Determinents of capital structure – a study with special reference to pharma industry

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Emperor International Journal of Finance and Management Research [EIJFMR] ISSN: 2395-5929

Emperor International Journal of Finance and Management Research [EIJFMR] ISSN: 2395-5929 Page 38

DETERMINENTS OF CAPITAL STRUCTURE – A STUDY WITH

SPECIAL REFERENCE TO PHARMA INDUSTRY

Mr. SELVABASKAR

Assistant Professor of Business Administration

Sri Sankara Arts and Science College

Enathur, Kanchipuram

Introduction of Capital Structure

Capital structure refers to a company’s

outstanding debt and equity. It allows a

firm to understand what kind of funding

the company uses to finance its overall

activities and growth. In other words, it

shows the proportions of senior debt,

subordinated debt and equity (common or

preferred) in the funding. The purpose of

capital structure is to provide an overview

of the level of the company’s risk. As a

rule of thumb, the higher the proportion of

debt financing a company has, the higher

its exposure to risk will be.

A company's proportion of short and

long-term debt is considered when analyzing

capital structure. When people refer to

capital structure they are most likely

referring to a firm's debt-to-equity ratio,

which provides insight into how risky a

company is. Usually a company more

heavily financed by debt poses greater

risk, as this firm is relatively highly

levered.

Capital structure is the mix of the

long-term sources of funds used by a firm. It is

made up of debt and equity securities and

refers to permanent financing of a firm. It

is composed of long-term debt, preference

share capital and shareholders’ funds.

Various authors have defined capital

structure in different ways.

Profile of the pharmaceutical industry

The number of purely Indian Pharma

companies is fairly low. Indian Pharma

industry is mainly operated as well as

controlled by dominant foreign companies

having subsidiaries in India due to

availability of cheap labour in India at

lowest cost. In 2002, over 20,000

registered drug manufacturers in India sold

$9 billion worth of formulations and bulk

drugs. 85% of these formulations were

sold in India while over 60% of the bulk

drugs were exported, mostly to the United

States and Russia. Most of the players in

the market are small-to-medium

enterprises; 250 of the largest companies

control 70% of the Indian market. Thanks

to the 1970 Patent Act, multinationals

represent only 35% of the market, down

from 70% thirty years ago.

Most Pharma companies operating in

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Indians almost exclusively from the lowest

ranks to high level management. Home

grown pharmaceuticals, like many other

businesses in India, are often a mix of

public and private enterprise.

In terms of the global market, India

currently holds a modest 1–2% share, but

it has been growing at approximately 10%

per year. India gained its foothold on the

global scene with its innovatively

engineered generic drugs and active

pharmaceutical ingredients (API), and it is

now seeking to become a major player in

outsourced clinical research as well as

contract manufacturing and research.

There are 74 US FDA-approved

manufacturing facilities in India, more

than in any other country outside the U.S,

and in 2005, almost 20% of all

Abbreviated New Drug Applications

(ANDA) to the FDA are expected to be

filed by Indian companies.

 To identity the key ratios influencing

the capital structure.

 To examine the selected ratios across

the selected companies

 To test the relationship among the selected ratios.

 To find the impact of key determinants

on capital structure.

Growths in other fields notwithstanding,

generics are still a large part of the picture.

London research company Global Insight

estimates that India’s share of the global

generics market will have risen from 4% to

33% by 2007. The Indian pharmaceutical

industry has become the third largest

producer in the world and is poised to

grow into an industry of $20 billion in

2015 from the current turnover of $12

billion.

Objectives of the study

The objectives are geared towards the

following:

Statement of problem

An important questions facing companies

in need of new finance is whether to raise

debt or equity. As for as capital structure

theories are concern they bring forth

common ideology in terms of profitability

and capital structure. As and when the

debt portion is more in the capital structure

subject to the condition of existence of tax,

the debt act as tax shield, thereby there is a

chance for increasing return on equity. On

the basis of above fact this projects makes

an attempt in identifying the relationship

between the key determinants like non

debt tax shield, profitability, asset

structure, liquidity and debt ratios have an

impact on leverages.

Limitations

 Sun Pharma is the only large cap

company available for study in that

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 Firm like Biocon which is into bio

pharmaceuticals is compared against

other regular drug makers.

 Also all firms do not have any

similarity in type of drugs they make

or markets they operate.

 Limited number of firms under study

can be a hindrance in external validity.

Research methodology

Research Methods more usually refers

primarily to the process you use to

investigate the subject(s) that are the focus

of your research interest. Research

methods include formulating research

questions, sampling (random,

opportunistic), measurement (surveys,

scales, qualitative measures etc), research

design (experimental and

quasi-experimental), data analysis of the data,

and describing your findings.

Review of literature

Matthews (1994) analyzed that the capital

structure theories grounded in the finance

paradigm (agency theory, transaction cost

theory) have contributed to the

understanding of capital structure decision

making. However, they do not address the

intricacies of capital structure decision

making from a managerial choice

perspective, especially in privately held

firms. This article brings together research

from strategic management, decision

sciences, and social psychology to develop

a conceptual model for understanding

capital structure decision making in

privately held firms. In general, it is

posited that capital structure decisions are

influenced by the firm owner's attitude

toward debt as moderated by external

environmental conditions.

Ronoowah (1995) examined that the

empirical research on capital structure has

largely been confined to the US and other

developed countries with very few studies

for developing economies. This paper

attempts to supplement the existing

literature by bringing new evidences on the

determinants of capital structure for the

case of companies listed on the Stock

Exchange of the Small Island Developing

State (SIDS) of Mauritius. Results from

the study reveal that certain firm specific

factors which explain capital structure in

developed countries are also relevant in a

small island economy like Mauritius.

Using panel estimations techniques for the

38 firms of the Stock Exchange of

Mauritius (SEM) for the period

1994-2004, the regression results show that the

most important firm specific factors that

influence capital structure choice in

Mauritius are profitability, size, tangibility

and liquidity.

Pathak (1997) examined the relative

importance of six factors in the capital

structure decisions of publicly traded

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emerging economies usually group several

countries together. The paper utilizes a

larger data set of 135 firms in the period of

1990-2009 listed on the Bombay Stock

Exchange (BSE) in comparison to the

earlier studies on India and examines

additional factors. The paper found that

factors such as tangibility of assets,

growth, firm size, business risk, liquidity,

and profitability have significant

influences on the leverage structure chosen

by firms in the Indian context.

Titman (2007) explored cross-sectional as

well as time-series variation in debt ratios.

The author paid particular attention to

interactions between financial distress

costs and debt holder/equity holder agency

problems and examine how the ability to

dynamically adjust the debt ratio affects

the deviation of actual debt ratios from

their targets. Regressions estimated on

simulated data generated by the model are

roughly consistent with actual regressions

estimated in the empirical literature.

Data analysis and interpretation

In this section, we discuss the variables

that affect the steel company’s debt ratio.

These variables are drawn from the earlier

theories and empirical researches. These

variables are discussed below

Interpretation

The above Table shows the Descriptive Statistics of Non Debt Tax Shield With respect to

Sun Pharma ARC Company during the period of 2010-14. The table exhibits the Mean value

of ratio stands at 0.675. The standard deviation is found to be 1.26. From the table the

Coefficient of variation is calculated to find the consistency of maintaining the Non Debt Tax

Shield of Sun pharma. Since the Co-efficient of variance is 0.535 it can be inferred that the

company exhibits more variation in maintaining Depreciation.

Variables Entered/Removedb

Model Variables Entered Variables Removed Method

1 profitablitya . Enter

2 Liquidity, Non-debt Tax

shield = Depreciation / Total Assets, Asset Structurea

. Enter

a. All requested variables entered.

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Coefficients

Model Unstandardized Coefficients

Standardized

Coefficients _T _Sig.

B Std. Error Beta

1 (Constant) .445 .031 14.120 .000

Profitability -.933 .005 -.999 -197.596 .000

2

(Constant) .243 .100 2.433 .017

Profitability -.966 .055 -1.034 -17.438 .000

Non-debt Tax shield =

Depreciation / Total Assets 5.666 2.677 .264 2.117 .037

Asset Structure -.314 .135 -.297 -2.336 .022

Liquidity .079 .034 .012 2.339 .021

a. Dependent Variable: Debt Ratio

Inference

After studying the correlation matrix we proceed with multiple regression method. Multiple

regression is used here to describe the dependence of debt ratio on the independent variables.

All the eight variables discussed in the previous section were taken as independent variable

for this study and the model is represented below.

Debtrat =  + 1 ndts + 2 profitab + 3 asststr + 4 liquidt + e

Where Debtrat = Debt ratio, ndts = non debt tax shield, profitab = profitability, Liquidit =

Liquidity, asststr = asset structure

Regression Analysis

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the Estimate

Change Statistics

R Square

Change F Change df1 df2 Sig. F Change

1 .999a .997 .997 .31409 .997 3.904E4 1 98 .000

2 .999b _.998 _.998 _.29883 _.000 _4.422 ₃ ₉₅ _.006

a. Predictors: (Constant), profitability

Model Sum of Squares df Mean Square F Sig.

1

Regression 3851.771 1 3851.771 3.904E4 .000a

Residual 9.668 98 .099

Total 3861.439 99

2

Regression 3852.956 4 963.239 1.079E4 .000b

Residual 8.483 95 .089

Total 3861.439 99

a. Predictors: (Constant), profitablity

b. Predictors: (Constant), profitablity, Liquidity, Non-debt Tax shield = Depreciation / Total Assets, Asset Structure

c. Dependent Variable: Debt Ratio

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All the above variables are based upon the

time series average over a period of five

years. This study is a cross section

regression analysis using time series

average data and this approach is preferred

over panel regression analysis. The results

of the regression analysis are shown

below.

From the above table, it can be seen that

the R square is .98, which means that

overall 18% of the variation happened in

the debt ratio is explained by the

independent variables. The F value of

1.079E4 and P value of 0.000 in the

ANOVA table says that the model is

statistically significant. Hence overall, it

can be said that the independent variables

that are used for this model is explaining

the dependent variable significantly and

the model is fit. If we look into the values

of regression coefficients then it is seen

here that independent variables like non

debt tax shield, asset structure, liquidity,

profitability are significant at 5% and

profitability is sharing a positive sign and

risk and growth is sharing a negative sign

with debt ratio as expected by the trade off

theory.

We can infer from the result that these

pharma companies’ debt portion is

negatively influenced by the profitability

and asset structure. Whereas non debt tax

shield and liquidity are positively

influenced by debt ratio. Because debt

portion of a firm can absorb the interest

cost so that the profit decreases.

Findings

Here stepwise multiple regressions are

used to get the best combination of the

independent variables that can predict the

dependent variable significantly. In

stepwise regression dependent variables

are entered into the equation one at a time

and each time the weak dependent variable

will be removed from the equation. Only

those variables are included that are really

significant to the equation.

It is found that these four variables in the

last model explain 99% of variability in

the debt ratio of pharmaceutical company.

From the ANOVA table it can be found

that all the models are statistically fit as all

the models are having p value of 0.000

respectively.

Conclusion

This paper is an experiment to analyze the

capital structure and its determinant of the

Indian steel companies with the help of the

correlation analysis, multiple regression

analysis and stepwise regression analysis.

It was found that the pharma companies

that are taken as sample are debt driven

means relying more on debt. So here we

tried to find out which factors that are

significantly affecting the debt ratio.

Hence Four explanatory variables are

taken into consideration and we found that

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shield, asset structure, liquidity

profitability are playing a significant role

in explaining the debt ratio of the Indian

pharma companies.

Hence pharma companies need to reduce

their cost or to improve their efficiency

level in order to increase their profitability.

Another important factor that companies

need to reduce is the risk factor, if the risk

factor will be high then automatically

lenders will move away and may not be

willing to put their money is risky

business. If we consider the signs of the

regression coefficients then we found that

these signs are following the trend of trade

off theory and not so much of pecking

order theory. However this study has

some limitations like here the size of the

sample is limited to only 20 companies

and limited to only one sector. Hence more

sectors can be taken and a cross sectional

study can be done.

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