Capital strucure and its impact on financial performance of indian steel industry

Capital Strucure and Its Impact on Financial Performance Of Indian Steel Industry, Ata Takeh , Dr. Jubiliy
Navaprabha, Journal Impact Factor (2015): 7.9270 Calculated By Gisi (www.jifactor.com)
www.iaeme.com/ijm.asp 29 editor@iaeme.com
1
Ph.D Scholar in Commerce, University of Kerala,
2
Associate Prof., P.G. Dept. of Commerce, S.D. College, Alappuzha,
ABSTRACT
Capital structure is one of the most important area of financial decision making. In this study we
attempt to examine capital structure and its impact on financial performance of selected Indian steel
Industry during 2007 to 2012. Multiple regression model, correlation matrix, ANOVA and descriptive
statistics are performed for the study. We computed OPM, ROA, ROE and ROCE as a indicator of
financial performance (dependent variables) and capital structure (TDER, TADR, ICR and FDR) as a
independent variables. The result of multiple regression and ANOVA indicated that there is a significant
impact of capital structure on financial performance of Indian steel Industry. Correlation results
confirmed that there is negative relationship between capital structure and financial performance. The
result of the study may guide creditors, companies and policy makers to formulate better policy decision.
Keyword: Capital Structure, Financial Performance, Profitability, Steel, Indian Steel Industry.
INTRODUCTION
Steel is very important for development of any economy in the present day. It is a foundation of
human civilization. The level of per capita consumption of steel in any country is considered as a critical
key factor for development of socio-economic and standard of living of the people. Indian steel industry
is important for growth of the country's economic. It is play significant role in traditional sectors, such as
transportation , constructions, automobile, industrial applications etc.
Effective management of finance is vital for the success of any company. According to John and
Mayor “financial structure” of a business as consisting three elements assets, liabilities and capital. The
financial structure provides an insight into the various types of sources tapped to finance the total assets
employed in a business enterprise that part of financial which represents long term sources is known as
“capital structure.”16
CAPITAL STRUCTURE AND ITS IMPACT ON FINANCIAL
PERFORMANCE OF INDIAN STEEL INDUSTRY
1
Ata Takeh, 2
Dr. Jubiliy Navaprabha
Volume 6, Issue 6, June (2015), pp. 29-38
Article ID: 10120150606004
International Journal of Management (IJM)
IAEME: http://www.iaeme.com/IJM.asp
ISSN 0976-6502 (Print)
ISSN 0976-6510 (Online)
IJM
© I A E M E

REVIEW OF RELATED LITERATURE
B. Nimalathasan and Valeriu Brabete (2010)1
pointed out capital structure and its impact on
profitability: a study of listed manufacturing companies in Sri Lanka. The analysis of listed
manufacturing companies shows that Dept equity ratio is positively and strongly associated to all
profitability ratios (Gross Profit, Operating Profit and Net Profit Ratios).
Hurdle (1973)2
revealed that financial leverage effects negatively with profitability in accordance
with two stage least squares(2SLS) and positively according to ordinary least squares(OLS).
Mc Connell and Servaes (1995)3
and Agarwal and Zhao (2007)4
presented additional evidence on
how the growth of the firm may affect on the relationship between capital structure and performance.
High growth firms effect negatively between financial leverage and firm value, while low growth firms
effect positively.
Choudhury (1993)5
mentioned that the decreased use of debt tends to decrease profitability of a
company. Because due to lack of adequate finances it has to give up some of the profitable opportunities
and vice-versa.
Banu (1990)6
stated that the capital structure of a firm has a direct impact on its profitability. She
suggested that the concerned financial executives should put emphasis on various aspects of capital
structure. Otherwise the capital structure of the enterprise will be unsound producing adverse impact on
its profitability.
As contained in Bauer (2004)7
, from the agency cost theory view point, firms with a more profit
should have higher leverage for income they shield from taxes. It holds the view that more profit firms
should make use of more debts purposely to serve as a disciplinary measure for the managers. Empirical
evidences from the previous studies are in consistence with the Agency Cost Theory for their reporting of
negative relationship between capital structure and profitability.
Friend and Lang (1988)8
; Barton et al., (1989)9
; Chittenden et. al., (1996)10
; Jordan et al.,
(1998)11
; Shyam-Sunder and Myers (1999)12
; Mishra and Mc Conaughy (1999)13
; Michaelas et al.,
(1999)14
are reported the negative relationship between capital structure and profitability but Petersen and
Rajan, (1994)15
reported a positive relationship.
RESEARCH OBJECTIVES
The objectives of the study are:
To identify the company’s capital structure.
To find out the relationship between capital structure and financial performance.
To examine the impact of capital structure on financial performance.
HYPOTHESIS
The following hypotheses are specified for the research study.
There is a negative relationship between capital structure and financial performance.
Capital structure has an impact on financial performance.
CONCEPTUALIZATION MODEL
According to the review of related literature and hypothesis, the following conceptual modal was
formulated to outline the impact of capital structure on financial performance.

Independent Variable Dependent Variable
Figure No.1
RESEARCH METHODOLOGY
a) Data Collection
The study is mainly based on secondary data. Relevant secondary data have been collected from Books,
Periodicals, Libraries of various Research Institutions, Financial reports, BSE Official Directory, NSE,
Guidelines and rules, and Internet etc. as and when required.
b) Period of study
The time period of the research is designed from 2007 up to 2012.
c) Sampling Design
The researcher has selected only 13 major steel Industries as a sample on the basis of availability of data
and listed in BSE and NSE. The companies that have been chosen for the study are: Steel Authority of
India Limited (SAIL), BHUSHAN Steel Ltd, VISA Steel Ltd, TATA Steel Ltd, JSW Steel Ltd, JINDAL
Steel and Power Ltd, FACOR Steels Limited, Jindal Stainless Limited (JSL), MSP Steel and Power
Limited, NOVA Iron and Steel Ltd, Steel Exchange India Ltd (SEIL), Uttam Galva Steels Limited
(UGSL) and Mahindra Ugine Steel Company Limited (MUSCO).
d) Mode of Analysis
In order to derive the accurate results, the researcher are applied various statistical tools like Mean, Min,
Max, Standard Deviation (S.D) to analysis the consistency and Correlation Matrix, Multiple liner
Regression, ANOVA are employed for test of hypothesis with the help of statistical package SPSS 22.
e) Research Model
Correlation analysis was used to examine the relationship between dependent and independent variables.
Regression analysis was used to find out the effect of capital structure on financial performance of
selected Indian steel Industries.
Capital structure (Financial debt ratio, Total debt equity ratio, Total asset debt ratio, Interest
coverage ratio ) are the independent variables and profitability as a indicator of financial performance
(Operating profit margin, Return on asset, Return on equity and Return on capital employed) are the
dependent variables.
Financial debt ratio
Total debt equity ratio
Total asset debt ratio
Interest coverage ratio
Operating profit margin
Return on asset
Return on equity
Return on capital employed
Capital Structure Financial Performance

RESULTS AND INTERPRETATION
a) Descriptive statistics
Table No.1: Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Operating Profit Margin (OPM) 13 -5.47 36.18 15.1092 12.38048
Return on Capital Employed (ROCE) 13 -2.14 27.96 10.6423 8.73365
Return on Equity (ROE) 13 -31.45 23.22 4.5954 16.40723
Return on Asset (ROA) 13 0.35 100.08 50.6846 29.72284
Total Debt Equity Ratio (TDER) 13 0.02 4.33 2.0523 1.39847
Total Asset Debt Ratio (TADR) 13 1.30 25.97 4.7100 6.64696
Interest Coverage Ratio (ICR) 13 0.17 24.20 5.0354 6.29564
Financial Debt Ratio (FDR) 13 0.27 0.79 0.5792 0.17666
Valid N (list wise) 13
The mean, min and max values with standard deviation of different variables of interest in the
study during the period 2007 to 2012 are presented in the Table No.1. In addition, it shows the min and
max values of each variable which essentially gives an indication of how wide ranging each respective
variable can be. All variables were calculated using financial ratios.
b) Correlation Analysis
Table No.2: Correlations Matrix
TDER TADR ICR FDR
OPM Pearson Correlation -0.157 -0.513 0.510 -0.249
Sig. (2-tailed) 0.609 0.073 0.075 0.413
N 13 13 13 13
ROCE Pearson Correlation -0.516 -0.291 0.702**
-0.512
Sig. (2-tailed) 0.071 0.334 0.007 0.074
N 13 13 13 13
ROE Pearson Correlation -0.186 -0.614*
0.490 -0.128
Sig. (2-tailed) 0.542 0.025 0.089 0.677
N 13 13 13 13
ROA Pearson Correlation 0.053 -0.454 0.403 -0.004
Sig. (2-tailed) 0.864 0.119 0.172 0.991
N 13 13 13 13
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
From the above table No. 2, we can found out a negative relationship exists between OPM with
TDER, TADR and FDR. There is a positive relationship between OPM and ICR. And it not significant.
There exist negative relationship between ROCE with TDER, TADR and FDR. And it not significant.
ROCE has a positive and strong relationship with ICR and Correlation is significant at the 0.01 level. The
coefficient of determination is 0.007. That is only 0.7 % of variance in the ROEC is accounted by the
ICR. There is a negative relationship between ROE with TDER, TADR and FDR. The Correlation is
significant between ROE and TADR at the 0.05 level. The coefficient of determination is 0.025. That is
only 0.2.5 % of variance in the ROE is accounted by the TADR. There exist positive relationship
between ROE and ICR. And it is not significant. we also observed that a negative relationship exists
between ROA with TADR and FDR. ROA has a positive relationship with TDER and ICR. And it is not
significant.

c) Regression Analysis
We develop 4 model for the study. The specified model for the study is:
Profitability = β0 + β1TEDR+ β2TADR+ β3ICR+ β4FDR+ e
where:
β0 = Intercept
β1, β2, β3, β4 = coefficient of the explanatory variable
TEDR = Total debt equity ratio
TADR = Total asset debt ratio
ICR = Interest coverage ratio
FDR = Financial debt ratio
e = Error term
Model 1.
Operating Profit Margin (OPM) = β0 + β1TEDR+ β2TADR+ β3ICR+ β4FDR+ e
Table No.3: Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 0.831a
0.690 0.535 8.44429
a. Predictors: (Constant), FDR, ICR, TADR, TDER
The above table No.3, indicates the R square is 0.69. It means 69 % of variance of OPM is
accurate by the capital structure and remaining 31 % of variance with OPM is attributed to other factors.
This showed that capital structure has at least 69% significant influence on the OPM of the firms.
Table No.4: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 1268.868 4 317.217 4.449 0.035b
Residual 570.448 8 71.306
Total 1839.316 12
a. Dependent Variable: OPM
b. Predictors: (Constant), FDR, ICR, TADR, TDER
The table No.4 explains the most possible combination of capital structure that could contribute to
the relationship with the OPM. The F value of 4.449 and P value of 0.035 (P<0.05) in the ANOVA table
says that the model is statistically significant. Thus There is a significant impact of capital structure on
OPM.
Table No.5: Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 58.554 25.228 2.321 0.049
TDER 2.704 5.798 0.305 0.466 0.653
TADR -1.690 0.508 -0.907 -3.323 0.010
ICR 0.071 0.599 0.036 0.119 0.908
FDR -71.462 54.787 -1.020 -1.304 0.228
a. Dependent Variable: OPM

Table No. 5 presents the data findings on the OPM regression model. According to the table the
findings indicated that the intercept was 58.554, that is, when all the factors are equated to zero the OPM
will be 58.554, while the coefficients for TDER will be 2.704, TADR proportion -1.69, ICR proportion
0.071 and FDR proportion -71.462.
OPM= 58.554 + 2.704TDER - 1.69TADR + 0.071ICR - 71.462FDR + e
According to the model, an increase in the level of TDER brings about a 2.704 increase in OPM,
it implying that an increase in the TDER is associated with increase in profitability. An increase in the
TADR on the other hand leads to an decrease of -1.69 in OPM. The model further shows that an increase
in ICR brings about a increase of 0.071 in OPM. This depicts that increase in ICR influence OPM thus
profitability positively. An increase in FDR brings 71.462 decrease in OPM. This can be explained by the
fact that FDR are relatively expensive and thus employing high proportions of them could lead to low
profitability.
Model 2.
Return on Capital Employed (ROCE) = β0 + β1TEDR+ β2TADR+ β3ICR+ β4FDR+ e
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
2 0.943a
0.888 0.833 3.57269
The above table No.6, manifested the R square is 0.943. It shows the 94.3% of variance of ROCE
is accurate by the capital structure and remaining 5.7% of variance with ROCE is ascribed to other
factors. This indicated that capital structure has at least 94.3% significant influence on the ROCE of the
companies.
Table No.7: ANOVAa
2 Regression 813.208 4 203.302 15.928 0.001b
Residual 102.113 8 12.764
Total 915.321 12
a. Dependent Variable: ROCE
The table No.7 expressed the most possible combination of capital structure that could contribute
to the relationship with the ROCE. The F value of 15.928 and P value of 0.001 (P<0.05) in the ANOVA
table says that the model is statistically significant. Thus There is a significant impact of capital structure
on ROCE.
Model
Standardized
Coefficients
2 (Constant) 12.696 10.674 1.189 0.268
TDER -6.858 2.453 -1.098 -2.796 0.023
TADR -0.848 0.215 -0.645 -3.940 0.004
ICR 0.705 0.253 0.508 2.782 0.024
FDR 21.517 23.180 0.435 0.928 0.380
a. Dependent Variable: ROCE

Table No. 8 indicated the data findings on the ROCE regression model. According to the table
the findings witnessed that the intercept was 12.696, that is, when all the factors are equated to zero the
ROCE will be 12.696, while the coefficients for TDER will be -6.858, TADR proportion -0.848, ICR
proportion 0.705 and FDR proportion 21.517.
ROCE= 12.696 - 6.858TDER - 0.848TADR + 0.705ICR + 21.517FDR + e
According to the model, an increase in the level of TDER brings about a 6.858 decrease in ROCE,
it implying that an increase in the TDER is associated with decrease in profitability. An increase in the
TADR on the other hand leads to an decrease of 0.848 in ROCE. The model further shows that an
increase in ICR brings about a increase of 0.705 in ROCE. This depicts that increase in ICR influence
ROCE thus profitability positively. An increase in FDR brings 21.517 increase in ROCE. Thus
employing high proportions of FDR could lead to high profitability.
Model 3.
Return on Equity (ROE) = β0 + β1TEDR+ β2TADR+ β3ICR+ β4FDR+ e
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
3 .937a
.878 .817 7.01727
The above table No.9, showed the R square is 0.937. It indicates the 93.7% of variance of ROE is
accurate by the capital structure and remaining 6.3% of variance with ROE is attributed to other factors.
This indicated that capital structure has at least 93.7% significant influence on the ROE of the
organization.
Table No.10: ANOVAa
3 Regression 2836.429 4 709.107 14.400 0.001b
Residual 393.937 8 49.242
Total 3230.366 12
a. Dependent Variable: ROE
The table No.10 showed the most possible combination of capital structure that could contribute
to the relationship with the ROE. The F value of 14.4 and P value of 0.001 (P<0.05) in the ANOVA table
says that the model is statistically significant. Thus There is a significant impact of capital structure on
ROE.
Model
Standardized
Coefficients
3 (Constant) -9.048 20.965 -0.432 0.677
TDER -16.037 4.818 -1.367 -3.328 0.010
TADR -2.080 0.423 -0.843 -4.922 0.001
ICR 1.198 0.498 0.460 2.406 0.043
FDR 86.872 45.529 0.935 1.908 0.093
a. Dependent Variable: ROE

Table No. 11 showed the data findings on the ROE regression model. According to the table the
findings manifested that the intercept was -9.048, that is, when all the factors are equated to zero the ROE
will be -9.048, while the coefficients for TDER will be -16.037, TADR proportion -2.080, ICR
proportion 1.198 and FDR proportion 86.872.
ROE= -9.048 - 16.037TDER - 2.080TADR + 1.198ICR + 86.872FDR + e
According to the model, an increase in the level of TDER brings about a 16.037 decrease in ROE,
TADR on the other hand leads to an decrease of 2.080 in ROE. The model further shows that an increase
in ICR brings about a increase of 1.198 in ROE. This depicts that increase in ICR influence ROE thus
profitability positively. An increase in FDR brings 86.872 increase in ROE. Thus employing high
proportions of FDR could lead to high profitability.
Model 4.
Return on Asset (ROA) = β0 + β1TEDR+ β2TADR+ β3ICR+ β4FDR+ e
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
4 0.600a
0.360 0.040 29.11912
The above table No.12, manifested the R square is 0.600. It implies the 60% of variance of ROA
is accurate by the capital structure and remaining 40% of variance with ROA is attributed to other factors.
This indicated that capital structure has at least 60% significant influence on the ROA of the organization.
Table No.13: ANOVAa
4 Regression 3817.977 4 954.494 1.126 0.409b
Residual 6783.386 8 847.923
Total 10601.363 12
a. Dependent Variable: ROA
The table No.13 expressed the most possible combination of capital structure that could contribute
to the relationship with the ROA. The F value of 1.126 and P value of 0.409 (P<0.05) in the ANOVA
table says that the model is statistically insignificant. Thus There is a no significant impact of capital
structure on ROA.
Model
Standardized
Coefficients
4 (Constant) 56.831 86.996 0.653 0.532
TDER -2.079 19.993 -0.098 -0.104 0.920
TADR -2.211 1.753 -0.494 -1.261 0.243
ICR 1.648 2.066 0.349 0.798 0.448
FDR 0.404 188.928 0.002 0.002 0.998
a. Dependent Variable: ROA

Table No. 14 indicated the data findings on the ROA regression model. According to the table the
findings showed that the intercept was -56.831, that is, when all the factors are equated to zero the ROA
will be 56.831, while the coefficients for TDER will be -2.079, TADR proportion -2.211, ICR proportion
1.648 and FDR proportion 0.404.
ROA= 56.831 - 2.079TDER - 2.211TADR + 1.648ICR + 0.404FDR + e
According to the model, an increase in the level of TDER brings about a 2.079 decrease in ROA,
TADR on the other hand leads to an decrease of 2.211 in ROA. The model further indicates that an
increase in ICR brings about a increase of 1.648 in ROA. This depicts that increase in ICR influence
ROA thus profitability positively. An increase in FDR brings 0.404 increase in ROA.
CONCLUSION
This paper examined capital structure and its impact on financial performance of Indian steel
Industry. The Correlation results confirmed that a negative relationship exists between OPM with TDER,
TADR and FDR. There is a positive relationship between OPM and ICR. There exist negative
relationship between ROCE with TDER, TADR and FDR. ROCE has a positive and strong relationship
with ICR and Correlation is significant at the 0.01 level. The coefficient of determination is 0.007. That is
only 0.7 % of variance in the ROEC is accounted by the ICR. There is a negative relationship between
ROE with TDER, TADR and FDR. The Correlation is significant between ROE and TADR at the 0.05
level. The coefficient of determination is 0.025. That is only 0.2.5 % of variance in the ROE is accounted
by the TADR. There exist positive relationship between ROE and ICR. we also observed that a negative
relationship exists between ROA with TADR and FDR. ROA has a positive relationship with TDER and
ICR. And it is not significant. Therefore, there is negative relationship between capital structure and
financial performance of Indian steel Industry. The result of multiple regression and ANOVA indicated
that There is a significant impact of capital structure on OPM, ROCE, ROE. There is a no significant
impact of capital structure on ROA. Thus capital structure has a great impact on financial performance of
the Indian steel industry.
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