Capital Structures and Profitability of Pharmaceuticals Companies Listed under Australian Stock Exchange

This research project analyzes the capital structures and profitability of pharmaceuticals companies listed under Australian stock exchange. The study is presented for the partial fulfillment of Degree in Master of Professional Accounting in Holmesglen Institute. The research will be based on the following companies Auscann Group Holdings Ltd, Avita Medical Ltd, Benitec Biopharma Limited, Biotron Limited and Bioxyne Limited. The study will discuss whether healthcare sectors recognize capital structure as a strategic opportunity to enhance the value of the organization or to view capital structure as a by-product of another decision making.

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Critical analysis of the capital structures and
profitability of Pharmaceuticals companies
listed under Australian stock exchange
Author- AARTI GOEL
This research project is presented for the partial fulfillment of
Degree in Master of Professional Accounting
In
Holmesglen Institute
Supervisor - Dr. Vasanthi Peter
This Master’s thesis is carried out as a part of the education at the
Holmesglen Institute and is approved as a part of this education.

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AUTHORISATION FORM
I_________________, authorize Holmesglen Institute to publish or use this research project
“Relationship of effective working capital and profitability based on company’s performance”,
for scholarly research. I acknowledge that photocopy or electronic transfer, the abstract or in
part, at the request of research institutions or individuals for the purpose of scholarly research.
Dated on: June 2018

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ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to my class coordinator and research project leader
Dr. Vasanthi Peter for the continuous support of my MPA study and research project for her
patience, motivation, enthusiasm and immense knowledge. Her guidance helped me in all the
time of research and writing of thesis. I could not have imagined having a better advisor and
mentor for my research project.
Besides my course leader, I would like to thanks my friend _____________who always took me
out from the hurdles I faced and for encouragement he gave me and for hard questions.

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Table of Contents
1.0 Introduction...........................................................................................................................................6
1.1 Review of Literature..............................................................................................................................7
1.2 Methodology.......................................................................................................................................39
1.3 Interpretation......................................................................................................................................41
1.4 Conclusion...........................................................................................................................................42
2.0 Introduction.........................................................................................................................................43
2.1 Objective.............................................................................................................................................43
2.2 Conceptual framework........................................................................................................................44
2.3 Review of literature.............................................................................................................................45
3.0 Introduction.........................................................................................................................................46
3.1 Research design...................................................................................................................................46
3.2 Data Collection....................................................................................................................................46
3.2.1 Data type......................................................................................................................................46
3.2.2 Variables included in the data set.................................................................................................47
3.2.2.1 Dependent variable:..............................................................................................................47
Debt equity ratio................................................................................................................................47
3.2.2.2 Research questions................................................................................................................47
3.2.2.3 Hypothesis.............................................................................................................................48
3.33 Data processing and Data analysis techniques..............................................................................48
3.34 Data pre- processing......................................................................................................................48
3.35 Data analysis techniques...............................................................................................................49
3.3 Descriptive analysis.............................................................................................................................49
3.3.1 Chi-square and cross tabulation...................................................................................................49
3.3.2 Inferential analysis........................................................................................................................49
3.3.3 Stationary test..............................................................................................................................49
3.3.4 Normality test and Heteroskedasticity.........................................................................................50
3.3.5 Correlation....................................................................................................................................50
3.3.6 Regression analysis.......................................................................................................................50
3.4 Limitation of the study........................................................................................................................51
4.0 Introduction.........................................................................................................................................52

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4.1 Descriptive statistics............................................................................................................................52
4.2.1 Cross tab and chi square test........................................................................................................55
4.2.2 Profitability...................................................................................................................................56
4.2.3 Inferential analysis........................................................................................................................57
4.2.4 Correlation analysis......................................................................................................................58
4.2.5 Regression analysis.......................................................................................................................59
4.2.6 Pooled OLS....................................................................................................................................60
4.3 Summary.............................................................................................................................................63
5.0 Introduction.........................................................................................................................................64
5.1 Findings and conclusion.......................................................................................................................64
5.2 Recommendations...............................................................................................................................66
6.0 Reference............................................................................................................................................67
7.0 APPENDIX.........................................................................................................................................79

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1.0 Introduction
Capital structure is generally classified into debt and equity to support assets. In the case of
healthcare systems, they are viewed as a strategic component for their financial planning.
However, for non- profit health care centers the capital structure is maintained nearly at a
constant level of debt over the past century, but investor-owned sectors have reduced their
relative use of debts (Akhtar, 2012).
According to Hackbarth and Mauer (2012), non-profit healthcare groups have not been able to
reduce their debts in the same way as investor-owned hospitals do. This is due to the use of debt
to support investments within the financial market. Since, pharmaceuticals, biotechnology, and
life science sectors don't have access to equity, high bond rating and solid investment earnings
which are the key asset for capital structure policies of preserving access to the debt market.
In this research study, we will analyze the firm's strategy to help brighten the choice managers
make between debt and equity financing the research will be based on the following companies
Auscann Group Holdings Ltd, Avita Medical Ltd, Benitec Biopharma Limited, Biotron Limited
and Bioxyne Limited. These companies are classified under Pharmaceuticals, Biotechnology &
Life Sciences in Australian Stock Exchange.
This research study will further discuss whether healthcare sectors recognize capital structure as
a strategic opportunity to enhance the value of the organization or to view capital structure as a
by-product of another decision making (Jin, 2005). For businesses, the form of competition each
entity chooses will determine the strategic value of the firm of maintaining financial slack. In this
research, we will use secondary data to analyze the strong support for the proposition that
financial slack should be a particularly critical strategic imperative for firms pursuing a
competitive strategy premised on innovation. In some cases, health care sectors employ a capital
structure in a strategic manner as they may have a specific capital structure to target independent
goals within the organization. The key criteria of capital structure policy are to reduce the overall

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cost of financing, thus lowering the firm’s cost and enabling the firm to pursue more value-
adding investments.
1.1 Review of Literature
According to Decker (1997), healthcare sectors have different policies and various kind of
market competition which creates more challenges to the providers, hence the possibility that
capital structure can be used strategically becomes increasingly more important. One of the
concerns regarding healthcare sectors is that it depends on management for their financial
positions to support challenges from the other side. When management continues to manage their
financial affairs to make up for the financial shortcomings of the business, then the likelihood of
continuing financial viability is increased. If these healthcare sectors find it difficult to continue
achieving their financial management goals, and at the same time failing to meet their
operational goals, the future viability of these organizations will be significantly challenged.
According to Naseem, Zhang, Malik and Ramiz Ur Rehman (2017), "Optimal capital structure is
a unique mix of debt and equity that minimizes the overall cost of financing assets". There are
various theories that suggest the derivation of an optimal capital structure depends mainly on the
existence of financial market imperfections.
One of the key importance of capital structure depends on the asset part of the balance sheet.
Decisions made by management about programs and the source needed to carry out these
programs are significant to strategic and financial success (Pyo, Shin, and Thompson, 2015).
Even though, decisions made by management about the mix of debt and equity employed to
support the firm’s assets are not relevant to the overall cost of financing. Thus, companies tend to
reduce the overall cost of financing and hence increase the value of the firm by shifting the
capital structure towards more debt or more equity is not regarded as successful.

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Under agency cost, a firm attempt to lower overall financing cost by shifting from relatively
costly equity to relatively lower cost debt which will result to be unsuccessful. This will impact,
the equity holders with increasing risk on their returns as it is associated with more debts, this, in
turn, will result in the business paying higher dividends (Stein, 2002). As a result, debt holders
may impose higher interest charges or more restrictive covenants on the borrowing firm which
have costs as well. The increase in required dividends, interest rates or cost of covenants will be
sufficient enough for the business to cancel out gains associated with using relatively cheap
debts, so the overall cost of financing stays constant. As a result, the firm can't do anything to
alter the capital structure that in turn will reduce the financing cost. Hence, the capital structure
will have no influence on the value of the firm.
Yang, Cheng, and Lee (2014) indicates that the altering the capital structure of the firm can
influence the overall cost of financing which depends on the nature of the financial market
implications faced by the firm. One of the key imperfections probably relates to access to
subsidized debt, restricted access to equity markets and the existence of significant transaction
cost associated with financial distress. Because high ratio shows that organization is not capable
of financing its project and a low ratio indicates that organization is not bold enough to take a
risk by borrowing from the market. Cost of capital is the expense related to the option chose for
raising funds (Naseem, Zhang, Malik and Ramiz Ur Rehman, 2017). Debt attracts interest but
provides tax deduction because it can be expensed. However, equity requires dividend which is
not an obligation but if paid there is no tax deduction on this amount. So, it can be said these two
factors determine the capital structure which further defines the amount of debt and equity.
According to Welch (2011), subsidized debts come in different forms. For instance, for a tax-
paying firm, the income tax deductibility of debt interest payments is a significant subsidy that
helps to support the use of debt, the magnitude of which depends on the marginal tax rate of the
firm. Since debt is taxed only once as income to individual and dividends are taxed twice as
income to the individual and as income to the firm. Non-profit health care sectors do not pay
income taxes; as a result, they don't face this income tax subsidy.

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The capital structure theory generally demonstrated the importance of financial market
conditions especially market imperfections that influence the capital structure strategy. However,
a high cost of acquiring equity through the provision of community benefits and other fund-
raising efforts will reduce the relative amount of equity on the balance sheet (Attaoui and Poncet,
2013). Deciding the optimal level of capital structure is very important for every organization.
Excess of either component can have left an organization in a difficult situation. The optimal
level capital structure is obtained when the cost of raising the funds is the minimum. However,
level of capital mix depends on various factors affecting the industry. The optimal capital mix is
not a rigid amount of capital mix. It provides a range within which the capital structure can be
considered as optimal. So, it can be said that cost of moving from one capital structure to other is
also considered while determining the debt and equity mix.
According to Bonini and Boraschi (2010), the relationship between capital structure and market
structure is that it demonstrates that low leverage may constitute a strategic advantage and that
attempts to gain this advantage may, in turn, influence the capital structure. The cost of capital
schedules is the existence of information asymmetries, particularly with regards to the quality of
investment opportunities available. The effect of information asymmetries has also been linked,
theoretically to capital rationing where information asymmetries are related to the problems that
are a more moral hazard and adverse selection. In this research, we will analyze the impact of
capital structure on are Auscann Group Holdings Ltd, Avita Medical Ltd, Benitec Biopharma
Limited, Biotron Limited and Bioxyne Limited. These companies are classified under
Pharmaceuticals, Biotechnology & Life Sciences in Australian Stock Exchange.
In the case of hospitals and healthcare information asymmetries is more relevant. This is due to
the nature of the business is highly risky and uncertain. Financial markets account for uncertainty
in setting the cost of capital. For healthcare entities, a large amount of capital must be invested
in order to make the business financially viable. For instance, a large amount of capital may be
needed to support the vertical and horizontal integration of the system. Positive net returns are

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generally enjoyed only when there is a large amount of capital investment. In the case of medical
sectors, since the entities are generally not for profit organizations, they have little investment
and may be unable to make a positive profit since it has very little to sell (Das, 2017).
Porter in 2008 mirrored the substance, where the strategist comprehended and adapted to the
opposition. Frequently, in any case, administrators characterize opposition too barely, as though
it happened among the present competitors. However, rivalry for benefits was mainly due to
industry opponents to incorporate other aggressive powers also: providers, potential contestants,
and clients. The broadened contention outcome from every one of the five powers characterizes
an industry's shapes and structure. The idea of aggressive association inside an industry was the
result.
Bharathi Kamath in 2008 in his paper, “Intellectual capital and corporate execution in Indian
pharmaceutical industry” accentuated on the reason for the paper was to think about the
relationship, assuming any, between the intellectual capital(IC) segments, to be specific human,
auxiliary, and capitals which were physical was the customary procedures of execution of the
organization, specifically benefit, efficiency and market valuation.
Abor in 2008 examined the capital structure of Ghanaian firms and their determinants. The
examination looks at the capital structures of freely cited firms, expansive unquoted firms, and
little and medium ventures in Ghana. Utilizing a board relapse display, the paper likewise looks
at the determinants of capital structure choices among the three example gatherings. The
outcomes demonstrate that cited and expansive unquoted firms show fundamentally higher
obligation proportions than do SMEs.
Mehralian et al. in 2012 explored the connection between intellectual capital(IC) parts with the
customary measures of execution of the Iranian pharmaceutical division. The examination shows
that the connections between the execution of an organization's IC and ordinary execution

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pointers were changed. The discoveries recommend that the execution of an organization's IC
can clarify gainfulness yet not profitability and market valuation in Iran.
Li and Pike in their exploration in 2008 researched the connection between intellectual capital
revelation and corporate administration factors, other firm‐specific attributes were controlled, for
an example of 100 UK recorded firms. Intellectual capital divulgence was estimated by a
revelation file score, upheld by word tally and level of word check measurements to survey the
assortment, volume and focal point of intellectual capital exposure individually. Consequences
of the investigation in light of the three measures of intellectual capital exposure show a critical
relationship with all the administration factors aside from part duality.
Alipour in 2012 concentrated the impact of intellectual capital on the firm execution of insurance
agencies in Iran. The reason for the paper was to dissect the part of IC and its association with
monetary execution of Iran insurance agencies amid the period 2005 to 2007. The consequences
of the examination uncovered that esteem included intellectual capital and its parts have a huge
positive association with organizations' benefit.
In 2012, Abuzayed in his paper in the Jordanian prospect talked about the reason for the paper
was to look at the impact of working capital administration on firms' execution for an example of
firms recorded on a little developing business sector, to be specific Amman Stock Exchange.
Utilizing vigorous estimation procedures the examination found that productivity was influenced
decidedly with the money change cycle. Furthermore, money related markets neglected to punish
directors for wasteful working capital administration in developing markets.
Sydler et al. in 2014 inquired about the estimation of intellectual capital(IC) was an
exceptionally examined point in the field of learning administration. The paper displays a
strategy for estimating IC to quantitatively survey whether IC bolsters the information based
perspective of the firm that clarifies long haul contrasts in firm gainfulness. We deliberately

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inspect the scene of IC valuation techniques connected and tended to in the writing, and we
broaden one model by choosing financial intermediaries for human, basic and social capital.
Azhagaiah in 2011 definite the effect of capital structure on benefit with extraordinary reference
to IT industry in India. Firms can utilize either obligation or value funding to back their benefits.
The best decision was a blend of obligation and value. The present investigation essentially
examinations the capital structure influences on the profitability of corporate firms. The study
was done in Indian. The investigation endeavors to build up the speculated relationship with
reference to how far the CS influences the business income of firms and the interrelationship was
amongst capital structure and Profitability.
Market analyst and scientists have invested their energy for new considerations around this
theory. Chowdhury in 2010 endeavored to exactly bolster the contention of Modigliani and
Miller display. The thesis examines the impact of obligation value structure on the estimation of
offers given distinctive sizes, ventures and development openings with the organizations. The
entire platform was taken as the Chittagong Stock Exchange and Dhaka Stock Exchange of
Bangladesh.
Ahangar in 2011 clarified the connection between intellectual capital and money related
executions. In the intellectual capital writing, just a couple of studies have examined the
connections between the segments of IC and hierarchical achievement. The examination gives
assist knowledge into the part of IC in hierarchical execution, particularly budgetary execution.
In the request, Value Added Intellectual Coefficient (VAICTM) technique has been utilized for
estimating the esteem based execution of the organization. Corporate execution measures utilized
as a part of the examination were productive.

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Hasan et al. in 2014 concentrated the Influence of capital structure on firm execution. The paper
basically thinks about the capital structure impact on association's execution. The examination
was about 36 firms recorded in Dhaka Stock Exchange amid the period 2007 to 2012. Few
execution measures were utilized; return on value, profit per share, the return of advantage and
Tobin's Q; as reliant factors and the capital structure proportions; here and now an obligation,
long-haul obligation and aggregate obligation proportions; as autonomous factors.
To break down the reasons for high gainfulness of pharmaceutical and biotechnology
organizations, Nickisch et al. in 2009 distributed their exploration. Biotechnology financial
specialists were progressively worried about going out on a limb of putting resources into the
advancement of inventive medications, and pharmaceutical organizations were stressed over
keeping up their high benefit later on. The inquiry was the means by which to manufacture an
arrangement of innovative work (R&D) ventures that satisfies the money related desires for
financial specialists and investors.
Australia has had an administration-sponsored all-inclusive arrangement of pharmaceutical
arrangement for a long time. The Pharmaceutical Benefits Scheme (PBS) devours around 14
percent of aggregate government medicinal services uses and has developed considerably in both
scope of medications secured, and consumption since it was first presented in 1950. It joins
understanding copayments (with differentials for the all-inclusive community contrasted and
concessional recipients). Before posting a medication on the PBS it was liable to a thorough cost-
viability examination. Duckett in 2004 concentrated on the above situation.
Beecroft in 2007 talked about the Generic medication approach in Australia. The article gives a
discourse, from a network drug store point of view, on the arrangement condition for the drug
store division in Australia, with a specific spotlight on exhibit challenges emerging from
recommendations to accomplish considerable PBS cost reserve funds from a foreseen surge of
new nonexclusive medications. Some $2 billion of solutions as of now on the PBS will fall off
patent in the following 4 years. The development originates from a low base where generics right
now represent just 15% of the aggregate PBS spending plan. Compensation for PBS

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administering was settled through multiyear concurrences with the administration, so exchanging
terms on generics were vital for the cross-sponsorship of other apportioning exercises and expert
administrations.
Brennan in 2001 analyzed the degree to which 11 knowledge‐based Irish recorded organizations
were receiving approaches for announcing of intellectual capital in their yearly reports. Their
market and book esteem were thought about and a substantial investigation of the yearly reports
of the 11 recorded organizations was directed. Except for two of the 11 recorded organizations,
huge contrasts in market and book esteems were discovered, proposing that knowledge‐based
Irish recorded organizations have a generous level of non‐physical, impalpable, intellectual
capital resources.
Mishra and Chandra in 2010 concentrated the Mergers, Acquisitions, and Firms Performance and
with regards to strategy changes in the 1990s by and large and three imperative alterations made
to the Indian Patent Act. The present paper constructs an endeavor to look at the effect of MA on
monetary execution of Indian pharmaceutical organizations. It was discovered that the benefit of
a firm depends straightforwardly on its size, offering endeavors and fares and imports forces
however contrarily on their piece of the overall industry and interest for the items.
Singh and Kansal in 2011 in their paper expect to research bury firm savvy capital (IC)
divulgences and its varieties in top 20 recorded pharmaceutical organizations in India, consider
the class astute and component insightful IC revelations (ICD), discover the effect of ICD on the
formation of IC in financial terms, discover relationship between's IC valuation and its exposure,
and test importance of connection.
Joshi et al in 2013 in their paper on Intellectual capital and budgetary execution, talked about the
reason for the look at the intellectual capital implementation of the financial Sector of Australia

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for the period 2006 to 2008. It intended to inspect the connection between IC execution and the
money related execution of the monetary segment. The capacity of money related segment in
Australia was exceptionally impacted by human capital. It was observed that 66% of the sample
organizations had small levels of proficiency in intellectual capital.
Brüggen, Vergauwen, and Dao in 2009 concentrated the stimulus following the paper was to
analyze determinants of the choice to uncover intellectual capital in yearly reports. The paper
finds that industry mark assumes a key part for the exposure of licensed innovation in yearly
reports. What's more, firm size was another determinant for scholarly divulgence of firms.
Conversely with prior investigations and hypothetical forecasts of willful divulgence, be that as it
may, the paper does not discover any connection among the level of intellectual capital exposure
and data asymmetry.
Vafaei, Taylor, and Ahmed in 2011 in their examination plan to analyze regardless of whether
recorded organizations' divulgence of intellectual capital was value‐relevant in share advertises
and to survey its directing part in the value‐relevance of announced profit and value following
the appropriation of universal money related detailing guidelines (IFRS). The outcomes uncover
that ICD was emphatically connected with showcase cost (i.e. has esteem significance) in
organizations in two of the four nations and in non‐traditional ventures. Further, the incremental
esteem significance of income and net resources was generally non‐significant; in any case, a
collaboration of these factors with ICD impressively builds the fundamental coefficients and the
illustrative intensity of the models.
Celenza and Rossi in 2014 in Intellectual capital and execution of recorded organizations
examined the expect to explore the connection between corporate executives and Value Added
Intellectual Coefficient (VAICTM) from one viewpoint, and the connection between the varieties
in showcase esteem and the varieties in VAIC then again. The results of the application stretch

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the significance of VAIC in the clarification of the varieties in MV and its part as "extra
coefficient" in the examination of value execution.
Anam Ousama, Fatima, and Rashid Hafiz-Majdi in 2012 set their goal to inspect the
determinants (i.e. firm size, productivity, use, and sort of review firm and industry write) of
intellectual capital (IC) exposure in the yearly reports of Malaysian recorded organizations. The
paper found that firm size, benefit, industry write were determinants of IC divulgence (ICD) in
the yearly reports of Malaysian recorded organizations, while use and sort of review firm did not
factually impact ICD.
Mohammadzadeh et al. in 2013 discussed the effect of capital structure on the benefit of
pharmaceutical associations the case of Iran. Sponsoring blend was the most indispensable issue.
The examination hopes to review the association between the capital structure and the
productivity of pharmaceutical associations in Iran. For the reason, as the investigation tests
described best 30 Iranian pharmaceutical associations and their budgetary data was gathered for
the season of 2001 to 2010. In the examination, the net edge advantage and commitments to asset
extent were used as markers of productivity and capital structure, independently and bargains
advancement was used as a control variable. Results exhibited that there was a gigantic negative
association between the advantage and the capital structure, which inferred that Pecking Order
Theory have been set up by the pharmaceutical associations. It also proved that within financing
has incited more noteworthy efficiency.
Goyal in 2013 inquired about the positive impacts of capital structure on the execution of
recorded Indian open area banks. The paper tries to think about the effect of capital structure on
gainfulness of open division banks in India recorded on national stock trade amid 2008 to 2012.
Regression Analysis was used for setting up the connection between Return on Assets, Return on
Equity, and EPS with capital structure results. The discoveries uncover optimistic relationship of
obligation with productivity as estimated by ROE, ROA, and EPS.

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Nimalathasan in 2009 examined the productivity of Listed Pharmaceutical Companies in
Bangladesh. The pharmaceutical market of Bangladesh especially powerful and focused and was
similar to those of created nations. It involved extraordinary joy that the division effectively
satisfies real part of the nearby request of pharmaceutical items and in the meantime, it was
pushing ahead to investigate the worldwide market for synthetic concoctions and
pharmaceuticals items. The commitment of pharmaceuticals organizations in Bangladesh to the
national economy was empowering. The interest in the part was expanding which talks about the
probability in the division. The division fulfills the request of the nearby market and furthermore
goes for fare to investigate the worldwide market. Directly the enterprises were sending out
prescriptions to in excess of 50 nations of the world.
Gill, Biger, and Mathur in 2011 concentrated the impact of capital structure on gainfulness from
the United States. The connection amid capital structure and benefit could not be ignored on the
changing grounds in the gainfulness, which was essential for the long haul survivability of the
firm. The paper expanded Abor's (2005) discoveries in regards to the impact of capital structure
on gainfulness by looking at the capital structure impact on the productivity of the American
administration and assembling firms.
Velnampy and Niresh in 2012 talked about the connection between capital structure and benefit.
Capital structure choice was the fundamental one since the benefit of an undertaking was
straightforwardly influenced by such choice. The motivation behind the examination was to
research the connection between capital structure and gainfulness often recorded Sri Lankan
banks in the course of the last multiyear time span from 2002 to 2009. The information has been
dissected by utilizing clear measurements and relationship investigation to discover the
relationship between the factors. Consequences of the examination demonstrate that there was a
negative relationship between capital structure and productivity aside from the relationship
between the obligation to value and profit for value. Advance the outcomes propose that 89% of
aggregate resources in the keeping money part of Sri Lanka were spoken to by obligation,
affirming the way that banks were profoundly outfitted organizations.

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De Mesquita and Lara in 2003 inspected the Capital structure and advantage in the Brazilian
case. The confirmation of an association's capital structure constitutes a troublesome decision,
one that incorporates a couple and antagonistic elements, for instance, danger and advantage.
That decision ends up being substantially more troublesome, in times when the financial
condition in which the association works presents an abnormal state of unsteadiness. Thusly, the
choice among the ideal degree of commitment and esteem can impact the estimation of the
association, as much as the landing rates can. In the present examination, the makers attempted
to review the effect of the capital structure of Brazilian associations as for the factor advantage.
El-Sayed Ebaid in 2009 looked into the impact of the capital-structure choice on firm execution:
correct evidence from Egypt. The inspiration driving the paper was to precisely analyze the
impact of capital structure choice on firm execution in Egypt as one of rising or advance
economies. Using three of accounting‐based measures of financial execution, and in light of a
case of non‐financial Egyptian firms from 1997 to 2005 recorded the results that revealed that
the decision of capital structure choice which had a weak‐to‐no impact on affiliation's execution.

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Table 1: Literature Review Journals
Title Journal Objective Variables Outcome
The debt ratio, Bart Canadian
Shareowner
To provide a
detailed
explanation of
debt ratios and
how it can be
used by
investors
Debt issued
and raised
Special
guidelines
provided to
investors for
analyzing the
debt ratio
Bankruptcy prediction
based on the debt
ratio. Daniel Brîndescu-
Olariu
Theoretical
and Applied
Economics
To provide
evidence that if
debt ratios are
analyzed
carefully then
can predict
bankruptcy
Debt ratios,
earnings and
period of
bankruptcy
The method
formulated was
not efficient
enough for
prediction but
provide risk
assessment
Long-term stock returns
after a substantial
increase in the debt
ratio. Huang& Chan
Applied
Financial
Economics
To analyze the
impact of debt
ratio on the
stock market
Announcing
time, stock
price, debt
ratio
Results indicated
that debt ratio, as
well as
management's
way of
presenting,
affects the stock
price
To have a target debt
ratio or not: what
difference does it make?
De Jonga & Verwijmere
Applied
Financial
Economics
To analyze the
tradition of
having a target
debt ratio
Debt ratio,
target debt
ratio
Though target
can help in
leveraging but
cannot help in
long-term
In search of an optimal
debt ratio for economic
growth. Smyth & Yu
Contemporar
y Economic
Policy
To analyze
whether debt
ratio can
improve
economic
growth
Debt ratio,
money stock,
and labor
Positive
relationship
established
Reducing agency
conflicts with target debt
ratios. Pyo, Shin &
Thompson
Journal of
Economics
and Finance
To analyze

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Two Common Problems
in Capital Structure
Research: The Financial-
Debt-To-Asset Ratio and
Issuing Activity Versus
Leverage
Changes. WELCH
International
Review of
Finance
To check
whether non-
financial
information
should be
considered
while
calculating
debt ratio
Debt ratio,
external
equity and
debt to asset
ratio
No evidence
found about the
relationship
Taxes and The Corporate
Sector Debt Ratio: Some
Time Series
Evidence. Rangazas &
Abdullah
Review of
Economics &
Statistics
Effect of the
tax rate on debt
raised by
companies
Debt ratio,
tax rates
Evidence found
regarding the
relationship of
tax rates with a
debt ratio
Determinants of Small
Firm Debt Ratios: An
Analysis of Retail Panel
Data. van der Wijst &
Thurik
Small
Business
Economics
To provide
various
determinants
which affect
debt
management in
small firms
Sales, debt
ratio, factors
determined
ratios
Some variables
affected debt
ratio directly
Optimal debt ratio and
consumption strategies in
the financial crisis. Jin
Journal of
Optimization
Theory and
Applications
To provide
benchmarking
regarding debt
ratios in the
financial crisis
Financial
determinants,
debt ratios
Illustration and
methodologies
provided
Ratio Analysis Uncovers
Fraud. FLEMING
Internal
Auditor
To provide a
quick look at
how ratios can
be used to
analyze a
company’s
position
Debt ratio,
finance ratio,
and
accounting
ratios
The framework
provided which
helped in
understanding the
ratios easily
The Effects of
Profitability Ratios on
debt Ratio: The Sample
of The Bist
Manufacturing Industry.
Korkmaz
Financial
Studies To establish
the relationship
among
profitability
and debt ratios
Profitability
ratios and
debt ratios
EPS and ROA
affect the current
as well as non-
current liabilities
Get debt ratios working
for you, Decker
Credit Union
Magazine
To provide a
framework for
understanding
debt ratios
Debt ratios
and
accounting
performance

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A Tale of Two Debt
Crises: A Stochastic
Optimal Control
Analysis. Stein
Journal of
Economics
To provide the
different level
of
conservatism
bank uses
while
sanctioning
loan
Leverage ratios, industry
norms, and stock price
reaction: An empirical
investigation of stock-
for-debt
transactions. Hull
Financial
Management To check how
the changed
leverage ratio
influences the
share price
Stock price,
Firms which uses
the norms have
an indirect
relationship with
leverage ratios
Optimal capital
structure. Dalal
International
Journal of
Education
and
Management
Studies
To provide
information
regarding the
perfect mix of
debt and equity
Debt, equity,
debt-equity
ratio
At macro level
0.25 and 0.79 at
the micro level
are an optimal
mix
Capital structure, cost of
capital, and voluntary
disclosures Bertomeu, J.,
Beyer & Dye
The
Accounting
Review
To frame a
model which
help both
investors as
well as entities
in determining
various
Corporate scandals and
capital structure. Bonini
& Boraschi
Journal of
Business
Ethics
To unveil the
fact whether
corporate
scandals in the
same sector
influence the
capital
structure of
peer companies
Failed
company’s
capital
structure and
peer
company’s
capital
structure
Failed companies
in the same
sector affect
capital structure
of organizations
Firm size and capital
structure: evidence using
dynamic panel
data. Gonzale&
Gonzalez
Applied
Economics
Applying
trade-off and
pecking theory
to determine
capital
structure as per
the size of
firms
Capital
structure and
investment
decisions of
small,
medium and
large firms
Favorable
response to
investment
opportunities
found in the debt
opportunities for
the company
Examining the theory of
capital structure: signal
Applied
Economics
To test the
existence of
Companies with
symmetric

22
factor hypothesis. Yang,
Chueh & Lee
optimal capital
structure by
using trade-off
and pecking
model
information have
an optimal
capital structure
and vice versa
Critique on the optimal
capital structure and
capital structure
decisions by firms in the
UK. Syed Shabib &
Abdur
Journal of
European
Studies
To apply
Modgillani and
Miller
approach in
real life
companies
Debt equity
ratio and
valuation of
the firm by
Modgilani
and Miller
approach
Hypothesis
proved that value
of firm differs if
debt-equity ratio
differs
Capital structure and
business cycles. Akhtar
Accounting &
Finance
To analyze the
effect of
business cycles
on unobserved
unchanged
capital
component
Leverage,
debt and
equity ratio
and different
business
cycle phases
Model framed for
explaining the
changes in risk
value during
different phases
in business
cycles
Capital Structure and
Debt Structure. Rauh &
Sufi
Review of
Financial
Studies
To analyze the
financial
statements of
public firm's
debt mix
Ways in
which debt is
raised,
capital
structure
The organization
can have more
optimal debt mix
if they raise
money from
different
resources
Optimal Priority
Structure, Capital
Structure, and
Investment. Hackbarth,
& Mauer
Review of
Financial
Studies
To check the
hypothesis
regarding
priority of
raising loan
where leverage
and market
condition are
changed
Growth
opportunities
, leverage at
different
times and
debt mix, the
power of
equity
holders
The relationship
established
between different
level of risks
calculated by
book and market
value figures
Capital structure and
profitability: Panel data
analysis. Das
Sumedha
Journal of
Management
To check how
different mixes
of capital affect
the earning
capacity of the
entities
Profitability,
capital
structure's
debt, and
equity mix
Results stated
that
organizations,
where money
was raised from
outside, are less
profitable than
own investment
Capital Structure and
Corporate
Governance. Naseem,
The Journal
of Developing
Areas
Studying
capital
structures with
Debt mix,
equity mix,
and level of
According to
results brand size
is also a factor in

23
Zhang, Malik & Ramiz-
Ur-Rehman
main theories
named as
agency, trade-
off and pecking
theory
governance
determining the
optimal level of
capital mix
Taxes and Capital
Structure. Faccio & Xu
Journal of
Financial &
Quantitative
Analysis
To analyze the
impact of
changing
corporate and
personal tax
rates on capital
structure
Corporate
and personal
tax rates,
level of
capital on
which
organization
get the tax
deduction
and dividend
policy
Organizations,
where most of
the investors
were individuals,
were affected the
most by changes
in tax rates
Adjustment Cost
Determinants and Target
Capital
Structure. Lambrinoudak
is
Multinational
Finance
Journal
To show the
relationship of
changing
capital mix
with increasing
cost and how it
further affects
the speed of
the process
Transaction
cost, debt-
equity mixes,
and target
debt-equity
mix
No significant
relationship
found between
the variables
International Sourcing
and Capital
Structure. Eun &
Lingling
Review of
Finance
Influence of
international
sourcing on
debt and equity
mix in US
firms
Risk factor,
interest rate
and existing
capital mix
Financial
leverage is
significantly
decreased due to
a loan from
international
resources
Capital Structure and
Debt Priority. Attaoui &
Poncet
Financial
Management
Analyzing
capital
structures of
organizations
which are
subjected to
solvency and
liquidity risks
Recommendation
s made for
determining debt
mix in case of
solvency and
liquidity risks
Capital Structure
Decisions. Dalal
Journal of
Business
Management
& Social
Sciences
Research
To analyze the
variability in
the capital
structure of
various firms
and its impact
The market
value of
Equity, Total
market value,
and Cost of
Capital
Significant
differences can
be seen in the
capital structure
of different
companies. This

24
on the value of
the firm.
is due to the
impact of
qualitative
factors which
vary from the
company as well
as an industry
perspective.
Innovation in the
pharmaceutical industry:
New estimates of R&D
costs. DiMasi,
Grabowski & Hansen
Journal of
Health
Economics
To conduct a
study on
developments
in biologics
and new drugs
to measure
their pre-tax
cost and its
capitalize
results.
R&D cost
estimates
The results
derived from the
sensitivity
analysis and
capitalized cost
of new drugs
shows that the
three has been a
significant
increase in the
capitalized cost
and the pre-
approval cost of
R&D in
comparison to
the previous
studies.
The influence of cash
flow volatility on capital
structure and the use of
debt of different
maturities. Keefe &
Yaghoubi
Journal of
Corporate
Finance
To measure the
quantitative
relationship
between cash
flow volatility
and capital
structure of a
firm.
Mean and
standard
deviation of
cash flow
volatility and
long-term
debt ratio.
The relationship
between cash
flow volatility
and capital
structure is non-
linear. Deviation
of cash flow
volatility by a
single unit cause
a significant
change in the
long-term debt
ratio and the
probability of
holding period of
debt.
New Financing Methods
in the Biopharma
Industry: A Case Study
of Royalty Pharma, Inc.
Lo & Naraharisetti
Social
Science
Research
Network
(SSRN)
To evaluate the
business model
of Royalty
Pharma and
assess the
The business
model of the
company is
efficient in
mitigating the

25
impact of the
capital
structure of the
company in the
Pharmaceutical
industry.
issues faced by
the industry due
to decline in
investment
capital and patent
expiration. The
comprehensive
financial
structure of
Royalty Pharma
including both
debt and equity
capital shall be
effective in
accordance with
the industry
scenario.
Corporate venture capital
and target companies
profiling in the
biopharma industry.
Corea
International
Journal of
Technical
Research and
Applications
To analyze the
changes taking
place in
investments
form Corporate
Venture
Capitalists in
the
pharmaceutical
industry
through the
study of
corporate
ventures in the
past years.
Number of
deals and
associate
investments
The Corporate
Venture
capitalists have
shifted their
investment from
high-risk
ventures to
moderate risk
opportunities.
The increase of
threats in the
pharmaceutical
industry has
enhanced the
complexities of
CVCs in
identifying viable
investment
opportunities.
Financial Ratio Analysis
as a Determinant of
Profitability in Nigerian
Pharmaceutical Industry.
Innocent, Mary &
Matthew
International
Journal of
Business and
Management
Evaluate the
impact of
financial ratios
on the profit
performance
with respect to
Pharmaceutical
firms.
Gross Profit
Margin,
Inventory
turnover
ratio,
Debtors’
turnover
ratio,
Creditors’
Velocity,
Inventory
turnover ratio,
Debtor’s turnover
ratio, Creditors’
Velocity and
Total Assets
turnover ratio are
highly and
negatively
correlated with

26
Total Assets
turnover ratio
the profitability
which makes
them critical for
profitability
analysis.
Global-innovation
strategy modeling of the
biotechnology industry.
Wu
Journal of
Business
Research
To evaluate the
impact of
ownership
structure,
innovation
strategies and
learning cycle
on the forecast
of financial
performance in
the context of
the
biotechnology
industry
It has been
established that
learning
orientation,
innovation
strategies, and
entrepreneurship
structure are
critical factors in
forecasting
performance of
the company.
Selection of
R&D strategies
and their
implementation
is significantly
affected by these
key determinants.
Capital structure for
multinational inter-and
intra-firm innovation
collaborations. Lee, Wu,
& Shen
International
Journal of
Electronic
Business
Management
To study the
influence of
capital
Structure
theories on the
financing
decision
making process
in the context
of
collaborations
between
various firms
for innovation
partnership.
Working
capital,
capital
expenditure,
debt
repayment,
operating
cash flows
The key
determinants for
capital structure
decisions in case
of the Pecking
order model are
operating cash
flows, working
capital, and debt
schedule and
investing
outflows for a
strategic
partnership.
However, static
trade-off model
considers
qualitative
factors such as
growth prospects
and distinctive
features to be the

27
variables critical
for capital
structure
decisions.
Capital structure effects
of international
expansion. Joliet &
Muller
Journal of
Multinational
Financial
Management
To evaluate the
impact of the
expansion of
domestic firms
to a global
level on the
capital
structure of the
firms.
The volume
of
international
transactions
Expansion of
firms to new
markets in new
geographical
areas does not
have a significant
impact on the
capital structure
of the firms.
Diversification of
firms in different
countries does
not influence
their existing
capital structure
as global firms
pursue stable
capital structure.
Impact of capital
structure on the financial
performance of the listed
trading companies in Sri
Lanka. Nirajini & Priya
International
Journal of
Scientific and
Research
Publications
To examine the
financial
reports of
selected listed
companies and
measure the
relationship
between capital
structure and
financial
performance.
Profitability
ratios, debt
assets ratio,
return on
capital
employed,
return on
assets
The study shows
that there exists a
positive
correlation
between the
capital structure
denomination
and performance
of the company.
Variation in
capital structure
influences the
profitability and
solvency ratios of
the company in a
significant
manner.
The Capital Structure
Decisions of New Firms.
Robb & Robinson
The Review of
Financial
Studies
To examine the
capital
structure
bifurcations
used by the
firms in the
initial
Data
retrieved
through
Kauffman
Firm survey
It has been
concluded that
new firms raise a
substantial
portion of their
capital through
external

28
operational
years and
impact of
changes in
markets
scenarios on
the firms.
financing options
rather than from
internal sources.
The firms take
advantage of the
debt leverage
which makes the
changes in
capital markets to
irrelevant to
them.
Capital structure–does
ownership structure
matter? Theory and
Indian evidence. Ganguli
Studies in
Economics
and Finance
To analyze the
mutual
relationship
between the
ownership
criteria and
capital
structure of the
firm and to
study their
interdependenc
e on each
other.
Ownership
structure and
leverage
The research
study shows that
capital structure
is influenced
substantially by
the ownership
criteria, however,
capital structure
does not impact
the ownership
control of the
firms.
Determinants of the
capital structure of small
and medium-sized
Brazilian enterprises.
Forte, Barros &
Nakamura
BAR-
Brazilian
Administratio
n Review
To critically
evaluate the
key elements
of capital
structure for
the small and
medium
enterprise.
Profitability,
growth in
assets and
financial
leverage
The findings
have suggested
that there exists a
negative relation
between
profitability and
leverage whereas
there is a positive
correlation
between assets
growth and
leverage. The
study further
implies that small
firms use debt
financing for
future growth
prospects.
Overconfidence and
optimism: The effect of
national culture on
capital structure.
Research in
International
Business and
Finance
To find out
whether
cultural and
regional
The degree
of
individualis
m in a region
The study
concludes that
managers
possessing

29
Antonczyk & Salzmann
aspects have
any relation to
the changes in
capital
structure.
and capital
structure of
firms
optimistic and
bullish nature
push the capital
structure towards
a riskier
variation.
Capital allocation and
delegation of decision-
making authority within
firms. Graham, Harvey,
and Puri
Journal of
Financial
Economics
To examine the
level of
delegation of
financial
functions by
the executive
level manager
and to
determine the
factors that
affect the
delegation
criteria.
Corporate
policies and
traits of
finance
managers
The analysis
shows that
delegation of
financial
functions by the
managers is
circumstantial
rather than on a
regular basis.
Further,
allocation of
capital is
influenced by the
personal traits of
finance managers
and their
financial
expertise and
estimation.
A panel data analysis of
capital structure
determinants: Empirical
results from Turkish
capital market.
Bayrakdaroglu, Ege &
Yazici
International
Journal of
Economics
and Finance
To examine
whether the
capital
structure
theories
developed
according to
developed
countries
would be
applicable to
the firms in
developing
economies.
Capital
market data
of listed
companies
It is concluded
that debt ratio
standards are not
followed by
Turkish
companies; rather
they follow a
systematic
approach towards
the capital
structure.
Further, pecking
order theory is
satisfied by the
Turkish
companies and
static trade-off
theory is not
relevant for this
economy.
Capital structure choice Journal of The objective Marginal tax The study shows

30
and company taxation: A
meta-study. Feld,
Heckemeyer & Overesch
Banking &
Finance
is to examine
the impact of
corporate
taxation on the
debt portion in
capital
structure with
the use of the
quantitative
technique.
rate, debt to
capital ratio
that taxation
policies
significantly
impact the debt
ratio of the firms
however the
scale of impact
depends on other
factors and
control variables.
The marginal
effect of tax rates
on debt has been
on an average
0.27 on the debt
financing.
Capital structure, free
cash flow, diversification
and firm performance: A
holistic analysis. Park &
Jang
International
Journal of
Hospitality
Management
To study the
relation
between capital
structures, free
cash flow,
expansion and
financial
performance in
a simulated and
comprehensive
manner.
The
individual
relation
between
these factors
The study
concludes that
free cash flows
do not impact
diversification
performance,
rather it is caused
by diversification
effects itself.
However, free
cash flows can be
influenced by
debt leverage to
enhance the
financial
performance of
the company.
The World Experience of
Transformation of
Innovative Approaches
to Assurance of Financial
Stability of Social
Economic Systems.
Dudin, Prokofey,
Fedorova & Frygin
Life Science
Journal
To determine
the measures
for the
financial
stability of
social
economic
systems by
determination
and analysis of
performance
trends.
Performance
trends
Financial
stability and
performance are
substantially
affected by the
external
environment
which can be
developed by a
balanced capital
structure and
counteraction
plan for

31
transforming risk
into the financial
advantage.
Mergers and innovation
in the pharmaceutical
industry. Comanor &
Scherer
Journal of
health
economics
To determine
whether
mergers and
acquisitions in
the
pharmaceutical
industry
declining
innovation.
Merger and
R&D
synergies
It has been
concluded that
the decline in the
industry
performance is
not due to lack of
innovation, rather
it is the
inefficiency of
capital structure
in accordance
with merger
scenarios.
The Effects of Capital
Structure on Profitability
of Listed Firms in
Ghana. Addae, Nyarko-
Baasi & Hughes
European
Journal of
Business and
Management
To determine
the relationship
between capital
structure and
profitability of
companies in
the context of
listed
companies in
Ghana.
Profitability,
short-term
debt, and
long-term
debt.
The study
concludes that
there exists a
strong positive
correlation
between
profitability and
short-term debt
and a strong
negative
correlation with
long-term debt.
However, the
study could not
generalize a
criterion for
optimum capital
structure.
Governance, Ownership
Structure, and
Performance of
Entrepreneurial IPOs in
AIM Companies. Farag,
Mallin & Ow-Yong
Corporate
Governance:
An
International
Review
The aim is to
establish the
relationship
between
corporate
governance,
venture capital
ownership and
the financial
performance in
the context of
UK Alternative
Corporate
governance
characteristic
s
It has been
derived that
venture capital
ownership
enhances
corporate
governance
compliance post
IPO. Further,
venture capital
ownership
imparts

32
Investment
Market.
managerial skills
to the firm which
consequently
improves their
financial
performance.
Funding biotech start-ups
in a post-VC world.
Bains, Wooder &
Guzman
Journal of
Commercial
Biotechnolog
y
To review the
venture capital
investments for
past seven
years and
predict the
future pattern
in the industry.
Angel
investments,
grants,
venture
investments
The research
suggests that
there is the shift
in the firm's
perception from
huge investment
to angel investor
schemes, grants
and new
investment
schemes
implemented by
the government.
The industry is
estimated to
pursue
crowdfunding
schemes in the
future periods.
Drivers influencing the
governance of inter-firm
relationships in the
biopharmaceutical
industry: an empirical
survey in the Italian
context. Billitteri, Lo
Nigro & Perrone
Technology
Analysis &
Strategic
Management
The aim is to
review various
aspects that
influence
governance
forms on the
basis of
relevant kinds
of literature.
Transaction
cost,
property
right theory,
real option
and the
resource-
based view
The research
concludes that
governance
forms are
significantly
impacted by
previous
partnership
arrangements and
product portfolio
of the company.
Capital structure
decisions around the
world: which factors are
reliably important?
Oztekin
Journal of
Financial and
Quantitative
Analysis
To scrutinize
the
international
factors which
influence the
capital
structure
decisions of
companies.
Firm’s
market
capitalization
, inflation,
profitability
The analysis
concludes that
financial
institutions attain
leverage in a
quick manner
whereas
safeguarding of
debt holders by
the government

33
provides higher
leverage.
Trade-off theory,
pecking order theory, and
market timing theory: a
comprehensive review of
capital structure theories.
Ahmadimousaabad,
Bajuri, N., Jahanzeb,
Karami & Rehman
International
Journal of
Management
and
Commerce
Innovations
The paper
examines
various capital
theories and
their
application in
the company’s
strategic
decision-
making
process.
Capital
structure, the
value of the
firm
The study finds
that the two
popular theories
i.e. pecking order
and trade-off
correlated and
have application
in the decision-
making process.
However, the use
of Market
Timing Theory
has gained
recognition by
the companies in
their capital
structure
decisions.
Determinants of capital
structure: the United
Kingdom versus
continental European
countries. Acedo‐
Ramírez & Ruiz‐
Cabestre
Journal of
International
Financial
Management
& Accounting
To critically
examine the
specific
variable of
different
companies and
difference in
the variables
based on the
country.
Country-
specific
determinants
and firm-
specific
determinants
The study shows
that there exists
huge disparity
between the
capital structures
of different
countries due to
the difference in
the financial
systems of each
country. Further,
the system is the
United Kingdom
shows
contrasting traits
from other
European
countries.
The Dynamics of Capital
Structure Decisions.
Antao & Bonfim
Social
Sciences
Research
Network
To review the
process of
companies to
achieve their
target leverage
ratios
Leverage
targets and
duration to
reach the
target
It is concluded
that smaller
companies
achieve their
optimum capital
structure faster
than large
companies due to

34
a rapid shift in
leverage ratios.
Further,
companies with
higher free cash
flows tend to take
longer duration
in reaching their
target ratios as
they have to
increase the debt
ratio.
Capital structure and its
determinants in New
Zealand firms. Wellalage
& Locke
Journal of
Business
Economics
and
Management
To examine the
relationship
between the
approaches of
companies,
corporate
governance
and capital
structure in the
context of
listed
companies in
New Zealand.
Corporate
governance,
capital
structure, and
firm's
strategies
The study
suggests that
capital structure
shall be based
upon specific
firm environment
and requirements
rather than on
corporate
governance.
Further, the
financial
decisions shall be
in line with the
firm’s
requirements and
borrowing
capacities.
Do financial crises alter
the dynamics of
corporate capital
structure? Evidence from
GCC countries. Zeitun,
Temimi & Mimouni
The Quarterly
Review of
Economics
and Finance
To study the
impact of
financial crisis
in 2008 on the
capital
structure of
companies in
Gulf region.
Corporate
leverage,
debt
availability
The analysis
implies
concludes that
the leverage
ratios of the
companies have
been adversely
impacted by the
financial crisis
situation due to
decline in debt
supply in the
financial market.
Determinants of the
capital structure of
Ghanaian firms, Abor, J.
Intellectual
capital and
corporate
capital
structure
unquoted firms
show
fundamentally

35
executives in
Indian
pharmaceutical
industry
higher obligation
proportions than
do SMEs
The relationship between
intellectual capital and
financial performance,
Ahangar, R.G.
African
journal of
business
management
an empirical
investigation in
an Iranian
company
Value Added
Intellectual
Coefficient
Corporate
execution
measures utilized
as a part of the
examination
were productive
The effect of intellectual
capital on firm
performance, Alipour,
M.
Measuring
Business
Excellence
Impact of
scholarly
capital on firm
execution: an
examination of
Iran insurance
agencies
scholarly capital
and its parts have
a huge positive
association with
organizations'
benefit
Determinants of
intellectual capital
reporting: Evidence from
annual reports of
Malaysian listed
companies, Anam
Ousama, A., Fatima,
A.H. and Rashid Hafiz-
Majdi, A
Journal of
Accounting in
Emerging
Economies
inspect the
determinants
(i.e. firm size,
productivity,
use, and sort
of review firm
and industry
write) of
intellectual
capital (IC)
firm size,
productivity
firm size, benefit,
industry write
were
determinants of
IC divulgence
(ICD) in the
yearly reports of
Malaysian
recorded
organizations
The impact of capital
structure on profitability
with special reference to
IT industry in India vs.
Domestic products,
Azhagaiah, R. and
Gavoury, C., 2011
Managing
Global
Transitions
effect of
capital
structure on
benefit with
extraordinary
reference to IT
industry in
India
capital
structure
(CS)
CS influences the
business income
of firms and
what the
interrelationship
was amongst CS
and Profitability
Generic drug policy in
Australia: a community
pharmacy perspective,
Beecroft, G.
Australia and
New Zealand
health policy
discourse,
from a
network drug
store point of
view, on the
arrangement
condition of
the drug store
division in
PBS cost
reserve
funds
so exchanging
terms on
generics were
vital for the
cross-
sponsorship of
other
apportioning
exercises and

36
Australia expert
administrations
Intellectual capital and
corporate performance
in Indian pharmaceutical
industry, Bharathi
Kamath, G
Journal of
Intellectual
Capital
think about the
relationship,
assuming any,
between the
intellectual
capital(IC)
segments, to
be specific
human,
auxiliary, and
physical
capitals with
the customary
measures of
execution of
the
organization,
specifically
benefit,
efficiency and
market
valuation.
intellectual
capital(IC)
Positive
measures of
execution of the
organization,
specifically
benefit,
efficiency and
market
valuation.
Determinants of
intellectual capital
disclosure: evidence
from Australia, Brüggen,
A., Vergauwen, P. and
Dao, M.
Management
decision
analyze
determinants
of the choice
to uncover
intellectual
capital in
yearly reports
firm size,
industry
mark
the paper does
not discover any
connection
between the
level of data
asymmetry and
intellectual
capital exposure
Intellectual capital and
performance of listed
companies: empirical
evidence from Italy,
Celenza, D. and Rossi, F
Measuring
Business
Excellence
the expect to
explore the
connection
between
corporate
executives and
Value Added
Intellectual
Coefficient
Value Added
Intellectual
Coefficient
The results of the
application
stretch the
significance of
VAIC in the
clarification of
the varieties in
MV and its part
as "extra
coefficient" in
the examination
of value

37
execution
Impact of capital
structure on firm's value:
Evidence from
Bangladesh, Chowdhury,
A. and Chowdhury, S.P
Business &
Economic
Horizons
impact of
obligation
value structure
on the
estimation of
offers given
distinctive sizes
impact of
obligation
value
ventures and
development
openings with
the organizations
fused in Dhaka
Stock Exchange
(DSE) and
Chittagong Stock
Exchange (CSE)
of Bangladesh.
Capital structure and
profitability: the Brazilian
case, de Mesquita,
J.M.C. and Lara, J.E
the impact of
the capital
structure of
Brazilian
organizations
with respect to
the factor
benefit
The impact of capital-
structure choice on firm
performance: empirical
evidence from Egypt, El-
Sayed Ebaid, I
The Journal of
Risk Finance
examine the
effect of
capital
structure
decision on
firm execution
in Egypt as one
of rising or
progress
economies
monetary
execution
capital structure
decision choice,
all in all, terms,
has a weak-to-no
effect on
association's
execution
The effect of capital
structure on profitability:
Evidence from the
United States, Gill, A.,
Biger, N. and Mathur, N
International
Journal of
Management
expand Abor's
(2005)
discoveries in
regards to the
impact of
capital
structure on
gainfulness by
looking at the
impact of
capital
structure on
the
productivity of
the American
The assertive
connection
between capital
structure and
benefit

38
administration
and assembling
firms
Impact of capital
structure on the
performance of listed
public sector banks in
India, Goyal, A.M.
International
journal of
business and
management
invention
effect of
capital
structure on
gainfulness of
open division
banks in India
recorded on
national stock
trade amid
2008 to 2012
ROE, ROA,
and EPS
the positive
relationship of
here and now
obligation with
productivity as
estimated by
ROE, ROA, and
EPS
Influence of capital
structure on firm
performance: Evidence
from Bangladesh, Hasan,
M.B., Ahsan, A.M.,
Rahaman, M.A. and
Alam, M.N
International
Journal of
Business and
Management
ROE, ROA,
and EPS
Intellectual capital and
financial performance:
an evaluation of the
Australian financial
sector, Joshi, M., Cahill,
D., Sidhu, J. and Kansal,
M.
Journal of
intellectual
capital
inspect the
connection
between IC
execution and
the money
related
execution of
the monetary
segment
intellectual
capital(IC)
execution
Around 66% of
the example
organizations
have low levels
of intellectual
capital
proficiency
Intellectual capital
disclosure and corporate
governance structure in
UK firms, Li, J., Pike, R.
and Haniffa, R
Accounting
and Business
Research
Intellectual
capital
divulgence was
estimated by a
revelation file
score
intellectual
capital(IC)
execution
intellectual
capital exposure
show a critical
relationship with
all the
administration
factors aside
from part duality

39
1.2 Methodology
This research study aims to critically analyze the capital structure of pharmaceuticals,
biotechnology and life sector within Auscann Group Holdings Ltd, Avita Medical Ltd, Benitec
Biopharma Limited, Biotron Limited and Bioxyne Limited. This research will mainly focus on
secondary data and the information will be extracted from annual reports, yahoo finance with
will be then generated into SPSS to create histograms, ANOVA and regression analysis.
Primary data is generally collected by researchers themselves for the purpose of their research.
The information collected can be in the form of experiments, surveys, questionnaires, interviews,
and observations (Pyo, Shin, and Thompson, 2015). This can then be extended in quantitative or
statistical forms such as charts, graphs, diagrams, and tables. On the other hand, secondary data
uses data that are already been collected by researchers and is ready to use. This is also known as
empirical data. The information would be in the form of annual reports and journal articles.
Qualitative research pertains to identify the insight, outlooks, mental state, and view of the
people on a specific subject (Jin, 2005). In contrast to numerical data used in quantitative
analysis, the qualitative analysis relies solely on textual, audio and visual data to discover themes
and patterns (Hackbarth and Mauer, 2012). For the purpose of this study content analysis from
annual reports, balance sheets and profit and loss statements were chosen as the preferred
method for this research project.
According to Attaoui and Poncet (2013), sampling method generally relates to either probability
or nonprobability. In probability samples, each member of the population has a known non-zero
probability of being selected. In this method, there are random sampling, systematic sampling,
and stratified sampling.

40
Empirical data that is already available is commonly known as secondary data. According to
Decker (1997), secondary data include both textual data that exist without any collecting
activities. In qualitative research, this can be classified as naturally occurring materials since they
are readily available irrespective of the researcher's actions and intentions. In this case within
Auscann Group Holdings Ltd, Avita Medical Ltd, Benitec Biopharma Limited, Biotron Limited
and Bioxyne Limited the information data gathered will be based on the Australian Securities
Exchange, Yahoo Finance, and all the company's annual reports.
According to Yang, Cheng and Lee (2014), there are a number of methods used to collect data
such as secondary data, textual data, and participant observation non- participant observation.
This research will include textual data as a part of this research. Descriptive research methods
describe the situations. They do not make accurate predictions, and they do not determine cause
and effect (Hackbarth and Mauer, 2012). There are three main types of descriptive methods:
observational methods, case-study methods and survey methods. This research will focus on
observational methods and case study methods gathered from a wide range of journal articles.
1.3 Interpretation
The table one shows the results of descriptive statistics from SPSS software. Total data points
studied are 62. The first variable studied shows that on average total liabilities in the sample
were 1629169. The deviation of total liabilities from mean is 1892901. Also, the sample for total
liabilities is positively skewed. The further information from descriptive statistics reveals that the
kurtosis is also positive. The positive skewness and kurtosis show that the frequency polygon is
left-skewed. The distribution of frequency polygon is very important for checking the
normalization in data. The second variable studied here is total equity. On an average, the equity
in the sample is 3800120. And the sample is different from its mean by the value of 5831098.
The skewness and kurtosis further reveal that they are positively skewed. The last information is
regarding total profit earned by the organization. Table one reveals that on an average the
companies selected earned the profit of 1965259. And the deviation of the sample from its
average is 2270521. The different impact in skewness shows that frequency polygon for profit is
negatively skewed. However, the kurtosis for the same is positive.

41
To analyze the nature and shape of frequency polygon histogram is also drawn for the variables.
The histogram for total profit is highly peaked and is right skewed than the normal distribution.
The next histogram for total equity reveals that frequency polygon is not highly peaked and is
also left-skewed. Furthermore, in case of total liabilities, the diagram is left skewed and is also
flatter than a normal distribution.
The value of R when total liabilities are considered as a constant variable is 0.62. However, it
decreased when constant values were changed and equity was also included in it. Analysis of
variance provides information regarding the variances in the data and shows the degree of
freedom for the variable. Results show that degree of freedom in case of total profit as the
dependent variable and total equity as an independent variable is 0.230 and the significance is
0.634. This shows that it is not significant as the value is high than 0.05. The next test shows that
when variables are changed and total liabilities and equity is considered as an independent
variable the degree of freedom is 3.648 and the value of significance is 0.032. These results
indicate that the relationship and degree of freedom are significant in this case.
Furthermore, the t-test is also conducted. The t-test shows when profit is taken as the dependent
variable the value of beta is 0.62.
1.4 Conclusion
In nutshell, capital structure determines the position of the company in the market. The
imbalanced capital structure can harm the goodwill of the company. The results interpreted
above shows that there is a relationship among the profitability and capital structure of the
organizations. This relationship shows that optimum mix of debt and equity can enhance the
earnings of an organization.

42
Chapter 2
RESEARCH PROPOSAL
2.0 Introduction
An organization raises funds from different sources to continue its operations and investing in
new opportunities. The choice of these sources of funds is called capital structure. There are
mainly two categories in which this capital structure is divided named as debt and equity. Equity
refers to that amount which an organization raised by giving ownership to the investors. The
term equity also includes the reserves, surpluses and retained earnings. The second component
debt refers to the amount raised from outside sources and its cost is in terms of interest. Though
equity also has a cost of the dividend it is not an obligation on the company to pay dividends to
its shareholders.
Deciding the optimal level of capital structure is very important for every organization. Excess of
either component can leave an organization in the difficult situation. The optimal level capital
structure is obtained when the cost of raising the funds is the minimum. However, level of capital
mix depends on various factors affecting the industry. The optimal capital mix is not a rigid
amount of capital mix. It provides a range within which the capital structure can be considered as
optimal. So, it can be said that cost of moving from one capital structure to other is also
considered while determining the debt and equity mix (Baker & Wurgler, 2002).

43
2.1 Objective
To explain the concept of optimal capital structure
To define various factors involved in determining the level of optimum mix
To study the level of debt and equity in a particular sector
To compare the debt-equity ratios of companies selected as sample
To provide recommendations regarding the changes in capital structure in order to have the least
cost of capital
2.2 Conceptual framework
The above diagram shows the conceptual framework for the research proposal. There are many
factors which determine the level of the capital mix but for this research proposal, only two
factors will be studied. According to this framework capital structure of an organization depends
on two factors named as debt-equity ratio and cost of capital. Debt mix or ratio defines the
percentage of external funds in the total capital. Both high and low debt-equity ratios are not
good for an organization. Because high ratio shows that organization is not capable of financing
its project and the low ratio indicates that organization is not bold enough to take a risk by
borrowing from the market. Cost of capital is the expense related to the option chose for raising
funds. Debt attracts interest but provides tax deduction because it can be expensed. However,
equity requires dividend which is not an obligation but if paid there is no tax deduction on this
DEBT
(EXTERNAL)
EQUITY
(INTERNAL)
CAPITAL
STRUCTURE
DEBT MIX
COST OF
CAPITAL

44
amount. So, it can be said these two factors determine the capital structure which further defines
the amount of debt and equity. The companies which will be studied under this research proposal
are Auscann Group Holdings Ltd, Avita Medical Ltd, Benitec Biopharma Limited, Biotron
Limited and Bioxyne Limited. These companies are classified under Pharmaceuticals,
Biotechnology & Life Sciences in Australian Stock Exchange (Brigham & Houston, 2004).
2.3 Review of literature
Various authors have provided their point of view regarding the determinants of capital structure.
According to Rangazas, P., & Abdullah, D. (1987) rates of corporate tax prevailing in a country
affects the amount of debt raised by an organization. He found these variables inversely related.
Daniel Brîndescu-Olariu. (2016) also linked the ratios of debt can provide an important
information regarding the liquidity position of the organization. Debt ratios are also studied
because they clearly define the impact of raising external funds. According to Akhtar, S. (2012)
the capital structure also changes according to the business cycle phase prevailing at a particular
time.
The above table provides information regarding various articles studied while conducting the
research. This table provides information regarding the name of the article, author name, journal
name, the objective of the article, variables used and outcome of the research. Different authors
have studied the determinants of capital structure in different ways and came out with different
results. Eun & Lingling (2016) studied the debt ratios after companies can borrow funds from
international resources. According to him, this new way of raising funds has increased the level
of risk because of fluctuations in exchange rates. Dalal, G. (2013) also studied various theories
(Modgilani and Miller, trade-off, pecker and agency theory) related to capital structure and
recommended a framework while determining the capital structure.

45
Chapter 3
Research Methodology
3.0 Introduction
The current chapter is devoted to explaining the research methodology used in the current
research. Research methodology is one of the most important parts of any research as the
answers to the research questions are on the basis of the collected data and the analysis. In this
chapter also all the relevant process about the data collection, data processing, data analysis
techniques and the tools used for the data collection have been discussed. In the first section, the
research method and research design have been discussed. The second section has been devoted
to data collection methods and the data processing techniques and the data analysis techniques
used in the study.
3.1 Research design
The current research is aimed to examine the capital structure of the selected sectors such as the
pharmaceuticals, Biotechnology and the life science sector. For the analysis purpose, the
quantitative analysis will be used. The quantitative analysis focuses more on the statistical,
numerical and the mathematical analysis of the collected data. Other than the quantitative
analysis there are the other research methods such as the qualitative analysis and the mixed
method. In case of the qualitative analysis, the data is in the form of text, audio or video whereas

46
for the quantitative method the data is in form of the numbers and the results can be presented in
the tabular and graphical form. The mixed method is the mixture of both the qualitative and the
quantitative analysis(Nandagopal et al., 2011; VARGAS-HERNÁNDEZ, LEÓN and VALDÉZ,
2011).
3.2 Data Collection
3.2.1 Datatype
For the current research secondary data has been collected from different sources. Since the
research is aimed to examine the capital structure secondary data is more appropriate as
compared to the primary data. The major sources of the secondary data include the annual
reports of the company, published journals, research papers, and newspaper articles.
Time period
The time period taken into consideration for the current research is from 1997 to 2017. So the
time series analysis will be conducted.
3.2.2 Variables included in the dataset
3.2.2.1 Dependent variable:
Debt equity ratio
The dependent variable, in this case, is the debt-equity ratio which is the ratio of the total debt to
total equity. This is one of the most used proxies for capital structure. The debt-equity ratio
indicates how much the firm is dependent on debt and equity for its capital. A high debt-equity
ratio shows that the firm is more dependent on debt for its capital. High dependency on debt may
lead to bankruptcy if they are not able to pay the interest on time and the principal amount at
maturity. On the other hand, the lower debt-equity ratio indicates that the firm has raised more
capital through shares as compared to debt. Higher equity may lead to a conflict of interest
among the shareholders as the voting rights are distributed. Also, there may the problem of
principle agent in case of shareholders. Furthermore, the advantages for the firm such as the tax
benefits cannot be enjoyed by the firm if the fund is raised through equity. So, it is important to
have an optimal capital structure for the firm(Mishra, 2011; Hailu, 2015; Mohammed, Kovan
and Islam, 2017). 710710

47
3.2.2.2 Research questions
Q1. What are the factors affecting the capital structure of the firm?
Q2. What is the capital structure (measured by debt-equity ratio) in different sectors?
Q3. Is there any difference in the capital structure (measured by debt-equity ratio) in
different sectors?
3.2.2.3 Hypothesis
i) There is a significant difference between the capital structures in different industries.
ii) Firm size and capital structure are significantly related.
iii) Profitability and capital structure are significantly related.
iv) Firm and capital structure has a positive impact on the capital structure.
3.33 Data processing and Data analysis techniques
As mentioned in the earlier section also, secondary data has been collected for the analysis
purpose and there are both the advantages and disadvantages of collecting the data through this
method. The major advantage of collecting the data through this method is that the data is easy to
collect and the cost of collecting the data is low. Similarly, the data is verified as the data is
provided by the companies itself. The reliability of the data is higher and there is no specific
requirement to check the reliability again. Furthermore secondary is more appropriate as per the
research questions proposed in the earlier section. Primary data will not be able to provide
enough information for the debt-equity ratio.
On the other hand, there are some disadvantages of using the secondary data. This is because
sometimes the required data set may not be available for free. In this case, the cost of data
collection might be higher. Similarly, the data was collected for some other specific problem so
the data for the required variable may not be available. This will affect the entire research.
Furthermore, there may be some privacy and data security issues related to the secondary data.
3.34 Data pre-processing
Data preprocessing is one of the most important parts of data analysis, especially when the
secondary data is being used. In this case, also the data preprocessing was conducted to remove
the outliers, missing values and other noises in the data set. Firstly the data was imported into

48
excel and identify the missing values in the dataset. The missing value was replaced with the
average values, which is one the most popular techniques for missing values. Once the missing
values have been taken care of then the outliers in the data set were identified in both the
graphics and numerical ways. The outliers were also removed to make the data set the balance.
Once the data was cleaned, the spreadsheet was exported to the analytics tool which has been
used for further analysis.
3.35 Data analysis techniques
There are various techniques which have been used to examine the capital structure of the firms
in different sectors. All the techniques used have been discussed below:
3.3 Descriptive analysis
The descriptive analysis gives the overall summary of the data set. It has been one of the most
popular analysis techniques for those who have little or no statistical knowledge. In this case,
also the descriptive statistics have been performed and results for central tendency such as mean,
median, the mode has been calculated and interpreted. Apart from that other values such as the
SD, variance, minimum and maximum, Skewness and kurtosis have also been tested. The
Skewness and Kurtosis have been calculated to examine if the data is skewed left, right or not at
all skewed. Furthermore, the graphical presentation of the variables has also been shown to
examine the movement of the variables over the period of time.
3.3.1 Chi-square and cross tabulation
One of the objectives of this research is to compare the capital structure of firms in different
sectors. So to examine the same the cross tabulation has been conducted. The results from the
cross tabulation show the debt-equity ratio separately for different sectors. In addition, the chi-
squared test is used to examine whether there is the statistically significant difference in the debt-
equity ratio in different sectors. If the chi-square statistics is significant at 5% (or the p-value is
less than 0.05), it can be said that there is a statistically significant difference. However, if the p-
value for the chi-square statistics is more than 0.05, it can be concluded that the there is no
statistically significant difference in the debt-equity ratio.

49
3.3.2 Inferential analysis
For the inferential analysis following tests have conducted:
3.3.3 Stationary test
Since the time series data has been used for the analysis, it is important to test the stationary of
the dependent variable. It is important to test whether there is a problem of unit root or not. So, to
test the stationary, the Augmented Dickey Fuller test has been conducted. If the data is not
stationary at the level, then a different version of the dependent variables are tested for
stationary. This included the log transformation, adding the lag value, taking the first difference
etc. Without the stationary of the variables, further, the test cannot be conducted.
3.3.4 Normality test and Heteroskedasticity
It is important to test the normality of the residuals; this is one of the major assumptions of the
linear regression model. In this case, the normality has been tested using the Jaque-Bera test.
This test is performed post the regression analysis. Normality ensures that most of the data points
are near its mean value. Similarly, the heteroskedasticity of the residuals has also been tested.
The heteroskedasticity is used to check whether the variances are constant or not. If the variance
is not constant then there is the problem.
3.3.5 Correlation
Correlation analysis is another important inferential analysis, which is used to examine the
relationship between the two variables. The relationship is measured in terms of the correlation
coefficients between the two variables. The range of the correlation coefficient is -1 to +1. If the
correlation coefficient of two variables is close to the lower range i.e. near to -1 then it can be
said that there is a strong negative relationship between the two variables. On the other hand, if
the correlation coefficient is close to the higher range, i.e. near to +1 then it can be said that the
relationship between the two variables is positive and strong. A positive correlation means that if
one variable increase the other variable also increases and vice a vise for the negative correlation.
However, there is the difference between the correlation and causation. Correlation only shows
the relationship, it cannot guarantee the causation.
3.3.6 Regression analysis
To examine the impact of the independent variables on the dependent variable, regression
analysis is conducted. The impact of the independent variable on the dependent variable is

50
measured in terms of the regression coefficients. Positive regression coefficient indicates that the
independent variable has the positive impact on the dependent variable. On the other hand,
negative regression coefficient indicates that the independent variable has a negative impact on
the dependent variable. The magnitude of the impact is also measured by the regression
coefficient. The statistical significance of the impact is measured by the t statistics and the
standard error. P value is calculated using the t-statistic and the standard error. If the p-value is
less than 0.05 or 95 % confidence level, then it can be said that the regression coefficient
has the statistically significant impact. However, p-value more than 0.05 indicates statistically
insignificant impact. The overall fitness of the model is measured in terms of R squared and the
F statistics. R squared shows the variation explained by the independent variables for dependent
variable and the R squared value of more than 0.6 considered to be a good model. However, the
low value of R squared do not necessarily mean the model is bad. Furthermore, the F statistic is
used to test the cumulative impact of the independent variables on the dependent variable. If F
statistic is statistically significant then it can be said that at least one of the regression coefficient
is non-zero(McCarty and Hastak, 2007; Armstrong, 2012; George, Seals and Aban, 2014).
3.4 Limitation of the study
In this research, only the quantitative analysis has been conducted and there is no qualitative
analysis. Furthermore, the sample for the current case is used only between 1997 and 2017.
Apart from that, no causation analysis has been conducted. Finally, there are limitations related
to time and cost related to the research.

51
Chapter 4
Results from Data analysis
4.0 Introduction
The results from the data analysis have been presented in the current chapter. For the analysis
purpose, both the descriptive and the inferential analysis has been conducted. For the descriptive
statistics, various measures of central tendencies such as mean, the median mode have been
calculated. Furthermore, the cross-tabulation has also been presented to examine if there is the
statistically significant difference in the capital structure and the profitability of the companies
included in the dataset. Finally, for the inferential analysis, the correlation and regression
analysis were conducted.
4.1 Descriptive statistics
The results from the descriptive analysis along with the interpretations have been discussed in
the current section. The descriptive statistics is one of the most popular ways to visualize the
collected data and it helps the researchers and the readers both to get an overview of the data
used in the study. The results from the descriptive statistics are shown in the table below.
Results show that the average return on assets (ROA) for the selected companies in the study is
7.30%. This shows that companies are performing well. The ROA shows how much the firms are
earning relative to the total assets of the company. It also evaluates the efficiency of the
management in the company to generate profits from the given assets. In this case, the firms are
efficient in generating the profit as the ROA is higher. Furthermore, the minimum ROA is -4
whereas the maximum is 67. This shows high variation among the companies in terms of ROA.
Similarly, the results for the debt-equity ratio show that the average debt-equity ratio is 0.73,
which suggest the proportion of equity is higher as compared to the debt. This is because the
ratio is calculated by dividing the total debt by total equity. A ratio of higher than 1 shows that
the proportion of the debt is higher whereas the ratio of less than 1 shows that proportion of
equity is higher as compared to the debt. The higher equity means that firms are raising fund
mostly through issuing the shares rather than borrowing from the market. Since firms do not

52
have to pay the fixed return on equity unlike the debt, some companies prefer equity over debt.
However higher equity may lead to the conflict of interest between the shareholders and the
management. On the other hand, the higher debt may lead to insolvency problem if the firm is
not able to pay back the interest regularly and principle at the maturity.
Statistics debt
equity ROA Tobin debt SF
N Valid 72 72 72 72 72
Missing 0 0 0 0 0
Mean 0.73 7.30 1.62 2407.44 2135.79
Median 0.55 5.88 0.98 794.45 1388.65
Mode 0.00 4a .6100a 0.00 0.00
Std. Deviation 0.98 8.29 1.82 3851.16 2110.36
Variance 0.96 68.71 3.31 14831403.15 4453631.32
Skewness 3.21 5.37 2.78 2.01 1.64
Std. Error of
Skewness 0.28 0.28 0.28 0.28 0.28
Kurtosis 12.25 38.65 7.54 3.25 2.06
Std. Error of
Kurtosis 0.56 0.56 0.56 0.56 0.56
Minimum 0.00 -4.00 0.14 0.00 0.00
Maximum 5.64 67.00 9.39 17001.00 9206.00
a. Multiple modes exist. The smallest value is shown
Table 2 Results from the descriptive statistics
Furthermore, the descriptive statistics of the Tobin's Q shows that the average value is 1.61.
Tobin's q is used as the measure of the firms' value. It is calculated by dividing the total market
value of the firm by total asset value of the firm. The value of less than 1 suggests that it takes
the higher cost to replace the assets of the firm when compared to the total worth of the firm. On
the other hand, Tobin's Q of higher than 1 suggests that value of the firm is higher than the total
cost of its assets. In this case, the average value of Tobin's Q is higher than 1 which suggests that
the firms are overvalued.

53
Descriptive statistics of the other variables used in the study are shown in the table below. As the
results show the average current assets (CA) is 2665.49 million with the standard deviation of
3132.42 which indicates higher variation in the dataset. The minimum and the maximum value
of the current assets also confirm the higher variation. The results for the total assets show that
the average total assets are 7750.79 with the standard deviation of 9291.19. Furthermore, the
average current liabilities of the firms included in the data set are 2362.97 with the standard
deviation of 3029. On the basis of these results, it can be said that on an average the current
assets are higher than the current liabilities indicating that the current ratio is higher than 1.
Similarly, the total liabilities results show that on an average company included in the data set
have 5389.66 million debts with standard deviation 7298.57. The minimum and maximum total
liabilities are 64 million and 26483 million respectively.
Statistics CA TA CL TL div MVE Goodwil
l BE
N
Valid 72 72 72 72 72 72 72 72
Missin
g 0 0 0 0 0 0 0 0
Mean 2665.49 7750.79 2362.97 5389.66 -
401.45
11375.
7 2240.23 120.91
Median 981.25 3251.25 748.05 1808.95 -74.85 3985.5 1396.7 364.3
Mode 280a 643a 62a 64a -114a 138a 31a -4292a
Std.
Deviation 3132.43 9291.2 3029.72 7298.58 729.17 17408 3215.17 1506.83
Variance 981211
1
8.6E+0
7
917918
3
5.3E+0
7
53168
8 3E+08 1E+07 227052
2
Skewness 1.271 1.347 1.311 1.392 -2.275 2.253 2.159 -1.128
Std. Error
of
Skewness
0.283 0.283 0.283 0.283 0.283 0.283 0.283 0.283
Kurtosis -0.015 0.158 -0.012 0.329 4.11 4.118 3.282 2.342
Std. Error
of Kurtosis 0.559 0.559 0.559 0.559 0.559 0.559 0.559 0.559
Minimum 142 643 62 64 -2770 138 31 -4292
Maximum 9944 31515 9610 26483 -12 70300 11656 3449
a. Multiple modes exist. The smallest value is shown
Table 3 Results from the descriptive statistics

54
Similarly, the results from the descriptive statistics for other variables such as market value of
equity (MVE), goodwill and book value of the equity has been shown in the above table.
4.2.1 Cross tab and chi-square test
Results from the cross tabulation have been discussed in the current section. The cross-tabulation
and the chi-square test is used to examine whether there is the statistical difference between the
different firm for the debt-equity ratio (a measure of the capital structure of the firm) and the
return on assets (a measure of the profitability of the firm).
Capital structure
Results from the cross tabulation and the chi-square tests have been shown in the table below.
Results from the chi-square show that the value of the Pearson chi-square is 504.00 with 469
degrees of freedom. However, the p-value is more than 0.05. Since the p-value is not statistically
significant at 5 % confidence level the null hypothesis cannot be rejected. In other words, there is
no statistical difference in the capital structure of the selected firm. All the firms have the similar
debt-equity ratio. These results can be due to the selection of the sample, as the firms in similar
industry and economy are more likely to have similar capital structure.
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 504.000a 469 .128
Likelihood Ratio 299.440 469 1.000
Linear-by-Linear
Association
2.662 1 .103
N of Valid Cases 72
a. 544 cells (100.0%) have expected count less than 5. The
minimum expected count is .13.
Table 4 Results from the cross tabulation

55
Symmetric Measures
Value Approx.
Sig.
Nominal by
Nominal
Phi 2.646 .128
Cramer's
V
1.000 .128
N of Valid Cases 72
Table 5 Results from the cross tabulation
Also, the results from the symmetric measures are not statistically significant at 5 % significance
level. So, there is no statistically significant difference in the capital structure of the firms which
are selected for the current research.
4.2.2 Profitability
Similar to the capital structure the cross tabulation and the chi-square test has also been
performed for the profitability. As already discussed the profitability of the firm is measured by
return on assets. The results from the cross tabulation are shown in the table below and the
results show that the value of the Pearson chi-square I 488 with 483 degrees of freedom.
However, the two-tail significance level is not statistically significant. So, on the basis of the
results, it can be concluded that there is no statistically significant difference in the profitability
of the firms included in the model. In other words, the null hypothesis cannot be rejected in favor
of the alternative hypothesis (Antão et al., 2014).

56
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 488.000a 483 .428
Likelihood Ratio 293.894 483 1.000
Linear-by-Linear
Association
3.878 1 .049
N of Valid Cases 72
a. 560 cells (100.0%) have expected count less than 5. The
minimum expected count is .13.
Table 6 Results from the cross tabulation
Symmetric Measures
Value Approx.
Sig.
Nominal by
Nominal
Phi 2.603 .428
Cramer's
V
.984 .428
N of Valid Cases 72
Table 7 Results from the cross tabulation
Furthermore, the results from the symmetric measures show that the phi value of 2.603 which is
not statistically significant. So, from these results also it can be said that the profitability of the
firms is not statistically significantly different.
4.2.3 Inferential analysis
After the descriptive statistics in the previous section, the results from the inferential analysis are
discussed in the current section. In the first part, the results from the correlation analysis have
been shown followed by the regression results in the next part.

57
4.2.4 Correlation analysis
The association of the two variables can be measured in terms of the correlation analysis. The
correlation coefficient lies between -1 and +1. If the coefficient is close to -1 then there is strong
negative correlation whereas correlation close to +1 shows the strong positive relationship. For
the research, the Pearson correlation coefficient has been used and the results from the analysis
are shown in the table below:
Correlations ROA div MVE Goodwill BE Tobin debt
equity
ROA
Pearson
Correlation 1 -0.176 0.231 0.18 -0.112 0.216 -0.08
Sig. (2-
tailed) 0.139 0.051 0.13 0.347 0.069 0.503
N 72 72 72 72 72 72 72
div
Pearson
Correlation
-
0.176 1 -.981*
* -.934** .688** -0.051 -.632**
Sig. (2-
tailed) 0.139 0 0 0 0.672 0
MVE
Pearson
Correlation 0.231 -.981*
* 1 .923** -.680*
* 0.206 .595**
Sig. (2-
tailed) 0.051 0 0 0 0.082 0
Goodwill
Pearson
Correlation 0.18 -.934*
* .923** 1 -.777*
* 0.015 .564**
Sig. (2-
tailed) 0.13 0 0 0 0.903 0
BE
Pearson
Correlation
-
0.112 .688** -.680*
* -.777** 1 -0.016 -.502**
Sig. (2-
tailed) 0.347 0 0 0 0.897 0
Tobin Pearson
Correlation 0.216 -0.051 0.206 0.015 -0.016 1 -0.143

58
Sig. (2-
tailed) 0.069 0.672 0.082 0.903 0.897 0.229
debt
equity
Pearson
Correlation -0.08 -.632*
* .595** .564** -.502*
* -0.143 1
Sig. (2-
tailed) 0.503 0 0 0 0 0.229
**. Correlation is significant at the 0.01 level (2-tailed).
Table 8 Results from the correlation analysis
Results show that the ROA is negatively correlated with the dividend paid, the book value of the
equity and the debt-equity ratio, whereas with other independent variables ROA has positive
correlations. However, none of the correlation coefficients are statistically significant. On the
other hand, the capital structure is negatively correlated with ROA, the dividend paid and book
value of the equity and Tobin's Q. Whereas the capital structure is positively correlated with the
market value of the equity and goodwill. The correlation coefficients of debt-equity with div,
MVE, Goodwill and BE are statistically significant. Correlation among the other variable is
shown in the table above. It should be noted that if the correlation coefficient is positive both the
variables move in the same direction, whereas for the negative correlation the variables move in
the opposite directions. Another important thing to note is that the correlation and causation is
not the same thing. Correlation does not guarantee the causation.
4.2.5 Regression analysis
Since the correlation analysis shows that most of the variable shows the strong correlation with
the main variable of interest, capital structure further analysis can be conducted. In this section,
the results from the regression analysis have been shown. Regression analysis is used to examine
the impact of the independent variables on the dependent variable. Since the main aim of the
research is to analyze the capital structure of the firms, it has been taken as the dependent
variable and various other factors as the independent variable. The independent variables include
return on assets, the dividend paid, the market value of the equity, goodwill, book value of the
equity and Tobin's Q. Results from the regression analysis are discussed below.

59
4.2.6 Pooled OLS
The results from the model summary show that the value of R square is 0.495 which means that
49 % of the total variance in the capital structure is explained by the independent variable
included in the regression model. The remaining variation in the capital structure is due to some
other factors which are not included in the current study. The adjusted R squared is less than the
r squared as the adjusted r square also takes into consideration the relevance of the variable in the
model. In other words, by including more variables one can improve the value of the R square
but not necessarily the adjusted R square.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .703a .495 .448 .72690
a. Predictors: (Constant), Tobin, Goodwill, ROA, BE, div,
MVE
b. Dependent Variable: debt equity
Table 9 Results from the Model summary
Another measure of the goodness of fit of the regression model is the F statistics. In this case, the
F statistics is 10.607 which is statistically significant at 5 % significance level. This indicates that
that cumulative effect of the explanatory variables included in the current regression model is
significant. The F statistics tests the null hypothesis that all the regression coefficients are equal
to zero. In this case, the p-value is less than 0.05, so the null hypothesis can be rejected. In other
words, there is at least on independent variables which are not zero.
Furthermore, since both the R squared and the ANOVA table shows the significant impact the
regression coefficients can be interpreted to examine the impact of the independent variables on
capital structure.

60
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1
Regression 33.628 6 5.605 10.607 .000b
Residual 34.345 65 .528
Total 67.973 71
a. Dependent Variable: debt equity
b. Predictors: (Constant), Tobin, Goodwill, ROA, BE, div, MVE
Table 10 Results from the ANOVA table
Lastly, the results from the regression coefficients are shown in the table below. The regression
coefficients show whether the independent variable has the positive or negative impact and also
the magnitude of the impact. If the regression coefficient is greater than 0 than it can be said that
there is a positive impact. On the other hand, there is a negative impact if the regression
coefficient is negative. The significance of the variable is measured in terms of the p-value
associated with each regression coefficient.
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) .894 .176 5.081 .000
ROA -.021 .011 -.178 -1.887 .064
div .000 .001 -.153 -.202 .841
MVE 5.101E-005 .000 .908 1.116 .268
Goodwill .000 .000 -.576 -1.907 .061
BE .000 .000 -.252 -1.766 .082
Tobin -.159 .083 -.296 -1.906 .061
a. Dependent Variable: debt equity
Table 11 Results from the regression coefficients

61
As shown above, the ROA has negative regression coefficient which means that increasing in
Roa will lead to a decline in the debt-equity ratio. The decline in debt-equity ratio means that
firm will raise funds through the equity. This was expected because with an increase in ROA the
shares of the company become more attractive so people do not hesitate to invest. So, the
proportion of the equity increased as compared to the debt. Similarly, the regression coefficients
of the dividend are close to 0 which indicates that dividend paid have positive but very low
magnitude. In terms of the significance, none of the variables have the statistically significant
impact as the p-value of none of the regression coefficient are less than 0.05. Results from some
of the previous studies also show a positive relationship between ROA and capital
structure(Awan, Rashid, and Zia-ur-Rehman, 2011; Supa Tongkong, 2012; Zabri, 2012;
Mohammed, Kovan and Islam, 2017; Vuran, Tas, and Adiloğlu, 2017). Some scholars have
earlier shown different results than the current study. Also, different researchers have used
different factors for capital structure(Yasin, 2014; Hailu, 2015; Vuran, Tas, and Adiloğlu, 2017).
Figure 1 Histogram for the residuals from the regression analysis

62
The normality of the residual has been examined using the histogram and the results show that
the residuals follow the normal distribution. So, the results from the regression analysis are
robust.
4.3 Summary
This research is aimed to examine the capital structure of the selected firms. Secondary data was
collected from 2007 -2015 for 8 different firms. Descriptive statistics and inferential analysis
(correlation and regression) were performed for the analysis purpose. On the basis of the results,
it can be said that profitability has a positive impact on the capital structure. However, there may
be other factors which affect the capital structure which has not been taken into consideration in
the current study.

63
Chapter 5
Findings & Conclusion
5.0 Introduction
This chapter gives the major findings, conclusion, and recommendations of the main results on
the relationship between the capital structure and the company value. It gives an overview of the
results obtained in chapter four. The data obtained from secondary sources like published
websites, reports etc. are used and the descriptive and the inferential analysis of the data is done.
The rest of the paper includes major findings, conclusions, and recommendations.
5.1 Findings and conclusion
The study tried to create the relationship amid capital structure and the company value for the
Pharmaceuticals, Biotechnology and Life Science sector is critically analyzed. The research
methodology used in this study is the time series analysis. The analysis is conducted for the data
of over a period of ten years i.e., from 1997 to 2017. The quantitative and inferential analysis is
done to understand the factors affecting the capital structure and also the difference in debt-
equity ratio amongst different sectors. The dependent ratio used for the analysis is debt equity
ratio. Capital structure is the mix of the capital raised by the organization. This mix or blend
impacts the general cost of capital. The capital structure is a combination of equity and debt of
the company. The decision on the ratio of equity and debt to the total funds is taken by the
company based on its monetary condition and capability to nurture the funds required. This
decision is important since it impacts the earning per share or the returns to the shareholders. The
correctness of the decision is direct influences the upcoming state of the company. The concept
of capital structure was introduced by Modigliani and Miller (1958). No general theory for
choosing the best debt and equity choice due to its nature (Myers, 2001). Numerous researchers
have worked on capital structures and have given their recommendations to explain the relation
amid equity mix and capital structure. The aim of the study is to bring conclusions on the effect
of debt and equity ratio to returns.

64
 The returns on the assets i.e., the ROA obtained for all companies under study is Results
shows that the average return on assets (ROA) for the selected companies in the study is
7.30. Thus, the companies’ profits are high with respect to the assets of the company.
 By looking at the ROA value, it can be commented that the management style of the
companies is efficient. As the management style strongly influences the company profits.
 The debt-equity ratio is the ratio between debt and the equity of the company. The
average value obtained is 0.73 which indicates that the companies have fewer debts and
more equity. This is a positive outcome of ROA.
 Having high equity than debt is a good sign for the company profits as the liability of the
company decreases.
 The descriptive analysis also included computation of Tobin q’s ratio, this gives an
insight of the company valuation. It is observed that the value is 1.61 this implies that the
companies under the study are overvalued.
 The overvaluation will lead to increase in the completion of the companies, as the
companies are earning at an increased rate. In future, this may lead to lowering of share
prices in future. Thus, the companies should prepare themselves according.
The regression analysis of the data set is also performed. The major findings of the regression
analysis are as follows:
 The ROA and debt-equity ratio are inversely proportional. That is an increase in ROA
will decrease the Debt equity ratio.
 The ROA and capital structure are directly proportional to each other. An increase in
ROA will improve the capital structure of the company.
The findings of data analysis can be summarized by stating that there exists an opposite
relationship in profitability represented by ROA and a capital structure represented by debt to
equity ratio. The results generated to support the hypothesis that firm's returns are related to its
capital structure formulation. However, the hypothesis that there exists a positive relation is
negated by the derived outcomes. It has been found that companies in a sector show similar
results with regard to capital structure. Further, capital structure is influenced by numerous other
factors such as market value of the firm.

65
5.2 Recommendations
The report gave the complete analysis by using statistical tools on the companies selected. The
debt proportion and debt value are proportional of the organization are low proposing that low
measure of obligation in the capital structure. It had diminished the proprietor's reserve and
certainty as to the danger of the value holder's expansion with an increment in advances. Thus,
the ROI of the three companies is approaching one. This implies that the company has good
market value. The study hence accomplishes that there is a significant and constructive
relationship between capital structure and company value of the companies under study. The
second hypothesis also turns out to be true that there is a positive relationship between the size of
the firm and value of the firm. The relationship has been proven in the literature reviewed for the
project. In future, the study can be spanned over companies from different sectors to understand
whether these hypotheses hold for other sectors also. It has been recommended for future studies
that capital structure decisions are also evaluated with respect to other parameters to gain a
comprehensive understanding of the research topic. Capital structure is a pervasive phenomenon
which needs for assertion for performance analysis of the companies as well as the industry as a
whole. Based on the analysis, it is recommended that companies give considerable weight age
towards stabilizing its capital structure. The decisions can be taken in a better manner in a
perspective is built on the relationship between profitability and value of the firm and debt to
equity ratio. Hence, companies are recommended to understand the relevance of various
financial outcomes and relate them to achieve the objective of maximizing firm value and stable
and growing financial scenario.

66
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78
7.0 APPENDIX
Descriptive Analysis
N Rang
e
Mini
mum
Maxi
mum
Sum Mean Std.
Deviat
ion
Variance Skewn
ess
Kurtos
is
Sta
tist
ic
Statis
tic
Stati
stic
Statis
tic
Statist
ic
Statist
ic
Std.
Error
Statisti
c
Statistic Sta
tist
ic
S
td
.
E
rr
o
r
Sta
tist
ic
S
td
.
E
rr
o
r
Tot
al
Lia
bilit
ies
62 7421
404.0
0
1053
.00
7422
457.0
0
10100
8537.
00
16291
69.95
16
24039
8.759
09
18929
01.721
96
35830769
28999.26
0
1.5
21
.3
0
4
1.4
11
.5
9
9
total
equi
ty
62 3723
2771.
00
-
3692
249.
00
3354
0522.
00
23560
7471.
00
38001
20.50
00
74055
0.227
84
58310
98.325
12
34001707
677213.1
50
3.4
69
.3
0
4
13.
96
2
.5
9
9
total
prof
it
62 9479
628.0
0
-
8229
667.
00
1249
961.0
0
-
12184
6105.
00
-
19652
59.75
81
28835
6.482
35
22705
21.212
54
51552665
76607.30
0
-.8
93
.3
0
4
.50
8
.5
9
9

79
TOTAL PROFIT_HISTOGRAM

80
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92
7.0 APPENDIX
Descriptive Analysis
N Rang
e
Mini
mum
Maxi
mum
Sum Mean Std.
Deviat
ion
Variance Skewn
ess
Kurtos
is
Sta
tist
ic
Statis
tic
Stati
stic
Statis
tic
Statist
ic
Statist
ic
Std.
Error
Statisti
c
Statistic Sta
tist
ic
S
td
.
E
rr
o
r
Sta
tist
ic
S
td
.
E
rr
o
r
Tot
al
Lia
bilit
ies
62 7421
404.0
0
1053
.00
7422
457.0
0
10100
8537.
00
16291
69.95
16
24039
8.759
09
18929
01.721
96
35830769
28999.26
0
1.5
21
.3
0
4
1.4
11
.5
9
9
total
equi
ty
62 3723
2771.
00
-
3692
249.
00
3354
0522.
00
23560
7471.
00
38001
20.50
00
74055
0.227
84
58310
98.325
12
34001707
677213.1
50
3.4
69
.3
0
4
13.
96
2
.5
9
9
total
prof
it
62 9479
628.0
0
-
8229
667.
00
1249
961.0
0
-
12184
6105.
00
-
19652
59.75
81
28835
6.482
35
22705
21.212
54
51552665
76607.30
0
-.8
93
.3
0
4
.50
8
.5
9
9

93
TOTAL PROFIT_HISTOGRAM

94
TOTAL EQUITY HISTOGRAM

95
TOTAL LIABILITIES_HISTOGRAM
CORRELATION
Model Beta In t Sig. Partial
Correlation
Collinearity
Statistics
Tolerance
1 total
equity
-.327b -2.654 .010 -.327 .996
a. Dependent Variable: total profit
b. Predictors in the Model: (Constant), Total Liabilities

96
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -
2085915.501
384209.824 -5.429 .000
Total
Liabilities
.074 .155 .062 .479 .634
2
(Constant) -
1643994.943
402293.757 -4.087 .000
Total
Liabilities
.099 .148 .083 .674 .503
total equity -.127 .048 -.327 -2.654 .010
COEFFICIENT TABLE

97
ANALYSIS OF VARIANCE
Model Sum of
Squares
Df Mean Square F Sig.
1
Regression 1198806750
216.625
1 1198806750
216.625
.230 .634b
Residual 3132724544
22828.800
60 5221207573
713.814
Total 3144712611
73045.500
61
2
Regression 3460600052
6743.438
2 1730300026
3371.719
3.648 .032c
Residual 2798652606
46302.060
59 4743478994
005.120
Total 3144712611
73045.500
61
a. Dependent Variable: total profit
b. Predictors: (Constant), Total Liabilities
c. Predictors: (Constant), Total Liabilities, total equity

98
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .062a .004 -.013 2284996.186
81
2 .332b .110 .080 2177952.936
59
a. Predictors: (Constant), Total Liabilities
b. Predictors: (Constant), Total Liabilities, total equity

99
TOTAL LIABILITIES_HISTOGRAM

100
CORRELATION
Model Beta In t Sig. Partial
Correlation
Collinearity
Statistics
Tolerance
1 total
equity
-.327b -2.654 .010 -.327 .996
a. Dependent Variable: total profit

101
b. Predictors in the Model: (Constant), Total Liabilities
COEFFICIENT TABLE
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -
2085915.501
384209.824 -5.429 .000
Total
Liabilities
.074 .155 .062 .479 .634
2
(Constant) -
1643994.943
402293.757 -4.087 .000
Total
Liabilities
.099 .148 .083 .674 .503
total equity -.127 .048 -.327 -2.654 .010

102
ANALYSIS OF VARIANCE
Model Sum of
Squares
Df Mean Square F Sig.
1
Regression 1198806750
216.625
1 1198806750
216.625
.230 .634b
Residual 3132724544
22828.800
60 5221207573
713.814
Total 3144712611
73045.500
61
2
Regression 3460600052
6743.438
2 1730300026
3371.719
3.648 .032c
Residual 2798652606
46302.060
59 4743478994
005.120
Total 3144712611
73045.500
61
a. Dependent Variable: total profit
b. Predictors: (Constant), Total Liabilities
c. Predictors: (Constant), Total Liabilities, total equity

103
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .062a .004 -.013 2284996.186
81
2 .332b .110 .080 2177952.936
59
a. Predictors: (Constant), Total Liabilities
b. Predictors: (Constant), Total Liabilities, total equity