There are several reasons why a closely held company may need to determine the value of its businesses. These include transactions in company stock; purchases of the interest of a retiring owner; gift and estate taxes; litigation caused by marital dissolution, minority oppression, and dissenting shareholder actions; estate and personal financial planning; and going private transactions or initial public offerings.

Business valuation techniques are important because they allow business owners, as well as other interested parties, to measure the value of a business in the absence of open market transactions. There are three main categories of approaches taken to determine fair market value: 1) the earnings approach, 2) the asset approach, and 3) the market approach. Under the market approach, company transactions are looked at or compared to other companies’ values. One of the methods of valuing a business within the market approach is the Guideline Company Method (GCM); this method uses public companies’ value as proxies for the value of the target company. The GCM calculates value indicators based on published data regarding the public companies’ earnings, sales, revenues, or other indicators, compared with the company’s market value, and uses the comparison to estimate the target company’s value.

This article focuses on the use of traditionally selected indicators and the illustration of economic value added (EVA) as an indicator to use in valuing a target company based on publicly traded company values. EVA represents the net operating income generated by a business after deducting a charge for the use of invested capital. It is crucial for CPAs to select the best indicators for a given engagement.

Which of the traditional value indicators from the GCM are most effective in a business valuation context, and how does using EVA as an indicator? For example, James Hitchner, in Financial Valuations: Applications and Models (Wiley Finance; 4th ed., 2017), suggests considering revenues; earnings before interest, taxes, depreciation, and amortization (EBITDA); earnings before interest and taxes (EBIT); debt-free net income; debt-free cash flows; assets; tangible book value of invested capital; pre-tax income; net income; cash flow; and book value of equity (p. 324). He suggests that the coefficient of variation may be useful in determining how much weight to accord each of the various multiples. Shannon Pratt and Alina Niculita, in Valuing a Business the Analysis and Appraisal of Closely Held Companies (McGraw Hill, 5th ed., 2008), state, “Earnings-based multiples, such as price to net income, and price to pretax income, are generally considered to provide the best indication of business value” (p. 320). Other commenters have supported other indicators such as gross profit.

It is crucial for CPAs to select the best indicators for a given engagement.

The authors’ research provides the following two insights. First, using only the two traditional value indicators of EBIT and EBITDA for the GCM yields reliable results and significant time savings by eliminating the other indicators; second, although EVA has some merit, theoretically speaking, the authors’ results indicate the method is too erratic (and complicated) to rely upon.

Determining Fair Market Value

Most valuations are made with the objective of determining fair market value. However, in many states there are provisions that mandate a fair value standard that differs from fair market value, such as in the case of marital dissolution and oppressed minority shareholder purposes. This article focuses exclusively on fair market value engagements.

This analysis provides a systematic overview of the GCM using publicly traded stock, their comparative usefulness, and an examination of which indicators, including EVA, may result in a consistently useful valuation approach. The analysis examined 727 company-years of data and filtered the results to a sample of 24 companies over the period of 2015–2017.

The GCM broadly follows two steps. The first step is to select and identify companies that are comparable to the target; the companies are usually identified from those that have similar risk profiles and that operate in the same Standard Industry Classification (SIC). The second step is to decide which value indicator to use.

G. Bennett Stewart III, in Best-Practice EVA (Wiley, 2013), proclaimed that EVA, which recognizes the cost of using capital, is a better measure of a company’s value than EBIT, EBITDA, or any other traditional method. “And if early returns are an indication, in two to three years EVA will be viewed as one of the dominant models of investing analysis” (p. 247). His prediction of EVA becoming one of the dominant models has not been borne out. This analysis will compare using EVA as a tool in the GCM and also consider the various other indicators proposed and selected by the coefficient of variation.


The data for the analysis came from two sources. The financial statement and stock price data were obtained from Compustat. The minimum requirements for companies to be in our data set were positive fiscal-year end values for stock price, sales, pre-tax income, total assets, debt, and depreciation; companies missing any of these were deleted from the analysis. The companies also had to be incorporated in the United States. Some reduction in sample size occurred due to lack of sufficient observations to calculate the valuation multipliers as well as the EVA calculations. After filtering the companies for having the data required, we were able to gather a sufficient number of companies in the following two-digit SIC codes: 20 (agricultural products food processing), 28 (chemical preparations), 35 (industrial machinery), 36 (electrical equipment and supplies), 37 (transportation equipment), 38 (measuring instruments and supplies), 49 (utilities), and 73 (business services).

After identifying the industries, we selected a random sample of five companies from each two-digit SIC code and calculated their estimated value based on the indicators developed from the remaining companies in the two-digit industry data. We then determined which indicator produced the closest estimate to actual market value. The total numbers of companies in each two-digit SIC code are presented in Exhibit 1.

Exhibit 1

Industries Selected

SIC Code; Description; Number of Observations 2015; 2016; 2017 20; Agricultural products food processing; 25; 26; 23 28; Chemical preparations; 38; 52; 37 35; Industrial machinery; 21; 21; 26 36; Electrical equipment and supplies; 19; 29; 28 37; Transportation equipment; 23; 27; 28 38; Measuring instruments and supplies; 21; 21;22 49; Utlities; 67; 61; 55 73; Business services; 19; 19; 19 Total observations; 233; 256; 238 SIC=Standard Industry Classification

Calculation of EVA.

In addition to the information available in Compustat, the estimate for EVA is based on the adjustments to operating income indicated in G. Bennett Stewart III (Best-Practice EVA, Wiley, 2013) and following the methods used in Ken C. Yook, (“Estimating EVA using Compustat PC Plus,” Financial Practice and Education, vol. 9, no. 2, 1999, pp. 33–38) and Richard S Warr (“An Empirical Study of Inflation Distortions to EVA,” Journal of Economics and Business, vol. 57, no. 2, 2005, pp. 119–137). Theoretically, EVA represents the net operating income generated by a business after deducting a charge for the use of the invested capital. This analysis adjusts GAAP-reported net operating profits by the corrective accounting adjustments proposed in Appendix B of Stewart (2013), essentially converting the information to be similar to a cash basis. The weighted average cost of capital (WACC) was obtained from Professor Aswath Damodaram’s New York University website ( The WACC was calculated as the average for the industry at the two-digit SIC level. The invested capital measure follows Yook (1999) and Warr (2005) and adjusts total assets to more reasonably reflect the investment in assets. Once EVA is calculated, the value indicator is the ratio of the market value of invested capital (MVIC) to EVA.

This section describes the value indicators used in the analyses. A combination of indicators that include two based on MVIC (EBITDA and EBIT), four on the market value of equity (MVEq; revenues, pre-tax income, net income, and book value of equity) and EVA, also based on MVIC. The indicators are calculated as the ratio of the value measure (i.e., MVIC or MVEq) to each financial statement item.

Results of Best Valuation Indicators

After determining the multiples using the indicators described in the variable definitions above, the coefficient of variation is calculated. (The coefficient of variation is the ratio of the standard deviation to the mean.) The lowest coefficient of variation is used to determine the best indicator, as suggested by Hitchner. Exhibit 2 presents the coefficient of variation results for each of the eight SIC codes/year combinations. Ranking the results for the 25 data sets shows that, in all cases either EBIT or EBITDA is the most statistically valid indicator of the multiples we tested. (See Exhibit 3 for the rankings.) In all but three instances, the two indicators also claim second place. For those three cases where they did not come in second, the ranking for second place was a virtual tie, with coefficient differences of .029 and .034 between EBIT/EBITDA and the runner-up.

Exhibit 2

Coefficients of Variation

SIC code; Year; EBITDA; EBIT; Book Value; Sales; Pretax Income; Net Income 20; 2015; .331694; .384357; .786594; .457697; .425486; .547044 20; 2016; .310165; .315422; .858629; .456431; .343861; .347597 20; 2017; .359269; .370823; 1.02360; .521914; .369582; .366335 28; 2015; .721195; .714442; 2.09436; .989966; .868997; 1.46922 28; 2016; .478348; .488636; 1.587093; .671086; .730180; .610923 28; 2017; .467802; .518292; 1.13439; .645660; 1.34798; .943837 35; 2015; .352449; .357378; 1.78463; .452381; .384496; .436020 35; 2016; .189148; .233658; 2.76185; .475193; .805815; .788210 35; 2017; .227054; .207437; 2.98918; .480358; .271176; .383074 36; 2015; .280184; .285644; .415931; .681213; .503611; .286670 36; 2016; .400130; .412698; .444398; .928131; 1.30117; .620796 36; 2017; .550788; .495216; .5869 76; 1.31098; .521876; 1.19609 37; 2015; .344415; .341275; .996977; .569227; .446617; .421241 37; 2016; .348176; .309673; 1.69101; .596171; .600750; .663771 37; 2017; .348811; .297327; .521333; .673365; .368966; .434972 38; 2015; .263323; .224945; .840475; .414072; 1.00633; .876739 38; 2016; .239342; .234305; .928973; .407012; .406658; .400088 38; 2017; .241838; .266308; .979995; .427016; .716005; 2.00780 49; 2015; .183512; .166515; .295766; .501228; .431931; .839049 49; 2016; .200523; .129636; 1.42120; .302615; .440369; .359167 49; 2017; .214368; .180952; .369364; .494180; .247198; .415443 73; 2015; .326569; .338348; .651031; .534693; 1.08199; .565018 73; 2016; .392813; .344864; .836284; .697775; .350070; .358715 73; 2017; .505116; .621444; .7477059; .787955; 1.12098; .525634 SIC=Standard Industry Classification EBITDA=Earnings Before Interest, Taxes, Depreciation, and Amortization EBIT=Earnings Before Interest and Taxes

Exhibit 3

Ranking of Indicators

SIC code; Year; 1; 2; 3; 4; 5; 6 20; 2015; EBITDA; EBIT; Pre-Tax; Sales; Net Inc; Bk Val 20; 2016; EBITDA; EBIT; Pre-Tax; Net Inc; Sales; Bk Val 20; 2017; EBITDA; EBIT; Net Inc; Pre tax; Sales; Bk Val 28; 2015 EBIT; EBITDA; Pre-Tax; Sales; Net Inc; Bk Val 28; 2016; EBITDA; EBIT; Net Inc; Sales; Pre-Tax; Bk Val 28; 2017; EBITDA; EBIT; Sales; Net Inc; Bk Val; Pre-Tax 35; 2015; EBITDA; EBIT; Pre-Tax; Net Inc; Sales; Bk Val 35; 2016; EBITDA; EBIT; Sales; Net Inc; Pre-Tax; Bk Val 35; 2017 EBIT; EBITDA; Pre-Tax; Net Inc; Sales; Bk Val 36; 2015 EBIT; EBITDA; Net Inc; Bk Val; Sales; Pre-Tax 36; 2016; EBITDA; EBIT; Bk Val; Net Inc; Sales; Pre-Tax 36; 2017 EBIT Pre-Tax; EBITDA; Bk Val; Net Inc; Sales 37; 2015 EBIT; EBITDA; Net Inc; Pre-Tax; Sales; Bk Val 37; 2016 EBIT; EBITDA; Sales; Pre-Tax; Net Inc; Bk Val 37; 2017 EBIT; EBITDA; Pre-Tax; Net Inc; Bk Val; Sales 38; 2015 EBIT; EBITDA; Sales; Bk Val; Net Inc; Pre-Tax 38; 2016 EBIT; EBITDA; Net Inc; Pre-Tax; Sales; Bk Val 38; 2017; EBITDA; EBIT; Sales; Pre-Tax; Bk Val; Net Inc 49; 2015 EBIT; EBITDA; Bk Val; Pre-Tax; Sales; Net Inc 49; 2016 EBIT; EBITDA; Sales; Net Inc; Pre-Tax; Bk Val 49; 2017 EBIT; EBITDA; Pre-Tax; Bk Val; Net Inc; Sales 73; 2015; EBITDA; EBIT; Sales; Net Inc; Bk Val; Pre-Tax 73; 2016 EBIT Pre-Tax; Net Inc; EBITDA; Sales; Bk Val 73; 2017; EBITDA; Net Inc; EBIT Bk Val; Sales; Pre-Tax SIC=Standard Industry Classification EBITDA=Earnings Before Interest, Taxes, Depreciation, and Amortization EBIT=Earnings Before Interest and Taxes Net Inc=net income BK val=book value of equity EVA=economic value added EBITDA=Earnings Before Interest, Taxes, Depreciation, and Amortization MVIC=market value of investment capital SIC=Standard Industry Classification

Comparison of Best Valuation Indicators and EVA

The next step of the analysis was to compare the results of the best indicators suggested by the results in Exhibits 2 and 3 with the results of using EVA as the indicator. For the 120 trials, EVA was the best in 22 cases, representing 18% or of the time, including six trials where there was no significant difference from the actual value. In 11 trials, no significant difference between EBIT/EBITDA and the actual value.

It is worth noting that EVA’s overall variance from the actual MVIC was significantly worse than EBIT/EBITDA. For example, Exhibit 4 illustrates the percentage difference by SIC code in 2016.

Exhibit 4

Percentage Difference between Calculated Value and Actual MVIC

Overall, the median percentage difference for EVA was 100%, whereas for EBIT/EBITDA it was 18%. Therefore, the results of this analysis suggest that EVA is less reliable than EBIT/EBITDA for business valuation purposes.

Back to Basics

This study produces two conclusions. First, although there are many multiples and value indicators proposed by advocates of the GCM, the evidence indicates that there does not seem to be any compelling reason to look beyond EBIT and EBITDA for estimating the value of a target company. Thus, unless there is a particular reason for the valuation being conducted to use other indicators, such as net asset value in an industry where asset value drives revenue rates (i.e., a rate-based industry, such as regulated utilities), EBIT and EBITDA outperformed all other indicators and should suffice. However, it is important to note that when these value indicators are used to determine the value of a closely held company, additional adjustments may be needed to account for size, growth, and other factors.

Second, the analysis using EVA as a value indicator shows that, in 18% of the cases, EVA produces results that are more accurate than those produced using EBIT/EBITDA. However, the analysis also reveals that EVA’s overall variance from the actual MVIC was significantly worse than EBIT/EBITDA, which makes the use of EVA less reliable than the other alternatives and seem to fall short of the expectations suggested by Stewart (2013). In addition, EVA is not a published statistic for public companies and requires additional effort to determine, which raises the question of whether the increased effort in calculating EVA is worth its lower reliability as a value indicator.

The authors believe that using EVA produces a questionable result a sufficient amount of the time and thus should not be used unless there is a specific reason that it is more relevant in a particular valuation. An individual company may find it useful to track EVA for their company. GCM is only one method valuators consider in determining a company’s value and this study does not suggest that it is better than other valuation methodologies. Publications including the Federal Tax Valuation Digest, which provides hundreds of case digests that analyze court decisions and the reasoning behind them, can assist valuators considering the approaches to be taken in a given case. The use of EVA is not covered in these cases.

Carlos E. Jiménez-Angueira, PhD, CPA is an associate professor of accounting in the Lynn Pippenger School of Accounting at the University of South Florida.
Nicholas J. Mastracchio, PhD, CPA was the Arthur Andersen alumni Professor of Accounting at the University at Albany where he has emeritus status and recently retired from the University of South Florida as an associate professor. He has taught valuation courses at Union College and University of South Florida, published books on valuation for the AICPA and Bloomberg BNA, and his testimony on valuation methodology is case law in New York. He is a member of The CPA Journal Editorial Advisory Board.