CHAPTER VI

Analysis of Economic Value Added (EVA) and Market Value Added (MVA)

Maximizing shareholders value is becoming the new corporate standard in India. The corporates, which give the lowest preference to the shareholders’ inquisitiveness, are now bestowing the utmost inclination to it. Shareholder’s wealth in terms of the returns they receive depends on their investment. The returns can either be in the form of dividends or in the form of capital appreciation or both. Capital appreciation in turn depends on the subsequent changes in the market value of the shares. This market value of shares is influenced by a number of factors, which can be company specific, industry specific and macro-economic in nature1.

An important goal of financial management is to maximise the wealth of the organisation, highest capital employees wealth and consequently enhance the value of the firm. Shareholder’s wealth is traditionally reflected by either standard accounting parameters (such as profits, earnings and cash flow from operations) or financial ratios (including earnings per share, return on capital employed, return on net worth, net profit margin, operating profit margin etc). All these indicators fail to measure the true economic worth due to manipulative accounting techniques to state higher or lower earnings, depending on non-meaningful decision on how to record revenues or expenses. Standard accounting principles fail to reflect the varying cost of capital among the business within a company or the difference in risk in the case of alternative business strategies in the earnings.

1.

Mangala, Deepa and Joura Simpy, (2002) Linkage between economic value added and market value: An Analysis in Indian context, Indian Management Studies, Journal, pp-55-56.

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This financial information is used by managers, shareholders and other interested parties to asses their firm’s current performance, and also by stakeholders to predict its future performance. The question that then arises is, whether these measures of corporate performance are linked to the expectation of the shareholders or not. The problem with their performance measure is the lack of a proper benchmark for comparison. To help corporate to generate value for shareholders, value-based management system has been developed. Indeed, value based management, which seeks to integrate finance hypothesis with strategic economic philosophy, is considered as one of the most significant contributions to corporate financial planning2.

Over the past several years, an alternative performance measure called the Economic Value Added (EVA) has been gaining acceptance around the globe and has also been acknowledged by institutional firms as a credible performance measure in order to overcome the limitations of accounting based measures of financial performance. Joel M stern and G. Bennett Stewart & co., introduced a modified concept of economic profit in 1990, in the name of Economic Value Added (EVA) as a measure of business performance. Stern Stewart has claimed that EVA, as a tool of financial management, is neither ‘just a phenomenon’ nor is it united to ‘for profit’ organizations. Economic value added has been put to use for management performance evaluation, and more than just a measure of performance, it is the framework for a complete financial management (for improving scarce capital allocation; and valuation of a target company at the time of acquisition).

EVA as a tool of financial performance measurement Shareholders value creation is the new buzzword today and Economic Value Added (EVA) is its most popular measure. In simple terms EVA is 2.

MC Taggart, James et al., (1994). The Value imperative, Free Press; New York, PP 4-6.

214

nothing but returns generated above cost of capital. It is the Net Operating Profit After Tax (NOPAT) minus an appropriate change for the opportunity cost of all capital invested (WACC) in an organization. EVA is an estimate of “economic profit” or the amount by which earnings exceed or fall short of the required minimum rate of return that shareholders and lenders could get by investing capital in other securities of analogous risk3.

EVA as a tool of financial measurement enlightens whether the operating profit is enough to cover the cost of capital. Shareholders must earn sufficient returns for the risk they have taken in investing their funds in companies’ capital. According to business standard-KPMG, if a company’s EVA is negative, the firm is destroying shareholder’s wealth even though it may be reporting a positive and growing earning per share and return on capital employed4. The EVA framework, which is becoming more and more admired tool for measuring the financial performance of corporate, offers a consistent approach to set goals and measure performance, communicate with investors, evaluate strategies, allocate capital valuing acquisitions and determine incentive bonuses. It is one of the several on going initiatives for new corporate.

The evaluation and growth of the concept EVA, which may be realistically in young age in the west has been going through its childhood in country like India. It may be quite an emerging concept in the minds of Indian corporate policy makers and managers. Hence this chapter examines in detail the EVA of selected automobile industry. It consists of sub-parts like EVAbased ranking of selected companies, industry-wise and sector-wise trends in EVA-based ranking. Results and discussion on statistically established trends.

3. 4.

Jaishweta, (2003). Godrej Retools for Value, Business Standard, P. 6. Purikh, Parag, (2002). The Universe of Wealth Creation, PPFAS-Financial Advisory Services Ltd-Online P. 2.

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217

This chapter also examines the linear regression analysis in the midst of Market Value Added (MVA) and other traditional financial variables like EVA, EPS, ROCE, NOPAT and RONW of sample companies. It also discusses multiple regression analysis and MVA and other financial variables of sample companies sector-wise.

EVA of selected companies EVA-based performance framework not only provides a far more accurate report card on corporate financial performance than conventional measures, but also has considerable implications for companies on how to make strategic decisions and manage the healthier financial performance in their pursuit of shareholder value. EVA created by the selected automobile industry during the study period is depicted in Table 6.1. The table shows that out of twelve industry, eleven industry has generated positive EVA during the study period except in the year 1998-99, 2002-03, 2004-05 and one company has destroyed their shareholder’s wealth completely.

It may be observed from Table 6.1 that Ashok Leyland Ltd and Eicher Motors Ltd out of twelve companies have been generating the positive EVA all the way throughout the period of study. On the other hand, Daewoo Motors India Ltd is the only company which has been annihilating the wealth of shareholders right through the period except in the year 1995-96. Tata Motors Ltd, Bajaj Auto Ltd, Maharastra Scooters India Ltd created positive EVA during the major part of eleven years period. Rest of the companies slightly showed instability on their front.

On the whole the Table 6.1 concludes that about one-third (4 out of 12), of the sample companies have been able to govern affirmative EVA during period under study whereas remaining companies are feasible to append a very little to the value of shareholders. 218

219

Table 6.3 EVA-Sector-Wise Trends (1995-96-2005-06) S.No

Mean (Rs.in.crores)

Industry

CV

CAGR

1.

Ashok Leyland Ltd

241.06

0.28

4.14

2.

Tata Motors Ltd

233.21

4.22

-6.89

3.

Eicher Motor Ltd

22.21

0.66

8.54

4.

Swaraj Mazda Ltd

12.05

0.46

5.20

127.33

2.03

-2.71

10.19

9.73

-6.15

149.55

3.24

-3.10

49.14

22.16

-22.32

-62.34

1.17

-0.77

36.63

11.38

-17.85

541.65

1.36

-12.57

Commercial Vehicles Sector 5.

Hindustan Motors Ltd

6.

Mahindra and Mahindra Ltd

7.

Maruthi Udyog Ltd

8.

Daewoo Motors India Ltd Passenger Cars and Multiutility Vehicles Sector

9.

Bajaj Auto Ltd

10

Maharastra Scooters Ltd

25.90

1.67

-3.23

11.

TVS Motors India Ltd

45.97

3.00

-17.28

12.

Hero Honda Motors Ltd

201.02

0.92

24.94

Two and Three Wheelers Sectors

203.65

0.92

-2.21

Whole Automobile Industry

122.54

1.86

-6.15

Source: Computed

220

EVA based ranking of selected companies Table 6.1 also presents EVA-based ranking of sample companies. It is evident from the table that companies like Tata Motors Ltd, Ashok Leyland Ltd are toping in the list during the study period. On the other hand companies like Hindustan Motors Ltd and Daewoo Motors India Ltd have been loosing the grounds. Rest of the companies have indexed unsteady position during the study period. EVA based frequencies distribution of sample companies are shown in Table 6.2. It is clear from that seven companies in 1998-99, 2002-03, two in 2003-04 and one company in 2004-05, 2005-06 are reporting negative EVA and the remaining companies are generating positive EVA during the study period. It is also observed that more than 33 1/3 per cent of the companies have added to the economic value between Rs.100-500 crores during the study period and only two companies in 1995-96, three in 1997-98, four in 1999-2000, two in 2001-02 and one in 2002-03 reported an EVA of over Rs.500 crores. Sector wise trends in EVA Table 6.3 presents sector wise EVA of sample companies. It is evident from Table 6.3 that the mean EVA generated for the automobile industry is Rs.122.54 crores during the study period. The mean EVA generated is highest in two and three wheelers sectors followed by commercial vehicles sectors and passenger cars and multiutility vehicles sector. Two and three wheelers sector and commercial vehicles sector should perform well in this regard because their average is more than the industry average. It is also evident from the table that all selected sectors and whole industry witnessed very high fluctuation in their EVA during the study period. Table 6.3 further reported that the commercial vehicles, passenger cars and multi-utility vehicle sector and few of two and three wheelers sectors and whole industry registered negative compound annual growth rate of EVA during the study period. 221

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The economic value added of selected industry under commercial vehicles sector during the study period is presented in Table 6.3. The mean EVA was highest in Ashok Leyland Ltd followed by Tata Motors Ltd, Eicher Motors Ltd and Swaraj Mazda Ltd. All the industry under the sector had registered very high fluctuation in their EVA during the study period. It is also evident from the table that Tata Motors Ltd registered negative compound annual growth rate of EVA during the study period.

Table 6.3 also depicts the EVA generated by the selected companies under passenger cars and multiutility vehicles sector during the study period. It portrays that Daewoo Motors India Ltd showed negative EVA throughout the study period. The mean of Mahindra and Mahindra Ltd was highest followed by Maruthi Udyog Ltd and Hindustan Motors Ltd. All the companies registered very high fluctuation in their EVA during the study period. All the companies witnessed negative compound annual growth rate of EVA.

The EVA generated by the companies under two and three wheeler sector during the study period is presented in Table 6.3. It is evident from the table that the mean of Bajaj Auto Ltd was highest followed by Hero Honda Motors Ltd, TVS Motors Company Ltd and Maharastra Scooters Ltd. All the companies registered very high fluctuation in their EVA during the study period. The compound annual growth rate of all companies was negative except Hero Honda Motors Ltd during the study period.

The sector wise paired test provides the value of t test in Table 6.4. The table exhibits that there has been significant deviation (at 5% level) in the EVA of respective years except for the year 2001-2002 to 2004-2005.

223

224

Comparison of EVA and conventional method of financial performance Analysing the corporate performance of Indian automobile industry based on Return On Capital Employed (ROCE) the conventional benchmarks and on the new “trendier” one i.e., EVA, the results can be well exhibited in Table 6.5. From the table, it can be inferred that Indian automobile industry depicts a ROCE picture in terms of return on capital employed. The mean value of return on capital employed of automobile industry during the study period 24.51 per cent i.e., for every Rs.100 investment, the return is Rs.24.51 whereas EVA as a percentage of capital employed is only 7.04 i.e for every Rs.100 investment the company has added value of Rs.7.04. The same picture is reflected as in case of all three sectors. Thus, the comparison shows that divergence is less existent between the performance results given by traditional measure and EVA. However, the traditional measures do not reflect the real value addition to shareholders wealth and thus EVA has to be measured to have an idea about the shareholders value addition.

Market Value Added (MVA) of selected companies The MVA explains the value added to a particular equity share over its book value. It informs how much value has been added in the economic value of the shareholders. In view of that, a company with an objective of pleasing to the eyes of the shareholders wealth should endeavor to take advantage of its MVA. MVA can be estimated by subtracting the book value of shares from the market value of shares. It is silent that EVA helps in pushing up the MVA of an organisation. Thus, EVA can be considered as an internal measure and MVA as the external measure of a company’s financial performance. Table 6.6 presents MVA calculation of selected companies of Indian automobile industry. On the base of the table, it may be observed that out of 12 companies, 11 companies have registered positive MVA throughout the

225

226

227

study period. It indicates that the market value of these companies is dominating over the book value. On the other hand, Hindustan Motors Ltd (1997-98, 1999-00 to 2001-02), Daewoo Motors India Ltd (1997-98, 1999-00 to 2003-04) have registered negative MVA during the study period. It shows that the book value of these companies is dominated over the market value.

MVA based ranking of selected companies Table 6.6 also provides MVA based ranking. Glancing all the way through the table, it is noticed that all companies like Maruthi Udyog Ltd, Bajaj Auto Ltd, Hero Honda Motors Ltd, Tata Motors Ltd, Ashok Leyland Ltd are topping the list and on the other side companies like Daewoo Motors India Ltd, Swaraj Mazda Ltd and Maharastra Scooters India Ltd, are struggling in terms of MVA over the period.

Sector wise trends MVA Table 6.7 portrays whole automobile industry and sector wise information pertaining to MVA. It is evident from Table 6.7 that among the three sectors, passenger cars and multiutility vehicles sector have been generating highest market value added throughout the study period. This was due to better market value added of Maruthi Udyog Ltd and Mahindra and Mahindra Ltd. It was followed by two and three wheeler sector. Table 6.7 also shows that all the selected sectors of automobile industry have been generating aggregate MVA throughout the period. The growth of MVA is consistent in case of passenger cars and multiutility vehicles whereas less consistent in case of commercial vehicles and two and three wheelers sector. Table also brings out that only the commercial vehicles sector had registered negative compound annual growth rate of MVA during the study period.

228

Table 6.7 MVA-Sector-Wise trends (1995-96 to 2005-06) S.No

Mean (Rs.in.crores)

Industry

CV

CAGR

1.

Ashok Leyland Ltd

1664.59

0.86

14.78

2.

Tata Motors Ltd

3695.73

0.96

-10.93

3.

Eicher Motor Ltd

245.68

1.24

30.59

4.

Swaraj Mazda Ltd

124.10

1.07

25.26

1432.52

0.60

-2.45

152.72

1.60

11.58

Commercial Vehicles Sector 5.

Hindustan Motors Ltd

6.

Mahindra and Mahindra Ltd

1978.38

0.88

15.32

7.

Maruthi Udyog Ltd

1227.95

0.35

9.60

8.

Daewoo Motors India Ltd

-60.84

2.93

2.41

Passenger Cars and Multiutility Vehicles Sector

3587.69

0.42

10.52

9.

Bajaj Auto Ltd

7076.19

0.56

12.37

10

Maharastra Scooters Ltd

116.75

0.99

20.58

11.

TVS Motors India Ltd

988.49

0.85

24.48

12.

Hero Honda Motors Ltd

5809.80

0.84

13.36

Two and Three Wheelers Sectors

3497.81

0.68

19.90

Whole Automobile Industry

2836.79

0.46

10.03

Source: Computed

229

230

The market value added of selected companies under commercial vehicles sector during the study period is also presented in Table 6.7. This table reveals that the mean MVA of Tata Motors Ltd were the highest followed by Ashok Leyland Ltd, Eicher Motors Ltd and Swaraj Mazda Ltd. Table 6.7 brings out that all the selected companies under the commercial vehicles sector had registered very high fluctuations in their MVA during the study period.

Table 6.7 presents MVA of selected companies under passenger cars and multiutility vehicles sector. The table shows that the companies like Maruthi Udyog Ltd and Mahindra and Mahindra Ltd are top in the list and it was followed by Hindustan Motors Ltd and Daewoo Motors India Ltd. All the selected companies except Maruthi Udyog Ltd has registered very high fluctuation in their MVA during the study period. Table 6.7 brings out values relating to compound annual growth rate of MVA selected companies. It is evident from the table that all the companies had registered positive growth, rate of MVA during the study period.

Table 6.7 brings out the values relating to MVA of selected companies under two and three wheelers. Table 6.7 showed that Bajaj Auto Ltd, Hero Honda Motors Ltd are comparatively top in the list. On the other hand, Maharashtra Scooters Ltd are struggling on their front with regard to MVA during the study period. It is also noticed that all the selected companies have registered very high fluctuations in their MVA during the study period. The analysis of compound annual growth rate of MVA showed mixed trend during the study period.

The sector-wise paired test provided the value of the t test in Table 6.8. The table exhibits that there has been no significant deviation in MVA in respect of years except for the year 1995-96, 2003-04 and 2004-05. 231

MVA vis-a-vis other financial variables-Linear Regression and Multiple Regression Analysis In this section an attempt to find the relevance of Stern and Stewart’s claim that MVA of the firm is largely positive association with or driven by its EVA generating capacity and other financial variables like EPS, ROCE, NOPAT and RONW. Based on the sample of 12 companies of Indian automobile industry for a period of 11 years, the analysis of this section is divided into two parts: in the first part, the linear regression analysis between dependent and particular selected independent variables (s) has been examined and in the second part multiple regression analysis between MVA and other financial variables has been looked at for the selected sectors of automobile industry during the study period.

Linear Regression Analysis of MVA and selected Financial Variables In this section, results of correlation co-efficient, linear regression, Durbin-Watson Model, F-Statistics and t-statistics have been determined between dependent variable (MVA) and Independent variables. The values hence obtained have their particular statistical sense. The regression coefficient for independent variables like EVA, EPS, ROCE, NOPAT and RONW so worked out portray the temperament of association between the dependent and particular independent variable. The F statistics and t statistics so calculated determine the level of significance and insignificance being associated between the variables. Durbin-Watson Model allows the researcher to establish the auto-correlation, if any between dependent and independent variable (the desirable value is two and any value more than two signifies negative auto-correlation and vice-versa); values of adjusted R2 indicate the extent of variation in the dependent variable which may be explicated by independent variables and the standard error speaks about the limits within which the estimated value as the dependent variable is expected to lie. 232

Table: 6.9 MVA-EVA: Linear Regression Analysis Dependent variable-Market Value Added (MVA) Independent variable-Economic Value Added (EVA) Independent variable

t

Multipl R

RSquare

Adjusted R Square

Std. Error of the estimate

DurbinWatson

F value

0.97

0.920

0.293

0.09

-0.02

864.22

0.72

0.85

0.25

0.207

0.069

0.01

-0.11

1586.28

0.53

0.04

-1.80

-0.432

0.143

0.02

-0.09

2480.13

0.17

0.19

-0.46

-0.241

0.080

0.01

-0.10

1381.10

0.34

0.06

Coefficient

Commercial Vehicle EVA Passenger Cars and Multiutility Vehicles EVA Two and Three Wheelers EVA Whole Industry EVA

Source: Computed Table 6.10 MVA- EPS: Linear Regression Analysis Dependent variable-Market Value Added (MVA) Independent variable-Earnings Per Share (EPS) t

Multi R

Rsquare

Adjusted R-square

Std. Error of the estimate

DurbinWatson

F value

45.22

1.60

0.47

0.22

0.14

797.77

0.66

2.56

-8.57

-0.83

0.27

0.071

-0.032

1532.55

0.67

0.69

Two and Three Wheelers EPS

134.06

1.27

0.39

0.153

0.058

2306.61

0.482

1.62

Whole Industry EPS

-12.23

-0.43

0.14

0.020

-0.089

1371.82

0.364

0.18

Independent variable

Coefficient

Commercial Vehicle EPS Passenger Cars and Multiutility Vehicles EPS

Source: Computed

233

MVA-EVA Analysis Table 6.9 offers the explanation about the regression on analysis between MVA and EVA during the study period for the whole automobile industry and its three sectors. Table 6.9 provides the values of R, R-square and adjusted R2 for the whole industry 0.080, 0.01, -0.10 respectively. It sounds that there exists poor relationship between MVA and EVA in automobile industry, as the value of R-square is negative. Interestingly, the t and F statistics give the identical results but both of them lead to insignificant association between the variables under reference. It is evident from the table that the overall result in passenger cars and multiutility vehicles does not differ from whole industry and statistical association between MVA and EVA is again insignificant. Tables 6.9 suggest that the adjusted R2 value is negative in all cases in all the selected sectors of Indian automobile industry. MVA-EPS Analysis The linear regression analysis between MVA and EPS is presented in Table 6.10 for the study period. It is evident from the Table 6.10 that the correlation co-efficient between MVA and EPS during the study period is 0.14 and the value of R-Square and adjusted R-Square is very low and may not be adequate for the fitness of the model. The t and F statistics suggest that the association between MVA and EPS of automobile industry is not significant and EPS does not suitably explain MVA. It is evident from the table that the correlation co-efficient between MVA and EPS in passenger cars and multiutility vehicles is 0.27 and the adjusted R-Square value is negative. This shows the poor fitness of the model. Both t statistics and F statistics certify that the association between these two variables is insignificant as presented in the table. The t and F statistics are resulting identical values and secured that EPS of commercial vehicles sector has been able to describe MVA in better term than the other sectors. The overall results showed that EPS is positively associated with MVA in all the three sectors and the whole industry during the study period. 234

Table: 6.11 MVA-ROCE: Linear Regression Analysis Dependent Variable: Market-Value Added (MVA) Independent Variable: Return on capital employed (ROCE) Independent variable

Coefficient

t

Multiple R

Rsquare

Adjusted RSquare

Std. Error of the estimate

DurbinWatson

F value

Commercial Vehicle ROCE

10.493

0.380

0.126

0.02

-0.09

896.80

0.72

0.14

6.359

0.125

0.042

0.01

-0.11

1588.68

0.55

0.02

47.853

0.659

0.214

0.05

-0.06

1353.34

0.34

0.43

-125.179

-0.968

0.307

0.10

-0.01

2384.55

0.35

0.94

Passenger Cars and Multiutility Vehicles ROCE Two and Three Wheelers ROCE Whole Industry ROCE

Source: Computed Table 6.12 MVA-NOPAT: Linear Regression Analysis Dependent Variable: Market-Value Added (MVA) Independent Variable: Net operating profit after tax (NOPAT) Independent variable

t

Multi R

Rsquare

Adjusted R-square

Std. Error of the estimate

DurbinWatson

F value

3.167

1.745

0.503

0.253

0.170

781.38

0.652

3.045

9.042

3.758

0.782

0.611

0.568

992.03

0.702

14.12

18.422

13.997

0.978

0.956

0.951

525.12

2.360

195.92

10.478

8.942

0.948

0.899

0.888

440.71

1.201

79.96

Coefficient

Commercial Vehicle NOPAT Passenger Cars and Multiutility Vehicles NOPAT Two and Three Wheelers NOPAT Whole Industry NOPAT

Source: Computed

235

MVA-ROCE Analysis Table 6.11 offers the explanation about the regression analysis between MVA and ROCE during the study period for the whole automobile industry and its three sectors. Table 6.11 provides the values of R, R-Square and adjusted R-Square which are 0.307, 0.10, -0.01 respectively. It sounds that the value is very low and may not be adequate for the fitness of the model. The results of whole industry are similar to passenger cars and multiutility vehicles. Table 6.11 suggests that the variables are clearly correlated in two and three wheelers but the adjusted R-Square value is negative. However, in case of passenger cars and multiutility vehicles and commercial vehicles sector the value of R, R-Square and adjusted R-Square showed that the values have resulted in poor relationship between MVA and ROCE in these sectors. The overall results showed that ROCE is negatively associated with MVA in all the whole industry during the study period. MVA-NOPAT Analysis Linear regression analysis between MVA and NOPAT is presented in Table 6.12. In Table 6.12 the statistical association between MVA and NOPAT of all the three sectors and the whole industry are provided. The table reveals that the value of R, R-Square and adjusted R-Square are high and it may be adequate for the fitness of the model in case of whole industry, passenger cars and multiutility vehicles sector and two and three wheelers sector. The t and F statistics also suggest that the association between MVA and NOPAT is significant and NOPAT is suitable to explain the MVA of these sectors and the whole industry during the study period. The table reveals that the value of adjusted R-Square is very low and it may not be adequate for the fitness of the model, the t and F statistics also suggest that the association between the MVA and NOPAT is not significant. The overall analysis showed that NOPAT is positively associated with MVA in all the three sectors and whole industry. 236

Table: 6.13 MVA-RONW: Linear Regression Analysis Dependent Variable: Market-Value Added (MVA) Independent Variable: Return on net worth (RONW)

Independent variable

t

Multi R

Rsquare

Adjusted R-square

Std. Error of the estimate

DurbinWatson

F value

50.435

1.906

0.536

0.288

0.209

762.93

0.982

3.64

7.930

0.202

0.067

0.005

-0.106

1586.47

0.529

0.041

-190.75

-1.392

0.421

0.177

0.086

2272.86

0.571

1.939

36.027

0.592

0.194

0.038

-0.069

1359.330

0.292

0.351

Coefficient

Commercial Vehicle RONW Passenger Cars and Multiutility Vehicles RONW Two and Three Wheelers RONW Whole Industry RONW

Source: Computed

237

MVA and RONW Analysis Table 6.13 tenders the elucidation concerning the regression analysis between the MVA and RONW during the study period. The table 6.13 provides the values of R, R-Square and adjusted R-Square. Table 6.13 suggests that variables are clearly correlated

in

the

whole

industry,

commercial vehicles and two and three wheelers sectors and adjusted RSquare value is positive in two cases. In passenger cars and multiutility vehicles the value of R, R-Square, adjusted R-Square value is positive in two cases. In passenger cars and multiutility vehicles the vale of R, R-square, adjusted R-Square are 0.067, 0.005 and -0.106 respectively. It sounds that there exists poor relationship between MVA and RONW in passenger cars and multiutility vehicles sector. The t and F statistics also give identical results but both of them lead to insignificant association between them. The overall analysis showed that RONW is negatively associated with MVA only in case of two and three wheelers sectors during the study period. MVA vis-à-vis other financial variables-Multiple Regression Analysis The evidence of the majority of empirical study regarding EVA suggests that there is a positive relationship between EVA and MVA. However, when the explaining power of EVA versus traditional performance measures regarding return is considered, the results are mixed. This is in continuation with the analysis made in the previous past, an attempt has been made in this part to find out sector-wise trends as far as the factors affecting MVA are concerned. The purpose of this analysis whether a particular independent variable or a set of variables emerges as the most explanatory variable of the MVA during the study period. In order to meet this objective, multiple regression analysis has been considered on sector-wise and whole industry during the study period. The results of multiple regression analysis are presented in this section.

238

Table: 6.14 Determinants of Market Value Added-Multiple Regression Analysis (Automobile Industry) Dependent Variable: Market Value Added (MVA) Independent variable

Significant / Not significant

Co-efficients

t-value

2113.97

4.101

EVA

0.14

1.983

Significant**

EPS

28.76

3.773

Significant*

128.89

2.648

Significant*

11.16

5.588

Significant*

100.48

2.234

Significant*

Constant

ROCE NOPAT RONW 2

R = 0.98 Adj R2 = 0.97 F = 55.91 DW = 1.88 EVA-Economic Value Added; EPS - Earnings Per share; ROCE-Return on capital employed; NOPAT-Net operating profit after tax; RONW-Return on Net worth. * - significant at 0.05 level; ** - significant at 0.10 level Source: computed.

Table: 6.15 Determinants of Market Value Added-Multiple Regression Analysis (Commercial Vehicles) Dependent Variable: Market Value Added (MVA) Independent variable

Significant / Not significant

Co-efficient

t-value

464.51

1.880

EVA

0.66

2.910

Significant*

EPS

38.87

2.864

Significant*

ROCE

108.88

3.157

Significant*

1.63

1.534

Significant**

150.79

3.671

Significant*

Constant

NOPAT RONW 2

R = 2 Adj R = F = DW =

0.81 0.63 4.38 3.09

EVA-Economic Value Added; EPS - Earnings Per share; ROCE-Return on capital employed; NOPAT-Net operating profit after tax; RONW-Return on Net worth. * - significant at 0.05 level ; ** - significant at 0.10 level Source: computed.

239

Whole Industry Table 6.14 brings out the determinants of market value added for whole automobile industry during the study period. It is observed from the Table 6.14 that all the selected independent variables exerts significant influence on MVA of automobile industry during the study period. Coefficient of determination, R2 in the case 0.98 implies that changes in MVA are predicted by these independent variables to the extent of 98 per cent. It is also evident from the table that ROCE is found in strong association with MVA followed by RONW, EPS, NOPAT and EVA. From the value of adjusted R2 and F value regression results, it can be concluded that all the selected independent variables well explain the MVA of automobile industry during the study period. Commercial Vehicles Table 6.15 portrays the results of multiple regression analysis for commercial vehicles sector. It is revealed from the table that the co-efficient of determination, R2 value which is 0.81 implies that change in MVA can be predicted by these independent variables to the extent of 81 per cent only. It is also found that RONW is strongly associated with MVA followed by ROCE, EPS, NOPAT and EVA during the study period. The value of F statistic and adjusted R2 showed the good fitness of the model. Passenger Cars and Multiutility Vehicles Table 6.16 gives an account of multiple regression analysis between MVA and other financial variables in respect of passenger cars and multiutility vehicles sector. The result provided by this table witnessed that the variables noticed significantly associated with MVA are EPS, NOPAT, RONW and EVA. The co-efficient of determination, R2 in this case is 0.90 implying that change in MVA is predicted by these independent variables to the extent of 90 per cent. The value of R2 and F shows the good fitness of the model. 240

Table: 6.16 Determinants of Market Value Added-Multiple Regression Analysis (Passenger Cars and Multiutility Vehicles) Dependent Variable: Market Value Added (MVA) Independent variable

Significant / Not significant

Co-efficient

t-value

2306.27

3.481

EVA

0.435

2.718

Significant*

EPS

11.918

1.726

Significant**

ROCE

-51.73

1.436

Not Significant

11.87

5.791

Significant*

4.36

2.092

Significant*

Constant

NOPAT RONW 2

R = 0.90 Adj R2 = 0.79 F = 8.73 DW = 1.67 EVA-Economic Value Added; EPS - Earnings Per share; ROCE-Return on capital employed; NOPAT-Net operating profit after tax; RONW-Return on Net worth. * - significant at 0.05 level ; ** - significant at 0.10 level Source: computed. Table: 6.17 Determinants of Market Value Added-Multiple Regression Analysis (Two and Three Wheelers) Dependent Variable: Market Value Added (MVA) Independent variable

Significant / Not significant

Co-efficient

t-value

192.95

0.177

EVA

1.75

2.267

Significant*

EPS

30.68

2.597

Significant*

ROCE

74.83

2.978

Significant*

NOPAT

16.47

6.485

Significant*

141.14

1.160

Not Significant

Constant

RONW 2

R = 2 Adj R = F = DW =

0.98 0.97 61.83 3.00

EVA-Economic Value Added; EPS - Earnings Per share; ROCE-Return on capital employed; NOPAT-Net operating profit after tax; RONW-Return on Net worth. * - significant at 0.05 level ; ** - significant at 0.10 level Source: computed.

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Two and Three wheelers Table 6.17 describes the results of multiple regressions for determinants of MVA for two and three wheelers sector during the study period. It is explicit from the table that all the independent variables are significantly associated with MVA of two and three wheelers sector during the study period. Co-efficient of determination, R2 in this case in 0.98 implying that changes in MVA is predicted by selected independent variables to the extent of 97 per cent. RONW is strongly associated with MVA followed by ROCE, EPS, NOPAT and EVA. The value of t, F and R2 sounds the good fitness of the model.

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