Is Economic Value Added Relevant for Market Value? A Sector and Industry Analysis of European Companies

Christos G. Kampouris

Department of Tourism Studies, University of Piraeus, Piraeus, Greece.

DOI: 10.4236/tel.[phone]                

Abstract

The objective of the present article is to examine the effect that Economic Value Added (EVA) has on the market values of companies. In doing so, we utilize a large sample of European stocks, over the period 2015-2020, and compare the above relationship between secondary and service sector firms, along with an industry analysis. The sample firms were classified into six industries, namely consumer cyclical, consumer non-cyclical, technology, communication, basic materials and industrial. A single index regression analysis model was employed having economic spread as the independent variable and the ratio of market value over the invested capital as the dependent variable. The results unveiled a statistically significant positive relationship between the ratio of value over invested capital and economic spread for both the service and secondary sector. On an industry basis the statistically significant positive relationship between the variables of the regression model exists only in the consumer cyclical, consumer non-cyclical and the basic materials industry.

Keywords

Economic Value Added, Market Value, Sector Analysis, Industry Analysis, European Stock Exchanges

JEL Classifications

G10, G32, M21

1. Introduction

It is widely accepted in finance theory that the primary objective of management is to maximize the value of the firm. This is achieved by investing in projects that have a return greater than the minimum acceptable hurdle rate (investment decision), choosing a financing scheme that minimizes the hurdle rate and matches the duration of the assets being financed (financing decision), and returning excess cash to stockholders when there are not enough investments that earn the hurdle rate (dividend decision), (Damodaran, 2001) .

In the financial literature internationally, through the years, several measures have been developed that are used to calculate the ability of a firm to create value. The one that has received great attention is economic value added (EVA¹), that was developed by Stern Stewart & Company and is based on the comparison between the profit a firm creates and the capital charge it has incurred for creating this profit. For a firm to have positive EVA it must have a positive economic spread (the difference between the return on capital invested and the weighted average cost of capital).

Stern Stewart & Company have been advocating the use of EVA, claiming that it has revitalized the financial performance of several U.S. companies such as Coca-Cola, CSX, SPX Corp, GE, and Chrysler (Tully, 1993; Walbert, 1994) . They argue that EVA drives stock prices higher, creates wealth and explains changes in shareholder wealth better than any other performance measure (Stewart III, 1994) .

¹EVA is a registered trademark of Stern Stewart.

The objective of the present article is to examine further the effect that EVA has on market values, or otherwise whether the ability of firms to create value, in the context of the EVA model, has a positive effect on their market price. Specifically, the relationship between economic spread and market value is examined, for all secondary and service sector firms, except financials, listed in the Athens Stock Exchange over the period 2000-2005, both on a sector and on an industry basis.

The remainder of the paper is constructed as follows. The next section provides a brief overview of the literature. Section 3 presents and analyses the methodology employed for examining the objective of the present research. Section 4 describes the data sources. The results from the empirical procedure are presented and discussed in Section 5. The last section presents an overview of the issues raised and draws some implications for future research.

2. Literature Review

Many companies have adopted EVA and are using it as an internal and/or external performance measure, as an analytical tool to make portfolio selection decisions, and as a management discipline (Teitelbaum, 1997) . A survey performed by the Institute of Management Accountants (ΙΜΑ) in 1996 showed that 35% of the sample firms are currently using EVA and 45% expect to use EVA in the future. There are number of academic articles in the finance literature internationally showing the advantages of the EVA over other measures of value creation along with success stories of firms that have adopted EVA for measuring management’s performance (Blair, 1996; Byrne, 1994; Carr, 1996; Copeland and Meenan, 1994; Gressle, 1996; Tully, 1993; Stern, 1990; Rice, 1996; Pallerito, 1997; Martin, 1996) .

The outburst of EVA literature belongs to Stewart III (1991) and his book “The Quest for Value”. The author scrutinizes the ability of EVA as a performance measure and concludes that EVA can reveal the real value of a company. His empirical research was conducted using a sample of 613 American companies comparing two periods, namely 1984-1985 and 1987-1988. The underlying issue was Stewart’s claim that changes in EVA drive changes in Market Value Added (MVA). The empirical outcome reports a strong correlation between EVA and MVA with a price of R² almost 97%. Though, in the same study, is noted that companies with negative EVA seem to weaken the above findings. In the same line, many studies focus on the relationship between shareholders’ value and EVA.

The findings provide a rather mixed result which means that more work is needed to be done in this field. Studies in favor of EVA Alsoboa (2017) , Khan et al. (2016) , Grant (1996) , O’Byrne (1996) . On the other side, a sample of studies arguing against the superior informational content of the EVA is the following: Chen and Dodd (1997) , Peterson and Peterson (1996) , Biddle et al. (1997) , Saha et al. (2016) .

Furthermore, the relation between EVA and market values has attracted a great deal of attention in the finance literature internationally. Abate et al. (2004) show that EVA can be a valuable investing tool to identify good companies with good stocks. Garvey & Milbourn (2000) used a relatively standard principal-agent model to ascertain the relative value of earnings and EVA based on two distinct uses of the stock price. They found that a simple correlation between EVA or earnings and stock returns existed, and that EVA could be used as a reasonably reliable guide to the firm value.

Biddle et al. (1997) concluded that EVA might be an effective tool for internal decision-making, performance measurement and incentive compensation. Their evidence suggests that EVA is more highly associated with stock returns and firm values than accrual earnings generally. Furthermore, they suggest that EVA components only marginally add to information content beyond earnings.

Ferguson & Leistikow (1998) used event study methodology to investigate whether firms adopting an EVA system lead to better stock performance (i.e., greater profitability). The results showed that there is insufficient evidence to conclude whether adopting EVA improves stock performance. Also, firms that adopted EVA appeared to have above average profitability relative to their peers both before and after the adoption of EVA. Furthermore, there is some evidence that EVA adopters experienced increased profitability relative to their peers following adoption.

On the other hand, Paulo (2002) argues that EVA is just another piece of accounting information, and—like other accounting information—it has become less relevant to stock returns and stock price changes. Farsio et al. (2000) studied the relationship between EVA and stock returns using as a sample constituent company of the S&P 500 index and the Dow Jones Industrial Average index. They concluded that EVA is not a good indicator of stock performance and represents just one of many available measures, explaining only a fraction of the variability in stock return fluctuation.

Chen & Todd (2001) examined the value relevance of three profitability measures: operating income, residual income, and EVA. Based on a formal valuation model of stock returns they found that all three profitability measures have information content in terms of value-relevance. However, contrary to the claim of EVA advocates, their evidence does not support the assertion that EVA is the best measure for valuation purpose. In contrast, the operating income regressions tend to show higher R² than the residual income regressions, which in turn have higher R² than the EVA regressions, although the differences are statistically insignificant.

Omneya et al. (2021) investigated the impact of EVA and EVA Momentum on firm financial performance indicators; ROA and ROE using Generalized Least Squares (GLS) regression. The results showed significant impact for both economic values added on firm financial performance, except for EVA with ROE. In addition, it was found that EVA Momentum could be considered as the most effective economic measure in improving and explaining the financial performance.

3. Methodology

The EVA model is based on the comparison between the profit a firm creates and the capital charge it has incurred for creating this profit. If a profit is generated that exceeds the charges of debt and equity, as well as covering all other expenses, then value is created; if only the charge of capital is generated, then value is merely preserved; if less than the charge of capital is generated, then value is destroyed.

The profit a firm creates is measured, within the framework of the EVA model, by the net operating profit after tax (NOPAT). Thus, the EVA measure can be calculated as:

EVA = NOPAT − Capital Charges (1)

The EVA is in essence an estimate of the residual income that a firm creates, since it considers not only the NOPAT the firm produces but also the capital charges, it has incurred to produce this profit. Since these charges are the product of the invested capital times the weighted average cost of capital (WACC), the EVA can also be defined as (Ehrbar and Stewart III, 1999) :

EVA = NOPAT − (Invested Capital × WACC) (2)

The NOPAT is a function of earnings before interest payments and taxes (EBIT) and the tax rate of the firm, that is (Young and O’Byrne, 2000) :

NOPAT = EBIT × (1 − Tax Rate) (3)

Now, if we define the return on invested capital (ROIC) as the ratio of the NOPAT over the invested capital then the EVA can be redefined as follows:

EVA = Invested Capital × (ROIC − WACC) (4)

The invested capital refers to the sum of the net operating capital and the operating long-term assets and is calculated as follows (Brigham and Ehrhardt, 2002) :

Invested Capital = Cash + Accounts Receivable + Inventories +

Operating Long Term Assets) − (Accounts Payable − Accruals) (5)

The WACC is the average of equity and debt cost of a firm weighted by the proportion of equity and debt in the total capital of the firm. The cost of equity was calculated by applying the Capital Asset Pricing Model. Denoting market return as rm, the cost of equity is equal to (Damodaran, 2002) :

Cost of Equity = rf + b (rm − rf) (6)

The before-tax cost of debt, Kdb, is the average interest rate on borrowed funds that is annual interest expenses over principal. The after-tax cost of debt, Kd, is equal to the before-tax cost of debt times (1 − tax rate).

Economic spread is the difference between ROIC and the WACC. This difference, which is the heart of the EVA model, is the net return the firm achieves for the capital it uses in its operations. Companies that have a positive economic spread will have positive EVA and thus create wealth, while companies that have a WACC larger than the ROIC (negative economic spread) will eventually destroy wealth.

The advantage of the economic spread as a measure of wealth creation is that it elegantly incorporates balance sheet data into an adjusted income statement metric. Furthermore, economic spread is justified by financial theory and is consistent with valuation measures. Finally, economic spread summarizes in a single statistic the value created above and beyond all financial obligations, since it recognizes that capital is not free through the deduction of the capital charge from the profit a firm creates (Harper, 2005) .

The primary objective of management is to maximize the value of the firm. However, it is quite interesting to explore whether the ability of a firm to create value, within the context of economic spread, is reflected in its stock price. To examine whether economic spread is related with stock price valuation the following regression model is applied:

${\text{FV}}_{i,t}/{\text{IC}}_{i,t}=a+b\cdot E{S}_{i,t}+{\epsilon }_{i,t}$ (7)

where,

FV_i = Firm Value of ith company in period t

IC_i = Invested Capital of ith company in period t

ES_i = Economic Spread of ith company in period t

The firm value of a company, that is the sum of market capitalization and debt, should be greater than its invested capital when it has positive economic spread, which means it is creating value. The higher the economic spread of a company the higher the ratio of firm value over invested capital.

4. Source of Data

The sample is comprised of all listed non-financial corporations in Greece, Spain, Italy, United Kingdom, Germany, France, and the Netherlands. The purpose of exempting banks, holding companies, and investment firms is that the companies examined compile comparable financial statements and accounts to allow comparability when concluding.

A broad sample of years (2015-2020) has been opted in this paper, with the goal of looking at the EVA behavior as a result, the sample’s time span contributed to the inclusion of a larger volume of observations in the analysis. Furthermore, content related to the Covid-19 pandemic was incorporated. This is because Covid-19 cases are recorded from 22 January 2020 until today, according to World Health Organization (https://covid19.who.int/). This is an innovative attempt that is not included in previous empirical research.

The sampled companies of the seven countries under the microscope were categorized based on the Bloomberg Industry Classification (BIC) criteria in six industries covering the full range of economic activity exempting the financial sector. The number of companies under review is 1016, divided per industry as follows: oil & gas (48), basic materials (33), consumer goods (385), health care (68), industrials (307), technology (95), telecommunication (17), and utilities (63). All data were extracted from the reputable databases Bloomberg and DataStream.

Bloomberg is a major global provider of 24-hour financial news and information, including real-time and historic price data, financials data, trading news, and analysis. Bloomberg terminal is a computer system that allows investors to access the Bloomberg data service, which provides real-time global financial data, news feeds, and messages.

Datastream is a global financial and macroeconomic time-series database, providing data on equities, stock market indices, currencies, company fundamentals, fixed income securities and key economic indicators for 175 countries and 60 markets. Datastream is published by Refinitiv, a subsidiary of the London Stock Exchange Group plc.

5. Results

The regression model was applied both for all years under consideration and for each year separately either on a sector or an industry basis. Thus, for each sector and for each industry five regressions were performed. Each regression equation was tested for the statistical significance of its variables and the Durbin-Watson test was used to examine if the data were serially correlated.

Results from applying the regression model for the secondary sector are disclosed in Table 1. The economic spread is positively related to the ratio of value over invested capital of a firm either for all years or for each year of the research separately, while the results are statistically significant in five out of seven cases. However, the coefficient of determination appears to have a satisfactory explanatory power only in 2018, 2019 and 2020 (R² equal to 64.44%, 52.85% and 64.23% respectively).

Results from applying the regression model for the service sector are disclosed in Table 2. The economic spread is positively related to the ratio of value over invested capital of a firm either for all years or for each year of the research separately, while the results are statistically significant in six out of seven cases. The coefficient of determination appears to have a satisfactory explanatory power in 2016, 2018, 2019, and 2020 (R² equal to 54.20%, 42.34%, 55.85% and 63.55% respectively).

In the consumer cyclical industry, the regression results indicate that the there is a positive statistically significant relation between the economic spread and the ratio of value over invested capital of a firm either for all years or for each year of the research separately. Moreover, the overall fit of the estimated equations, as measured by the coefficient of determination is satisfactory in four out of seven cases (Table 3). Specifically R² is equal to 42.37% in 2017, 47.92% in 2018, 44.41% in 2019 and 42.36% in 2020.

In the case of the industrial companies, although the results indicate that the

Table 1. Regression model for the secondary sector.

t-stats in parentheses; *Not significant at the 90% or higher confidence level; **Not significant at the 95% of higher confidence level; ***Not significant at the 99% confidence level.

Table 2. Regression model for the service sector.

t-stats in parentheses; *Not significant at the 90% or higher confidence level; **Not significant at the 95% of higher confidence level; ***Not significant at the 99% confidence level.

Table 3. Regression model for consumer cyclical companies.

t-stats in parentheses.

there is a positive relation between the independent and dependent variable, this relation is statistically significant in only three cases. Moreover, the overall fit of the estimated equations, as measured by the coefficient of determination is poor in all seven cases where all R² values are below 40% (Table 4).

In the consumer non-cyclical industry, the economic spread is positively related to the ratio of value over invested capital of a firm for all years or for each year of the research separately, while the results are statistically significant in four out of seven cases. However, the coefficient of determination is satisfactory only in 2018 and 2019 (Table 5). Specifically R² is equal to 46.30% in 2018 and 49.72% in 2019.

In the basic materials industry, the results indicate that the there is a positive relation between the economic spread and the ratio of value over invested capital of firm value either for all years or for each year of the research separately, while there are statistically significant in only three out of seven cases. Moreover, the coefficient of determination is quite satisfactory in only three out of seven cases (Table 6). Specifically R² is equal to 43.54% in 2017, 47.45% in 2019 and 46.30% in 2020.

In the technology sector the results are statistically significant only in two cases. Furthermore, the coefficient of determination is satisfactory in only one out of seven cases (Table 7). Specifically R² is equal to 46.39% in 2015.

Finally, in the communication industry the results are statistically significant only in two cases. Furthermore, the coefficient of determination is poor in all seven cases where all R² values are below 40% (Table 8).

Table 4. Regression model for industrial companies.

t-stats in parentheses; *Not significant at the 90% or higher confidence level; ***Not significant at the 99% confidence level.

Table 5. Regression model for consumer non-cyclical companies.

t-stats in parentheses; *Not significant at the 90% or higher confidence level; ***Not significant at the 99% confidence level.

Table 6. Regression model for basic materials companies.

t-stats in parentheses; *Not significant at the 90% or higher confidence level; **Not significant at the 95% of higher confidence level; ***Not significant at the 99% confidence level.

Table 7. Regression model for technology companies.

t-stats in parentheses; *Not significant at the 90% or higher confidence level; **Not significant at the 95% of higher confidence level; ***Not significant at the 99% confidence level.

Table 8. Regression model for communications companies.

t-stats in parentheses; *Not significant at the 90% or higher confidence level; **Not significant at the 95% of higher confidence level.

6. Conclusion

The objective of the present article was to examine further the effect that EVA has on market values. Specifically, the relationship between economic spread and market value was examined, for all secondary and service sector firms, except financials, listed in European markets over the period 2015-2020, both on a sector and on an industry basis.

On a sector basis the results indicated that the economic spread is positively related to the ratio of value over invested capital both for the secondary and the service sector firms. Put in another words the ability of Greek secondary and service sector firms to have positive economic spread, that is to create wealth, is reflected on the market price of their stock. However, the explanatory power of the research model is greater for the service sector firms.

Turning to the industry basis the results showed that the economic spread and the market value of the firm are positively correlated on all sectors except the technology sector, where the beta coefficient of the regression model was negative in 66.67% of the cases.

However, the above results are statistically significant in all cases in the consumer cyclical sector and in 50% of the cases in the consumer non-cyclical sector. Thus, it can be concluded that ability of consumer sector firms, either cyclical or non-cyclical, to have positive economic spread, that is to produce wealth on the EVA context, is reflected on the market price of their stock.

Declarations

The paper has not been previously published, in English or another language.

The paper is the work of only the listed author.

The work is original and all the work of others is appropriately acknowledged.

No funding was received to assist with the preparation of this manuscript.

Conflicts of Interest

The author declares no conflicts of interest regarding the publication of this paper.

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