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- 1. 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
- 2. 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 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
- 3. 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 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.
- 4. 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 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
- 5. 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 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.
- 6. 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 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 Source: Capitaline database 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 Source: Capitaline database 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
- 7. 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 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 Source: Capitaline database 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
- 8. 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 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.
- 9. 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 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.

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