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

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 nature 1. 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. 213

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 planning 2. 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 risk 3. 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 employed 4. 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. Jaishweta, (2003). Godrej Retools for Value, Business Standard, P. 6. 4. Purikh, Parag, (2002). The Universe of Wealth Creation, PPFAS-Financial Advisory Services Ltd-Online P. 2. 215

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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

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Table 6.3 EVA-Sector-Wise Trends (1995-96-2005-06) S.No Industry Mean (Rs.in.crores) 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 Commercial Vehicles Sector 127.33 2.03-2.71 5. Hindustan Motors Ltd 10.19 9.73-6.15 6. Mahindra and Mahindra Ltd 149.55 3.24-3.10 7. Maruthi Udyog Ltd 49.14 22.16-22.32 8. Daewoo Motors India Ltd -62.34 1.17-0.77 Passenger Cars and Multiutility Vehicles Sector 36.63 11.38-17.85 9. Bajaj Auto Ltd 541.65 1.36-12.57 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

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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

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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 Industry Mean (Rs.in.crores) 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 Commercial Vehicles Sector 1432.52 0.60-2.45 5. Hindustan Motors Ltd 152.72 1.60 11.58 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 Passenger Cars and Multiutility Vehicles Sector - 2.93 2.41 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

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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 R 2 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

Independent variable Commercial Vehicle Table: 6.9 MVA-EVA: Linear Regression Analysis Dependent variable-market Value Added (MVA) Independent variable-economic Value Added (EVA) t Multipl R R- Square 233 Adjusted R - Square Std. Error of the estimate Coefficient Durbin- Watson F value EVA 0.97 0.920 0.293 0.09-0.02 864.22 0.72 0.85 Passenger Cars and Multiutility Vehicles EVA 0.25 0.207 0.069 0.01-0.11 1586.28 0.53 0.04 Two and Three Wheelers EVA -1.80-0.432 0.143 0.02-0.09 2480.13 0.17 0.19 Whole Industry EVA -0.46-0.241 0.080 0.01-0.10 1381.10 0.34 0.06 Source: Computed Independent variable Table 6.10 MVA- EPS: Linear Regression Analysis Dependent variable-market Value Added (MVA) Independent variable-earnings Per Share (EPS) t Multi R R- square Adjusted R-square Std. Error of the estimate Coefficient Durbin- Watson F value Commercial Vehicle EPS 45.22 1.60 0.47 0.22 0.14 797.77 0.66 2.56 Passenger Cars and Multiutility Vehicles EPS -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 Source: Computed

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 R 2 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 R 2 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

Independent variable Commercial Vehicle Table: 6.11 MVA-ROCE: Linear Regression Analysis Dependent Variable: Market-Value Added (MVA) Independent Variable: Return on capital employed (ROCE) t Multiple R R- square 235 Adjusted R- Square Std. Error of the estimate Durbin- Watson F value ROCE 10.493 0.380 0.126 0.02-0.09 896.80 0.72 0.14 Passenger Cars and Multiutility Vehicles ROCE 6.359 0.125 0.042 0.01-0.11 1588.68 0.55 0.02 Two and Three Wheelers ROCE 47.853 0.659 0.214 0.05-0.06 1353.34 0.34 0.43 Whole Industry ROCE -125.179-0.968 0.307 0.10-0.01 2384.55 0.35 0.94 Source: Computed Independent variable Commercial Vehicle Table 6.12 MVA-NOPAT: Linear Regression Analysis Dependent Variable: Market-Value Added (MVA) Independent Variable: Net operating profit after tax (NOPAT) t Multi R R- square Adjusted R-square Std. Error of the estimate Coefficient Coefficient Durbin- Watson F value NOPAT 3.167 1.745 0.503 0.253 0.170 781.38 0.652 3.045 Passenger Cars and Multiutility Vehicles NOPAT 9.042 3.758 0.782 0.611 0.568 992.03 0.702 14.12 Two and Three Wheelers NOPAT 18.422 13.997 0.978 0.956 0.951 525.12 2.360 195.92 Whole Industry NOPAT 10.478 8.942 0.948 0.899 0.888 440.71 1.201 79.96 Source: Computed

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 Commercial Vehicle t Multi R R- square Adjusted R-square Std. Error of the estimate Coefficient Durbin- Watson F value RONW 50.435 1.906 0.536 0.288 0.209 762.93 0.982 3.64 Passenger Cars and Multiutility Vehicles RONW 7.930 0.202 0.067 0.005-0.106 1586.47 0.529 0.041 Two and Three Wheelers RONW -190.75-1.392 0.421 0.177 0.086 2272.86 0.571 1.939 Whole Industry RONW 36.027 0.592 0.194 0.038-0.069 1359.330 0.292 0.351 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 R- Square 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 Co-efficients t-value Constant 2113.97 4.101 Significant / Not significant EVA 0.14 1.983 Significant** EPS 28.76 3.773 Significant* ROCE 128.89 2.648 Significant* NOPAT 11.16 5.588 Significant* RONW 100.48 2.234 Significant* R 2 = 0.98 Adj R 2 = 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 Co-efficient t-value Constant 464.51 1.880 Significant / Not significant EVA 0.66 2.910 Significant* EPS 38.87 2.864 Significant* ROCE 108.88 3.157 Significant* NOPAT 1.63 1.534 Significant** RONW 150.79 3.671 Significant* R 2 = 0.81 Adj R 2 = 0.63 F = 4.38 DW = 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, R 2 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 R 2 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, R 2 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 R 2 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, R 2 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 R 2 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 Co-efficient t-value Constant 2306.27 3.481 Significant / Not significant EVA 0.435 2.718 Significant* EPS 11.918 1.726 Significant** ROCE -51.73 1.436 Not Significant NOPAT 11.87 5.791 Significant* RONW 4.36 2.092 Significant* R 2 = 0.90 Adj R 2 = 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 Co-efficient t-value Constant 192.95 0.177 Significant / Not significant EVA 1.75 2.267 Significant* EPS 30.68 2.597 Significant* ROCE 74.83 2.978 Significant* NOPAT 16.47 6.485 Significant* RONW 141.14 1.160 Not Significant R 2 = 0.98 Adj R 2 = 0.97 F = 61.83 DW = 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. 241

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, R 2 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 R 2 sounds the good fitness of the model. 242