FACULTY OF ECONOMICS AND BUSINESS
Determinants of capital structure for football clubs
Miroslava Kopečná
S2753766 Thesis MSc Finance Supervisor: Dr. P.P.M SmidABSTRACT
Capital structure is very interesting domain for research. There is a lot of previous research on capital structure; however the main focus of these studies is regular companies, across industries and across country. None of the previous researches consider including sport industry in their sample. The goal of this research is to provide empirical evident on what determine the capital structure of the football club industry and to asses if these determinates are different compare to other regular companies. We founded that several determinates, namely: tangibility, profitability, size, liquidity can help explain the composition of capital structure for football clubs. Some significant differences also have been found between football clubs and other regular companies.
JEL codes classification: G32, C33
PREFACE
After several year of studying I can finally finish this credible journey with satisfaction. This past few years gave me so much. I didn’t only gain important knowledge for my future carrier but also learned valuable life lessons. I defeated all obstacles my studies and life threw at me so I can be here now, ready to start new chapter. I could not wish for better university or for better city to do my master’s degree.
My passion for sport and finance leaded me to choose for my master thesis project a subject that I can combine them. At the beginning of the study I had some doubts, that I can find such a topic. Capital structure and its determinants is very interesting topic and has been studied from many different perspective. Luckily for me, sport clubs have not been one of them. Although I did not have lot of insights on this topic after conducting this study, I have better understanding on how the composition of capital structure is determined. This knowledge can help me in my future career.
Table of Contents
1. Introduction ... 6
2. Literature Review ... 9
2.1. The determinants of capital structure ... 9
2.1.1. Tangibility ... 11 2.1.2. Profitability... 12 2.1.3. Size ... 13 2.1.4. Growth opportunities ... 13 2.1.5. Volatility ... 14 2.1.6. Liquidity ... 15
2.1.7. Non debt tax shield ... 16
2.1.8. Cost of Financial distress ... 16
2.1.9. Ownership structure ... 16 2.1.10. Tax ... 17 2.2. Hypotheses development ... 18 3. Methodology ... 24 3.1. Model specification ... 24 3.2. Dependent variable... 25 3.3. Independent variables ... 26 4. Data ... 30 4.1. Sample selection ... 30 4.2. Descriptive statistics ... 31 5. Results ... 35 5.1. Results of regression ... 35
5.2. Results of robustness test ... 37
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1. Introduction
Capital structure is a subject that has been, and still is, a very interesting subject for the research. It is such a hot topic among researchers because the composition of capital structure not only affects the value of a firm, but the level of debt and equity can also affect the cost of capital. The revolutionary work of Modigliani and Miller (1958) initiated a chase for optimal capital structure that would maximize the firm’s value.
Since the original proposition of Modigliani and Miller (1958), the theory on capital structure has evolved to a great deal. Now, it provides us with at least some clarification for choices of financial sources. Three main theories have been developed that can explain the choices for financing: trade-off theory, pecking order theory, and principal agent theory. Each theory provides its own explanation for managers’ choices of capital structure.
Although in theory, the choice for a specific capital structure can be justified, in practice is not that simple. Worldwide, we can observe that different firms have different capital structure composition, even within the same industry or country.
Recently, a particular industry was in the spot light. The media was filled with reports of the 2016 European football championship. Enthusiastic fans closely watched and supported their teams to the end. Without a doubt, football is one of the most popular sports in the world. The football industry is also one of the biggest among the sport industry. Yearly, the most famous football clubs such as Real Madrid and FC Barcelona generate revenues from selling tickets or broadcast rights exceeding 500 million €. As football clubs are such a glamorous subject these days, curiosity drew me to understand how managers of football clubs decide on a capital structure. After all, one of the most important tasks of strategic treasury management is to choose the right proportion of debt and equity - the “optimal” capital structure that maximizes company value and minimizes the cost of capital (Brusov et al., 2015).
7 football clubs are usually excluded because they are not same as regular corporations. The football industry is very different from traditional industries because they do not offer products or services. Football clubs’ financial statements considerably differ from the financial statements of regular companies. One difference between football clubs and regular companies can be presented by the asset structure. Football clubs are assumed to have large amount of intangible assets (human capital) - the football players. Football players are the main force that drives the revenues and existence of the football clubs. It’s almost impossible to put a price on a football player, however, they are treated as intangible assets and they are even amortized (the contract of the player). The theory and empirical evidence (Huang and Song, 2006; Titman and Wessels, 1988; Chung, 1993; Booth et al., 2001) shows that assets composition can determine the level of debt and equity. However, considering that football clubs have different composition of assets, we ask if this assumption will also holds for special companies such as football clubs? Football clubs are distinct from traditional companies in many other ways. Therefore, the question that needs to be answered is:
R1a: What are the main determinants of the capital structure for football clubs?
R1b: Do the relationships between these determinants and capital structure differ between football clubs and regular firms?
The aim of this study is to contribute to the already rich empirical field by providing empirical evidence on the determinant of capital structure for football clubs. The finding of this paper will bring new knowledge on what determine the choices for financing, which provide practitioners and management with deeper insight so that they can better understand the financial composition. Researchers could use this knowledge to further investigate other industries that have not been studied before and managers could use it for analysis of the current financial situation, as well as for decision making purposes.
8 the relationship. The data sample consists of 22 football clubs, 202 panels from 12 different countries. Time period covered is from 2000 to 2015.
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2. Literature Review
2.1. The determinants of capital structure
From the revolutionary work of Modigliani and Miller (1958), three important theories have been developed that help explain financial behavior that leads to different compositions of capital structure. These theories include the static trade-off theory, the pecking order theory, and the agency theory.
Static trade-off theory implies that the firm’s decision for capital structure is based on the trade-off between advantages and disadvantages of using debt. Companies are allowed to deduct the interest payments from taxable income, but there is a risk that companies will not be able to fulfill their obligations if they use too much leverage. This theory predicts that there is an optimal debt-equity ratio which helps maximize the value of a firm (Myers, 1984; Ross et al., 2011).
Pecking order theory suggests that there is no such thing as an optimal debt-equity ratio and instead, firms prefer to finance new investments by internally raised funds (such as retained earnings), and then by debt. Equity financing is the last resort (Myers, 1984; Tang and Jang, 2007).
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Table 1
Determinants of capital structure from empirical evidence
Determinant Predicted Relationship Empirical evidence
Tangibility + (Trade-off, Agency) Kayo and Kimura (2011), De Jong et al. (2008), Huang and Song (2006), Rajan and Zingales (1995), Wald (1999)
Profitability + (Trade-off) Tang and Jang (2007);
- (Pecking order, agency) Kayo and Kimura (2011); Huang and Song (2006); Bevan and Danbolt (2002); Booth et al. (2001); Chen (2004) Size + (Trade-off) Bevan and Danbolt (2002); Fama and French (2002);
- (Pecking order) Huang and Song (2006)
Growth opportunities +(Pecking, Agency) Pandey (2001) and Chen (2004);
- (Trade-off) Huang and Song (2006); De Jong et al. (2008); O’Brien (2003)
Volatility +(Pecking order) Keefe and Yaghoubi (2016); Huang and Song (2006), Huang and Song (2002),
- (Trade-off) Mocnik 2001, Titman and Wessels (1988), Booth et al (2001)
Liquidity - (Pecking order) Lipson and Mortal (2009), Anderson and Carverhill (2012)
Non-debt tax shield -(Trade-off) Titman and Wessels (1988), Wald (1999) Huang and Song (2006)
Ownership structure +(Agency) Bradley et al., 1984, Jensen and Meckling (1976); Berger et al. (1997)
Financial distress cost, +(Trade-off) Byoun (2008); Bradley et al. (1984)
- Kayo and Kimura (2011)
Tax +(Trade-off) Shyam-Sunder and Myers (1999), Bradley et al. (1984) - Huang and Song (2006), Chen (2004), De Jong et al.
(2008)
11 Table 1 summarizes the determinants of capital structure from empirical evidence1 (Kayo and Kimura, 2011; De Jong et al., 2008; Huang and Song, 2006;. Buferna et al., 2005; Tang and Jang, 2007; Rajan and Zingales, 1995,Bevan and Danbolt, 2002; Booth et al., 2001; Chen, 2004; Byoun, 2008; Bradley et al., 1984; O’Brien, 2003; Fama and French, 2002). The specific determinants would have a positive or negative relationship with leverage, depending on the theoretical approach.
2.1.1. Tangibility
Tangibility relates to the assets of the firm. In general, this theory predicts that tangibility is positively correlated with leverage because if a firm has more fixed assets (such as machinery, buildings, lands, and inventory) it could provide a better guarantee to investors for the taken risks (provided loan). On the other hand, if a firm has more intangible assets such as human capital or intellectual property, in practice, is not common to use it as collateral. The agency theory suggests that after issuance of debt, the firm may engage in riskier investments, which transfer wealth from creditors to shareholders. If a firm owns more tangible assets, then more of these assets can be used as collateral, mitigates the lender’s risks. A more tangible assets can lead to a higher capacity to borrow debt (Huang and Song, 2006; Jensen and Meckling, 1976).
Based on the trade-off theory, tangible assets will have more value for creditors than intangible assets. In case of bankruptcy, fixed assets can be sold on the market close to their cost price. Intangible assets, on the other hand, only present high value for the company, and not the lenders, because they cannot be sold for full price on the open market. (De Jong et al., 2008; Titman and Wessels, 1988).
The empirical evidence presented by Kayo and Kimura (2011), De Jong et al. (2008), Huang and Song (2006), Rajan and Zingales (1995), and Wald (1999) confirms the theoretical prediction that there is a positive correlation between leverage and tangibility.
1
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2.1.2. Profitability
A company is profitable when the cost of their operations is lower than the revenue. Profitability is an important factor because it reflects how much funds a company has to re-invest.
The trade-off theory implies that firms will borrow more because of the advantage of the tax shield. Companies with higher profits will have a greater need to shield their income from taxation therefor they will use more debt. On the other hand, companies with lower profits are associated with increased bankruptcy costs, thus for them the costs associated with debt would be higher than the advantages (tax shield) that debt provides. Less profitable companies will therefore use smaller amounts of debt and more profitable companies will use larger amounts of debt (Huang and Song, 2006; Fama and French, 2002).
However, the pecking order theory suggests an opposite relationship. According to this theory firms will use retained earnings first as investment funds and then move to bonds and to equity only if necessary. More profitable firms have more internal funds, thus less of a need for the external financing. As a result, more profitable firms would use less debt. Companies with smaller profits have less internal funds and greater need to issue debt (De Jong et al., 2008; Huang and Song, 2006).
Agency theory makes another conflicting prediction. Debt is considered a device that ensures managers pay out profits (in the form of interest payments) rather than build personal empires. Firms with higher profits have more free cash flow, thus using high amounts of debt can restrain management discretion because debts must be repaid (Jensen, 1989; Huang and Song, 2006).
13 that in reality, it is possible that there is no such relationship for some industries as opposed to capital structure theories.
2.1.3. Size
Theory suggests that there is a positive relationship between leverage and size. The trade-off theory assumes that large firms are more diversified and would have more stable cash flows. Consequently, they are less likely to go bankrupt and have access to lower interest rates so they can afford higher levels of debt. Furthermore, large firms have more complex operations, thus they are in greater need of financing and can issue larger amounts of debt. (Kayo and Kimura, 2011; Titman and Wessels, 1988; Huang and Song, 2006).
Empirical studies of Booth et al. (2001), Rajan and Zingales (1995), Bevan and Danbolt (2002), Kayo and Kimura (2011), and Barclay et al. (1995) find a positive relationship between leverage and company size as predicted by trade-off theory. However, the study of Wald (1999) finds that larger firms in certain countries - like Germany - tend to have less debt. He justifies his findings by noting that in Germany, a small number of managers control a sizable portion of their firms (such Daimler-Benz and Siemens). This means their performance affects their profitability and thus management is encouraged to act in the best interest of shareholders. Based on this fact, he argues that centralized control contributes to negative correlation between leverage and company size.
2.1.4. Growth opportunities
14 opportunities and leverage. When companies have new opportunity to invest, but does not have enough internal financing, they will issue new debt first (Kayo and Kimura, 2011). On the other hand, the trade-off theory suggests a negative relationship. Growth opportunities are not confirmed income and thus are insufficient collateral for creditors. Consequently, companies with higher growth opportunities will choose not to incur additional debt (Bevan and Danbolt, 2002).
Just like prediction from theories, findings from empirical studies are not uniform. Studies of Titman and Wessels (1988), Chung (1993), Booth et al. (2001), and Huang and Song (2006) find evidence of a negative relationship between growth opportunities and leverage. They suggest companies have specific debt-equity ratios. Pandey (2001) and Chen (2004) find a positive correlation, but their finding may be biased by the sample selection which focuses on developing countries, unlike other studies that focus only on developed countries (Huang and Song, 2006; Chung, 1993; Booth et al., 2001).
2.1.5. Volatility
Volatility refers to the variation in level of earnings. Theoretical studies are generally inconsistent in predicting the relationship between volatility and leverage. The trade-off theory suggests that when a firm’s earnings are highly volatile, it lowers their ability to cover fixed obligations which evokes uncertainty. This uncertainty would make creditors see the company as a riskier investment therefore they would require more compensation in the form of a higher interest rate. Most of the investors also try to avoid investing in companies with higher earnings volatility because they want to reduce their exposure to risks. Consequently, firms would prefer to use less debt in their capital structure, resulting in a negative relationship between leverage and volatility (Kayo and Kimura, 2011).
15 how much internal funds it will have access to, they will issue debt to cover the operations as the safest option (Ni and Yu, 2008).
Consistent with theoretical literature, there is contradicting empirical evidence on the relationship between leverage and volatility. Some studies find a negative relationship (Mocnik 2001, Titman and Wessels (1988), Booth et al (2001)) while others did find a positive relationship (Huang and Song, 2006; Huang and Song, 2002; Keefe and Yaghoubi, 2016).
2.1.6. Liquidity
Liquidity is related to how quickly asset or securities can be sold on the market. The pecking order theory suggests that there is a negative relationship between liquidity and leverage. Cash, cash reserves, and other forms of liquid assets serve as internal resources for companies to fund their operations, thus firms with more liquid assets would have less need for external sources of financing (De Jong et al., 2008).
The trade–off theory has similar prediction. According to Lipson and Mortal (2009), shareholders need to be compensated not only for the risks taken, but also for the transaction costs incurred from trading shares on the open market. Recent evidence (Butler et al, 2005) shows that less liquid equity has higher issuance costs, thus raising the overall cost of equity. Therefore, when the choices of capital structure are made based on trade-offs between debt and equity, firms with more liquid assets should choose to be financed with less leverage and more equity (Lipson and Mortal, 2009).
16 more liquid assets have less debt because the costs associated with holding large amounts of cash are lower than the costs of issuing more debt.
2.1.7. Non debt tax shield
Non- debt tax shield is associated with the tax deduction for depreciation and investment tax credits. According to trade of theory firms with larger non-debt tax shield is expected to use less debt because as argued by DeAngelo and Masulis (1980) non-debt tax shield is the substitute for the tax benefits of debt financing. Most of the empirical studies confirm the prediction. Negative relationship between leverage and non-debt tax shield is confirmed by Bradley et al. (1984), Huang and Song (2006), Titman and Wessels (1988), Wald (1999) and Fama and French (2002).
2.1.8. Cost of Financial distress
Financial distress costs or the distance from bankruptcy is associated with the issuance of debt. The more debt a company uses to finance its operations the more it is at risk of experiencing financial distress. Financial distress is occurring when a company has difficulty of repaying its financial obligations. Trade –off theory predicts a negative relationship between distance to bankruptcy and leverage. Financial health companies (able to fulfill their commitments without any trouble) tend to have a lower level of debt. This prediction is confirmed by the empirical evidence of Kayo and Kimura (2011); Byoun (2008) and Bradley et al. (1984).
2.1.9. Ownership structure
17 is a positive relationship between the ownership structure and leverage. This theory suggests that the companies will have more leverage because it can be used to minimize total agency cost. Also, there are two types of conflict of interest: between shareholders and managers, and shareholders and debtholders, therefore it is expected that there would be some correlation between ownership structure and leverage (Huang and Song, 2006;Jensen and Meckling, 1976; Jensen, 1986).
Most of the empirical studies (Jensen and Meckling, 1976; Berger et al. 1997) confirmed the positive relationship. However, Huang and Song (2006) fail to find statistical significant results between leverage and ownership structure suggesting that there is no relationships. They argue that they failed to significant result because the Chines companies are different from other companies because the state is the controlling shareholder or the companies are becoming more privately owned due to more competitive environment.
2.1.10.
Tax
Another determinant used in many studies is tax. The impact of tax on capital structure is the main theme of the work done by Modigliani and Miller (1958). Most of the researchers therefore, believe that taxes must be important in determining the capital structure of companies. Trade- off theory predicts that firms with a higher effective marginal tax rate should use more debt to obtain a tax-shield.
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2.2.
Hypotheses development
Tangibility
Football clubs have a special composition of assets. Firstly, most of the clubs own at least one or two football stadiums. As a fixed asset, the stadiums have a lot of value, thus it provide a great guarantee to creditors in the form of collateral. However, in the event of a bankruptcy, when the ownership is transferred to the creditors, it can be very hard to sell a stadium for real value. As we can imagine, football stadiums are usually very expensive to build and maintain, they are not like regular commodities that are traded daily on the market. Secondly, a big portion of football club assets is human capital. After all, football players are the main spectacle of this sport. Football players generate huge streams of revenue for the football club. They generate ticket purchases and build a loyal customer base, not to mention other commercial value they bring to the club. Thirdly, goodwill of a well-established team name has incredible value therefore, it is a major asset itself.
Regular companies usually have more fixed assets (buildings, lands, machinery) that can be used as collateral for provided loan. Empirical evidence presented by Kayo and Kimura (2011), De Jong et al. (2008), Huang and Song (2006), Rajan and Zingales (1995), and Wald (1999) confirms that there is a positive correlation between leverage and tangibility, that more fixed assets mean more leverage. However, we argue that the relationship would be reversed for Football clubs since they have more intangible composition of assets than regular companies.
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Profitability
Looking at profitability as a determinant of capital structure, football clubs find themselves in a special category because they generate their revenue differently from regular corporations. The first stream of revenue originates from sponsor deals and commercial partnerships. These investors will gladly pay a huge amount of money to promote their brand with certain football clubs. This source of capital has almost no cost but a football club cannot rely solely on this form of income because sponsors can withdraw their finances at any time. A second source of revenue comes from selling tickets on match days. However, matches are not played regularly, only on occasion and during certain seasons so the revenue from selling tickets is very volatile and depends on factors such as popularity of the club, popularity of the competitor, weather, public support for the team, and more. Beyond selling tickets, revenue also comes from selling broadcasting rights. This can jeopardize the revenue from ticket sales as the fans could choose to watch matches from the comfort of their homes rather than go to the stadium. This means if the football club isn’t strategic with broadcast right pricing and contracts, it could contribute to revenue volatility. Overall, revenue of football clubs is very unstable and unpredictable. Costs, on the other hand, are a different story. Football clubs have a lot of expenditures associated with their players. They have to pay very high salaries to players, sometimes even reaching several million euros depending on their popularity and skill level. Additional expenditures are encountered when clubs want to acquire new players. Furthermore, high costs are also associated with preparing players for competitions, sending them to matches, and maintaining the field and stadium.
Considering that football club revenue is very volatile and cannot be easily predicted, as well as the high costs associated with running a football club, clubs cannot solely rely on their internal funds for financing. Therefore, we argue that profitability and leverage have a positive relationship.
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Size
If we look at the size of the football clubs, most of them, especially the most famous ones are large in size. Just their assets can range around a few million Euros. The larger football clubs are more established and have been around for decades. Hence, in the eyes of investors, they are considered to be more trustworthy and therefore resulting in cheaper long-term debt and effectively lower risk of bankruptcy. This means the larger the football club, the cheaper their debt and the greater their access to it. We assume a positive relationship between leverage and company size as confirmed by most of the empirical evidence (Booth et al., 2001; Rajan and Zingales, 1995; Bevan and Danbolt, 2002; Kayo and Kimura, 2011).
H3: There is a positive relationship between size and leverage.
Growth opportunities
In the past decade, football clubs have been presented with huge growth opportunities. In the past decade, technological has advanced in a ways we could not even have imagined. Internet, social media, easy access to information, content sharing, digital platforms, all these technology advancements have provided companies with great opportunities for new revenue streams. A football club’s existence depends on the fans. Thanks to social media, fans can stay connected to the team and their favorite players. On the web, they can watch live-streams of football matches. On news sharing platforms, they can choose to just watch the highlights of the match or previous footage of past games. The possibilities are infinite and thanks to this, football clubs are better able to expand their businesses and grow values from sources that haven’t existed before.
21 invest in these new projects. Therefore, we suggest a negative relationship between growth opportunities and leverage
H4: There is a negative relationship between growth opportunities and leverage.
Volatility
As for the discussion about, football clubs earning are highly volatile compare to regular corporations. Their main stream of revenue is from selling tickets during match-day, which is influenced by many factors such as seasonality, weather, fans base, competitors, etc. A football club’s second income is derived from sponsorships and commercial partners, which also cannot be predicted. The third revenue comes from growth opportunities such as internet, social media, digital platform, which can be also volatile at the moment, because it´s not that well establish yet. Regular corporation may stream of revenue comes from selling products and services. This stream of revenue can also be volatile although we argue that less than for football clubs.
However, there is no clear empirical evidence on the relationship between leverage and volatility for regular corporations, therefore to predict the relationship between volatility and leverage for football clubs we have followed the study of Keefe and Yaghoubi (2016). They specifically focus on the influence of cash flow volatility on capital structure and the use of debt and use several measurements of the firm’s cash flow volatility (other studies are using only one) and several economic methods that also account for non-linearity. They find that cash flow volatility is an important determinant for the use of debt (positive and significant). Therefore, for football clubs we assume there would be a positive relationship between volatility and leverage
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Liquidity
Football club liquidity comes from two sources. On one side, they can hold a large amount of cash which comes from sponsors and commercial partners, which is the most liquid asset. On the other side, a big portion of their assets are very illiquid. Take for example the stadium or the football players’ contracts. None of those are like regular commodities that are traded daily on the open market. Special investors need to be found in order for football club owners to liquidate these assets. This takes time and effort, and in the end, there is no guarantee it will be sold for full value.
Empirical evidence on this relationship for regular companies is also inconclusive. Therefore, we follow Lipson and Mortal (2009), who’s finding suggests that more liquid equity mean lower leverage for regular corporations. We predict a negative relationship between leverage and liquidity for football clubs.
H6: There is a negative relationship between liquidity and leverage.
Other determinants
For purposes of this research study, only several determinants of capital structure were selected, namely: profitability, tangibility, size, growth opportunities, volatility, and liquidity. Firm-specific factors (tangibility, profitability, size and growth) are commonly used in empirical studies because they have been repeatedly found to be significant and consistent with the prediction of conventional capital structure theories (De Jong et al, 2008; Bevan and Danbolt, 2002; Titman and Wessels, 1988, Kayo and Kimura, 2011).
23 Table 2 summarizes the predictions between chosen determinants (tangibility, profitability, size, growth opportunities, volatility and liquidity) and leverage for football clubs.
Table 2
Summary of Hypotheses
H1: There is a negative relationship between tangibility and leverage. H2: There is a positive relationship between profitability and leverage. H3: There is a positive relationship between size and leverage.
H4: There is a negative relationship between growth opportunities and leverage. H5: There is a positive relationship between volatility and leverage.
H6: There is a negative relationship between liquidity and leverage.
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3. Methodology
3.1. Model specification
To test whether selected determinants have a significant effect on the composition of capital structure of football clubs, we apply regression analysis. According to Serrasquiro and Nunes (2008), the most common way of assessing the correlation between leverage and its determinants is by using panel data. By pooling random samples drawn from the same observations, but at different points in time, we can get more precise estimators and more powerful test statistics. Based on literature (Wooldridge, 2013; Brooks 2008), panel data should be used when the dataset contains both time series and cross-sectional elements - which it does in our sample.
25 Considering the previously defined determinants, equation 1 describes the econometric relationship between the independent and dependent variables.
TOTLEVi,t =β0 + β1TANGi,t +β2PROFii,t + β3LNSIZEi,t + β4GROWi,t + β5VOLATi,t + β6LIQUIDi,t +dt + εi,t
(1)
Where TOTLEVi,t is the dependent variable (leverage), β0 is the intercept term, β is a k×1
vector of parameters to be estimated on the explanatory variables, TANGi,t is tangibility of the
company, PROFi,t is the profitability, SIZEi,t is the size of the firm, GROWi,t represent the growth
opportunities, VOLATi,t is the volatility, LIQUIDi,t is the liquidity, dt are temporal dummy variables,
εi,t represent error term, i represent each individual company, t represent the period of time.
3.2. Dependent variable
Leverage
26 the significance differences between long-term and short-term debt. Using the approach by Bevan and Danbolt (2002), Buferna et al. (2005) and Pandey (2001) as a proxy for leverage, we will use book value of total-debt. The long-term-debt (book value) and short-term-debt (book value) will be used for robustness test, which will strengthen the result of our analysis. Fama and French (2002) pointed out that most of the predictions about the capital structure apply directly to book value of leverage, thus the book value is preferred. Book value of debt is also used in most of the empirical studies we follow (Fama and French, 2002, Bevan and Danbolt, 2002; Hung and Song, 2006). More detailed descriptions and calculation of the ratios can be found in Appendix B.
3.3. Independent variables
Tangibility
Debt holders require the firm to provide some kind of guarantee, collateral, for provided funds, that can be sold in case of default. Therefore the relative level of tangible assets to total assets presents a good proxy for tangibility. This proxy is also used in most of the empirical studies (Titman and Wessels, 1988; Bevan and Danbolt, 2002; De Jong et al., 2008; Huang and Song, 2006; Raja and Zingales, 1995). Followed the approach of Huang and Song (2006) and Raja and Zingales, (1995), in this study, we will use as a proxy for tangibility the ratio of fixed assets (book value) scaled by total assets (book value).
27
Profitability
There are three well know measurements of profitability: Profit margin, ROA (return on assets) or ROE (return on equity). Most of the empirical studies (Raja and Zingales, 1995; Huang and Song, 2006; Jong et al., 2008; Bevan and Danbolt, 2002) use ROA as a proxy for profitability. Huang and Song (2006) argue that profitability has the largest single effect on debt/asset ratio, so it should be reflected in measurements, therefore they use ROA ratio as a proxy for profitability. Ross et al. (2011) do not recommend using as a proxy profit margin because it takes into consideration in calculation all expenses including taxes and interest, which are related to leverage. ROA, on the other hand, measures the amount of profit made by company per one currency of asset therefore it shows the company’s true ability to generate profit before leverage. ROE is also not a good proxy for examining relationship between profitability and leverage, because it does not include all of company’s assets, but only shows the profitability of equity (Ross et al., 2011). Following example of Raja and Zingales (1995) Huang and Song (2006) Jong et al. (2008) Bevan and Danbolt (2002) we will use ROA ratio as a proxy for profitability.
Size
28
Growth opportunities
Adam and Goyal (2000) examine Three most commonly use proxies to measure growth opportunities, namely Tobin’s Q (market to book asset ratio), MBE ratio (market to book equity ratio) and E/P ratio (Earnings-price ratio). Tobin’s Q represents the ratio of the present value of expected cash flow to the replacement value of assets in place. This proxy is used in study of Rajan and Zingales (1995), Huang and Song (2006) and Bevan and Danbolt (2002). MBE ratio measures the extent to which a firm’s return on its assets-in-place and expected future investments exceeds its required return on equity and it is used in study of Booth et al. (2001). According to Chung and Charoenwong (1991) higher E/P ratio indicates that a larger portion of equity value can be attributed to assets in place relative to growth opportunities. However, Huang and Song (2006) argue that E/P ratio is the past growth experience, while Tobin’s Q can better reflect future growth opportunities, therefore, Tobin’s Q is better measurement to study growth opportunities. Adam and Goyal (2000) also show that the Tobin’s Q ratio is the most informative proxy because it contains the highest relative information content with respect to a firm’s investment opportunities. Based on arguments stated above, as proxy for Growth opportunities Tobin’s Q will be used for measuring growth opportunities.
Volatility
29 volatility and leverage because it excludes interest expense and taxes, which are related to debt (dependent variable) and include depreciation and amortization expenses, which are non-cash expenses. Following Keefe and Yaghoubi (2016) the standard deviation of earnings before interest will be used as a measurement of volatility.
Liquidity
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4. Data
4.1. Sample selection
The data set contains the financial information of all football clubs listed on the public stock exchange. The list of publicly traded football clubs was obtained from the ORACLE database. In this database, we use the Standard Industrial Classification (SIC) code of 7941 (Professional football clubs and promoters) to define our sample. We obtained a total of 35 listed companies. From this list, all the companies that are not sports clubs were excluded. We ended up with a sample of 25 clubs from which 22 were football clubs and two were rugby club one U.S association of eight teams of football clubs. We excluded the rugby clubs and the U.S association. The specific description about the data sample, the list of all the names of the clubs and ISIN codes can be found in appendix A. The time period covered is the last 15 year, from 2000 to 2015. The observations are collected for different amount of time for each football club, because the clubs started to get listed in different years (unbalanced data set). The data used for the empirical analysis was derived from the DataStream database using respective ISIN codes. This database contains information from balance sheets, income statements, and cash flow statements for publicly listed companies in various countries. Some information2 was also collected from company´s annual financial statements, as it was not available on DataStream. The specific information collected from the annual financial statements and the specific variables from DataStream can be found in Appendix B.
Dependent variable leverage (TOTLEV) is calculated as total debt divided by total assets (book value). The total debt is calculated as short-term debt plus long-term debt. The dependent variable for robustness test of leverage, short-term debt ratio (SHORTL) is calculated as short-term debt (book value) divided by total assets. Long-term debt ratio (LONGL) is
2
31 computed as long-term debt (book value) divided by total assets. Independent variable tangibility (TANG) is calculated as fixed assets divided by total assets. For measurement of profitability (PROF) we use ROA ratio, which is calculated as earnings before interest, taxes, depreciation and amortization (EBITDA) divided by total assets. Explanatory variable size (lnSIZE) is calculated as the natural logarithm of sales. For growth opportunities (GROW) we used as measurement Tobin’s Q, which is calculated as market value of total assets divided by book value of total assets. Volatility (VOLAT) measurement is calculated as the standard deviation of earnings before interest and taxes (EBIT) scaled by total assets. For liquidity (LIQUID) measurement we use current ratio, calculated as current assets divided by current liabilities.
4.2. Descriptive statistics
32
Table 3
Descriptive statistics of the collected sample
Variables Mean Std. Dev. Maximum Minimum Observations
TOTLEV 0.273 0.272 1.562 0.000 270 SHORTL 0.121 0.215 1.562 0.000 267 LONGL 0.153 0.171 0.762 0.000 270 TANG 0.322 0.298 0.929 0.001 271 PROF -0.137 1.862 0.768 -19.015 259 SIZE 597681.9 2572597 15427641 -1194 303 lnSIZE 11.324 1.661 17.078 3.971 303 GROW 1.231 0.396 2.462 0.494 210 VOLAT 0.182 0.896 9.509 0.001 266 LIQUID 0.996 0.935 6.570 0.100 227
The table displays mean, max, min, standard deviation and number of observation of dependent and independent variables. TOTLEV is the ratio of total debt, SHORTL is the ratio of short-term debt, LONGL is the ratio of long-term debt, TANG is the ratio of tangibility, PROF is ratio of profitability, SIZE is total sales, lnSIZE is natural logarithm of SIZE, growth represents the growth opportunities as Tobin’s Q, VOLAT is ratio of volatility, LIQUID is liquidity ratio
The average ratio of total debt to total asset is 27%. This indicates that football clubs are more funded with equity than debt. The maximum ratio is 156%. A ratio this high means that a company has more debt than assets, so the book value of equity is negative, which mean large accumulated losses. On the other hand the minimum is 0 which would mean full equity funding and no tax-advantages. If we compare the average to regular companies (Huang and Song, 2006; Bevan and Danbolt, 2002)3 we see that the mean is about 18%, which suggests that football clubs are funded with more debt as regular companies.
The average short term-debt ratio and long-term debt ratio are 12% and 15%, respectively. The short-term leverage mean is 13% for regular companies (Bevan and Danbolt, 2002), which means that football clubs have about the same amount of short-term debt on
3
33 average. The long-term leverage for regular companies is 6% (Chen, 2004; Huang and Song; 2006), meaning that football clubs use more long-term leverage than regular companies.
The ratio for tangibility is not very constant across our sample. On the one side, the maximum tangibility ratio is 93%, suggesting that football clubs have high portion of fixed assets. However, on the other side, the minimum ratio is 0, meaning that clubs do not have fixed assets what so ever. The mean of tangibility ratio is at 32%, which means that the football clubs have more intangible assets as hypothesized. Although, if we compare the mean with regular firms (Huang and Song, 2006, Bevan and Danbolt, 2002), they are about the same (around 35%). This means that football clubs do not have more intangible assets than other regular companies.
The mean of profitability ratio is around -14%, suggesting that, the football clubs are not very profitable. As we hypothesized previously the profit is very volatile, depending on many factors such as seasons, fans, competitors, etc. The mean of profitability ratio for regular companies is 16% (Bevan and Danbolt, 2002), meaning that regular companies are more profitable. However we need to take into consideration how the profitability measurements were constructed.
The median lnSize for regular companies range from 19% to 9% (Huang and Song, 2006; Bevan and Danbolt, 2002; Chen, 2004), whereas the mean lnSize for football clubs is around 11%. Therefore, on average, football clubs are same size as other regular companies in terms of revenue.
If we compare the mean of the growth opportunities (1.23) of football clubs with the mean (2.7) of regular companies (Huang and Song, 2006), we can see that regular companies are presented with more growth opportunities. This may be due to more flexibility regarding market orientation for regular companies.
34 problems with statistical interpretations and can make the hypothesis testing less conclusive. Table 4 reports the correlations between each dependent and independent variable.
Table 4
Correlation Matrix
TOTLEV SHORTL LONGL TANG PROF SIZE GROW VOLAT LIQUID
TOTLEV 1 SHORTL 0.671 1 LONGL 0.821 0.127 1 TANG 0.231 -0.048 0.346 1 PROF -0.245 -0.168 -0.197 -0.098 1 LNSIZE -0.204 -0.136 -0.168 -0.081 0.043 1 GROW 0.094 0.232 -0.053 -0.234 0.036 0.148 1 VOLAT 0.007 0.117 -0.081 0.014 -0.291 -0.078 0.077 1 LIQUID -0.425 -0.318 -0.323 -0.272 0.071 0.251 -0.145 -0.134 1
The table shows the correlation coefficient between each dependent and independent variables. Variables are negatively correlated when the sign upfront the coefficient is minus, positively correlated when it is plus. TOTLEV is the ratio of total debt, SHORTL is the ratio of short-term debt, LONGL is the ratio of long-term debt, TANG is the ratio of tangibility, PROF is ratio of profitability, SIZE is total sales, lnSIZE is natural logarithm of SIZE, growth represents the growth opportunities as Tobin’s Q, VOLAT is ratio of volatility, LIQUID is liquidity ratio
35
5. Results
5.1. Results of regression
In this section we present the result of the empirical analysis on the determinants of capital structure. However, before we can conduct any analysis, we had to make sure that our model has number of desirable properties (homoscedasticity, no autocorrelation, exogeneity, non-stochasticity, normal distribution), so that hypotheses tests regarding the coefficient estimates could be constructed with validity. If our model does not have the aforementioned desirable properties it can cause that the standard errors estimates are wrong, which can underestimate their true variability leading to biased probabilities.
The LM test and Hausman test revealed that random effect model should be used for analysis. The results are presented in table 5.
Table 5
Random effects GLS model total leverage
Variable Coefficient t-Statistic Probability
TANG 0.114 2.168** 0.031 PROF -0.332 -3.385*** 0.000 LNSIZE -0.028 -2.354** 0.019 GROW 0.057 1.467 0.144 VOLAT -0.313 -1.769* 0.078 LIQUID -0.084 -5.048*** 0.000 R-squared 0.288 Adjusted R-squared 0.262 Sum squared residuals 7.301
TOTLEVi,t = βo + β1TANGi,t +β2PROFii,t + β3LNSIZEi,t + β4GROWi,t + β5VOLATi,t +
β6LIQUIDi,t +dt + µi,t
The sample consist of 1352 observation from period 2000-2015, 202 unbalanced panels, Method: Panel EGLS (Cross-section random effects),
36 The results show that not all independent variables are significant, which is surprising. Therefore, to make sure that the right model was used for the analysis, we performed robustness test using alternative model. The results of this regression can be found in appendix E and are similar (in terms of significance) to the results in table 5. The R-squared value shows that 27% of total leverage is explained by the dependent variables in the sample.
The coefficient of tangibility is positive at 5% significance level, therefore we reject the null hypothesis that there is no relationship between leverage and tangibility. Although the relationship between leverage and tangibility was assumed to be negative, the results show otherwise. A positive relationship was assumed by Trade-off theory and confirmed by most of the prior empirical evidence (Kayo and Kimura, 2011; De Jong et al., 2008; Huang and Song, 2006; Rajan and Zingales, 1995; and Wald, 1999). The coefficient of 0.12 suggests that the tangibility is also economically significant. For leverage to increase by 1% the tangibility ratio will increase by 8.3% (1/0.12).
The coefficient of profitability is negative at 1% significance level. The negative coefficient means that football clubs will have more debt when they are less profitable. Although the relationship was predicted to be positive, the negative relationship is in line with the finding of Kayo and Kimura (2011); Huang and Song (2006); Bevan and Danbolt (2002); Booth et al. (2001); Chen (2004) and supports the pecking order theory. This is contradictory to prior finding for tangibility determinant which assumes the trade-off theory. The coefficient of -0.33 shows that when profitability of the football clubs decreases by 3% (1/0.33), the leverage decreases by 1%.
37 The coefficient of growth is positive, but insignificant even on 10% significance level, therefore we fail to reject the null hypothesis of no relationship between growth opportunities and leverage. These results are surprising, because growth opportunities have been found to be related to leverage. by most of the empirical papers (Titman and Wessels, 1988; Chung, 1993; Booth et al., 2001; and Huang and Song, 2006)
The coefficient of Volatility is negative and significant only at 10% significance level. However the probability is above 5% significance level, thus we fail to reject the null hypothesis of no relationship between volatility and leverage. The results suggest that volatile income of football clubs have no effect what-so-ever on the amount of debt they have. However, volatility has been found insignificant by some of the previous empirical studies (Booth et al., 2001; Mocnik, 2001).
Although there is not that much empirical evidence on the relationship between liquidity and leverage; we found that the coefficient of liquidity is negative and statistically significant at 1% level. A negative relationship was also confirmed by the three prior studies who used liquidity as a determinant (De Jong et al., 2008; Lipson and Mortal, 2009; Anderson and Carverhill; 2012). The coefficient of -0.08 suggests that for leverage to increase by 1% the liquidity measurement has to decrease by 12.5%., thus the results are significant economically too.
5.2. Results of robustness test
In this part, we analyzed the result of robustness analyses. Two robustness analyses were performed with same independent variables, but different dependent variables to check the stability of the relationship between total debt and the explanatory variables. The equation used for the analyses are presented in appendix C and the results of the regression in appendix D.
First, we compare the R –squared coefficients to see how well the model fits. The R-square are 18% and 24% for short-term and long-term model, respectively. R-square for
38 different determinants that can explain why football clubs use short-term debt. The R-squared is higher for long-term model, 24% of variance is explained, but still smaller that the R-square of total leverage model (29%). Therefore, the short-term and long-term models have less explanatory power. This is reflected also in the significance of coefficients. The results from short-term model show that the tangibility coefficient is no longer significant at 5% or even 10% significance level. This can be explained by the fact that usually there is no need for collateral (as a form of fixed assets) to obtain short- term debt. The coefficient of profitability is negative and significant at 5% interval, same as total leverage model. The size coefficient is insignificant, suggesting that size of the football club have no effect on the amount of short-term debt. Another difference is between the growth opportunities coefficients. The coefficient of growth opportunities from short-term leverage model is positive and significant at 5% interval. This fining is in line with the finding of Buferna et al. (2005) and Bevan and Danbolt (2002). Bevan and Danbolt (2002) assume that the relationship exists because firms with high level of growth opportunities prefer short-term to long-term debt, because short-term debt provides more financial flexibility. Another difference between the first and third equations is the significance of volatility. We fail to reject the null hypothesis that there is no relationship between volatility and short-term debt at 5% and 10% significance level. This suggest that cash flow volatility will not affect he short-term borrowing. The coefficient of volatility and liquidity in short- term model is the same compared with total leverage model. Volatility is insignificant at 10% interval and liquidity is negative and significant at 1% interval.
The long-term model does not differ that much from the initial model such as short-term leverage model. The relationships between long-term debt and independent variables are confirmed to be same as the initial model except for the volatility. The coefficient of volatility is negative and significant at 1% significance level. The negative relationship between volatility and leverage is explained by the trade-off theory and also confirmed by the empirical evidence of Huang and Song (2006) and Booth et al. (2001).
39
6. Conclusion
The regression analyses provided us with empirical evidence that some determinants identified by prior empirical studies (table 1) have in fact an influence on the composition of capital structure for football clubs. However, the results are also surprising because not all of the selected determinants have been found to affect the composition of debt as hypothesized.
The tangibility has been found to be positively related to leverage, with more tangible assets there is more leverage. A positive relationship between tangibility and leverage is found by most of the empirical studies (Kayo and Kimura, 2011; De Jong et al., 2008; Huang and Song, 2006; Rajan and Zingales, 1995; Wald, 1999) for regular companies as well and can be explained by either trade-off theory or agency theory. However, we hypothesized a negative relationship. As mention before, we assumed that football clubs have a special composition of assets, because their assets mostly include human capital and stadiums, which in most cases cannot serve as collateral to creditors for taken risks. The descriptive statistics show that on average football clubs have about the same amount of fixed assets as regular companies. Thus, the football clubs are not an exception and there is a positive relationship between tangibility and leverage.
The determinant of profitability was also hypothesized as opposite to findings. As mentioned in section 2.2, the football clubs have a few different source of revenue which is all very volatile and unpredictable (cannot be forecasted). They also have a high level of steady expenditures. Therefore, we assumed that they cannot rely only on their internal funds and will issue debt. The results show the exact opposite relationship. As the football clubs are more and more profitable they use less and less debt. These findings are confirmed by the empirical evidence of Kayo and Kimura (2011); Huang and Song (2006); Bevan and Danbolt (2002); Booth et al. (2001) for regular companies. The results suggest that companies will first finance their operations with internal funds and then leverage, confirming the pecking order theory.
40 evidence for regular companies founded also a positive relationship. The football clubs are the exception here. The composition of debt will decrease with the size of the firm, which is supported by the pecking-order theory. Bigger clubs have more internal funds, thus less need for external financing. A negative relationship is also found in the study of Wald (1995) for regular companies in Germany.
Surprising results were fined on the relationship between growth opportunities and leverage. We assumed that because of the technology advancement, the football industry have more growth opportunities, thus would want to maintain the lower level of debt. Lower amounts of debt provide the firm with more freedom to choose opportunities in which to invest. The negative relationship was found by the empirical evidence of Huang and Song (2006); Jong et al. (2008); O’Brien (2003) for regular companies. In this case the football clubs are exception. We assume that there isn’t a relationship because growth opportunities are not relevant determinate. Even when the football clubs are presented with growth opportunities they will choose not to issue more debt to invest in these growth opportunities as regular companies because the purpose for which they been created is different.
According to the result volatility and leverage has no relationship. We assume that there would be a positive relationship because football clubs are uncertain about the magnitude of future earnings, thus they will use debt to cover their obligations. Positive relationship is found by the empirical evidence of Keefe and Yaghoubi (2016); Huang and Song (2006), Huang and Song (2006) for regular companies. However, Booth et al (2001) and Mocnik (2001) fail to find a positive relationship for regular companies, same as in this study. No relationship suggests that the capital structure will not be affected by the earnings volatility.
41 This study provides us with new knowledge on the understanding of how the capital structure of football clubs is determined. Given that the company’s capital structure can significantly affect the cost of its capital, a better understanding of what determine the composition of equity and debt should enable management to maximize value for all stakeholders.
6.1. Limitations
42
6.2. Further research
43
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I
APPENDIXES
Appendix A: List of football clubs
Table A1
List of the football clubs
Company name REVENUE
2015* Country ISIN number Comments
1 MANCHESTER UNITED PLC 395178 Cayman Islands KYG5784H1065 FC in England 2 ARSENAL HOLDINGS PLC 344524 United Kingdom GB0030895238 FC in England,
Holloway -London 3 BORUSSIA-DORTMUND 276048 Germany DE0005493092 FC in Dortmund,
Germany
4 JUVENTUS 324666 Italy IT0000336518 FC in Turin, Italy
5 A.S. ROMA 180626 Italy IT0001008876 FC in Rome
6 S. S LAZIO SPA 110795 Italy IT0003621783 FC Lazio in Rome 7 AFC AJAX. 105412 Netherlands NL0000018034 FC in Amsterdam 8 CELTIC PLC 51080 United Kingdom GB0004339189 FC in Glasgow,
Scotland 9 SPORTING CLUBE DE
PORTUGAL 58382 Portugal PTSCP0AM0001 FC in Portugal
10 BRISBANE BRONCOS 40438 Australia AU000000BBL6 FC in Brisbane 11 SPORTING CLUBE DE BRAGA 21027 Portugal PTSCB0AM0001 FC in Portugal 12 NORTHAMPTON SAINTS PLC 16480 United Kingdom GB0030904733 FC in UK
13 BRONDBYERNES 135834 Denmark DK0010247956 FC in Denmark
14 AZUL AZUL S.A. 13669430 Chile CL0000006172 FC in Chile 15 AIK FOTBOLL AB 155877 Sweden SE0000598278 FC in Sweden 16 CRUZADOS S.A.D.P. 9666636 Chile CL0000006743 FC in Chile 17 AALBORG BOLDSPILKLUB 78773 Denmark DK0010247014 FC in Denmark
18 SILKEBORG IFS 61597 Denmark DK0010128008 FC in Denmark
19 OXFORD CITY FC. 617 United States of
America US69141A2015 FC in USA 20 GKS GIEKSA KATOWICE 2304 Poland PLGKS0000016 FC in Katowice,
Poland 21 RANGERS FC 16470 United Kingdom GB00B90T9Z75 FC in UK 22 NEWCASTLE UNITED 83086** United Kingdom GB0006572795 FC in UK
Note: information for other sports club were no available in the list downloaded from ORBIS using the sic code 7491 (Professional football clubs and promoters) The list of all 35 companies is available on request.
*: Revenue is display in thousands in each country home currency
II
Appendix B: Definition of variables
Table B1
Source of variables
Variable DataStream code Comment
1 Debt WC03255 Book value of debt*
2 Short-term debt WC03051 Book value* 3 Long-term debt WC03251 Book value *
4 Total assets WC02999 Book value of total assets* 5 Fixed assets WC02501 Book value of fixed assets*
6 EBIT WC18191 Net income plus taxes and interest* 7 EBITDA WC18198 EBIT plus depreciation and amortization* 8 Sales WC01001 Gross value of Sales or Revenue*
9 Equity WC03995 Book value of Equity*
10 Number of shares WC05651 Number of share outstanding* 11 Traded volume NOSH Volume of trade shares* 12 Current assets WC02201 Total current assets* 13 Current ratio WC03101 Total current liabilities*
14 MTB MTBV Market to book value*
15 MV MV Market value*
