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Impact of financial leverage on the

profitability of real estate companies

A quantitative study from Swedish Stock Exchange

BACHELOR DEGREE PROJECT

THESIS WITHIN: Business Administration

NUMBER OF CREDITS: 15 credits

PROGRAMME OF STUDY: International Management,

International Marketing

AUTHORs: Vladyslav Deboi, Harbi Kurmakhadov, Meng Li

JÖNKÖPING May 2021

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Bachelor Thesis in Business Administration

Title: Impact of financial leverage on the profitability of real estate companies: A quantitative

study from Swedish Stock Exchange

Authors: Vladyslav Deboi, Harbi Kurmakhadov, Meng Li

Tutor: Ulf Linnman

Date: 2021-05-24

Key terms: Financial leverage, Profitability, Capital structure, Real-estate industry, ROA

Abstract

Prudent usage of financial leverage by managers can significantly impact business operations

and a corporate’s performance. Thus, the determination and the understanding of the influence

of financial leverage on the profitability of a corporation are intrinsic and indispensable for not

only maximising the value of a firm but also improving its financial performance. This study

adopted a quantitative research method, in which the theories were tested by multiple regression

analysis in line with the positivism paradigm and deductive measure. Moreover, ontology

belongs to the objectivist perspective, in which the authors viewed reality as a mechanism from

the outside and focused only on observable and measurable facts. The authors investigated the

capital structure and profitability of the 18 largest listed real estate companies in Sweden from

2016 to 2020. Leverage essentially consists of total liability to assets, short-term liability to

assets and long-term liability to assets. Profitability is defined as the rate of return on assets

(ROA), which represents the company's degree of profitability relative to total assets from an

overall business perspective widely used for financial analysts. In order to accomplish the

trustworthy study in the regression model, control variables were also introduced that comprised

company size, liquidity and solvency. The result of this paper reveals that financial leverage is

irrelevant for determining ROA in the real estate industry in Sweden.

_____________________ _____________________ _____________________

Meng Li Vladyslav Deboi Harbi Kurmakhadov

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Table of Contents

1. INTRODUCTION................................................................................................................ 5

1.1 PROBLEM BACKGROUND.................................................................................................... 5

1.2 PROBLEM DISCUSSION ....................................................................................................... 8

1.3 RESEARCH QUESTION ...................................................................................................... 10

1.4 PURPOSE .......................................................................................................................... 10

1.5 PERSPECTIVE OF THE STUDY ........................................................................................... 10

1.6 DELIMITATIONS ............................................................................................................... 11

1.7 DEFINITIONS .................................................................................................................... 12

2. FRAME OF REFERENCE ............................................................................................... 15

2.1 LITERATURE REVIEW ....................................................................................................... 15

2.2 MODIGLIANI AND MILLER ............................................................................................... 19

2.3 THE STATEMENTS MADE ON CAPITAL STRUCTURE ........................................................... 20

2.4 TRADE-OFF THEORY ....................................................................................................... 20

2.5 PECKING-ORDER THEORY ............................................................................................... 21

2.6 HYPOTHESIS .................................................................................................................... 22

3. METHODOLOGY ............................................................................................................ 26

3.1 RESEARCH THEORY ......................................................................................................... 26

3.2 EPISTEMOLOGY ............................................................................................................... 26

3.3 ONTOLOGY ...................................................................................................................... 26

3.4 RESEARCH STRATEGY ..................................................................................................... 27

4. METHOD ........................................................................................................................... 28

4.1 SAMPLING METHOD ......................................................................................................... 28

4.2 DATA COLLECTION .......................................................................................................... 28

4.3 MULTIPLE REGRESSION ANALYSIS ................................................................................... 29

4.4 DATA ANALYSIS .............................................................................................................. 30

4.5 MULTICOLLINEARITY ...................................................................................................... 31

4.6 HOMOSCEDASTICITY ....................................................................................................... 31

4.7 SPURIOUS CORRELATION AND NON-STATIONARITY ........................................................ 32

4.8 VALIDITY AND RELIABILITY ............................................................................................ 33

5. EMPIRICAL FINDINGS & ANALYSIS ........................................................................ 35

5.1 MULTICOLLINEARITY ...................................................................................................... 35

5.2 DESCRIPTIVE STATISTICS ................................................................................................. 36

5.3 HETEROSCEDASTICITY .................................................................................................... 37

5.4 MODEL SUMMARY .......................................................................................................... 37

5.5 STATIONARITY ................................................................................................................ 38

5.6 COEFFICIENTS ................................................................................................................. 39

5.7 CONTROL VARIABLES ...................................................................................................... 43

6. CONCLUSION .................................................................................................................. 45

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6.1 SUMMARY OF THE FINDINGS ........................................................................................... 45

6.2 THEORETICAL AND PRACTICAL CONTRIBUTION ............................................................... 47

6.3 SOCIAL AND ETHICAL CONSIDERATIONS ......................................................................... 47

6.4 LIMITATION ..................................................................................................................... 48

6.5 SUGGESTIONS FOR FUTURE RESEARCH............................................................................ 48

7. REFERENCE ..................................................................................................................... 50

8. APPENDIX ......................................................................................................................... 59

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1. Introduction

1.1 Problem background

In retrospect, it can be observed that profitability has always been an indispensable and intrinsic

aspect that gauges and determines a firm’s performance as keeping the profitability at an

adequate level is imperative for a company’s long-term success and survivability. Ergo, a

plethora of researchers illuminated below have been minutely scrutinising and avidly

discerning the core drivers behind profitability.

McGahan and Porter (1997, p. 29-30) made a deduction that the firm-specific effect, which

indicates the slow gradual changes in the industry structure, is less persistent in comparison

with the industry effects. Another researcher scrutinised the impact of financial leverage on

industry profitability, and he concluded that companies, which had a low degree of financial

leverage, received systematically higher returns (Baker, 1973., p. 503). Additionally, two

researchers Dewenter and Malatesta (2001) endeavoured to analyse the dichotomy between

state-owned and privately-owned corporations in the form of labour intensity, leverage and

profitability. The researchers discerned that privately-owned companies have in general a

relatively lower degree of leverage and higher profitability in comparison with the state-owned

companies, which is in line and aggrandise the credibility of the results found by Baker (1973)

(Dewenter & Malatesta, 2001., p. 321-322). However, they emphasised the fact that

governmentally owned companies do not have the possibility of issuing stocks and therefore

have a reliance on borrowed capital provided internally generated funds are not adequate

(Dewenter & Malatesta, 2001, p. 321)

The findings emphasised above implies that profitability can differ depending on the factors

i.e., firm’s characteristics, privately or publicly owned, and industry. Ergo, it is imperative to

consider these aspects in the conduct of the research to increase the probability of fully

comprehending generated outcomes.

Moreover, it is possible that earlier empirical outcomes differ, due to the various ways of

measuring profitability. The choice of a profitability measure varies throughout previous

studies, and a measure that has been common in previous research is Return on Assets or

abbreviated ROA, which is an indicator that reveals how profitable a company is relative to its

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total assets (Le & Phan, 2017; Simerly & Li, 2000; Gill et al., 2009; Barton & Gordon, 1988;

Ahmed Sheikh & Wang, 2013, Nguyen et al., 2019). Additionally, another measure that can

be seen in previous research is Return on Equity or abbreviated ROE, which is an indicator that

reveals how profitable a firm is relative to its total equity (Le & Phan, 2017; Chaklader &

Chawla, 2016; Abor, 2005). Albeit, as it was aforementioned that both ROE and ROA are the

most frequently utilised measures, it can be also observed that such measure as EBIT to Total

Assets is also used in a research conducted by Margaritis & Psillaki (2010). The three measures

mentioned above are not superior to each other, and the choice of a measure depends on the

stakeholders that intend to utilise the data as well as the perspective of the research (Margaritis

& Psillaki, 2010). Additionally, it is emphasised by the authors that ROA also indicates how

effectively and efficiently assets are managed to generate revenue. Thus, the measure is mostly

suitable for all stakeholders and managers of the firm. Alternatively, ROE reveals and indicates

how effectively and efficiently the management of the organisation is utilising shareholders’

invested capital or shareholders’ equity to generate revenue, and therefore is more suitable and

of utter interest to investors.

A plethora of factors exist that explains the achievement of maximal profitability. The

imperative and frequently debated factor for maximising profitability is the choice of capital

structure. Brealey et al (2013, p. 427) asserted that the core resource of a company is the cash

flow generated by its assets, which can be categorised as safe cash-flow-stream that goes to

debtholders and risky cash-flow-stream that goes to stockholders, and this financing mix of

equity and liability in a company is called capital structure. In the process of measuring a capital

structure, leverage is often utilised in studies as the aspect explains the extent to which

companies have a reliance on debt in financing their business (DeMarzo & Berk, 2013, p. 39).

As it was aforementioned, leverage has been frequently utilised in previous studies and many

researchers have scrutinised and examined how profitability is dependent on capital structure

(Nunes et al., 2009; Khan, 2012; Kester, 1986; Avci, 2016; Margaritis & Psillaki, 2010; Abor,

2005, Nguyen et al., 2019). Different conclusions were made, where Margaritis & Psillaki

(2010), Abor (2005) and Avci (2016) discerned that a positive relationship exists between

profitability and leverage, and it implies that companies with a higher degree of debt are more

profitable. Contrarily, the researchers Nunes et al. (2009), Khan (2012) and Kester (1986)

found and made a conclusion that financial leverage and profitability have a negative

relationship, which implies that companies with a low degree of leverage are more profitable.

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However, a significant aspect to shed light upon is that the relationships mentioned above do

not claim causality, which explains that it is arduous to assert that leverage influences

profitability, and vice versa. The phenomenon is called reversed causality and it is imperative

to consider as both profitability and leverage possibly impacts each other over the years.

In this day and age, several theories on the optimisation of companies’ capital structure exist.

Modigliani and Miller (1958) presented and illuminated a proposition, which stated the

irrelevance of capital structure. The theory established by the authors implied that the choice

of capital structure does not influence a company’s cost of capital in a perfect market. However,

the theory was revised by the researchers a few years later, which included the statement that

the degree of leverage was relevant (Modigliani and Miller, 1963). The theory vividly

conveyed the fact that a company can increase its profitability and value in a form of a tax

shield begot by financial leverage, indicating that companies should imply a high degree of

debt in their capital structure to maximise the firm’s value.

Another prominent and intrinsic theory in the field related to the capital structure is the

Pecking-order theory established by Myers and Majluf (1984) that states that companies prefer

internal financing to fund business operations. However, external financing is required in cases,

and companies firstly issue the safest security viz. debt, and equity will be issued as a last resort

(Myers, 1984, p. 581).

In contrast to the Pecking-order theory, the Trade-off theory illuminated the statement that

companies can attain an optimal degree of leverage, in which tax shields’ benefits are offset by

expenses from financial distress (Myers, 1984; Kraus & Litzenberger). Thus, a deduction can

be made based on the theory that firms should balance the capital structure and swap debt for

equity and vice versa to reach the maximisation of the value of the company (Myers, 1984, p.

577).

The theories aforementioned are central and crucial in the sphere of capital structure and imply

that a certain correlation and relationship is expected between profitability and financial

leverage. The irrelevance of financial leverage in the cost of capital illuminated by Modigliani

and Miller in 1958 would indicate that a certain relationship is not expected. The revision of

the theory established by the authors in 1963 and the Trade-off theory presented by Myers in

1984 do state that a higher degree of debt is preferable to increase a firm’s value, which begets

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an expectation of a positive relationship between profitability and financial leverage. Last but

not least, the Pecking-order theory presented by Myers and Majluf in 1984 suggests internal

financing, which leads to a relatively low degree of financial leverage and indicates a negative

relationship between profitability and leverage.

This paper will scrutinise and investigate the real estate industry in Sweden that has seen a

gradual growth in the past year. The relevance of capital structure in the real estate industry is

relatively unknown as well as its relationship with profitability. Moreover, as it was already

presented above, the findings on the relationship between profitability and financial leverage

vary depending on the context of research. Ergo, it gives the basis for the choice of discerning

how profitability is related to financial leverage in the real estate industry in Sweden, which is

a relatively unexplored industry on the mentioned topic.

1.2 Problem Discussion

In the section “the problem background”, it is illuminated the imperativeness of capital

structure and its debated relationship with profitability as well as established theories that shed

light upon the relation between the two variables in order to provide relevant theoretical

background. The following section has an aim to examine further the previous study on the

relationship between profitability and leverage as well as to link the problem background to

the real estate industry in Sweden.

As it was mentioned above, earlier studies have generated different results on the relationship

between profitability and financial leverage. No relationship between the two variables for

companies listed in the Johannesburg Stock Exchange in South Africa was found by two

researchers Mashavave and Tsaurai (2015, p. 85). Another study conducted by

Moahammadzadeh et al. (2013, p. 576) found a negative relationship between financial

leverage and profitability for pharmaceutical companies listed in Iran. Additionally, previously

mentioned researcher Abor (2005, p. 443) found a positive relationship between the two

variables by scrutinising listed companies on the Ghana Stock Exchange. Moreover, Grycova

and Stekla (2015, p. 39-40) came to the conclusion that there is a negative relationship between

the variables in the agricultural industry. Last but not least, two researchers Yazdanfar and

Ohman (2015, p. 113) examined and analysed Swedish SMEs (Small and Medium-sized

Enterprises) and made a deduction that a negative relationship exists between both short-

term/long-term debts and profitability.

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The following study has an aim to investigate the real estate industry in Sweden. Palm (2016)

refers to a definition of real estate as a property consisting of land and the facilities on it as well

as its natural resources i.e., minerals, water, crops. The intrinsic factor to emphasise is the

imperativeness of the real estate industry in countries’ economy as the collapse and formation

of real estate bubbles have a significant influence on political and economic trajectories

(Edvinsson et al., 2020). With the benefit of hindsight, it can be concluded that the bursting of

the real estate bubble in Japan in the early 1990s begot a long-term economic stagnation, and

one of the major causes of the Great Recession was the collapse of real estate prices (Edvinsson

et al., 2020).

In retrospect, the recent financial crisis of 2008 can be taken as an instance that vividly conveys

the detrimental and atrocious impact caused by the burst of the real estate bubble, which is

elaborately explained and analysed in the book “The 2008 Global Financial Crisis in Retrospect”

written by Aliber and Zoega (2019). The authors emphasised in the book that the global

financial crisis led to the most severe decline in employment, production and in world trade

since the Great Depression. The catalysator that ignited the financial calamity was uncontrolled

lending of capital by financial institutions to individuals and companies that took significant

financial leverage but were not solvent enough (Aliber and Zoega, 2019).

Swedish real estate industry has been growing and flourishing especially since the 1960s

(Edvinsson et al., 2020). According to the data provided by analysts at CEIC, the Swedish real

estate industry saw a growth of an average of 7,4% during the period of March 1987 –

December 2020, which is also estimated and supported by Edvinsson et al. (2020) (CEIC,

2021). The capital structure in the real estate industry can be characterised by relatively high

capital intensity and due to the significant amount of tangible assets in the form of real estate,

it can be seen as a relatively high book value of assets in relation to the revenue earned by the

company. The nature of the capital structure of the companies operating in the real estate

industry is an interesting area to analyse and investigate as it may augment the existing

knowledge and theories related to capital structure. Additionally, our endeavours to find

research that analyses the relationship between financial leverage and profitability of Swedish

real estate companies were to no avail, which begets an assumption that the area is

unexamined.

In light of the above, the problem background and the problem discussion have built the

foundation for the underlying research question of the study.

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1.3 Research Question

The authors’ goal is to fill the research gap conveyed in the previous sections. Thus, this study

will investigate how financial leverage is related to profitability for real estate companies in

Sweden. The research question that will be explored throughout the thesis is:

Is there a relationship between profitability and financial leverage of real estate companies on

the Swedish market?

1.4 Purpose

The aim of the following study is to analyse and explore the relationship between financial

leverage and profitability of publicly owned Swedish real estate companies. Total debt is the

core variable for leverage in order to answer the research question and fulfil the purpose.

Additionally, the study has a sub-purpose, which is to scrutinise the relationship between short-

term and long-term debt with profitability. It is believed that the research will contribute to the

existing literature and the results will be of utter interest as well as beneficial not only to

managers working in the real estate industry that have to be cognisant of maximising the impact

of the degree of financial leverage on profitability, but also to investors that can have a benefit

of understanding whether a company’s management is utilising its leveraged capital prudently.

1.5 Perspective of the Study

Moreover, the goal of the study is to present the results that would be helpful and beneficial for

managers operating in the real estate industry to determine the degree of financial leverage in

the capital structure of real estate companies. Due to the fact that it is aimed towards managers,

Return on Assets (ROA) is suitable for taking as a profitability measure. It is also stated by

Penman (2013, p. 371) that ROA is of interest to managers as it accounts for the return on total

capital.

It was also aforementioned that the study is also beneficial for investors as public Swedish real

estate companies are scrutinised. Although ROE would be a more suitable measure as it gauges

the return on shareholders’ equity and reveals how efficient the management is utilising the

company’s capital to generate profit, ROA is also helpful and of interest to investors as it

measures a company’s efficiency to allocate its assets (DeMarzo and Berk, 2013, p. 42). Thus,

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it can be asserted that investors can also use the findings of this research as ROA is often

utilised and can be put into comparison with other industries to give useful implications.

1.6 Delimitations

This section of the research aims to provide a narrower scope on the purpose and research

question of the study. Moreover, another vital goal of the delimitation is to provide specified

and relevant outcomes that will beget a more graspable, comprehensive and focused conclusion.

As it was written in the problem background section, profitability can differ depending on the

factors i.e., firm’s characteristics, privately or publicly owned, and industry. Thus, it is

important to take these aspects into consideration in the conduct of the research to increase the

probability of fully comprehending generated outcomes.

- The first limitation to shed light upon is that the research will focus only on publicly

owned real estate companies listed on OMX STOCKHOLM REAL ESTATE GI

(abbreviated as SX35GI). The research was narrowed down to the index as it measures

how share prices develop and also takes into account all share dividends that companies

pay. The return index thus provides a clearer picture of the total return (Nasdaq, 2021).

- Corporations included in the research will be limited companies that are listed on the

Stockholm Large Cap in order to reduce the risk of the results and scrutinises being

afflicted by potential outliers from differing characteristics that these companies may

possess.

- The research will explore and analyse companies within the timeframe of 2016-2020

except for a company named Nyfosa as it was established in 2018 and does not show the

results for the period of 2016-2018.

Based on the delimitation aforementioned, eighteen conglomerates in the real estate industry

were identified that share the same characteristics. In the commencement of writing this

research paper, it was assumed to utilise the annual reports of these companies to find the

relationship between financial leverage and profitability. However, the number of observations

that amounted to 88 was insufficient, which would beget unreliable results and errors in the

research. Thus, a conclusion was made by the authors of this paper to discern the relationship

between the variables by retrieving the secondary data from quarterly reports, which

consequently limited and increased the number of observations to 348 and reduced the

probability of committing the error in the research.

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1.7 Definitions

Financial leverage

Financial leverage is a financial term that refers to how much debt is utilised by a company to

finance its assets, due to the lack of cash flows for short-term debts or a need for more capital

to finance investment (Myers, 1984).

Capital structure

Gangeni (2006) stated that the capital structure attempts to explain the mix of securities and

financing sources used by corporations to finance real investment. Also, the author highlighted

that internal finance sources i.e., retained earnings and stock issuance as well external finance

sources i.e., loans and bonds can be utilised by a firm to finance the investments required to

maintain its business operations and enhance its survivability on the market (Gangeni, 2006).

Net income

According to McCamish (2021), the term net income is the amount of money that’s left after

taxes and certain deductions are made from gross income. It is also known as net profit, the

bottom line, and net earnings.

EBITDA

McCamish (2021) stated that earnings before interest, tax, depreciation, and amortisation

(EBITDA) is a measure of a company's operating performance. In practice, it is utilised to

evaluate a firm’s performance without factoring in financing decisions or tax environments.

McCamish (2021) acronym EBITDA by the following components: “Earnings” - stands for the

income; “Before” - excludes the following items from the metric: “Interest”, which depends on

the financing structure of a company; “Taxes”, which are set by the geographic location of a

company; “depreciation and amortisation” based on past investments.

Return on Assets (ROA)

Return on assets (ROA) is a financial ratio that can help to analyse the profitability of a

company. ROA measures how much profit the relative total assets generate for the business.

The calculation and tracking of ROA allow investors, analysts, and managers to analyse if the

company provides a good return on assets. Additionally, return on assets is a comparison metric

that can be used to examine the past performance of a company (McCamish, 2021).

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Return on Equity (ROE)

It is mentioned by Fridson & Alvarez (2011) that return on equity or abbreviated ROE is a

measure of a company’s financial performance that is calculated by dividing net income

illustrated in the income statement by shareholders’ equity that can be found on the balance

sheet. ROE is conceived as a measure of the profitability of a company in relation to

stockholders’ equity (Fridson & Alvarez, 2011).

Variance Inflation Factors (VIF)

The variance inflation factor (VIF) is a method of detecting the severity of multicollinearity by

looking at the extent to which a given explanatory variable can be explained by all the other

explanatory variables in the equation (Studenmund, 2016, p. 234). In the application of

Statistics VIF, if the value is greater than 10 there is multicollinearity (Pallant, 2016).

Market capitalisation

The aggregate dollar market value of a company's outstanding shares of stock is referred to as

market capitalisation (Fridson & Alvarez, 2011). The authors emphasise that it is determined

by multiplying the current market price of one share by the total number of a company's

outstanding shares (Fridson & Alvarez, 2011)

Long-term debt

According to Porter & Norton (2015), long-term debt is an obligation that will not be paid or

otherwise satisfied within the next year or the operating cycle, whichever is longer, is classified

as a long-term liability, or long-term debt. Additionally, the author emphasises that notes

payable and bonds payable, both promises to pay money in the future, are two common forms

of long-term debt.

Short-term debt

Short-term debt/liability or current liability is an obligation, which comprises such aspects as

accounts payable, wages payable, income taxes payable that will be satisfied within the next

operating cycle or within one year if the cycle is shorter than one year (Porter & Norton, 2015).

Short-term debt + Long-term debt = Total Debt

The addition of long-term and short-term debt is equal to total debt, which is the whole debt

that a company possesses.

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Current ratio

Financial analysts often compare a firm’s current assets and current liabilities to assess whether

the firm has sufficient working capital to meet its short-term needs (Berk & DeMarzo, 2017).

This metric is widely used across the industry to access the ability of liquidity. And the formula

is shown as:

𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝑅𝑎𝑡𝑖𝑜 =𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡𝑠

𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠

Interest coverage ratio

Financial analysts and debt collectors assess a firm’s ability to meet its interest obligations by

comparing its earnings with its interest expenses using an interest coverage ratio (Berk &

DeMarzo, 2017, p.72). It is the ratio indicating the ability of a company to pay off its long-term

debt and the interest on that debt (Goel, 2016). Insolvency, which is widely used for financial

risk analysis determining the outcomes of companies' long-term survival (Goel, 2016). The

formula is shown as:

𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑐𝑜𝑣𝑒𝑟𝑎𝑔𝑒 𝑟𝑎𝑡𝑖𝑜 =𝐸𝐵𝐼𝑇

𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝐸𝑥𝑝𝑒𝑛𝑠𝑒

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2. Frame of reference

This section takes a deep look at the previously published research as well as theories to be

utilised in the empirical studies of this paper. The frame of reference structure has two main

parts. In the first part, the authors of this paper read the conducted studies finding both positive,

negative and absence of correlation and relationship between profitability and financial

leverage. Additionally, the aspects such as the date of publication, industry, country

classification in the world's economies, and theories applied are considered to draw up the full

picture of the investigated topic. In the second section, the most common theories applied in

the previous studies i.e., Modigliani and Miller, Trade-off theory and Pecking-order theory are

presented, described and discussed. Finally, the formulation of the hypotheses to be tested is

presented for answering the research question.

2.1 Literature review

A number of papers are published on the subject of financial leverage and its impact on

profitability. Sundry and numerous ways exist for the measurement of profitability, and the

choice depends on the group at which the research is targeting. Thus, the choice of the measure

utilised in this research is selected based on relevant previous studies.

In Kujaca and Pygman’s (1988) article “Financial leverage in real estate investing”, it was

mentioned that the use of leverage permits broader diversification of a fund’s real estate

portfolio, and the diversified investment minimises the risk of large losses. Moreover, in the

studies made by Margaritis & Psillaki (2010), in which French textiles and chemicals

manufacturing firms were investigated, it was found a positive relationship between capital

structure and firms’ financial performance. To identify the relation the regression model was

applied, and the result showed that the influence on profitability appears to be more powerful

for firms with higher leverage. The same positive result was in the research conducted by Avci

(2016), who examined Turkish manufacturing firms from 2003 through 2015. The author shed

light upon the fact that the industry defines the composition of capital structure that influences

the relationship. Thus, based on his observations, consulting companies are less capital

intensive compared to manufacturing firms that require heavy investments in long-term assets.

Similar results were obtained by Abor (2015) who found a positive relationship between

leverage and profitability retrieving the data from Ghana Stock Exchange companies. In his

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research, he found that both the short-term debt and total debt reveal a positive relationship

with profitability in comparison to long-term debt that reveals a negative relationship with

profitability (Abor, 2005, p. 443). Thus, the results showed that the short-term debt serves as

the primary financing way in the given case. Another research made by Gill et al. (2011)

analysed how leverage impacts profitability considering the sample of 272 U.S. companies

from 2005 to 2007 as well as the regression model that was applied in the research. It showed

that the debt to total assets has a positive relationship with profitability in the manufacturing

companies. In Sarda studies (2016), a linear regression model was utilised to measure the

relationship between leverage and companies’ profitability. A sample of 32 large private

manufacturing companies performing in Ethiopia from 2006 to 2010 was utilised and a positive

relationship appeared to exist between debt and profitability. In the analysis made by Akhtar

(2012), the relationship between financial leverage and financial performance was observed in

Pakistan within the energy and fuel field. The author presented a positive association between

financial performance and financial leverage of the firm, and thus proved that the companies

with higher profitability can improve their performance by utilising high levels of financial

leverage. Moreover, this study has a plethora of evidence which states that the internal

stakeholders are able to strengthen their financial position on the energy and fuel market due

to external funds such as financial leverages. The imperative aspect for the company's future

flourishing is the willingness to make strategic decisions related to the choice of capital

structure. In another research, the author Al-Taani (2013) investigated the relationship between

capital structure and firm's performance including in sample 45 Jordanian manufacturing firms

available on Amman Stock Exchange from 2005 to 2009. The variables utilised in the study

were the following: return on assets, profit margin, total debt to equity, total assets to short

term debt ratios, and long-term debt to total assets (Al-Taani, 2013). The dependent variable

represented by return on assets and profit margin as indicators for financial execution, and

independent variables such as long-term debt to short term debt to total assets represented for

capital structure. Consequently, Al Taani’s (2013) study found that no significant relation

among Short Term Debt to Total Assets and Return on Assets, Total Debt to Equity and Return

on Assets, Short Term Debt to Total Assets and Profit Margin, long term debt to total assets

and Profit Margin, and Total Debt to Equity and Profit Margin. Based on Asif (2011) statement,

the growth and earning from leverage are significant. Looking from the market perspective,

companies that tend to use loans increase their market value. Moreover, the wealth of the

shareholders is maximised when the firm is able to employ more debt (Memon, Fozia & Bhutto,

2012).

17

In contrast to the findings above, multiple authors Kester (1986), Friend and Lang (1988, p.

275, 277) and Nunes et al. (2009) concluded a negative relationship between leverage and

profitability. Looking from Jarrow (2012) perspective, the capital structure that consists largely

of loans has the danger of default and the less worthy of its capital. Authors Titman & Wassels

(1988) stated that firms that utilise the firm's earnings generate more profit compared to others

that rely on outside capital. The price of a company's stock defines its performance, and when

the stock price is high, instead of relying on outside capital companies may choose to issue

equity to cope with the leverages. In the research made by Wald (1999) the drawn-up result

was that the debt to assets ratio has a significant negative relation with the firm profitability.

The findings were based on the capital structure of firms in such countries as France, Germany,

the United Kingdom and the United States taking into consideration firm size, growth and

firm’s riskiness as explanatory variables (Wald, 1999). In addition, Mandelker & Rhee (1984)

made it clear in their study that in most business sectors, the companies with high profits have

the lowest leverage ratio. In fact, the greatest returns are experienced by a company's

stockholders when such events as a stock repurchase or debt for equity exchange occurs rather

than issuing stocks. Although Nguyen et al. (2019) that analysed the real estate industry in

Vietnam found a negative relationship between the dependent variable ROA and financial

leverage, it has been shown to be insignificant. It was reported by Amsaveni (2009), after the

examination of the relationship between leverage and growth utilising a dataset of 20 years,

about the existence of a negative relation. The reasons stated are either the unrecognized growth

opportunities by the capital market or the inability to overcome the leverage overhang. When

analysing the relationship of these variables in Malaysia companies, Ting et al. (2011) selected

the data from 1997 to 2008 and utilised a panel data analysis to build a regression model which

helped them to conclude that the total debt and tangible assets had a positive relationship, but

the profitability was influenced negatively. Looking at the long-term debt it was found the

negative influence on profitability and tangible assets, and the short-term showed significant

negative effects. Additionally, Jordanian public companies from the Amman stock market

investigated by Soumadi and Hayajneh (2015) with the time frame of 5 years (2001-2006), and

multiple regression models applied in the research showed that both leverage and capital

structure had a negative impact on companies’ execution and financial performance. The

imperative aspect to shed the light upon was that the financial leverage is the same between the

low-growth and high growth companies for the Jordanian companies. In the research by

Vijeyaratnam and Anandasayanan (2015), in which the manufacturing companies in Sri Lanka

18

analysed between 2008 to 2012 years, the concluded result stated that the non-debt tax shield

had an imperative negative impact on the profitability of these companies. Another interesting

finding was in the research made by Yinusa & Rodnonova (2018) in which he selected up to

115 Nigerian companies in the period of 1998 to 2016 and studied the relationship between

profitability and leverages. It was concluded in the research that the companies’ performance

was negatively related to leverages. The proof is found in Dalbor and Upneja (2002) reports

that enhanced firm quality and growth opportunities were related negatively to long-term debt

usage.

Looking at the profitability, Chaklader & Chawla (2016, p. 271), which made an investigation

of the determinants of capital structure where ROE was utilised as one of the core independent

variables, made a deduction that more profitable companies would use internal funds and issue

debt to benefit from the tax shields, the authors’ motivation to utilise ROE as the measurement

of profitability was unclear in their research. Another researcher Le & Phan (2017) that

scrutinised the impact of capital structure on profitability had ROE as the core element for

analysing. However, unlike the study priorly mentioned, the authors included other variables

that convey profitability i.e., ROA and Tobin’s Q. The target group in this research, which are

managers of the real estate industry, paved the way for the choice of ROA as the core

measurement of profitability as this ratio is ubiquitously and frequently utilised by managers

and stakeholders and is an adequate measure that reflects return controlled by management

(Bettis, 1981, p. 384). Furthermore, another author Simerly & Li (2000, p. 40) asserted that

ROA is as intrinsic as ROE, due to the fact that the latter measure ignores that influence begot

by certain types of resource investment. Thus, the researchers concluded that ROI (Return on

Investment) and ROA are more appropriate measurements of profitability for capturing a

company's contribution to the more general investment of resources. Finally, asserting that

ROA is superior to ROE would be misleading as the choice of the ratio depends on the type of

research conducted and the target group. In the case of this study, it was decided to utilise the

ROA as the core measurement of profitability due to the arguments aforementioned.

The following theories, which also have been mentioned in most previous studies are central

for the area of capital structure:

• The revised theorem of Modigliani and Miller (1963).

19

• The Trade-off theory (Myers, 1984) does in general advocate higher debt levels for

increasing firm value, which leads to a positive relationship between leverage and

profitability,

• The pecking-order theory (Myers & Majluf, 1984) suggests internal financing, leading

to relatively lower debt levels and a negative relationship between leverage and

profitability”.

In the light of the above mentioned, a plethora of sources has been reviewed and analysed to

provide solid information to be used in the further discussion part of this paper. The review

vividly shows how such factors as time frame, countries’ economy and financial systems,

industry, methods and applied theories impact the result of the studies. The proof is retrieved

from McGahan and Porter (1997, p. 29) who said that the drivers of profitability may vary

depending on the industry as well as in the composition of capital structure”. Due to this reason,

researchers have not reached a consensus. In Sweden, which is the research focus in this study,

there is a limited number of studies related to our topic. However, the research conducted by

Kokko (1990) emphasised that the number of real estate and financial companies grew fast as

businesses were financed by borrowed funds. By scrutinising and utilising the data retrieved,

it allowed us to formulate precise hypotheses, and write the proper analysis that fills the found

gaps and provide tremendous value to the existing research.

2.2 Modigliani and Miller

Modigliani and Miller presented its prominent paper in 1958 where the researchers shed light

upon the choice of capital structure and the way of implying it on the businesses (Modigliani

& Miller, 1958). Modigliani and Miller gave compelling arguments for the irrelevance of the

company's capital structure based on a few assumptions. However, five years later, the authors

revised, altered the theorem and presented it in the article named “Corporate Income Taxes

and the Cost of Capital: A Correction”, where they concluded that capital structure is relevant

(Modigliani & Miller, 1963). It can be thought that the theorem is obsolete, however, the

theorem of Modigliani and Miller has been at the core in the field related to capital structure

and is still taught at educational institutions in this day and age. Due to the intrinsic role the

theorem has been having in the discussions of capital structure, the authors of the following

paper concluded to include the theorem as a core theorem for the theoretical framework.

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2.3 The statements made on capital structure

Two prominent researchers Modigliani & Miller (1958, p. 268) made a statement that a

company’s capital structure has no relevance with its total value. However, the statement was

based on the assumption that companies operate in a perfect market, which means that

companies operate in a market where there are no taxes and where the cost of capital is equal

amidst companies in the same class, bonds give a constant return etc. (Modigliani & Miller,

1958, p. 266-268, 273-274). Albeit the theorem aforementioned is unrealistic, it is intrinsic to

understand what degree of capital leverage would be most optimal for a company.

After strong criticism towards the theorem due to the exclusion of taxes in the research, the

authors revised their theorem in 1963. Modigliani & Miller (1963) emphasised and confessed

that the negligence of taxes in their research was a mistake as the tax shield accompanying debt

had relevance with the firm's value. The authors continued with the statements that arbitrage is

not just a function of an expected net-tax return, but also of the degree of leverage and tax rate

(Modigliani & Miller, 1963, p. 434). Consequently, the researchers concluded that a company

can increase its value by maximising its financial leverage and as a result utilise the benefits

begot by tax shields (Modigliani & Miller, 1963, p. 434).

The theorem is at the core of this study, which indicated that a positive relationship between

profitability and financial leverage is expected.

2.4 Trade-Off Theory

On the contrary, opposed to Modigliani and Miller’s theorems related to capital structure and

their statements on the relevance of financial leverage on company performance, two

researchers Kraus & Litzenberger (1973) introduced the trade-off theory, which states that

companies can attain and maximise their value by having an optimal capital structure. In lieu

of having companies unlimitedly rising levels of leverage to maximise company value, the

trade-off theory asserts that companies have a particular capital structure to strive for reaching

the point of having the highest possible and maximum company value. Consequently, it begets

a form of balancing where companies deliberately should increase the level of debt or decrease

it to reach the optimum (Myers, 1984, p. 577). The balance of capital structure emphasised

above stem from the profit gained from tax shields and the expenses of potential financial

21

distress and bankruptcy, where the optimum is met when the benefits of tax shields are

counterbalanced by the expenses related to debt (Myers, 1984).

Kraus & Litzenberger (1973, p. 911) endorsed the proposition made by Modigliani & Miller

(1958) that capital structure is irrelevant under the assumption of the perfect market. The

authors asserted that bankruptcies, taxes and other imperative aspects in the imperfect market

do have an impact on the capital structure and its effect on company value. Moreover, the

researchers stated that the benefit of tax shields stems from the tax-deductibility of interest

expenses, where financial leverage causes a decrease in a company’s income tax liability and

an increase in after-tax profit. Ergo, the implication of the trade-off theory established by Myers

(1984) asserts that an increase in a company’s financial debt is beneficial but to a certain level

or limit. The excess in the financial leverage that exceeds the limit beget costs that erode the

benefits (Myers, 1984).

The theory indicates that the level of financial leverage has initially a positive relationship with

profitability, however, the relationship becomes negative after costs of financial distress and

benefits from tax shields reach a point of break-even. Thus, it can be asserted that the traditional

approach of the theory established by Myers (1984) has a concave relationship with

profitability than linear.

2.5 Pecking-Order Theory

Myers and Majluf (1984) established a fundament for the pecking-order theory that shed upon

a different view on capital structure in comparison with the trade-off theory. The theory

established by the researchers endeavours to illuminate how companies choose their capital

structure. One of the core ideas proposed by Myers and Majluf (1984) was that the fact that

managers have access to insider information that is not available to stakeholders can affect the

managerial decision-making in what project to undertake and how it should be financed. As it

is asserted by the authors, issuing stocks to generate more equity to finance the business

operations can be perceived as a negative sign and news for shareholders, thus managers may

relinquish a positive NPV (Net Present Value) opportunity to shy away from giving inadequate

and potentially unpleasant signals to shareholders (Myers & Majluf, 1984, p. 188).

The researchers mentioned above concluded that if investments possibilities occur over time,

then shareholders will conceive the financial slack as valuable as the company can benefit from

22

these opportunities. However, if the company does not have availability to low-risk debt or an

adequate amount of internal funds, then it might be reasonable for a company to omit projects

to shy away from issuing risky securities, albeit they have a positive net present value (Myers

& Majluf, 1984).

Myers (1984, p. 581) drew the dichotomy between the trade-off theory and pecking-order

theory and asserted that in lieu of endeavouring to balance and find the optimal degree of

financial leverage, the company will follow a particular financing pecking order. The pecking

order states that companies will primarily utilise internally generated funds when financing is

needed. The theory also states that if the requirement of external funding is inevitable, then

companies will initially issue debt; the second option will be the issuance of mixed hybrid

securities, and the last option will be the issuance of additional equity (Myers, 1984, p. 581).

2.6 Hypothesis

Short-term debt and profitability

By scrutinising the literature aforementioned, it can be observed that earlier empirical results

and conclusions came to various outcomes on the relationship between profitability and short-

term debt as such authors as Khan (2012), Zeitun & Tian (2007), Yazdanfar & Öhman (2015)

found a negative relationship between the variables whereas Gill et al. (2011) and Abor (2005)

came to the deduction that there is a positive relationship between profitability and short-term

debt. However, it is imperative to emphasise that the authors priorly mentioned who found a

positive relationship between the variables utilised Return on Equity as a measurement of

profitability, which might impact the conclusion the researchers made. Albeit it can be seen

different results on the relationship between profitability and short-term debt, it can be asserted

that a relationship exists, and this begets the following hypothesis:

H0: No relationship exists between short-term debt and profitability

HA: A relationship exists between short-term debt and profitability

Long-term debt and profitability

In retrospect, it can be concluded that earlier empirical results made by Abor (2005), Yazdanfar

& Öhman (2015) indicated a negative relationship between profitability and long-term debt

whereas Gill et al. (2011) that analysed the firms in the manufacturing sector found a significant

positive relationship between the variables.

23

The real estate industry can be observed to have a high degree of long-term debt and it is of

utter interest to analyse and see the impact of long-term debt and its significance in relation to

profitability. Thus, the following hypothesis was established:

H0: No relationship exists between long-term debt and profitability

HA: A relationship exists between long-term debt and profitability

Total debt and profitability

There is a significant number of research and theories in regard to total debt. As it was observed,

in the research by Margaritis and Psillaki (2010, p. 628) authors found that total debt to assets

has a positive and significant relationship with profitability and efficiency. The same result

about the positive relationships was found in the study by Gill et al. (2011) and Abor (2005).

In comparison to these findings, both researchers' Khan (2012) and Nunes (2009) found a

negative relationship between total debt and profitability. Since the research proved to show

the discrepancy in results as well as variation of theories to forecast the relationship between

total debt and profitability, it becomes necessary to enquire about the relation existence and

compare findings to the theories utilised previously. From the previously mentioned theories,

the Modigliani and Miller (1963) and the Trade-off theory established by Myers (1984) are

true when a positive relationship exists while the Pecking-order theory is false by supporting

the negative relationship (Myers & Majluf, 1984)

H0: No relationship between total debt and profitability

HA: A relationship between total debt and profitability

Size

Multiple researchers found a negative correlation between size and profitability (Yazdanfar &

Öhman, 2015; Goddard et al., 2005). On the other hand, some studies found the opposing

relationship when size is positively related to profitability (Chadha & Sharma, 2015; Simerly

& Li, 2000). There are previous studies in which size was calculated in a variety of ways. In

this study, market capitalisation takes the place since it captures the potential value to be created

in real estate companies. Based on the relationship shown in the results from studies before, it

is assumed that some form of relationship between size and profitability exists. Thus, the

following hypothesis should be considered:

24

H0: No relationship between size and profitability

HA: A relationship between size and profitability

Liquidity and profitability

Financial analysts and creditors examine a firm's current assets and current liabilities to

estimate where the firm has sufficient working capital to meet its short-term need (Berk &

DeMarzo, 2017). Liquidity has been selected as one of the control variables by many previous

researchers. Andersson & Minnema (2018) selected the current ratio for the control variables,

Goddard et al. (2005), found the positive relationship between liquidity and profitability, and

Samo & Murad (2019) also indicates that there is a positive relationship between liquidity and

profitability in their research. Previous research elaborates various relationships between

liquidity and profitability. Hence, this research adopted the same notion, and the following

hypothesis is established:

H0: No relationship exists between liquidity and profitability

HA: A relationship exists between liquidity and profitability

Solvency and profitability

Solvency denotes the ability of a company to pay off its long-term debt and the interest on that

debt (Goel, 2016). It also indicates tremendous importance for financial risk analysis, which

determines the outcomes of companies' long-term survival (Goel, 2016). The financial ratio

used in this research is the interest coverage ratio. It is commonly used to discover how

efficiently a company can pay interest on outstanding debt, considering that all real-estate

companies have high leverage.

Solvency has not been widely used in previous research to profitability. However, Hapsari et

al. (2016) drew the conclusion in their research that solvency has no significant influence on

profitability. Another study from Sucipto & Chasanah (2019) also states that the solvency ratio

measured by the interest coverage ratio has no effect on profitability. Thus, the following

hypothesis is established:

H0: No relationship exists between solvency and profitability

HA: A relationship exists between solvency and profitability

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Figure 1. Summary of Hypothesis

Long-term debt

Short-term debt

Total debt

Size

Liquidity

Solvency

Profitability

H1

H2

H3

H4

H5

H6

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3. Methodology

3.1 Research theory

The deductive theory is defined by Bryman and Bell (2015) as representing the most common

view of the nature of the relationship between theory and research. In general, the process of

deduction is very linear, and can be summarised in six steps-theory, hypothesis, data collection,

findings, a hypothesis confirmed or rejected and revision of theory (Bryman & Bell, 2015,

p.11). Another approach to the research theory is called inductive, which is the process of

induction involving drawing generalisable inferences out of observations (Bryman & Bell,

2015, p.12). Oppositely, induction is the reversed process whereas deduction, from

observations to theory (Bryman & Bell, 2015). The characteristics of this study are suitable to

deductive measure, which the essential emphasis in our thesis is to examine the applicable

hypothesis between profitability and capital structure for real-estate companies in the Swedish

market based on existing theories, not endeavour to generate new theory. Hence, the six steps

method as mentioned above in deduction is indicated for this thesis.

3.2 Epistemology

It was lucidly demonstrated by Van de Ven (2007) that the definition of any frame of research

is a philosophy of science that acquaints a scholar’s approach to the essence of the phenomenon

examined (ontology) and methods for understanding it (epistemology). In another expression,

an epistemological issue is regarded as acceptable knowledge in a discipline (Bryman & Bell,

2015). This research paradigm belongs to positivism, which is an epistemological stance that

advocates the application of the methods to study social reality in natural sciences (Bryman &

Bell, 2015, p. 15). The study purpose is to discover the relationship between capital structure

and profitability rely on empirical findings and theoretical framework. In line with the

positivism paradigm, the authors maintained an objective, and value-free approach throughout

the whole research paper, which the authors will not try to discover the reason in depth from

an interpretive perspective.

3.3 Ontology

The question of social ontology is regarded with the nature of social entities, which the

orientation is whether social entities can and should be viewed as objective entities that have a

27

reality external to social actors or be considered as social constructions built up from the

perceptions and actions of social actors (Bryman & Bell, 2015, p. 20). These positions are

referred to respectively as objectivism and constructionism (Bryman & Bell, 2015, p. 20).

Objectivism refers to the ontological view that implies that social phenomena are beyond our

reach or influence for people as external facts (Bryman & Bell, 2015, p. 21). An alternative

ontological position, constructionism, implies social phenomena and categories are not only

produced through social interaction but also that they are in a constant state of revision (Bryman

& Bell, 2015, p. 22). This study involved an objectivist perspective, in which the author views

reality as a mechanism from outside and focusing only on observable and measurable facts.

3.4 Research Strategy

Quantitative and qualitative research both refer to the useful methods of business

research. The distinction between quantitative and qualitative are majorly different in their

distinctive cluster of research strategies (Bryman & Bell, 2015). Quantitative research can be

construed as the research strategy that emphasises quantification in the collection and analysis

of data, which orientation of the research belongs to deductive, testing of theory, positivism

and objectivism (Bryman & Bell, 2015, p. 27). By contrast, qualitative research was a research

strategy that usually emphasizes words rather than quantification in the collection and analysis

of data, which correspondingly are inductive, generation of theory, interpretivism and

constructionism (Bryman & Bell, 2015, p. 27).

Hence, according to Bryman and Bell (2015), the chosen approaches are adequate and suitable

for conducting a quantitative study, in which the theories would be tested by regression analysis

in quantitative research in line with the positivism paradigm. The authors of this research

maintained an objective and value-free approach throughout the whole paper to demonstrate

the impact between leverage and profitability for public real-estate companies in Sweden.

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4. Method

4.1 Sampling method

Non-probability sampling refers to the non-random selection of cases for a study (Bryman &

Bell, 2013). Based on the characteristics and application conditions of our research objects, a

type of non-probability sampling called judgemental sampling is selected as an appropriate

sampling method that fulfils the purposive sampling function in this study. Judgemental

sampling refers to the measure in convenience sampling that the population are chosen by the

researcher`s criteria (Malhotra, et al., 2017). The researchers exercised the expertise to choose

the certain companies to be included in the sample because we believe the similar

characteristics of the public companies are representative of the population in study purpose

without potential interference from outliers. Thus, sorted companies by market capitalisation,

and select companies with a market capitalisation in large and exclude companies that have

just gone public or will be privatised.

Determining the sample size is a crucial measure for a regression model, that is, with small

samples examination might obtain results not generalised with other samples (Pallant, 2016).

Ergo, we implemented the notion from Tabachnick & Fidell (as cited in Pallant, 2016) that

intaking independent variables attended to the minimal requirement of N > 50 + 8M, where M

indicates a number of the independent variables and N represents a number of the observations.

4.2 Data collection

The data utilised in this research paper is secondary data that is already being collected for

specific purposes (Malhotra, et al., p. 111). We practice the advantages of secondary data in

terms of relatively inexpensive and quickly obtained. The population for this study is real-estate

public companies floated in OMX Stockholm Real Estate GI and corresponding data were

collected directly on the official website of each company. Researchers of this study have

selected 5 years interim reports and financial ratios for purposely selected companies in the

REAL ESTATE index of X35GI, for our study sampling, in which 18 companies (see below

as figure 4) in the entire index that fulfil the criteria of large capitalisation. All require sufficient

debt capacity and considerable turnover to eliminate the risk of misleading results. The span of

this study started from 2015 to 2020, meanwhile in total 348 observations have been collected

29

to meet the study purposes since the continuous five-year data can avoid temporary effects and

improve reliability.

However, a few data were missing in the original report. The researchers utilised the solution

of interpolation, which accordingly the missing variable was theoretically substituted by proxy

variables due to the proportionality of regression analysis relationships between changes

among variables, rather than the absolute level of the variables (Studenmund, 2016, p. 346).

Moreover, the potential error and inaccuracy of secondary data also have been considered. The

data used in the research is public external secondary data that the audit company has notarised.

Therefore, it provides a significant degree of accuracy and credibility for the present study.

When collected data, the entry process is applied directly to the Excel table created from the

website and then imported into the SPSS analysis software. To improve the overall operability,

the researchers also conducted manual screening to ensure that the correct data was collected

for the target population.

4.3 Multiple regression analysis

Regression analysis is utilised by econometricians to make quantitative estimates of economic

relationships that previously have been completely theoretical (Studenmund, 2016, p. 5).

Multiple regression or multivariate regression is a class of measures to discover the correlation

among one continuous dependent variable and two or more independent variables or predictors

(Anderson, 2014). Researchers applied the most common analysis in regression named

standard multiple regression, in which all independent variables are entered into the model

simultaneously (Pallant, 2016). The experimental process refers to the 6-steps regression

analysis method, including reviewing the literature and develop the theoretical model, specify

the model, hypothesise the expected signs of the coefficients, collect the data, estimate and

evaluate the equation and document the results (Studenmund, 2016, p. 66). Equally important,

the regression model requires a sound theoretical foundation and conceptual reasons.

Whereupon researchers formulated theoretical frameworks by consulting peer-reviewed

literature and existing economic models, in which the corresponding financial indicators were

implemented representatively as variables and participated in the regression model.

The variables in this study are categorised into variables of interest and control variables. A

control variable is not the object of interest in the study. It is rather the regressors included to

30

hold constant factors, which possibly affect interest variables suffering from omitted variable

bias if neglected (Stock & Watson 2020). Standardised Coefficients used in the regression

indicates the means that these values for each of the different variables have been converted to

the same scale, the unstandardised coefficient values listed as Beta (B) is the major appliance

in this study, which elaborate the degree of correlation in the ranges between -1 and 1. R=.10-

0.29 is small correlation, R= .30-.49 medium correlation, R=.50-1.0 is large correlation (Pallant,

2016, p. 137). Therefore, the equation described how firm’s profitability Y (dependent variable)

is related to the capital structure, solvency, liquidity and size (independent variables) was

formed:

Figure 2. Regression formula

Where:

ROA= Return to assets

β= Constant

SHORTERM2A= Total liability / Total Assets

LONGTERM2A= Short-term liability / Total Assets

TOTAL2A= Long-term liability / Total Assets

Size= Market capitalisation

Liquidity= Current ratio

Solvency= Interest coverage ratio

ε = Error term

i,t= For firm i on year t

4.4 Data analysis

The statistics program SPSS will be used to examine our collected data and offer answers to

the hypotheses mentioned above. Initially, we will conduct descriptive statistics of the sample

to get an elaborated picture of the sample. Subsequently, a multiple regression will be adopted

to test our model’s rationality. This multivariate analysis method helps to explore the

relationship between a continuous variable and various independent variables (Pallant, 2016),

31

meaning that we could separately identify the correlation between our independent variables

and dependent variables as well as the direction of each correlation. According to Pallant

(2016), Cronbach’s Alpha needs to be above 0.7. Before examining the actual results of the

Multiple regression, the assumptions of multicollinearity, normality and homoscedasticity need

to be met (Pallant, 2016). Thereafter, the most commonly used measure called R² or “goodness

of fit” applied to the regression model. it can be explored how much of the variance in the

dependent variables is explained by the regression model by checking the R² (Pallant, 2016).

The higher R², the closer the estimated regression equation fits the sample data, which R² line

in the interval 0R²1 (Studenmund, 2016). The R² close to one shows an excellent fit overall.

The Standard Coefficients give evidence on which variable makes the strongest contribution

to explaining the dependent variable (Pallant, 2016), in this case, profitability. Lastly, possible

correlations and their significance as seen in the ANOVA will help to answer each hypothesis

individually.

4.5 Multicollinearity

Multicollinearity refers to the correlation among the independent variables, which makes it

difficult to make inferences about the individual regression coefficients and their individual

effects on the dependent variable (Lind et al., 2006, p. 439). Therefore, it is very essential to

check collinearity and also it is the first step the author of this study diagnoses after data

collection.

Researchers applied the Collinearity Diagnostics and Coefficients to test collinearity in SPSS.

The general rule is if the correlation between two independent variables is between −0.70 and

0.70, it is possible to use both of the independent variables (Lind et al., 2006, p. 440). In the

application of Statistics VIF, if the value is greater than 10 there is multicollinearity. Another

way is to observe the condition index in the collinearity diagnostics table. When the data is

greater than 15 could indicate a problem of collinearity and greater than 30 is a strong sign

(Hair, et al. 2013). After the two methods of diagnosis, the research could exclude the influence

of collinearity since the data is proved less than the reference value.

4.6 Homoscedasticity

The definition of heteroskedasticity and homoscedasticity indicates the error term ε is

homoscedastic if the variance of the conditional distribution of ε given X is constant otherwise

32

the error term is heteroskedastic, viz. if the distribution at each scatter point in the regression

model is almost the same, it is called homoscedasticity, and if the scatter point distribution of

each X is different, it is called heteroscedasticity (Stock & Watson, 2020, p. 188). The

occurrence of heteroscedasticity will cause problems for the regression model; thus, it needs to

be tested and eliminated. The practical implication of the heteroskedasticity diagnosis is

whether one should use heteroskedasticity-robust or homoskedasticity-only standard errors and

a large number of software programs report homoskedasticity only standard errors as their

default setting (Stock & Watson, 2020, p. 188).

The researchers could implement the diagnosis through the observation from the PP-plot from

SPSS, see Figure 2. Since the research`s model appears to approximate homoskedasticity, no

additional setting needed to be carried out in the software. Just in case, the reachers applied a

double check by running the Breusch–Pagan test for squared residual values as a dependent

variable in the regression equation since the variance of the conditional distribution of ε given

X is constant, the independent variable does not affect the residual value (Studenmund, A.

2016). Test the overall significance with a chi-square test. If the significance in ANOVA shows

P>0.05, then it is homoscedasticity.

4.7 Spurious Correlation and Non-stationarity

One problem with time series data is that if the independent variable has the same underlying

trend as the dependent variable, then the independent variable becomes apparently more

significant (Studenmund, 2016, p.376-377). Studenmund defined such a problem as spurious

correlation (2016), the strong relationship between two or more variables through undetected

underlying relationship, hence the regression is likely untrustworthy and overstated. One of the

typical problems is nonstationary, which refers to the series having one or more basic properties

that do change over time (Studenmund, 2016). As the previous chapter illuminated,

heteroskedasticity is one of nonstationary. The major consequence of nonstationary for

regression analysis inflates R² and the t-scores of the nonstationary independent variables,

which in turn leads to incorrect model specification (Studenmund, 2016, p. 378).

In this study, the data was collected in a seasonal interim report in which all companies

followed the approximate same timetable according to the accounting criterion. In the design

phase of the experiment, such a test was conducted by the experimenters and the measure for

33

delimitation called random walk will be applied by researchers if the nonstationary appears,

which allow the next period’s value to equal this period’s value plus a stochastic error term

(Studenmund, 2016, p. 378). By applying the extension functionality of stationarity in SPSS,

the authors implement the augmented Dickey-Fuller Test which examines the hypothesis if the

variable has a unit root, viz. The null hypothesis of the test is that the time series can be

represented by a unit root, that it is not stationary (Studenmund, 2016, p. 378; Brownlee, 2020).

Thus, If the p-value > 0.05 in the t-test indicates to reject the null hypothesis (H0), the data has

a unit root and is non-stationary and vice versa (Brownlee, 2020).

4.8 Validity and Reliability

Validity incorporates internal validity, external validity, and ecological validity (Bryman &

Bell, 2015). In the case of this research paper, the core focus was on external validity and

ecological validity. The external validity is concerned with the notion of generalisation, which

is the question of whether the results of a study can be generalised beyond the specific research

context (Bryman & Bell, 2015, p. 43). The authors of this paper have already reviewed some

previous literature that to some extent share a similar topic and research problem but in a

different country or region. It was expected that our results could also fit in another country’s

context, meaning that the results should partially align with some of the previous literature.

Ecological validity concentrates more on practical considerations, in which the social scientific

findings are applicable to people’s everyday life (Bryman & Bell, 2015). One of the research

purposes is grounded in a very practical way, in which authors of this study want research

findings to guide management to better evaluate a company’s profitability to invest smartly.

Reliability makes mention of the consistency when measuring a concept, including stability,

internal reliability, and inter-observer consistency (Bryman & Bell, 2015). The notion of

stability also refers to test-retest reliability, in which the test-retest ability of a scale mainly

involves administering it to the same items on two different occasions and calculating the

correlation between the two scores obtained (Pallant, 2016). Preliminarily, it is focused on

collecting 5 years of data from the official websites. The higher test-retest correlation suggests

a more reliable scale, which employed this test-retest reliability in our research through

collecting 5 years more data from these same companies to check if the two different scales

have highly correlated results.

34

Another aspect for assessing reliability is internal consistency (also called internal reliability).

This concerns the degree to which the items that make up the scale are all measuring the same

underlying attribute (Pallant, 2016, p. 6). By checking the internal consistency, it can assist to

identify how strong the items hang together. It was determined to use the concept of financial

leverage as one of our independent variables and it incorporates short-term debt, long-term

debt and total debt. There are several ways to examine the internal consistency, one adopted

way in this study applying Cronbach’s coefficient alpha as the most common method for this.

A reliability test was run in SPSS to see the Cronbach’s alpha of the three items, which ideally

expected a minimum level of 0.7 Cronbach alpha values. If the reliability test result fulfilled

the expectation, it could be combined into an index as the financial leverage for one of our

independent variables.

The authors of this study also engaged in a triangulation strategy to reassure research’s validity

and reliability, which entails adopting more than one method or source of data in the study of

a social phenomenon (Bryman & Bell, 2015). What was done for this triangulation mainly

focused on multi-sources of data and it was always double-checked. As mentioned above,

researchers of this paper utilised secondary data which was published on digital platforms and

gathered through different websites, including annual reports, interim reports, and calculated

financial ratios for triangulation. Additionally, if the data of the same companies on different

platforms, a recheck of the data will perform whether the numbers remain the same among

different platforms. In addition, since the units of the numbers in the reports of each company

are different to display and calculate the number, units were unified and carefully conducted

into account by manual screening.

35

5. Empirical Findings & Analysis

5.1 Multicollinearity

Table 1. Correlation

Table 2. Collinearity Diagnostics

According to the demonstration in 4.5, the multicollinearity makes it challenging to make

presumptions about the individual regression coefficients and their individual effects on the

dependent variable (Lind et al., 2006). Thus, it is imperative to examine collinearity and it is

the first aspect the author diagnoses after data collection. Researchers applied the Collinearity

Diagnostics and Coefficients to test collinearity in SPSS. According to Linda et al. (2006),

from the general rule, if the Pearson correlation between two independent variables is between

−0.70 and 0.70, it is possible to use both of the independent variables. By analysing the

correlation, all variables remain in the accepted range, which all variables can safely be

assumed. The observation from Collinearity diagnostics has also confirmed the result, which

the reading in condition index has only one dimension with the result greater than 15.

Consequently, no diagnosis has confirmed the potential multicollinearity that existed in the

regression model.

36

5.2 Descriptive statistics

A summary that comprises three aspects: mean value, standard deviation and number of observations

for the included variables are illustrated below:

Table 3. Descriptive Statistics

As it was previously mentioned, ROA plays the core role as the dependent variable and is

intrinsic for answering the underlying research question. It can be observed in the table

illustrated above indicating the mean ROA of 0,0194 or 1,94% for the real estate conglomerates

listed on OMX Stockholm Real Estate GI. Additionally, it can be observed that a considerable

reduction of mean ROA is caused by some observations that have a negative ratio, which

includes the minimum ROA at -0,0223 or -2,23%. Moreover, albeit the ratio sees a reduction

due to the effect of limiting the maximum ROA value to 6,33% after adjusting the outliers in

the 99th percentile, the mean ROA is also positively impacted by the limit existing on the

observations that have a negative ratio after adjusting for outliers in the 1st percentile.

Other aspects that are indispensable for discerning the relationship of financial leverage to the

dependant variable ROA, and ergo is imperative for answering the research question are Total

Debt to Asset ratio (TOTAL2A), Short-term Debt to Asset ratio (SHORTTERM2A) and Long-

term Debt to Asset ratio (LONGTERM2A). After adjusting the outliers in the 1st and 99th

percentiles, it was found that TOTAL2A has a mean of 0,569 with MIN and MAX values

ranging from 0,284 to 0,832, whereas SHORTTERM2A has a mean of 0,130 with minimum

and maximum values ranging from 0,017 and 0,439, and LONGTERM2A has a mean of 0,439

with MIN and MAX values ranging from 0,132 and 0,667.

37

5.3 Heteroscedasticity

As described in the method section, a Breusch–Pagan test has been performed to investigate if the

heteroscedasticity is excited in the model. The result demonstrated below:

Table 4. BP test for heteroscedasticity

Due to the fact that a direct function to test heteroskedasticity is not provided by SPSS, the

authors grasped the theoretical method of Breusch–Pagan test for the diagnosis of the problem.

Since the variance of the conditional distribution of ε given X is constant, the independent

variable does not affect the residual value (Studenmund, A. 2016). The authors applied the

characteristic to test the overall significance with a chi-square test. If the significance in

ANOVA shows P>0.05, then it is homoscedasticity. By analysing Table 4 it can be observed

that the significance is 0.232, greater than 0.05, which can be interpreted to reject the null

hypothesis. The model has proven homoscedasticity.

5.4 Model Summary

Table 5. Model Summary

38

The most commonly used measure called R² or “goodness of fit” is applied to the regression

model. As the method section has illuminated that the higher R², the closer the estimated

regression equation fits the sample data. However, low R² doesn't necessarily indicate that the

model failed. According to Frost (2019), some fields of study have an inherently greater amount

of unexplainable variation. In these areas, R² values are bound to be lower. Ergo, the

demonstration of R² will be integrated with the statistical significance, which the author allows

to draw important conclusions about the relationship between the variables (Frost, 2019).

From the Model Summary table, the value of R-square is 0.149 which means approximately

14.9% of the variability of dependent variable “Profitability” is explained by the control

variables “Financial Leverage” and the remaining of the variance is unexplained. However,

Samo and Muradm (2019, p. 78) found their R² is 0.298, Karlsson and Nordström (2007, p.34)

showed the R² in regression results equals 0.199 and Nguyen et al., (2019, p. 2322) also had

comprehensive lower R² in 0.013. More study in literature review such as Dalci (2018),

Alexander and Joel (2015), Reddy and Narayan (2018), had respectively 0.1743, 0.018 and

0.2499. Through a large number of literature reviews on related studies that have shown

relatively low values in common. The author has reason to believe that the readings of this

study are normal and reliable.

5.5 Stationarity

In the design phase of the experiment, such tests

were mentioned by the experimenters and the

measure for elimination called the Dickey-Fuller

test that is applied by researchers. By the

extension functionality of stationarity in SPSS,

authors implement the augmented Dickey-Fuller

Test which examines the hypothesis if the

variable has a unit root, viz. The null hypothesis

of the test is that the time series can be

represented by a unit root, that it is not stationary

(Studenmund, 2016, p. 378) (Brownlee, 2020).

Thus, if p-value > 0.05 in the t-test indicates to

reject the null hypothesis (H0), the data has a

unit root and is non-stationary and vice versa

Table 6. Time series tests for variable:

DATE_

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(Brownlee, J., 2020). From the above table, the tested p-value was shown to be 0.01 which is

less than 0.05. Hence, the null hypothesis is rejected, which proves the time serial is

stationarity.

5.6 Coefficients

OLS Regression Model

The following part of the paper conveys the results from the OLS regression that will be also

mentioned as the simple regression model. The model was chosen due to the fact that it is

frequently utilised throughout previous research, which is a validation of its inclusion by rising

comparability with earlier empirical outcomes.

Table 7. OLS Regression – TOTAL2A

The table above conveys the outcomes from the regression test. The first term illustrated in the

table is the unstandardized coefficient beta (B) that represents the slope of the line between the

dependent variable and the predictor variable. The unstandardized beta for each variable

divulges a unit change in Return on Assets ratio for an increase of one point of an independent

variable, given that the rest of the independent variables are fixed (Pallant, 2016). However,

the author emphasised the fact that it is imperative to look at the standardised coefficients in

lieu of looking at the unstandardized coefficient as standardised has a meaning that the values

for each of the various variables have been converted to the same scale to facilitate to compare

the values between each other (Pallant, 2016, p. 201). By observing the standardized coefficient

beta, which conveys the degree to which every independent variable has a contribution to the

dependent variable ROA, a deduction can be made that independent variable TOTAL2A has a

weak unique contribution of 0,039 to explaining the dependent variable whereas independent

variable ICR (Interest Coverage Ratio) has a strong and the strongest unique contribution to

explaining the dependent variable ROA. Another significant aspect that is under observation is

the Significance value (Sig.), which indicates whether a variable is conducting a statistically

significant unique contribution to the equation (Pallant, 2016, p. 201). It can be seen that the

40

independent variables except TOTAL2A and Size have a significance value less than 0,05,

which means that these variables are making a significant unique contribution to the

prognostication of the dependent variable ROA whereas TOTAL2A that has 0,579 and Size

with 0,212 in significance indicating an insignificant unique contribution to the prognostication

of ROA.

It is important to observe the performance of collinearity diagnostics on the variable that is a

part of the multiple regression procedure, due to the fact that it can disclose the issues with

multicollinearity that may not be shown in the correlation matrix (Pallant, 2016, 198). It can

be seen in Table 7 that tolerance for all variables, which indicates to what extent the variability

of specified independent is not explained by the other independent variables in the model, are

over 0,10 that shows that the multiple correlation with other variables is low and suggests a

low possibility of multicollinearity. Variance inflation factor (VIF) that is inverse of the

Tolerance value is less than 10 for all variables, which indicates that multicollinearity does not

exist in the model.

It can be evident that the real estate companies listed on the large cap tend to be capital intensive

and have a high degree of total debt amounting to an average of 56,9% based on Table 7. In

conjunction with the result and analysis aforementioned, it can be observed that t-statistics for

long-term debt to assets amounted to 0,555, which is between the critical values of -1,960 and

1,960 that are derived from the Student’s T-distribution table with 300+ in degrees of freedom

and (0,05/2) in significance value. Thus, it can be concluded that the null hypothesis is not

rejected, and no evidence shows a relationship between the independent variable TOTAL2A

and dependent variable ROA.

Table 8. OLS Regression - SHORTTERM2A

The regression model illustrated above that incorporates short-term debt (SHORTTERM2A)

sheds light upon similar outcomes as Table 7. According to the statistics, the independent

41

variable SHORTTERM2A has a negative and insignificant relationship with the dependent

variable ROA as the standardised coefficient beta is -0,014 indicating that an increase in

SHORTTERM2A causes a decrease in ROA. Additionally, it can be observed that the

independent variable ICR has again a significant relationship with the dependent variable ROA

and the Current Ratio (CR) has a strong negative relationship with the profitability metric as in

Table 7. Moreover, it can be concluded that independent variables SHORTTERM2A and SIZE

are making an insignificant unique contribution to the prognostication of the dependent

variable ROA as the first variable has 0,799 and the second has 0,292 in significance, which is

over 0,05. The tolerance is over 0,10 and VIF is less than 10 for all variables presented in Table

8, which indicates no possibility of multicollinearity.

By analysing the descriptive statistics illustrated in Table 3, it can be seen that the analysed

companies operating in the real estate industry do not possess a high degree of current debt

amounting to an average of 13% of total assets, which conveys the fact that these companies

do not heavily rely on short-term debt. Although the correlation between the independent

variable SHORTTERM2A and dependent variable ROA is insignificant, the t-statistics that

amounted to -0,254 indicated that there is no relationship between the variables and we should

not reject the null hypothesis, due to the fact that t-stat is between the critical values of -1,960

and 1,960 that were retrieved from the Student’s T-distribution table. Other researchers i.e.

Khan (2012), Yazdanfar & Öhman (2015), Ebaid (2009) that were scrutinising sundry

industries found also a negative relationship between the variables, however, it was significant.

Table 9. LSO Regression - LONGTERM2A

The regression model illustrated in Table 9 incorporates long-term debt (LONGTERM2A).

According to the statistics, the independent variable LONGTERM2A has a positive

relationship with the dependent variable ROA as the standardised coefficient beta is 0,032,

which indicates that a decrease in LONGTERM2A begets a decrease in ROA. Although the

independent variable has a positive relationship with ROA, its significance value is above 0,05,

42

which indicates that it is making an insignificant unique contribution to the prognostication of

the dependent variable ROA. Moreover, it can be observed that the independent variables CR,

SIZE and ICR have the same values as Tables 7 and 8 illustrated above. The tolerance is over

0,10 and VIF is less than 10 for LONGTERM2A indicating no possibility of multicollinearity.

It can be observed by analysing Table 3, which illustrates the descriptive statistics that the

companies operating in the real estate industry have a high degree of long-term debt amounting

to the average of 44 per cent of total assets. However, albeit it might be evident that an

insignificant positive relationship exists between independent variable LONGTERM2A and

dependent variable ROA as the standardised coefficient beta amounted to 0,032, it can be

concluded that the variables possess an insignificant relationship between each other and the

null hypothesis should not be rejected, due to the fact that t-statistics amounted to 0,562 and is

between the critical values of -1,960 and 1,960, which are derived from the Student’s T-

distribution table with 300+ in degrees of freedom and (0,05/2) in significance value.

The result found by the authors of this paper is not in line with the findings illustrated in the

research conducted by Abor (2005), Avci (2016), Yazdanfar and Öhman (2015), which found

a significant negative relationship between the variables and with the findings shown by the

researchers Gill et al. (2011) that found a positive relationship between LONGTERM2A and

profitability. However, the findings are in line with the research conducted by Nguyen et al.

(2019), which analysed the relationship between financial leverage and profitability of real

estate companies located in Vietnam and found a relationship between the variables where a

1% increase in financial leverage begot a 0,011% in ROA that is insignificant and aggrandises

the result found in our paper. Moreover, the results of this paper are in line with the findings

made by Al-Taani (2013), which investigated the relationship between capital structure and

firm's performance including in sample 45 Jordanian manufacturing firms available on Amman

Stock Exchange from 2005 to 2009 and found insignificant relationship between the variables.

Since the statistical tests do not show a significant relationship between the variables when

applying the OLS Regression Model, it can be argued that long-term debt is of no relevance

for generating profits for the analysed large-cap companies operating in the real estate industry,

which could be in line with the capital structure irrelevance theorem presented by Modigliani

and Miller (1958). Additionally, it could be asserted that long-term debt might be of relevance

for companies in the process of decision-making, however, it is assessed as an insufficient

source of financing for maximising profits.

43

It was vividly explained previously in the research paper that profitability can differ depending

on the factors i.e., firm’s characteristics, privately or publicly owned, and industry. It can be

observed that research aforementioned i.e., Yazdanfar and Öhman (2015), Khan (2012) saw

differing results, due to the fact that one group analysed human-intensive companies i.e.,

consulting companies that have an insignificant degree of debt in their capital structure whereas

another group of researchers i.e., Avci (2016), Margaritis & Psillaki (2010), Gill et al. (2011)

analysed capital intensive companies such as companies operating in the manufacturing

industry that has on average a high degree of debt. Albeit it can be assumed that the results of

this research paper should be similar to the results shown by the prior group, owing to the fact

that the real estate industry has on average a high degree of debt and is capital intensive as it

can be observed in the case of manufacturing firms, the results are different as real estate

industry has dissimilar nature of the business operation and the procedure of recording its

profits on the income statement (Andersson & Landberg, 2005). The industry includes and

records changes in the value of the property as income, which is impacted by external factors

i.e., interest rate, inflation rate etc., and is not under the company’s control (Andersson &

Landberg, 2005).

Ergo, in light of the above mentioned, all endeavours to find a relationship between the

independent variables i.e., LONGTERM2A, SHORTTERM2A, TOTAL2A and dependent

variables ROA were to no avail and a deduction can be made that ROA is of no relevance in

determining the impact of capital structure on profitability in the real estate industry.

5.7 Control variables

The selection of variables is partly based on previous research in the literature review of a

similar study and selected by the authors of this paper in industry relevance. This part links

with economic theory to interpret the meaning behind the numbers.

Size

The result of multiple regression in size is shown insignificant and the null hypothesis cannot

be rejected. For each variable tested in Sig, this elaborates whether the variable is making a

statistically significant unique contribution to the equation (Pallant, 2016, p.202). If the Sig.

value is less than .05 the variable is making a significant unique contribution to the prediction

of the dependent variable or reduce to conclude the significance to the dependent variable

44

(Pallant, 2016, p.201). By reading tables of 7,8,9, the significant values are relatively 0.21 for

total-debt to asset, 0.231 for long-debt to asset and 0.292 for total-debt to asset, which is all

insignificant on a 5% significance level. Thus, it was no possibility of statistically confirming

that there is any relationship between the firm's size and profitability.

The reason for the insignificance is unknown by precise reason. However, the authors of this

study assume that it may be caused by a limitation in quantity of sampling or the choice of

variables to measure the size of the company. Simerly and Li (2000) had a relatively abundant

population for their research which consisted of 700 large U.S firms from different industries,

successfully indicating the positive correlation to profitability. Another suspected reason in this

study is the market capitalisation, which takes the place due to the fact that it captures the

potential value to be created in real estate companies. Some previous reach conducts the

employee number to represent the size as a regressor.

Liquidity

Unlike size, the liquidity results convey the outcomes from the unstandardized coefficient beta

(B) demonstrate the weak negative correlation in relatively -.302 for long-term debt to asset,

-.303 for short-term debt to asset, and -.296 for total-debt to the asset. ALL Sig. readings remain

at 0.00 which prove significant in all tests on the 5% significance level. Liquidity has been

selected as one of the control variables by many previous researchers. Goddard et al. (2005),

found a positive relationship between liquidity and profitability, and Samo and Murad (2019)

indicate that there is a positive relationship between liquidity and profitability in their research

as well. Unlike previous research, it can be evident that the real estate companies listed on the

large-cap have averaged a negative 30% weak correlation in their liquidity to profitability,

while working capital to meet its short-term need has weak negative impacts. Thus, the null

hypothesis cannot be rejected. However, alternative hypotheses cannot be approved either,

because the weak correlation is shown as the result that the authors of this study cannot

conclude any decisive relation.

Solvency

Solvency denotes the ability of a company to pay off its long-term debt and the interest on that

debt (Goel, 2016). The results convey similar outcomes as liquidity from the unstandardized

coefficient beta (B) demonstrate the significant medium correlation in relatively .440 for long-

term debt to asset, .437 for short-term debt to asset, and .445 for total-debt to asset. Ergo, the

45

authors of this study cannot reject the null hypothesis and either accept the alternative

hypothesis, which was statistically confirmed only as a medium relationship between the firm's

solvency and profitability.

Hapsari et al. (2016) drew the conclusion in their research that solvency has no significant

influence on profitability, Sucipto and Chasanah (2019) stated that the solvency ratio measured

by the interest coverage ratio has no effect on profitability. Unlike previous researchers, this

study shed light upon how efficiently a company can pay interest on outstanding debt impact

the profitability in a positive relation for large-cap real-estate companies in Sweden.

Considering all have high leverage, this result can lead to the further direction of the study.

6. Conclusion

In the following chapters, the authors of the paper vividly shed light upon the results of the

analysis. Subsequently, the theoretical and practical contributions in conjunction with ethical

considerations are discussed. Last but not least, the paragraph about future research presents

topics to be explored further.

6.1 Summary of the Findings

It was hypothesised that no relationship exists between the independent variable TOTAL2A

and the dependent variable ROA. The results showed that there is an insignificant positive

relationship between financial leverage and firm profitability. The statistical test showed that

we should not reject the null hypothesis, which indicates that there is no relationship between

TOTAL2A and ROA. Additionally, it was hypothesised that no relationship exists between

independent variables SHORTTERM2A, LONGTERM2A and dependent variable ROA. The

results showed that there is an insignificant negative relationship between SHORTTERM2A

and ROA as well as an insignificant positive relationship between LONGTERM2A and firm

profitability. The statistical tests showed that we should not reject the null hypothesis both for

LONGTERM2A and SHORTTERM2A, which indicates that there is no relationship between

LONGTERM2A and ROA in conjunction with SHORTTERM2A and ROA. It can be generally

observed that financial leverage has an insignificant positive relationship with firm profitability

metric ROA, which is in line with the statement and findings illustrated by previous researchers

Nguyen et al. (2019) that found a relationship between the variables where a 1% increase in

financial leverage begot a 0,011% in ROA that is insignificant and aggrandises the result found

46

in our paper. However, other researchers such as Avci (2016) that scrutinised the relationship

between financial leverage and profitability in the manufacturing industry found a significant

negative relationship whereas Gill et al. (2011) that analysed the same industry in another

country found a significant positive relationship between the variables. The reason for the

contradiction between different findings can depend on the ratios that are utilised in the

research as well as other intrinsic aspects i.e., firms’ characteristics, industry, ownership. In the

case of the following research paper, it can be observed that the nature of the business operation

and the procedure of recording its profits on the income statement in the real estate industry is

different from other industries, which consequently beget the statement that ROA is of no

relevance in determining the impact of capital structure on profitability in the real estate

industry.

It was hypothesised that there is no relation between size and profitability. However, the results

showed that they are insignificant and not able to statistically confirm that there is any

relationship between the firm's size and profitability. The authors of this study suspect it may

be caused by a limitation in quantity of sampling or the choice of variables to measure the size

of the company. Simerly and Li (2000) had a relatively abundant population for their research

which consisted of 700 large U.S firms from different industries, successfully indicating the

positive correlation to profitability. Another suspected reason in this study is the market

capitalisation, which takes the place due to the fact that it captures the potential value to be

created in real estate companies. Some previous reach conducts the employee number to

represent the size as a regressor. Additionally, liquidity was hypothesised to have no relation

to profitability. Unlike size, the liquidity results convey the outcomes demonstrate the

significant weak negative correlation of all tests on the 5% significance level. Goddard et al.

(2005), found a positive relationship between liquidity and profitability, and Samo & Murad

(2019) also indicates that there is a positive relationship between liquidity and profitability in

their research. Unlike previous research, it can be evident that the real estate companies listed

on the large cap have a weak correlation in their liquidity to profitability, while working capital

to meet its short-term need has weak negative impacts. Thus, the null hypothesis cannot be

rejected. However, alternative hypotheses cannot be approved either, because the weak

correlation is shown as the result that the authors of this study cannot conclude any decisive

relation. Similar results convey solvency, the outcomes as liquidity demonstrate the significant

medium correlation. Ergo, the authors of this study cannot reject the null hypothesis and either

accept the alternative hypothesis, which was statistically confirmed only as a medium

47

relationship between the firm's solvency and profitability. Hapsari et al. (2016) drew the

conclusion in their research that solvency has no significant influence on profitability. Sucipto

& Chasanah (2019) stated that the solvency ratio measured by the interest coverage ratio has

no effect on profitability. Unlike previous researchers, this study shed light upon how

efficiently a company can pay interest on outstanding debt impact the profitability in a positive

relation for large-cap real-estate companies in Sweden. Considering all have high leverage, this

result can lead to a further direction of study.

6.2 Theoretical and practical contribution

The authors of this study aimed to generalise whether leverage impacts the profitability of real

estate companies in Sweden confiding the set boundaries. This research serves to be useful

since it fills the existing scientific gap providing the analysis of such a heavily capital-intensive

industry as real estate. Despite its scale, a limited number of studies concentrated, especially,

on the financial aspects including the leverage utilisation and its impact on profitability. The

current research provides an overview of the capital structure and profitability of companies

contributing to more studies to be conducted in the future that will consider the most up-to-

date data, solid hints, insights and relative conclusions. It is imperative to emphasise that the

research is supposed to be well readable and helpful for concerned managers who are aiming

to see the full picture of the chosen debt level that influences the final result. Proper

management and prospects tend to be detected by the existence of growth opportunities that

reveal a good signal to the bond market hence influencing the speed at which firms adjust their

leverage. By being able to make proper decisions concerning profitability, maximization

indeed guarantees a sound market standing that stimulates investors to join and invest in

companies. Thereafter, it is necessary to keep the debt finance at the optimal level ensuring

adequate utilization of the firm's assets.

6.3 Social and Ethical Considerations

The authors of this paper are assured that their study does not serve to be the subject of any

ethical dilemmas. The secondary data gathered for this study is publicly accessible and any

form of sensitive information is absent. Moreover, questionnaires or similar collection methods

that gather personal information, views, opinions were not utilised. As to the social dilemmas,

maximisation of profitability, which is the main objective of the research, is beneficial for firms,

however, firms that are driven only by this goal may put aside environmental and societal

48

factors as well as the social effect within organisations. For instance, managers decide to avoid

debt utilisation in the upcoming project, and the quality issuing source seems to be an adequate

and applicable financial alternative. However, stakeholders are experiencing more pressure,

stress, which is socially inadequate and consequently leads to a higher rate of sick leave. Ergo,

it is hard for employees to be effective and concentrated in such an unbalanced environment

which directly leads to the inability of generating and increasing revenue counteracting the core

set aim.

6.4 Limitation

Due to the limitation of the length of the research, our data collection only covers a period of

5 years. Professionals will study for a longer time when completing the same type of work.

More data provides relative larger sample size, which may have a positive impact on the model.

In addition, some data is somewhat difficult in the collection phase, which data is missing in

the interim report, and the researchers use the average estimation method to complete the data.

It is a well-known fact that COVID-19 has wreaked havoc in many industries, and it is

imperative to mention that the results of the following research could potentially be afflicted

by the situation of COVID-19 as not only the coronavirus has impacted the returns generated

in the real estate industry, but also other factors i.e., interest expense, changes in the property

value etc.

Another limitation that was identified refers to the lack of previous studies in the research area.

The literature review served to be useful for identification of the scope of works, however if

there were more solid and relevant information about Swedish real estate market it would

definitely strengthen authors’ position. To minimize the limitation gap authors avidly

scrutinized available researches from other countries considering the specification and

differences of Sweden in such sectors as politics, economics and technologies.

6.5 Suggestions for Future Research

The limitation in this paper turned out to be the recommendation for further studies, and it will

be to collect data of all real estate firms from both Swedish Stock Exchange and private, and

not sampling only publicly owned. Another crucial aspect would be to increase the period of

the study by at least 10 years, since currently, the observation included 5 years, from 2016 to

49

2020. In retrospect, there are permanent fluctuations in the economy, politics, and society that

influence the outcome. Being capable of seeing the most crisis years as well as the most

flourishing years and comparing them with each other will provide more clarity to the results.

Finally, future studies similar to the conducted one are recommended, however, implementing

other analytical tools, theories or methods. For instance, the utilization of the dynamic panel

data regression can reveal fascinating relationships between short and long-term leverage. Thus,

the carried-out studies will provide the ability to compare how the results either differ or end

up with similar effects identifying the contributing factors and causes. The recommendation to

real estate companies would be to invest in an analysis that statistically shows the proper

balance of leverage to be utilized to convert them into profitability.

Last but not least, the authors of this research paper would give a recommendation to scrutinise

the relationship between financial leverage and profitability by utilising different profitability

measures that are not impacted by external factors i.e., change in the property value. It would

be of utter interest to observe the cash inflow beget by financial leverage, in lieu of the impact

on the profit as profitability is easily manipulated.

50

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8. Appendix

Figure 1. Summary of Hypothesis

Figure 2. Regression formula

Table 1. Correlation

Long-term debt

Short-term debt

Total debt

Size

Liquidity

Solvency

Profitability

H1

H2

H3

H4

H5

H6

60

Table 2. Collinearity Diagnostics

Table 3. Descriptive Statistics

Table 4. BP test for heteroscedasticity

61

Table 5. Model Summary

Table 6. Time series tests for variable:

DATE_

Table 7. OLS Regression – TOTAL2A

62

Table 8. OLS Regression - SHORTTERM2A

Table 9. LSO Regression - LONGTERM2A

Figure 3. Scatterplot

63

Figure 4. Real Estate Companies