Examining Association Of Vaic And Eva With Mva Accounting Essay

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An economys success and growth primarily depends on the suitable and instantaneous operation of the banking system. Components of intellectual capital i.e. human capital and customer capital are widely present in the banking sector (Kamath, 2007). These are very important factors for the smooth and successful working of the banks. Different researchers are interested in measuring intellectual capital and therefore various models are developed to measure it. Value Added Intellectual Co-efficient (VAIC) and Economic Value Added (EVA) are two models which are largely used by the researchers. EVA is the method used for the evaluation of the shareholders wealth earned by the organization. Higher EVA means the company is earning more value for the invested money by them. VAIC model is used for the measurement of intellectual capital in the organization. It is the value added by the presence of the intellectual capital in the organization. With the growing concern about measurement and financial reporting of intellectual capital in the organizations, the question arises whether intellectual capital has significant association with the economic value added and market value added of the organizations. Market Value Added (MVA) is the market invested capital subtracted from market value of the organization. If the MVA of the company is greater than zero then the company is generating returns more than the capital invested. Market Value Added (MVA) is supposed to reflect the intellectual capital and economic value added by the banks. The present paper is an attempt to present a comparative analysis of VAIC, EVA and MVA of Indian banking industry.

Review of Literature

For the expediency of the scholars engaged in the field, the paper divides the survey of literature into two key parts. Those researches who have contributed to the measurement of intellectual capital by applying value added intellectual co-efficient (VAIC) and those who have measured economic value added (EVA). Both these models are used to measure intellectual capital in the organizations but so far no such detailed study has been carried out to assess the association between these two models.

A small number of researchers have tried to capture the disclosing patterns of intellectual capital in different countries. Valuation and proper measurement of intellectual capital requires appropriate methods. Goh & Lim (2004) conducted the study to assess qualitative and quantitative information disclosed by the Malaysian companies. The study found that disclosure about the intellectual capital of the companies was more of qualitative manner than the quantitative manner. Various models were developed for measuring it but Bontis et al. (1999) analyzed that none of the model can be considered best as each model have its own criteria for measuring intellectual capital. Bozzolan, Favotto & Ricceri (2003) conducted the study to assess the disclosure pattern of intellectual capital of Italian companies. The results of study concluded that disclosure of intellectual capital was mainly related with external capital of the companies. Industry and size of the companies were two important factors affecting the disclosing pattern of the companies. Other researchers investigated the relationship between the intellectual capital and business performance of the companies in different countries. Zeghal & Maahoul (2010) carried out study in UK on 300 firms and found a positive relationship between intellectual capital and firms' economic and financial performance. Zerenler, Hasilogu & Sezgin (2008) analyzed intellectual capital influence over innovative performance of Turkish firms and found a positive association between them. Tan, Plowman & Hancock (2007) investigated and found positive relationship between intellectual capital and future financial performance in Singapore companies. Chen, Cheng & Hwang (2005) evaluated the relationship between intellectual capital and market valuation of Taiwanese companies. Many similar studies were carried out to check the relationship between intellectual capital and firms' financial performance by Kamath (2008) on pharmaceutical industry in India, Gan & Saleh (2008) in Bursa Malaysia and Firer & Stainbank (2003) in 65 South African companies listed on the Johannesburg Securities Exchange (JSE).

EVA is yet another method used for the evaluation of intellectual capital in the companies. But this method is not a direct method for measuring intellectual capital. Calculation of EVA requires many adjustments of the accounting entries, as it is different from the accounting value added of the companies. Both these measuring tools of intellectual capital are used in the paper in attempt to find out any association between the VAIC and EVA of the companies.

Bontis et al. (1999) argued even if EVA did not relate to the intangible assets but its management can improve the EVA of the firms. Abdeen & Haight (2001) studied Economic Value Added with the purpose focusing on advantages and the limitations of the model. The study is based on a sample of Fortune 500 corporations with a time period of 1997 and 1998. Results of the study found that the EVA users were better performers than non-users in case of revenues, assets and stockholders' equity.

Palliam (2006) evaluated the relationship between the Economic Value Added and stock returns and firm values. A total of 108 companies with 33 non-EVA users and 75-EVA users were taken as sample. The study found that EVA method was no better than other available metrics used for the measurements of shareholders value. The study also analyzed that there were no association between the shareholders return and companies' EVA.

Maditions, Sevic & Therious (2009) used Easton and Harris formal valuation model to evaluate the performance measures. The main purpose of the study was to compare valued based performance measured by Economic Value Added (EVA) and Shareholder Value Added (SVA) with traditional measures as Earnings Per Share (EPS), Return on Investment (ROI), and Return on Equity (ROE) for the analysis of the stock market returns. The study was based on the Athens Stock Exchange with a time period of 1999-2001. Results found the association between the stock market returns with Earning Per Share (EPS) rather than EVA.

Paulo (2010) examined EVA model as superior model in comparison to other financial performance metrics. The association between EVA and MVA was also analyzed. It was concluded that EVA method still did not validate to be the appropriate measure for meeting the corporate goal for the UK Companies Act, 2006.

Sharma, Hui & Tan (2007) conducted a case study in the New Development Company Ltd. (NWDC) based in Hong Kong SAR for the analysis of the knowledge management program. The paper analyzed that EVA method was a better proxy for the measurement of the intellectual capital. Popa, Mihailescu & Caragea (2009) evaluated EVA model in the Roman banks as the performance indicator. Findings of the paper evaluated that EVA could be used as the tool for the improvement the financial performance of the banks and could be used by the management for taking decisions.

Jones (2006) compared the calculated beta of the companies provided by the Stern Stewart & Company's annual EVA scoreboard with the estimated beta. A sample of 399 companies was taken for a period of 1995 to 2001. There was significant difference between the calculated beta coefficient and the market calculated beta. The results also highlighted that market beta was considerably higher than the expected EVA value of the companies. Geyser & Liebenberg (2003) examined the EVA method in the South African agricultural business and co-operatives. Results of the study analyzed that EVA was a value enhancing technique for the agriculture co-operatives.

Dagogo & Ollor (2009) analyzed the use of venture capital in small and medium scale enterprises in Nigeria. A comparison of EVA was made between the venture capital backed SME's with that of non-venture capital backed SMEs. Variables used for the study were equity finance, management support and technical support. From results it was analyzed that venture capital backed SMEs were providing more revenues to the government and fulfilling their social responsibilities. There was a significant difference between EVA of venture capital backed SMEs and non-venture capital backed SMEs for the debt equity and therefore these SMEs grew more in comparison with others.

2.1 Research gaps indentified

As from the review of literature above, intellectual capital and its measurement and management has been the area of interest of corporate world from the past few decades. But there is no agreement among researchers and academicians about any single reporting and measurement model for the intellectual capital. EVA and VAICTM being two models, used separately but until now no study tried to assess association between them as far as known. This paper tries to analyze link if any between the two models and Market Value Added (MVA) of the Indian banks.

Research Objectives and Methodology

For the purpose of this paper, thirty seven banks are taken. These banks are divided into two major categories of private and public sector banks. Data is obtained from the database of Centre for Monitoring Indian Economy (CMIE) called Prowess. However, the present study is limited to only three factors i.e. VAIC, EVA and MVA and more factors could be considered for broadening the horizon of such study in future.

3.1 Objectives of the study

The main objective of the present paper is to examine the association and inter-relationship among the models of intellectual capital i.e. Value Added Intellectual Co-efficient (VAIC), Economic Value Added (EVA) and Market Value Added (MVA) of the selected banks for the time period of ten years from 1999-2000 to 2008-2009.

3.2 Hypothesis of the study

The following hypotheses have been formulated for this study:

H01: there is no association between the intellectual capital measured by Value Added Intellectual Co-efficient (VAICTM) and the Economic Value Added (EVA) of the banks for the time period 1999-2000 to 2008-2009.

H02: there is no association between the intellectual capital measured by Value Added Intellectual Co-efficient (VAICTM) and the Market Value Added (MVA) of the banks for the time period 1999-2000 to 2008-2009.

H03: there is no association between the Economic Value Added (EVA) and Market Value Added (MVA) of the banks for the time period 1999-2000 to 2008-2009.

3.3 Measurement of Value Added Intellectual Co-efficient (VAICTM)

VAICTM model is used to measure the intellectual capital of the companies. As calculation of VAIC is based on the audited accounts of the companies, it is considered as reliable measure. It is the difference of input and output of the companies.


Value added can be calculated as follows:

VA= I + DP + D + T + M + R

Where I stand for interest expenses, DP represents depreciation expenses; D is used for dividends paid, T represents corporate tax, M is the equity of minority shareholders and R is the retained earnings.

Value Added Intellectual Co-efficient (VAIC) can be divided into three components namely Capital Employed Efficiency (CEE), Human Capital Efficiency (HCE) and Structural Capital Efficiency (SCE). VAIC is calculated as follows:


For the purpose of this study, value added as suggested by Chen, Cheng & Hwang (2005) is calculated in the following manner:

VA = W + I + T + NI


W = wages of the employees

I = Interest

T = Taxes

NI = Profits after taxes



CEE = Capital Employed Efficiency

VA = Value added

CE = Capital employed taken as net worth of the company.



HCE = Human Capital Efficiency

VA = Value added

HC = Total of wages and salaries of the employees.

SC = VA - HC



SCE = Structural capital efficiency and

SC = Structural capital

Economic Value Added (EVA)

Economic Value Added (EVA) was developed by Joel Stern and G. Bennet Stewart in 1989. It was previously used by the accountant as the term residual income of the organizations. So, many of the researchers do not consider it as a new model. But the term being relatively new and involves a complex process in its calculation. EVA can be calculated by the following method.



NOPAT = Net Operating Profits After Tax

WACC = Weighted Average Cost of Capital

IC = Invested Capital

Calculation of EVA has to be done after making adjustments because without these adjustments it is not an economic term but an accounting term. In calculating EVA, following adjustments have to be made

Loan loss provision and Loan loss reserve


Restructuring charges

Security accounting

General risk reserve

Research and Development (R&D) costs and training costs

Operating lease expenses

Cost of Debt (Kd) has been computed as:

Kd =

While calculating beginning borrowings, all deposits as well as long term borrowing has to be included as all debt (deposits and borrowings) are interests bearing. Therefore, interest paid in the financial year has been considered as a total interest expenses.

Cost of equity (Ke) is calculated by using Capital Assets Pricing Model (CAPM). According to this model, Ke is the shareholder expected return and this expected return (Rj) is as follows:

Rj = Rf + (Rm - Rf) βi


Rf = Risk free rate of return

Rm = Market rate of return, and

βi = Sensitivity of the share price in relation to market return.

Risk free rate of return is taken by the proxy of 364 days Treasury bill rate of return (T-bill). For the calculation of market rate of returns daily banks index closing price of year to year basis is taken. Bank index is calculated by using 30 banks from 1996 to 2007. Closing price is used after making adjustments for bonus, dividend and right issue.

The daily market return has been calculated by taking logarithm of prices instead of:


Rt = Daily bank Index return

Pt = Current Index closing price

Pt-1 = Previous day closing price.

The βi coefficient in the standard regression equation (referred as to beta in this case) measures the sensitivity of dependent variable to per unit change in independent variable.

For the purpose of ascertaining the cost of equity, the individual bank equity share price has been taken as the dependent variable and the return on the market (computed as daily return of bank index) has been taken as the independent variable. To find out receptiveness of individual bank's equity return (taken as proxy for the cost of equity) to the market rate of return, the Beta co-efficient has been calculated as follows:


βi = The beta of the security in the question,

COVim = Covariance between return of the bank equity and market return of the bank index,

= Variance of the market return.

Mathematically Stewart presented EVA model as:

EVAt = NOPATt - (WACCt * ICt-1)


NOPAT = Net operating profit after tax

= Earnings before interest and tax * (1 - corporate Tax rate)

WACC = Weighted average cost of capital

IC = Invested capital

NOPAT is calculated as per the Calabrese (1999). Rakshit (2006) carried out a case study of Dabur India Limited and found that EVA is a new technique and should be used in taking various decisions.

Market Value Added (MVA)

The term Market Value Added (MVA) tells the market value created by the company.

Market Value Added (MVA)

= Market Value of Stock - Equity capital Supplied by shareholders.

= (Current Market Price) Ã- (No. of Share outstanding) - Total Common Equity.

Results and Analysis

Figure 1.1 shows selected Indian banks divided into public and private sectors. Table 1.1 shows the Value Added Intellectual Co-efficient (VAIC) with their mean and standard deviation. Mean of the VAIC in the year 2000 is 8.50 which keep on increasing till the year 2002 and starts declining thereafter till the year 2006 and increased after it. The mean of VAIC is highest in the year 2009 which is 11.55. The result depicts that mean of the VAIC in the selected period is from 7.22 to 11.55 which indicate that banks are utilizing their intellectual capital to a good extent.

Table 1.2 shows the Economic Value Added (EVA) of the selected banks. From the table, it can be analyzed that EVA of the banks in the time period from 2000 to 2009 shows an increasing trend. It is lowest in the year 2000 (i.e. 950.25) and highest in the year 2009 (i.e. 3615.77). Mean EVA of the banks indicated that shareholders value is increasingly added by the banks.

Libenberg (2004) carried out a study to calculate EVA of South African agricultural firms and found that the selected companies were not adding value to the members' interest. In addition, no correlation was found between EVA performance of the firms and individual groups of the co-operatives in the study period. Iseri & Kayakutlu (2006) analyzed in Istanbul Stock Exchange that richest intellectual capital retail companies were found to have highest EVA but the results was not generalized because study was based on a small sample size.

Table 1.3 shows the mean value of Market Value Added (MVA). MVA of the banks shows increasing trend from the year 2000 to 2008 and declined in the year 2009 (from 12295.65 in the year 2008 to 9119.47 in the year 2009). Overall market value of the banks has increased in the given period.

Figure 1.1 : Shows the selected banks used in the study.

For the purpose of checking association between these variables, Pearson correlation is used. The results (Table 1.4) show that association between the EVA and VAIC is not found to be significant in given period. The association between MVA and EVA is 41.1 to 89.3% and significant in all nine cases except in the year 2003. No correlation is found between VAIC and MVA in the given period. By the use of Shapiro-Wilk Test for normality data are analyzed and found to be non-normal. So, outliers are removed to have consistency in the results. Data is made normal by taking base 10 logarithms and inverse of the data.

Table 1.5 shows the results of the multiple regression of dependent and independent variables. Results of the multiple regression highlight that independent variables i.e. EVA and VAIC explain 24.6% to 67.6% of the dependent variables which is also significant at 5% and 1% level of significance. The model is significant in all ten years at 1% and 5 % level of significance. Form the results it can be concluded that Value Added Intellectual Co-efficient (VAIC) used to measure intellectual capital efficiencies explains only 29.5 to 37.5% of the variables and significant at 5 and 10% level of significance. Economic Value Added (EVA) explains 48 to 83.5% of the variables and significant at 5% to 1% level of significance.

Findings and Conclusion

From the foregoing discussion, it may be concluded that intellectual capital is associated with the market valuation of the banks. Intellectual capital that is measured by both Value Added Intellectual Co-efficient (VAICTM) and Economic Value Added (EVA) is found to be associated with the Market Value Added (MVA) of the banks. EVA is more closely associated and it means that economic value added may be considered as the better utilization of intellectual capital of the banks. Contrary to our results, Kyriazis & Anastassis (2007) found that there was no stronger correlation between EVA and MVA of the Greek companies but on the other hand, Kramer & Peters (2001) analyzed that EVA should be considered as a better proxy for the MVA in comparison to net operating profit after tax (NOPAT). It can be summarized that economic valuation of the Indian banks is considered as essential yardstick by the investors as market valuations may reflect significant association among economic value added and market value added of the banks. Since market valuation is considered as right always, hence, EVA may attempts to be the close proxy of MVA as evidenced in the analysis of this study.