EVA: Does Size Matter?

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Review of Pacific Basin Financial Markets and Policies Vol. 12, No. 2 (2009) 267–287 c World Scientific Publishing Co. and Center for Pacific Basin Business, Economics and Finance Research

EVA: Does Size Matter? Janet Hamilton School of Business Administration Portland State University Portland, OR 97201, USA Shafiqur Rahman∗ School of Business Administration Portland State University Portland, OR 97201, USA [email protected] Alice C. Lee Department of Finance San Francisco State University San Francisco, CA 94132, USA Value-added performance measures, such as economic value added (EVA), are promoted as a means to better align managerial incentives and improve firm performance. This paper empirically examines whether EVA adopters outperform a peer group of non-adopters over a long term horizon. It also explores the determinants associated with differences in relative market performance of these two groups. We find mixed results consistent with previous studies. In examining risk adjusted market returns, we find that the full sample significantly underperforms the market. However, during the period of the study, EVA adopters exhibit less negative performance than non-adopters. Moreover, over the entire study period, adopter performance improves in a positive direction, while non-adopters experience a performance decline. Adopting firms also exhibit higher earnings growth and higher returns. In perspective, these results suggest there is some benefit to EVA adoption, relative to a peer group, as adopters outperform their peer group. In a comparison

∗

Corresponding author.

267

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268 • Janet Hamilton, Shafiqur Rahman & Alice C. Lee

of peer matched groups, firm size and growth opportunities are found to have a significant impact on performance for three size-based groups. Keywords: Economic value added; market value added; value-added performance; size; buy and hold compound monthly returns; market model abnormal returns.

1. Introduction Value-added performance measures were enthusiastically embraced by corporations in the 1990s as a means to better align managerial incentives and improve firm performance. In theory, better alignment of manager and shareholder interests reduce agency costs and improve firm performance. Advocates also argue that value-based measures are better indicators of firm performance than accounting measures since they provide a more accurate and timely measure of residual income. The extraordinary rate of adoption of such measures was in part fueled by the economic value added (EVA) measure promoted by Stern Stewart. When incorporated into a performance management system, the EVA measure is promoted as reducing accounting distortions and improving alignment of managerial interests with shareholders. Despite eager adoption of the EVA concept by the corporate world, the evidence of its impact on shareholder performance has been mixed. Previous research has found both positive and negative returns to EVA adopters. This paper adds to the body of evidence by examining whether EVA adopters outperform a peer group of non-adopters over a long term horizon. It also explores the determinants associated with differences in relative market performance of these two groups.

2. EVA Evidence Revisited The concept of EVA is not new. It is grounded in the economic principle of residual income whereby to add value, companies must earn a rate of return on invested capital greater than the cost of that capital. If firms are successful in this, the value of the firm will increase, and, in an efficient market, so will shareholder wealth. Firms that take on projects whose value is less than their cost will experience declines in value and shareholder wealth. While simple in concept, the implementation of residual income measures has not been without difficulty. The measurement of economic returns and costs often relies on accounting cost and return proxies which are incomplete when it comes to measuring true economic profit. Two issues lie at

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EVA: Does Size Matter? • 269

the heart of the measurement problem: accounting adjustments that distort economic earnings, and the omission of a charge for the cost of equity capital. Since accounting measures use historical, cost-based information, the resulting accounting values are typically not reflective of economic value. Moreover, the typical return proxies only capture interest bearing capital charges. Consequently, there exists a potential bias to decisions based on accounting metrics. Stewart (1991) advocates EVA as a performance measure that overcomes these problems to provide a truer measure of economic earnings. It accomplishes this by adjusting accounting income for numerous Generally Accepted Accounting Principles (GAAP) and non-US GAAP adjustments that distort economic profit. Among the most common adjustments are those for research and development, advertising, expense, deferred taxes, provision for bad debts, depreciation, goodwill, restructuring charges, and LIFO reserves. In addition, EVA includes a capital charge that accounts for all sources of capital, not just interest bearing debt. As developed, EVA is defined as: EVA = NOPAT − Cost of Invested capital, where NOPAT is the net operating profit after tax, and the cost of invested capital is the weighted average cost of capital (RWACC) multiplied by invested capital. Invested capital is typically defined as interest bearing debt and long term capital. EVA measures the amount of economic profit resulting from managerial decisions. Firms can enhance EVA by increasing profitability through improving operational efficiency, divesting poorly performing assets, or investing in assets that earn a return greater than their cost of capital. As a measure of wealth creation, it allows companies to track a manager’s decisions and develop compensation packages that reflect the amount of additional value created. The appeal of the EVA approach is that it directly links changes in economic value to managerial decisions in areas where performance measures are most often used — compensation, firm performance, and capital expenditures. Traditionally, a variety of metrics have been used, often conflicting. Capital expenditure metrics come closest to using an economic income measure through the employment of discounted cash flow techniques. Incentive compensation and firm performance metrics have often relied on accounting based measures such as return on investment (ROI), return on equity (ROE), and earnings per share based measures. EVA provides a performance

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measure that marries these three areas with a consistent, and economically defensible, set of metrics. Whole Foods, a large, specialty grocer retailer in the US, states that the use of EVA “provides useful information to management, analysts and investors regarding certain additional financial and business trends relating to its results of operations and financial condition. In addition, management uses these measures for reviewing the financial results of the Company and for incentive compensation and capital planning purposes.” (Whole Foods Market Inc. Annual Report, 2007) EVA, as measured by Whole Foods in 2007, was calculated as: ($ in thousands) Net operating profit after tax (NOPAT) Capital charge EVA

2007 $201,934 166,480 35,454

Infosys, a large software company domiciled in India, is another example of a firm that uses EVA extensively in evaluating company performance and provides results as an indicator of shareholder value wealth creation. In their 2006–2007 annual report, they report compound EVA growth of 47% over the previous four years and a range of 18% to 26% return as measured by EVA to capital employed. 2.1. The practice of EVA The estimation of EVA is not without complexity. As Weaver (2001) notes in developing an EVA estimate for a hypothetical firm, there are up to 164 potential items that require adjusting to move from an accounting to an economic based measure of NOPAT and Capital. These adjustments reflect both US GAAP and Non-US GAAP adjustments. The concept behind the adjustments to net income is to remove the effect of one-time events and nonoperating items, and to reflect more closely the economic cash flow arising from the firm’s asset base. For the calculation of EVA capital, the goal is to develop a measure of long term capital, and exclude such items as noninterest bearing liabilities and other short term accounts not related to the acquisition and use of long term assets. At the same time, adjustments to capital include such non-GAAP adjustments as restructuring charges and the capitalization of goodwill. More details associated with the potential number of adjustments can be found in Young and O’Bryne (2001), and Young (1999).

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Despite the great number of potential adjustments, the actual number of adjustments made by companies is much smaller (Young 1999; Anderson et al., 2005), and ranges between zero and about 15 adjustments. Young and O’Bryne (2001) suggest that the majority of companies make at most five or fewer adjustments, while many firms make no adjustments at all. This adjustment process can be seen in Whole Foods reported calculation of NOPAT and Capital for 2006 and 2007 (as reported on the Whole Foods website, http://www.wholefoodsmarket.com/company/eva.php): Whole Foods NOPAT Calculation from Net Income: ($ in thousands) 2007 GAAP net income $182,740 Provision for income taxes 121,827 Interest expense and other 31,989

2006 $203,828 135,885 19,088

Net operating profit before taxes (NOPBT) Taxes (40%)

336,556 134,622

358,801 143,520

NOPAT

$201,934

$215,281

Whole Foods ending EVA capital from total net assets: ($ in thousands) 2007 Total assets $2,208,124 Total liabilities 759,354

2006 $2,040,422 663,006

Net assets Long-term debt and capital lease obligations Implied goodwill (from pooling-of-interest transactions) Other*

1,448,770 58,307 162,803 179,897

1,377,416 12,214 162,803 123,911

EVA capital

$1,849,778

$1,676,344

∗

Accumulated components of net income not included in NOPAT.

2.2. EVA and discounted cash flow Prior to the advent of the EVA measure, the discounted cash flow (DCF) measure was perhaps the most familiar and widely adopted performance measure in corporate America. It is typically applied to a firm valuation (Rappaport, 1986) or an investment in assets, when it is commonly known as the net present value (NPV) method. The concept is simple: the market value is the present value of all future expected cash flows, discounted back at the cost of capital. In a recent survey, Ryan and Trahan (1999) report that while CFOs identified EVA as the most familiar value based performance method (94% of responding CFOs reported familiarity), the second most familiar method was discounted cash flow (DCF), with 86% CFO familiarity. In principle, the DCF and EVA measures provide consistent value metrics. As reported by Weaver and Weston (2002), discounted cash valuation

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and economic profit, as measured by EVA, are highly correlated. In fact, the two approaches will produce equal results in valuation. In terms of traditional net present value analysis, this means the present value of a project’s EVA cash flows is equal to the NPV of the investment. While the treatment of capital outlay differs in the two approaches (NPV records the outlay in the initial period; EVA allocates it over the depreciable life of the investment), the resulting value is the same. While EVA has been readily adopted by firms, whether it actually increases long term economic performance is an unresolved issue. 2.3. EVA and accounting measures of performance Due to the potential link between managerial compensation and value creation, EVA is frequently advocated as superior to accounting based performance measures for aligning managerial and stockholder incentives (e.g., Dodd and Johns, 1999; and Ehrbar, 1999). By providing improved estimates of economic returns managers and investors can make more informed decisions. With superior information and better decisions, an increase in EVA result can ultimately lead to an increase in shareholders wealth. The assertion that EVA is superior to accounting performance measures has found little empirical support. In an early study, Biddle et al. (1997) find that earnings are more highly related to stock returns than EVA, residual income, or cash flow from operations. Their tests of the unique information content of EVA suggest that it is only slightly higher than that of earnings and not economically significant. Chen and Dodd (2001) report similar results when evaluating the relative and incremental information content of operating income, residual income and EVA. While they determine that EVA measures contain unique information relative to operating and residual income, EVA measures do not provide significant incremental information content when compared with residual income measures. An earlier study by Chen and Dodd (1997) suggest that while EVA measures provide additional information over accounting measures of performance, they provide only slightly more information than residual income when explaining stock returns. The size of the additional information content may be due in part to the nature of adjustments companies make when implementing EVA. Weaver (2001) finds that on average, firms make 19 adjustments to EVA. In a small sample field study of New Zealand firms, McLaren (2003) finds that firms evolve to making fewer adjustments to EVA over time, and often made less than five EVA adjustments.

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In a departure from these results, a recent study by Feltham et al. (2004) reexamines the Biddle et al. (1997) research using a sample based upon the Stern Stewart 1000 for year ending 2000. The result is a slightly smaller sample than that of Biddle et al. (1997), which covered the 1996 Stern Stewart 1000. However, tests across the same time period, as well as the subsequent five years, provided evidence that suggests EVA measures dominate the relative information content of both earnings and operating cash flow measures. 2.4. EVA and firm performance The link between manager performance systems, EVA and shareholders wealth has been examined by several researchers. In one such study, Wallace (1997) considers the relationship between value-based management systems and firm performance from an agency perspective. Wallace theorizes that if incentives are better aligned, then improvements in operating decisions could be expected of adopting firms. In examining the post-adoption performance of 40 matched-pair firms, his findings support improved operations. Specifically, adopting firms experience improvements in residual income and asset efficiency, while reducing new investment and increasing stock repurchases. Some evidence that shareholders wealth increased as a result of adoption is found but appears to be not significant statistically. Bacidore et al. (1997) also examine the relationship of EVA to performance. In research evaluating abnormal returns, the authors compare EVA to an alternative measure, refined economic value added (REVA). The primary difference between EVA and REVA is that the latter uses the market value of the firm, rather than the book value, in determining cost of capital. Employing a sample of 600 firms over a 10-year period, their finding suggests that REVA explains abnormal returns better than EVA and is better able to predict shareholder wealth creation. 2.5. EVA adopters versus nonadopters While much of the research to date focuses on questions related to whether residual based income measures are superior to other measures of income, limited research exists that compares the impact of stock market performance of EVA adopters to non-adopters. In addition to Wallace (1997), Kleiman (1999) reports evidence of significant differences between adopting and non-adopting firm performance. Using a matched-pair sample of 71 firms, EVA adopter excess returns are found to be greater than their matched

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competitors over the three years following adoption. Further evidence suggests that post-adoption operating performance is also improved, primarily through improved per-employee operating margin and cash flow. Analyses provided by Ehrbar (1999) and Stern Stewart (2002) also suggest that EVA adoption results in superior market performance relative to peers. In a comparison of Fortune 500 EVA adopters and non-adopters, Abdeen and Haight (2002) report mixed results when examining EVA relationships. While they provide evidence that the mean of most profitability-based performance measures are higher for EVA users, they also find earnings per share (EPS)related and total return measures are lower for EVA adopters. Griffith (2004) examines EVA adopters and finds that they significantly under-perform the market for up to three and four years after adoption. Using security analysts’ forecasts as a proxy for performance, Cordeiro and Kent (2001) find no significant relationship between EVA and firm performance for a group of EVA adopters and a control group. Their results suggest security analysts’ forecasts are related to firm size, past five-year EPS, industry average EPS and the number of analysts making forecasts, but not EVA adoption. Sparling and Turvey (2003) challenge the notion of comparing adopter versus non-adopter changes in EVA firms, arguing that “if the link does not exist (between EVA and returns), there is absolutely no point in adopting EVA.” They test whether shareholders returns are correlated with changes in EVA over varying time periods and provide results that are not supportive of a strong, positive relationship in all periods. Although a weak correlation over long term horizons of five and 10 years is evident, they maintain it is not strong enough to conclude that there is a sufficient link between EVA and returns. While the concept of EVA is compelling in abstract, the lack of a consistent body of research raises the question of why so little evidence has been found to support the relationship between EVA and various performance measures. The lack of relationship has been attributed to many factors, including the complexity of calculating EVA; the market’s focus on earnings, and not EVA; the length of time to implement EVA and the short time frame of many studies; and the wide diversity in implementation practices and culture change among adopting firms (e.g., Abdeen and Haight, 2002; Weaver, 2001; Sparling and Turley, 2003; Kleiman, 1999; McLaren, 2003; and Malmi and Ikaheimo, 2003). The purpose of this research is to examine the determinants of the long term price reaction to EVA adopters. The longer time frame allows for a lag between adoption, implementation and realization of EVA benefits, which

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is often characteristic of the culture change necessary to communicate and understand a new performance measurement system. We extend the studies of Kleiman (1999), Wallace (2001) and Griffith (2004) by examining buy and hold returns to account for the bias inherent in cumulative abnormal returns measured over a long time period (Barber and Lyon, 1997). The agency conflicts noted in the works of Lovata and Costigan (2002) and Wallace (1999) are drawn on to examine the role of agency conflicts. Further, we include an analysis of size effects to explain return differentials among a matched peer group of adopters and non-adopters. 3. Research Issues 3.1. Alignment of managerial interests Agency conflicts arise when managerial and shareholders interest are not aligned. Management acting in their own interests will consume more resources and act less efficiently than if they perceive themselves to be owners. EVA adoption has the potential to better align managerial incentives (Wallace, 1997; Stewart, 1991) and improve operational efficiency and decision making. Organizational strategy also may play a role. Lovata and Costigan (2002) present findings that suggest EVA adopters are more likely to be defender firms (i.e., those who compete on cost leadership) because of their focus on financial performance. These firms, in turn, are more likely to have agency conflicts and turn to an EVA system as a mechanism to mitigate agency costs and improve performance. They find that EVA adopters are more likely to have higher institutional ownership and less insider ownership than a peer group of non-adopters. Together, these factors lead to the hypothesis that as a result of improved managerial incentives, EVA adopters should see improved post-adoption returns relative to non-adopters. In the post-adoption period, it is expected that successful implementation will lead to more alignment of shareholders and managerial interests for adopters, and thus greater return performance. Growth opportunities, as evidenced by price to book (P/B), are related to EVA adoption in that defender firms are likely to have fewer opportunities than non-adopters (Lovata and Costigan, 2002). As such, post-adoption P/B is expected to be related to greater post-adoption return performance. 3.2. Size effects A firm size effect has been related to differential abnormal returns (Brown and Warner, 1985). As smaller securities tend to trade at lower prices often

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with greater volatility, the combination of small and large firms in an event study may mask underlying relationships not related to size. In addition, firm size has also been associated with the amount of pre-disclosure information. Larger firms are more likely to have more sources disseminating information about them, suggesting that there is less unexpected information conveyed to the market as a result of an announcement (see, for example, Bamber, 1987; and Ziebart, 1990). This would suggest the size of the unexpected abnormal return would be smaller as firm size increases. Studies to date have focused on excess returns to EVA adopters in aggregate. Additional size related factors may be associated with the success of EVA adoption. Implementation of a residual performance system is costly and time consuming. Larger firms are more able to take on the investment necessary to implement EVA than smaller firms. The cost is a heavier burden to bear for these firms and is likely to reduce the size of abnormal returns for smaller firms. Yet the increased complexity and size of larger firms may work against performance improvement and customer satisfaction. Challenges of communicating and enforcing a new performance system increase as firm size increases and may affect the depth and success of implementation. Managerial focus on customer satisfaction may be diverted with a new system if performance incentives are not adequately developed. At the same time, monitoring costs increase as size increases, thereby increasing agency costs (Lovata and Costigan, 2002). The existence of a size effect is suggested by this constellation of factors. While we would expect to see smaller firms exhibiting changes in performance before larger firms, the direction of the net impact on returns for each size group could go either way. 4. Data and Methodology The sample for this study consists of 67 firms identified as EVA adopters from a sample client list published by Stern Stewart in 1999. A control group of firms is matched to the sample based first on industry, as determined fourdigit SIC code, and then on firm size within the SIC code. When required due to lack of data or size differences, the three- and then two-digit SIC code are used. Firm size is measured by sales in the year prior to EVA adoption. Firms are deleted from the sample if they do not have data available in the Research Insight and CRSP databases for a minimum of 72 months in the pre-adoption year and 72 months in the post-adoption year. This requirement reduces the sample to 45 firms. In the 10 years prior to 1994,

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approximately 23% of the sample companies adopted EVA. The remaining firms adopted EVA, in roughly equal proportion over each year between 1994 and 1997. To provide for both implementation and sufficient time to demonstrate results, we apply a long time horizon. The sample period is defined as five years prior to plan adoption (pre-adoption period), the year of adoption, and five years following adoption (post-adoption), for a total of 11 years. Thus, for those firms with an adoption year of 1997, the last data point in the series occurs in 2002. Table 1 provides descriptive statistics for mean values of size, performance and operating variables for both adopting and matched peers in the

Table 1.

Descriptive statistics of sample, 1994–2002.

Variable

Mean

Panel A: Pre-Adoption Period1 Net Sales Adopters 3, 159.3 Non-adopters 3, 650.3

Std. dev

t-stat for Mean Difference

Probability > |t|

4, 205.0 6, 439.8

−0.42

0.678

3, 368.9 6, 359.6

−0.61

0.541

Total Assets

Adopters 2, 882.0 Non-adopters 3, 498.9

Total Debt/Capital

Adopters Non-adopters

33.8 36.0

17.6 21.9

−0.50

0.622

Return on Assets


4.7 4.9

3.7 8.0

−0.22

0.828

Return on Equity


10.4 12.2

7.2 17.4

−0.62

0.534

Panel B: Post-Adoption Period2 Net Sales Adopters 4, 492.7 Non-adopters 5, 236.7

6, 188.9 6, 973.4

−0.526

0.600

5, 592.6 7, 158.4

−0.552

0.583

−0.820

0.414

Total Assets

Adopters 4, 401.6 Non-adopters 5, 162.5

Total Debt/Capital


38.2 42.9

18.56 34.26

Return on Assets


3.3 1.1

9.9 12.9

0.877

0.383

Return on Equity


9.7 23.1

19.3 158.2

−0.551

0.583

Notes: 1 The mean is calculated for each firm in the pre-adoption period and represents the average level in the year prior to adoption. 2 The mean is calculated for each firm as of the end of their respective post adoption period.

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year prior to adoption (Panel A) and again at the end of the post adoption period (Panel B). Adopting companies tend to have lower mean sales and assets relative to the peer group, along with slightly lower returns on assets, and equity in the pre-adoption period. Although it could be argued that firms with lower profitability sought measures to improve, such as EVA adoption, the differences between the adopter and peers are not significantly different in the pre-adoption period. Similarly, significant differences between groups on these measures are not evident in the post-adoption period. 4.1. Abnormal return calculations Given the long run focus of this study, buy and hold compound monthly returns are the basis for the abnormal return calculations (Barber and Lyon, 1997). Abnormal returns are calculated with two methods. The first method employs traditional event study methods with a single index market model to estimate expected returns over a 60-month period ending one year prior to the year of EVA adoption (Brown and Warner, 1985). The abnormal return using a market model approach is defined as: αf + βˆj Rmt ). Ajt = Rjt − (ˆ

(1)

The buy and hold returns (BHAR) are computed by estimating the abnormal compound return for each sample security for each month and aggregating across trading months for each sample period. For the peer matched portfolio, the calculation is:   T2 (1 + Rjt ) − 1 − [(1 + α ˆ j )(T2 −T1 +1) − 1] BHARjT1 ,T2 =  t=T1



− βˆj 

T2

 (1 + Rpt ) − 1 ,

(2)

t=T1

where the interval of trading months begins with month T1 and ends with month T2 . Correspondingly, the buy and hold average compounded abnormal return is: 1 BHARj,T1 ,T2 , (3) ACART1 ,T2 = N where t is defined in trading months relative to adoption month and year. Similar methods are used to estimate peer adjusted abnormal returns, average abnormal peer adjusted returns, and buy and hold returns. Peer adjusted returns are computed by subtracting the observed return of the

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market (peer matched stock) for day t, Rpt , from the rate of return of the sample stock of the jth firm on day t. Stated in terms of the peer matched portfolio, the abnormal return calculation is: Ajt = Rjt − Rpt .

(4)

All other return measures are similarly adjusted, replacing the market return with the peer return. Bootstrap versions of the transformed normal test (Hall, 1992) and the cross-sectional standard deviation test discussed by Brown and Warner (1985) are used to test for abnormal returns. The bootstrap versions provide nonparametric analogs of these tests. In addition, the nonparametric generalized sign test discussed in Cowan (1992) is estimated and used to test for abnormal returns. 5. Empirical Results 5.1. Market model abnormal returns – Do EVA adopters experience greater returns? Table 2 presents the results of computing market model based BHARs for each of the sample periods. As Panel A indicates, adopters in the 60-month Table 2.

Mean abnormal buy and hold return differences market model benchmark. Significant Tests

Mean Compound Abnormal Return (BHAR)

Bootstrap CsectErr t

Sign Test z

Bootstrap Skewness Corrected t

Panel A: Pre Period All 90 Adopters 45 Non-adopters 45

−43.77% −106.58% 19.04%

−1.47 −4.43∗∗ 0.36

−3.10∗∗ 2.93∗∗ −1.46

−1.30 −4.95∗∗ 0.37

Panel B: Post Period All 90 Adopters 45 Non-adopters 45

−75.00% −26.38% −124.72%

−2.69∗∗ −1.04 −2.52∗∗

−3.38∗∗∗ −2.12∗ −2.67∗∗

−3.62∗∗ −1.02 −3.72∗∗

Panel C: Adopters Pre Period Post Period

−106.58% −26.38%

Panel D: Non-adopters Pre Period Post Period

19.04% −124.72%

Sample Size

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pre-adoption period significantly underperformed the market with a BHAR of –106.6%, while the peer group of non-adopters earned abnormal compound returns of 19.0%, although not at a significant level. This relationship reverses in the post-adoption period results of Panel B, which shows EVA adopter BHARs of –26.4% compared to a significant –124.7% BHARs for the non-adopters. The BHARs of adopting EVA firms (Panel C) becomes less negative between pre- and post-adoption periods, while the peer firms exhibited increasingly negative BHAR (Panel D) returns. These results are consistent with those of Griffith (2004), who finds significant underperformance of EVA adopters subsequent to adoption. The market underperformance in the pre-adoption period may well have been a strong motivation to adopt EVA. At the same time, the relatively improved performance of adopters in the post-adoption period relative to the peer group attests to the potential of EVA implementation. What is not apparent are the drivers behind the greater underperformance of the non-adopter peer group in this period. Given the multiple industries represented by the sample, it is unlikely the results are due to an industry related effect. If EVA adoption is successful, the expectation is that EVA will increase and accordingly, so will market value. So what explains the observed postadoption market adjusted negative returns for adopters and non-adopters alike? 5.2. Does size matter? Size based companion portfolio comparisons To examine size-based effects, peer-based abnormal returns, stratified into three size groups, were estimated for each matched pair in the adoption period. Table 3 presents descriptive statistics for these size portfolios. On average, EVA adopters had better return performance and earnings per share growth in the five year post-adoption period. Both small and medium size adopters turned in better performance on average, while large adopters saw comparable or lower returns and EPS growth, when compared to non-adopters. The results of the size based peer-matched portfolios are presented in Table 4. As shown, the full sample did not demonstrate that EVA adoption is related to an increase in market value in any of the five years after the adoption year. Mean BHARs average 5.6% the year after adoption, move to negative returns in the three subsequent years, and return to an average 15.64% by the end of the fifth year. In none of the years are these results significantly different from non-adopters.

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EVA: Does Size Matter? • 281 Table 3.

Descriptive statistics for size based groups — Five years post-adoption. N

Market Cap

Total Assets

5-Year EPS Growth

45 11 15 19

$3,005,171 $1,583,246 $2,813,279 $4,618,987

$4,727 $2,320 $4,336 $7,525

7.6% 13.3% 7.0% 2.5%

Non-adopters All 45 Small 11 Medium 15 Large 19

$3,838,946 $1,316,739 $3,422,280 $6,777,818

$5,979 $2,940 $4,589 $10,409

5.5% 6.5% 4.5% 5.5%

Adopters All Small Medium Large

ROA 3.5% 0.7% 4.2% 5.7%

ROE 10.3% 3.1% 12.2% 15.7%

1.8% 4.9% −1.5% −14.0% 2.9% 11.5% 3.9% 17.1%

Interestingly, over the five-year period, each size group experiences significant cumulative returns for different periods. The cumulative average BHAR for the large firm peer-matched groups is increasingly negative, reaching a significant negative peak of –5.2% in the third year post adoption. Thereafter, abnormal returns remain negative but begin to decrease. The BHAR for the medium firm group shows increasing, but not significant returns in the first two years post adoption but then revert to negative returns, peaking in the fourth year at a significant level and becoming less negative by the final period. In a departure from this pattern, the small group BHAR is increasing and positive in all but one year with a significant positive difference in the fifth year post adoption. For small adopters, it appears that EVA adoption does have a demonstrable positive difference on market performance over the long run. Large and medium size adopters exhibit negative significant returns but with a decreasing trend by the end of the period. There is a marked time lag for the year of impact — large adopters are most affected in the third year, medium adopters in the fourth, and small adopters in the fifth year post adoption. Plots of the BHARs for each group are shown in Figure 1 and are consistent with the observed pattern of returns for each of the periods. 5.3. What explains these size based results? The size hypotheses are further tested by estimating the following multiple regression model: BHRi,0,59 =

f {Adopt, MkCap, PB, InsideOwn, EBIT%, EMPSales}. + ± + − + ±

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282 • Janet Hamilton, Shafiqur Rahman & Alice C. Lee Table 4. Mean abnormal buy and hold return differences — EVA adopters vs matched peer control group.1,2 BHAR Significant Tests Mean Mean Cumulative Compound Bootstrap Sample Abnormal Abnormal Bootstrap Sign Skewness Size Return (CAR) Return (BHAR) CsectErr t Test z Corrected t

Year Past Adoption Year Full Sample 1 2 3 4 5

45

Large Firm Sample 1 2 3 4 5

11

Medium Firm Sample 1 2 3 4 5

15

Small Firm Sample 1 2 3 4 5

19

3.10% −4.38% −4.79% −5.85% −6.0%

5.6% −4.59% −7.76% −5.63% 15.64%

−0.22 0.08 −0.82 −1.42† −1.12

0.88 −0.32 −0.66 −0.33 0.88

0.94 −0.32 −0.66 −0.33 0.95

−2.83% −14.25% −28.41% −19.75% 18.06%

−4.12% −8.37% −45.21% −25.65% −25.96%

−0.94 −0.94 −2.14∗ −0.94 −1.54†

−0.51 −0.54 −2.05∗ −0.55 −1.20

−0.50 −0.53 −2.05∗ −0.54 −1.26

1.83% 4.92% −14.30% −21.74% −28.20%

5.10% 11.25% −22.69% −32.27% −23.93%

−0.55 0.38 −0.04 0.42 −0.55 −1.32† ∗∗ −1.59† −2.62 † −1.59 −1.03

0.42 0.47 −1.28 −1.33† −0.10

7.52% −6.01% 16.39% 14.75% 18.51%

11.63% −14.92% 25.70% 27.00% 70.96%

0.86 0.86 0.86 0.86 0.86

1.17 −0.58 1.39† 1.08 2.20∗

1.27 −0.63 1.40† 1.15 2.80∗∗

Notes: 1† , ∗ , ∗∗ , and ∗∗∗ denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a two-tail test. 2 Results are similar for the CARS results and therefore not reported.

The sign under each variable represents the expected direction of the relationship. Each variable is defined as: = The 60-month post-adoption period cumulative return of firm i. Adopt = A dummy variable equal to 0 if the firm was an EVA adopter; and 1 otherwise.

BHR

i,0,59

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Fig. 1.

BHAR plots of size-based matched-peer portfolios — post adoption period.

MkCap = Log of the market value of firm i in the 60th month post adoption. P/B = The price to book ratio of firm i in the 60th month post adoption. This measure is a proxy for growth opportunities. InsideOwn = The percentage of total shares outstanding held by insiders to provide a measure of the degree of agency conflict. EBIT% = The ratio of EBIT to sales, to represent a measure of operating efficiency. EMPSales = The ratio of total employees to sales, to represent a measure of operating efficiency and customer satisfaction. Buy and hold total returns (BHR) are evaluated to provide a more direct connection to the peer based companion portfolios.

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284 • Janet Hamilton, Shafiqur Rahman & Alice C. Lee Table 5. Cumulative return regressions — EVA adopters vs matched peer control group.1

Expected Sign Intercept Adopt MkCap PB InsideOwn EBIT% EmpSales Adjusted R2 F-ratio

+ ± − − ±

Model 1 Full Sample (n = 90)

Model 2 Adopters (n = 45)

1.65 0.16 −0.12∗∗∗ 0.14∗∗∗

3.96

2.30

−0.26∗∗ 0.07∗ −0.01 0.04∗∗ −17.84 0.53 7.97∗∗∗

−0.13† 0.09∗ −0.026∗ 0.01 3.06 0.26 3.15∗

0.30 13.0∗∗∗

Model 3 Non-adopters (n = 45)

Note: 1† , ∗ , ∗∗ , and ∗∗∗ denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively.

5.4. Regression results Table 5 reports the regression results of the 60-month return period against the applicable independent variables. The results for the entire sample, Model 1, suggest that both size and P/B are significant contributors to the return performance of all firms over the post-adoption period. Firm size is negatively related to total period returns, while greater growth opportunities, as measured by P/B, are positively related. Although the sign of the ADOPT coefficient is in the predicted direction, it is not statistically significant. This lack of significance may be attributable to the differential financial performance of large and small firms. An analysis of adopters and non-adopters provides some additional insight. Model 2 presents the results for adopter firms. Both the size effect, MkCap and the growth proxy, P/B, remain statistically significant and of the expected sign. These results suggest that for adopter firms growth opportunities, as measured by P/B ratios, have a positive impact on cumulative returns while firm size exerts a negative influence on post-adoption returns. In addition, the significant coefficient on EBIT margin suggests that increased operating performance is a significant contributor to cumulative returns. The distinction between adopters and non-adopters is highlighted in Model 3. Only growth opportunities and insider ownership have significant coefficients at or above the 5% level. The coefficient of size is of the expected direction but is only marginally significant. For non-adopters, these results

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suggest that as the level of insider ownership increases, returns fall, which suggests that agency conflicts are related to performance for this group. 6. Discussion and Conclusion EVA is a performance measurement system aimed at aligning managerial incentives with shareholders interest. If successful, it is expected that adopters of EVA systems would outperform non-adopters. The evidence on the efficacy of EVA is by no means conclusive. Previous research has found mixed results and this study is no exception. In examining risk adjusted market returns, we find that the full sample significantly underperforms the market. However, during the period of the study EVA adopters exhibit less negative performance than non-adopters. Moreover, over the entire study period, adopter performance improves in a positive direction while nonadopters experience a performance decline. Adopting firms also exhibited higher earnings growth and higher returns. In perspective, these results suggest there is some benefit to EVA adoption, relative to a peer group, as adopters outperform their peer group. What might explain the poor results relative to the market as a whole? EVA implementation is not without its problems. As Weaver (2001), McLaren (2003) and others have pointed out, it is a complex and expensive system to both implement and convey. Moreover, some have argued that implementation of a system that focuses on one measure diverts attention from the collection of factors, tangible and otherwise, that affect a firm’s success in the market, including customer satisfaction. Ignoring these factors can adversely affect firm performance. Moreover, inconsistent use of EVA adjustments across firms may lead to difficulty in comparison and uneven performance outcomes. A factor not accounted for in this study is the nature of market-wide performance during the period of the study. The bulk of market returns went to smaller capitalization and high technology firms over this period. To the extent that this sample is more heavily biased toward larger industrial and service firms, relative performance may in fact be much less than the market index. Finally, with an investment environment heavily focused on EPS, the use of an EVA performance system may not be sufficient to warrant attention, especially if EPS falls in early years as the cost of adoption are absorbed. The determinants of return differentials between adopters and nonadopters were also examined. Size and growth opportunities were found to have a significant impact on performance. Larger returns were associated

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with smaller size and greater growth opportunities for both the full sample and adopters only. Smaller firms may benefit from EVA adoption more readily than larger firms due to less complex organization structures and ease of communication across the firm. When considering adopters only, EBIT margin was also significant, suggesting the importance of efficiency in operations for this group. For non-adopters, the lower the insider ownership, the lower the relative returns. This result suggests that there is less alignment of managerial interests with shareholders in non-adopting firms.

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