View of A STUDY ON IMPACT OF LEVERAGE ON DIVIDEND PAYMENT BEHAVIOUR

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Vol.04,Special Issue 05, (ICIR-2019) September 2019, Available Online: www.ajeee.co.in/index.php/AJEEE

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A STUDY ON IMPACT OF LEVERAGE ON DIVIDEND PAYMENT BEHAVIOUR (With special reference to Indian listed firms)

1Ravleen Kaur, ²Dr Pratima Jain

1Research Scholar, SRGPGPI, Indore, MP,

2Research Guide, DAVV, Indore

Abstract - The challenge before every financial manager in today’s developing market is to keep its investors contended. Apart from investing decision the focus today is deciding right mix of financing structure and along with it maximizing the shareholders wealth. A firm should focus its productive activities and select such a financing source, which will achieve the objective of wealth maximization. The financial leverage is an essential requirement for achieving optimal capital structure. An optimal capital structure affects the value of firm through reduced cost of capital. In the present study, an attempt was made to determinate optimal financial leverage and to find out its impact on the firm’s over all capital structure.

SPSS was used in data analysis using multiple regression models. The findings reveal that that variable financial leverage strongly and positively influences the EPS, ROE, RG and Size of sample companies.

Keywords: Dividend policy, leverages, Capital Structure 1 INTRODUCTION

The business objective of nearly all companies is to earn profits and maximises its firm value. Once the profits are generated, it then decides on what to do with those profits. The company may plan to hold back or retain the profits within the company, or they could pay it out to the owners of the firm in the form of dividends. The dividend policy decision involves two questions. First, what fraction of earnings should be paid out, on average, over time? Second, what type of dividend policy should the firm follow? Issues such as whether it should maintain stable dividend policy or growth in dividends or bonus issues etc. are important deciding factors. These decisions are crucial, as management has to satisfy various stakeholders from the profit. Out of the Stakeholders, priority is to be given to equity share -holders, as they are being the highest risk.

Dividend policy determines the distribution of the firm’s earnings and choosing between retention (that is reinvestment) and cash dividend payments to shareholders. The dividend policy theories focus on the issue of the relevancy of dividend policy to the value of a firm.

There are thus, conflicting viewpoints regarding the impact of dividend decision on firm’s value.

For financing their investments sources namely, internal and external funds are used.

Retained earnings and depreciation are included in the internal sources and new borrowings or the issue of stock is the external ones. Thus, the financing decision involves the decision of two choices. The first deals with the dividend, which involves the amount of retained earnings to be ploughed back and the remainder to be paid out as dividends. The second is the capital structure decision involving the portion of external finance to be borrowed and the portion to be raised in the form of new equity. Neither the dividend nor the capital structure decision should have an impact on the value of the firm. This is because both these decisions can be related to either the type of security, form of distribution, or mix of the ownership structure, but not to the investment decision. The financing decision determines the optimum mix of debt and equity, the relative numbers of shareholders and debt holders, and the decision for distribution of earnings between interest, dividends and capital gains. Financing and investment decisions are independent of each other and the latter determine the value of the firm. Therefore, as financing decisions have no effect on firm’s value, they are irrelevant. However, in practice, firms, managers, and investors, devote much time and resources in analysing financing decisions about dividends and capital structure.

2 LITERATURE REVIEW

The major contributors of the agency theory include Jensen and Meckling, 1976, Roseff, 1982 and Easterbrook, 1984. Agency cost theory explains the relationship between principals, for example shareholders and agents, such as a firm's executives. Agency cost is an economic concept concerning the cost to a principal, when the principal chooses or hires

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an agent to act on its behalf. Due to the separation between ownership and control, managers (agents) may not always act in the best interest of the firm’s owner. This encourages shareholders to incur agency costs to monitor managers’ behaviour. Dividend payments may help in reducing agency costs between managers and shareholders. As the two parties have different interests and the agent has more information, the principal cannot directly ensure that its agent is always acting in its best interests. Employing leverage in the company is one of the effective methods against manager’s behavior.

Al-Najjar (2009) conducted research in Jordan and used dividend per share, leverage, earning per share, institutional ownership, return on equity, and business risk as variables. He concluded, “The factors which affect the likelihood of dividend payment are similar to those which affect the dividend policy”. The results showed strong negative relation between institutional ownership and dividend policy; which were in accordance with signaling theory. Finally, the results showed that the Linter model is valid for Jordanian data, and that Jordanian firms have target payout ratios and that they adjust to their target relatively faster than firms in more developed countries.

Short et al. (2002) examine the potential association between ownership structures and dividend policy using three alternative dividends models for the UK companies. They were the pioneer to present results for UK, and concluded that ownership structures are different from those of the US. The results consistently showed positive association between dividend payout policy and institutional ownership. The results found that a negative association exists between dividend payout policy and managerial ownership. Gugler &

Yurtoglu (2003) investigates the relationship between dividend and ownership and control structure of the firm for 214 Austrian and German non-financial firms over the period of 1991-1999. The results indicate that state-controlled firms engage in dividend smoothing, whereas family-controlled firms do not. The findings show that over the years the dividend payouts have declined and high growth oriented firms have larger payout targets irrespective in whose hands the control of the firm is vested. Kumar (2003) examines the possible association between ownership structure, corporate governance and firm's dividend payout policy of all manufacturing firms over the period 1994-2000. He examines the payout behavior of dividends and the association of ownership structure for Indian corporate firms. Kumar finds positive association between ownership structure and dividend payout policy and that dividend payout are not influenced by the ownership structure of the firms. The results reveal that Debt and equity are negatively related whereas past investment opportunities have positive relationship with dividends.

Asif, A., Rasool, W., & Kamal, Y (2011) conducted a research on Pakistani companies to examine the effects of financial leverage on dividends. For the study, they collected the data of 403 companies registered on the Karachi stock exchange between the period 2002 and 2003. Descriptive analysis was carried out and regression and correlation analysis was used to examine the data. The results depicted that dividend policy is negatively affected by financial leverage as the firms having high debt capital tend to pay less dividends. The study concluded that among other variables debt ratios and dividend yield are highly significant determinants while framing dividend policy.

In addition, Ozdagli (2009) shows that there is a significant relationship between dividends offer by companies related to financial leverage. In fact, the leverage level of the company will reflect changes in returns to their investors. The higher the leverage levels of the company, the lower the return to the shareholders as companies have to bear the costs to pay their financing costs. However, Al-Kuwari (2009) emphasized that the relationship between financial leverage and company dividend payments was positive when the optimum capital structure was able to pay a high dividend compared to other companies.

Tobin's Q is a measure of the marginal efficiency of capital, computed for any asset or investment project. It is measured as the ratio of the market value of the asset to its replacement cost. The firms which invest optimally, choose only those projects in which Q ratio is more than or equal to one. Firms that choose projects with Q ratios of less than 1.0 are investing in projects having negative net present value and thus decreasing the wealth of its shareholders.

Jensen, Solberg and Zorn (1992) examined the joint determination of dividends, insider ownership of stock and leverage. They provided empirical evidence that dividends serve as means of reducing the conflict of interest between managers and shareholders. The study found that dividends are negatively related to leverage and also to other insider holdings

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after controlling profitability, growth prospects and investment opportunities. These results remain consistent with Jensen’s free cash flow explanation of dividend policy. Lang and Linzenberger (1989) compared investor reaction to dividend changes by managers suspected of over investing. Managers, who optimally invest, generate a market –to book ratio (called Tobin’s Q ratio) that exceeds 1 because the market value reflects the investment (the book value) plus the net present value of the investment. Using the same logic, a Q ratio of less than 1 indicates overinvestment .An increase in the dividend payout by a firm with Q ratio of less than 1 is good news because it means lesser money spent on sub optimal investment. For a firm with a Q ratio exceeding 1, however such a dividend increase merely reflects optimal investment decisions. A mirror argument applies to dividend decreases. Lang and Litzenberger found that action to dividend changes by firms having a low Q ratio. This evidence supports the argument that dividends may constrain management’s ability to invest beyond the levels that shareholders desire.

2.1 Interrelation of Leverage & Dividend payments

According to the signaling theory, if a company pays out more dividends then this acts as a signal about its strong financial performance among its investors. As it is common notion that only profit making firms, pay out cash dividends. It also increases the credit worthiness of the firm. Moreover, dividends paying companies are able to raise more debt hence dividend payment is determinant of leverage. Thus on this ground it can be said that dividend payout positively influences leverage. However, contrary, leverage is not found to be a determinant of dividend payments. Thus, there exists a negative relation between the two because firms having more borrowed funds prefer to retain earnings to repay debt instead of paying more dividends. This argument was in support to the arguments of Al- Malkawi (2007), Patra et al. (2012) and Al-Najjar (2009).

2.2 Objectives of The Study

1. To study the impact of leverage and dividend payment behaviour of companies of different sectors.

2. To study the leverage of Indian listed companies during the period 2009- 2017 and to know about the impact of financial leverage (Fixed financial expenses) on Earnings per Share (EPS).

3. To study the undervalued and overvalued firms based on Tobin’s Q.

3 METHODOLOGY

Current study is divided into two parts; in first part, we have drawn the conclusion based on capital structure ratio, while in second part we tried to establish the relationship between dividend policy, financial leverage and capital structure of the firms.

3.1 Research Methodology

Present study was descriptive in nature, by using the systematic research conclusion was drawn. For this purpose, data was collected in its current state and both quantitative and qualitative data was used. Data was collected from different websites (like, nseindia.com, moneycontrol.com, and yahoofinance.com), Print media, newspapers, accounts that are produced by the company to the stock exchange and official website of the respective company. We used the data of top 25 market capitalization companies, which are listed in index of national stock exchange during the period of 1 April 2009 to 31 March 2017. The data period starts from 1 April 2009 to 31 March 2017. The data used in this category was collected from official website of national stock exchange, consider that data sources used as highly reliable due to its function for the financial markets and is core business competences within data supply and gathering. Data were analyzed through SPSS (version 18).

3.2 Data Analysis

Data were analyzed through SPSS (version 18). Variables were calculated. The descriptive statistics were utilized to further analyze the data and included percentage, mean, median, maximum, minimum and median.

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4 3.3 Three Stage Least Squares Modeling

For this criterion three stages least square modelling was used, for this purpose the 3 different equations for the determination of relationship between leverage, capital structure and dividend policy were used. Above-mentioned variables taken as dependent variable while other factors taken as explanatory variables of the relationship between these three.

For completion of this firstly the ordinary least square method have been estimated, then the two stage least square method was computed.

3.4 Hypothesis Testing

Being followed above model impact of financial leverage on dividend payment behaviour of all 25 companies of different sector was tested through regression analysis. For which dividend payment behaviour were measured through EPS, DPS, ROE, Tax, and RG. Further more impact of all these variables were calculated on Size of that firm.

In first equation relationship between financial leverage and all variables of dividend payment behaviour were established for all 25 companies in table below.

In the second equation relationship between all variables of dividend payment behaviour and size were established for all 25 companies in table below.

Hypothesis for the research was set as follows:

H01: There is negative relationship between Financial Leverage and earnings per share, dividend per share, ROE, tax, revenue growth and size

H02: There is negative relationship between size and EPS, DPS, ROE, Tax and RG 4 RESULTS & DISCUSSIONS

4.1 Correlation

Correlation and regression analysis are related in the sense that both deal with relationships among variables. The correlation coefficient is a measure of linear association between two variables. Values of the correlation coefficient are always between -1 and +1. A correlation coefficient of +1 indicates that two variables are perfectly related in a positive linear sense, a correlation coefficient of -1 indicates that two variables are perfectly related in a negative linear sense, and a correlation coefficient of 0 indicates that there is no linear relationship between the two variables. For simple linear regression, the sample correlation coefficient is the square root of the coefficient of determination, with the sign of the correlation coefficient being the same as the sign of b1, the coefficient of x1 in the estimated regression equation.

4.2Regression Analysis

RG Tax Financial

Leverage ROE

DPS EPS

Size (Capital Structure)

RG Tax Financial

Leverage ROE

DPS EPS

Size (Capital Structure)

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Regression analysis involves identifying the relationship between a dependent variable and one or more independent variables. A model of the relationship is hypothesized, and estimates of the parameter values are used to develop an estimated regression equation.

Various tests are then employed to determine if the model is satisfactory. If the model is deemed satisfactory, the estimated regression equation can be used to predict the value of the dependent variable given values for the independent variables.

In table below, only having significant relationship between and among independent and dependent variables were taken with r square, beta, F value, T test, and slope.

Table1: Significant relationship among Variables (R square, F value, T Test and slope)

Company 1: ACC

Independent Dep R² F Sig T Sig Beta a b

ROE Size .686 13.105 .011^a -3.620 .011 -.828 14439.965 -186.782

RG Size .863 37.759 .001^a 6.145 .001 .929 5915.291 .550

Debt Equity Size .625 10.018 .019^a -3.165 .019 -.791 12594.561 -23835.566 Company 2: Adani Port

Size FL .476 5.449 .058^a 2.334 .058 .690 1.077 8.382E-6

FL Size .476 5.449 .058^a 2.334 .058 .690 -52516.622 56783.792

RG Size .940 93.853 .000^a 9.688 .000 .969 -2926.861 4.979

Company 3: Asian Paint

FL Tax .619 9.757 .020^a 3.124 .020 .787 1.006 3.143E-5

FL RG .449 4.885 .069^a 2.210 .069 .670 1.004 1.710E-6

FL Size .578 8.217 .029^a 2.866 .029 .760 1.007 2.610E-6

FL Size .578 8.217 .029^a 2.866 .029 .760 -220800.846 221448.573

Tax Size .953 121.294 .000^a 11.013 .000 .976 67.256 11.356

RG Size .972 210.108 .000^a 14.495 000 .986 -1821.235 .733

Debt Equity Size .544 7.145 .037^a -2.673 .037 -.737 7350.797 -66610.280 Company 4: Bajaj Auto

FL ROE .531 6.803 .040^a -2.608 .040 -.729 1.149 -.004

DPS Size .864 38.228 .001^a 6.183 .001 .930 -2694.203 340.326

Tax Size .899 53.592 .000^a 7.321 .000 .948 2769.247 8.454

RG Size .861 37.316 .001^a 6.109 .001 .928 -147.109 .657

Debt Equity Size .629 10.177 .019^a -3.190 .019 -.793 13457.082 -9416.507 Company 5: Bharti Airtel

ROE Size .481 5.550 .057^a -2.356 .057 -.693 155799.583 -3898.772

Tax Size .788 22.270 .003^a 4.719 .003 .888 11608.492 55.195

RG Size .744 17.421 .006^a 4.174 .006 .862 -86785.102 3.636

Company 6: BHEL

FL Tax .442 4.762 .072^a 2.182 .072 .665 .862 5.975E-5

FL Debt Equity .814 26.235 .002â -5.122 .002 -.902 69377.921 -863.875 DPS Size .814 26.235 .002â -5.122 .002 -.902 69377.921 -863.875 EPS Size .714 14.974 .008â -3.870 .008 -.845 68496.400 -316.876 ROE Size .558 7.577 .033â -2.753 .033 -.747 71163.958 -637.350 Company 7: BOSCH

FL EPS .428 4.495 .078^a 2.120 .078 .654 .821 .000

FL Tax .439 4.700 .073 a 2.168 .073 .663 .805 .000

FL RG .750 17.978 .005 a 4.240 .005 .866 .716 2.670E-5

FL Size .707 14.448 .009 a 3.801 .009 .841 .759 2.290E-5

FL Debt Equity .853 34.749 .001 a -5.895 .001 -.923 1.029 -2.154

EPS Size .827 28.595 .002^a 5.347 .002 .909 1688.199 19.547

FL Size .707 14.448 .009^a 3.801 .009 .841 -20850.392 30858.973

Tax Size .802 24.359 .003^a 4.936 .003 .896 1096.916 17.671

RG Size .745 17.576 .006^a 4.192 .006 .863 -159.830 .977

Debt Equity Size .931 81.072 .000^a -9.004 .000 -.965 11416.363 -82636.441 Company 8: Cipla

FL ROE .515 6.373 .045 a -2.524 .045 -.718 1.139 -.007

FL RG .570 7.963 .030 a 2.822 .030 .755 .947 1.084E-5

FL Size .702 14.153 .009 a 3.762 .009 .838 .947 8.732E-6

FL Size .702 14.153 .009^a 3.762 .009 .838 -72894.060 80423.400

ROE Size .777 20.9277 .004^a -4.575 .004 .882 2227.050 -831.193

RG Size .925 74.035 .000^a 8.604 .000 .962 -781.302 1.324

Company 9: Eicher Motors

FL Debt Equity .535 6.913 .039 2.629 .039 .732 1.000 .445

DPS Size .989 455.258 .000 21.337 .000 .995 346.034 33.832

EPS Size .973 177.667 .000 13.329 .000 .986 696.825 6.247

ROE Size .710 12.228 .017 3.497 .017 .842 402.567 41.103

Tax Size .938 75.283 .000 8.677 .000 .968 821.487 4.144

RG Size .988 420.359 .000 20.503 .000 .994 256.466 .442

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Company 10: HCL Technology

FL Tax .619 9.757 .020 a -3.124 .020 -.787 1.065 -4.574E-5

FL RG .530 6.768 .041 a -2.601 .041 -.728 1.085 -4.757E-6

FL Size .470 5.320 .061 a -2.307 .061 .686 1.079 -3.004E-6

DPS Size .639 10.643 .017^a 3.262 .017 .800 4932.182 734.628

FL Size .470 5.320 .061 a -2.307 .061 -.686 177007.819 -156471.64

Tax Size .946 106.110 .000 a 10.301 .000 .973 6173.786 12.908

RG Size .871 40.515 .001^a 6.365 .001 .933 66.197 1.392

Debt Equity Size .503 6.072 .049^a -2.464 .049 -.709 21020.139 -62648.665 Company 11: Hindalco

FL Tax .697 13.799 .010 a -3.715 .010 -.835 4.426 -.007

FL RG .627 10.090 .019 a 3.176 .019 .792 -2.615 .000

FL Size .525 6.621 .042 a 2.573 .042 .724 -1.421 5.475E-5

DPS Size .719 15.387 .008^a -3.923 .008 -.848 134430.149 -60434.073 EPS Size .610 9.378 .022^a -3.062 .022 -.781 87146.894 -3576.576

FL Size .525 6.621 .042^a 2.573 .042 .724 41971.527 9581.726

ROE Size .871 40.347 .001^a -6.352 .001 -.933 9196.730 -9653.666 Tax Size .821 27.534 .002^a -5.247 .002 -.906 96719.468 -96.254

RG Size .891 49.018 .000^a 7.001 .000 .944 -10677.691 2.593

Debt Equity Size .498 5.951 .051^a 2.439 .051 .076 28228.243 57774.316 Company 12: Hind Uni Lever

DPS Size .575 8.116 .029^a 2.849 .029 .758 7874.482 311.166

EPS Size .836 30.489 .001^a 5.522 .001 .914 4807.517 442.491

Tax Size .948 109.789 .000^a 10.478 .000 .974 7044.234 3.503

RG Size .881 44.445 .001^a 6.667 .001 .939 2685.889 .350

Company 13: Infosys

FL ROE .672 12.296 .013 a -3.507 .013 -.820 32.409 -.002

FL Tax .673 12.327 .013 a -30511 .013 -.820 34.356 .000

FL RG .668 12.090 .013 a -3.477 .013 -.818 32.692 .000

ROE Size .438 4.669 .074 ^a -2.161 .074 -.662 5.417 .000

Tax Size .444 4.800 .071 ^a -2.191 .071 -.667 5.748 -3.228E-5

RG Size .542 7.102 .037 ^a -2.6665 .037 -.736 5.589 -2.812E-5

Company 14: IOC

FL Tax .544 7.147 .037 a -2.673 .037 -.737 2.901 .000

FL RG .176 1.283 .300 1.133 .300 .420 -.892 7.055E-6

RG Size .532 6.834 .040 2.614 .040 .730 23942.765 .449

Company 15: ITC

EPS Size .556 7.521 .034 2.743 .034 .746 -4501.470 3826.425

FL Size .252 2.019 .205 -1.421 .205 -.502 1.768E6 -1.725E6

Tax Size .999 4.691E3 .000 68.491 .000 .999 6319.223 8.500

RG Size .941 95.004 .000 9.747 .000 .970 -5443.567 1.227

Company 16: LT

DPS Size .677 12.574 .012 3.546 .012 .823 -22729.184 5980.425

ROE Size .905 57.382 .000 -7.575 .000 -.951 137357.434 -3764.255

RG Size .876 42.516 .001 6.520 .001 .936 -42308.554 1.964

Debt Equity Size .475 5.421 .059 -2.328 .059 -.689 123461.284 -164591.54 Company 17: Maruti

DPS Size .839 31.340 .001 5.598 .001 .916 15056.849 795.729

EPS Size .774 20.568 .004 4.535 .004 .880 6976.818 200.763

Tax Size .558 7.563 .033 2.750 .033 .747 12470.686 13.674

RG Size .968 184.041 .000 13.566 .000 .984 -7983.809 .718

Company 18: PFC

Tax Size .972 206.621 .000 14.374 .000 .986 56261.787 66.713

RG Size .966 172.822 .000 13.146 .000 .983 27619.530 7.523

Company 19: PNB

DPS Size .827 23.857 .005 -4.884 .005 .909 666740.952 -9612.288

EPS Size .843 26.939 .003 -5.190 .003 -.918 666143.733 -1521.039 ROE Size .733 13.705 .014 -3.702 .014 -.856 639180.625 -9367.221

Tax Size .556 6.264 .054 -2.503 .054 -.746 619840.455 -66-.724

RG Size .903 46.310 .001 6.805 .001 .950 -55846.040 12.956

Book to market

Size .532 5.676 .063 2.383 .063 .729 333269.376 177173.041 Company 20: Power Grid

DPS Size .501 6.016 .050^a 2.415 .050 .708 31707.860 39156.147

EPS Size .915 64.854 .000^a 8.053 .000 .957 -11818.581 15361.619

Tax Size .703 14.183 .009^a 3.766 .009 .838 14746.908 92.361

RG Size .961 148.682 .000^a 12.194 .000 .980 13098.915 7.419

Company 21: Reliance

FL Tax .614 9.549 .021 a -3.090 .021 -.784 1.145 -8.227E-6

FL Size .515 6.363 .045 a -2.523 .045 -.717 1.142 -1.353E-7

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DPS Size .897 52.388 .000a 7.238 .000 .947 -283093.86 70533.392

FL Size .515 6.363 .045 a -2.523 .045 -.717 4.523E6 -3.804E6

ROE Size .504 6.091 .049 a -2.468 .049 -.710 1.128E6 -64611.731

Tax Size .945 103.753 .000^a 10.186 .000 .972 20979.203 54.139 Company 22: Reliance Infra

DPS FL .967 173.821 .000^a 13.184 .000 .983 -5.408 .920

Size FL .600 9.007 .024^a 3.001 .024 .775 -.580 5.150E-5

DPS Size .681 12.813 .012^a 3.579 .012 .825 -45338.170 11611.695

FL Size .600 9.007 .024^a 3.001 .024 .775 24615.355 11653.280

Debt Equity Size .620 9.776 .020^a 3.127 .020 .787 25781.810 36289.717 Company 23: Shree Cement

FL Tax .587 8.514 .027^a -2.918 .027 -.766 1.357 .000

DPS Size .598 8.926 .024^a 2.988 .024 .773 5862.674 39.987

EPS Size .537 6.948 .039^a 2.636 .039 .733 3891.756 15.073

RG Size .798 23.733 .003^a 4.872 .003 .893 374.705 1.170

Debt Equity Size .534 6.888 .039^a -2.625 .039 -.731 8988.638 -4394.746 Company 24: Siemens

FL Tax .523 6.591 .042^a -2.567 .042 -.724 1.117 .000

Tax Size .513 6.322 .046^a 2.514 .046 .716 9834.568 1.727

Company 25: Tata Power

FL ROE .841 31.718 .001^a -5.632 .001 -.917 5.380 -.460

FL Size .569 7.935 .030 2.817 .030 .755 -.1.596 .000

DPS Size .575 8.114 .029 -2.848 .029 -.758 33466.063 -1085.771

EPS Size .634 10.384 .018 -3.222 .018 -.796 33443.794 -346.104

FL Size .569 7.935 .030 2.817 .030 .755 20076.842 4723.900

ROE Size .564 7.749 .032 -2.784 .032 -.751 46881.603 -2356.937

The regression is calculated by taking the total of different combinations of dependent and independent variables by using SPSS software. The above table comprises coefficient table, Model summary^b and anova table which indicates beta value above 0.5 tested through t- test having t value of above 2.0 which is significant at 0.05%. Since the r² value of is above .5, which means significant impact on dependent variables. Linear Regression between independent variables and dependent variables has good fit as indicated by F-test value, which is above 5.0 and significant at least .05^a level.

In the end, different ratios and Tobin’s Q was calculated and result of this ratio indicates that in 46% of the firm’s stock are more expensive than the replacement cost of its asset, and for rest of the firm the stock price is undervalued. While in most of the firm current ratio and quick ratio have been found much higher than normal value, which indicates that most of the companies have better liquidity in day-to-day business. Moreover, we also computed book to market value ratio and result indicates that 70% of companies have been underperforming during the analysis period.

Company Tobin’s Q Current ratio Quick ratio Book to market ratio

ACC 1.08 4.05 3.01 0.35

ADANI PORTS 1.056 3.54 2.96 0.512

ASIANPAINT 0.865 5.46 3.86 0.201

BAJAJ-AUTO 1.005 6.44 4.253 0.186

BHARTIARTL 0.94 4.25 3.25 0.264

BHEL 1.853 1.36 1.21 1.256

BOSCH 0.751 3.25 2.69 4.24

CIPLA 1.089 3.254 0.964 0.456

EICHER MOTORS 1.02 6.55 4.67 6.241

HCLTECH 0.752 8.652 6.521 0.159

HINDALCO 1.185 6.254 5.05 1.025

HINDUNILVR 0.9554 8.054 6.91 0.185

INFY 0.756 7.052 4.99 0.215

IOC 1.056 3.02 2.057 1.053

ITC 0.954 6.154 4.081 0.524

LT 0.642 3.058 1.99 0.764

MARUTI 0.954 6.024 4.509 2.15

PFC 1.65 2.84 2.084 2.351

PNB 0.99 3.05 1.05 0.128

POWERGRID 1.452 4.90 3.04 1.648

RELIANCE 1.26 5.412 4.015 0.856

RELINFRA 1.09 5.0142 2.66 0.612

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SHREE CEMENT 1.26 3.052 2.06 6.275

SIEMENS 1.054 3.0543 2.789 0.5124

TATAPOWER 1.259 3.0124 2.05 1.006

5 CONCLUSIONS AND SUGGESTIONS

1. Study reveals that the variable financial leverage strongly and positively influences the EPS, ROE, RG and Size of companies.

2. Study also reveals that the variables EPS, ROE and RG strongly and positively influence the Size of companies.

3. Study also reveals that impact of independent variables varies sector wise.

4. It is suggested to the research scholars to include wide spread areas for further studies so that the results may be generalized for the entire business sectors of the country. In addition, the researchers are suggested to collect data for more years for further studies. The study reveals that some companies provide lack of financial information, which was a limitation for the present study. Therefore, to overcome the problem the companies are suggested provide adequate information required to enhance the leverage and dividend payment behaviour.

5. Last but not the least, all business sectors are advised to reduce their business risks regarding the factors that influence the revenue growth of the companies.

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