THE PERFORMANCE OF GLOBAL BRANDS IN THE 2008 FINANCIAL CRISIS

Johny K. Johansson
Georgetown University

Claudiu V. Dimofte*
Georgetown University*Johny K. Johansson (johanssj@georgetown.edu) is the McCrane/Shaker Professor ofmarketing and international business and Claudiu V. Dimofte (dimofte@msb.edu) is
assistant professor of marketing, both in the Department of Marketing and International
Business, McDonough School of Business, Georgetown University, Washington DC
20057. The authors thankfully acknowledge the financial support provided by
Georgetown University’s Capital Markets Research Center. Thanks also to Sanal
Mazvancheryl who provided useful insights and to Brata Yudha for data collection andcomments.

THE PERFORMANCE OF GLOBAL BRANDS IN THE 2008 FINANCIAL CRISISABSTRACTPrevious research has demonstrated that high brand equity helps reduce volatility in the stock
market. This suggests that the leading global brands should perform better than other brands in adown market, This research investigates how 58 of the top global brands fared in the stock
market downturn in the Fall of 2008. The results are not supportive of the hypothesis. On
average, the top global brands performed worse than the market. The 29 global brands withhighest equity performed slightly better than the 29 with lower equity – but lagged behind themin four daily upturns. Controlling for fundamental financial factors and industry effects showsthat global brands have no advantage over other brands in a down market.
Keywords: International marketing management, Marketing strategy and implementation,
Multinational marketing operations, Global brandingA number of recent studies have attempted to link marketing performance to stock
market performance (e.g., Srinivasan and Hanssens 2009; Krasnikov, Misra and Orozco 2009).
The studies are part of a wider effort to generate marketing metrics that link marketing
expenditures to bottom-line results. Such analyses have consistently shown that a firm’s brandassets have a significantly positive impact on stock market performance (Aaker and Jacobson
2001; Mizik and Jacobson 2009). One result emerging is that high brand equity lowers the risk
associated with stocks. For example, one recent finding is that high brand equity reduces
volatility and thus risk (Rego, Billett and Morgan 2009).
This reduced risk effect should be particularly advantageous in a downturn. When thestock market trends downwards, investors are likely to search for less risky investments, andhigh equity brands could provide more financial security. If so, strong brands should fare better
than other brands during a crisis.
The year 2008 offers a particularly dramatic example of a stock market downturn,especially in the four-month period from early September (Lehman Brothers failed on September
15) through the end of December. With a 38.5% loss, 2008 was the worst year for the Standard& Poor’s 500 since 1937 and the worst since 1931 for the Dow Jones Industrials, which dropped
33.8%. The vast majority of stocks (almost 9 out of 10 of those in the S&P 1500 and more than
90% of those in the S&P 500) lost value during the year. On average, these losing stocks
dropped more than 40% of their value and almost $7 trillion in market value was wiped out.
Shares of large companies with value-priced stocks, generally considered a safer part of themarket, lost 38% of their value as measured by the Vanguard Value exchange traded fund. 1

1

See www.vanguard.com for the data quoted here. The numbers come from on-line data readily available also on

Google Finance, Yahoo Finance, Bloomberg and other sources.

1

Every major stock market in the world declined, such that the basic tenets of diversifying aportfolio simply did not apply in 2008.
The Fall 2008 downturn provides a natural setting for testing the effect of brand equity on
share prices and volatility further. The current research investigates whether firms with strong
global brands really performed better than the market in this critical period.

THE DATAThe brands selected for the study were all part of the 100 “Top Global Brands” publishedby Business Week on September 18, 2008. These data come from Interbrand, a branding
consultancy based in London and New York specializing in calculating global brand value. 2
Since some of the brands are only a part of a firm’s brand portfolio (e.g., Gillette now belongs toProcter & Gamble), and since privately owned firms are not listed in the stock market (e.g.,
IKEA), not all the 100 brands could be included. For practical reasons, we also limited the data
to shares listed on the three American stock exchanges–NYSE, ASE, and NASDAQ. Although
this necessarily limited the generalizability of the results, a number of non-American brands
were included. The final sample of 58 brands included 10 European and 5 Japanese brands, inaddition to 43 North-American brands. The list of brands as well as their Interbrand rankingsand scores are presented in Table 1.
-----------------------------Insert table 1 about here------------------------------

2

See www.interbrand.com for the full list of the 100 brands. The degree of globalness likely varies across these 100

brands, but for our purposes here we simply follow the Interbrand’s choice to label them as “global” (the degree of
globalness is discussed by Steenkamp, Batra and Alden 2003).

2

Stock Returns
The typical measure of a stock’s performance in a certain period is its share price“return,” usually calculated as the percent by which its share price has either increased or
decreased during the period. In this work, we used daily closing prices on the New York Stock
Exchanges for the period between September 1 and December 31, 2008. This four-monthinterval encompassed most of the downturn in the financial crisis of 2008. The daily return isthen computed as the share price at the end of day 2 minus the price at the end of day 1, divided
by the price at end of day 1. To get percentages, we multiply by 100. 3
Counting the trading days when the markets were open during the four-month period
yielded a time-series of 85 daily returns for each firm. The same calculations using the S&P 500
index yielded the time series for the market returns during this period. Regressing daily returns
against the market returns yielded the well known “beta” of the stock (Fama and French 1993).

Industry Returns
Research on marketing effects on share prices typically starts by adjusting the observed
returns for financial “fundamentals” (Srinivasan and Hanssens 2008). The basic adjustment is torelate returns to the market as a whole. The first fundamental adjustment is to account forindustry effects, typically by using dummy variables for each industry (e.g., Rego et al. 2009).
In the present case, where the sample was limited to the top global brands and several industries
were represented by only one brand, such a procedure was not feasible.
The early study by King (1966) was one of the first to establish industry effects on stock
prices. Later research confirmed King’s basic argument that in addition to the basic market
3

An alternative measure, also common, is to take the natural log of price at the end of day 2 minus the natural

logarithm of price at the end of day 1. The two procedures yield very similar results.

3

effect there is also a significant industry effect that needs to be accounted for. In our case, inparticular, industry variables were necessary since they would include brands with lower equity
scores.
The traditional way to classify industries is using the three- or four-digit SIC codes. For
example, Fama and French (1997) identify 48 industries based on SIC codes, and others haveoffered a revised version which yields a more compact set of 14 industries (Barth et al. 2001).
However, the SIC codes do not necessarily yield industry groupings which represent theconsumer or industrial markets in which the firms operate. For example, in our case, FedEx(SIC 4513) and UPS (SIC 4215) would not be classified together, and neither would Sony (SIC
3651) and Panasonic (SIC 6719). We opted instead for the Google Finance industry groupings,
one of the several on-line services which offered slightly more market-oriented groupings. In
this way we classified our 58 brands into 33 industries.
To calculate the industry returns for each of the 33 industries, we first identified each of
the brands in the industry, which Google Finance also listed. The number of firms in an industry
varied considerably, from as few as 4 (air couriers) to as many as 392 (regional banks). We thenused the Wharton WRDS data base which draws on Compustat to extract the share prices foreach of the brands in the industry from September 1 to December 31, 2008. The percentage
returns were then computed for each brand in the industry. A weighted average was used to
calculate the mean daily return for the industry in the period, the weights based on the market
cap of the firm (its share price multiplied by the number of shares outstanding). The result was
33 time-series, each with 85 observations, of the mean industry returns during the period. We
also computed an adjusted set of industry mean returns, where each of the 58 brands was

4excluded from the computation. When one firm is a large player in an industry, its returns will
dominate the industry average.

Financial Variables
There are several financial variables that need to be accounted for before conclusions canbe properly drawn about brand equity effects. Here we again followed standard financial
analysis (see Fama and French 1993).
Total assets. Larger firms tend to be more stable and provide more security for investors.
Their returns typically show less variability and thus less volatility, which should be attractive
during a downturn. Following financial analysis, the measure used is natural logarithm of total
assets, in order to protect against outliers dominating the results.
Age. Much like total assets, older firms tend to be more established and have alreadyproven capable to withstand disruption across time. In a sense, they are “survivors” that will be
more likely to attract investors in a downturn. This variable was coded as “1” for firms with lessthan 25 years since establishment, “2” for those between 25 and 50 years, and “3” for firms
established more than 50 years ago.
Leverage. Leverage is the degree to which borrowed funds are used to operate a business.
The debt/equity ratio should have a negative effect on returns in a crisis, when high leverage islikely to expose investors to more risk.
Credit rating. The firm’s credit rating is one signal of how robust the stock is. A
stronger credit rating should give confidence to investors in a downturn. It was coded as “10”for an AAA rating, down to “1” for a DDD.

5

ROA. Return-on-assets is a measure of the degree to which the firm is able to utilize its
resources efficiently. If historical returns-on-assets have been high, the stock is likely to weather
a storm better than other firms.
Diversification. The more businesses the firm operatess in, the lower its stock’s risk,
which should be attractive in a downturn. Following Rego et al. (2009), this variable was coded“1” for a single industry firm, and “2” for a firm with more diversification. We also introduced asecond measure of diversification – the percentage of revenues generated outside of NorthAmerica. The geographically diversified firm should have a better risk profile.

The Brand Equity Measure
The brand equity measure used is already well known among academicians andpractitioners.4 Given that Interbrand scores come from proprietary sources, however, not all thesteps in the computations have been published. Here we can only present some of the basic steps.
Interbrand.5 The Interbrand formula first deducts from total company earnings (1) thecosts of brand sales, (2) marketing costs, (3) overhead costs, including depreciation, (4) a charge
for capital employed (5-10% of capital), and (5) taxes. The result is then adjusted to account for
non-brand factors, such as distribution strength, in order to avoid attributing revenues to thebrand when in reality other factors are responsible for the earnings. The resulting brand earnings
are then further adjusted by brand strength.
Brand strength involves several factors: leadership (25%) or the brand’s ability todominate a market (positive factor); stability (15%) assessing how long the brand has been
4

It should be acknowledged that Interbrand calls the scores “brand value” rather than “brand equity.” The latter has,ironically enough, gained a more behavioral than financial slant. In this paper we treat the names as synonymous.
5

Source: www.interbrand.com .

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established (positive factor); market volatility (10%) accounting for the risk of new technology
and low entry barriers (negative factor); reach (25%) or the geographic spread of the brand sales(positive factor); trend (10%) capturing the upward (or downward) trajectory of the brand;
support (10%) assessing the consistency of marketing support (positive factor); and legal
protection (5%) dealing with the firm’s problems with protecting the brand name in different
markets (negative factor). These factors are combined according to the percentage weights into abrand strength index which is used to derive a discount factor for the projected future earnings.
A strong brand with a high index score will be sure to yield strong future earnings, and thus its
discount rate is small, around the 5% which is typical of a low-risk investment. A weaker brandwill have a higher discount rate, reflecting the higher risk of its future earnings. The resulting
net present value for each brand is the number used to derive the list of the top 100 brands.
The Interbrand data are typically collected and analyzed annually. For the 2008 year,
Interbrand published its top 100 rankings in the Business Week issue of September 18. Thus, atthe very beginning of the time period selected here, September 1, the 2008 Interbrand rankings
were not yet available. We considered whether the 2007 Interbrand data should be used. But the
rankings show considerable stability over time, with 54 of the selected 58 brands appearing inboth years, and a correlation of .996 between the 2007 and 2008 brand value scores. Although
the analysis could have been done using the 2007 data, we deemed it more appropriate to use the2008 data since the customer data would come from 2008.6

6

According to Business Week’s September 18 issue, the brand values were based on data collected during the 12months prior to June 30, 2008. This meant that the later developments were not factored into the brand valuations.

7

METHODOLOGYHow one should relate annual brand value data to daily share prices is not obvious. The
explanatory factor, annual scores of brand equity, is basically constant over the days analyzed,
making any assessment of its impact on any one individual brand impossible. It is thus
necessary to compare daily returns across brands. Such cross-sectional analysis forces one to use
summary measures of the swings in the daily share prices for each brand. While the raw dataoffers 85 observations of daily returns over the four months, in the actual analysis of brandeffects, we have only one summary measure. Clearly, a lot of information gets lost.
To try to alleviate this problem, we have chosen to test several possible summary
measures. First, we used the percentage difference between share prices at the end of the period
and the beginning of the period–Sep-Dec–to capture the cumulative effect of the downturn,
expecting the difference to be lower for these strong global brands (Barber and Lyon 1997). We
also used the Mean of the daily returns over the four-month period, but since results are virtually
identical, we do not report them here. A second measure is the standard deviation of the returns,
a common way to capture Volatility in finance. Volatility can be interpreted as a measure of risk,
and based on previous research (e.g., Rego et al. 2009) we expected high brand values to help
reduce volatility.
We also used the beta as a summary measure, computing a four-month brand beta byregressing daily returns against S&P 500 daily returns for the period. A firm that showed less
impact from the downturn should have a beta below 1.0. If its beta is high (over 1.0), the impact
from the crisis is exacerbated. Given earlier findings, we would expect global brands withhigher equity scores to do better, meaning that their betas should be lower.

8

To incorporate industry effects on the share prices, we introduced industry averages foreach measure – Sep-Dec drop and Volatility – as explanatory factors. In the case of the betas,
we regressed each of the 33 industries’ four-month returns (with the focal brand’s contribution
eliminated) against the S&P returns, to derive 33 “industry betas.” An industry beta–like thefirm’s own beta–should be less than 1.0 for industries less affected by the market downturn, andabove 1.0 for industries influenced more. We expected each high equity global brands to showless of an impact than its industry, which includes also low equity global brands as well as more
local brands.
The analysis was done with gradually more complex specifications. First, the average
performance of the 58 global brands was compared to the market’s performance as measured by
the S&P 500. Then we compared the average performance to the respective industry
performance, to assess the extent to which industry effects could account for some of the results.
We also performed a median split of the 58-firm sample into high and low scorers, and compared
their average performance to the market and the industry.
We then introduced financial variables to evaluate whether the results could be due not tobrand equity scores but financial fundamentals instead. These models consisted of regressing
each summary measure first against the financial variables alone, and then with the brand equity
measures included. This made it possible to test whether the addition of the brand equity
measures was significant.
We also decided to go deeper into the analysis of the daily prices during the four month
period. We wanted to test whether whatever results we could uncover with the summary
measures would hold up in particularly extreme situations. Consequently, using the S&P 500
index, we selected the four largest daily market drops and the four largest daily market gains

9during the four month period. We then used the percentage daily returns during these eightoccasions as a measure of performance. Since this measure pivots at 0, we ran the analysis
separately for gains and for losses, again using the gradual procedure – first basic averages, thenregression models with financial variables only, then adding the brand measures - to test thesignificance of the high equity of the global brands.

THE FOUR-MONTH PERFORMANCE
Share Price Performance
The initial analysis compared the average drop for the 58 brands over the four-month
period to that of the market and the rest of the respective industry over the same timeframe. The
results were not in line with our hypothesis that these brands would beat the market.
The S&P 500 index, the standard market index in financial analysis, dropped 30.27%
between September 1 and December 31, 2008.7 The average drop during the same period for the
58 firms in our sample was 34.80%. A t-test confirms the difference to be significant (p < .04).
Selecting out the 12 brands representing institutions from the hard hit financial sector showedslightly better results, with a drop of 31.36% for the remaining 46 brands (still worse than themarket, although not significantly so–p = .56). The global brands did not do better than themarket, and if anything they did worse.
The next step was to analyze the possible industry effect. The selected brands may havecome from particularly exposed industries. The analysis first involved comparing the averageindustry performance of the 33 industries against the market. The mean drop for the industries(with the focal brand excluded) was 30.36%, virtually identical to the market. Remarkably,
7

The perhaps more popular index, Dow-Jones Industrial Average, only covers 50 firms. It dropped 22.3% in thefour-month period, somewhat less than the S&P 500.

10

looking at the previous results, the global brands dropped significantly more (p < .04) than theirrespective industries. The correlation between the firms’ returns and the industry returns was asignificantly positive (p < .03) but relatively low r = .29. These global brands are not following
their industries very closely. 8
We next split the 58 brands into high and low scorers, using a cutoff of the medianInterbrand score. Here, the global brand equity had slightly more positive impact in accordancewith our basic hypothesis, as can be seen in Table 2.
-----------------------------Insert table 2 about here-----------------------------The 29 brands above the median show non-significant differences when compared withthe market and their industries. By contrast, the 29 brands with below-median values fromInterbrand scored significantly worse than both the market and their respective industries (seeTable 2). The stronger among the global brands did not suffer quite as much as the weaker of the
top global brands. The difference between the above and below median brand returns was
significant (p = .05).
We next introduced the financial fundamentals to help explain the results. In addition to
the financial variables already described, we also introduced two additional dummy variables.
One captured the 15 Foreign brands from Europe and Japan, and another identified the Financial
brands, combining investment services, insurance companies, and banks to capture the strong
financial character of the crisis. We ran regression models in two steps, first with the financial

8

A natural question here is the degree to which the brands correlate with the market. We will cover the betas inwhat follows. Here, where the analysis is cross-sectional with summary measures, the S&P 500 market drop is
essentially a constant for all the brands.

11

variables only and then with the Interbrand scores added, to identify their incremental
contribution. The results are given in Table 3.
-----------------------------Insert table 3 about here-----------------------------As can be seen in the Table, these regression models are not very strong. None of thefinancial fundamentals enter significantly to help explain the share price performance over theperiod. There is clearly a possibility of multicollinearity obscuring single variables’ impact, butsince the emphasis here is upon the incremental impact from the brand value scores, we will not
discuss the estimates in detail, but instead make sure that the standard fundamental factors areaccounted for. It is clear that the marginal improvement from high equity scores is very small. 9
We did attempt one improved specification of the model, testing whether the foreign
brand or the portion of revenues coming from the North-American market could significantly
affect firm performance. Neither of the two variables entered the models significantly (all pvalues above .10). The data are very limited, but for these brands the diversification of therevenue stream does not seem to matter much–the crisis was of course quite global in any case.

The Impact on Volatility
The next step in the analysis was to assess whether the top global brands showed lessvolatility than the market. Volatility, as measured by the standard deviation in the daily pricechanges, is a common measure of risk in financial markets. The hypothesis here was that the
global brands with brand equity would show less volatility and thus be less risky.

9

It is important to note that because the included 58 brands all have relatively high scores on the Interbrand measure,

this does not mean that Interbrand scores overall have no impact on share prices. This is not an analysis of thecomplete range of Interbrand scores, and thus is not an assessment of the scores’ validity.

12

The volatility in the market, measured as the standard deviation of S&P 500 of the daily
returns across the four months from September 1 to December 31, 2008, was 4.07%. The
average volatility of the 33 industries (again without the focal brand included) in that period was
4.58%, significantly higher (p < .01). The selected 58 brands, remarkably, had an even higher
degree of volatility, at 5.61%, significantly higher than either the market (p < .001) or therespective industries (p < .01). Selecting out the financial brands yielded some improvement,with average volatility for 42 brands at 4.76%, still significantly higher than the market (p< .001), but not from their industry (p = .29). These results suggest that global brands with highbrand values not only dropped further during the crisis, but actually showed higher risk than theother brands in the market and in their industry.
We again split the data into brands above and below the median. The results were in linewith the previous analysis, but strikingly different from the initial hypothesis (see Table 4).
-----------------------------Insert table 4 about here-----------------------------As can be seen in the Table, the higher equity global brands show somewhat lower
volatility than lower equity brands–but these top global brands are still showing higher risk thantheir industries and the market.
We then replicated the two regression runs with the financial variables, one with and one
without the Interbrand values. The results are presented in Table 5.
-----------------------------Insert table 5 about here-----------------------------Again, the results are not very strong. The adjusted R-squares are improved, and thedummy variable capturing the financial institutions is clearly significant, showing the greater risk

13

associated with them. But the brand equity scores do not lower risk significantly, although thenegative effect is in the hypothesized direction of lower risk. Strikingly, the association with theindustry (calculated with the focal brand excluded) is again weak.

Analyzing the Betas
We next examined the market betas of the firms and the related industry betas. As
discussed earlier, the betas are the regression coefficients of the share price changes against themarket changes, and basically measure the degree to which a firm’s shares co-vary with themarket. They can be computed for various time horizons, such as a year, using different change
periods, such as daily or weekly. Here, we calculated the four-month betas for the firms and theindustries using the 85 daily price changes.
The average beta for the 58 firms over the four months was equal to 1.07. A firm that
follows the market exactly would have a beta of 1.0. The difference is not significant (p = .19),
suggesting that these firms on average tend to follow the market. Similarly, the 33 industry-tomarket betas averaged 1.03, also not significantly different from the market. A beta above 1.0
indicates higher risk, and a beta below 1.0 indicates less risky shares. In terms of the betas,
therefore, these firms were not more or less risky than the market.
The average difference of .035 between the firm and the industry beta is not significantly
different from zero (p = .55). Still, the difference is in the direction opposite to our expectations.
These 58 brands are, if anything, riskier than their respective industries.
Splitting the sample into the above-median and below-median halves did produce somepositive results for the high scorers. The average betas are shown in Table 6.
-----------------------------Insert table 6 about here14

-----------------------------None of the average betas in the Table are different from the market. However, the beta
for the above-median brands is significantly lower (p< .04) than that of the below-median brands.
Thus, within the 58 brands, those with higher equity scores show less riskiness than the brands
with lower scores.
We next re-ran the two regressions with financial fundamentals included. The results are
given in Table 7.
-----------------------------Insert table 7 about here-----------------------------Again, the results are not impressive. The Financial dummy variable is the only
significant variable, although the total assets estimate weakly suggests that firms with larger
assets were generally riskier. The Interbrand values do not make a significant difference.

FOUR DAILY UPTURNS AND FOUR DOWNTURNS
These results clearly do not support our expectations of superior stock market
performance for high equity brands. Therefore, to investigate in more depth the events during
the four-month period, we selected the market’s four largest daily upturns and the four largest
daily downturns according to the S&P 500. The hypothesis was that by looking at lessaggregated events, we might be better able to identify the brand equity effect. Again, given
published findings by Rego et al. (2009) and others, we expected in particular the dramatic daily
downturns to show the resilience of strong brands.
As before, we first analyze how the 58 brands performed on average, then how the aboveand below median equity brands performed, and then estimate the incremental impact of equity

15

scores once financial fundamentals have been accounted for. The data matrix here was“stacked” to make up a 4x58 (thus N = 232) data set, separately for the four loss dates and thefour gain dates.

Four Large Losses
The S&P 500 mean return during the four drops was -8.60%. The mean drop for the 33
industries involved was -8.35%. The mean drop for the shares of the 58 brands was -8.58%,
close to the market overall, but worse than their respective industries (although not statisticallysignificant: p = .46). The median split revealed more positive findings for Interbrand scores,
however (see Table 8).
-----------------------------Insert table 8 about here-----------------------------As can be seen from the high and low scorers, the Interbrand measures successfully
separated brands that lost less than the market from those that lost more than the market.
Nevertheless, in either case the differences with the market and the industry performance are notstatistically significant. Again, the average for these 58 top global brands is not different fromthe market or their industries–although the very high equity brands do better than the relatively
lower equity brands.
Again we introduced the financial fundamentals in order to try to explain theperformance returns over the four drops, and to identify any brand equity impact. We also
included the S&P 500 drops for each of the four loss occasions. The regression results areshown in Table 9.
-----------------------------Insert table 9 about here16

-----------------------------The results are very similar to earlier results in terms of brand equity impact–it isvirtually absent. On the other hand, these results show a closer connection between the 58
brands and their industries than earlier results. They also show how both industry and market
affect these brands, and also the role of size and age, in addition to the powerful impact on thefinancial institutions.10

Four Large Gains
Turning to the four largest one-day gains in the Fall of 2008 reveals an even gloomier
picture for the high equity brands. The market as a whole and the 33 industries climbed higher
than the average of the 58 brands. The S&P 500 index gained 8.90%, whereas the top global
brands rose only 7.62% (p < .02). Industry gains were at 8.82%, with the difference between
industry and firms also significant (p < .02).
Comparing brands above and below the median brand equity score showed a remarkable
result (see Table 10).
-------------------------------Insert table 10 about here-------------------------------As the Table shows, the global brands with particularly high equity gained significantly
less in the four upturns. They did worse than the market, worse than their industries, and worse
than the brands with lower equity levels. Clearly, the strongest global brands did not get toparticipate very much in these four large upward swings in the market.
10

The correlation between the industry and the market for the eight daily instances was a high r = .83, indicating

possible multicollinearity. However, since both enter with significant estimates, we kept both variables in theequation. In any case, our focus is on the brand equity contribution.

17

Finally, we ran the two regressions again with the financial fundamentals in place. The
results confirm the worst (see Table 11).
-------------------------------Insert table 11 about here-------------------------------The adjusted R-squares are very small, suggesting that the four upturns are difficult to
explain on a fundamentals basis. The only real impact comes from the market itself, with somepossible negative impact from diversification. Brand equity surely seems to have very little
impact here, and if anything high equity serves as a brake on the upswing.

SUMMARY OF RESULTS
The results can be summarized quite briefly. First, in the Fall 2008 stock market crisis
the global brands with high Interbrand value scores did not do better than the market,–if anything,
they did worse. They also did consistently worse than the rest of the industries they were in,where less prominent global brands and local brands were included. The average drop in theshare prices of the top-ranked global brands was greater than the loss in the market or in theirrespective industries.
Separating out the higher brand equity scorers among these top brands, the result issomewhat more in line with our hypotheses. The global brands with above-median scores
performed about the same as the market and their industries. The global brands with belowmedian Interbrand values did significantly worse then either the market or the industry.
In terms of volatility, similar results hold. The 58 brands were more volatile than bothmarket and their industries, against expectations that high brand equity would reduce risk.
Splitting the sample into above- and below-median equity scorers showed a somewhat

18attenuating effect, with high scorers showing less risk than low scorers, but still significantly
worse than the market and slightly worse than their industries.
As for betas, another risk measure, the 58 brands did not do significantly worse than themarket, or their industries, but they did not do better either. The above average equity scorers
did do significantly better, with lower betas, than the low scorers, but neither group beat themarket or their respective industries.
Investigating the largest four upturns and largest four downturns during the period does
little to improve this picture. The average share returns during the four losses are notsignificantly different from the negative market returns or the respective industry returns. When
split into above average equity scorers, the top global brands almost did beat the market, but notquite. They did beat the lower equity scorers significantly, however.
This is the good news. The bad news is that in the four upturns, the high brand equity
seemed to have slowed the rise in the share prices. The 58 brands gained significantly less thaneither the market or their respective industries. Furthermore, the above-median brand equity
scorers encountered a significantly lower rise in their share prices than the below-median scorers,
who were close to both market and their industries.
Finally, the regressions attempting to clarify the incremental impact of brand equity over
and above financial fundamentals, showed consistently insignificant contributions from theInterbrand scores. The regressions were uniformly weak, with consistent impact only from thefinancial institutions indicator variable.
.
DISCUSSION

19

Given all the previous findings about positive branding effects on stock markets, a firstnatural discussion point is whether these results might be spurious. The data are obviously all
publicly available, and although the compilation is a tiresome task, especially for the industry
averages, we have checked them for reliability. The share price data for the 58 firms are
relatively simple to compile, and the other financial variables are not particularly difficult, eventhough some judgment is required. This is offset by using fairly simple and standard cutoffs,
such as was used for the diversification dummy, the foreign ownership dummy, and others suchas the age variable.
We focused on the four-month period from September 1 to December 31, 2008. This
was the most critical period of the 2008 year, but several things did develop earlier in the year, so
the starting point is somewhat arbitrary. We used the September 15, 2008, bankruptcy filing by
Lehman Brothers as a natural starting point, backing up two weeks to the beginning of the month.
As for the cutoff date of December 31, 2008, this seemed also quite natural, although someindices did start going up even before that date. On the whole, we think we captured the maincrisis period.
There are difficult problems in weighing together the firms to get the industry data.
Following standard financial procedures, we used the market capitalization of the firms (shareprice times outstanding shares) as the basis, and this number of course varies across the fourmonths forcing us to use averages. But any weighting scheme (book value, total assets, or total
revenues) has its own advantages and drawbacks. Also, the industry identification using theGoogle Finance categories can be criticized, although other alternatives (SIC codes, Bloomberg,
and others) have their own strengths and weaknesses. We also ran the industries without the20

focal brands’ contributions–the correct approach so as not to confound the measure, but one
which undoubtedly lowers the estimated industry impact.
Selecting the Interbrand Top 100 Global Brands as the sample frame is of course a clear
deficiency in terms of representativeness. These are certainly not all the global brands in theworld’s marketplace–they were simply the ones for which equity data were available. Thus, itwould not be correct to extend the findings to the larger universe of global brands but limit them
only to those with high Interbrand scores. Even though we compare these brands to theirindustries, it is not clear whether this is the correct comparison. The industries include not only
other global brands but also local brands. Thus, when the industry seems to perform better than
the selected global brands (as we have shown in some cases above), it might be due either to theweaker global brands or local brands (or both). Thus our results cannot be interpreted cleanly
when it comes to the underlying explanation. However, the fact that high equity global brands
do not perform as well as one might expect, given past research, still stands.
One can also question whether the consequent choice of the 58 brands somehow distorted
the results. Some strong brands belong to conglomerate companies (P&G is an obvious
example) and in that sense our sample is not representative of all type of brands. Global brands
from foreign countries were clearly under-represented (e.g., BMW, Mercedes, Samsung andothers) because of the limitation to American stock exchanges. We might have over-represented
the financial brands, and even though we tried to control for that (with the relevant industry
averages and with the Financial dummy variable) it is possible, albeit unlikely, that somefinancial-based bias still persists.
We used the Interbrand 2008 brand equity measures even though they did not actually get
published until September 18 that year. Here we think the effect is very minimal, as these scores

21

show very little movement over any two years, and as noted, the correlation with 2007 scores is avery high .996. The median split we used might seem–and is, in fact–arbitrary, but it is difficult
to find a good rationale for any other split here.
If the data are not a problem (except, perhaps, for a noisy industry variable), can themethodology be faulty? The measures used to summarize the four-month data clearly ignore a
lot of variation, but it is hard to see how to avoid this when the brand equity scores are annual
and not varying over the period. This is of course also why we decided to check a few criticalincidents over the four months, picking four very highs and four very low shifts in the stock
market. One might question whether averaging over the four gains and the four losses makes
sense, but we think just picking one daily shift for each could be quite misleading simply bychance. In the analysis we have used very simple statistical tools, relying only on simple t-tests
and linear regression models. It is of course possible that one might find some new informationby using more advanced tools, but it is not clear that the basic research question posed in thisstudy requires that.

CONCLUSION
There is no evidence that these top global brands in their respective industries
performed better than global or local competitors with lower brand equity. In fact, the evidence
seems to suggest the opposite–brands which were not ranked among the top in their industries(and not among the top on a global basis) did not perform any worse and might in fact have
performed better. The industries’ average performance during the four months was close to themarket (thus not very good) but still slightly better than the average of the selected top firms.

22

The lack of a significant industry effect in the four-month performance of the selected
brands suggests that the firms are not very closely linked to their respective industries. This isperhaps an artifact of the particular industry classification used, but nevertheless raises a question.
Is it possible that in a downturn, it is the large and well-known brands that take most of thebeating?11
On the theoretical side, this study questions whether a strong brand is in fact a positive
asset for share prices. Lower profile brands may do as well or better, because the leading brands
may be more at risk than the anonymous also-rans. However, a more radical interpretation ispossible. The consistent impact of the financial indicator variable in the regressions estimatedhere, coupled with the lack of any clear impact of other financial fundamentals, suggests that thestock market crisis in the Fall of 2008 was in fact an abnormal event in all respects. The“normal” market rules no longer applied. If so, the lack of positive impact from the top global
brands’ high equity scores on the stock market is simply a reflection of what happens whenmarkets fail, and the results offered here should not be used to generalize beyond the crisis.

11

A run on the shares of a smaller brand is not likely to create the same downward momentum as a run on a large

brand. In a similar vein, the largest global brands are the ones singled out by anti-globalization activists.

23

REFERENCES
Aaker, David A. and Robert Jacobson (2001), “The Value Relevance of Brand Attitudes in HighTechnology Markets,” Journal of Marketing Research, 38, (4), 485-93.
Fama, Eugene F. and Kenneth R. French (1993), “Common Risk Factors in the Returns on
Stocks and Bonds,” Journal of Financial Economics, 33, (1), 3-56.
-------- and -------- (1997), “Industry Costs of Equity,” Journal of Financial Economics, 43, (2),
153-93.
Barber, Brad M. and John D. Lyon (1997) “Detecting long-run abnormal stock returns: The
empirical power and specification of test statistics,” Journal of Financial Economics, 43, (3),341-372.
Barth, Mary, Donald P. Cram and Karen K. Nelson (2001), “Accruals and the Prediction of
Future Cash Flows,” The Accounting Review, 76, (1), 27-58.
King, Benjamin F. (1966), “Market and Industry Factors in Stock Price Behavior,” The Journalof Business, 39, (1),139-90.
Krasnikov, Alexander, Saurabh Misra and David Orozco (2009), “Evaluating the Financial
Impact of Branding Using Trademarks: A Framework and Empirical Evidence,” Journal ofMarketing, 73, (1), 154-66.
Mizik, Natalie and Robert Jacobson (2009), “Valuing Branded Businesses,” Journal ofMarketing, Vol. 73, (1), 137-53.
Rego, Lopo L., Matthew T. Billett and Neil A. Morgan (2009), Consumer-Based Brand Equity
and Firm Risk,” Journal of Marketing, 73, (1), 47-60.
Srinivasan Shuba and Dominique M. Hanssens (2009), Marketing and Firm Value: Metrics,
Methods, Findings, and Future Directions,” Journal of Marketing Research, 73, (2), 293-312.
Steenkamp, Jan-Benedict E.M., Rajeev Batra, and Dana L. Alden (2003), “How Perceived Brand
Globalness Creates Brand Value,” Journal of International Business Studies, 34 (1), 53-65.

24

TABLESTABLE 1: Sample of 58 of Interbrand’s 2008 Top 100 Global Brands
Brand

Value* Rank

Brand

Coca Cola
IBM
Microsoft
GE
Nokia
ToyotaIntel
McDonalds
DisneyGoogleHP
American Express
Cisco
Citibank
Honda
Oracle
Apple
Sony
Pepsi
HSBC
Nike
UPS
SAP
Dell
Canon
J.P.Morgan
GoldmanKellogg's
UBS

66667
59031
59007
53086
35942
34050
31261
31049
29251
25590
23509
21940
21306
20174
19079
13831
13724
13583
13249
13143
12672
12621
12228
11695
10876
10773
10331
9710
8740

Morgan Stanley
PhilipsThomsoneBay
Accenture
Siemens
Ford
Harley Davidson
AIG
AXA
Heinz
Colgateamazon.com
Xerox
Yahoo!
Caterpillar
Avon Products
Gap
Panasonic
Tiffany
AllianzBP
Starbucks
ING
Motorola
Johnson & Johnson
Marriott
Fed EX
Visa

1
2
3
4
5
6
7
8
9
10
12
15
17
19
20
23
24
25
26
27
29
30
31
32
36
37
38
39
41

Value* Rank
8696 428325 43
8313 447991 467948 477943 487896 49
7609 507022 547001 556646 566437 576434 586393 595496 655288 685264 604357 774281 784208 804033 82
3911 843879 853768 86
3721 873582 92
3502 963359 993338 100*Brand Values in Millions of U.S. Dollars.

25

TABLE 2: Percent Share Price Drop for High and Low Equity Brands Sep-Dec 2008

Interbrand Values

Share Price % change

Industry % change

S&P 500 % change

Above median:-31.55%-31.95%-30.27%-38.05%*-28.76%-30.27%(N = 29)

Below median:(N = 29)*Significantly worse than both the market (p < .03), and the industry (p < .01).

26

TABLE 3: Two Regression Models of Share Price Changes

Dependent variable: Percent share price change, Sep. 1 - Dec. 31, 2008
Independent
Variables

Estimated coefficients (sig. levels in parentheses)
MODEL 1

MODEL 2-34.940 (.134)-32.120 (.182)

Industry change.004 (.973).008 (.952)

Ln(Total assets)-.544 (.771)-.893 (.654)

Age5.700 (.058)

5.617 (.064)

Leverage-.053 (.652)-.058 (.624)

Credit rating

3.335 (.167)

3.010 (.229)

Return-on-assets.702 (.985)-1.920 (.959)

Number of Industries-7.482 (.088)-7.373 (.096)

North-American %.041 (.736).047 (.706)

Financial-16.540 (.070)-14.798 (.129)

Foreign-1.637 (.679)-1.047 (.800)

Intercept

Diversification

Interbrand.085 (.596)

Adj. R2

.163

.150

N

58

58

27

TABLE 4: Volatility among High and Low Equity Brands

Interbrand Values

Firm Volatility

Industry Volatility

S&P 500 Volatility

Above median:

5.15%**

4.51%*

4.07%

6.10%***

4.65%*

4.07%(N = 29)

Below median:(N = 29)* Not significantly different from the market.
** Significantly worse than the market (p < .01) but not worse than the industry (p = .10).
*** Significantly worse than the both the market (p < .001) and the industry (p < .02).

28

TABLE 5: Two Regression Models of Share Price Volatility

Dependent variable: Standard deviation of share price changes, Sep. 1 - Dec. 31, 2008
Independent
Variables

Estimated coefficients (sig. levels in parentheses)
MODEL 1

MODEL 2

Intercept

2.088 (.435)

1.236 (.648)

Industry volatility.254 (.151).249 (.155)

Ln(Total assets).366 (.132).484 (.058)

Age-.350 (.329)-.324 (.360)

Leverage-.010 (.490)-.008 (.561)

Credit rating-.567 (.063)-.467 (.128)

Return-on-assets

2.529 (.574)

3.338 (.457)

Number of Industries.522 (.317).483 (.350)

North-American %-.008 (.595)-.010 (.521)

Financial

3.643 (.002)

3.082 (.009)

Foreign-.432 (.437)-.615 (.278)

Diversification

Interbrand-.027 (.152)

Adj. R2

.493

.505

N

58

58

29

TABLE 6: The Average Betas for High and Low Equity Brands

Interbrand Values

Firm Betas

Industry Betas

S&P 500 Baseline

Above median:

1.005*

0.994

1.0

1.130

1.071

1.0

(N = 29)

Below median:(N = 29)* Significantly different from the betas of firms below the median (p < .04).

30

TABLE 7: Two Regression Models of the Firm Betas

Dependent variable: The firm-to-market betas, Sep. 1 - Dec. 31, 2008
Independent
Variables

Estimated coefficients (sig. levels in parentheses)
MODEL 1

MODEL 2

Intercept.218 (.594).121 (.772)

Industry betas.172 (.138).165 (.155)

Ln(Total assets).064 (.091).078 (.052)

Age-.051 (.355)-.048 (.384)

Leverage-.002 (.427)-.002 (.480)

Credit rating-.567 (.063)-.467 (.128)

Return-on-assets.161 (.820).246 (.729)

Number of Industries.052 (.264).047 (.551)

North-American %-.001 (.655)-.001 (.604)

Financial.516 (.003).451 (.014)

Foreign-.032 (.684)-.053 (.518)

Diversification

Interbrand-.003 (.278)

Adj. R2

.514

.516

N

58

58

31

TABLE 8: Mean Share Loss among High and Low Equity Brands

Interbrand Values

Firm Loss

Industry Loss

S&P 500 Loss

Above median:-7.93%*-7.96%**-8.60%-9.22%-8.73%-8.60%(N = 116)

Below median:(N = 116)* Marginally different from the market (p = .09) but significantly different from below-median brands(p < .001).
** Not significantly different from the market (p = .16) but significantly different from below-medianindustries (p = .09).

32

TABLE 9: Two Regression Models of the Four Loss Occasions

Dependent variable: Percentage share loss for the firm
Independent
Variables

Estimated coefficients (sig. levels in parentheses)
MODEL 1

MODEL 2

Intercept

10.725 (.026)

11.084 (.022)

S & P 500 change

1.347 (.004)

1.347 (.004)

Industry change.175 (.026).175 (.026)

Ln(Total assets)-.782 (.002)-.798 (.001)

Age.880 (.028).848 (.035)

Leverage.007 (.642).006 (.711)

Credit rating.519 (.115).443 (.193)-3.092 (.516)-3.789 (.433)

Number of Industries.645 (.273).623 (.291)

North-American %-1.143 (.486)-.953 (.565)

Financial-4.087 (.001)-3.900 (.001)

Foreign.192 (.729).285 (.614)

Return-on-assets
Diversification

Interbrand.017 (.377)

Adj. R2

.373

.372

N

232

232

33

TABLE 10: Mean Share Gain among High and Low Equity Brands

Interbrand Values

Firm Gain

Industry Gain

S&P 500 Gain

Above median:

6.24%*

10.14%**

8.90%

8.99%

9.19%

8.90%

(N = 116)

Below median:(N = 116)* Significantly different from the market and industry (p < .001 in each case) and significantly different
from below-median brands (p < .001).
** Not significantly different from the market (p = .17) and not different from below-median industries(p = .30).

34

TABLE 11: Two Regression Models of the Four Gain Occasions

Dependent variable: Percentage share gain for the firm
Independent
Variables

Estimated coefficients (sig. levels in parentheses)
MODEL 1

MODEL 2

Intercept-2.551 (.666)-3.229 (.588)

S & P 500 change

1.165 (.000)

1.159 (.000)

Industry change-.088 (.210)-.083 (.242)

Ln(Total assets).685 (.156).712 (.142)

Age.013 (.987).078 (.922)

Leverage-.101 (.737)-.008 (.801)

Credit rating-.578 (.366)-.419 (.528)

Return-on-assets

1.408 (.880)

2.825 (.764)

Number of Industries-2.813 (.016)-2.771 (.018)

North-American %2.605 (.413)

2.191 (.496)

Financial.168 (.943)-.179 (.940)

Foreign.816 (.438).614 (.569)

Diversification

Interbrand-.034 (.372)

Adj. R2

.098

.097

N

232

232

35