an empirical analysis on the impact of capital structure …€¦ · increases with its debt...

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International Journal of Management (IJM)

Volume 9, Issue 2, March–April 2018, pp. 16–30, Article ID: IJM_09_02_002

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AN EMPIRICAL ANALYSIS ON THE IMPACT

OF CAPITAL STRUCTURE DETRIMENTS ON

PROFITABILITY OF FIRM: A CASE ON LISTED

IT COMPANIES IN INDIA

Dr. Sudhendu Giri

Deputy Dean and Professor of Finance,

Maharshi Law School, Maharshi University of Information and Technology, Noida, India

ABSTRACT

To operate efficiently firms are free to raise equity or debt or any combination of

two to optimally manage the financing of its assets. Because of tax advantage and as a

cheaper source of finance Debt component is very often preferred and given a

significant proportion in determining the capital structure of the firm. In present study

we are mainly focusing on the analysis that whether or not capital structure has any

impact over the profitability of Listed IT companies in Indian market. Through the

present elaborated we are trying to establish the relationship between capstr(capital

structure) and profitability and its effects on business revenue also. For a more

purposeful analysis, selected firms are grouped under three categories on the basis of

two attributes i.e revenue earned by business and firms’ assets size. At the very first

stage, firms are grouped into low, medium and large on the basis of their respective

assets size to test the hypothesis established that capstr has a significant impact on

selected profitability measures of listed IT companies in India. For the purpose of our

analytical study we choose a sample size of 96 IT companies through multi stage

sampling technique for 10 years of data sets ranging from 2007 to 2017 for analysis

purpose. Two dependent variables and four independent variables with one controlled

variable are taken under consideration for analysis using regression. We used

descriptive statistics such as Mean, S.D. and Ratio along with the techniques such as

Pearson Coefficient of Correlation, which is primarily used for testing the relationship

between capstr variables and profitability measures under scope of our study along

the side we also used Regression analysis(OLS Model) to test the unidentified impact

of capstr on profitability variables under scope. The use of correlation tools are

mainly done for the purpose of finding out multicolinearity among independent

variables to decide that what variables can further be tested in our regression models.

The study proves the relationship of capstr variables and profitability variables with

the help of analysis that capital structure has significantly influenced the profitability

of the firms and a substantial increase in debt component in capital structure leads to

a decrease in Net Profit of Listed IT companies in Indian market.

An Empirical Analysis on the Impact of Capital Structure Detriments on Profitability of Firm: A Case on Listed

IT Companies in India


Key words: Capital Structure, Determinant, Profitability, Impact, Ratios.

Cite this Article: Dr. Sudhendu Giri, An Empirical Analysis on the Impact of Capital

Structure Detriments on Profitability of Firm: A Case on Listed IT Companies in

India. International Journal of Management, 9 (2), 2018, pp. 16–30.

http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=9&IType=2

1. INTRODUCTION

The financing pattern of a firm and its associated component cost presents different dynamic

dimensions of the competition between firms and an essential part of firms capital structure

decision to maximize its market value and growth to shareholders‘ investment. Capital

structure strongly deals with the long term mode of finance to generate sound results pertinent

for its value maximization. The debt to equity ratio reflects the two components of capital

structure viz. debt and equity. For our clear understanding there is a difference between

capital structure and finance structure, which besides long term debt also includes short term

debts(Nistor,2004). Investment opportunities have broadly expanded with liberalization and

globalization of world economies and also the investment opportunities and financing options,

and reason being the dependence of Capital Markets witnessed an unprecedented increase.

Every firm requires capital to establish and more of it requires if it wants to grow. The sources

of this capital can be different that suits to the need of the firm. They can use either equity of

debt or any combination of the two in any proportion which yields the maximum value to

shareholders. The issue regarding the best capital structure(debt-equity mix) is the most

perplexing issue in the life of a finance manager. The debt is considered irrelevant in the firms

capital structure if it is based on critical assumption of non existence of corporate taxes. But

corporate taxes do exist and interest of debentures is treated as allowed expenditure which

yields higher returns to a levered firm and the reason one can conclude on a general term that

levered firms are valued more than an unlevered firm because of tax saving on interest paid

on debt and the fact alone makes debt advantageous over equity. The firms‘ market value

increases with its debt proportion in capital structure that makes a levered firm more valuable

over unlevered firms.

The seminal work on the subject by Modigliani and Miller(1958,1963)dicussed the

determinants of capital structure- whether interest on debt is tax deductible or not, was a

pioneering work. If consider the non tax deductibility of Debt-Interest, the firm owners will

be indifferent between the use of equity and debtors as they will yield the same thing to firm.

But on the other hand where tax deductibility is considered, the market value of the firm can

be maximized upto 100 percent by employing 100 percent debt financing. Despite the fact

that Debt Interest is tax deductible, the use of debt component, in capital structure of firms

varies in all firms, which gives rise to a new puzzle that what determines capital structure and

what should be ideal debt ratio for firms. In last couple of years, the demarcation line has been

created between small firms and large firms and recognition that former has different aspects

regarding their capital structure than to later. One thing which is clearly observed in

determining the capityal structure is level of risk attached with debt component, the higher the

debt, the greater the risk and the interest rates but at the same time this increased tax rate also

provides higher tax advantage to the firm in the form of tax saving. But contrarily, if firms do

not have sufficient operating profit in any of coming year, the firm can go up in bankruptcy.

The paper of Modigliani and Miller(1958) founded the approach for modern theory of

capital structure presenting a view that Capital Structure can be irrelevant under what

circumstances and since then many other economist also followed the same view which

Modigliani and Miller followed. Some other recent studies include Taggard(1977),

Dr. Sudhendu Giri


Masulis(1983), Ravid(1988) and Allen(1991), comment by Bhattacharya(1979) on

Miller(1977), Modigliani(1982),Ross(1977) and Stiglitz(1974) and Masulis(1980) are all that

includes general survey studies. Jensen and Meckling(1976) was the initiative in this area

which was build on an earlier research work of Fama and Miller(1972). It has been proven

empirically that profitability of firms in concentrated sector firms are totally different from the

level of firms that are in competitive business.

2. STATEMENT OF PROBLEM, IT’S SIGNIFICANCE AND SCOPE

In the present paper the impact of capital structure on profitability of Indian firms is analyzed.

While determining the profitability of Indian firms two important factors viz. assets size and

business revenue are considered important determinant. In Indian context, only few studies so

far, have analyzed the size and revenue and its analytical impact on profitability of the Indian

firms. The present study is formulated after categorizing the selected Indian IT Firms into

three different categories which are further based on two attributes. After very first, firms are

classifieds on the basis of revenue i.e. total income as low, medium and high income groups

and secondly they are classified on the basis of asset size into small, medium and large to

formulate the hypothetical relationship that capstr has significant impact on the profitability of

selected IT companies listed in Indian Stock Market.

In spite of the fact that many research studies have been undertaken on the subject of

capital structure, there are a few which focuses on relationship between capstr and

profitability. Thus the present attempt is a maiden attempt to analyze the profitability of

selected Indian IT companies and the relationship amongst grouped firms in terms of their

capital structure and their profitability.

The present paper is attempted to conduct an empirical study to the hypothesis that there is

a relationship exist between capstr and profitability.

3. OBJECTIVES AND HYPOTHESIS

The present empirical paper is aimed

To analyze the factors determining capstr of Indian IT firms listed on BSE on the basis of their

respective size and total income groups.

To analyze the interrelationship of firms‘ capital structure and their profitability based on the

two attributes viz. assets size and revenue.

H01

The selected capstr variables and Return on Assets variable of all three different Income

group IT firms viz. Low Income IT firms, Medium Income IT firms and High Income IT

firms do not have any significant relationship.

H02

The selected capstr variables and Return on Assets variable of all three business size IT

firms viz Small size IT firms, Medium size IT firms and Large size IT firms do not have any

significant relationship.

H03

The selected capstr variables and Return on Capital Employed variable of all three

different Income group IT firms viz. Low Income IT firms, Medium Income IT firms and

High Income It firms do not have any significant relationship.

H04

The selected capstr variables and Return on Capital Employed variable of all three

business size IT firms viz. Small size IT firms, Medium size IT firms and Large IT firms do

not have any significant relationship.

H05 The selected capstr variables and Return on Assets variable for overall IT companies do

not have any significant relationship.




H06

The selected capstr variables and Return on Capital Employed for overall IT companies

do not have any significant relationship.

4. LITERATURE REVIEW

A Capital structure exist whenever there is a debt component exist in a firm and therefore it

can be concluded that capital structure(capstr) and debt are variables that are interlinked. The

value of debt cannot be determined easily if we do not have clear idea about the capital

structure(capstr), which in turn is useful to determine the default or bankruptcy levels, even in

turn, the capital structure can not be optimized at all if we do not know the leverage‘s impact

on final debt value. Modigliani and Miller(1958) presents clearly an overview that

admissibility of Interest as an allowed expenditure for the purpose of corporate tax creates a

good incentives to companies to increase the debt levels in their capital structure design.

Titman(1988) changed the implications when he breaks down the total debt into short term,

long term and convertible debt rather taking aggregate debt into considerations. Whereas,

Barton and Godon(1988) presented the view of managerial perspective of determining capital

structure at the determinant level of company analysis. Vogt(1994) used simultaneous

equation set to analyze the debt component the test the pecking order theory(Myers 1984).

Rajon and Zingales(1995) come to conclusion that factors identified in other previous studies

in the US are correlated similarly in other countries as well. Jenson and Meckling(1976) the

optimal capital structure is obtained by trading off the agency cost of debt against the benefit

of debt. In his research, Jenson and Mackling identified disputes between the concerns of

shareholders and managers because of the fact that managements‘ ownership being less than

100% of the equity. Jensen(1986) proposed that this problem could be sorted out by

increasing the management‘s ownership or by reducing the debt component in capital

structure. Gratom(2000) estimated tax advantage to debt component in capital structure.

Stein(2001) concluded that tax advantages increases as firm chooses to increase debt level.

Booth at al(2001) concluded that ratios pertaining to debt in developing countries have the

same implications as they have in developed countries but there are differences in the way

that these ratios are connected by country factors like, GDP growth rates, inflation rates and

capital market developments. Um(2001) stated that the static trade off theory is obtained,

where the net tax advantage of debt financing balances leverage related costs such as

bankruptcy and suggests that a high profit level gives rise to a higher debt capacity and

interest tax shield, therefor, a profitability and financial leverage is positively related. Guney

and Paudyal(2002) concluded that capital structure of companies not only affected by its own

characteristics but by other surrounding environment factors also, such as the state of the

economy, stock market and bank sector. Harris and Raviv (1990) in their research state that

the optimal structure is obtained through a trade-off between liquidation decisions and higher

investigation costs. They concluded that high leverage can be an outcome with large firm

value, lower probability of reorganization following default, and higher debt level. Stulz

(1990) stated that the optimal capital structure can be designed by a trade-off between benefit

of debt and cost of debt. His arguments were based on the fact that managers issue debt only

if they fear a takeover.Diamond (1989), and Hirschleifer and Thakor (1989) in their research

argued that the asset substitution problem (such as using debt to finance high risk projects

instead of equity) could be reduced because of the management‘s reputation being at stake.

While shareholders preferred to maximize an expected return, managers maximized the

possibility of being successful. Diamond (1989) argued that as a firm gets older, it chooses

less risky projects, thereby reducing its defaults which would lead to a lower cost of debt.

This theory suggests that younger firms will have less debt than older ones. The size of

companies is another feature that may influence the companies‘ capital structure. According

to Titman and Wessels (1988), size of companies is positively related to debt. Since then,

Dr. Sudhendu Giri


extensive research work has been developed over this topic and the consensus is that SMEs

are expected to show a positive relationship between size and LTD, but a negative one with

STD (e.g. Michaelas et al., 1999; Hall et al., 2000; Esperança et al., 2003; Vieira and Novo,

2010). Warner (1977) said that a large company has lower transactions costs of financing

externally than a small company, making it harder for the small companies to access debt and

keeping them away from outside financing. In general, large companies follow a strategy of

diversified business, enabling them to have stable earnings reducing the risk of bankruptcy

and contributing to meet their debt obligations (Warner, 1977; Marsh, 1982). Moreover,

SMEs are averse to risk because they are less leveraged and prefer to use more self-financing

(Gallo and Vilaseca, 1996). According to GABA, D., & GARG, R. (2012), the effect of

different company specific variables includes asset composition, business risk, size, debt

service capacity, growth rate and earning rate on the capital structure of selected variables.

Regression results revealed that asset composition, business risk and debt service capacity are

significant factors in context of automobile industry. According to Ashraf, T., & Rasool, S.

(2013), Out of 7 variables only 3 are significant (Size, tangibility and growth). It means that

the firms in automobile sector should keep in mind these factors because these factors

determine the leverage decision in this sector. The remaining three factors (profitability, tax,

risk and NDTS) are insignificant and do not play any role in the determination of leverage in

non-financial firms of automobile sector of Pakistan. According to Ani, M. A., & Amri, M. A.

(2015), higher fixed assets, higher risk and size encourage firms to use the debts in the capital

structure. According to Boateng, A. (2004), Firm characteristics such as size of the JV, type

of the industry have effect on the capital structure. Also the level of ownership of partners of

the JV influences the capital structure of the firm. According to Vicente-Lorente, J. (2001).

Reputational assets technological capabilities & specific human capital affect the firm‘s debt

ratio in spite of the fact that they are expected to have similar implications as intangible assets

and non debt tax shields.

Gorden (1962) observed that with the increase of size, return on investment was

negatively related to debt ratio. He also confirmed the negative association between operating

risk and debt ratio. Baxter (1967) articulated that the degree of degree of financial leverage

would depend on the variance of net operating earnings, since; business with relatively stable

income streams is comparatively least prone to bankruptcy. Hence, a negative association

exists between variance of net operating earnings and degree of financial leverage. A cross

sectional study Gupta (1969), on the financial structure of American Manufacturing

Enterprises during 1961-62 confirmed^ that total debt ratios were positively related to growth

and negatively related . ,to size. Toy et al (1974} found higher level of operating risk is

associated with higher debt ratio and growth, typically measured in terms of sales, is

negatively related to debt ratio while financial leverage is indirectly tied with return on

investment (ROI). Ferri and Jones (1979) investigated the relationship between firm's

financial structure and its industrial class, size, variability of income and operating leverage.

They found that the industry class was linked to the firm's leverage, but not in a direct manner

as was suggested in other researches. Secondly, a firm's use of debt is related to its size.

Finally, operating leverage does influence the percentage of debt in a firm's financial

structure. In the same manner, .Venkatesan (1983) analyzed the relationship between seven

variables; industry categorization, size, operating leverage, debt coverage, cash flow

coverage, business risk, growth ratio and financial structure of firms. It was observed that,

only debt coverage ratio was found to be important variable significantly affecting the

financial structure of the firm. Careltori and Siberman (1997) concluded that higher the

variability in ROI lower will be the degree of financial leverage in firms. Bradley, Jarroll and

Kim (2002} found that debt to asset ratio is negatively related to both volatility of annual




operating earnings and advertising and Research and Development expenses. Mohanty found

that financial leverage is negatively related to profitability and value of the firm within an

industry in Indian context

5. METHODOLOGY

5.1. Data Sources

For the purpose of present study secondary data sources are used. The secondary data is

extracted from CMIE‘s proprietory data base package PROWESS. The listed IT firms on BSE

are divided in three different categories as Los, Medium and High based on Buisness Income

of the firms. The categorization is based on the following premise:

Low Income Group < Rs. 25 Crore

Medium Income Group >= Rs. 25 Crore & < Rs. 100 Crore

High Income Group >= Rs. 100 Crore

Firms are further categorized on the basis of their respective size which is based on their

total assets size. This categorization is done on the following premise:

Small Size Firms < Rs. 25 Crore

Medium Size Firms >= Rs. 25 Crore but < Rs. 100 Crore

Large Size Firms >= Rs. 100 Crore

5.2. Sample Design for Analysis

Total number of IT firms listed in BSE as on 31st March, 2017 were 5315, out of these total

firms 915 firms were listed as IT Sector firms, 836 were listed as software firms and firms

doing business other than software were 107,only 797 firms out of total 836 software firms

were continuously listed in BSE and the reason being only 797 firms were considered for the

study. After considering the data availability and firms listing criteria for the 10 years

period(2007-08 to 2017-18), 113 firms were selected as sample for further analysis. Out of

these 113 firms further adjustment is made in regard to 15 firms which shows extreme

values(outliers) and finally 98 firms were selected by using Multi stage sampling technique.

5.3. Data Analysis

In present study Pearson Coefficient of Correlation is used to analyze whether there is any

significant relationship is there between capstr and Profitability variables along with this

Regression analysis using Ordinary Least Squares Model is carried out to find out the unique

impact of capstr on Profitability measures. Descriptive statistics is also used for setting

relationship with the help of Mean, Standard Deviation and Ratios Analysis.

For the purpose of analysis two dependent variables are taken in consideration i.e. Return

on Assets and Return on Capital Employed as Profitability variables. To study capstr(Capital

structure) paper considers Total Debt to Total Assets and Debt Equity Ratio as proxy to

capital structure(capstr). Expense Ratio and Current Ratio are used as controlled variables in

present study. Below is the Dependent and Independent Variables used through out the paper

for analysis purpose:

1. Dependent (Profitability) Variables

Return on Assets

Return on Capital Employed

2. Independent (Capstr) Variables Total Debt to Total Assets Ratio

Dr. Sudhendu Giri


Expense to Income Ratio

Debt Equity Ratio

Current Ratio

3. Controlled Variables

Expense Ratio

Current Ratio

In furtherance to the above analytical process, correlation analysis is used to check out the

multi collinearity amongst variables to decide what variables should we include in our

regression model or how all independent variables could be used in the regression model.

(Multiple) Regression Model

γe = α + β1exp_inc + β2TD_TA + β3CR + β4DER + ε

Where,

γe = Dependent (Profitability) variables(Return on Assets and Return on Capital Employed)

exp_inc = Expense to Income Ratio

TD_TA = Total Debt to Total Asset Ratio

CR = Current Ratio

α = Intercept of the Model

β1, β2, β3, β4= Coefficient

and, ε = Residual error

The study includes the time period of 10 years ranges from 2007-08 to 2017-18. Since all

the IT firms are not listed continuously, multi stage sampling technique is used for sample

identification.

6. LIMITATION OF THE STUDY

The data is collected from CMIE‘s PROWESS database, which is subject to accuracy and

reliability of secondary data.

Trend identification analysis could not be done due to lack of resources.

From the point of view of sample identification data is restricted to 103 firms.

Firms could not be exclusively identified as Hardware IT firms Software IT firms as Hardware

firms easily get entry in Software domain and outsourcing also loses their domain as

Hardware firms.

Table 1 Regression Results for RoA based on Business Income

Variable Coefficient

(Low Income Group)

Coefficient

(Medium Income Group)

Coefficient

(High Income Group)

Intercept

18.6964*** 113.3397*** 137.5883***

Exp_inc -0.2937*** -0.9198*** -2.0083***

TD_TA -0.0983 -0.3107 0.0032

CR -0.0843 0.3129*** 0.0032

D_E Ratio 0.0983 -2.1041*** -13.9766***




R2 0.3103 0.8923 0.6893

Adjusted R2 0.3007 0.8813 0.6837

F Statistic 27.19*** 143.93*** 98.39***

P-Value(F Statistic) 0.000 0.000 0.000

*** Significant at 1% level

Present empirical study is focused on IT firms listed on BSE. The inferences and results

conclude thereon can be useful for further studies carried on other sector firms for drawing

conclusions and inferences regarding capital structure variables and profitability measures.

Further inferences can be drawn on other firms from different sectors and different

conclusions can be drawn for firms other than IT firms.

Conclusion can be drawn on the basis of firm‘s size whether it has significant relationship or

not for firms‘ other than IT firms.

Studies could also be carried out about to find out whether there is any significant relationship

exist between fixed assets, assets structure, investment and volatility, the probability of

bankruptcy and uniqueness of the product etc. in respect to capital structure and profitability.

7. MAJOR FINDINGS AND ANALYSIS

The major findings from data analysis pertaining to capital structure and profitability are

presented in Table1. The use of debt component in capital structure and Return on Assets of

Low Income group IT firms(β=0.2937, t = -10, p < 0.01); (R2

= 0.3103, F = 27.19, p < 0.01);

31 percent of variation is reported in RoA. Therefor, H1

0 ‗ The capital structure(capstr)

variables and Return on Assets for low income group IT firms has no significant relationship‘

is accepted. But, in case of medium income group IT firms a significant relationship is found

between capstr variables and profitability. A significant but negative relationship is there with

profitability and capstr (R2

= .8923, F = 143.93, p < 0.01). Therefore H1

0 in regard to medium

income group IT firm is rejected. In case of High income class IT firms debt component in

capital structure shows a significant negative impact on profitability by using assets(RoA).(R2

= 0.6893, t = 101.12, p < 0.01), D_E Ratio( β = -13.9766, t = -4.09, p = <0.01) is found

significant statistically. So, Null Hypothesis H1

0 in the case of High Income class IT firms is

rejected.

In order to find out the relationship between capital structure and profitability in respect to

small size firm(on the basis of asset size) the relationship expressed by correlation analysis

shows that exp_inc with RoA and TD_TA with RoA is negatively significant. Only the

individual β coefficient amongst others, the β coefficient of exp_inc(β = -0.3119, t = 11.53 ,

p= < 0.01); β coefficient of TD_TA(β = -0.2193, t = -5.03, p = <0.01) (R2 = 0.4363, F= 31.64,

p <0.01) is significantly negative(see Table 2). Hence H2

0 ‗ The selected capstr variables and

RoA of small size group IT firms do not have any significant relationship‘ is subject to be

rejected.

However, in case of Medium size IT firms, Profitability has inverse relationship by the

use of debt component in capital structure, the β coefficient for exp_inc ratio is with negative

sign(β = -0.1097, t = -6.93, p = <0.01); for TD_TA(β = -0.0473, t = -3.19, p< = 0.01); for CR

the β coefficient is found to be(β = -0.3153, t = -4.03, p = <0.01); and for D_E Ratio(β = -

2.2359, t = -3.03, p = <0.01) are found to be significant. Hence, H2

0 is rejected in respect to

Medium size IT firms. In respect to large size IT firms capital structure tends to reduce the

Dr. Sudhendu Giri


level of Net Profit with an increase in debt component. The RoA has inverse relationship with

exp_inc, TD_TA and D_E Ratio; exp_inc(β = -1.9783, t = -17.79, p = <0.01); TD_TA(β = -

0.0573, t = -22.38, p = <0.01); D_E Ratio(β = -9.8759, t = -3.87, p = <0.01) and ,therefore,

H2

0 is also rejected in case of large size IT firms.

In case of all selected firms the profitability variable RoA is significant negatively with

capstr variables [ exp_inc, TD_TA, CR]. Net Profit(Profitability) relative to Total Asset Size

tends to show an increase in Total Debt where there was an increase in CR. The β coefficient

for exp_inc (β = -0.1783, t = -5.93, p < 0.01) and for CR (β = -0.2153, t = -2.95, p <0.01) are

significant negatively(see table 2). Hence H05 that ‗The selected capstr variables and Return

on Assets for overall IT firms do not have any significant relationship‘ is rejected.

Table 2 Regression Analysis for Return on Assets Based on Firms‘ Size


(Small Size Firms)

Coefficient

(Medium Size Firms)

Coefficient

(Large Size Firms)

Coefficient

(Overall Firms)

Intercept 29.5493*** 25.1548*** 113.1223*** 35.9378***

Exp_Inc -0.3119*** -0.1097*** -1.9783 -0.3179***

TD_TA -0.2193*** -0.0973*** -0.0573 -0.1783***

CR 0.0418 -0.3153** 0.3150 -0.2153***

D_E Ratio -0.0617 -3.2359** -9.8759** -0.3771

R2 0.4363 0.2153 0.6739 0.3183

Adjusted R2 0.4256 0.2031 0.6613 0.3097

F Statistic 31.64*** 18.19*** 103.53*** 61.97***

P Value

(F Statistic)

0.0000 0.0000 0.0000 0.0000

**Significant at 5% level, ***Significant at 1%level.

If we look at Table 3 than it can be found that selected capstr variables and RoCE of low

Income IT firms do not have any significant raltionship. Exp_inc(β = -0.1083, t = 11.93, p

<0.01); TD_TA (β = -0.0219, t = 17.93, p <0.01); CR(β = -0.0217, t = -19.29, p<0.01); D_E

Ratio(β = 0.2138, t = 2, p<0.01). Profitability in respect to Return on Capital Employed has

negatively and significantly impacted by expenditure, TD_TA, and D_E Ratio(see table 3);

therefore H03 ‗ The selected capstr variables and Return on Capital Employed for Low

Income IT firms do not have any significant relationship‘ is accepted. The fit of regression is

found to be good as (F=25.17 at 1% level of significance) where as the value of R2 is very

low(0.2317) supporting the acceptance of H03

.

But, in case of Medium Income IT girms, the paper witnessed a significant relationship

between capstr variables and Return on Capital Employed(R2=0.5851, F= 52.37,p<0.01). The

negative sign used in case of exp_inc, TD_TA, CR and D_E Ratio indicates that debt

component in capital structure is subject to reduce the Net Profit component of the firms in

this group. Therefore, H03 with respect to Medium Income Firm is subject to be rejected.

Table 3 Regression Analysis for RoCE Based on Income Group


(Low Income Group)

Coefficient

(Medium Income Group)

Coefficient

(High Income Group)

Intercept 11.5836*** 81.7915*** 54.5927***

Exp_inc -0.1083*** -0.6789*** -0.4183***

TD_TA -0.0219 -0.5773*** -0.2637***

CR -0.0217 -0.5389*** -0.9471***

D_E Ratio 0.2138 -3.3575*** 7.1739

R2 0.2317 0.5851 0.1779

Adjusted R2 0.2239 0.5709 0.1613




F- Statistic 25.17*** 52.37*** 14.81***

P Value

(F-Statistic)

0.0000 0.0000 0.0000

****Significant at 1% level

Table 4 Correlation Matrix Results

Variables RoA RoCE Exp_inc TD_TA CR D_E Ratio

RoA 1.0000

RoCE 0.8372*** 1.0000

Exp_inc -0.5335*** -0.4759** 1.0000

TD_TA -0.2737*** -0.2993*** 0.0836 1.0000

CR -0.2197*** -0.1983*** 0.2004** -0.1037** 1.0000

D_E Ratio -0.0971 -0.0530 -0.0218 0.0913** -0.1149 1.0000

**Significant at 5%level, ***Significant at 1% level

Through examining the table 3 we found a significant relationship between debt

component in capital structure and Return on Capital Employed in the group of High Income

IT firms(R2= 0.1779, F= 14.81,p<0.01) and the reason being H0

3 is subject to be rejected in

High Income group IT firm.

Table 5 Regression Analysis for RoCE Based on Firms‘Size

Variables Coefficient

(Small Size Firms)

Coefficient

(Medium Size

Firm)

Coefficient

(Large Size

Firm)

Coefficient

(Overall Firm)

Intercept 29.3179*** 23.1873*** 66.2973**** 28.4539***

Exp_inc -0.2193*** -0.0863*** -0.5817*** -0.2347***

TD_TA -0.4161*** -0.0693*** -0.2739*** -0.1193***

CR -0.0397*** -0.3307*** -0.6357*** -0.2301***

D_E Ratio 0.2763 -3.7438*** 6.9753* -0.1953

R2 0.4167 0.1639 0.3397 0.1937

Adjusted R2 0.4053 0.1527 0.3263 0.1813

F Statistic 35.71*** 14.73*** 32.07*** 47.93***

P Value

(F-Statistic)

0.0000 0.0000 0.0000 0.00000

*Significant at 10%level, **Significant at 5%level, ***Significant at 1%level.

Considering the small size IT firms we found that profitability has affected inversely with

the use of debt component in capital structure. Return of Capital Employed(RoCE) is found

significant(R2 = 0.4167) with F Value of 35.71(p<0.01); exp_inc(β = -0.2193, t = -7.39,

p<0.01); analysis found an increase in Total Debt proportionate to Total Assets Size(β = -

0.4161, t = -8.97, p<0.01). The use of debt component in overall capital structure negatively

affected the profitability of small size IT firms(see table 5). Therefore, H04, ‗The selected

capstr variables and Return on Capital Employed for small size IT firms do not have any

significant relationship‘ is subject to be rejected.

In case of Medium size IT firms net earnings reduced significantly with an increase in

debt component in capital structure. Exp_inc(β = -0.0863, t = -5.79, p<0.01); TD_TA(β = -

0.0693, t = -3.43, p<0.01); CR(β = -0.3307, t = -4.78, p<0.01); D_E Ratio(β = -3.7438, t = -

4.31, p<0.01 is significant negatively at 1% significant level. Therefore H04 in respect to

medium size IT firms is subject to be rejected.

Dr. Sudhendu Giri


If we consider Large Size IT firms then the use of debt component in capital structure is

less profitable. The Regression result for Return on Capital Employed for Large Size IT

firms(R2 = 0.3397, F = 32.07, p<0.01) are significant negatively. It is found during study

period that the use of debt component in capital structure is less profitable. Hence, H04 with

respect to large size IT firms is subject to be rejected. The profitability measure checked in

terms of RoCE is subject to decline with an increase in TE,TD, CAS and CLS and β

coefficient is found significantly negative except for D_E Ratio. Return on Capital

Employed(RoCE) with exp_inc(β = -0.2347, t = -12.17, p<0.01); for TD_TA(β = -0.1193, t =

-6.79, p<0.01); for CR(β = -0.2301, t = 4.75, p<0.01) are found significantly negative(see

table 5). Thus it is concluded that capital structure has a significant impact on IT firms in

India. Therefore, Ho6 ‗The selected capstr variables and Return on Capital Employed for

overall IT companies do not have significant relationship‘ is subject to be rejected.

8. CONCLUSIONS

In present empirical study we considered two variables as profitability control variables i.e.

RoA and RoCE and as a proxy to capstr Total Assets to Total Debt ratio and Debt Equity

Ratio(D_E Ratio) are considered. For the purpose of empirical study, Pearson‘s coefficient of

correlation and Regression (OLS Model) analysis is used along with descriptive statistics i.e.

mean, standard deviation through out the paper. For analytical purposes sample data is further

categorized on the basis of business income into three categories viz. High income, medium

income and low income IT firms and another classification is made on the basis of assets size

of the firms viz. Large size, medium size and Low size IT firms. Then statistical tools are

applied to the data set for analysis and inferences are drawn from them. The profitability and

the use of debt in the capital structure over different variables under scope for profitability are

studied and analyzed for all two different attributes and conclusions are drawn thereon.

The present study proves, considering the data on the basis on business income, Low

income IT firms shows highly profitable outcome with low expenses but profitability of these

groups of firms is totally independent of the debt component in capital structure. Hence,

profitability in terms of RoCE is inversely impacted by exp_inc and is free from any affect of

capital structure of low income IT firms. Medium income IT firms shows good results by

generating good profit with low level of debt. The capital structure of medium income IT

firms shows a significant impact on profitability and the use of debt component in capital

structure shows a substantial decrease in net income of these firms. Better management of

capital structure is seen in case of high income IT firms that most of revenue is increased with

the use of debt portion in capital structure.

If size of business is taken into account, it can be concluded that the small size IT firms

did not show any favorable performance in generating revenue. The concerned profitability

variables are inversely affected by the increase in expenses and increase in Total Debt

proportionate to Total Asset Size and capital structure shows a significant impact over

profitability variables taken into account for analysis for small size IT firms and a remarkable

negative influence of expenses on profitability is found. In a gist it can be inferred through

overall regression analysis results that profitability measured by Return on Capital

Employed(RoCE) is negatively(significantly) affected by use of debt component in capstr for

small size IT firms. In case of Medium size IT firms the present empirical study proves that

the net income stood at 10 percent to their Total Asset Size and capital employed and debt

component is lesser in capstr in Medium size IT firms. Hence, in case of Medium size IT

firms profitability measured through RoA and RoCE is inversely affected by the use debt

component in capstr and net income is subject to decrease with an increase in debt component

in capstr significantly. As far as Large size IT firms are subject to analysis, the results reveals




that the large size IT firms never relied on debt component in their capstr and yielded better

results even without employing debt in capstr. Further to analysis, the increase in the use of

debt component in their capstr reduced the profitability scaled by Total Asset Size for large

size IT firms and less profitability is recorded with the use of debt component during the

study period.

Therefore, it can be concluded that there has been a strong one to one relationship

between capstr variables and profitability and capstr variables has a significant impact on

profitability variables and an increase in debt component in capstr significantly reduced the

profitability of IT firms listed in Bombay Stock Exchange(BSE).

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