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10520130201001

  1. 1. Journal of Management (JOM), ISSN 2347-3940 (Print), ISSN 2347-3959 (Online), Volume 2, Issue 1, January-June (2014), pp. 01-08 © IAEME 1 CAPITAL STRUCTURE DETERMINANTS AND VARIATIONS: A STUDY OF AGRICULTURE SECTOR FIRMS Dr. Anshu Bhardwaj Assistant Professor, Faculty of Commerce and Management, BPS Mahila Vishwavidyalaya, Khanpur Kalan, Sonipat. ABSTRACT The capital structure decisions are taken judiciously will not only reduce the operating risk but also leads to maximization of firm value. The approach will also help in achieving the fundamental objective of any business firm i.e., wealth maximization that ultimately leads to value maximization.The objectives of the study are to assess the determinants of capital structure of Agriculture Sector Firms and to assess the impact of firm specific determinants of Agriculture Sector Firms in deciding the financial structure. The findings of Agriculture Sector firms show that there is a positive relationship between return on net worth, non-debt tax shield, profitability, and growth with financial leverage. The relationship between return on capital employed, interest cover ratio, collatralizable value of assets and size is negative indicating that the negative relationship existing between collatralizable value of assets and financial leverage is due to the reason that increase in a debt is used to finance the current assets due to which the proportion of net fixed assets in total assets reduces. The negative relationship between size and financial leverage is due to the reason that the availability of information that outsiders have about the firm and thus increases the preference for equity relative to debt. The present study investigates various competing theories in explaining the capital structure decisions of Agriculture Sector Firms. For this purpose, a measure of leverage is regressed on firm characteristics that have been identified by previous research as important determinants of capital structure. Keywords: Capital Structure Determinants. Financial Leverage, Firm Value, Financial Structure. JOURNAL OF MANAGEMENT (JOM) ISSN 2347-3940 (Print) ISSN 2347-3959 (Online) Volume 2, Issue 1, January-June (2014), pp. 01-08 © IAEME: http://www.iaeme.com/jom.asp Journal Impact Factor (2014): 1.5952 (Calculated by GISI) www.jifactor.com JOM © I A E M E
  2. 2. Journal of Management (JOM), ISSN 2347-3940 (Print), ISSN 2347-3959 (Online), Volume 2, Issue 1, January-June (2014), pp. 01-08 © IAEME 2 1. INTRODUCTION Capital structure deals with proper mix of debt and equity so as to obtain optimal level and maximize returns of shareholders.Franco Modigliani and Merton Miller’s proposition of irrelevance theory is based on the notion that firm’s financing mix does not impact the firm value. But in real world such assumptions does not hold good and irrelevance hypothesis mentions that there is conservation of firm value independent of the mix of corporate capital structure. However, empirical evidence also suggests that there is a positive relationship between optimal capital structure and a maximised firm value. In previous research, scholars have also considered various factors impacting the capital structure decision considering value maximisation and importance of these variables. The present study is focused on assessing the determinants of capital structure of Agriculture Sector Firms and assessing the impact of firm specific determinants of Agriculture 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 debt to equity ratio was used as a proxy for financial leverage. 2. RESEARCH METHODOLOGY This study is spread over a period of 9 years from 2001-2009 for Agriculture Sector Firms, which are listed on the Bombay Stock Exchange (BSE-500).The techniques used in this study are regression analysis and correlation analysis.The total numbers of firms which are selected for Agriculture Sector Firms is 18.The present study will rely on the data collected from secondary sources such as Annual reports of the companies, CMIE (Centre for Monitoring the Indian Economy) and Capitaline database. Model: Financial Leverage is dependent variable LEVi, t = β0 + β1RONWi,t+ β2 ROCEi,t+ β3ICRi,t+ β4NDTSi,t+β5PROFi,t+ β6COVAi,t + β7SIZEi,t+β8GROWTHi,t+ εit 3. OBJECTIVES OF THE STUDY 1. To assess the determinants of capital structure of Agriculture Sector Firms. 2. To assess the impact of firm specific determinants of Agriculture Sector Firms in deciding the financial structure. 4. HYPOTHESES Hypothesis 1: The capital structure of Agriculture Sector Firms has no impact on the value of the firm. Hypothesis 2: The firm-specific determinants of capital structure of Agriculture Sector Firms do not have any impact on the financial structure.
  3. 3. Journal of Management (JOM), ISSN 2347-3940 (Print), ISSN 2347-3959 (Online), Volume 2, Issue 1, January-June (2014), pp. 01-08 © IAEME 3 5. REVIEW OF LITERATURE Eriotis, Nikolaos (2007)conducted a study on “How firm characteristics affect capital structure: an empirical study” for 129 Greek companies using panel data procedure for the firm listed on the Athens stock exchange during 1997-2001. The focus of the study was to analyze the firm characteristics as determinants of capital structure according to different explanatory variables. The findings of the study justify that there is a negative relationship between the debt ratio of the firms and their growth, their quick ratio and their interest coverage ratio. The firms that maintain a relatively high interest cover ratio prefer to use less debt capital. Further, the debt ratio of the firm is positively related to its size. Thus, larger firms employ more debt capital in comparison with smaller firms, a finding which is consistent with the theoretical background of capital structure. The findings can be considered as an indication that firms generally finance their activities following the financing procedures implied by the pecking order theory. The study also proves to some extent on the financial theory and provides insights into understanding the impact of chosen financing mix on firm value. Serrasquiero and Rogao (2009)conducted a study on “Capital structure of listed Portuguese companies: Determinants of debt adjustment” to find out the impact of listed Portuguese companies specific determinants on adjustment of actual debt towards target debt ratio. The determinants are assets to tangibility, size, profitability, and market to book ratio to determine the optimal level of debt by using dynamic panel estimators and OLS repressions. The paper investigates the explanatory power of trade-off, Pecking Order Theory and Market Timing Theory which further contributes to a deeper understanding about the capital structure decision. The findings of the study are that the tangibility of assets and size are determinant that contribute for a greater adjustment of debt towards optimal level. The results also suggest that the transaction costs are relevant in listed Portuguese company’s access to debt. The findings of the study revealed that tangibility of assets and size are determinants that contribute for a greater adjustment of debt towards optimal level. Further, the influence of profitability and market to book ratio on adjustment of actual debt towards the optimum level of debt cannot be considered relevant. The results also suggest that the capital structure decisions of listed Portuguese companies can be explained in the light of Trade-off and Pecking Order hypothesis and not according to market timing theory. Sheikh, Nadeem Ahmed, Wang Zongjum (2011) conducted a study on “Determinants of capital structure: An empirical study of firms in Manufacturing industry of Pakistan” for a sample of 160 firms listed on Karachi stock exchangeduring 2003-2007 using panel data procedures. The aim of this empirical study is to explore the factors that affect the capital structure of manufacturing firms and to investigate whether the capital structure models derived from western settings provide convincing explanations for capital structure decisions of the Pakistani firms. The study has reviewed different conditional theories in order to formulate testable propositions concerning the determinants of capital structure of the manufacturing firms. The findings of the study are that profitability, liquidity, earnings volatility, and tangibility (asset structure) are related negatively to the debt ratio, whereas firm size is positively linked to the debt ratio. The findings of this study are consistent with the predictions of the Trade-Off Theory, Pecking Order Theory and Agency Theory.
  4. 4. Journal of Management (JOM), ISSN 2347-3940 (Print), ISSN 2347-3959 (Online), Volume 2, Issue 1, January-June (2014), pp. 01-08 © IAEME 4 6. ANALYSIS AND INTERPRETATION 6.1 Correlation Analysis for Agriculture Sector Firms The correlation coefficient was used to assess the determinant of capital structure and its influence in deciding the financial structure of Agriculture Sector. The independent and dependent variable are used to explain the inter-industry variation of capital structure determinants and its influence in deciding the financial structure. Table1.1 below exhibits the Pearson correlation between the financial leverage and the determinants of the capital structure for Agriculture Sector Firms. Table 1.1: Correlation for Independent and Dependent Variable for Agriculture Sector Firms FL RONW ROCE ICR NDTS PROF CVA SIZE GROW FL Pearson Correlation 1 .110 -.380 -.850** .883** .043 -.698** -.470* .370 Sig. (2-tailed) .663 .120 .000 .000 .866 .001 .049 .131 N 18 18 18 18 18 18 18 18 18 RONW Pearson Correlation .110 1 .751** .196 .294 .074 .520* .787** -.120 Sig. (2-tailed) .663 .000 .435 .236 .770 .027 .000 .634 N 18 18 18 18 18 18 18 18 18 ROCE Pearson Correlation -.380 .751** 1 .511* -.155 .129 .796** .902** -.214 Sig. (2-tailed) .120 .000 .030 .539 .611 .000 .000 .394 N 18 18 18 18 18 18 18 18 18 ICR Pearson Correlation -.850** .196 .511* 1 -.617** -.106 .732** .736** -.374 Sig. (2-tailed) .000 .435 .030 .006 .676 .001 .000 .127 N 18 18 18 18 18 18 18 18 18 NDTS Pearson Correlation .883** .294 -.155 -.617** 1 -.098 -.572* -.192 .113 Sig. (2-tailed) .000 .236 .539 .006 .699 .013 .445 .654 N 18 18 18 18 18 18 18 18 18 PROF Pearson Correlation .043 .074 .129 -.106 -.098 1 .159 .015 .425 Sig. (2-tailed) .866 .770 .611 .676 .699 .529 .954 .079 N 18 18 18 18 18 18 18 18 18 CVA Pearson Correlation -.698** .520* .796** .732** -.572* .159 1 .830** -.211 Sig. (2-tailed) .001 .027 .000 .001 .013 .529 .000 .402 N 18 18 18 18 18 18 18 18 18 SIZE Pearson Correlation -.470* .787** .902** .736** -.192 .015 .830** 1 -.307 Sig. (2-tailed) .049 .000 .000 .000 .445 .954 .000 .215 N 18 18 18 18 18 18 18 18 18 GROWTH Pearson Correlation .370 -.120 -.214 -.374 .113 .425 -.211 -.307 1 Sig. (2-tailed) .131 .634 .394 .127 .654 .079 .402 .215 N 18 18 18 18 18 18 18 18 18 **. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed). Source: Capitaline database
  5. 5. Journal of Management (JOM), ISSN 2347-3940 (Print), ISSN 2347-3959 (Online), Volume 2, Issue 1, January-June (2014), pp. 01-08 © IAEME 5 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 is positively correlated with return on net worth, non-debt tax shield, profitability, and growth. The positive relationship between return on net worth and financial leverage depicts that it has considerably increased due to which there is an increase in the use of reserves and surplus. An analytical study suggests that the company with high growth rates are likely to pay lower dividends, thus retained earnings are also cheaper source of finance. The positive relationship between return on capital employed and financial leverage is because of better utilization of both the sources of finance i.e. debt and equity. The positive relationship between financial leverage with non-debt tax shield shows that there is a strong and direct relationship between leverage and relative amount of non-debt tax shield. Another determinant of capital structure is growth that is likely to place greater demand on internally generated funds and push the firms into borrowing and hence suggest that future opportunities will be positivelyto leverage. The Static Trade-off Hypothesis pleads for the low level of debt capital of risky firms. The higher profitability of firms implies higher debt capacity for AgricultureSector Firms. Thus, the findings of the study are consistent with the previous findings which suggest that there is a positive relationship between the capital structure and profitability. The findings also revealed that the relationship between financial leverage and profitability depends on the effectiveness of the market for corporate control. If the market for corporate control is effective, managers of profitable firms are more interested in borrowing funds and lenders are also more willing to lend to profitable firms. Further results of the study suggest that financial leverage is negatively related with return on capital employed, interest cover ratio, collatralizable value of assets and size. The negative relationship between collatralizable value of assets and financial leverage indicates that increase in debt is used to finance current assets due to which the proportion of net fixed assets in total assets reduces. The negative relationship between size and financial leverage is found and the reason attributed for the same is that larger firms have more access to the equity market and may have accumulated internal finances than smaller firms. The highest degree of correlation can be observed between return on capital employed and size (0.902) which is statistically significant at0.01 level of significance. 6.2 Regression Analysis for Agriculture Sector Firms To study the variations in capital structure, the various dependent and independent variables are considered in Agriculture Sector Firms. Regression analysis was carried out for Agriculture 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. The adjusted R-square measures the proportion of the variation in the dependent variable taking into account the loss of degrees in freedom associated with adding extra variables. Table 1.2 depicts the summary statistics of Regression Analysis for Agriculture Sector Firms and the study also made use of ANOVA to examine the nature and differences in the capital structure of Agriculture Sector Firms as depicted in Table 1.3.
  6. 6. Journal of Management (JOM), ISSN 2347-3940 (Print), ISSN 2347-3959 (Online), Volume 2, Issue 1, January-June (2014), pp. 01-08 © IAEME 6 Table 1.2: Model Summary of Agriculture Sector Firms Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 .983a .966 .942 .0636542 1.515 a. Predictors: (Constant), GROWTH, NDTS, ROCE, PROF, ICR, RONW, CVA b. Dependent Variable: FL Source: Capitaline database Table 1.3 : ANOVA of Agriculture Sector Firms Model Sum of Squares Df Mean Square F Sig. 1 Regression 1.151 7 .164 40.581 .000a Residual .041 10 .004 Total 1.192 17 a. Predictors: (Constant), GROWTH, NDTS, ROCE, PROF, ICR, RONW, CVA. 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 Table 1.2 and 1.3 above. Table 1.3depicts 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 Agriculture Sector analysis, the correlation between dependent variables and predictor is represented as (0.983) 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 (0.966) explains that how much variation is explained by all the independent variables or predictors 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 (0.942) was statistically significant. It was suggested that the determinants of capital structure explained 94.2 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 D-W (Durbin-Watson) statistics is also applied. In the present study, the value (1.515) 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 1.3depicts F-values calculated to measure the significance of the model. It is observed that the overall regression model is significant (F=40.581, p<0.00) and also interprets that the model is constructed well. The reason for the same is that Agriculture Sector Firms are profitable firms and can exploit their market power in a situation of intensifying competition by increasing their borrowings to expand their output. The strategy is also beneficial for Agriculture Sector Firms as such firms have more profits to shield from taxes. Furthermore, agency costs will be higher once firms reach higher levels of profitability. The output of Multivariate Regression against financial leverage for Agriculture Sector is shown in the Table 1.4.
  7. 7. Journal of Management (JOM), ISSN 2347-3940 (Print), ISSN 2347-3959 (Online), Volume 2, Issue 1, January-June (2014), pp. 01-08 © IAEME 7 Table 1.4: Output of Multivariate Regression against Financial Leverage for Agriculture Sector Firms Model Unstandardized Coefficients Standardized Coefficients T Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) .412 .090 4.590 .001 RONW .316 .149 .289 2.126 .059 .184 5.421 ROCE -.159 .098 -.209 -1.618 .137 .204 4.907 ICR -.443 .115 -.388 -3.848 .003 .334 2.991 NDTS .470 .144 .445 3.256 .009 .182 5.497 PROF .012 .066 .012 .179 .862 .711 1.406 CVA -.117 .183 -.117 -.639 .537 .102 9.818 GROW .151 .079 .135 1.924 .083 .695 1.438 a. Dependent Variable: FL Source: Capitaline database From the 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 Agriculture 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 (0.412). The coefficient of return on net worth β1 is (0.316) and it is predicted that for every unit increase there is (0.316) unit increases in the financial leverage, holding all other variables constant. The coefficient of return on capital employed β2 is (-0.159), it is predicted that for every unit decrease in return on capital employed there is (0.159) unit decrease in financial leverage, holding all other variables constant. The coefficient of interest cover ratio β3 is (-0.443). So for every unit decrease (as it is negative value), there is a (0.443) unit decrease in financial leverage is predicted, holding all other variables constant. The coefficient of non-debt tax shield β4 is (0.470). So for every unit increase (as it is positive value) there is a (0.470) unit increase in financial leverage is predicted, holding all other variables constant. The coefficient of profitability β5 is (0.012). So for every unit increase (as it is positive value) there is a (0.012) unit increase in financial leverage is predicted, holding all other variables constant. The coefficient of collateralized value of assets β6 is (-0.117). So for every unit decrease (as it is negative value) in collateralized value of assets there is a (0.117) unit decrease in financial leverage is predicted, holding all other variables constant. The coefficient of growth β7 is (0.151). So for every unit increase (as it is positive value) in growth there is a (0.151) unit increase in financial leverage is predicted, holding all other variables constant. The higher the beta coefficient more is the contribution of determinants in explaining the variation in capital structure of Agriculture Sector Firms. As shown in the Table 1.4 above, results indicate that financial leverage is highly influenced by non-debt tax shield and considered as the most important determinant of capital structure, beta coefficient is (0.445). A negative coefficient of collatralizable value of assets for financial leverage indicates that increase in a debt is used to finance current assets due to which the proportion of net fixed assets in total assets reduces. Table 1.4 depicts that the highest VIF was about (9.818) which is less than 10 and hence multicollinearity is not a problem in case of Agriculture Sector.
  8. 8. Journal of Management (JOM), ISSN 2347-3940 (Print), ISSN 2347-3959 (Online), Volume 2, Issue 1, January-June (2014), pp. 01-08 © IAEME 8 7. CONCLUSION The positive relationship between return on net worth and financial leverage depicts that the return available to the firm has considerably increased and resulted into increase in the use of reserves and surplus.The reason for positive relationship with growth resulted into high growth rates and the firms are likely to pay lower dividends and thus retained earnings are considered as a cheaper source of finance. The positive relationship between return on capital employed and financial leverage is because of better utilization of both the sources of finance i.e. debt and equity.The relationship between non-debt tax shield and financial leverage is significant and positive and the reason for the same is that the use of non-debt tax shields i.e. depreciation etc. increases the effective tax rate and thus the value of the debt tax shield. The positive relationship of profitability with financial leverage is positive and the reason for the same is that the Agriculture Sector firms are efficient and their effectiveness of the market for corporate control is resulted into their commitment to pay out the debts in cash. BIBLIOGRAPHY [1] Eriotis, Nikolaos. ” How firm characteristics affects capital structure: an empirical study.” Managerial finance. Vol. 33, No. 5, 2007: 321-331. [2] Harris Milton and Arthur Raviv. “The Theory of capital structure.” Journal of Finance. Vol.46, 1991: 297-335. [3] Serrasquiero and Rogao. “Capital structure of listed Portuguese companies Determinants of debt adjustment.” Review of Accounting and Finance. Vol. 8, No.1, 2009: 54-75. [4] Sheikh, Nadeem Ahmed, Wang, Zangjun. “Determinates of capital structure: An empirical study of firms in Manufacturing Industry of Pakistan.” Managerial Finance. Vol.37, No.2, 2011: 117-133. [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] 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. [8] 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. [9] 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.

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