Master Thesis in Finance - Tilburg University

2015-2016

Master Thesis in

Finance Capital structure decisions for public and

private firms in the United Kingdom

Student: F. Psipsilis (671310) Supervisor: dr. M. Da Rin

Master Thesis in Finance Tilburg University

i

Master Thesis in Finance

Topic: Capital Structure decisions of public and private firms in the

United Kingdom

November 2016

Student Information:

Name: Fotios Psipsilis ANR: 671310

Master in Finance student Tilburg University

Supervisor Information: Name: dr. M. Da Rin

Associate Professor of Finance Tilburg University

Second Reader Information: Name: Prof. Mina Vlachaki PhD student

Tilburg University

I certify that the work presented here, is to the best of knowledge and belief, original and the results

of my own investigation, except as acknowledged, and has not been submitted, either in part or in

whole, for a degree at this or any University.


ii

Abstract

For my study, I have investigated which factors could be considered important when considering

the capital structure decisions of firms. My sample consists of private and public firms in the

United Kingdom and covers the period from 2006 to 2015. I used ordinary least square (OLS)

regressions in order to test variables that have been used previously in the relevant literature, based

on trade off theory, pecking order theory, and agency cost theory. Due to limitations in the data of

private firms, only the growth is used as a factor for agency cost theory. Public firms follow the

pecking order theory. The empirical evidence show that public firms base their capital structure

decisions on profitability and liquidity. Public firms that have higher profitability and liquid ity

tend to reduce their debt levels. Private firms provide similar evidence but business risk and growth

has to be added to the key factors that lead their capital structure decisions. Private firms that face

higher business risk tend to maintain low levels of debt. Moreover, private firms with more growth

potentials have more leverage. In addition to the above, the leverage ratio of public and private

firms has a negative correlation and persistence to time. However, based on the variance

decomposition analysis, firm’s specific characteristics dominate the capital structure decisions of

private and public firms in the UK.


iii

Table of Contents:

Abstract

Table of Contents

List of Tables

Section 1: Introduction ..................................................................................................................1

Section 2: Current Literature .......................................................................................................3

2.1. Modigliani and Miller Theory ...............................................................................................3

2.2. Trade off Theory....................................................................................................................4

2.3. Pecking order Theory ............................................................................................................5

2.4. Agency cost Theory...............................................................................................................6

2.5. Factors that affect Capital Structure of Public and Private Firms .........................................7

Section 3: Sample ...........................................................................................................................8

3.1. Sample ...................................................................................................................................8

3.2. Summary Statistics ..............................................................................................................10

Section 4: Research Question and Methodology .......................................................................10

4.1. Research Question and Hypotheses.....................................................................................10

4.2. Methodology........................................................................................................................12

Section 5: Evidence of my Research ...........................................................................................14

5.1. Regression Results...............................................................................................................14

5.2. Variance Decomposition .....................................................................................................23

Section 6: Conclusion...................................................................................................................26

References


iv

List of Tables:

Table A: Summary Statistics

Table B: Independent and Control Variables

Table 1: Test the Trade-off Theory

Table 2: Test the Pecking Order Theory

Table 3: Test Firm’s Growth Effect

Table 4: Leverage Determinants

Table 5: Variance Decomposition: Public Firms

Table 6: Variance Decomposition: Private Firms


1

1. Introduction

One of the fundamental problems that firms have to solve is how they will maintain an optimal

capital structure. Many studies try to address which factors that affect capital structure of firms

should be consider important (De Jong, Kabir and Nguyen 2008; Frank and Goyal 2009; Lemon,

Roberts, and Zender (2008); Rajan and Zingales 1995; Titman and Wessels 1988).

Modigliani and Miller (1958) proposed that firm’s total market value is independent of its capital

structure under certain strict assumptions. Since financial markets are not frictionless, latter

research is focused to surpass market failures in order to shape the optimal capital structure of a

firm. Thus, Theories of optimal capital structure try to explain the optimal leverage level should

be undertaken according to a cost and benefit analysis of adding more debt into a firm. Starting

with trade off theory which suggests that firms try to balance the benefits and the costs of debt.

Next is pecking order theory which implies that firms tend to use their internal funds. If these funds

are insufficient to finance their projects or operations, then firms use external financing. In this

case, debt is preferred over equity. Lastly, agency cost theory tries to explain the problems that are

caused due to conflicts of interests between firm’s managers and shareholders.

Frank and Goyal (2009) investigated several factors that tend to affect the capital structure

decisions of firms. Their sample consists of American public firms and covers the period of 1950

to 2003. De Jong, Kabir and Nguyen (2008) suggest that specific firm and country factors play a

key role in the optimal capital structure of corporations. Their sample consists of 42 countries. In

contrast to previous studies, the determinants of firm’s capital structure are different between each

country. In order to examine the optimal capital structure decisions, Rajan and Zingales (1995)

analyzed the financing decisions of public firms which were listed in the G-7 countries. Lastly,

Titman and Wessels (1998) examined several theories that are related to the capital structure

decisions. The majority of all capital structure studies is focused on US public firms. Therefore, it

is usually assumed that private entities could follow a same pattern. However, there is a major

difference between private and public firms. According to Brav (2009), private firms have limited

access to capital market and as a result the cost of debt and equity is higher compared to their

public counterparties. This master thesis is going to focus on private and public firms that operate

within the United Kingdom. Thus, I tried to identify which factors could be considered key drivers


2

of a firm’s capital structure decisions. In order to do so, I had to first investigate each of the

mentioned theories separately.

My sample consists of 352 public and 1275 private firms that operate within the United Kingdom.

I have included only non-financial firms and specific legal entities based on Brav (2009) paper.

Financial firms and these legal entities differ significantly to their capital structure decisions due

to regulations that are imposed to them. The sample period is from 2006 to 2015. I used the

Amadeus dataset managed by Bureau van Dijk (BvD) hosted at Wharton Research database in

order to obtain my data and Ordinary least square (OLS) regressions for my analysis.

According to my empirical analysis there are a few factors that play a significant role in capital

structure decisions of firms. First of all, it is worth to mention that private firms tend to have on

average a higher leverage ratio compared to their public counterparties. Moreover, there is a

difference in the key drivers in their decisions. UK’s public firms that are more profitable and have

higher liquidity tend to have lower leverage ratio. Based on pecking order theory, these firms will

use their internal funds to finance new projects and maintain the required levels of liquidity. If the

profits or the liquidity levels are not sufficient then public corporations will prefer debt over equity

as an external source of financing. Regarding private firms, the results are on the same directions

but I have also to consider the importance of business risk and the growth of these firms. It seems

that private firms that face more business risk are these with lower debt. Moreover, private firms

with more growth potentials have more debt in their books. I have also investigated the correlation

between leverage and time. My results impose a negative correlation and a persistence of leverage

through time in both private and public firms. This means that firms with higher (lower) leverage

will reduce (increase) their leverage but they will maintain their leverage ratio in a low but stable

ratio over time (Lemmon, Roberts and Zender 2008). Even though the factors that are mentioned

are important, my evidence also indicates that firm specific characteristics, which are measured as

firm fixed effects, have the most important role in firm’s capital structure decisions.

The study of private firms is usually limited due to date availability and it is common to generalize

the results of public firms to private. Therefore, my study contributes to the current literature in

the following ways. Firstly, it investigates the factors that drive the capital structure decisions not

only for public but also for private firms. Moreover, this study confirms that some factors such as


3

profitability, liquidity for public firms and business risk, profitability, liquidity and growth for

private firms could be considered key drivers for their capital structure decisions.

In this section, I have presented an introduction to the selected thesis topic as well as a general

discussion of my results. The remaining contents are presented in the following way: section 2

covers a literature review, section 3 includes the sample description and summary statistics table,

section 4 states the research question and the methodology analysis and it is followed by section 5

and 6 where the results and the overall conclusion are presented respectively.

2. Current Literature

This section provides a literature review of the capital structure theories along with predictions of

factors that according to previous research affect the capital structure decisions of firms.

2.1. Modigliani and Miller Theory

Modigliani and Miller (1958) have proven that a firm’s total market value is independent of its

capital structure under certain assumptions. They have assumed that there are perfect financ ia l

markets and no taxes, all agents have the same information, and a firm’s cash-flows do not depend

on its financial policy. In reality though, these assumptions do not hold. Frictions do exist in

financial markets. The current financial crisis has shown that a lot of firms faced bankruptcy or

went bankrupt. Moreover, each country imposes a tax rate in the corporations that operate within

the country. Firms have to meet their tax obligations in order to continue their smooth operation.

It is also well known that managers of a firm know more information about the firm and its

performance than outside investors. This confirms that the assumption of no information

asymmetry is not realistic. Lastly, another common problem of corporations is the conflict of

interest between the management and the shareholders of firm. Shareholders want to ensure that

managers use the free cash flows of the firm in order to maximize shareholders’ value and for their

benefit. Quite often, firms that have large amount of free cash flows undertake more debt in order

to commit these free cash flows to future debt repayment.

However, latter research is focused on surpassing market failures in order to shape the optimal

capital structure of a firm. Theories of optimal capital structure try to explain the optimal leverage

level which should be maintained according to a cost and benefit analysis of adding more debt into


4

the firm. Among them, trade-off, pecking order and agency cost theories have been used to provide

an explanation of firms’ capital structure decisions.

2.2. Trade-off Theory

A firm has to choose the amount of debt capital and equity capital in order to finance its

investments. According to static trade-off theory, the decision has to balance the cost and benefits

of bearing more debt. Kraus and Litzenberger (1973) state that a corporation has to balance the

dead-weight cost of bankruptcy and the tax saving benefits of debt. Myers (1984) reports that firms

borrow up to the point where the marginal tax benefit cancels out the marginal costs of bankruptcy.

Evidence suggests that decisions of a firm capital structure are in line with trade-off theory (Frank

and Goyal 2009). Brounen, de Jong and Koedijk (2006) use the non-debt tax shield and the cost

of financial distress in order to measure the trade-off theory. According to DeAngelo and Masulis

(1980), non-debt tax shield does effect the capital structure decisions of a firm. Business risk,

tangibility and size have been used as proxies of measurement of cost of financial distress. De

Jong, Kabir and Nguyen (2008) provide evidence that business risk does effect the financ ia l

leverage ratio of corporations. Titman and Wessels (1988) suggest that tangibility is related with

the levels of financial leverage within a corporation. Therefore, I have used non-debt tax shield,

business risk and asset tangibility as variables to test the trade-off theory.

The relation between leverage and non-debt tax shields

Firms have to pay interest on the outstanding debt. However, interest payments have tax benefits.

According to trade-off theory, entities tend to use more debt in order to achieve higher tax benefits

that occur from higher interest payments. DeAngelo and Masulis (1980) suggest that non debt tax

shield can be used as an alternative to the tax benefits that occur from debt. Therefore, non-debt

tax shields can reduce corporate taxes and as a result firms with higher non-debt tax shields will

pay low taxes and have lower outstanding debt. Based on this, I would expect a negative correlation

between leverage ratio and non-debt tax shields.

The relation between leverage and business risk

Business risk is related to earning volatility. Firms with higher earnings volatility are exposed to a

higher cost of financial distress. As a result, the probability of a firm going bankrupt increases with

earnings volatility. Therefore, these entities should maintain lower level of debt because they might


5

not be able to repay their lenders. Many previous studies impose that a firm’s debt level tend to

decline as firm’s earnings become more volatile (Titman and Wessels 1998). In their study, De

Jong, Kabir and Nguyen (2008), confirm that firms which face high business risk have low debt

levels and based on this I should expect a negative correlation.

The relation between leverage and asset’s tangibility

Bankruptcy costs are decreased as the number of tangible assets of a firm is increased. Tangib le

assets are easier to be valued and therefore to be claimed by the lenders if firm goes bankrupt.

Firms use their tangible assets as collaterals to issue debt. Therefore, lenders are able to pledge

more assets when they are lending their funds in firms which hold more tangible assets. The more

tangible assets a firm holds the higher debt amounts it can attract. Frank and Goyal (2009) state

that under trade off theory there is a positive relation between leverage and tangibility.

2.3. Pecking order Theory

Pecking order theory assumes that an organization should follow an order preference of its

financing decisions due to information costs (Myers and Majluf 1984). Firms have to decide

between internal financing and external financing. External financing is related to information

asymmetry and transaction costs. Myers (1984) and Donaldson (1961) indicate that firms tend to

prefer internal financing to external financing and in case of external financing debt is preferred to

equity. There is also evidence that a firm decides its capital structure by pecking order theory

(Shyam-Sunder and Myers 1999). Profitability, liquidity (De Jong, Kabir and Nguyen 2008) and

asset tangibility (Frank and Goyal 2009) have been used as proxies to measure the pecking order

theory.

The relation between leverage and asset’s tangibility

Whenever firms need to issue debt they have to use their tangible assets as collaterals. Tangib le

assets of a firm are observable and easier to be valued by the lenders. According to pecking order

theory, firms will use their retained earnings to finance their operations and new projects and if

these are not sufficient then they will use debt over equity. If firms provide more collaterals, then

debt issuance becomes less costly. Therefore, Frank and Goyal (2009) suggest that adverse

selection is increased by tangible assets and as a result it will increase the debt capacity of a firm.

However, when information asymmetry of tangible assets is low, firms can issue equity with a


6

lower cost (Frank and Goyal 2009). Relying on previous literature, I would expect a positive

correlation between tangibility and leverage ratio (Frank and Goyal 2009; Rajan and Zinga les

1995).

The relation between leverage and profitability

Firm’s profitability is a measure that is used to measure the profit that a firm is able to make.

Retained earnings of firms are linked to the ability of a firm to make profit. Therefore, profit could

be considered as a form of internal financing (De Jong, Kabir and Nquyen 2008). According to

pecking order theory, firms will firstly use their internal funds and then any form of external funds.

If a firm has higher profitability, then the leverage over time will be reduced (Frank and Goyal

2009). Moreover, Myers (1984) provides evidence that firms with higher profits have a lower

leverage ratio compared to firms with lower profits. As a result, I would expect a negative

correlation between leverage ratio and firms’ profitability.

The relation between leverage and liquidity

Liquidity is related to the liquid assets and cash related assets that a firm holds. As it is already

mentioned, pecking order theory suggests that firms will use their internal funds in order to finance

their operations and new projects and if these funds are not sufficient then they will issue debt

rather than equity. Firms’ that hold more liquid assets or have more cash available will use them

for their operations or projects and therefore the debt capacity will not change because there will

be no need for further external financing. Based on the study of De Jong, Kabir and Nguyen (2008),

I would expect a negative relation between the liquidity and firms’ leverage ratio. Thus, firms will

use their liquid assets as a form of internal funds and only if these funds are not suffic ient

corporations will attract debt.

2.4. Agency cost theory

As reported by Jensen and Meckling (1976), agency cost theory is a determinant of organizat ions

capital structure decisions as well as trade-off and pecking order theory. The separation of

ownership and management of firms essentially results in conflict of interest between shareholders

and managers. Agency theory is based on the costs that occur due to these conflicts of interest.

Jensen (1986) introduced the well-known free cash flow problem. He suggests that managers may

overinvest the excess free cash flows into value destroying investments in the pursuit of empire


7

building. Thus, firms can use leverage as a measure of discipline and reduce the exploit of private

benefits by managers. The conflict of interest implies to bondholders and equity holders as well

(Jensen and Meckling 1976). Myers (1977) suggests that the problem of underinvestment is

significantly strong for growth companies because they tend to overlook profitable investments.

Free cash flows and growth opportunity have been used as proxies to measure agency cost theory.

Jensen (1986) imposes a positive relationship between free cash flow and leverage ratio of a

corporation. Titman and Wessels (1988) suggest that growth opportunity has a negative effect on

leverage of firms. Although free cash flow could be an important factor of agency cost theory

based on previous literature, this research is limited to the use of growth as a variable for agency

cost theory, due to the limitation of information that is provided for private firms.

The relation between leverage and growth

According to agency cost theory, managers do not always undertake investments that will

eventually maximize shareholders value. This behavior often causes conflict of interest between

shareholders and managers of firm. The cost that occurs from this agency conflict tends to be

higher for firms that operate within growing industries (Titman and Wessels 1988). Thus, future

growth which is related to these investments will have a negative relation to debt levels. However,

convertible debt ratios could have a positive correlation with firm’s growth because this kind of

debt is able to reduce agency costs (Jensen and Meckling 1976). Since I have used the most

common leverage ratio definition, I would expect to find a negative relation between firms’ growth

and leverage.

2.5. Factors that affect Capital Structure of Public and Private firms

Current literature has pointed out several factors which have an impact of financial behavior of

public corporations. To begin with, Titman and Wessels (1988) recognize as important factors the

asset structure, non-debt tax shield, growth uniqueness, industry classification, size, earnings

volatility and profitability. Moreover, Rajan and Zingales (1995) suggest that firms’ size, asset

tangibility, profitability and growth are the most reliable determinants of leverage. Last but not

least, Frank and Goyal (2009) provide evidence of a positive correlation between median industry

leverage, tangibility, log of assets, and expected inflation with market leverage. They also impose

a negative correlation for market-to-book asset ratio and profits. Lastly, their evidence is in line

with trade off theory up to a point.


8

Most of these capital structure studies are based on US publicly traded firms. According to Brav

(2009), there is also interesting evidence related to capital structure decisions of private firms. This

study aims to investigate which factors are important in capital structure decisions in both private

and public UK firms. The results that Brav (2009) obtains for public firms are in line with the

current literature. This implies that firm size and asset tangibility are positively related to leverage.

On the other hand, profitability and growth opportunities are negatively correlated to debt ratios

that are used in his research. Brav (2009) predicts that the leverage ratio of private entities is more

sensitive to firm’s profitability and negatively correlated. Moreover, there is a negative relation

between the leverage ratio of private firms and their growth opportunities as proxies of capital

expenditures and the growth rate of sales (Brav 2009). Leverage ratio increases as the size of

private firms’ increases which is line with trade off theory. Regarding private firms’ leverage and

asset tangibility relation, the results are in line with the static trade off theory which implies a

positive relation.

In addition to these, Lemmon, Roberts and Zender (2008) research study investigates the trend of

leverage through time. In their study, they observe that firms that are ranked as high (low) leverage

firms tend to reduce (increase) their debt capacity as time passes by. Moreover, firms tend to

decrease or increase their debt to equity ratio and beyond a point to maintain this ratio stable over

time. This suggests a persistence effect of leverage over time.

Taking all the above into account, I will investigate the capital structure decisions of public and

private firms in UK. Moreover, I will use the key factors that have been used in trade-off, pecking

order and agency cost theory in order to provide evidence of their decisions. The upcoming section

provides information about my data sample as well as the descriptive statistics analysis.

3. Data Analysis

This section provides an insight of how I have treated my data set. Additionally, in the subsection

3.2, I analyze the results of the table of summary statistics which includes the means and the

standard deviations of my variables.

3.1. Sample

In order to imply my quantitative analysis, I used the AMADEUS database managed by Bureau

van Dijk (BvD) which is provided at Wharton Research Database Services. AMADEUS is a pan-


9

European financial database which contains information for both private and public firms. I

downloaded financial statement information regarding private and public firms in the UK. My

sample period covers the period of 2006 to 2015 and consists of very large and large firms as are

defined by the BvD database.

In my analysis, the following types of firms has been included: Private limited company, Public

company AIM (Alternative Investment Market), public company not quoted, public company

quoted, and public company quoted OFEX (Off Exchange). I excluded the following entities types:

Charitable organization, Legal form unknown, Limited company guarantee, Limited liability

partnership – LLP, Not companies act, Public investment trust, Royal charter, Unlimited company.

Additionally, I had to exclude firms of financial and insurance activities (NACE-Rev 2: 64-66),

utility firms (NACE-Rev 2: 35-39), public administration and defense, and compulsory social

security firms (NACE-Rev 2: 84), due to the fact that these firms have a different nature of

operations and accounting information and their capital structures have to be regulated by

legislation (Brav 2009). Additionally, I have considered as “public firms” all the entities that are

quoted and as “private firms” the unquoted ones.

All firms that operate in the United Kingdom should prepare and provide their financial statements

in accordance with U.K accounting standards or IFRS regardless of being public or private. Brav

(2009) excludes firms that their annual sales do not exceed one million pounds after June 2000.

Recent regulatory changes imply that all firms should be audited except if they are qualified as

small entities. Small companies are defined as the ones which have an annual turnover under 6.5

million pounds or a balance sheet value under 3.26 million pounds. Thus, I excluded firms which

met these conditions.

Rajan and Zingales (1995) suggest that there is a difference in reported leverage between firms

with unconsolidated balance sheets and those which provide consolidated financial statements.

Thus, I excluded firms that do not provide consolidated financial statements. Last but not least, I

have set the leverage to be within the closed unit interval (Lemmon, Roberts, and Zender 2008)

and firms that do not have an active legal status for 10 years are excluded.


10

3.2. Summary Statistics

Table A provides results of summary statistics of public and private firms in the United Kingdom

as well as the difference between each variable’s mean. As it was expected, I was able to observe

significant differences between public and private corporations. First of all, private firms have a

significant higher leverage ratio compared to their public counterparties. However, public

organizations are larger and have more growth potentials. In the same direction are the results for

non-debt Tax-Shields which suggest that public firms use more the non-debt Tax-Shield benefits.

Moreover, public firms face higher level of business risk compared to the private ones. Public

firms tend to be more profitable but my evidence suggests that their liquidity ratio is lower than

the one of private firms.

Summary Statistics

Table A. The sample consists of very large and large public and private firms which operate within the United

Kingdom. The data period is from 2006 to 2015. The table below provides variable means and standard d eviations of

public and private firms as well as the difference of each variable’s mean. Three stars, two stars and one star impose

the significance of this difference in 99%, 95%, and 90% confidence level respectively.

Public Firms Private Firms

Variable Mean (SD) Mean (SD) Difference

Leverage 0,451 0,19 0,541 0,22 (-0,089) ***

Firm Size 19,466 2,02 17,500 1,40 (1,966)*** Non-Debt TS 0,046 0,04 0,038 0,03 (0,008)***

B. Risk 0,044 0,04 0,038 0,03 (0,007)***

Asset Tang. 0,233 0,22 0,301 0,24 (-0,068)***

Profitability 0,054 0,08 0,042 0,07 (0,013)***

Liquidity 1,664 1,06 1,698 1,13 (-0,034)*

Growth 0,003 0,01 0,003 0,01 (0,001)***

No. Obs 3013 10272

4. Research Question and Methodology

In order to provide the methodology that I have used to facilitate my quantitative analysis, I have

to address first the research question that is going to be tested in this study. Based on the review

of the current literature which is provided in section 2, I have used several hypotheses in order to

test my research question.

4.1. Research Question and Hypotheses

As it was already mentioned, Frank and Goyal (2009) provide evidence about which factors should

we consider as important determinants of capital structure studies. Their study is based on US


11

publicly traded firms as well as many other capital structure studies. Brav (2009) suggests that

there is also important evidence regarding the capital structure decisions of private firms. In this

study, I investigated which factors could be considered important in capital structure decisions in

both private and public UK firms. Thus the main research question is the following:

Main Question: “Which firm specific characteristics could matter in capital structure decisions

of private and public firms in the UK?”

In order to do so, I had to investigate the theories which are related to capital structure. Hence, the

key factors of trade-off and pecking order theory were investigated. In addition to this and due to

limitations regarding the date about private firms, the growth opportunities will be tested as the

variable of the agency cost theory. Thus, the questions that had to be answered first were the

following:

Question (A): “Under trade-off theory, which factors are important for capital structure

decisions of private and public firms in the UK?”

Question (B): “Under pecking order theory, which factors matter for capital structure

decisions of private and public firms in the UK?”

Question (C): “Under agency cost theory, is the growth opportunities factor significant for

capital structure decisions of private and public firms in the UK?”

Based on these questions, I formulated the following hypotheses in respect of the current literature :

(A) Trade-off theory hypotheses:

H1a: There is a negative relation between business risk and leverage ratio of public UK firms.

H1b: There is a negative relation between business risk and leverage ratio of private UK firms.

H2a: There is a negative relation between non debt tax shields and leverage ratio of public UK

firms.

H2b: There is a negative relation between non debt tax shields and leverage ratio of private UK

firms.

H3a: There is a positive relation between tangible assets and leverage ratio of public UK firms.


12

H3b: There is a positive relation between tangible assets and leverage ratio of private UK firms.

(B) Pecking order theory hypotheses:

G1a: There is a positive relation between asset tangibility and leverage ratio of public firms in the

UK.

G1b: There is a positive relation between asset tangibility and leverage ratio of private firms in

the UK.

G2a: There is a negative relation between profitability and leverage ratio of public firms in the

UK.

G2b: There is a negative relation between profitability and leverage ratio of private firms in the

UK.

G3a: There is a negative relation between liquidity and leverage ratio of public UK firms.

G3b: There is a negative relation between liquidity and leverage ratio of private UK firms.

(C) Agency cost theory hypotheses:

S1a: There is a negative relation between growth opportunities and leverage ratio of public UK

firms.

S1b: There is a negative relation between growth opportunities and leverage ratio of private UK

firms.

4.2. Methodology

In order to answer my main and sub research questions, I had to use a quantitative analysis. In my

analysis, I used components of balance sheets and income statements of public and private UK

firms in order to calculate my dependent and independent variables. I considered it useful to use a

pooled Ordinary Least Square (OLS) regression since I had to deal with panel data. My main OLS

regression model was the following:

𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 = 𝛽0 + 𝛽1𝑆𝑖𝑧𝑒𝑖,𝑡−1 + 𝛽2𝐵𝑅𝑖𝑠𝑘𝑖 ,𝑡−1 + 𝛽3𝑁𝑑𝑇𝑆𝑖,𝑡−1 + 𝛽4 𝑇𝑎𝑛𝑔𝑖,𝑡−1 + 𝛽5𝑃𝑟𝑜𝑓𝑖,𝑡−1

+ 𝛽6𝐿𝑖𝑞𝑖,𝑡−1 + 𝛽7𝐺𝑟𝑜𝑤𝑡ℎ𝑖,𝑡−1 + 𝜀𝑖,𝑡


13

In this model Leverage is my dependent variable of each firm at year t and was calculated as Total

debt to total assets of firm. I implied 1- year lag for the independent variables. Thus, firm’s leverage

decisions of year t are affected by the decisions of previous year. At this point, it is useful to

provide the definition of each variable.

Variables

Frank and Goyal (2009) point out that there are quite a few definitions of leverage that have been

used in previous literature. Their main differences are about the use of market or book values as

well as debt maturity. Welch (2004) suggests interest coverage ratio as a measure of leverage.

Frank and Goyal (2009) use the following definitions of leverage in their study: (1) total debt to

market value of assets ratio, (2) total debt to book value of assets ratio, (3) long-term debt to market

value of assets, and (4) long-term debt to book value of assets.

In this study, I have used the total debt to total assets as my leverage ratio measure. The total debt

is the sum of current liabilities and non-current liabilities.

The following table provides an overview of the variables that I used as well as their definitions.

Independent and Control Variables

Table B. An overview of variables. Definitions are provided as well as papers that have used similar proxies.

Control Variable:

Firm Size (Frank and Goyal 2009)

𝑆𝑖𝑧𝑒 = 𝑁𝑎𝑡𝑢𝑟𝑎𝑙 𝐿𝑜𝑔𝑟𝑖𝑡ℎ𝑚 𝑜𝑓 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠

Trade off theory:

Business Risk (De Jong, Kabir, and Nguyen

2008)

BRisk= standard deviation of EBIT to Total Assets over a period of 5 years

Non-debt Tax Shield (Frank and Goyal 2009)

𝑁𝑑𝑇𝑆 =𝐷𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛

𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠⁄

Tangibility (Frank and Goyal 2009)

𝑇𝑎𝑛𝑔 =𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐹𝑖𝑥𝑒𝑑 𝐴𝑠𝑠𝑒𝑡𝑠


Pecking order theory Tangibility (Frank and Goyal 2009)

𝑇𝑎𝑛𝑔 =𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐹𝑖𝑥𝑒𝑑 𝐴𝑠𝑠𝑒𝑡𝑠


Profitability (Frank and Goyal 2009)

𝑃𝑟𝑜𝑓 = 𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠⁄

Liquidity (De Jong, Kabir, and Nguyen

2008)

𝐿𝑖𝑞 = 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠⁄


14

Agency cost theory:

Growth opportunities (Titman and Wessels 1988)

𝐺𝑟𝑜𝑤𝑡ℎ

= ((𝐿𝑛(𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠)𝑡 − 𝐿𝑛(𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠)𝑡−1)

𝐿𝑛(𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠)𝑡−1⁄ )

5. Evidence of my research

In this chapter, I have included the results of my OLS regression models. First, I tested each sub-

question separately and afterwards the main research question. In sub-section 5.2, I provide the

evidence of a variance decomposition analysis which I applied in each model.

5.1. Regression Analysis

In this section, I present the output of my research. The first question in the table was tested below

regarding the factors based on trade off theory which could be consider important. The results

reported in models A1 and A3 are for public firms and for private in models A2 and A4

respectively. In order to consider each firm’s specific characteristics fixed firm effects (FE) are

taken into account in models A3 and A4. Last but not least, all models in this section include year

dummies in order to observe the leverage ratio through time. Therefore, the results of my first sub

question regarding the trade-off theory can be seen below.

Test the Trade-off Theory

Table 1. This table provides estimates from OLS regressions of Leverage ratio as dependent variable and independent

variables that are based on Trade-off theory. Business risk, Non-Debt Tax-Shield and assets’ tangibility have been

used as independent variables. Size of a firm is used as control variable. All models include year dummies. Models

A1 and A3 provide results for public firms in the UK and models A2 and A4 for private firms. Firm Fixed Effects are

taken into account in models A3 and A4. The sample period is from 2006 to 2015. Clustered standard errors are

reported below the coefficient estimates.

Dependent Variable: Leverage Ratio

Model A1 Pub. Firms

Model A2 Priv. Firms

Model A3 Pub. Firms (FE)

Model A4 Priv. Firms (FE)

Size 0.0250*** 0.0018 0.0190 0.0113 (0.0046) (0.0042) (0.0155) (0.0095)

Business Risk -0.4038* -0.5983*** 0.1996* -0.2438*** (0.2098) (0.1481) (0.1203) (0.0912)

Non-Debt Tax-Shield 0.4905*** 0.4748*** 0.2089** 0.1024 (0.1736) (0.1564) (0.0888) (0.1094)

Assets’ Tangibility -0.0522 -0.1477*** -0.0063 -0.0013

(0.0426) (0.0259) (0.0685) (0.0240) Year 2009 -0.0305*** -0.0177*** -0.0289*** -0.0227***

(0.0054) (0.0039) (0.0057) (0.0033) Year 2010 -0.0471*** -0.0122** -0.0467*** -0.0218***


15

(0.0074) (0.0050) (0.0063) (0.0041) Year 2011 -0.0476*** -0.0172*** -0.0486*** -0.0236***

(0.0082) (0.0054) (0.0075) (0.0045) Year 2012 -0.0566*** -0.0298*** -0.0566*** -0.0353***

(0.0086) (0.0056) (0.0088) (0.0048) Year 2013 -0.0579*** -0.0398*** -0.0587*** -0.0464***

(0.0090) (0.0059) (0.0097) (0.0052) Year 2014 -0.0647*** -0.0466*** -0.0561*** -0.0553***

(0.0098) (0.0059) (0.0105) (0.0054) Year 2015 -0.0589*** -0.0533*** -0.0521*** -0.0642***

(0.0103) (0.0061) (0.0116) (0.0059)

Constant 0.0173 0.5822*** 0.1069 0.3778** (0.0906) (0.0723) (0.2919) (0.1656)

Observations 2,532 8,711 2,532 8,711 # of Firms 352 1,275 352 1,275

R-squared 0.072 0.033 0.873 0.885 Firm Fixed Effects No No Yes Yes

As it can be observed, there is a positive and significant correlation between leverage and firm’s

size of public firms in the UK (model A1). This is in line with the trade-off theory based on the

evidence of previous literature. The negative correlation between business risk and leverage of

public firms is consistent with previous literature. However, the coefficient is significant in 90%

confidence level. I would expect a negative correlation between non-debt Tax shield and Leverage.

In public firms’ sample this correlation is positive and statistica lly significant in 99% confidence

level. A potential explanation could be that the changes in taxes during this period of time still

allow firms to take advantage of the interest tax benefits of debt, but as debt capacity increases

firms start to rely on non-debt tax shields as well. Moreover, the correlation between tangibility of

firm and leverage is negative but it is not statistical significant in any confidence level.

When I used firm fixed effects for public firms (model A3) there are some minor changes. Firm’s

size is still positively correlated to leverage but not statistically significant. However, business risk

and leverage turn to be positively correlated and still significant at the same confidence level.

According to this, firms with high business risk which are more volatile tend to maintain high

levels of debt which is not consistent with previous literature. There are not significant changes in

the non-debt tax shields’ and tangibility’s results.

On the other hand, private firms in the United Kingdom provide slightly different evidence. Firm’s

size is positively correlated but it is not statistically significant as it is in public firms. Private


16

firms’ sample provides a negative correlation between business risk and leverage which is

statistically significant in 99% confidence level. Thus, private firms that face low business risk are

willing to undertake more debt. There is no difference between public and private firms’ results

regarding non-debt tax shield. Private firms, compared to their public counterparties, have a

negative and statistically significant correlation between leverage and the nature of assets which

are measured by tangibility. A potential explanation is that private firms tend to have large

expenditures in research and developments. Therefore, in order to protect these patents, they will

avoid to undertake further debt. Titman (1984) suggests that if a firm produces unique products

then it should maintain a low leverage ratio, which is not consistent with trade off theory.

Additionally, when firm fixed effects are used in the private firms’ sample (model A4), I observed

that non debt tax shields and asset tangibility are no more statistically significant. Comparing the

models A3 and A4 (firm fixed effects) for public and private firms respectively there is one major

difference. The correlation between business risk and leverage of public firms’ changes to positive

when firm fixed effects are used. However, this correlation remains negative and statistica l ly

significant in 99% confidence level for private firms’ sample even when firm fixed effects are

used.

In the above table, year dummies have been used. The results show a strong negative correlation

between time and leverage either the firm is public or private. Based on Lemmon, Roberts and

Zender (2008), firms that have a high leverage ratio tend to reduce this ratio as time passes, and

vice versa. However, they observed a persistence in the leverage ratio of firms which implies that

beyond a certain point firms tend to maintain a low and stable level of leverage ratio over time.

Next table presents results between leverage ratio and variables that are used in previous literature

to test the pecking order theory. The same pattern is used in this table. Size of a firm is used as a

control variable and year dummies are used as well. Models B1 and B3 show results of public

firms and models B2 and B4 for private. In last two models, B3 and B4, firm fixed effects are used.

Thus, the results of my second sub question regarding pecking order theory are presented below.


17

Test the Pecking Order Theory


variables that are based on Pecking Order theory. Assets’ tangibility, Profitability and Liquidity have been used as

independent variables. Size of a firm is used as control variable. All models include year dummies. Models B1 and

B3 provide results for public firms in the UK and models B2 and B4 for private firms. Firm Fixed Effects are taken

into account in models B3 and B4. The sample period is from 2006 to 2015. Clustered standard errors are reported

below the coefficient estimates.

Dependent Variable: Leverage Ratio Model B1

Pub. Firms Model B2 Priv. Firms

Model B3 Pub. Firms

(FE)

Model B4 Priv. Firms

(FE)

Size 0.0202*** 0.0064* 0.0111 0.0136 (0.0039) (0.0033) (0.0155) (0.0085)

Assets’ Tangibility -0.0744** -0.2599*** -0.0718 -0.0451* (0.0355) (0.0218) (0.0659) (0.0255)

Profitability -0.2818*** -0.4467*** -0.2255*** -0.2910*** (0.0775) (0.0483) (0.0500) (0.0306)

Liquidity -0.0827*** -0.1191*** -0.0230*** -0.0271*** (0.0078) (0.0051) (0.0050) (0.0036)

Year 2009 -0.0448*** -0.0213*** -0.0315*** -0.0244*** (0.0060) (0.0041) (0.0055) (0.0033)

Year 2010 -0.0621*** -0.0116** -0.0491*** -0.0259*** (0.0075) (0.0048) (0.0062) (0.0038)

Year 2011 -0.0591*** -0.0232*** -0.0470*** -0.0290*** (0.0077) (0.0049) (0.0070) (0.0041)

Year 2012 -0.0666*** -0.0324*** -0.0557*** -0.0392*** (0.0083) (0.0050) (0.0085) (0.0044)

Year 2013 -0.0635*** -0.0380*** -0.0566*** -0.0491***

(0.0087) (0.0053) (0.0093) (0.0049) Year 2014 -0.0647*** -0.0426*** -0.0542*** -0.0572***

(0.0099) (0.0053) (0.0100) (0.0052) Year 2015 -0.0536*** -0.0436*** -0.0496*** -0.0627***

(0.0106) (0.0055) (0.0112) (0.0056) Constant 0.2798*** 0.7476*** 0.3439 0.4050***

(0.0793) (0.0574) (0.2932) (0.1470)

Observations 2,532 8,711 2,532 8,711 Number of Firms 352 1,275 352 1,275


Public firms’ size is positively correlated with leverage ratio and statistically significant in 99%

confidence level. The literature imposes a positive correlation between leverage and tangibility.

Based on my results there is a negative correlation. According to pecking order theory, issuing

equity could have a low cost due to low information asymmetry of tangible assets that a firm holds

(Frank and Goyal 2009). The pecking order theory imposes a negative correlation between


18

leverage and profitability, as well as liquidity. The results regarding the public firms are in line

with the previous literature and are statistically significant. As it can be seen in model B3, firm

fixed effects that are used there have two major changes. Firm’s size and tangibility are no more

statistically significant.

The results of private firms are consistent with pecking order theory. Asset tangibility and leverage

has a negative correlation in private firms’ sample. It might be the case that private firms tend to

have large expenditures in research and development. In order to protect the assets that are going

to be produced in case of a successful research and development investment, firms will avoid to

undertake further debt. Moreover, the correlation between leverage and profitability as well as

liquidity is negative and statistically significant in 99% confidence level. This implies that private

firms that are more profitable or hold more liquid assets tend to have low debt levels. This is

consistent with pecking order theory which imposes that firms prefer to use their internal financ ing

rather than external financing. When firm fixed effects (model B4) are used there is only one major

change. The coefficient of tangibility is statistically significant in 90% confidence level. There are

no significant differences between public and private firms based on the results of this table and

therefore can be concluded that private and public firms are consistent with pecking order theory

in respect to the tested variables.

Regarding year dummies, I can still observe a negative correlation between time and leverage ratio

of both private and public firms in the United Kingdom. This persistence is still statistica l ly

significant as in Table 1.

Below in Table 3, I used the difference of natural logarithm of total assets as a proxy of firm’s

growth. Based on agency cost theory, there is a negative relation between growth opportunities of

firms and leverage ratio. This implies that firms with a higher growth rate tend to maintain lower

levels of debt. Therefore, the results of this study are presented in the following table.


19

Test Firm’s Growth Effect


variable firm’s Growth which is based on Agency cost theory. Size of a firm is used as control variable. All models

include year dummies. Models C1 and C3 provide results for public firms in the UK whereas models B2 and B4 for

private firms. Firm Fixed Effects are taken into account in models C3 and C4. The sample period is from 2006 to

2015. Clustered standard errors are reported below the coefficient estimates.

Dependent Variable: Leverage Ratio Model C1

Pub. Firms Model C2 Priv. Firms

Model C3 Pub. Firms (FE)

Model C4 Priv. Firms (FE)

Size 0.0250*** -0.0001 0.0136 0.0039

(0.0046) (0.0042) (0.0166) (0.0101) Firm’s Growth -1.3062** 1.1017*** 0.0978 0.5710***

(0.6039) (0.2958) (0.3343) (0.1594)

Year 2009 -0.0331*** -0.0205*** -0.0252*** -0.0233*** (0.0051) (0.0039) (0.0054) (0.0033)

Year 2010 -0.0614*** -0.0129** -0.0391*** -0.0204*** (0.0078) (0.0050) (0.0069) (0.0040)

Year 2011 -0.0607*** -0.0192*** -0.0419*** -0.0241*** (0.0079) (0.0051) (0.0076) (0.0043)

Year 2012 -0.0671*** -0.0316*** -0.0498*** -0.0364*** (0.0086) (0.0052) (0.0092) (0.0047)

Year 2013 -0.0695*** -0.0395*** -0.0527*** -0.0459*** (0.0095) (0.0057) (0.0103) (0.0053)

Year 2014 -0.0733*** -0.0459*** -0.0500*** -0.0545*** (0.0103) (0.0058) (0.0112) (0.0056)

Year 2015 -0.0650*** -0.0503*** -0.0477*** -0.0623*** (0.0107) (0.0061) (0.0123) (0.0061)

Constant 0.0229 0.5625*** 0.2231 0.5007*** (0.0877) (0.0718) (0.3161) (0.1751)



According to model C1, public firms’ size is positive correlated and statistically significant. The

coefficient of growth variable predicts a negative relation with leverage which is in line with the

agency cost theory and trade off theory based on the previous literature. According to Frank and

Goyal (2009), firm’s growth leads to higher cost of distress and it is reasonable for this kind of

firms to reduce their debt levels. This negative correlation turns to positive when firm fixed effects

are used in Model C3 but it is not statistically significant.

On contrast to public firms, private firms’ growth seems to be in line with pecking order theory

instead of agency cost theory. The correlation between growth and leverage of private firms is


20

positive. This implies that private firms with more growth opportunities tend to have higher debt

levels. The results do not change even when firm fixed effects are implied to the model.

The results of my main research question are presented in Table 4. I am testing several variables

with leverage in order to conclude which ones could be considered important drivers of capital

structure decisions of public and private firms in the United Kingdom.

According to model D1, public firms’ size is still positively correlated to leverage and statistica l ly

significant as trade off theory predicts (Frank and Goyal 2009). The prediction of business risk,

which is negatively correlated to leverage but not statistically significant, is in line with trade off

theory. DeAngelo and Masulis (1980) suggest that non debt tax shield can be used as alternatives

to the tax benefits that occur from debt. The non-debt tax shield of public firm’s sample has a

positive correlation with leverage ratio. A possible interpretation of this could be that firms prefer

to use debt tax benefits up to an optimal point. Beyond this point, they start to substitute them with

non-debt tax shields but not entirely. However, the coefficient result is not statistically significant.

The results of tangibility, profitability and liquidity are in line with pecking order theory and are

statistically significant. According to pecking order theory more profitable firms tend to mainta in

lower levels of debt which impose a negative correlation. Public firms in the UK will use their

excess profits to finance new projects as well as their daily operations rather than issuing debt or

equity. As it is already mentioned pecking order theory predicts a negative correlation between

tangibility and leverage in the presence of low information asymmetry which is in line with the

below results. According to the table below, public firms with low liquidity tend to have higher

leverage which implies a negative correlation between them. Liquid assets such as inventories or

cash & cash equivalent can be used as funds for financing and firms do not have to go to external

markets to raise new funds. Last but not least, growth for public firms is negatively correlated to

leverage which is consistent with trade off theory and agency cost theory

.


21

Leverage Determinants

Table 4. This table provides estimates from OLS regressions of leverage ratio as dependent variable and independent

variables several factors that are used in previous studies to test the determinants of firm’s capital structure. All models

include year dummies. Models D1 and D3 presents the results of the public firms in the UK and models D2 and D4

for the private ones. Firm fixed effects are taken into account in models D3 and D4. The sample period is from 2006

to 2015. Clustered standard errors are reported below the coefficient estimates.

Dependent Variable: Leverage Ratio Model D1

Pub. Firms Model D2 Priv. Firms

Model D3 Pub. Firms (FE)

Model D4 Priv. Firms (FE)

Size 0.0195*** 0.0050 0.0083 0.0001

(0.0039) (0.0032) (0.0162) (0.0093) Business Risk -0.2622 -0.2029** 0.0962 -0.2607***

(0.1732) (0.1025) (0.1201) (0.0790) Non-Debt Tax-Shield 0.0161 -0.0786 0.0248 -0.0001

(0.1642) (0.1332) (0.0939) (0.1052)

Tangibility of Assets -0.0796** -0.2576*** -0.0612 -0.0376 (0.0364) (0.0232) (0.0679) (0.0254)

Profitability -0.2956*** -0.4778*** -0.2286*** -0.3205*** (0.0856) (0.0512) (0.0615) (0.0307)

Liquidity -0.0817*** -0.1184*** -0.0227*** -0.0259*** (0.0079) (0.0051) (0.0050) (0.0035)

Growth opportunities -0.3091 1.0623*** 0.5423 0.8310*** (0.4976) (0.2498) (0.3313) (0.1553)

Year 2009 -0.0429*** -0.0197*** -0.0315*** -0.0211*** (0.0061) (0.0042) (0.0059) (0.0034)

Year 2010 -0.0601*** -0.0028 -0.0465*** -0.0166*** (0.0086) (0.0051) (0.0069) (0.0040)

Year 2011 -0.0550*** -0.0161*** -0.0460*** -0.0203*** (0.0085) (0.0051) (0.0077) (0.0043)

Year 2012 -0.0632*** -0.0268*** -0.0543*** -0.0307*** (0.0087) (0.0052) (0.0090) (0.0046)

Year 2013 -0.0614*** -0.0311*** -0.0541*** -0.0395***

(0.0093) (0.0055) (0.0097) (0.0051) Year 2014 -0.0636*** -0.0367*** -0.0514*** -0.0482***

(0.0103) (0.0055) (0.0105) (0.0054) Year 2015 -0.0535*** -0.0381*** -0.0466*** -0.0537***

(0.0108) (0.0056) (0.0118) (0.0058) Constant 0.3041*** 0.7725*** 0.3859 0.6391***

(0.0798) (0.0572) (0.3069) (0.1609)


R-squared 0.304 0.398 0.881 0.894 Firm Fixed Effect No No Yes Yes

Firm fixed effects have been used in model D3 for the sample of public firms. There are a few

differences that should be mentioned. Firm’s size remains positively correlated to leverage but not


22

statistically significant. Business risk is positively correlated but not statistically significant to

leverage. The results regarding profitability and liquidity remain the same. Public firms’ growth

sign turns to positive but it is not statistically significant. As pecking order theory predicts, debt

levels, given a fixed profitability level, are higher in firms with higher growth potentials (Frank

and Goyal 2009).

Moving to model D2, which refers to private firms there is some evidence that is worth to be

discussed. The results of size, tangibility, profitability and liquidity are the same as these of public

firms. What should be mentioned is that the size of a private firm does not affect its leverage

decisions. Even though size is positively correlated to leverage it is not statistically significant.

Moreover, private firms tend to prefer non-debt tax shield benefits but the evidence are not

statistically significant. Another major difference between public and private firms in the United

Kingdom, based on the above table, is the role of firm’s growth to the leverage ratio. In contrast

to the public sample, private firms’ evidence imposes a positive correlation between firm’s growth

and leverage which is statistically significant. This is in line with pecking order theory which

implies that firms that have more growth potentials tend to have higher leverage ratios.

The only major change which can be observed when firm fixed effects are used in the regression

model for the sample of private firm is that tangibility’s coefficient result is not statistica l ly

significant. Comparing these results with those of public firms there are three differences that have

to be mentioned. In case of private firms, business risk is negatively correlated to leverage ratio

and statistically significant but this is not the case in the sample of public firms. Although, there is

still difference in the sign of the correlation between non-debt tax shield and leverage for the public

and private firms with the sign for the latest to be negative but still not statistically significant. The

last difference can be spotted in the significance of the growth coefficient. This coefficient is not

statistically significant in case of public firms but it is in 99% confidence level in the case of private

firms.

Last but not least, it is important to mention once more that the correlation between year dummies

and leverage ratio remains negative and statistically significant.


23

5.2. Variance Decomposition

After completing my regression analysis, I conduct a covariance analysis (ANCOVA), which

allowed me to decompose the variation in leverage which is attributed to each different variable

of the above models. In my analysis, which can be seen in the following tables, I used the same

models as in my regression analysis section (5.1). Table 5 presents results for the public firms

whereas Table 6 provides evidence for private. Each column of each table presents a different

model which leverage ratio was tested previously. Following Lemmon, Roberts and Zender

(2008), I normalized the partial sum of squares in order to sum to one in each column. Thus, the

fraction of the partial sum of squares is presented in each row. However, the last two rows present

the R2 and the adjusted R2. The logic behind the following tables is to show the explanatory power

of each particular “factor” to the model. For instance, in column (b) where I used only year

dummies as my explanatory variable, this “factor” has the value of 1.00 which implies that the

whole model is affected by year dummies.

Observing Table 5, which provides results for public firms, I was able to draw a few important

conclusions. The main conclusion is that firm fixed effects contribute the most in each model. In

the absence of firm fixed effects in model A1, which tests the relation between leverage and factors

that support trade off theory size has the higher explanatory power (58.7%). Business risk and non-

debt tax shields have about 10% each. Size, Business risk, non-debt Tax Shield attribute 0.1%

each, when firm fixed effects are in place. FE are able to explain 98% of model which tests trade

off theory. Moving to model B1, variables that support pecking order theory are tested, liquid ity

seems to be the key factor with 76.8% explanatory power. Control variable of size explains 10%

of the model while tangibility and profitability roughly 9% together. When FE are used the

attributable effect of liquidity in the model drops to 0.6% while the rest are below 1% as well.

Year dummies have low explanatory power in models where variables of pecking order theory are

tested. Again Firm fixed effects (FE) contribute 97% in pecking order theory’s model. In model

C1, where public firms’ growth options are regressed with leverage, the results show that the key

factor is the control variable of size (72.5%) and year dummies (21.4%). The variable of growth

affects the model only by 6%. This changes to 0% when firm fixed effects are in place. Lastly in

model D1, where all variables are included, it seems that capital structure decisions of firms could

be affects by liquidity (75.8%), size (9.4%), tangibility (3.8%), profitability (5.7%), and year


24

dummies (3.7%). These numbers shrink dramatically when firm fixed effects are used in model

D3. Concluding, each public firm’s decision regarding each capital structure could be explained

by firm’s liquidity, size, tangibility and profitability. However, each firm’s decision is unique and

relies mostly in firm’s specifics characteristics which are measured by firm fixed effects. The

models with the highest adjusted R square and R square are the model in which pecking order

theory is tested by using firm fixed effects (model B3) and the model where all variables and firm

fixed effects are used (model D3).

Variance Decomposition: Public Firms

Table 5. The sample period is from 2006 to 2015. The table provides evidence of variance decomposition for the

models that have been used for the public firms in the United Kingdom. R squares and Adjusted R squares are reported.

I have computed the partial sum of squares for each variable in each model. Afterwards, I had to normalize each

estimate so each column sums to one. For instance, in Model A1 for Leverage ratio, 60% of the explained sum of

squares captured by the included covariates can be attributed to firm size. Firm FE and Year FE stand for firm fixed

effects and year fixed effects respectively.

Trade Off Theory

Pecking Order Theory

Growth Opportunities

All Variables Models

Model A1

Model A3

Model B1

Model B3

Model C1

Model C3

Model D1

Model D3 Variable (a) (b) (c)

Firm FE 1.00 . 0.99 . 0.989 . 0.973 . 0.993 . 0.977

Year FE . 1.00 0.01 0.128 0.009 0.041 0.012 0.214 0.007 0.037 0.009 Size . . . 0.587 0.001 0.100 0.000 0.725 0.000 0.094 0.000

B. Risk . . 0.107 0.001 . . . . 0.014 0.000 Nd. TS . . . 0.109 0.001 . . . . 0.000 0.000

Tang. . . . 0.069 0.000 0.036 0.001 . . 0.038 0.000 Prof. . . . . . 0.055 0.008 . . 0.057 0.007

Liq. . . . . . 0.768 0.006 . . 0.758 0.006 Growth . . . . . . 0.061 0.000 0.001 0.001

R2 0.838 0.010 0.846 0.072 0.873 0.299 0.880 0.060 0.871 0.304 0.881

Adj. R2 0.815 0.007 0.824 0.068 0.852 0.297 0.860 0.057 0.850 0.300 0.861

Table 6 presents the results of variance decomposition of private firms. The results are slightly

different compared to those of public entities. In model A2, the attributable effect of tangibility on

the model which tests the trade-off theory is 55,7%. The rest variables, which are size, year

dummies, business risk, and non-debt tax shields, attribute 44.3% altogether. This seems more

balanced compared to the one of public firms, which the attributable effect was mainly related to

size. However, the attributable effect of these variables drop almost to 0% when firm fixed effects

are used. FE are able to explain 99% of model A4 which test trade off theory’s factors. In the

column where model B2 is presented, I observe that liquidity and tangibility are the factors that


25

attribute more in this model. Once more, their explanatory power changes dramatically when firm

fixed effects are used in the next column where model B4 is presented. In the model C2, where the

growth effect is presented the results are different for private firms compared to those of public.

Year dummies have a higher explanatory power in case of private firms as well as the growth

factor which is 27% compare to 6% for the same model of public firms’ sample. The outcome

when firm fixed effects are used is the same though both for private and public. Lastly, in model

D2, the factors that seem to have a significant impact on firm’s leverage decisions are liquid ity

(77%), tangibility (17%) and profitability (5%). In the case of private firms, size does not seem to

attribute as much as it does in public ones. As it is expected in the last model (D4), where firm

fixed effects are used, 97% of the model is affected by the unique characteristics of each firm

(Fixed effects) and the rest 3% is attributed to the remaining variables. In line with their public

counterparties, private firms’ model B4, where pecking order theory is tested, and model D4,

where all factors are used as well as FE, provide the higher R square and adjusted R square.

Variance Decomposition: Private Firms

Table 6. The sample period is from 2006 to 2015. The table below provides evidence of variance decomposition for

the models that have been used for the private firms in the United Kingdom. R squares and Adjusted R squares are

reported. I have computed the partial sum of squares for each variable in each model. Afterwards, I had to normalize

each estimate so each column sums to one. For instance, in Model A2 for Leverage ratio, 16.8% of the explained sum

of squares captured by the included covariates can be attributed to business risk that firm faces. Firm FE and Year FE

stand for firm fixed effects and year fixed effects respectively.

Trade Off Theory

Pecking Order Theory

Growth Opportunities

All Variables Models

Model A2

Model A4

Model B2

Model B4

Model C2

Model C4

Model D2

Model D4 Variable (a) (b) (c)

Firm FE 1.00 . 0.989 . 0.992 . 0.974 . 0.993 . 0.974 Year FE . 1.00 0.011 0.151 0.007 0.010 0.011 0.729 0.006 0.001 0.008

Size . . . 0.003 0.000 0.004 0.000 0.000 0.000 0.002 0.000

B. Risk . . 0.168 0.001 . . . . 0.002 0.001 Nd. TS . . . 0.121 0.000 . . . . 0.000 0.000

Tang. . . . 0.557 0.000 0.182 0.000 . . 0.166 0.000 Prof. . . . . . 0.043 0.008 . . 0.049 0.009

Liq. . . . . . 0.761 0.007 . . 0.766 0.006 Growth . . . . . . 0.271 0.001 0.004 0.002

R2 0.860 0.008 0.869 0.033 0.885 0.395 0.892 0.008 0.885 0.398 0.894

Adj. R2 0.839 0.007 0.850 0.031 0.865 0.394 0.873 0.007 0.865 0.397 0.875


26

Based on variance decomposition analysis, I could conclude that although some factors are able to

drive the decisions of firms’ capital decisions, the main driver is the unique specific characterist ics

of each firm no matter if it is a public or private corporation.

6. Conclusion

This Master thesis study provides evidence of capital structure decisions of public and private

firms in the United Kingdom. The sample period of this study is from 2006 to 2015. The main

research question that have been addressed was the following:

“Which firm specific characteristics could matter in capital structure decisions of private and

public firms in the UK?”

Based on Ordinary Least Square analysis of the previous section, I can provide results which

further support the existing literature of capital structure decisions of public firms. Since this study

also investigates private firms, it adds value to private firms’ literature which is limited. According

to my results, UK public firms’ size, nature of assets (tangibility), profitability and liquidity are

the main factors that affect their capital structure decisions. The correlation of public firms’ size

and leverage ratio is positive and it is consistent with the trade-off theory. This implies that larger

firms are able to attract more loans due to the fact that these firms have diversified activities and

are able to generate stable cash flows compared to small ones. Although previous literature

imposes a positive correlation between leverage and tangibility, my results predict a negative one

which can be considered as an effect of information asymmetry. According to pecking order

theory, issuing equity costs less for firms if there is low information asymmetry about the tangible

assets which they hold and this will result to a lower leverage ratio. Results of UK public firms

sample suggest that profitability and liquidity have a negative correlation to leverage. Pecking

order theory imposes that firms prefer internal over external financing. Debt is a source of external

financing. Profitable firms tend to have more earnings which are considered as internal funds.

Therefore, more profitable firms will use these earnings to finance their operations and new

investments and only when these are not sufficient will use debt financing. This will result to lower

leverage ratios for more profitable firms. Liquidity is linked to liquid assets of firms such as firm’s

inventories and cash & cash equivalents. Thus, firms with higher liquidity ratio will have lower

leverage ratio due to the fact that they will use these liquid assets to finance their operations or

new projects and go to debt capital markets only when these liquid assets are not sufficient. When


27

firm fixed effects are in place, only profitability and liquidity factors matter in UK public firms’

capital structure decisions. Thus, I can conclude that during the last 10 years, capital structure

decisions of public firms in the United Kingdom follow the principals of pecking order theory.

On the other hand, the sample of UK private firms provides slightly different evidence. The factors

that can be considered key drivers of UK private firms’ capital structure decisions are business

risk, tangibility, profitability, liquidity and firm’s growth. Business risk is in line with trade off

theory which imposes a negative relation with leverage. The earnings volatility that a firm faces at

any given time tends to increase or decrease the probability of the firm to go bankrupt. Therefore,

business risk is associated with earnings volatility and as it is obvious firms that have more volatile

earnings will have lower debt in their balance sheets in order to be able to continue their operations.

According to UK legislation, private firms have to report their financial statements following the

same standards as listed entities. Therefore, this could reduce the information asymmetry of the

assets that private firms hold and this will result to a lower cost equity issuance. Profitability and

liquidity results of private firms are in line with pecking order theory. This means that firms that

have higher profitability and liquidity will have less debt in their balance sheet. Regarding firm’s

growth variable, the results of private firms suggest a positive relationship with leverage. However ,

this is not in line with trade off or agency cost theory prediction but it is consistent with pecking

order theory. Thus, private firms that have more growth potentials tend to maintain more debt in

their balance sheets. When firm fixed effects are used business risk, profitability, liquidity and

growth are significant factors. Therefore, I can conclude that private firms follow trade off theory

in respect of business risk and pecking order theory in respect of profitability, liquidity and growth.

In addition to the above, I would like to mention that I have included year dummies in my models.

The results of public and private firms in the United Kingdom point out to the same direction. I

was able to record a negative correlation between leverage and time. This negative correlation is

significant and negative during all tested years. Therefore, I can conclude that firms with high

(low) leverage tend to reduce (increase) their leverage through time.

As many other studies, this study has limitations. The first limitation is in agency cost theory. In

my study, I have only included growth as a representative variable of this theory. Firm’s free cash

flows as well as managerial variables such as ownership concentration, board of directors have

been used in previous research in order to provide evidence which support agency cost theory. The


28

second limitation is the fact that I have studied only three capital structure theories. Market timing

theory has also used in previous studies to provide evidence for firms’ capital structure decisions.

This theory predicts that managers observe debt and equity market conditions and finance their

projects with funds raised from the most favorable market at this point in time. When neither bond

nor equity market looks attractive they will postpone debt or equity issuance. However, if a market

looks attractive, they might raise funds even though the firm might not need them at this point.

Lastly, a future research could include market timing theory in addition to the three theories that

this study includes. Moreover, someone could also include more leverage definitions or more

definitions for each variable in order to test for robustness in the results.


29

References

Brav, Omer. 2009. "Access To Capital, Capital Structure, And The Funding Of The Firm". The

Journal of Finance 64 (1): 263-308.

Brounen, Dirk, Abe de Jong, and Kees Koedijk. 2006. "Capital Structure Policies In Europe:

Survey Evidence". Journal of Banking & Finance 30 (5): 1409-1442.

de Jong, Abe, Rezaul Kabir, and Thuy Thu Nguyen. 2008. "Capital Structure Around The World:

The Roles Of Firm- And Country-Specific Determinants". Journal of Banking & Finance

32 (9): 1954-1969.

DeAngelo, Harry and Ronald W. Masulis. 1980. "Optimal Capital Structure under Corporate and

Personal Taxation". Journal of Financial Economics 8 (1): 3-29.

Donaldson, Gordon. 1961. Corporate Debt Capacity. Boston: Division of Research, Graduate

School of Business Administration, Harvard University.

Frank, Murray Z. and Vidhan K. Goyal. 2009. "Capital Structure Decisions: Which Factors Are

Reliably Important?". Financial Management 38 (1): 1-37.

Jensen, Michael C.. 1986. “Agency costs of free cash flow, corporate finance, and takeovers”.

American Economic Review 76, 323–329.

Jensen, Michael C. and William H. Meckling. 1976. "Theory Of The Firm: Managerial Behavior,

Agency Costs And Ownership Structure". Journal Of Financial Economics 3 (4): 305-360.

Kraus, Alan and Robert H. Litzenberger. 1973. "A State-Preference Model Of Optimal Financia l

Leverage". The Journal Of Finance 28 (4): 911-922.

Lemmon, Michael L., Michael R. Roberts, and Jaime F. Zender. 2008. "Back To The Beginning:

Persistence And The Cross-Section Of Corporate Capital Structure". The Journal Of

Finance 63 (4): 1575-1608.

Miller, Merton H. and Franco Modigliani. 1958. “The cost of capital, finance, and the theory of

investment”. American Economic Review 48(3): 433-443.

Myers, Stewart C. 1977. "Determinants Of Corporate Borrowing". Journal Of Financia l

Economics 5 (2): 147-175.


30

Myers, Stewart C. 1984. "The Capital Structure Puzzle". The Journal Of Finance 39 (3): 575-592.

Myers, Stewart C. and Nicholas S. Majluf. 1984. "Corporate Financing And Investment Decisions

When Firms Have Information That Investors Do Not Have". Journal Of Financia l

Economics 13 (2): 187-221.

Rajan, Raghuram G. and Luigi Zingales. 1995. "What Do We Know About Capital Structure?

Some Evidence From International Data". The Journal Of Finance 50 (5): 1421-1460.

Shyam-Sunder, Lakshmi and Stewart C. Myers. 1999. "Testing Static Tradeoff Against Pecking

Order Models Of Capital Structure”. Journal Of Financial Economics 51 (2): 219-244.

Titman, Sheridan. 1984. "The Effect Of Capital Structure On A Firm's Liquidation Decision".

Journal Of Financial Economics 13 (1): 137-151. doi:10.1016/0304-405x(84)90035-7.

Titman, Sheridan and Roberto Wessels. 1988. "The Determinants Of Capital Structure Choice".

The Journal Of Finance 43 (1): 1-19.

Welch, Ivo. 2004. "Capital Structure And Stock Returns". Journal Of Political Economy 112 (1):

106-132.

Master Thesis in Finance - Tilburg University

Documents

Transcript of Master Thesis in Finance - Tilburg University