how useful are brand valuation methods for brand ......brand valuation philosophies and prior tests...

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How useful are Brand Valuation Methods for Brand Management? A Validation Study

Marc Fischer1)

Tobias Hornig2)

August 2014

1 Professor of Marketing and Market Research, University of Cologne, and Associate Professor

of Marketing at UTS Business School, Sydney. Contact: University of Cologne, The Faculty of Management, Economics, and Social Sciences, Chair for Marketing and Market Research, Albertus-Magnus-Platz, 50923 Cologne, Germany, Phone: +49 (221) 470-8675, Fax: +49 (221) 470-8677, e-mail: [email protected]

2 Siemens AG, e-mail: [email protected] The authors gratefully acknowledge the support of Corebrand and Harris Interactive for the usage of their data in this study. They also thank Jan-Benedict Steenkamp and participants of seminars at the University of Groningen, UTS Business School, and Marketing Science Conference 2014 in Atlanta for valuable comments on this paper.

This study is fully independent. The authors declare that they have no professional relationship with the providers of investigated brand valuation models. They did not receive financial support from the providers.

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ABSTRACT

Many methods for measuring the financial value of a brand have been suggested in the past.

Their results, however, differ to a great extent, which raises serious concerns about the validity

of these methods. In this large-scale study, we adapt the established construct validation

methodology in social sciences to assess the validity of financial brand valuation methods.

Specifically, we test the validity of brand valuation methods with respect to their

reliability/stability, convergent validity, discriminant validity, nomological validity, and

predictive validity.

We apply the test procedure to nine prominent valuation methods that cover the basic

philosophies in brand valuation, which are cost-based, market-based, and income/DCF-based

approaches. The data cover a period of 22 years from 1990 to 2012. The sample includes 36,992

financial values of 4,879 brands that originate from 87 countries and represent more than 70

industries.

Generally speaking, brand valuation models produce reliable and stable results. Only few models

show convergent validity across different valuation approaches. Nomological validity cannot be

established for any method and a few models demonstrate predictive validity in stock-return

forecasts. Considering all validity test criteria together, it appears that the market-based methods

perform best in meeting the validity requirements.

Keywords: Brand valuation, brand equity, scale validation, time-series tests

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INTRODUCTION

The financial value of a brand is of high interest to many stakeholders and decision

makers. As a result, several methods for measuring the brand value have been suggested in the

past. Their results, however, differ to a great extent, which raises serious concerns about the

validity of these methods. Marketing’s seat at the table in the boardroom is severely hampered if

the value of the asset that attracts most of marketing’s expenditures cannot be established in a

credible way. Hence, it is crucial that marketers agree on a generally accepted valuation method

that produces valid results and makes marketing accountable. This study is a first step into that

direction.

Unfortunately, the true value of a brand cannot be observed. Without knowing the true

value it is hard to assess how close a model’s estimate comes to the true value. While

unpromising at first glance, this dilemma is not new but well known in the social sciences that

frequently need to measure unobserved constructs. In fact, this discipline has developed a

rigorous framework of test procedures and statistics for evaluating the validity of a construct

(e.g., Churchill 1979; Peter 1979, 1981). Construct validity refers to the correspondence between

a construct at the conceptual level and the operational procedure to measure that construct.

Assessing construct validity usually involves several tests including tests on reliability,

convergent validity, discriminant validity, nomological validity, and predictive validity of the

measurement model. Construct validity cannot be assessed directly but is inferred from the

measure’s variance and covariance with other measures.

A drawback of this validation approach is that it is ignorant about the measurement level,

i.e. the absolute metric. The absolute brand value is particularly relevant to applications for

accounting and transaction purposes, such as mergers and acquisitions, licensing, or

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securitization. The other major group of applications includes valuation for brand management

purposes, such as brand strategy, brand portfolio management, or marketing budget allocation.

Here, the absolute brand value plays a minor role. It is rather essential that the metric reflects the

effects of brand decisions in a consistent and valid way. According to a recent survey among

senior executives, the reasons for brand valuation are equally distributed across the two groups

of applications (PWC 2012). Hence, even though testing for construct validity does not solve the

issue of diverging absolute brand valuation results, it helps establish confidence in using a

method for brand management purposes that are by no means less important in practice. This is

the focus of our study.

We assess nine brand valuation methods that cover the three basic philosophies in brand

valuation, i.e. cost-based, market-based, and income/DCF-based approaches. Specifically, we

compare brand valuation results produced by the following models: historical cost of creation

and advertising stock (cost-based approaches), Simon and Sullivan (1993) and CoreBrand

(market-based approaches), and Interbrand, Millward Brown, Semion, Brand Finance, and

Ailawadi, Lehmann, and Neslin (2003) (income/DCF-based approaches). Our data covers a

period of 22 years ranging from the start of brand valuation in 1990 to 2012. It includes 36,992

financial values of 4,879 brands that originate from 87 countries and represent more than 70

industries. The scope of our data thus provides the basis for truly generalizable results on the

validity of brand valuation methods.

This study offers several contributions. It is the first attempt to bring light into the jungle

of brand valuation methods. We achieve this by testing the construct validity of established

valuation methods, which is a prerequisite for using brand values to assess and monitor the

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outcomes of important brand management decisions. We show which methods and which

valuation philosophies satisfy the validity criteria.

Second, since the test procedures for validating social constructs were mainly developed

for cross-sectional data we need to make appropriate adaptations to our data that are generated

across both brands and time. Specifically, we extend the test framework by established time-

series tests that account for both stationary and non-stationary time-series. We also introduce

Granger-causality tests as a powerful means to test for nomological validity, which is not

applicable with purely cross-sectional data.

Finally, we provide important implications from our findings for the use of brand

valuation methods in management practice. In addition, we discuss consequences for using brand

values in research on brand management. Our conclusions also stimulate the development of an

ideal, generally accepted measurement approach.

The paper is structured as follows: In the next section we briefly review the literature on

brand valuation philosophies and prior tests of methods. We then describe our test methodology.

In the following sections, we introduce our data and present the empirical test results. We

continue with discussing the managerial and research implications. We conclude the paper with

its limitations and suggestions for further research.

LITERATURE REVIEW

We define brand value as the incremental discounted future cash flows accruing from a

branded product compared with an identical but unbranded product (Simon and Sullivan 1993).

Salinas (2009) provides a complete summary of 39 proprietary financial brand valuation

methods. We do not repeat the detailed discussion here but refer to this excellent review. The

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main purpose of this section is to delineate the ideas behind the three major brand valuation

philosophies that emerge from the literature (e.g., ISO Standard 2010; Salinas 2009). These

philosophies are: cost-based, market-based, and income/DCF-based approaches.

Approaches of Brand Valuation

Cost-based approach. This approach determines the brand value in terms of prior

investments into developing and building the brand. A straightforward method is to measure the

historical cost of creation by accumulating all expenditures on the brand until the period of

valuation (Barwise et al. 1989). A more realistic approach is to follow the idea of an advertising

stock where brand expenditures build up the stock that decays at a constant rate over time

(Nerlove and Arrow 1962). While cost-based measures are attractive due to the objective and

easy collection of data they are heavily criticized for their theoretical weaknesses. Prior brand

expenditures measure past efforts for brand building but not future outcomes, such as excess

profits, that accrue from the brand (Salinas 2009; 60f).

Market-based approach. Following the efficient market hypothesis (Fama and French

2006), the market price for an asset represents the fair value for that asset, provided that all

investors have the same amount of information available and engage in many transactions.

Unfortunately, there does not exist a liquid market for brand transactions, i.e. transaction prices

are not readily available. But given that a brand is part of the firm value, its value is implicitly

included in the market value of a company. The idea of a market-based approach, such as the

CoreBrand model or the model by Simon and Sullivan (1993), is to separate the brand value

from the observed market capitalization of the firm. The major advantage of this approach is its

consistency with capital asset pricing theory and fair valuation principles. But there is no general

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agreement about the right approach to isolate the brand value from the company’s market value

and applications are limited to publicly listed firms (Fischer 2007).

Income/DCF-based approach. The income/DCF-based approach attempts to project the

(future) stream of profits or cash flows, respectively, due to the brand (e.g., Salinas 2009; Fischer

2007). In theory, the present value of this stream equals the market price of the brand. There are

multiple ways to arrive at the estimate for the present value of brand-generated cash flows, such

as the relief-from-royalty method (Brand Finance), income split method (Interbrand, Millward

Brown), or incremental income method (Semion). We also consider the revenue premium model

by Ailawadi, Lehmann, and Neslin (2003) as a current-period income-based method as it can

easily be used to determine the incremental cash flow that is attributable to the brand. The major

advantage of these models is that they are consistent with the basic principles of fair asset

valuation that is inherent to DCF models. Major concerns exist about the subjectivity and

uncertainty that is often associated with the forecast and separation of expected brand cash flows

(Ailawadi, Lehmann, and Neslin 2003).

Prior Literature on Validating Brand Valuation Models

Little research exists that offers orientation in the validation of brand valuation models. In

fact, many (commercial) models are presented in a way that the model and its results stand alone

and convince by its face validity. Additional validity tests are not performed. Rigorous tests of

model validity are also very limited in the academic literature. Simon and Sullivan (1993) show

in a descriptive way how the estimated brand value for Coke and Pepsi changes in response to

important events in the soft drink market. Fischer (2007) compares model-implied market

capitalization with actual values for two automobile firms. Only Ailawadi, Lehmann, and Neslin

(2003) adopt a more rigorous approach and test the stability of their measure and its correlation

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with several other measures in two different data sets. To the best of our knowledge, no study

has attempted to develop and apply a comprehensive testing framework to test several

established brand valuation models at once.

METHODOLOGICAL FRAMEWORK FOR INTERNAL AND EXTERNAL VALIDATION

Our suggested framework for testing the validity of brand valuation methods follows the

paradigm for measuring marketing constructs (Churchill 1979; Peter 1979, 1981). According to

this paradigm, construct validity is established by internal and external validation of the measure.

Internal validation refers to trait validity and is achieved by demonstrating reliability, convergent

validity, and discriminant validity for the measure. Internal validation is a necessary condition

for the validity of a measure, but it is not sufficient. The brand value method must also enable

observable predictions derived from theoretical propositions (Peter 1979). Hence, we also

require the method to demonstrate nomological and predictive validity (external validation).

In the following, we describe how we approach the measurement of the various types of

validity. We use established procedures of analyzing correlations and covariances among

constructs (e.g., DeVellis 2012; Netemeyer, Bearden, and Sharma 2003). When necessary, we

also introduce adaptations and extensions to the framework that result from the time-series

nature of brand value data. Table 1 summarizes the methodological framework with its test

statistics and thresholds, which we follow in this study.

== Table 1 about here ==

Reliability/Stability over Time

Reliability measures the degree to which a measure is free from random error. It is

assessed in terms of (1) test-retest correlation and (2) internal consistency (Cronbach’s Alpha)

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(Netemeyer, Bearden, and Sharma 2003, 10). We consider reliability as an integral part of a

construct’s validity because any subsequent correlation-based validity test depends on the

measures’ reliability. In fact, the correlation between two measures cannot be higher than the

square root of the smaller reliability (Peter 1981).

Test statistics. The test-retest correlation is the key measure for our reliability test.

Internal consistency tests do not apply to brand valuation methods as they refer to multi-item

scales. For each brand valuation method, we calculate the correlation of the measure in t with its

lagged value in t-1, where t refers to the year of valuation. Strictly speaking, the autocorrelation

coefficient does not only reflect the reliability of the method since the underlying brand value

may change from one year to the next year. However, stickiness or stability over time,

respectively, is inherent to the concept of brand equity. Thus, low autocorrelation suggests poor

reliability of the method (Ailawadi, Lehmann, and Neslin 2003). In addition, we make use of the

combined time-series and cross-sectional nature of our data. Specifically, we decompose the total

variance in a valuation method’s results into its cross-sectional variance and time variance.

Following the same argument about stickiness, time variance should be significantly smaller

relative to cross-sectional variance.

Threshold values. In order to attest a method a high reliability we need to define the

desired value for the test statistic. Unfortunately, there is no theory from which we can deduce

the appropriate threshold level (DeVellis 2012, 67; Churchill 1979). We will rely on thresholds

used in the literature and consider the amount of shared variance that is implicit to the

correlation. For reliability (correlation) coefficients, the literature considers a value of .90 as the

minimum and a value of .95 as desirable standard for measures in applied settings where

important decisions are made (e.g., Ailawadi, Lehmann, and Neslin 2003; Churchill 1979;

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DeVellis 2012, 109). We adopt the value .90 for the brand value’s correlation with its prior year

value. As a consequence, the shared variance over the two periods is larger than 80%. For

methods with two measurement points within the same year, we set a stricter threshold of .95.

When decomposing the observed variance of a method, we require that the (cross-sectional)

variance across brands be significantly larger than the (time) variance across time, which is

satisfied at a ratio of 3 (see table 1).

Convergent Validity

Convergent validity describes the extent to which independent brand valuation methods

yield similar results. Conceptually, this means that they should be highly correlated among each

other (Churchill 1979; Peter 1981).

Test statistics. The standard test statistic here is the correlation coefficient for two

independent valuation methods, which may be corrected for the known unreliability in the

measures (DeVellis, 66). It should be noted, however, that the correlation between two time

series requires that their means and variances remain constant over time (Aldrich 1995). Such

series are said to be stationary. If they are non-stationary, the traditional validation test is

meaningless and produces spurious correlation coefficients. For such cases, we suggest applying

Kao’s (1999) panel co-integration test. Two series are co-integrated if they converge towards a

common and stable equilibrium in the long run (Engle and Granger 1987). By accounting for the

trend in the time-series, this test reveals whether the two measures are associated with each other

or not. Figure 1 summarizes the steps we suggest for analyzing the association between measures

with time-series (panel) data.

== Figure 1 about here ==

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Threshold values. Again, we need to define desired values for the correlation statistics.

The psychometric literature does not suggest a minimum level of correlation but only requires

that the correlation among alternative measures be higher than the correlation with distinct

measures (discriminant validity) (e.g., Peter 1981; DeVellis 2012, 67ff). This rule leaves room

for different threshold values, which is not acceptable for our application. Consistent with prior

research in branding (e.g., Fischer, Völckner, and Sattler 2010), we require an average minimum

correlation of .50 that corresponds to an average shared variance of at least 25% to demonstrate

convergent validity. Unlike most scale validation studies, our study includes 9 alternative

measures leading to 36 potential pairwise correlations. Before obtaining the average correlation,

we require that 75% of all correlations for a measure with other measures must be manifest and

statistically significant (p < .05) (see table 1).

For non-stationary time-series, we require that ADF < tADF. ADF denotes the augmented

Dickey-Fuller statistic that is obtained for a panel, i.e. we estimate brand-specific constants, and

tADF is the respective t-statistic (Engle and Granger 1987; Kao 1999).1

Discriminant Validity

Discriminant validity refers the extent to which brand valuation methods correlate with

constructs that are designed to measure distinct concepts. The measures should be uncorrelated if

they represent different constructs (Churchill 1979; Peter 1981).

Test statistics. The standard test statistic is either the correlation coefficient between the

brand value and the divergent measure or the panel co-integration test if series are non-stationary

(see figure 1 again). We assess discriminant validity with respect to two important constructs:

customer satisfaction and corporate reputation. Customer satisfaction measures the degree to

1 Note that we do not compute correlation coefficients for differenced, non-stationary time-series. Although

differencing translates the series into stationary series, it also removes any cross-sectional variance. As a result, correlations tend to be much lower and are no longer comparable with correlations in levels.

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which purchase and consumption experiences confirm purchase expectations (Fornell et al.

1996). It focuses on the relationship of the customer with the company. In contrast, brand value

focuses on the product. Corporate reputation reflects the overall credibility and respect that an

organization has among a broad set of constituents (e.g., employees, investors, regulators,

customers). Reputation arises from several dimensions including financial soundness,

innovation, product/services quality, and quality of management (Fombrun and Shanley 1990).

Hence, the concept is broader than that of the brand.

Threshold values. Correlations between brand values and distinct measures should be

lower than the correlation among alternate brand values (e.g., Peter 1981; DeVellis 2012, 67ff).

We consider a maximum correlation of .50 (see our threshold for convergent validity before) as

too high since this would allow for a shared variance of up to 25%. We rather require the

correlation not to exceed .30, i.e. the shared variance between the divergent constructs should be

less than 10%. For non-stationary time-series, we require that the co-integration test be rejected

(ADF < tADF) (see table 1).

Nomological Validity

Nomological validity describes the extent to which brand valuation methods are

associated with measures of other constructs that is consistent with theory (Netemeyer, Bearden,

and Sharma 2003, 86). Recall that we investigate the use of brand valuation methods for brand

management purposes such as brand portfolio strategies, brand investment decisions, etc. In

these applications, brand value is a key intermediate marketing variable that fits into a logical

chain of brand value creation (Keller and Lehmann 2003). From the brand value chain, we derive

the nomological network of antecedents and consequences as shown in Figure 2. Previous

advertising and other brand expenditures that are included in selling, general, and administrative

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(SG&A) expenditures drive brand value. We also consider customer-based brand equity

measured by Harris EquiTrend scores as an antecedent. The strength of the brand in the heart and

minds of customers is a prerequisite for future returns due to the brand (Fischer 2007). If the

brand value increases we suppose this to have positive economic consequences for the firm in

the future. Specifically, we expect that sales, profit and finally the market valuation of the firm

improve (see figure 2 again).

== Figure 2 about here ==

Test statistics. The standard test statistics is again the correlation coefficient between the

brand value and the antecedent and consequence measures. If time-series are non-stationary we

need to test for co-integration. However, correlation does not imply causation. Since the

nomological framework implies a causal ordering of variables it can be tested more rigorously by

using the concept of Granger Causality. The idea here is to use the temporal ordering of events to

distinguish between leading (antecedents) and lagging (consequences) variables on empirical

grounds (Granger 1969). We employ the established regression-based methods (see Appendix A

for details) to test the validity of the Granger-causal ordering of figure 2.

Threshold values. For the correlation analysis, we require that 75% of pairwise

correlations between the brand value measure and both its antecedent and consequence measures

must be statistically significant (p < .05). In addition, the average correlation should be .40 or

greater, i.e. the shared variance is at least 15% (e.g., Fischer, Völckner, and Sattler 2010; Finn

and Kayande 2005). For non-stationary time-series, we require that the co-integration test be not

rejected (ADF > tADF) (see table 1).

In our Granger-causality tests, we allow for up to 4 lags. For each antecedent, we test

whether it Granger-causes the respective brand value measure. The same applies to each

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consequence, which we assume to be Granger-caused by the brand value measure. Here again,

we require that 75% of these tests be not rejected to establish confidence in the causal

relationships. More importantly, we also test the reverse of all postulated variable relationships.

In fact, if the reverse relationship is supported by the Granger-causality test it casts doubt on the

presumed causal ordering of variables. We require that the number of supported Granger-causal

relationships must be significantly greater than the number of unexpected reverse relationships

across all antecedents and consequences. The ratio should be at least 3 to 1 (see table 1).

Predictive Validity

Predictive validity demonstrates the ability of a measure to predict an external criterion

that is independent of the valuation result but related to it in a practical and meaningful way.

Unlike nomological validity, predictive validity does not require that the reason for the empirical

association is understood (DeVellis 2012, 61). It is rather important that the external criterion can

be considered a “gold standard” and has relevance for actual decision-making.

Real brand transaction prices obviously represent a “gold standard” that reflects

managerial decisions. Unfortunately, we could only obtain a very limited number of transactions

prices. Sample size is not sufficient to correlate M&A values with individual method’s results.

But we can obtain insights from a pooled correlation across methods.

The stock price of a firm is another useful external criterion for prediction. Consistent

with the call for marketing accountability, changes in brand values as a result of brand

management decisions should have an effect on firm value. Prior research (Mizik and Jacobson

2008) has shown that a perceptional brand measure, which is an antecedent in our nomological

framework (figure 2), indeed drives stock prices. Hence, testing whether brand value estimates

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impact stock prices qualifies as a powerful way to demonstrate the predictive validity of a

valuation method (Srinivasan and Hanssens 2009).

Model and test statistic. We implement the stock return-modeling framework of Mizik

and Jacobson (2008) that builds on the efficient market hypothesis (Fama and French 2006) and

test the predictive ability of brand value measures. The model controls for risk factors according

to Carhart’s (1997) four-factor model, period fixed effects, and unanticipated changes in

accounting performance. Based on the residuals of a fixed-effect, first-order autoregressive

model, we add unanticipated changes in brand value to the model because investors only respond

to new information. For further modeling details, we refer to Mizik and Jacobson (2008) and

Appendix B. We estimate the immediate impact on stock return. Since it may take some time

until investors fully understand and appreciate the financial implications a change in brand value,

we also estimate the effect for 1 month, 5 months, and 11 months after the announcement.

Predictive validity is shown if the coefficient estimate associated with the brand value measure is

positive and statistically significant (p < .05) (see table 1 again).

DATA AND MEASURES

Data Sources

We test nine brand valuation models: historical cost of creation (Barwise et al. 1989),

advertising stock (Nerlove and Arrow 1962), Simon and Sullivan (1993), CoreBrand (2013),

Interbrand (2012), Millward Brown (2013), Semion (2013), Brand Finance (2013), and

Ailawadi, Lehmann, and Neslin (2003). We do not repeat details on the methodology of the

models but refer to the respective original source. Salinas (2009) also covers the models in her

review. CoreBrand, Interbrand, Millward Brown, Semion, and Brand Finance are commercial

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vendors that developed their own proprietary models. Semion is based in Germany, the country

with the second most model contributions. All commercial vendors publish brand valuation

results for international brands in ranking lists that are either cross-sectoral or focus on a specific

industry. We collected these information from their websites, press releases, and outlets such as

Financial World or Business Week, respectively. We also gathered data on revised brand

valuation results whenever possible. We approached each vendor to obtain brand values that

were not published in the ranking list. Only CoreBrand was willing to share their proprietary data

for the purpose of our study.

For the other four (academic) models, we collected published brand values and

reproduced brand values according to the methodology described by the authors. For this

purpose we collected financial, marketing, and capital market data from various sources

including COMPUSTAT, Thomson Banker One, SEC filings, and the Center for Research in

Security Prices (CRSP).

Finally, for the tests on discriminant and nomological validity, we used data provided by

the American Customer Satisfaction Index (ACSI), Fortune’s corporate Reputation Index, and

Harris EquiTrend. The data collection spans a period of 22 years from 1990 to 2012. Depending

on the model, brand values are available from the start in 1990 or at a later date. A detailed

summary of data sources and periods for each model is available in Appendix C.

Measures

We need to report on a few decisions and assumptions we had to make when compiling

our database.

Advertising stock model. We follow the Nerlove and Arrow (1962) approach to measure

brand value in terms of an advertising stock. The advertising stock in a given year t is composed

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of the advertising expenditures in t and the stock value in t-1 that is carried over at a constant rate

measured by the carryover coefficient. Since we do not observe brand-specific carryover

coefficients, we use a value of .50 that has been established as an empirical generalization

(Sethuraman, Tellis, and Briesch 2011). Following Fischer et al. (2011), we estimate the stock

value in the initial year of our observation period by dividing brand expenditures in that year by

the decay rate of .50 (= 1 – carryover).

Ailawadi, Lehman, and Neslin (2003) model. This model suggests computing brand value

by the revenue premium a brand has over a benchmark product with no or low brand equity. This

could be a private label brand in a consumer packaged goods category or simply the lowest-share

brand in a market. We use the lowest-share brand as benchmark that can be consistently applied

across industries (p. 15). To obtain a profit-based measure, we follow the authors’ suggestion (p.

6) and apply a profit margin that reflects average profitability in the market by period. However,

we do not project revenues and costs into the future to obtain a future-oriented measure. Thus,

though valuation results from this model are income-based they refer only to the current period.

Customer satisfaction and corporate reputation. Fornell et al. (1996) provide details on

the calculation of the satisfaction rating scale (0-100) used for the ACSI. Ratings are collected on

an annual basis for more than 230 companies. Fortune’s annual corporate reputation index is an

overall reputation score ranging from 0-10. It builds on ratings from eight dimensions (Fombrun

and Shanley 1990).

Customer-based brand equity. Harris Equitrend provides the data for our customer-based

brand equity measure. The Equitrend measure is a brand rating scale from 0-100 that captures

US customer perceptions on a representative basis for more than 1,500 brands per year.

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M&A brand values and stock returns. We reviewed SEC filings of publicly listed firms to

obtain brand values from real market transactions. Since the revision of international accounting

standards in 2001, firms are required to recognize the brand value that is part of the purchase

price in an acquisition (for details, see Fischer 2007; Salinas 2009). We estimate expected stock

returns according to the four-factor model (Carhart 1997), where risk factors are provided by the

CRSP database. The dependent variable in our stock return model is the geometric 12-month

mean of abnormal stock returns since our temporal interval is the year.

Comparability and Time Alignment

Two independent experts evaluated each brand to ensure that brand values and other

information are comparable and appropriately aligned on the time scale. Both evaluators had to

agree that the brand satisfies the following rules.

Brand values always reflect the value of a specific product or service, respectively, not a

portfolio. When company data had to be used to obtain brand values (e.g., for the Simon and

Sullivan model), we verified that the valued brand is a corporate brand or clearly dominates the

company’s business. For example, BMW satisfies this requirement as the brand accounts for

more than 90% of firm sales. In contrast, Colgate-Palmolive, Procter and Gamble, or Unilever do

not satisfy the rule since we cannot identify one major brand. Note, however, that their product

brands are still be part of our database if they are produced by other valuation models such as

Interbrand or Millward Brown.

A brand’s value always refers to the global value of the brand in a given year. Foreign

currencies are converted into US dollars at the average annual exchange rate, i.e., all figures are

in US dollar.

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Finally, we carefully align brand values and other variables, so that they are comparable

in terms of the period of measurement. Commercial vendors announce brand values at different

dates during the year. We treat the year of announcement as the valuation year by assuming that

it incorporates the most recent information available to the valuator. We use financial and other

information from that year to produce brand values for the other methods. For nomological

validity tests (see figure 2 again), we consider financial and Equitrend data of the preceding and

following year to generate variables for antecedents and consequences, respectively. For the

stock return response model, we align abnormal returns and brand value data at the monthly

level. For example, if Interbrand announces the new brand value in August of year t we calculate

the 12-month abnormal return for the parent company that covers the preceding 12 months

including August of year t. The abnormal returns within 1 month after the announcement

includes only the returns of September, the returns after 5 months only the returns for September

thru January, etc.

Descriptive Statistics

Our data collection effort resulted into a healthy sample size of 36,992 brand values that

span a period of 22 years from 1990 to 2012. This sample includes 3,879 brands that originate

from 87 countries and cover virtually all industries. Table 2 summarizes our database with

respect to periods, brands and observations.


Since valuation models were introduced at different points in time, time-series for brand

values have different lengths and starting years across the methods. Due to time and other data

limitations the number of brands by method varies from 78 (Semion) to 2,752 (Brand Finance).

On average, each method contributes 727 brands to our analysis. 4,110 brand value observations

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are, on average, available across methods. The mean length of individual brand time-series varies

from 2.16 years (Brand Finance) to 13.22 years (historical cost and advertising stock models).

The average brand time-series length amounts to 5.66 years. Thus, our sample exhibits a typical

panel structure. Actual brand values (not shown) range from zero to more than US$150 bn. The

mean value is US$ 2.54 bn. The standard deviation amounts to US$ 6.04 bn. We observe a

highly skewed and leptokurtic distribution of values for each method. This suggests that there are

only a few brands carrying very large values. The vast majority of brands, however, have

relatively low values compared to the top brands.

An important characteristic of our database is that it provides enough observations for

conducting the various validity tests. As a rule, we only compute a correlation or regression

coefficient if more than 50 observations are available. As table 2 shows, the number of joint

observations between two methods averages 706 observations. The number of joint observations

only falls below the minimum of 50 for Semion with respect to three other methods. Considering

the variables for testing discriminant and nomological validity, all methods reach the minimum

level. The sample sizes average several hundred observations. Appendix D provides detailed

information on joint observations by method and variable.

Panel Unit Root Tests

Consistent with the nature of a data, we apply the panel unit root test according to Levin,

Lin, and Chu (2002) to test for stationarity of variables. Unit root is rejected for most brand

valuation methods and validation variables. Brand values by the historical cost model and the

Simon-Sullivan model as well as SG&A expenditures and firm revenues do not pass the test.

Hence, we need to make appropriate changes to our tests following the procedure of figure 1.

21

EMPIRICAL RESULTS

We present the aggregated results of our validity tests in Tables 3 to 8. More detailed test results

are provided in Tables E.1 to E.4 in Appendix E. Recall that all tests are based on at least 50

(joint) observations. The vast majority of tests exceed this minimum level by far.

Reliability and Stability Results

Table 3 shows that test-retest correlations are very high for the nine valuation methods.

All methods satisfy the correlation threshold of .90 (between-year correlation) or .95 (within-

year correlation), respectively. The picture does not change if we consider the decomposition of

the variance of brand values into its cross-sectional and time variance. Cross-sectional variance

is more than three-times larger than time variance, except for the historical cost model. We

conclude that the reliability and stability is very strong for most methods.


Convergent Validity Results

Table 4 summarizes the pairwise correlations of brand values produced by different

methods. Note that only 7 correlation coefficients are available for the cost-based methods and

CoreBrand due to insufficient joint observations with the Semion model. Most methods correlate

positively and significantly with each other. If time-series are non-stationary, the panel co-

integration test suggests that model results converge towards a common equilibrium in the long

run, i.e. they are associated with each other. All methods pass the threshold for the proportion of

significant correlations, except for the Millward Brown model. This model correlates only with

50% of the other models. In fact, Millward Brown only correlates with other (commercial)

models of the same category, i.e. future-oriented income/DCF-based approaches such as

Interbrand.

22


Considering the average significant correlation among methods, it turns out that four

models do not pass the threshold of .50. These models are historical cost of creation, Interbrand,

Semion, and Ailawadi, Lehmann, and Neslin. As we show later in our robustness checks, it does

not matter whether we include Millward Brown and/or Semion or not into this analysis. If we

consider only correlations among models within their subcategory of valuation approach all

methods except for Semion pass the threshold, i.e. demonstrate sufficient convergent validity.

Discriminant Validity Results

Our discriminant validity tests in Table 5 suggest that brand valuations of all models are

distinct from the concept of customer satisfaction (correlation < .30). However, correlations of

CoreBrand, Millward Brown, and Brand Finance with Fortune’s Corporate Reputation Index are

slightly larger than our threshold of .30. These correlations are still smaller than their average

significant correlations in the convergent validity test. But concerns about these models’

differentiation from overall firm reputation remain.


Nomological Validity Results

Correlations. The correlation results inform about the strength of supposed relations

between brand values and their antecedents and consequences (see Figure 2 again). For a strong

association, we require that the majority of correlations is significant and averages .40 or higher.

Non-stationary time-series should be co-integrated to support the association assumption. With

respect to consequences, Table 6 shows that these requirements are satisfied by all methods

except for Millward Brown. For antecedents, we find that Interbrand, Millward Brown, and

Ailawadi, Lehmann, and Neslin do not pass the minimum correlation of .40. Hence, full

23

nomological validity in terms of the strength of associations cannot be established for these

models.


Granger-causality tests. Table 7 summarizes our findings from Granger-causality tests.

With these tests, we check for the plausibility of the assumed direction of causality according to

our nomological framework. When testing for Granger-causal relations between antecedents and

consequences variables we account for up to four lags. A relation between two variables is

counted in table 7 if we find support for at least one lag. As the results show, we find support for

most expected relations between brand values and its antecedents and consequences across the

various models. One exception is again Millward Brown’s model. Another observation,

however, is most striking. We also find that the reverse of the assumed relations is supported for

most variables and across models. Considering the threshold of 3 for the ratio of supported

versus reverse relations, we conclude that not a single method demonstrates sufficient

nomological validity.


Predictive Validity Results

Correlation. We start our predictive validity tests with the correlation of brand value

estimates with real brand transaction prices, which we consider as a ‘gold standard’. Since only

102 transaction prices are available to us, we can only correlate them with the pooled outcomes

of the valuation models. The correlation, which is corrected for measurement error due to

varying model estimates (DeVellis 2012, 66), amounts to .482 (p < .01). We interpret this a first

evidence for predictive validity of brand valuation models in general with respect to real

24

transaction prices. Due to data limitations, however, we cannot establish that result for any

specific valuation method.

Stock return response model. Table 8 presents the results of the stock return response

model. Recall that we control for several risk factors and the impact of unanticipated accounting

performance information in this model (Mizik and Jacobson 2008). We consider immediate

stock response and future stock response within 1, 5, and 11 months after the new brand value

information is available to the public.

Generally, we find only weak support for predictive validity across brand valuation

models. For most models, the coefficient associated with the unanticipated change in brand value

is not significant. However, we do find positive and significant parameter estimates for the

models of CoreBrand, Interbrand, and Semion in the immediate stock response model. We also

find a significant estimate for the Ailawadi, Lehmann, and Neslin model if we consider future

stock return response within 1 month after announcement. Most interestingly, the CoreBrand

model further demonstrates a strong predictive ability for future returns with respect to 5 and 11

months after new brand value estimates are available.


Robustness Checks

We performed several analyses to check the robustness of our results. First, we

substituted the valuation year of the commercial methods for the preceding year. It could be that

the brand values announced in year t in fact represent the information level of year t-1. The

change in periods does not have a substantial impact on our results. Second, we performed the

convergent validity analysis again by excluding the models of Millward Brown model and/or

Semion. Recall that Millward Brown’s performance is very poor. So, we could argue that the

25

inclusion of this model adversely affects the results for other models, especially those that do not

pass the threshold. Another adverse effect might result from including the Semion model since

we have significantly less brand values available from this model. Excluding these models from

the analysis does not change our conclusions. Third, we took the log of all variables in the

nomological validity tests (except for EBIT due to potentially negative values) to check the

stability of Granger-causality results. Our conclusions do not change. Fourth, we considered all 4

lags in calculating the ratio threshold with respect to the Granger-causality tests. Again, our

conclusions do not change.

Summary of Validation Test Results

Table 9 integrates the findings from all validation tests, which helps developing an

overall evaluation of the brand valuation methods. The check marks and crosses indicate whether

a method has passed a specific test or not. We summarize first results across methods and focus

then on the performance of individual methods.


The last row of table 9 shows the number of methods that passed a specific test. From this

summary, we draw the following conclusions:

1. Brand valuation methods generally produce reliable and stable results. 2. The results also appear to be largely distinct from other constructs (discriminant

validity). 3. Convergent validity, however, is only sufficiently established within the valuation

category but not across categories. Only market-based methods demonstrate unconstrained convergent validity across all categories.

4. While common correlation analysis suggests nomological validity, there is no support at all from Granger-causality tests. Hence, nomological validity cannot be shown for any method.

5. Only a minority of methods provides evidence of predictive validity. None of the cost-based models shows predictive validity.

26

It is apparent from table 9 that no single method does pass all tests together. If we claim

this to be the standard we have no choice but to conclude that none of the brand valuation

methods is valid and thus useful. However, such a standard is probably too strict. Simply

counting the check marks selects the two market-based methods and the advertising stock model

as best performing models. However, such counting might ignore differences in importance of

the validity dimensions. From a brand management perspective, we consider the

reliability/stability of a method, its convergent validity and its predictive validity as relatively

more important than discriminant and nomological validity. This is because a measurement

method must be reliable if management continuously uses it for decision-making. Convergent

validity strongly indicates that the measure does reflect the conceptual essence of the construct,

which is important to establish a shared understanding about investment activities and objectives

in the company. Finally, predictive validity ensures that the measure is indeed linked with

relevant external performance criteria such as stock price. Following this prioritization, we draw

the following conclusions about individual methods:

1. Only the market-based CoreBrand model convinces across all tree priority validity dimensions reliability, convergent validity, and predictive validity.

2. If we accept that convergent validity within only the valuation category of Income/DCF-based methods is sufficient, the Interbrand model is also performening quite well.

3. The value of the Millward Brown model is highly questionable. It does not pass a single validity test, except for the reliability/stability test.

4. Although the cost-based valuation approach lacks theoretical foundation, the performance of the ad-stock model with respect to reliability, convergent validity, and discriminant validity is remarkable and warrants further attention.

27

DISCUSSION

Managerial Implications

Companies that invest a lot in building and nurturing their brands want to monitor the

outcome of these investments. Measuring the brand value thus is of utmost importance to brand

managers and ensures the attention of top management. While it is crucial to know the absolute

value for transactional purposes it is less relevant in brand management applications. Here, it is

important that management can trust the measure that is to reflect the effects of brand decisions

in a meaningful and consistent way. Our validation study is a first step to provide guidance

through the jungle of brand valuation models available to managers. Managers may learn from

our study in several ways. We cannot fully solve the issue of the best brand valuation model, as

our validation test does not reveal the one superior model that passes every single test. But we

can differentiate between models and valuation approaches that come closer to the ideal and

those that are further away.

It appears that the market-based methods generally perform best along the various

criteria. Specifically, the CoreBrand model turns out to be reliable and to converge with the

results across different valuation categories. Most importantly, it has an impact on both

immediate and future stock returns. Conceptually, we agree with Salinas (2009, 388-395) that the

model setup raises concerns. It uses brand familiarity and favorability from surveying a specific

audience - top managers and decisions makers of other corporations. This might explain why the

model does not fully discriminate from the corporate reputation construct. But it simply performs

better than many other suggested models. It would be worthwhile to further investigate the

reasons for this better performance.

28

The major drawback of the CoreBrand model is that it only applies to corporate brands or

dominant brands of a company. But many companies manage a portfolio of brands that requires

monitoring the value of individual product brands. For these applications, our analysis suggests

the use of the Interbrand model. Conceptual and methodological concerns are also associated

with this model (e.g., Salinas 2009, 215-232). Putting these limitations aside, our study shows

that the model is reliable and has predictive relevance. However, we could not establish

convergent validity across valuation categories but only within its category. If the user is willing

to accept this limitation, the model certainly represents the best choice among future-oriented

Income/DCF-based valuation approaches.

Alternatively, brand managers should consider the use of the Ailawadi, Lehmann, and

Neslin model. It demonstrates both high reliability/stability and predictive validity. Its rather low

performance on convergent validity may be due to the fact that this approach focuses on current-

period brand revenues and profits. All other models involve longer time horizon by either

incorporating expected future returns or accumulating past brand investments. If a focus on only

current-period results is acceptable, the Ailawadi, Lehmann, and Neslin model presents a

valuable alternative that is particularly easy to implement.

Research Implications

Our brand validation results also have implications for researchers and hopefully

stimulate the further development and refinement of valuation models. First, we think that the

suggested methodological framework is a valuable contribution to the measurement and scaling

literature. The availability of time-series data offers new possibilities for testing concepts such as

nomological validity or predictive validity in a more rigorous way than before. Although

generating data over several periods involves higher costs it also offers the chance to verify the

29

causal ordering of nomological networks. Such verification is important if researchers want to

use new constructs for theory testing in their empirical models. In addition, it seems worthwhile

to carefully think about meaningful external criteria that are linked with management objectives

and can be used for testing predictive validity.

Second, in contrast to managerial applications, nomological validity has a higher value to

researchers. Our validation results obviously do not attest nomological validity but rather suggest

that brand valuation results are highly endogenous with their antecedents such as advertising

expenditures and consequences such as profit. This finding is not necessarily surprising, as brand

value is defined as the incremental cash profit that accrues from effective brand investments.

Hence, the construct essentially is defined in terms of its antecedents and consequences. As a

consequence, financial brand equity measures do not qualify as a measure to be used in empirical

models of firm performance unless the performance or criterion variable, respectively, can be

separated in a convincing manner.

Finally, our results should stimulate the research on new valuation models. The surprising

good performance of the ad-stock model deserves more attention. Although it uses past brand

investments it produces reliable results that correlate strongly with other valuation approaches.

Apparently, it shows empirical evidence of capturing the value of a brand. Conceptually, the

concept of an advertising stock is consistent with the idea of brand equity that provides the

potential for future performance even if investments are cut back to a minimum. This might

explain why the ad stock provides a meaningful way to reflect brand value. It should be noted

that we had to use the generalized carryover coefficient of .50 due to the lack of more specific

data. Brand-specific carryover coefficients will better capture the true differences in brand

strength. We encourage more research in this direction, which might lead into new, powerful

30

approaches for brand valuation. In fact, an important advantage of the advertising stock approach

is that it is consistent with the accounting practice of recognizing the value of an asset, which is

depreciated over time.

Limitations and Further Research

Our study is subject to limitations. The results of our study do not inform about the model

that performs best with respect to the absolute metric. Given the large differences between brand

value estimates, this is still an issue of high importance that warrants a solution. The true value

of a brand is not observed. A promising avenue is to use data from real brand transactions

provided the sample is large enough. While we consider nine valuation models, there are many

more models. Salinas (2009) discusses 39 models. Provided that sufficient data are available it

would be interesting to extend our validation tests to other models. Finally, we had to make an

assumption about the choice of the lowest-share brand as benchmark brand for the Ailawadi,

Lehmann, and Neslin (2003) model. This is fully consistent with the recommendation by the

authors (p. 15) and they present evidence that their measure is robust with respect to this

assumption. But it would be interesting to learn more about the potential influence of this

assumption on our validation results.

31

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34

FIGURE 1 TESTING FOR MEANINGFUL ASSOCIATIONS WITH TIME-SERIES DATA

No

Correlation analysis Co-integration test (Kao

1999)

Yes

Are both time-series evolving?

Are both time-series stationary?

No

Yes

Test for unit root (Levin, Lin, and Chu 2002)

35

FIGURE 2

NETWORK OF NOMOLOGICAL RELATIONSHIPS

Antecedents(Period't)1' Period't' Period't+1'

Consequences(

Log'of'Adver1sing''expenditures'

Log'of'Selling'&'general'administra1on'expenditures'

Brand'value'

measure'

Firm'market'capitaliza1on'

Price)to)'book'value'

Customer)''based'brand''equity'

Sales'

Profit'

36

TABLE 1 METHODOLOGICAL FRAMEWORK

Criterion Test statistic Threshold Source Reliability/ stability

1. Test-retest correlation r between brand value in year t and t-1

2. Test-retest correlation r of brand value within year t

rt-1 ≥ .90 rwithin ≥ .95

Ailawadi, Lehmann, and Neslin (2003); Churchill (1979); DeVellis (2012, 109)

3. Variance decomposition into cross-sectional (brands) and time variance

Cross-sectional varianceTime variance

≥ 3

Convergent validity

4. Correlation r between brand values of different methods (stationary series)

a) At least 75 % significant pairwise correlations (p < .05) b) Average significant rconv ≥ .50

Fischer, Völckner, and Sattler (2010)

5. Panel co-integration test for brand values of different methods (non-stationary series)

ADFconv > tADF Kao (1999)

Discriminant validity

6. Correlation r between brand values and distinct measures (stationary series)

Average rdiscr < .30

7. Panel co-integration test for brand values and distinct measures (non-stationary series)

ADFdiscr < tADF Kao (1999)

Nomological validity

8. Correlation r between brand values and nomological variables (stationary series)

a) More than 50 % significant pairwise correlations (p < .05) b) Average significant rnomol ≥ .40

Finn and Kayande (2005); Fischer, Völckner, and Sattler (2010)

9. Panel co-integration test for brand values of different methods (non-stationary series)

ADFnomol > tADF Kao (1999)

10. Granger causality test for relations between brand values and both antecedents and consequences

a) More than 50 % significant supported relations (p < .05)

b)

# supported relations# reverse relations

> 2

Predictive validity

11. Stock return response model (t-test)

Positive and significant estimated coefficient for brand value measure (t > 1.96; p < .05)

Carhart (1997); Fama and French (2006); Mizik and Jacobson (2008)

37

TABLE 2 OVERVIEW OF BRAND VALUE DATABASE

Period Brands Observations Mean no. of

observations per brand

Median no. of observations

per brand

Mean no. of joint observ. per

method Cost-based methods Ad-stock model 1990-2011 186 2,458 13.22 14.00 818 Historical costs 1990-2011 186 2,458 13.22 14.00 818 Market-based methods

Simon and Sullivan (1993) 1992-2012 438 5,571 12.72 14.00 913

CoreBrand 2002-2012 672 3,979 5.92 6.00 982 Income/DCF-based methods

Future-oriented Interbrand 1992-2012 1,027 3,841 3.74 3.00 557 Millward Brown 2006-2012 324 1,175 3.63 4.00 305 Semion 1997-2012 78 774 9.92 12.00 58 Brand Finance 2006-2012 2,752 5,950 2.16 2.00 521 Current-period oriented

Ailawadi, Lehmann, and Neslin (2003)

1997-2012

876

10,786

12.31

13.00

1,381

Total 1990-2012 3,879 36,992 5.66 3.00 706

38

TABLE 3 RELIABILITY AND STABILITY TEST RESULTS

Test-retest correlation Variance decomposition

Year t

with year t-1

Within year t

Ratio of cross-sectional to time variance

Cross-sectional variance

Time variance

Threshold ≥ .90 ≥ .95 ≥ 3.00 ≥ 75.0 % ≤ 25.0 % Cost-based methods Ad-stock model .996 n.a. 3.26 76.5 % 23.5 % Historical costs .998 n.a. 1.64 62.1 % 37.9 % Market-based methods

Simon and Sullivan (1993)

.993 n.a. 7.70 88.5 % 11.5 %

CoreBrand .964 n.a. 10.49 91.3 % 8.7 % Income/DCF-based methods

Future-oriented Interbrand .990 .983 8.35 89.3 % 1.7 % Millward Brown .952 .997 5.76 85.2 % 14.8 % Semion .987 .970 14.63 93.6 % 6.4 % Brand Finance .942 .984 13.08 92.9 % 7.1 % Current-period oriented


.901 n.a. 3.12 76.1 % 23.9 %

Notes: All correlation coefficients are highly significant at p < .01; n.a. = not applicable due to missing observations.

39

TABLE 4 CONVERGENT VALIDITY TEST RESULTS

Within and across valuation categories Within valuation category No. of significant

correlations (p < .05) Average significant

correlation

Average significant correlation

Threshold ≥ 75 % ≥ .50 ≥ .50 Cost-based methods Ad-stock model 6 (86%)1) .554 .928 Historical costs 6 (86%)1) .4622) .928 Market-based methods Simon and Sullivan (1993) 7 (88%) .6072) .693

CoreBrand 7 (100%)1) .583 .693 Income/DCF-based methods

Future-oriented Interbrand 8 (100%) .494 .660 Millward Brown 4 (50%) .540 .543 Semion 5 (100%)3) .4423) .401 Brand Finance 8 (100%) .520 .625 Current-period oriented Ailawadi, Lehmann, and Neslin (2003) 7 (88%) .397 n.a

Notes: n.a. = not applicable since only one current-period income model 1)

The total number of pairwise correlations is 7 due to insufficient joint observations with the Semion model. 2)

Correlation between historical costs model and Simon and Sullivan (1993) model not included due to non-stationary time-series. Series are co-integrated (ADF = 38.10; p < .01). 3)

The total number of pairwise correlations is 5 due to insufficient joint observations with all models.

40

TABLE 5 DISCRIMINANT VALIDITY TEST RESULTS

Average correlation across methods

(convergent validity)

American Customer Satisfaction Index

Fortune Corporate Reputation Index

Threshold < .30 < .30 Cost-based methods Ad-stock model .554 .219 .047NS Historical costs .462 .258 .007NS

Market-based methods Simon and Sullivan (1993) .607 .061NS .051NS

CoreBrand .583 .101 .339 Income/DCF-based methods

Future-oriented Interbrand .494 -.267 .251 Millward Brown .540 -.078NS .310 Semion .442 n.a. -.073NS Brand Finance .520 .002NS .345 Current-period oriented Ailawadi, Lehmann, and Neslin (2003) .397 .016NS .073

Notes: NS = not significant (p > .05; two-sided t-test); n.a. = not applicable since less than 50 observations available.

41

TABLE 6 NOMOLOGICAL VALIDITY TEST RESULTS (CORRELATIONS)

Three antecedents (year t-1) Four consequences (year t+1) No. of significant


correlation No. of significant


correlation Threshold > 50% ≥ .40 > 50% ≥ .40 Cost-based methods ! = .634 ! = .430 Ad-stock model 2 (66%) .653 3 (75%) .456 Historical costs 2 (66%) .6141) 3 (75%) .4031) Market-based methods ! = .519 ! = .584 Simon and Sullivan (1993) 2 (66%) .6241) 3 (75%) .5881)

CoreBrand 3 (100%) .414 4 (100%) .579 Income/DCF-based methods

Future-oriented ! = .413 ! = .431 Interbrand 3 (100%) .359 4 (100%) .407 Millward Brown 3 (100%) .235 4 (100%) .277 Semion 3 (100%) .627 4 (100%) .449 Brand Finance 3 (100%) .431 3 (75%) .591 Current-period oriented Ailawadi, Lehmann and Neslin (2003) 3 (100%) .266 3 (75%) .732

Across all methods .469 .498 Notes: ! refers to the average correlation within the respective subcategory. 1) Correlation between Log(SG&A) expenditures and both historical costs model and Simon and Sullivan (1993) model not included due to non-stationary time-series. Series are co-integrated (ADF = 12.52 and 40.52, respectively; p < .01).

42

TABLE 7 NOMOLOGICAL VALIDITY TEST RESULTS (GRANGER CAUSALITY)

Notes: Non-stationary time-series are transformed into first differences for the Granger-causality tests. 1) Only two variables considered due to insufficient sample size.

No. of significant supported relations

(p < .05)

No. of significant reverse relations

(p < .05)

Ratio of supported to reverse relations

(p < .05) Antecedents Consequences Antecedents Consequences Antecedents Consequences

Threshold > 50% > 50% > 2 > 2 Cost-based methods Ad-stock model 3 (100%) 3 (75%) 3 (100%) 3 (75%) 1 1 Historical costs 1 (33%) 3 (75%) 3 (100%) 3 (75%) .33 1 Market-based methods Simon and Sullivan (1993)

2 (66%) 3 (75%) 3 (100%) 3 (75%) .66 1

CoreBrand 2 (66%) 3 (75%) 2 (66%) 3 (75%) 1 1 Income/DCF-based models

Future-oriented Interbrand 2 (66%) 3 (75%) 1 (33%) 4 (100%) 2 .75 Millward Brown 0 (0%) 2 (50%) 1 (33%) 3 (75%) 0 .66 Semion 1 (50%) 1) 3 (75%) 2 (100%) 1) 3 (75%) .50 1 Brand Finance 3 (100%) 3 (75%) 3 (100%) 3 (75%) 1 1 Current-period oriented Ailawadi, Lehmann and Neslin (2003)

2 (66%) 3 (75%) 3 (100%) 3 (75%) .66 1

Across all methods 1.8 (60%) 2.9 (72%) 2.3 (81%) 3.1 (78%) .79 .93

43

TABLE 8 PREDICTIVE VALIDITY TEST RESULTS (STOCK RETURN RESPONSE MODEL)

Ad-stock model

Historical costs


CoreBrand Interbrand Millward Brown

Semion Brand Finance

Ailawadi, Lehmann, & Neslin (2003)

Immediate stock return response UΔROA1) 6.095

(0.99) 6.042 (.990)

5.315 (0.395)

3.114 (0.763)

2.637 (1.262)

4.595NS (2.895)

-1.880NS (2.092)

5.649 (1.497)

9.853 (.512)

UΔBV1) -.003NS (.019)

-.017NS (.015)

-.008NS (.455)

.006 (.003)

.007 (.004)

.002NS (.002)

.032 (.014)

.004NS (.005)

.000NS (.001)

F-Value 6.41 6.49 16.33 3.38 2.56 4.25 4.19 6.67 26.96 N 1,759 1,759 4,215 2,736 1,099 380 575 1,520 8,005 Future stock return response within 1 month after announcement UΔBV1) -.083NS

(.071) -.083NS (.054)

-.001NS (.017)

.014NS (.011)

-.012NS (.013)

-.015NS (.008)

-.025NS (.054)

.003NS (.018)

.017 (.005)

F-Value 2.69 2.75 2.92 4.91 2.64 7.36 1.51NS 4.63 4.08 N 1,802 1,802 4,226 2,738 1,101 380 576 1,519 8,029 Future stock return response within 5 months after announcement UΔBV1) -.500NS

(.305) -.493 (.232)

-.033NS (.068)

.144 (.043)

.000NS

(.005) .000NS (.004)

.003NS (.021)

-.007NS

(.007) -.003NS (.021)

F-Value 3.25 3.35 5.87 3.21 4.79 1.53NS 5.94 6.96 5.94 N 1,802 1,802 4,420 2,736 1,101 380 576 1515 8,013 Future stock return response within 11 months after announcement UΔBV1) -.446

(.019) -.045 (.015)

-.009NS (.005)

.011 (.003)

-.003NS (.004)

-.001NS (.002)

-.005NS

(.014) -.003NS (.004)

.000 NS (0.001)

F-Value 3.23 3.46 5.75 2.12 3.14 2.05NS 4.70 5.30 8.91 N 1,799 1,799 4,206 2,734 1,101 380 576 1,502 7,973 Notes: Standard error are in parentheses; NS = not significant (p < .05; one-sided t-test); UΔROA = unanticipated change in accounting performance; UΔBV = unanticipated change in brand value; N = sample size. Parameter estimates for intercept, annual dummies, and accounting performance (for 1, 5, and 11 months future stock return models) are not reported in the table. 1) For reading convenience we multiply coefficients by 10,000.

44

TABLE 9 SUMMARY OF VALIDATION TEST RESULTS

Reliability/stability Convergent validity Discriminant validity

Nomological Validity Predictive validity

Test statistic Test-retest correlation

Variance decomposition

Correlation / Co-integration test



Granger causality test

Stock return response model (t-test)

Threshold rt-1 ≥ .90 / rwithin ≥ .95

Ratio of cross-sectional to time variance ≥ 3

rconv ≥ .50 / ADFconv > tADF rdiscr < .30 /

ADFdiscr < tADF rnomol ≥ .40 / ADFnomol > tADF Ratio of supported

to reverse relations > 2

t > 1.96, p < .05

Across all methods

Within categories

Cost-based methods Ad-stock model ✓ ✓ ✓ ✓ ✓ ✓ ✕ ✕ Historical costs ✓ ✕ ✕ ✓ ✓ ✓ ✕ ✕

Market-based methods Simon and Sullivan (1993)

✓ ✓ ✓ ✓ ✓ ✓ ✕ ✕

CoreBrand ✓ ✓ ✓ ✓ ✕ ✓ ✕ ✓

Income/DCF-based methods

Future-oriented Interbrand ✓ ✓ ✕ ✓ ✓ ✕ ✕ ✓ Millward Brown ✓ ✓ ✕ ✓ ✕ ✕ ✕ ✕ Semion ✓ ✓ ✕ ✕ ✓ ✓ ✕ ✓ Brand Finance ✓ ✓ ✓ ✓ ✕ ✓ ✕ ✕ Current-period oriented


✓ ✓ ✕ n.a. ✓ ✕ ✕ ✓

Across all methods 9 of 9 8 of 9 4 of 9 7 of 8 6 of 9 6 of 9 0 of 9 4 of 9 Notes: ✓ passed, ✕ not passed; r denotes the correlation coefficient; ADF is the augmented Dickey-Fuller test statistic applied in co-integration tests;

and t refers to the respective t-statistic.

45

APPENDIX

Appendix A: Granger-causality tests

We test Granger (1969) causality between a antecedent variable, x (e.g., advertising), and a

brand value metric, y (e.g., Interbrand), by the following regressions:

(A.1a)

yt =α0,1a + α1,1a,l yt−l +l=1

L=4

∑ α 2,1a,l xt−l + ε1al=1

L=4

∑ (supposed relation)

(A.1b)

xt =α0,1b + α1,1b,l xt−l +l=1

L=4

∑ α 2,1b,l yt−l + ε1bl=1

L=4

∑ (reverse relation)

where denotes the parameters to be estimated, the error term, t the year, and L the

number of lags. We further specify the estimation equations for brand value metrics and each

consequence variables, z (e.g., firm profit):

(A.2a) zt =α0,2a + α1,2a,l zt−l +

l=1

L=4∑ α2,2a,l yt−l + ε2a

l=1

L=4

∑ (supposed relation)

(A.2b)

yt =α0,2b + α1,2b,l yt−l +l=1

L=4

∑ α 2,2b,l zt−l + ε2bl=1

L=4

∑ (reverse relation)

Note that we take the first differences for non-stationary time series in each regression of eq.

(A.1a) to (A.2b) to ensure that variables are integrated by the same order.

Granger-causality is supported if the F-value for the joint hypothesis of α 2,i,l = ...=α 2,i,L = 0

exceeds the respective Wald statistic. The effective number of estimation coefficients relates to the number of lags l that eventually has been specified. We test four lag structures l

= 1,..., 4.

α ε

α

46

Appendix B: Stock return response model We adopt the framework of Mizik (2009) and expect abnormal returns to depend on

unexpected changes in profitability and financial brand values. Abnormal return is the

difference between actual and expected stock return. We estimate expected stock return

according to the four-factor model (Carhart 1997, Fama and French 2006). The four factors

are provided by the Center for Research in Security Prices (CRSP) and are based on regional

values depending on the primary listing of the company. They reflect the country-specific

risk more precisely than the Global factors, i.e. the aggregate of the regional factors. Regional

factors are available for Europe, Japan, Asia Pacific, and the US. Only firms that are listed in

these regions entered our analysis. We use monthly stock returns to ensure correct alignment

with the month in which financial brand equity metrics become available. Since financial

brand values are measured on an annual basis we take the geometric mean of abnormal

returns within the corresponding 12 months brand value measurement period.

We measure unexpected changes in profitability and brand value as the residuals of a fixed-

effect, first-order autoregressive model of firm profitability and the financial brand equities,

respectively. The usage of unanticipated changes instead of actual levels follows the efficient

market hypothesis (Fama 1970). They also avoid error terms to be serially correlated (Greene

2011). We additionally control for economy-wide risk by yearly dummies and estimate

abnormal returns on financial brand values by the following regression:

(B.1) AStkRetit = β0 + β1UΔAccPit + β2UΔBVit + βk+1YD +

k=1

K−1∑ ηit , with

where AStkRetit denotes abnormal stock return of brand i‘s parental firm in period t, UΔAccPit

and UΔBVit unanticipated changes in profitability and financial brand values, respectively, β

parameter estimates, YD yearly dummies, and and the error term and variance.

Recent research further considers capital markets not only to respond when new information

arrives but also when their total financial implications have been fully understood (e.g., Brav

and Heaton 2002, Srinivasan and Hanssens 2009). We thus test for lagged effects that might

occur within 1, 5, and 11 months after the brand value announcements. In these cases, the

brand value variable effectively precedes future abnormal stock returns in eq. (B.1). Again,

abnormal stock returns are measured as geometric mean.

ηit～

i.i.d

N (0,ση2 )

η ση

2

47

Appendix C: Overview of periods and data sources Variable Period Data source Commercial financial brand values

Interbrand 1992-2012 Interbrand, www.interbrand.com Millward Brown 2006-2012 Millward Brown, www.millwardbrown.com Semion 1997-2012 semion® brand-broker Gmbh, www.semion.de

Brand Finance 2006-2012 Brand Finance, www.brandfinance.com Corebrand 2002-2012 Corebrand, www.corebrand.com

Academic financial brand values Cost-based metrics

Advertising 1990-2011 Compustat Simon & Sullivan (1993)

Intangible asset value, concentration ratio, advertising spending, -share, market share, R&D share, brand age, order of entry

1992-2012

Compustat, Thomson Banker One, company reports, company founding dates (Field and Karpoff 2002, Loughran and Ritter 2004), Internet research

Ailawadi, Lehmann & Neslin (2003) Revenues, industry profit margin 1997-2012 Compustat, SIC classification

Brand transactions prices Brand values derived from M&As 2001-2012 SEC filings

Additional data for construct validity tests

American Customer Satisfaction Index 1994-2012 www.theacsi.org Fortune Reputation Index 1992-2012 Fortune’s Most Admired Companies Advertising spending, SG&A spending, revenues, EBIT, market capitalization, market-to-book value

1992-2012 Thomson Banker One

Harris EquiTrend 2005-2012 Harris Interactive Additional data for predictive validity tests

Stock return 1992-2012 Thomson Banker One Market factor, size factor, value factor, momentum factor

1992-2012 Center for Research in Stock Prices (CRSP)

Profitability 1992-2012 Thomson Banker One

48

Appendix D: Overview of joint observations Table D.1: Number of joint observations between financial brand valuation methods

Ad stock model

Capitalized costs


Corebrand Interbrand Millward Brown


Ailawadi, Lehmann & Neslin

(2003) Cost-based models Ad-stock model 2,458 Historical costs 2,458 2,458 Market-based models Simon and Sullivan (1993)

763 763 5,571

Corebrand 929 929 1,489 3,979 Income/DCF forecast-based models

Future-oriented Interbrand 628 628 566 507 3,841 Millward Brown 207 207 131 285 539 1,175 Semion 51) 51) 98 191) 101 53 774 Brand Finance 299 299 435 556 728 681 102 5,950 Current period-oriented Ailawadi, Lehmann, and Neslin (2003)

1,258 1,258 3,147 3,140 756 333 84 1,069 10,786

1) We do not calculate correlation coefficients of these relationships since the no. of joint observations is less than 50.

Table D.2: Number of joint observations with distinct marketing constructs

American Customer Satisfaction Index

Fortune Corporate Reputation Index

Cost-based methods Ad-stock model 888 967 Historical costs 888 967

Market-based methods Simon and Sullivan (1993)

351 700

Corebrand 726 1,183 Income/DCF forecast-based methods

Future-oriented Interbrand 437 598 Millward Brown 182 257 Semion 151) 89 Brand Finance 292 565 Current period-oriented Ailawadi, Lehmann, and Neslin (2003)

1,135 1,723

1) We do not calculate the correlation coefficients of this relationship since the

no. of joint observations is less than 50.

49

Table D.3: Number of joint observations with nomological variables

Antecedents (t-1) Consequences (t+1) Log Ad

spending Log SG&A spending

Harris EquiTrend

Sales EBIT Market capitalizatio

n

Market-to-book value

Cost-based methods Ad-stock model 2,046 1,903 409 2,096 2,096 2,124 2,116 Historical costs 2,046 1,903 409 2,096 2,096 2,124 2,116 Market-based methods Simon and Sullivan (1993)

1,973 4,984 339 4,729 4,748 4,757 4,733

Corebrand 1,877 3,167 658 3,384 3,384 3,369 3,347 Income/DCF-forecast based methods

Future-oriented Interbrand 946 1,624 520 1,820 1,820 1,798 1,793 Millward Brown 328 510 642 507 507 492 490 Semion 60 583 50 651 651 643 643 Brand Finance 908 2,402 875 3,114 3,114 3,094 3,117 Current-period oriented Ailawadi, Lehmann, and Neslin (2003)

3,777 7,566 807 8,978 8,978 8,871 8,823

50

Appendix E: Detailed validation test results Table D.1: Results on convergent validity

Ad stock model

Capitalized costs




Ailawadi, Lehmann & Neslin

(2003) Required thresholds Cost-based models Ad-stock model 1 Historical costs .928 1 Market-based models Simon and Sullivan (1993)

.817 .2101) 1

Corebrand .472 .406 .693 1 Income/DCF-based models

Future-oriented Interbrand .375 .373 .319 .658 1 Millward Brown .018NS .040NS .092NS .530 .672 1 Semion n.a. n.a. .795 n.a. .525 .264 1 Brand Finance .334 .276 .510 .698 .770 .690 .411 1 Current-period oriented Ailawadi, Lehmann, and Neslin (2003)

.401 .328 .510 .623 .240 -.015NS .209 .470 1

Notes: Bold = significant, NS = Not significant (p > .05; one-sided t-test); n.a. = not applicable since less than 50 observations 1)

The correlation coefficient between these series bases on first differences since they are non-stationary. The panel co-integration test indicates convergence towards a common and stable equilibrium for these variables in the long run (ADF = 38.10; p < 01).

51

Table D.2: Results on nomological validity

Antecedents (t-1) Consequences (t+1) Log Ad

spending Log SG&A spending

Harris EquiTrend

Sales EBIT Market capitalizatio

n

Market-to-book value

Cost-based methods Ad-stock model .720 .585 -.015NS .469 .420 .480 -.018NS Historical costs .614 -.0461) .039NS .0781) .386 .420 -.019NS Market-based methods Simon and Sullivan (1993)

.624 .1561) -.021NS .1141) .538 .637 -.001NS

CoreBrand .519 .517 .207 .680 .710 .901 .023NS Income/DCF- based methods

Future-oriented Interbrand .442 .436 .198 .474 .444 .641 .070 Millward Brown .217 .346 .143 .169 .309 .552 .078 Semion .845 .758 .277 .798 .524 .583 -.110 Brand Finance .543 .550 .201 .646 .456 .670 .024NS Current-period oriented Ailawadi, Lehmann, and Neslin (2003) .457 .432 -.091 .890 .670 .636 -.008NS

Notes: No. of observations in parentheses; Bold = significant, NS = Not significant (p > .05; one-sided t-test) 1)

The correlation coefficient between these series bases on first differences since they are non-stationary. The panel co-integration test indicates a stable and long run relationship between these variables. The corresponding ADF statistic for Simon and Sullivan (1993) and for the capitalized costs model is 12.52 and 40.52, respectively (both p < .10).

52

Table D.3: F-values of bivariate Granger-causality regressions (Eq. A.1a and A.1b)

Dependent variable

Ad-stock model

Historical costs1)

Simon and Sullivan (1993)1)



Ailawadi, Lehmann & Neslin (2003)

Predictor variable Advertising Lags (t) 1 4577.99 (2044) .00 (1934) 6.10 (1550) .11 (1557) 3.44 (661) 2.43 (219) 2.15 (58) 2.08 (528) 1.4 (3555) 2 2.54 (1813) n.a. 62.04 (1335) 5.54 (768) 5.93 (488) .84 (149) n.a. 5.04 (312) .12 (3014) 3 8.30 (1619) n.a. 24.11 (1148) 6.36 (990) 2.37 (377) .46 (97) n.a. 2.15 (161) 3.86 (2531) 4 n.a. n.a. 19.05 (981) 5.54 (768) 1.03 (301) .17 (57) n.a. 1.07 (55) 3.12 (2077) SGA1) 1 174.90 (1701) 2.76 (1636) .02 (4108) 2.28 (2556) .02 (1100) 2.42 (355) 17.19 (510) 7.64 (1208) 277.81 (6607) 2 2.80 (1521) 3.65 (1460) 5.60 (3696) 53.73 (1299) .27 (810) 1.20 (263) 8.93 (446) 3.54 (594) 9.77 (5768) 3 7.13 (1355) 5.64 (1297) 21.37 (3310) 37.48 (1667) .51 (594) .99 (181) 12.62 (392) .65 (299) 51.88 (5029) 4 4.42 (1224) 4.47 (1169) 2.76 (2953) 53.73 (1299) 1.66 (457) .47 (111) 9.77 (342) .91 (107) 31.63 (4303) Harris

Equitrend

1 1.32 (392) 1.05 (387) .42 (304) .57 (600) 5.90 (452) 1.68 (488) n.a. 11.63 (593) .53 (766) 2 1.08 (299) .96 (295) .63 (224) 2.26 (185) 4.20 (328) 2.84 (352) n.a. 5.76 (387) 1.74 (579) 3 2.37 (214) 2.45 (211) .06 (161) .64 (299) 1.17 (233) .40 (232) n.a. 3.10 (230) .24 (434) 4 2.54 (134) 1.98 (132) 2.33 (100) 2.26 (185) .61 (168) 1.78 (140) n.a 1.37 (97) .60 (295) Predictor variable

Ad-stock model

Capitalized costs1)





Dependent variable

Advertising Lags (t) 1 1.15 (2044) 35.67 (1934) 2.88 (1550) 65.84 (1557) .13 (661) 4.88 (219) 6.01 (58) 5.88 (528) 2.45 (3555) 2 .03 (1813) n.a. 5.48 (1335) 59.25 (768) .51 (488) 3.52 (149) n.a. 2.12 (312) 16.83 (3014) 3 2.09 (1619) n.a. .55 (1148) 64.08 (990) 1.98 (377) 2.29 (97) n.a. .39 (161) 1.93 (2531) 4 n.a. n.a. 14.13 (981) 5.54 (768) 1.24 (301) 4.14 (57) n.a. 3.34 (55) 9.43 (2077) SGA1) 1 .70 (1701) 6.15 (1636) .08 (4108) 19.02 (2556) .26 (1100) .05 (355) 11.14 (510) 4.49 (1208) 331.53 (6607) 2 22.62 (1521) 21.25 (1460) 3.20 (3696) 6.82 (1299) .12 (810) .05 (263) 4.14 (446) 1.14 (594) 159.81 (5768) 3 14.68 (1355) 17.21 (1297) 4.47 (3310) 9.35 (1667) .03 (594) .20 (181) 2.98 (392) 2.14 (299) 92.42 (5029) 4 12.86 (1224) 14.06 (1169) 56.55 (2953) 6.82 (1299) .03 (457) .96 (111) 2.51 (342) 2.34 (107) 63.18 (4303) Harris

Equitrend

1 .14 (392) .16 (387) 4.31 (304) .34 (600) 1.88 (452) .79 (488) n.a. 1.31 (593) 2.97 (766) 2 3.09 (299) 4.33 (295) 3.98 (224) .86 (185) 5.37 (328) .30 (352) n.a. 6.64 (387) 2.95 (579) 3 2.09 (214) 4.40 (211) 3.21 (161) .39 (299) 4.20 (233) .08 (232) n.a. 5.92 (230) 6.02 (434) 4 4.42 (134) 5.41 (132) 1.45 (100) .86 (185) 3.09 (168) 1.36 (140) n.a. 3.68 (97) 4.90 (295) Notes: No. of observations in parentheses; Bold = significant, otherwise p > .05; n.a. = not applicable 1) First differences are used to ensure that time series are integrated by the same order.

53

Table D.4: F-values of bivariate Granger-causality regressions (Eq. A.2a and A.2b)

Dependent variable

Ad-stock model

Historical costs1)





Predictor variable Sales1) Lags (t) 1 101.31 (1761) .13 (1688) 3.92 (3973) 29.26 (2792) 19.68 (1238)

(1238) 2.65 (430) 36.96 (619) 47.52 (2088) 203.32 (8290)

2 1.31 (1567) .75 (1500) 4.70 (3551) 16.37 (1425) 9.84 (884) .32 (313) 17.60 (551) 11.78 (1096) 175.63 (7332) 3 8.58 (1386) 5.37 (1324) 5.84 (3156) 12.54 (1826) 7.14 (643) .16 (212) 11.66 (489) 7.63 (535) 45.43 (6471) 4 4.13 (1251) 4.55 (1192) 3.89 (2796) 16.37 (1425) 5.09 (492) .89 (129) 13.42 (432) .63 (155) 101.89 (5624) EBIT 1 22.72 (1886) 10.43 (1708) 27.75 (4020) 3.89 (2805) 3.24 (1255) 1.57 (431) .00 (623) 101.59 (2091) .01 (8362) 2 12.38 (1688) 13.99 (1518) 36.85 (3593) 2.97 (1431) 14.43 (893) 5.74 (314) 11.63 (555) 5.16 (1101) .89 (7399) 3 1.06 (1500) 8.08 (1339) 23.08 (3197) 1.72 (1835) 11.72 (650) 2.33 (212) 5.88 (493) 13.39 (538) 2.05 (6532) 4 6.68 (1324) 4.51 (1206) 18.53 (2833) 2.97 (1431) 7.26 (497) 1.20 (129) 5.80 (435) 2.55 (156) 3.88 (5682) Market capitalization 1 67.86 (1911) 14.09 (1737) 162.52 (4122) 2.61 (2818) 47.78 (1274) 18.30 (434) .83 (616) 335.44 (2140) 284.49 (8382) 2 26.61 (1716) 24.32 (1548) 138.69 (3712) 12.89 (1451) 53.70 (908) 42.99 (319) 11.87 (549) 285.66 (1135) 176.77 (7435) 3 16.85 (1528) 13.36 (1369) 54.85 (3326) 19.67 (1854) 3.04 (662) 24.48 (217) 1.04 (488) 66.25 (559) 157.69 (6578) 4 1.18 (1350) 11.07 (1236) 47.00 (2971) 12.89 (1451) 2.26 ( 510) 14.83 (133) 12.56 (431) 12.74 (158) 119.76 (5734) Price-to-book value 1 .04 (1898) .02 (1725) .93 (4085) .00 (2803) 9.60 (1271) 4.57 (433) 1.04 (616) .24 (2151) .05 (8321) 2 .03 (1697) .03 (1530) .66 (3667) .05 (1441) 1.83 (908) 1.86 (318) .83 (549) .68 (1139) .03 (7367) 3 .08 (1503) .09 (1346) .62 (3277) .02 (1844) .87 (663) .17 (216) 1.06 (488) .23 (560) .00 (6516) 4 .08 (1321) .06 (1210) .62 (2921) .05 (1441) .50 (511) .04 (132) 1.70 (431) .77 (158) .00 (5679) Predictor variable

Ad-stock model

Capitalized costs 1)





Dependent variable

Sales1) 1 .62 (1761) 4.88 (1688) 98.19 (3973) 190.60 (2792) 9.94 (1238) .97 (430) 14.67 (619) 12.31 (2088) 268.80 (8290) 2 8.07 (1567) 25.92 (1500) 75.7 (3551) 52.96 (1425) 7.56 (884) 6.22 (313) 5.49 (551) 1.11 (1096) 173.42 (7332) 3 2.22 (1386) 21.72 (1324) 62.28 (3156) 83.50 (1826) 5.46 (643) 7.40 (212) 7.47 (489) 7.26 (535) 44.25 (6471) 4 15.86 (1251) 20.37 (1192) 41.64 (2796) 52.96 (1425) 5.10 (492) 11.06 (129) 8.66 (432) 4.97 (155) 18.58 (5624) EBIT 1 33.09 (1824) 54.02 (1708) 27.17 (4020) 470.81 (2805) 7.98 (1122) 2.6 (357) 27.73 (529) 4.45 (1223) 14.16 (6727) 2 35.61 (1636) 7.08 (1518) 31.03 (3593) 58.61 (1431) .16 (819) .21 (265) 11.71 (465) .74 (601) 169.41 (5882) 3 36.70 (1460) 7.67 (1339) 31.06 (3197) 94.44 (1835) .09 (601) .33 (182) 8.73 (408) 3.22 (305) 101.49 (5135) 4 27.50 (1297) 4.92 (1206) 36.57 (2833) 58.61 (1431) .10 (462) .87 (112) 6.95 (357) 4.60 (107) 68.61 (4406) Market capitalization 1 7.56 (1911) 7.95 (1737) 5.12 (4122) 51.77 (2818) 23.89 (1274) 27.14 (434) 2.04 (616) 32.27 (2140) .71 (8382) 2 2.44 (1716) 0.93 (1548) 5.79 (3712) 45.71 (1451) 2.68 (908) 11.32 (319) 8.74 (549) 13.42 (1135) 3.97 (7435) 3 1.19 (1528) 2.24 (1369) 7.71 (3326) 69.83 (1854) 1.20 (662) 16.20 (217) 4.99 (488) 2.82 (559) 6.57 (6578) 4 2.7 (1350) 4.06 (1236) 4.61 (2971) 45.71 (1451) 2.85 (510) 4.60 (133) 9.35 (431) 2.32 (158) 1.83 (5734) Price-to-book value 1 .27 (1898) .05 (1725) .95 (4085) .17 (2803) 2.75 (1271) 1.71 (433) .60 (616) 1.19 (2151) .45 (8321) 2 .15 (1697) .54 (1530) .54 (3667) .21 (1441) .56 (908) 1.96 (318) .49 (549) .08 (1139) .20 (7367) 3 .46 (1503) .48 (1346) .78 (3277) .10 (1844) .68 (663) .74 (216) 1.26 (488) .91 (560) .14 (6516) 4 .46 (1321) .36 (1210) .25 (2921) .21 (1441) .64 (511) .47 (132) 1.20 (431) 1.36 (158) .10 (5679) Notes: No. of observations in parentheses; Bold = significant (p < .05), NS = Not significant (p > .05); n.a. = not applicable 1) First differences are used to ensure that time series are integrated by the same order.

54

Appendix References

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——— and Kenneth R. French (2006), “The Value Premium and the CAPM,” Journal of Finance, 61 (5), 2163–2185.

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how useful are brand valuation methods for brand ......brand valuation philosophies and prior tests...

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