CAPITAL STRUCTURE AND ITS IMPACT ON FINANCIAL PERFORMANCE OF INDIAN STEEL INDUSTRY

CAPITAL STRUCTURE 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 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 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 www.iaeme.com/ijm.asp 29 editor@iaeme.com

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. www.iaeme.com/ijm.asp 30 editor@iaeme.com

Independent Variable Capital Structure Dependent Variable Financial Performance 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 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. www.iaeme.com/ijm.asp 31 editor@iaeme.com

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. www.iaeme.com/ijm.asp 32 editor@iaeme.com

c) Regression Analysis We develop 4 model for the study. The specified model for the study is: Profitability = β 0 + β 1 TEDR+ β 2 TADR+ β 3 ICR+ β 4 FDR+ e where: β 0 = Intercept β 1, β 2, β 3, β 4 = 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) = β 0 + β 1 TEDR+ β 2 TADR+ β 3 ICR+ β 4 FDR+ e Table No.3: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.831 a 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: ANOVA a Model Sum of Squares df Mean Square F Sig. 1 Regression 1268.868 4 317.217 4.449 0.035 b 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: Coefficients a Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 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 www.iaeme.com/ijm.asp 33 editor@iaeme.com

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) = β 0 + β 1 TEDR+ β 2 TADR+ β 3 ICR+ β 4 FDR+ e Table No.6: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 2 0.943 a 0.888 0.833 3.57269 a. Predictors: (Constant), FDR, ICR, TADR, TDER 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: ANOVA a Model Sum of Squares df Mean Square F Sig. 2 Regression 813.208 4 203.302 15.928 0.001 b Residual 102.113 8 12.764 Total 915.321 12 a. Dependent Variable: ROCE b. Predictors: (Constant), FDR, ICR, TADR, TDER 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. Table No.8: Coefficients a Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 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 www.iaeme.com/ijm.asp 34 editor@iaeme.com

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) = β 0 + β 1 TEDR+ β 2 TADR+ β 3 ICR+ β 4 FDR+ e Table No.9: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 3.937 a.878.817 7.01727 a. Predictors: (Constant), FDR, ICR, TADR, TDER 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: ANOVA a Model Sum of Squares df Mean Square F Sig. 3 Regression 2836.429 4 709.107 14.400 0.001 b Residual 393.937 8 49.242 Total 3230.366 12 a. Dependent Variable: ROE b. Predictors: (Constant), FDR, ICR, TADR, TDER 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. Table No.11: Coefficients a Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 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 www.iaeme.com/ijm.asp 35 editor@iaeme.com

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, 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 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) = β 0 + β 1 TEDR+ β 2 TADR+ β 3 ICR+ β 4 FDR+ e Table No.12: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 4 0.600 a 0.360 0.040 29.11912 a. Predictors: (Constant), FDR, ICR, TADR, TDER 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: ANOVA a Model Sum of Squares df Mean Square F Sig. 4 Regression 3817.977 4 954.494 1.126 0.409 b Residual 6783.386 8 847.923 Total 10601.363 12 a. Dependent Variable: ROA b. Predictors: (Constant), FDR, ICR, TADR, TDER 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. Table No.14: Coefficients a Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 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 www.iaeme.com/ijm.asp 36 editor@iaeme.com

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, 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 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. REFERENCE 1. Nimalathasan, B., Valeriu B.,''Capital Structure and Its Impact on Profitability: A Study of Listed Manufacturing Companies in Sri Lanka'', Revista Tinerilor Economisti/The Young Economists Journal. 2010, 13,55-61 2. Hurdle, G. ''Leverage, Risk, Market Structure and Profitability'', The Review of Economics and Statistics : 1973, 478-485. 3. McConnell, J., and Servaes, H. ''Equity Ownership and The Two Faces of Debt''. Journal of Financial Economics, 1995, 39: 131 157. 4. Agarawal, R and Zhao, X. '' The Leverage Value Relationship Puzzle: An Industry Effects Resolution''. Journal of Economics and Business, 2007, 59:286-297. 5. Choudhury, J.A. ''Evaluation of Capital Structure of Three companies Listed with Dhaka Stock exchange''. The Cost and Management, XXI. 1993, (4). 6. Banu, S. ''Evaluation of Financial Structure of Rajshahi Jute Mills Ltd''. The Islamic University Studies,. 1990. 7. Bauer, P. ''Determinants of capital structure empirical Evidence from the Czech Republic''. Czech Journal of Economics and Finance, 2004. 8. Friend, 1. and L. Lang ''An Empirical Test of the Impact of Managerial Self-Interest on Corporate Capital Structure''. Journal of Finance (June), 1988, 271-281. www.iaeme.com/ijm.asp 37 editor@iaeme.com

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