10220140503002

International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),
ISSN 0976 – 6332 (Online), Volume 5, Issue 3, May- June (2014), pp. 11-19 © IAEME
11
CAPITAL STRUCTURE DETERMINANTS: A STUDY OF METAL,
METAL PRODUCTS AND MINING SECTOR FIRMS
Dr. Anshu Bhardwaj
Assistant Professor, Faculty of Commerce and Management,
BPS Mahila Vishwavidyalaya, Khanpur Kalan, Sonipat.
ABSTRACT
Capital structure decisions are of paramount importance in Indian business
environment as the firms operating in Metal, Metal Products and Mining Sector Firms are
becoming cautious about the choice of source of funds and forming their capital structure to
be called as optimal capital structure. Thus, reducing the cost of capital and maximization of
the value of the firm has become the main objective of financial managers and the process of
liberalization has given more flexibility to the Indian financial managers in choosing the
capital structure of the firm. The objectives of the study are to assess the determinants of
capital structure of Metal, Metal Products and Mining Sector Firms and to assess the impact
of firm specific determinants of Metal, Metal Products and Mining Sector Firms in deciding
the financial structure. The dependent variable is the financial leverage and is defined as the
ratio of total debt to total equity.The techniques used in this study were regression analysis
and correlation analysis.Regression analysis is one of the most pervasive of all statistical
analysis methods due to its generality and applicability although it does not account for cause
and effect relationships.The findings of Metals, Metal Products and Mining Sector suggest
that there is a negative relationship of collatralizable value of assets and size with financial
leverage. The findings of Metals, Metal Products and Mining Sector indicate that there is a
negative relationship of return on capital employed, return on net worth, interest cover ratio,
non-debt tax shield, profitability, and growth with financial leverage.
Keywords: Financial Leverage, Firm Value, Capital Structure Determinants.
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH
IN MANAGEMENT (IJARM)
ISSN 0976 - 6324 (Print)
ISSN 0976 - 6332 (Online)
Volume 5, Issue 3, May-June (2014), pp. 11-19
© IAEME: www.iaeme.com/ijarm.asp
Journal Impact Factor (2014): 5.4271 (Calculated by GISI)
www.jifactor.com
IJARM
© I A E M E

12
1. INTRODUCTION
Empirical evidence suggests that there are large numbers of factors as determinants of
a firm’s financing choices. The financial manager has to take judicious mix of debt and
equity so that total cost of capital can be reduced by increasing the proportion of cheaper
source of capital so that the firm value can be increased. The earlier studies do agree with
Modigliani and Miller (1963) that the gains from the leverage are significant and that the use
of debt increases the market value of a firm.During the later years Modigliani and Miller
emphasized that in an idealized situation with the non-existence of taxes, the value of the firm
is independent of the debt-equity mix meaning thereby capital structure is irrelevant to the
value of the firm is further supported by studies conducted in the past. But, these observations
are not consistent and practically applicable in the real life situations. Thus, capital structure
decision is considered to be more complicated in case of developing countries or emerging
economies as compared to developed countries. The present study focuses on the
determinants that have considerable impact on capital structure decisions in case of Metal,
Metal Products and Mining Sector Firms.
2. RESEARCH METHODOLOGY
The present study will rely on the data collected from various sources such as Annual
reports of the companies, CMIE (Centre for Monitoring the Indian Economy) and Capitaline
database. This study is spread over a period of 9 years for Metal, Metal Products and Mining
Sector Firms which are listed on the Bombay Stock Exchange (BSE-500). The total numbers
of firms which are selected from Metal, Metal Products and Mining Sector Firms is 31. The
techniques used in this study are regression analysis and correlation analysis.
3. OBJECTIVES OF THE STUDY
1. To assess the determinants of capital structure of Metal, Metal Products and Mining
Sector Firms.
2. To assess the impact of firm specific determinants of Metal, Metal Products and
Mining Sector Firms in deciding the financial structure.
4. HYPOTHESES
Hypothesis 1: The capital structure of Metal, Metal Products and Mining Sector Firms has
no impact on the value of the firm.
Hypothesis 2: The firm-specific determinants of capital structure of Metal, Metal Products
and Mining Sector Firms do not have any impact on the financial structure.
5. REVIEW OF LITERATURE
Dragota, Mihaela, SemenescuAndreea (2005) conducted a study on “Debt –equity
choice in Romania: The role of firm specific determinants” for publicly traded companies for
the period 1997-2007. The explanatory variables are based on linear multiple regression
model for analysis of the capital structure determinants in the Central and Eastern European

13
Countries. The dependent variable considered in the study is the leverage and the independent
variables are asset structure, firm size, profitability and market to book ratio. The analysis is
done using cross sectional OLS regression, and the results indicate that Romanian listed
companies financed their assets through equity, commercial debt and other financial debt.
The variables considered in the study are significant, but some of them are relevant only for
one type of debt or only for the accounting values and not for the market ones or vice-versa.
The finding of the study are consistent with Pecking order theory and seems to be more
appropriate for the Romanian capital market but signally theory is not entirely rejected.
Das Sumitra and Roy Malabika (2007)conducted a study “Inter-industry difference
in Capital structure: The Evidence from India” for heterogeneous set of twelve Indian
Manufacturing industries for the period 1979-1998. The time period is further divided into
two parts, i.e., Pre-liberalization period 1979-1991 and post-liberalization period 1992-1998
to capture the effect of policy break on the capital structure of firms. The purpose of the study
is to investigate empirically the existence of inter-industry differences in the capital structure
of Indian firms and identify the possible sources of such variations in capital structure. The
technique used for the analysis is cross sectional and covers the pre and post liberalization
periods separately to indicate if there is a clear break in the financing pattern of the Indian
firms due to policy shift. Further, the findings also represent that the variation in the ratio of
individual firm’s debt ratio to mean industry ratio across different classes is insignificant.
Momani, Alsharayri and Dandan (2010) studied the impact of firm’s characteristics
on determining the financial structure on the insurance sector firms in Jordan. The study
aimed at examining the effect of volume, asset structure, return on assets, growth rate etc. for
25 companies during the year 2000-2007. The method applied for analysis of data was simple
and multiple regressions where the study showed a significant difference between the
company’s characteristics mentioned under the study. Thus, the researcher found that it is
important to study the financial decision of the manager in determining the ratio of debt, the
impact on the cost of funds and therefore, the market value of the business and indicators of
the company when they are choosing the company’s financial structure. The findings of the
study are that there is a statistically significant relation between firm size, structure of the
company assets, return on assets, rate of growth and capital structure.
6. ANALYSIS AND INTERPRETATION
6.1 Correlation Analysis for Metal, Metal Products, and Mining Sector
The correlation coefficient was used to assess the determinants of capital structure and
its influence in deciding the financial structure. The independent and dependent variable are
used to explain the inter-industry variations of capital structure for Metal, Metal Products and
Mining Sector Firms. Table 1.1 exhibits the Pearson Correlation between the financial
leverage and the determinants of the capital structure for Metal, Metal Products, and Mining
Sector Firms.

14
Table 1.1: Correlation for Independent and Dependent variable for Metal, Metal
Products and Mining Sector
FL RONW ROCE ICR NDTS PROF CVA SIZE GROW
FL
Pearson
Correlation
1 -.199 -.435*
-.431*
-.036 -.680**
.092 .125 -.090
Sig. (2-tailed) .284 .014 .015 .849 .000 .622 .503 .631
N 31 31 31 31 31 31 31 31 s31
RONW
Pearson
Correlation
-.199 1 .824**
.292 -.087 .780**
.225 .255 .029
Sig. (2-tailed) .284 .000 .111 .641 .000 .223 .167 .876
N 31 31 31 31 31 31 31 31 31
ROCE
Pearson
Correlation
-.435*
.824**
1 .545**
-.112 .898**
.164 .253 -.001
Sig. (2-tailed) .014 .000 .002 .548 .000 .378 .170 .996
N 31 31 31 31 31 31 31 31 31
ICR
Pearson
Correlation
-.431*
.292 .545**
1 -.056 .548**
.041 .125 -.010
Sig. (2-tailed) .015 .111 .002 .766 .001 .829 .501 .956
N 31 31 31 31 31 31 31 31 31
NDTS
Pearson
Correlation
-.036 -.087 -.112 -.056 1 -.118 -.232 -.340 .849**
Sig. (2-tailed) .849 .641 .548 .766 .526 .209 .062 .000
N 31 31 31 31 31 31 31 31 31
PROF
Pearson
Correlation
-.680**
.780**
.898**
.548**
-.118 1 .117 .245 -.004
Sig. (2-tailed) .000 .000 .000 .001 .526 .530 .183 .984
N 31 31 31 31 31 31 31 31 31
CVA
Pearson
Correlation
.092 .225 .164 .041 -.232 .117 1 .333 -.323
Sig. (2-tailed) .622 .223 .378 .829 .209 .530 .067 .076
N 31 31 31 31 31 31 31 31 31
SIZE
Pearson
Correlation
.125 .255 .253 .125 -.340 .245 .333 1 -.362*
Sig. (2-tailed) .503 .167 .170 .501 .062 .183 .067 .045
N 31 31 31 31 31 31 31 31 31
GROWTH
Pearson
Correlation
-.090 .029 -.001 -.010 .849**
-.004 -.323 -.362*
1
Sig. (2-tailed) .631 .876 .996 .956 .000 .984 .076 .045
N 31 31 31 31 31 31 31 31 31
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Source: Capitaline database

15
To test the correlation between each dependent and independent variable, Karl
Pearson correlation is being calculated and presented in Table1.1. It can be interpreted that
Financial Leverage (FL) is positively correlated with collateralizable value of assets and size.
The size of the firm is positively related to leverage and statistically significant at 1% level.
The findings support the prediction of the Trade-off Theory over the Pecking Order Theory
and suggest that borrowing capacity for Indian firms is significantly limited by their
bankruptcy or financial distress risks. It also supports the view that larger firms may be more
diversified and fail less often. As large Indian firms are diversified in their product market,
their risk to face financial distress is expected to be low, where the failure of one market can
be compensated by the other. To the extent that this is the case, this finding implies that the
cost of the bankruptcy or financial distress is one of the main determinants of the leverage
ratio for the Metal, Metal Products, and Mining Sector Firms. In the present study, fixed
assets to total assets ratio is used to measure the collatralizable value of firm assets. The
findings revealed that firms can use their fixed assets as collateral to secure debt financing
and firms can use their tangible assets as securities when raising low cost secured debt. It was
also found that in case of Metal, Metal Products and Mining Sector Firms that have higher
liquidation value of tangible assets have more debt than other firms. Further, results of the
study suggest that financial leverage is negatively related with return on net worth, return on
capital employed, interest cover ratio, non-debt tax shield, profitability and growth. The
findings of these studies do not support the theoretical foundation that was put forward by
Modigliani and Miller in 1958 and corrected in 1963. The theory suggests that use of debt
leads to an increase in the value of firm by reducing the cost of capital and magnifying
returns to owners. The inconsistency can be attributed to high interest rate and high cost of
funds and the result support pecking order theory thereby suggesting that profitable firms
prefer internal financing and leverage has negative relationship with profitability. The
negative sign of the b-coefficient for the growth of the assets conforms to the conclusion
reached by earlier researches as firms expecting high future growth use a greater amount of
equity finance. As for collatralizable value of assets, its contribution is very small, although
the positive sign means the increase of fixed assets has a collateral value for higher debt.
There is a negative relationship between growth and financial leverage and the possible
explanation for the same is that growth opportunities are not considered as a collatralizable
assets to borrow in case of Metal, Metal Products, and Mining Sector Firms. The highest
degree of correlation can be observed between return on capital employed and profitability
(0.898) which is statistically significant at 0.01 level of significance.
6.2 Regression Analysis for Metal, Metal Products and Mining Sector Firms
Regression analysis was carried outfor Metal, Metal Products and Mining Sector
Firms using relevant techniques to identify the major variables which have impact on capital
structure decisions. The various tests are conducted to assess the relative significance,
desirability and reliability of model estimation parameters. Thus, Regression Analysis was
used to see how far the explanatory variables were related with capital structure decisions and
also to examine the inter-industry difference in determinants of capital structure. Table 1.2
depicts the summary statistics of regression analysis for Metal, Metal Products and Mining
Sector Firms and the study also made use of ANOVA to examine the nature and differences
in the capital structure of Metal, Metal Products and Mining Sector Firms as depicted in
Table 1.3.

16
Table 1.2: Model Summary of Metal, Metal Products and Mining Sector Firms
Model R R Square
Adjusted R
Square
Std. Error of the Estimate Durbin-Watson
1 .915a
.837 .778 .10738 2.072
a. Predictors: (Constant), GROWTH, ROCE, CVA, SIZE, ICR, RONW, NDTS, PROF
b. Dependent Variable: FL
Table 1.3 : ANOVA of Metal, Metal Products and Mining Sector Firms
Model Sum of Squares Df Mean Square F Sig.
1
Regression 1.304 8 .163 14.134 .000a
Residual .254 22 .012
Total 1.558 30
a. Predictors: (Constant), GROWTH, ICR, NDTS, PROF, SIZE, RONW, CVA, ROCE
b. Dependent Variable: FL
The summary of regression analysis results showing determinants of capital structure
as predictors and capital structure decision as criterion variables are shown in above Tables.
Table 1.2depicts R which is the square root of R-Square and is showing the correlation
between the observed and predicted values of dependent variable i.e. financial leverage. In
case of Metals, Metal products and Mining Sector analysis, the correlation between
dependent variables and predictor is represented as (0.915) which is considered to be a high
value and it shows that they are positively and significantly correlated with each other. The
value of R- Square (0.837) explains the degree of variation that is explained by all the
independent variables or predictors i.e. determinants of capital structure taken together. It is
considered to be an overall measure of the strength of association and does not reflect the
extent to which any particular independent variable is associated with the dependent variable.
In terms of the impact of capital structure determinants on the financial leverage, the adjusted
R Square (0.778) was statistically significant. It was suggested that the determinants of
capital structure explained 77.8 per cent of the variance in the overall decisions with regard to
debt and equity which constitute the capital structure. In order to test the first degree serial
correlation among variables Durbin-Watson statistics is also applied. In the present study, the
value (2.072) is less than the critical range of 2.25, thus considered to be acceptable and
concludes that the presence of first order serial correlation is not found.Table 4.19 depicts F
values that are calculated to measure the significance of the model. It is observed that the
overall regression model is significant (F=14.134, p<0.00) and also interprets that the model
is constructed well. Durbin-Watson statistics is also applied to test the first degree serial
correlation among variables. In the present study, the value (2.072) is less than the critical
range of 2.25, thus considered to be acceptable and concludes that there is not any presence

17
of first order serial correlation. The findings support the view that increase in stock prices
have encouraged Metals, Metal Products and Mining Sector to go to the stock market for
financing and also that the cost of debt has increased due to conservative credit policy and
removal of restrictions on deposit and lending interest rates. The output of Multivariate
Regression against financial leverage for Metal, Metal Products and Mining Sector is shown
in the Table 1.4.
Table 1.4: Output of Multivariate Regression against Financial Leverage for Metal,
Metal Products and Mining Sector
Model
Unstandardized
Coefficients
Standardized
Coefficients
T Sig.
Collinearity
Statistics
B
Std.
Error
Beta Tolerance VIF
1
(Constant)
.711 .084 8.493 .000
RONW
.538 .132 .689 4.066 .118 .258 3.879
ROCE
.427 .227 .432 1.879 .174 .140 7.129
ICR
.023 .134 .019 .170 .219 .594 1.683
NDTS -.083 .216 -.065 -.382 .212 .259 3.856
PROF -1.802 .221 -1.680 -8.168 .364 .175 5.713
CVA
-.027 .094 -.028 -.287 .113 .780 1.281
SIZE
.259 .104 .244 2.479 .100 .763 1.310
GROW .024 .222 .019 .107 .267 .239 4.184
Dependent Variable: FL
From Table 1.4 it can be interpreted that the various coefficients considered in the
study are explaining the overall impact in deciding the financing pattern of Metal, Metal
Products and Mining Sector Firms. In the present study, the dependent variable is financial
leverage which is constant and other variables are independent variables. The financial
leverage β0 is constant with a value of (0.711). The Second Colum (B) reports the values for
the regression equation for predicting the dependent variable from the independent variable.
The coefficient of return on net worth β1 is (0.538). So for every unit increase (as it is
positive value) there is a (0.53) unit increase in financial leverage is predicted, holding all
other variables constant. The coefficient of return on capital employed β2 is (0.427). So for
every unit increase (as it is positive value) there is a (0.42) unit increase in financial leverage
is predicted, holding all other variables constant. The coefficient of interest cover ratio β3 is
(0.023). So for every unit increase (as it is positive value) there is a (0.023) unit increase in
financial leverage is predicted, holding all other variables constant. The coefficient of non-
debt tax shield β4 is (-0.83). So for every unit decrease (as it is negative value) there is a
(0.83) unit decrease in financial leverage is predicted, holding all other variables constant.
The coefficient of Profitability β5 is (-1.802). So for every unit decrease (as it is negative
value) there is a (1.802) unit decrease in financial leverage is predicted, holding all other

18
variables constant. The coefficient of collateralized value of assets β6 is (-0.027). So for
every unit decrease (as it is negative value) there is a (0.027) unit decrease in financial
leverage is predicted, holding all other variables constant. The coefficient of size β7 is
(0.259). So for every unit increase (as it is positive value) in size there is a (0.259) unit
increase in financial leverage is predicted, holding all other variables constant. The
coefficient of growth β8 is (0.024). So for every unit increase (as it is positive value) in
growth there is a (0.024) unit increase in financial leverage is predicted, holding all other
variables constant. Table 4.20 indicates that the highest VIF is (7.129) which is below the cut
off rate thus indicates that the statistical relation can be established between dependent and
independent variable and the estimated coefficients are considered to be reliable. The growth
is estimated to have a positive impact on the financial leverage and the results contradict with
the Static Trade of Theory and Agency Cost Theory but support the Pecking Order Theory
and the possible reason for the same is that Healthcare Sector Firms ought to use external
financing then prefer debt over equity. The higher the beta coefficient more is the
contribution of determinants in explaining the variation in capital structure of Metal, Metal
Products and Mining Sector firms. As shown in the Table, result indicate that financial
leverage is highly influenced by return on net worth and considered as the most important
determinant of capital structure, beta coefficient (0.689). The result implies that firms with
higher growth rate maintain higher financial leverage ratio in case of Metal, Metal Products
and Mining Sector firms.
7. CONCLUSION
The findings of Metals, Metal Products and Mining Sector suggest that there is a
negative relationship of collatralizable value of assets and size with financial leverage. The
negative relationship of collatralizable value of assets is due to the fact that asset substitution
is less likely to occur when the firm has more assets already in place and another reason is
that tangible assets often reduces the cost of financial distress because they tend to have
higher liquidation value.The findings of Metals, Metal Products and Mining Sector revealed
that there is a positive relationship of size with financial leverage because larger firms have
higher debt capacity and are less prone to bankruptcy resulting into more usage of debt than
smaller firms.The findings of Metals, Metal Products and Mining Sector indicate that there is
a negative relationship of return on capital employed, return on net worth, interest cover ratio,
non-debt tax shield, profitability, and growth with financial leverage. The negative
relationship of non-debt tax shield is due to the reason that non-debt tax shield are the
substitutes for the debt tax shield. It is interpreted that firms with high non-debt tax shield
relative to their expected cash flows will have less debt in their capital structure.The findings
of Metals, Metal Products and Mining Sector also shows that due to the negative relationship
of profitability with financial leverage, the firms are expecting high future profitability and
are likely to rely more on equity and resulting into less dependence on debt.The findings of
Metals, Metal Products and Mining Sector also revealed that there is a negative relationship
of growth which implies that growth prospects provide owners with more opportunities to
expropriate wealth from debt holders through sub-optimal investment or risk shifting. Hence,
firms with high investment opportunities relative to their tangible assets should have low debt
levels.

19
BIBLIOGRAPHY
[1] Das Sumitra and Roy Malabika. “Inter-industry difference in Capital structure: The
Evidence from India.” Finance India. Vol.21, 2007: 517-532.
[2] Dragota, Mihaela, SemenescuAndreea. “Debt –equity choice in Romania: The role of
firm specific determinants.” Finance India. Vol. XXIII, No. 2, 2005: 541-574.
[3] Momani, F. Ghazi, A., lsharayri, A., Majed, Dandan, M., Muafaq. “Impact of firm’s
characteristics on determinants the general finance structure of the insurance sector
firm in Jordan.” Journal of Social Sciences. Vol. 6, No.2, 2010: 282-286.
[4] Modigliani, F., and Miller, M., (1958), “The cost of capital, corporation finance and
the theory of investment.” The American Economic Review. Vol. 48, No.3, 1958:
261-297.
[5] Rajan G. Raghuram and ZingalesDuigi. “What do we know about capital structure?
Some evidence from International date.” The Journal of Finance. Vol. 50, No. 5, 1995:
1421-1460.
[6] Arunkumar O. N and T. Radharamanan, “Working Capital Management and
Profitability: An Empirical Analysis of Indian Manufacturing Firms”, International
Journal of Management (IJM), Volume 4, Issue 1, 2013, pp. 121 - 129, ISSN Print:
0976-6502, ISSN Online: 0976-6510.
[7] Dr.V.Sarangarajan, Dr.S.A.Lourthuraj and Dr.A.Ananth, “Capital Structure Efficiency
of Cement Industry in Tamil Nadu”, International Journal of Management (IJM),
Volume 4, Issue 1, 2013, pp. 190 - 196, ISSN Print: 0976-6502, ISSN Online:
0976-6510.
[8] Arunkumar O. N and T. Radharamanan, “Working Capital Management and
Profitability: An Empirical Analysis of Indian Manufacturing Firms”, International
Journal of Management (IJM), Volume 4, Issue 1, 2013, pp. 121 - 129, ISSN Print:
0976-6502, ISSN Online: 0976-6510.
[9] Pasquale De Luca, “Capital Structure and Economic Performance of the Firm:
Evidence from Italy”, International Journal of Management (IJM), Volume 5, Issue 3,
2014, pp. 1 - 20, ISSN Print: 0976-6502, ISSN Online: 0976-6510.

10220140503002

More Related Content

What's hot

Viewers also liked

Similar to 10220140503002

More from IAEME Publication

Recently uploaded

10220140503002