Equation 5.

1 1 2 1 2 Accordingly, the metric does not consider taxes, neither year-end accounting adjustments. Group contribution is excluded since it cannot be derived directly from the day-to-day activities of the company while other extraordinary incomes and expenses can, at least from a long-term perspective (Bertmar & Molin, 1977). Interest costs are excluded in order to make the metric independent of the firm’s capital structure. Average total assets are simply generated from the Total Assets line in the balance sheet and represents an average, i.e. average total assets for 2012 corresponds to the average of the numbers from the year-end reports of 2011 and 2012.

3.1.2.2 Return on equity Return on equity is calculated from the following numbers:

Equation 6.

1

The bottom line from the income statement, net income, is used since that represents the profit attributable to the shareholders. Again, the definition from Bertmar and Molin (1977) has been applied to calculate equity in the denominator. More specifically, the entries gathered from the balance sheet were Total Equity and Untaxed reserves. Untaxed reserves are postponements of taxable income and therefore, one part of it can be considered as unpaid tax debt, while the other is considered as equity (Hoogendoorn, 1996). As tax rate, the Swedish corporate tax for each year has been used, meaning 28.0% for years 2005-2008 and 26.3% for years 2009-2012 (Ekonomifakta, 2014). Again, an average is used for the numbers taken from the balance sheet.

3.1.2.3 Debt‐to‐equity ratio The debt-to-equity measure is used to represent the capital structure of the firm’s over the studied period. There are a number of ratios used for this, where total liabilities divided

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by equity is the basic one most often used by Swedish companies (Johansson & Runsten, 2005). However, this measure includes also non-interest-bearing debt, which does not inflict any financial costs on the company and thus is not associated with the same financial risk. Therefore, the number may not always provide an accurate picture of a firm’s financial position (Johansson & Runsten, 2005). For example, a company can increase their total liabilities-to-equity ratio if they gain a better market position that allows them to increase the days payable outstanding. This would result in a higher amount of accounts payable and thus a higher total liabilities-to-equity ratio, which could lead to the belief that the firm’s financial position has weakened when the company in fact has strengthened their market position. Due to this, the globally more widely used definition of debt-to-equity ratio, the interest-bearing liabilities divided by equity, is better suited for this study. The data used for this study does not include any information about which parts of the liabilities in the balance sheet that is interest bearing and which is not. Consequently, we needed to decide on another measure that as accurately as possible could represent a firm’s financial position while being feasible to use given the data available. In the balance sheets, a distinction is made between short- and long-term liabilities. Short-term liabilities are loans and obligations that last less than a year. It includes for example accounts payable, tax debt and advance payments, which are normally categorized as non-interest bearing (Johansson & Runsten, 2005). Therefore, these liabilities have been excluded from the debt-to-equity ratio used in this study, which now can be defined by the following formula:

Equation 7.

/

1

Long-term debt includes the entries bond loans, long-term liabilities to credit institutions, long-term liabilities to group/associated companies and other long-term liabilities in the balance sheet. Equity has been calculated the same way as in 3.1.2.

3.1.2.4 Average interest on debt Like with the debt-to-equity ratio, calculating average interest on debt based only on non-interest-bearing liabilities can provide a better picture of a firm’s financial position (Johansson & Runsten, 2005). Again, it is not possible to distinguish between interest-bearing and non-interest-bearing debt in the data so long-term debt will serve as an estimate. Also, using the same type of debt as for the debt-to-equity ratio gives the opportunity to better study a possible correlation between the two in the analysis. The following formula will be used:

Equation 8.

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Interest costs include the entries interest expense to group companies and external interest costs. The long-term debt is calculated the same way as in 3.1.3.

3.2 Methodology for linear regression analysis In order to test the relationship between different variables, a linear regression analysis will be carried out. Hypotheses will be formed about the linear relationships in the following form:

Equation 9.

The basic question of this equation is how y varies with x. In any econometric study, there are basically three issues that have to be covered (Wooldridge, 2012). The first issue concerns how other factors that influence y is allowed in the model. The second issue is to describe or determine the functional relationship between y and x. The third issue is to determine how the ceteris paribus effect between the variables can be captured. The simple regression model represents the ambiguity of these three issues (see equation above). The dependent variable, y, is representing the return on equity and is the variable that is supposed to be explained in the analysis (Montgomery & Runger, 2007). Debt-to-equity ratio is the dependent, or explanatory, variable. That is, the objective is to find out if, and in that case how, the dependent variable is causing any variance in the independent variable. Specifically, the intention is to understand if changes in return on equity can be explained by changes in debt-to-equity (Wooldridge, 2012). As mentioned initially in this chapter, however, one of the main issues with the simple regression model for empirical studies is that it is difficult to make ceteris paribus conclusions about how the studied variables actually affect each other (Wooldridge, 2012). In our case, a simple regression analysis would assume that all other factors that affect ROE are completely uncorrelated with D/E, which is unrealistic. Therefore, a multiple regression model is necessary because it allows for several factors, which all together affect the independent variable. The power of the multiple regression model is that it makes it possible to change one dependent variable at the same time as the other dependent variables are held fixed (Montgomery & Runger, 2007). The multiple regression model is presented in the following equation.

Equation 10.

⋯ The disturbance term in the regression model contains all other factors that are not included as control variables. These are the unobserved factors of the model (Wooldridge, 2012). The β1 –term in the equation will explain the functional relationship between leverage and return on equity.

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Throughout the study, ordinary least squares (OLS) method is used to estimate the unknown parameters in the linear regression model. The reason for choosing the OLS estimator method is justified by the Gauss-Markov Theorem, because it creates the result with the smallest variance in the coefficients (Wooldridge, 2012). That is the OLS estimators are the best estimators which minimize the vertical square errors between the data points and the regression line (Wooldridge, 2012).

3.2.1 Description of control variables The criteria for selecting the control variables are that they are factors, which affect the ROE and are correlated with D/E. If these types of factors were not explicitly included as control variables in the multiple regression model, it would generate a biased OLS estimator of β1 (Wooldridge, 2012). That is, the benefit of explicitly including the control variables in the regression model, as opposed to keep them in the error term of the model, is that the variables will be held fixed as the model is executed. Several other studies of similar nature were used to derive proper control variables and avoid under-specification of the model and biased results (Simerly & Li, 2000; Margaritis & Psillaki, 2010; Abor, 2005). The control variables used in this study is size, growth, asset turnover, and business risk. The size of a firm has been found to be influencing several important aspects of a firm, such as structure, decision-making, and performance (Simerly & Li, 2000; Abor, 2005). The sizes of the sample companies are measured by the natural logarithm of yearly turnover (Margaritis & Psillaki, 2009). Size is therefore included explicitly in the study as a control variable. The sales is used in logarithmic form to make the data more normally distributed (Wooldridge, 2012). Researchers of social sciences often use the log values for large integer values (Wooldridge, 2012; Freund et al. 2006). The second control variable is business risk, which is defined as the fluctuations in return on assets (Johansson & Runsten, 2005). Business risk will influence both D/E and ROE for firms. Since the business risk is often industry specific, firms will look at their total risk preference and then adjust the gap between the industry-specific business risk and the total risk they are willing to take (Johansson & Runsten, 2005). For instance, commercial banks tend to have a very low business risk and hence is compensating with taking a higher financial leverage in order to generate an attractive return on equity for shareholders (Johansson & Runsten, 2008). Some similar studies have used industry as a control variable (Bradley et al., 1984). The reason industry is not included as a control variable in this study is two-fold. First, the industry data that could be generated in the employed database was of poor quality. The second reason is that industry is often correlated with business risk (Johansson & Runsten, 2005), and including such a variable would increase the multicollinearity of the results. The third control variable is growth, defined as yearly growth in total assets. Firm growth has been found to have a positive correlation with firm performance (King & Santor,

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2008; Claessens et al., 2002). Growth has also been used as control variable in other studies (Abor 2005). The fourth control variable is total asset turnover. The rationale for including this variable was the absence of good quality data on industry and asset turnover is rather correlated with different industries (Johansson & Runsten, 2005). For instance, capital intensive energy firms tend to have asset turnover ratios in ranging from 0.4 to 0.7, whereas manufacturing firms might have between 0.8 and 1.6 (Johansson & Runsten, 2005). The general form of the multiple regression model was then developed to the following model, containing the main variable under investigation and the control variables.

Equation 11.

∗ ∗ SIZE ∗ GROWTH ∗ ASS. TO ∗ BUS. RISK

If a coefficient of a factor in the model is zero even after controlling for the other variables in the model, that factor is probably an inclusion of an irrelevant variable (Wooldridge, 2012). All coefficients of factors included in the model will be analyzed after the computations of the model in order to identify potential over-specifications in the model.

3.2.2 Hypothesis development The problem set up for investigation in this study is to test whether the D/E has an effect on ROE. The null hypothesis is therefore H0: β1 = 0. The alternative hypothesis is H1: β1 ≠ 0, which means that there is a statistically significant relationship between D/E and ROE. A t-test was conducted in order to ensure that the estimated coefficient is not due to sampling error. Rather than only selecting a specific critical value for the t statistic, an analysis of the p-value will be conducted. This approach is more informative than using a predetermined critical value for rejecting the null hypothesis (Wooldridge, 2012). The p-value expresses the smallest significant value at which the null hypothesis would have been rejected (Montgomery & Runger, 2007). Practically, this means that a small p-value is evidence against the null hypothesis and vice versa. The significance level of the regression model is selected as 99%. That is, the required p-value for rejecting the null hypothesis was selected to be 0.01. The reason for selecting a high significance level is that the sample size is fairly large (Wooldridge, 2012). Even though there are no standard rules for selecting significant levels, 99% is common when having a few thousand data points (Wooldridge, 2012). For example, if the p-value is 0.01, it means that if the null hypothesis were true, one could observe an equally large t statistic 1% of the time, providing some evidence against the null hypothesis. Specifically, the p-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed (Wooldridge, 2012). The null hypothesis is summarized in the table below.

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Table 1

The F test is conducted in order to evaluate how much of the variation that was captured by the regression. The F test statistic is calculated as the mean squared error of regression (MSR), which is the mean squared error of the regression, divided by the mean squared error (MSE), which is the average square of errors of the OLS estimator. MSE is the squared distance that the OLS estimator is from the population value (Wooldridge, 2012), and it is that same as the variance plus the square of any bias. The F-value is then translated to a significance level automatically by the model. The F value measures the significance of the multiple regression overall. The significance of the F test is equal to the p-value of the test.

Equation 12.

3.2.3 Multicollinearity A multicollinearity test was conducted in order to ensure that the regression model had no multicollinearity. The test was performed with the NumXL Pro add-in in Microsoft Excel with the Variance Inflation Factor (VIF) method (See Appendix A). High correlation between two or more of the independent variables is referred to as multicollinearity (Wooldridge, 2012). The problem of multicollinearity is commonly discussed in econometric studies (Bradley et al., 1984), and sometimes even overly empathized by econometricians (Wooldridge, 2012). The effect of multicollinearity is basically the same as having a small sample size because both drive a high variance of the coefficients (Wooldridge, 2012). The only way to reduce the multicollinearity problems in this study would be to exclude one of the control variables. By doing excluding one of the explanatory variables, however, might cause the results of the model to be biased (Wooldridge, 2012). In spite of the attempt to minimize multicollinearity, it is always a fundamental problem in cross-sectional regression studies.

3.2.4 Goodness-of-fit

H0 β1 = 0 There is no a statistically significant relationship between D/E and ROE

H1: β1 ≠ 0 Null hypothesis rejected

Significant at 0,01

Hypothesis test ‐ Testing correlation between D/E and ROE

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The coefficient of determination, or R-squared, in the regression analysis measures how much of the variation in ROE that can be explained by the debt-to-equity ratio (Wooldridge, 2012). It is important to note that R-squared is not a variable that will influence the generation of the model. Some studies tend to construct their studies in order to get a desirable R-squared (Wooldridge, 2012). As a result, they are likely to introduce multicolliniearity in the model (Wooldridge, 2012). R-squared is defined as follows:

Equation 13.

In addition, the adjusted R-squared will also be generated in the model. This is calculated in addition to R-squared because R-squared is always increasing as more regressors are added to the model. The adjusted R-squared penalizes the R-squared for having numerous explanatory variables according to the equation below (Wooldridge, 2012), where k denotes the number of explanatory variables and n is the total number of observations.

Equation 14.

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It is important not to overemphasize the goodness-of-fit by including too many control variables in the model (Wooldridge, 2012; Freund et al. 2006). As mentioned earlier, the selection of control variables is a trade-off between biased results and introducing multicollinearity. That also means that there is always a good idea to include explanatory variables that affect y and are uncorrelated with all the other independent variables (Wooldridge, 2012).

3.2.5 Causality versus correlation The present study has a similar challenge as many other statistical studies, which is to evaluate whether the leverage actually has got a causal effect on firm performance as opposed to having merely a correlating relationship, which can be achieved by keeping all factors but the test variables equal (Wooldridge, 2012). It is rarely possible, however, to keep all other factors equal when performing analysis on economic data, but it is rather a question of holding enough other factors fixed to make a case for causality (Wooldridge, 2012). In order to make the case for causality in this study, a number of control factors derived from theory will be included in the regression analysis (see “description of control variables”). This is an attempt to isolate the ceteris paribus effect of the studied variables, which is carried out using the Data Analysis add-in in Microsoft Excel. By explicitly including factors that has been found to affect the ROE by other authors, these factors can be held fixed and a ceteris paribus effect can be analyzed (Freund et al., 2006).

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One part of this study was to find evidence that the D/E does have an effect on the ROE. An equally important part, however, was to describe the functional relationship between the variables. The coefficient β1 will explain that if that relationship is positive or negative as well as to give an indication of the magnitude of the causality. It is also important to emphasize the difference between statistical significance and economic significance. Statistical significance is only related to the size of the t statistic, whereas the economic significance is more related to the size and sign of the coefficients (Wooldridge, 2012).

3.3 Data collection The sample used for this study consists of Swedish public companies. Numbers are obtained from the database Retriever Business that has annual account figures for all companies listed on the Swedish stock exchange. Since the information dates back to 2004 and averages are used for calculating some of the variables, the presented numbers in the analysis start at year 2005. The majority of the companies have not registered numbers for year 2013 in the database, thus making 2012 the last year analyzed. The original sample consisted of 492 companies. Some of the companies have incomplete numbers for the period, i.e. they became listed on the stock exchange later than 2004. After excluding these companies the sample consisted of 369 companies. Additionally, the sample was adjusted to exclude very small companies with below SEK 10m in revenues. Specifically, all companies that did not have at least SEK 10m in revenues every year of the studied period were excluded. The reason was that some of these smaller companies were having highly questionable data logged in the “Retriever Database”. For instance, some of these companies were not having any sales at all some of these years. After excluding these companies the final sample consisted of 299 companies. For a more detailed description of the studied companies, see Appendix D & E. For the regression analysis, another type of adjustment to the data was made, where a number of outliers were excluded. A residual output analysis was used to identify the outliers, see Appendix C. These single observations had a major influence on the result of the model. An outlier is defined as an observation that is so extreme that when dropping it, the OLS estimators change by a practically large amount (Wooldridge, 2012). Because the original sample is a few thousand data points, the impact on the quality of the study should not be noticeable (Wooldridge, 2012). After adjusting the data set, 2337 data points remains. The data is based on figures presented by the studied companies, and can therefore be subject to accounting choices made by the companies. That is, we can only make conclusions about the volatility in certain financial measures presented by the companies. However, we have not used any of the KPIs already calculated by the companies and thus the study will not be affected by company-specific choices regarding KPI definitions.

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4. Results In this section, the results of the study are presented. First, the developments in the different studied variables are described. Secondly, the results from the regression analysis are presented.

4.1 Variable developments The description of firm performance is made based on the measures return on equity and return on assets.

Figure 1. Return on equity for Swedish companies 2005-2012

Figure 1 shows the return on equity by two metrics, the aggregate of all studied companies and the median of the sample, over the years 2005 to 2012. It shows a steady decline in the aggregate metric between 2005 and 2007 before the financial crisis in 2008, which results in a steeper downwards curve, hitting the bottom at around 3%. The aggregate curve rebounds the year after with an increase of approximately 9 percentage points and in 2010 the number is back to the level of 2007. In 2011, the aggregate number takes a downturn again from 17% to around 11% before a slight increase in 2012 to end the period. For the median number, the fluctuations are smaller than for the aggregate number. The largest difference between two years for the median is 8 percentage points and 13 for the aggregate, both between 2007 and 2008. There are differences in the direction of the curves for several of the analyzed years. In 2005 as well as in 2011, the median number increases while the aggregate decreases and the opposite occurs in both 2009 and 2012. The values for the median are generally lower than for the aggregate number with the exception of 2008 where the median value is 2 percentage points higher. Over the whole

2005 2006 2007 2008 2009 2010 2011 2012

Aggregate 21,3% 19,2% 16,5% 3,3% 12,2% 17,0% 10,8% 12,8%

Median 13,0% 14,4% 13,7% 5,7% 4,9% 9,3% 9,7% 6,3%

0,0%

5,0%

10,0%

15,0%

20,0%

25,0%

ROE

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period, the aggregate number is on average 4.5 percentage points higher. Both curves have a substantial decrease in 2008 corresponding to the financial crisis and they end the period with significantly lower values than they started with in 2005.

Figure 2. Return on assets for Swedish companies 2005-2012.

Figure 2 shows the median and aggregate number for return on assets in Swedish public companies over the period from 2005 to 2012. Similar to return on equity, the aggregate number shows a steady decline between the years 2005 and 2007. During 2008, there is a significant drop from 12% to 5%, which is the lowest point of the period. Then in the two following years the measure recovers by increasing 3 percentage points each year before turning down again in 2011. Altogether, the aggregate number drops 6 percentage points from 14% to 8% between 2005 and 2012. While the aggregate number decreases between 2005 and 2007, the median increases during these years, though not by much. As with return on equity, the median number for return on assets varies less than the aggregate number. It also takes a lower value for the whole period with the aggregate number being on average 4 points higher.

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Figure 3. Debt-to-equity ratio for Swedish companies 2005-2012.

The description of capital structure is made based on the measure debt-to-equity ratio. Figure 3 shows the aggregate and median value of the debt-to-equity ratio for Swedish companies during the years between 2005 and 2012. The values show that there is a significant difference between the aggregate number and the median company with the aggregate number being on average 157% larger over the period. While the median has been fairly steady over the period, the changes in the aggregate number is considerable. The ratio decreases slightly during 2006 before starting to trend upwards. In 2008, there is an increase of approximately 0.100 followed by a slight increase also during 2009. The peak is reached in the last year of the period, 2012, when the debt-to-equity ratio increases to 0.473, a value 52% higher than in 2005.

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Figure 4. Average interest on debt for Swedish companies 2005-2012.

Figure 4 shows the average interest on debt for Swedish public companies from 2005 to 2012, both by the aggregate and median number. In this case, the median is consistently larger than the aggregate number. The curves are very similar, both in terms of their variation and in the direction of the slopes. Every time the median increases, so does the aggregate number. There is an evident increase in average interest at the beginning of the financial crisis in 2008, most clearly shown in the median curve. Thereafter comes a sharp decline until the lowest level for both curves is hit in 2010. From the start of the period to the last year in 2012 the aggregate number of average interest decreases by 2 percentage points. However, the median company has approximately the same average interest on debt in 2012 as in 2005.

4.2 Regression analysis The result of the full period in of the multiple regression model is presented in table 2. The Analysis of the Variance (ANOVA) table includes valuable data for understanding the characteristics of the test. As expected, the model has five degrees of freedom which corresponds to the number of explanatory variables in the regression model.

Table 2

R‐squared 24.1% F 148

Adjusted R‐squared 23.9% Significance F 1,8E‐136

N 2337 df 5

Regression statistics

Regression output

ANOVA

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The purpose of the regression model is, naturally, to explain the variation in ROE with the variation in D/E. The F statistic is 148, which gives a significance for the entire multiple regression model of well under the selected significance level of 0.01. The R-squared of the regression is 24.1% for the full period, which means that 24.1% of the total variation in ROE can be explained by the explanatory variables. In addition, the adjusted R-squared of the model is just slightly lower at 23.9% The coefficients of the multiple regression model is read from table 3 and represents the estimates of the coefficients in the regression equation. The most interesting coefficient in this study is the coefficient for debt-to-equity, which explains the functional relationship between ROE and leverage. The null hypothesis cannot be rejected in the period between 2010 and 2012. For the other two sub-periods and the full period, the null hypothesis can indeed be rejected and hence debt-to-equity has a significant statistical impact on ROE. The negative sign for β indicates a negative relationship between the debt-to-equity and ROE for all periods, with the strongest functional relationship during the crisis years in 2008 and 2009. That is, companies with lower debt-to-equity any given year, presents a higher ROE in the end of that year.

Table 3

Furthermore, it is also interesting to discuss the impact of control variables on the independent variable, ROE. Size has a p-value under 0.01 in all periods and hence the null hypothesis can be rejected, which indicate that larger companies are having higher ROE during the studies period. In contrast, the null hypothesis cannot be rejected for growth during any period at the 0.01-level. There are however large differences between the periods. When looking at the full period, the null hypothesis would have been rejected at a 3% significance level. For the periods 2005 to 2007 and 2012 to 2012, however, the null hypothesis would not have been rejected even at the 10% significance level, which is commonly used as the highest acceptable value by many researchers (Wooldridge, 2012). The asset turnover ratio is positive and significant for all periods as the p-value is less than 0.01.The positive sign of the OLS estimators indicate that companies with higher total asset turnover are generally having a higher ROE. Lastly, the null hypothesis can be rejected for business risk in all periods except for the period 2010 to 2012. Business risk has the steepest slope of the OLS estimators in all periods, and has a negative sign, which indicate that companies with lower business risk are having higher ROE.

R2

Year β p‐value* β p‐value* β p‐value* β p‐value* β p‐value*

2005‐2007 ‐0,080 0,001 0,045 0,000 0,000 0,697 0,060 0,000 ‐3,721 0,000 33%

2008‐2009 ‐0,136 0,000 0,056 0,000 0,000 0,040 0,070 0,001 ‐1,121 0,000 24%

2010‐2012 ‐0,018 0,478 0,064 0,000 0,000 0,304 0,093 0,000 ‐0,318 0,099 23%

2005‐2012 ‐0,070 0,000 0,054 0,000 0,000 0,031 0,081 0,000 ‐1,444 0,000 24%

*significant at p<0.01; marked with bold text

Summary statistics of dependent variables ‐ size and significance

Debt‐to‐Equity Size Growth ATO Business risk

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5. Analysis The analysis of the presented results is divided into two parts. First, the financial ratios are analyzed and possible explanations for the developments are discussed. Regression model output is discussed in the second part, where the results are analyzed from the perspective of capital structure theories and other empirical studies.

5.1 Discussion of financial ratios The performance of the companies during the studied years has varied greatly, in part due to the financial crisis, which takes place in the middle of the period. The median metric has a smaller variance in both of the performance measures. Looking at the characteristics of the data, this is not all that surprising. A small number of companies have a large impact on the aggregate number, whereas all participating companies affect the median number equally. For example, in 2012 ten companies represented 70% of the aggregate net income. Consequently, a significantly changed result for one of the largest companies has considerable impact on the aggregate performance measures. It is also clear from the results that the financial crisis really hit Swedish companies in the year of 2008. Performance had been trending downwards in the years before as well but not to the same extent. There is a larger drop for the aggregate number of both the ROA and ROE measure, indicating that larger companies were more affected in that year. However, they also seem to recover quicker as evidenced by the fact that the aggregate number changes direction in 2009 while the median keeps sloping downwards. A trend during the period is that performance turns for smaller companies seem to lag behind larger companies. This can be seen in 2005-2006 and 2011-2012 apart from the already mentioned 2009. The observation holds for both measures, even though the pattern is clearer in return on equity. Another observation is that the aggregate number outperforms the median over the whole period with the only exception being ROE in 2008, meaning larger companies in general perform better than smaller companies. The slopes for ROA and ROE look very similar, which is to be expected since they both measure the profitability of a firm in some way. Furthermore, the value of ROE is generally higher than for ROA, which can be explained by financial decisions that generates leverage effects on ROE, see Equation 1. Leverage also explains the fact that the ROE measure is fluctuating more than the ROA. Only during 2008 does ROE fall below the value for ROA, indicating that the financial part of the equation has negative impact and thus that the ROA is lower than the average interest on loans1. Fluctuations in the performance measures can to a large degree be attributed to changes in the result rather than in equity or assets. While the aggregate profit/loss is very much affected by the unstable business cycle, the growth in assets and also equity is fairly stable over the period. The steady equity growth means that the increase in long-term

1 The average interest on loans in this case shall not be confused with our definition of average interest on debt but refers to the Rs used in Johansson and Runsten’s equation.

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debt-to-equity ratio is a result of the long-term debt levels rising even more. During the crisis years, 2008 and 2009, companies’ long-term debt increases with a total of 37% compared to 2007. This happens even though the credit supply is supposedly tightened during these years (Cornett et al., 2011; Ivashina & Scharfstein, 2010). As expected, this results in large interest costs during these years, especially during 2008 before the average interest on debt starts to fall in 2009. Logically, the average interest should rise because of the lower credit supply but such an effect is not seen except for a little bit during 2008. For the remainder of the period the average interest lies below the levels from before the crisis. This can probably be explained by the very low prime rate set by the Swedish National Bank in the beginning of 2009. The rate was held under 1% for most of 2009 and for the whole 2010, while for a large part of this period being as low as 0.25% (Riksbanken, 2014). These numbers are very low, especially compared to the rate levels in 2007 and 2008 of between 3 and 5%. Small increases in the official rate were done during 2011 and a subsequent increase of average interest on debt for Swedish companies followed. Regarding the debt-to-equity ratio, the aggregate number does, as previously mentioned, rise during the financial crisis. The median is, however, remarkably steady during the whole period, while also residing at significantly lower levels than the aggregate. This could be interpreted as a sign that larger companies tend to have higher debt-to-equity ratio than smaller companies. Furthermore, the aggregate number changes in accordance with the market timing theory that says debt-to-equity ratio should be lower during good times than bad. A possible explanation to the fact that the median does not change during the crisis is that banks may have been more reluctant to issue loans to smaller companies. The corporate tax rate in Sweden was reduced in 2009, which according to the trade-off theory should lessen the incentives of carrying debt. No correlation between the debt-to-equity rate and the reduced tax rate can be distinguished from the data since many factors impact the results but it could be interesting to evaluate if such an effect occurs for the larger tax reduction in 2013. Average interest on debt is interesting since it is the only measure where the median consistently shows higher values than the aggregate. This could be due to the fact that banks give larger companies better terms on their loans and as mentioned earlier, larger companies will largely affect the aggregate number. The two curves are otherwise very similar and they both correlate significantly with the changes in the Swedish prime rate. Bertmar and Molin (1977) found a positive correlation between average interest on debt and debt-to-equity ratio. Such correlation cannot be seen in this sample, rather the opposite. Judging by the aggregate numbers, companies reach the highest point of leverage during the end of the period when the average interest is at its lowest. This seems reasonable from the borrowing companies’ perspective since loans become more attractive when the rate is low.

5.2 Analysis of regression results

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The results of the regression model presented in this report clearly suggest a negative relationship between debt-to-equity and return on equity. The overall model has a R-squared at about 24%, which means that the regression model can explain about 24% of the variation in return on equity. This means that the model explains a decent share of the total variation and that the model has some credibility. A more interesting question, however, is whether the identified relationship is actually demonstrating a causal effect or if it is merely a correlation. The ambition of the study was to simulate a ceteris paribus effect, and the important question is whether that is achieved to a satisfying degree. Of course, a real ceteris paribus effect in this kind of study is illusionary and not really attainable, which is also demonstrated by the R-square of 24%. The control variables selected in the regression model are surely relevant factors but there are certainly relevant factors that are not explicitly included in the model. As a result, the answer to the causality versus correlation question is indeed rather subjective and it is up to each reader to gauge the quality of the methodological approach. It should strengthen the credibility of this report, however, that there are both theoretical and empirical evidence for its conclusions. The significance of three out of four control variables indicates that the selected control variables indeed are relevant factors to consider. The negative relationship between debt-to-equity and return on equity suggested in the regression model is more comparable to the pecking order theory than the trade-off theory. There is an important difference between these theories and this study, however. The trade-off theory and the pecking order theory is focused on the reverse relationship of debt-to-equity and profitability. That is, they suggest that the profitability of the firm drives the capital structure decisions whereas this study is concerned with the reversed causality. However, the trade-off theory suggests that more profitable firms should increase their debt because they have low expected bankruptcy costs and benefit more from the tax shield benefits. The main purpose of this study is descriptive in nature and do not aim to completely explain the reasons for the results. There could be some interest in looking at some of the results from a trade-off theory perspective, however. Using the trade-off perspective, one possible explanation for the negative relationship between debt-to-equity and return on equity could be that companies are having higher debt than what is optimal from a tax shield and expected bankruptcy cost perspective. For instance, if a company is not profitable, the tax benefit of higher debt will evaporate at the same time as the expected bankruptcy costs would increase. That is, the company fails to utilize the full potential benefits of increased debt while at the same time having to bear the drawbacks of increased risk. One can get additional support for that argument by looking at the functional relationship between debt-to-equity and return on equity during the different periods (See table 3). During the boom between 2005 and 2007, the OLS estimator had a value of -0.08. In contrast, when the financial crisis hit and profits decreased, the slope of the OLS estimator increased to -0.136, indicating a stronger functional relationship. That is, decreasing debt during times with lower profitability was even more important, indicating that firms could utilize even less potential benefits related to the tax shield possibility.

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As presented in the literature review, researchers have been generating different results on what the optimal capital structure really is in the trade-off theory. Some research has come to the conclusion that firms should increase their debt and some has come to the opposite conclusion. The results of this study cannot tell anything about what the level of that optimum might be, but only that Swedish companies seem to benefit from a lower debt-to-equity ratio during the studied period. Although the pecking order theory provides limited guidance for explaining the reverse causality of debt on profitability, it does provide some insight in combination with the result of the study. The regression analysis suggests a significantly negative relationship between debt-to-equity and return on equity, which means that higher profitability is often associated with lower levels of debt-to-equity ratios. This is in line with what the pecking order theory suggests. So while the pecking order theory explains cause and effect between capital structure and profitability from the reverse standpoint, the general negative relationship between profitability and debt is consistent with the regression model. In that sense, the pecking order theory is more aligned with the conclusions form this study, which is also consistent with what other empirical studies have found when comparing trade-off theory with pecking order theory (Tong & Green, 2005; Fama & French, 2002; Shyam-Sunders & Myers, 1999). One explanation of these results might indeed be due to the complexity in measuring imperative variables of the trade-off theory as discussed by Graham (2000) and Ju et al. (2005). The negative relationship between debt-to-equity and return on equity is also supported by a number of other empirical studies. Bertmar and Molin (1977) found also found a negative relationship between the two variables in Swedish firms between 1966 and 1972.

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6. Conclusions The results presented in this thesis show that firms’ performance has been significantly affected by the financial crisis in 2008 and 2009. The two performance measures, ROE and ROA, fluctuate in a similar manner, with a huge drop coinciding with the first crisis year. Furthermore, Swedish companies are yet to recover fully and reach the profitability levels they had before 2008. Companies’ debt-to-equity ratios start the period on a low level before rising during the crisis, something that is consistent with existing research on the subject. The pattern is mainly existent in larger companies, while smaller companies seem to keep a steady debt-to-equity ratio throughout the period. Average interest on debt show signs of correlating with the prime rate and the measure reaches its peak at the beginning of the financial crisis. The regression model suggests a significant relationship between the debt-to-equity and return on equity for the full period between 2005 and 2012. The result is also consistent over all sub periods, which represents the periods before, during and after the financial crisis. The result was strongest during the actual crisis, which might indicate that Swedish firms are having higher than optimal debt-to-equity ratios. Indeed, the results show that Swedish firms could increase return on equity by reducing debt-to-equity. In addition, the results are consistent with some of the earlier research on the topic as well as with the pecking order theory. The study has both practical and theoretical contributions. The practical contributions consist mainly of decision support for managers operating in Swedish companies. Although there have been quite some research on capital structure during the last decade, few studies are committed to Swedish companies. Another important edge of this study is that it concentrates on the reverse causality between return on equity and debt-to-equity compared to the most widely recognized capital structure theories as pecking order theory and trade-off theory. The main academic contribution lies in the large amounts of quantitative data and sophisticated statistical methods utilized in materializing the regression model. A number of future research ideas have been identified during the project. One such idea would be to look closer at optimal capital structures for Swedish companies, using parts of the data generated in this study. Such a study would be an attractive complement to the present study because these results indicate that companies would benefit from lower debt-to-equity but not how low they should go.

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Appendix A. Multicollinearity test

The multicolliniearity test was conducted with the Variance Inflation Factor (VIF) method. A rule of thumb is that there is no problem with multicollinearity if VIF is below 4, which means that the current levels are very good (O’Brien, 2007). B. Regression output

Variable VIF

Debt‐to‐equity 1,08

Size 1,30

Growth 1,04

ATO 1,10

Business risk 1,19

Multicollinearity test

SUMMARY OUTPUT 2005‐2007

Regression Statistics

Multiple R0,575475

R Square 0,331172

Adjusted 0,327301

Standard 0,439181

Observati 870

ANOVA

df SS MS F Signif. F

Regressio 5 82,5162162 16,5032432 85,56233 4,47E‐73

Residual 864 166,6481199 0,19287977

Total 869 249,1643361

Coeff. Std. Err. t Stat P‐value Lo 95% Up 95% Lo 95,0% Up 95,0%

Intercept ‐0,49906 0,075586053 ‐6,6025197 7,05E‐11 ‐0,64741 ‐0,3507 ‐0,64741 ‐0,3507

D/E ‐0,07991 0,023129569 ‐3,4547096 0,000578 ‐0,1253 ‐0,03451 ‐0,1253 ‐0,03451

Size(log o 0,045462 0,005891037 7,71717547 3,28E‐14 0,0339 0,057025 0,0339 0,057025

Growth (i 1,12E‐09 2,86791E‐09 0,38967824 0,696871 ‐4,5E‐09 6,75E‐09 ‐4,5E‐09 6,75E‐09

Kapitalom 0,060462 0,015464725 3,90968607 9,97E‐05 0,030109 0,090815 0,030109 0,090815

Business r ‐3,72106 0,291787657 ‐12,752618 2,98E‐34 ‐4,29375 ‐3,14836 ‐4,29375 ‐3,14836

31



Multiple R0,488473

R Square 0,238606

Adjusted 0,231997

Standard 0,405805

Observati 582

ANOVA


Regressio 5 29,72555 5,945111 36,10142 3,51E‐32

Residual 576 94,85453 0,164678

Total 581 124,5801


Intercept ‐0,77277 0,092959 ‐8,31304 6,74E‐16 ‐0,95535 ‐0,59019 ‐0,95535 ‐0,59019

D/E ‐0,13551 0,029217 ‐4,63807 4,36E‐06 ‐0,19289 ‐0,07813 ‐0,19289 ‐0,07813

Size(log o 0,056124 0,007086 7,92009 1,23E‐14 0,042206 0,070042 0,042206 0,070042

Growth (i 6E‐09 2,91E‐09 2,060216 0,039827 2,8E‐10 1,17E‐08 2,8E‐10 1,17E‐08

Kapitalom 0,069545 0,021664 3,210104 0,001401 0,026994 0,112096 0,026994 0,112096

Business r ‐1,12088 0,273061 ‐4,10488 4,63E‐05 ‐1,6572 ‐0,58457 ‐1,6572 ‐0,58457

32



Multiple R0,479054

R Square 0,229493

Adjusted 0,22511

Standard 0,390269

Observati 885

ANOVA


Regressio 5 39,8757 7,97514 52,36132 1,34E‐47

Residual 879 133,8803 0,15231

Total 884 173,756


Intercept ‐0,95262 0,074849 ‐12,7272 3,53E‐34 ‐1,09953 ‐0,80572 ‐1,09953 ‐0,80572

D/E ‐0,01783 0,025107 ‐0,71 0,477894 ‐0,0671 0,031451 ‐0,0671 0,031451

Size(log o 0,06352 0,005609 11,3246 7,57E‐28 0,052511 0,074529 0,052511 0,074529

Growth (i 3,37E‐09 3,28E‐09 1,027685 0,304381 ‐3,1E‐09 9,81E‐09 ‐3,1E‐09 9,81E‐09

Kapitalom 0,093314 0,015748 5,925434 4,47E‐09 0,062406 0,124223 0,062406 0,124223

Business r ‐0,31778 0,192411 ‐1,65155 0,098983 ‐0,69541 0,059862 ‐0,69541 0,059862

33

The Sum of Squares (SS) column presents important information about the variation in the test. The SST measures the total variation in the test. The SSR can be described as the amount of the variability that can be explained by the regression. The SSE is the difference between the SST and the SSR and hence the amount of the variation that cannot be explained by the regression. C. Residual output

SUMMARY OUTPUT ‐ full period


Multiple R 0,49047

R Square 0,240561

Adjusted 0,238932

Standard 0,423073

Observati 2337

ANOVA


Regressio 5 132,1617 26,43234 147,6741 1,8E‐136

Residual 2331 417,2282 0,178991

Total 2336 549,3899


Intercept ‐0,73883 0,046731 ‐15,8104 1,45E‐53 ‐0,83047 ‐0,64719 ‐0,83047 ‐0,647191

D/E ‐0,06952 0,014954 ‐4,64891 3,52E‐06 ‐0,09884 ‐0,0402 ‐0,09884 ‐0,040195

Size(log o 0,053842 0,003558 15,13187 2,05E‐49 0,046865 0,06082 0,046865 0,0608198

Growth (i 3,78E‐09 1,75E‐09 2,16107 0,030792 3,5E‐10 7,22E‐09 3,5E‐10 7,216E‐09

Kapitalom 0,080743 0,009984 8,087216 9,71E‐16 0,061164 0,100321 0,061164 0,1003213

Business r ‐1,44398 0,143627 ‐10,0537 2,62E‐23 ‐1,72563 ‐1,16233 ‐1,72563 ‐1,162333

34

Sample of residual output used for identifying outliers and influencers in the regression data. D. Companies Companies included in the study 2E Group AB Bergs Timber AB (publ) AB Novestra Betsson AB

AB Sagax Betting Promotion Sweden AB AB Traction Bilia AB ABB AB BillerudKorsnäs Aktiebolag (publ) Acando AB BioGaia AB ACAP Invest AB BioInvent International AB Accelerator Nordic AB Biotage AB

RESIDUAL OUTPUT full period

Observation Predicted Re Residuals Standard Residuals

1 0,164871602 ‐0,122134894 ‐0,288994345

2 ‐0,319939792 0,228358737 0,540340122

3 ‐0,185824032 0,333985467 0,790273015

4 0,007438747 0,130025781 0,307665681

5 0,290543007 ‐0,133357435 ‐0,315549007

6 0,141748937 ‐0,037150562 ‐0,087905281

7 0,043269168 ‐0,38957935 ‐0,921818696

8 ‐0,206963961 0,1137859 0,26923904

9 ‐0,106651112 ‐0,309106905 ‐0,731405614

10 0,106330691 0,00448987 0,010623885

11 0,232587879 0,071269129 0,168636288

12 ‐0,050887617 ‐0,329716903 ‐0,780172783

13 ‐0,208106374 ‐1,11976966 ‐2,649587583

14 ‐0,195855267 0,269514592 0,637722687

15 ‐0,035597612 ‐0,127578861 ‐0,301875803

16 0,07150775 0,103298601 0,244424098

17 0,33947827 ‐0,210553224 ‐0,498208898

18 0,099460173 ‐0,898490975 ‐2,126000209

19 ‐0,06763249 0,319022696 0,754868258

20 0,259325755 ‐0,031253939 ‐0,073952754

21 0,254779771 ‐0,131432815 ‐0,31099499

22 0,233960725 ‐0,022333431 ‐0,052845138

23 ‐0,203676152 0,204914715 0,484867114

35

ACTIVE Biotech AB Björn Borg AB Addnode Group Aktiebolag (publ) Boliden AB Addtech AB Bong AB ADDvise Lab Solutions AB (publ) Boule Diagnostics AB Aerocrine Aktiebolag Bredband2 AB Agellis Group AB Bringwell AB (publ) AIK Fotboll AB BTS Group AB

Aktiebolag Fagerhult Bure Equity AB Aktiebolaget Electrolux Business Control Systems Sverige AB Aktiebolaget Geveko Caperio AB Aktiebolaget Industrivärden Castellum Aktiebolag Aktiebolaget SKF Catena AB Aktiebolaget Volvo CellaVision AB Alfa Laval AB Cision AB Allenex AB Commodity Quest AB (publ) AllTele Allmänna Svenska Tfn.aktie.bol. Concordia Maritime Aktiebolag ALM Equity AB Confidence International Aktiebolag AlphaHelix Molecular Diagnostics AB Connecta AB Amasten Holding AB (publ) Conpharm AB (publ) Anoto Group AB Consilium Aktiebolag

AQ Group AB Corem Property Group AB Aqeri Holding AB Cryptzone Group AB Arc Aroma Pure AB CTT Systems AB Arcam Aktiebolag (publ) CybAero AB Arctic Gold AB (Publ) CYBERCOM GROUP AB Arctic Paper Sverige Aktiebolag DEFLAMO AB (publ) Aspiro AB DGC One AB ASSA ABLOY AB DIBS Payment Services AB (publ)

AstraZeneca AB DORO AB Atlas Copco Aktiebolag Drillcon Aktiebolag Atrium Ljungberg AB Duni AB Auriant Mining AB Duroc Aktiebolag Autoliv Aktiebolag ECOMB AB (publ) Availo AB (publ) EcoRub AB Avanza Bank Holding AB Elanders AB Avega Group AB Electra Gruppen AB (publ)

AVTECH Sweden AB (publ) Elekta AB (publ) Axfood Aktiebolag Ellen Aktiebolag Axis Aktiebolag Elos AB B&B TOOLS Aktiebolag Elverket Vallentuna AB BE Group AB (publ) Empire AB Beijer Alma AB Enea Aktiebolag

36

Beijer Electronics Aktiebolag Eolus Vind Aktiebolag (publ). eWork Scandinavia AB KABE AB Exini Diagnostics Aktiebolag KappAhl AB (publ) Fabege AB Kentima Holding AB (publ) Fastighets AB Balder Klick Data Aktiebolag FastPartner AB Klövern AB Feelgood Svenska Aktiebolag (publ.) Knowit Aktiebolag (publ)

Fenix Outdoor AB Kungsleden Fastighets AB Fingerprint Cards AB Lagercrantz Group Aktiebolag FormPipe Software AB Lammhults Design Group AB Forsstrom High Frequency AB Lappland Goldminers AB G & L Beijer AB LifeAssays AB (publ) Generic Sweden AB (publ) Lovisagruvan AB (publ) Genesis IT AB Lundin Mining AB Genovis Aktiebolag Lundin Petroleum AB

Getinge AB Mackmyra Svensk Whisky AB

Ginger Oil AB Malmbergs Elektriska Aktiebolag (publ)

Glycorex Transplantation AB (publ) Mangold Fondkommission AB Gunnebo Aktiebolag Meda Aktiebolag H & M Hennes & Mauritz AB Medirox Aktiebolag

Haldex Aktiebolag Medivir Aktiebolag Heba Fastighets Aktiebolag Micro Systemation AB (publ) Hemtex Aktiebolag Micronic Mydata AB (publ) Hexagon Aktiebolag Micropos Medical AB (publ) Hexatronic Scandinavia AB (publ) Midsona AB Hifab Group AB Midway Holding Aktiebolag

HiQ International AB Modern Ekonomi Sverige Holding AB (publ)

HMS Networks AB MSC Konsult Aktiebolag Holmen Aktiebolag MultiQ International Aktiebolag HomeMaid AB (publ) NCC AKTIEBOLAG Hufvudstaden AB Nederman Holding Aktiebolag Husqvarna Aktiebolag Net Entertainment NE AB

I.A.R. Systems Group AB Net Insight AB ICA Gruppen Aktiebolag New Wave Group AB IDL Biotech AB NGS Group Aktiebolag Image Systems AB NIBE Industrier AB Impact Coatings AB (publ) Nischer Aktiebolag (publ) Industrial and Financial Systems, IFS Aktiebolag Nobia AB Indutrade Aktiebolag Nolato Aktiebolag Insplanet AB (publ) Nordic Camping & Resort AB

37

International Hairstudio M & R AB Nordic Service Partners Holding AB Intrum Justitia AB NordIQ Göteborg AB Investment AB Kinnevik Nordkom AB Investment AB Öresund Nordnet AB Investmentaktiebolaget Latour NOTE AB (publ) Investor Aktiebolag Oasmia Pharmaceutical AB Invisio Communications AB Obducat Aktiebolag

InXL Innovation AB Odd Molly International AB ITAB Shop Concept AB Oden Control AB JLT Mobile Computers AB (publ) OEM International Aktiebolag JM AB Oniva Online Group Europe AB Josab International AB (publ) Online Brands Nordic AB Opcon Aktiebolag Skånska Energi Aktiebolag Opus Group AB (publ) Smarteq AB (publ) OraSolv AB Softronic Aktiebolag

Orexo AB SSAB AB Oriflame Cosmetics AB Starbreeze AB Ortivus Aktiebolag Stille AB PA Resources Aktiebolag Stockwik Förvaltning AB Paradox Entertainment AB (publ). Stora Enso AB Parans Solar Lighting AB (publ) Stora Enso Skog Aktiebolag PartnerTech AB Studsvik AB Paynova AB SWECO AB (publ)

Peab AB Svedbergs i Dalstorp AB Pfizer Health AB Swede resources AB (publ) Pilum AB (publ) Swedish Match AB PolyPlank Aktiebolag (publ) Swedish Orphan Biovitrum AB (publ) Poolia AB Swedol AB (publ) Precise Biometrics AB SwitchCore AB (publ) Precomp Solutions Aktiebolag (publ) Systemair Aktiebolag Prevas Aktiebolag TagMaster Aktiebolag

Pricer Aktiebolag Tele2 AB Proact IT Group AB Telefonaktiebolaget L M Ericsson PROBI Aktiebolag TeliaSonera Aktiebolag

Proffice Aktiebolag Thenberg & Kinde Fondkommission Aktiebolag

ProfilGruppen AB Tieto Sweden AB

Rasta Group AB TradeDoubler Aktiebolag Ratos AB Transcom Aktiebolag RaySearch Laboratories AB (publ) Trelleborg AB ReadSoft Aktiebolag TrustBuddy International AB Real Point Investment AB ( publ ) Uniflex AB Rederi AB Transatlantic United Media Group Nordic AB

38

Rejlers AB (publ) Unlimited Travel Group UTG AB (publ) Respiratorius AB (publ) Wallenstam AB RNB RETAIL AND BRANDS AB (publ) VBG GROUP AB (publ) Rottneros AB VENDATOR AB Russian Real Estate Investment Company AB Venue Retail Group Aktiebolag Rörvik Timber AB WeSC AB (publ) Sandvik Aktiebolag West International Aktiebolag (publ) SAS AB Vindico Security AB (publ) SBC Sveriges BostadsrättsCentrum AB Vitec Software Group AB (publ) Seamless Distribution AB Vitrolife AB SECTRA Aktiebolag XANO Industri AB Securitas AB XCounter AB

Selena Oil & Gas Holding AB ZetaDisplay AB Semcon Aktiebolag ÅF AB Sensys Traffic AB Senzime AB (publ.) SinterCast Aktiebolag SJR in Scandinavia AB Skanska AB SkiStar Aktiebolag

Skåne‐möllan Aktiebolag E. Sampling of companies

# of companie

Available in database 492

Missing data for the wh 124

Turnover below SEK 10 69

Final sample 299

Sample selection

a study of swedish public companies

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