Journal of Marketing Research
Vol. XLVI (June 2009), 293–312
293
© 2009, American Marketing Association
ISSN: 0022-2437 (print), 1547-7193 (electronic)
*Shuba Srinivasan is Associate Professor of Marketing, School of Man-
agement, Boston University (e-mail: [email protected]). Dominique M.
Hanssens is Bud Knapp Professor of Marketing, Anderson School of Man-
agement, University of California, Los Angeles (e-mail: dominique.
[email protected]). The authors acknowledge the helpful com-
ments of the editorial team and Donald Lehmann, Canlin Li, David May-
ers, Natalie Mizik, and Hyun Shin on previous versions of this article.
Shuba Srinivasan acknowledges the seminar participants at Boston Univer-
sity for their insightful comments. Dominique Hanssens acknowledges the
role of the Marketing Science Institute in encouraging and funding
research on marketing and firm value. Donald Lehmann served as associ-
ate editor for this article.
SHUBA SRINIVASAN and DOMINIQUE M. HANSSENS*
The marketing profession is being challenged to assess and
communicate the value created by its actions on shareholder value.
These demands create a need to translate marketing resource
allocations and their performance consequences into financial and firm
value effects. The objective of this article is to integrate the existing
knowledge on the impact of marketing on firm value. The authors first
frame the important research questions on marketing and firm value and
review the important investor response metrics and relevant analytical
models as they relate to marketing. Next, they summarize the empirical
findings to date on how marketing creates shareholder value, including
the impact of brand equity, customer equity, customer satisfaction,
research and development and product quality, and specific marketing-
mix actions. Then, the authors review emerging findings on biases in
investor response to marketing actions. They conclude by formulating an
agenda for future research challenges in this emerging area.
Keywords
: marketing and firm valuation, financial performance, market
valuation modeling, return on marketing investment, empirical
findings
Marketing and Firm Value: Metrics, Methods,
Findings, and Future Directions
Investors trade companies’ shares because their expecta-
tions of these companies’ future earnings differ. This trad-
ing activity results in a share price that represents the valua-
tion, or consensus forecast of the financial health, of these
companies. To aid in this process, industry experts (“ana-
lysts”) publish their own earnings expectations, which are
based in part on meetings with senior company executives
that focus on strategies and business plans for the foresee-
able future. The importance of this expectation setting is
evident every quarter when companies’ earnings announce-
ments are followed by sometimes drastic stock price adjust-
ments when the actual earnings deviate from expectations
(i.e., when there is an earnings surprise).
This continuous firm value adjustment process is of
major importance to senior executives and, in particular, to
the stewards of demand generation for the firm (i.e., the
marketing and sales managers). Individual executive com-
pensation packages are often tied to stock price, and more
important, when stock prices do not trend upward, this is
perceived as a failure of management strategy. Conse-
quently, managerial actions may be influenced by past
movements in share price; in other words, there is a feed-
back loop of investor sentiment into managerial resource
allocation. Thus, it is of the utmost importance to under-
stand how managerial actions translate into the consensus
forecast of financial health (i.e., stock price) and, in particu-
lar, what influences the consensus formation.
In recent years, researchers in marketing have begun to
examine the demand creation aspect of firm valuation.
Although demand creation is but one aspect of management
strategy, it is arguably the most important and the most
challenging. Its critical importance stems from the notion
that customers have become the ultimate scarce resource
(e.g., Peppers and Rogers 2005). To demonstrate this chal-
lenge, consider that the tenure of chief marketing officers is
comparatively short (Nath and Mahajan 2008), which is
another way of saying that chief executives and boards of
directors are more often disappointed in the performance of
294 JOURNAL OF MARKETING RESEARCH, JUNE 2009
1
The finance literature distinguishes among weak, semistrong, and
strong efficiency (Fama 1991). In a marketing context, the semistrong
definition is the most appropriate because, by definition, marketing actions
are publicly observable.
their chief marketing officers than in that of the other senior
executives in the firm.
If marketing’s contributions were readily visible in quar-
terly changes in sales and earnings, the task would be sim-
ple because investors are known to react quickly and fully
to earnings surprises. However, much of good marketing is
building the intangible assets of the firm—in particular,
brand equity, customer loyalty, and market-sensing capabil-
ity. Progress in these areas is not readily visible from quar-
terly earnings, not only because different nonfinancial
“intermediate” performance metrics are used (e.g., cus-
tomer satisfaction measures) but also because the financial
outcomes can be substantially delayed. As with research
and development (R&D), marketing is requesting that the
investor community adopt an investment perspective on its
spending.
The following recent examples from the business press
serve as illustrative examples of investor response to spe-
cific marketing actions and nonfinancial performance
movements in different areas:
•Pricing. In September 2007, when Apple announced a $200
price cut on its cell phone, iPhone, investor reaction to this
presumed “bad news” led to a stock price drop of Apple by 5%
to $136.36 (Information Week 2007).
•Channels of distribution. In July 2006, when Wal-Mart closed
its operations in Germany, its share price increased by 1% to
$43.91 (Reuters 2006).
•New product introductions. In April 2006, the introduction of
Boot Camp software by Apple, which allows users to operate
Windows XP on Mac computers, led to an increase of $6.04 in
Apple’s share price (Wingfield 2006).
•Perceived quality. In September 2006, General Motors
announced that it would extend warranties to 100,000 miles on
2007 cars and trucks as part of a plan to tout quality and win
back buyers lost to Toyota and other rivals. Investor reaction
led to a 2.4% increase in General Motors stock price (Chon
2006).
•Customer satisfaction. In August 2005, when Dell’s customer
satisfaction rating dropped a steep 6.3% to 74 of a possible
100, the biggest drop among major PC makers, its shares
closed down from $41.79 to $36.58 (DiCarlo 2005).
These examples suggest that investors react quickly,
rewarding firms with a higher stock price as information
perceived as “good news” becomes available, and vice
versa. Are these financial-market reactions in sync with
product-market reactions, which, de facto, are the revenue
sources for the firm? According to the well-known efficient
markets hypothesis (EMH) in finance, these investor reac-
tions fully and accurately incorporate any new information
that has value relevance.
1
Thus, insofar as marketing drives
product-market performance, new marketing developments
could be value relevant.
Finance theory supports the value relevance of marketing
through its effect on the firm’s cash needs (Rao and
Bharadwaj 2008). Given that marketing affects the shape of
the probability distribution of future sales revenues, it helps
determine the firm’s working capital requirements (see Rao
and Bharadwaj 2008). Thus, the study of marketing’s
impact on valuation proceeds by way of its impact on cash
flows—in particular, their magnitude, speed, and volatility
(Srivastava, Shervani, and Fahey 1998). Both tangible and
intangible impact routes need to be considered. However, it
is not clear a priori that the investor reaction mechanism
will always be complete and accurate, as the EMH predicts.
In the iPhone example, did the investors accurately infer the
price elasticity for this new cell phone? Indeed, there are
two reasons accurate investor response to marketing devel-
opments is inherently difficult to assess. First, because
investors are not necessarily marketing experts, they may
wrongly evaluate the impact of a marketing driver on future
cash flows. For example, it has been reported that the
shares of “intangible-intensive” firms are systematically
undervalued (Lev 2004). This results in adverse conse-
quences, including excessively high costs of capital for
such firms, leading them to underinvest in intangibles, such
as brand building, which could limit the future earnings
growth that investors seek. Second, investors may be influ-
enced by persuasive communication by company execu-
tives or stock analysts (e.g., Gallaher, Kaniel, and Starks
2005; Sirri and Tufano 1998) and by a host of other mediat-
ing factors.
This article examines the methods for determining the
impact of marketing on investor valuation and summarizes
the existing findings in this area. We first frame the impor-
tant research questions on marketing and firm value and
review the key investor response metrics and relevant ana-
lytical models as they relate to marketing. We then summa-
rize the empirical findings to date on how marketing creates
shareholder value, including the impact of brand equity,
customer equity, customer satisfaction, R&D and product
quality, and specific marketing-mix actions on firm value.
Finally, we conclude with several directions for further
research.
MARKETING AND FIRM VALUE: METHODS AND
METRICS
Summary Steps in Market Valuation Modeling
The starting point for tackling the marketing valuation
question is the Fama–French factor model developed in the
finance literature (e.g., Fama and French 1992, 1996) (see
Table 1, Row 1). This model recognizes the random-walk
nature of stock prices and therefore is expressed as stock
returns, which are stationary.
The Fama–French model also recognizes three system-
atic factors that explain cross-sectional differences among
stock returns. It states that the additional returns investors
can expect to receive by investing in stocks of companies
are explained by three factors: the excess return on a broad
market portfolio (market risk factor), the difference in
return between a large-cap and a small-cap portfolio (size
risk factor), and the difference in return between high and
low book-to-market stocks (value risk factor). These three
factors are augmented with a fourth factor, momentum, to
obtain the Carhart (1997) four-factor financial model. Mar-
keting valuation models then act on the unanticipated com-
ponent of stock returns. From a finance perspective, such
efforts complement the Carhart four-factor financial model
because they demonstrate how firm-specific managerial
actions can either add or subtract shareholder value. The
Marketing and Firm Value 295
Approach Characteristics of Approach Limitations of Approach
Representative Studies/
Sample from Approach
Dependent/Predictor
Variable Used in Study
1. Four-factor
model
Recognizes systematic sources of cross-
sectional differences among firms: the size
factor, the market-to-book value factor, the
market risk factor, and the momentum factor.
Relies on EMH.
Straightforward to estimate.
Can assess cross-sectional variation in investor
response.
Inferences from the
portfolio approach are
sensitive to the choice of
the benchmark portfolio.
Is correlational in nature.
Is subject to omitted-
variable bias.
For application outside the
United States, three of the
four factors are not readily
available.
Rao, Agarwal, and
Dahlhoff (2004) (across
industries)
Barth et al. (1998)
(across industries)
Madden, Fehle, and
Fournier (2006)
Tobin’s q/branding
strategy
Firm valuation/brand
value estimates
Stock returns/brand
valuation
2. Event
study
Assesses the abnormal return for a stock as the
ex post return of the stock during the course of
the event window less the normal expected
return, assuming that the event had not taken
place.
Relies on EMH.
Easy to implement because key data are event
dates and stock prices around the events.
Analysis is causal in nature.
Inappropriate for
measuring long-term
abnormal returns to events
that are clustered in time.
Horsky and
Swyngedouw (1987)
(across industries)
Chaney, Devinney, and
Winer (1991) (across
industries)
Lane and Jacobson
(1995) (within industry)
Geyskens, Gielens, and
Dekimpe (2002)
(within industry)
Stock returns/name
change events
Stock returns/ new
product announcements
Stock returns/brand
extension announcements
Stock returns/Internet
channel investments
3. Calendar
portfolio
Constructs a single portfolio including stocks of
firms with the event to measure the long-term
abnormal returns to that portfolio.
Accounts for cross-sectional correlation of
returns.
Statistical inferences are likely more accurate
than those obtained with event studies.
Does not produce separate
measures of abnormal
returns for each event.
Inferences from the
portfolio approach are
sensitive to the choice of
the benchmark portfolio.
Sorescu, Shankar, and
Kushwaha (2007)
(within industry)
Stock returns/new
product announcements
4. Stock
return
response
model
Establishes whether investors perceive
information on marketing activity, such as
advertising spending, as contributing to the
projection of future cash flows.
Based on the Carhart (1997) four-factor model.
Relies on the EMH.
Provides insights into the market’s expectations
of the long-term value prospects associated with
changes in marketing strategy.
Takes into account the dynamic properties of
stock returns.
Requires detailed
marketing data at the brand
or strategic business unit
level.
Marketing measures must
reflect information that is
available to market
participants because the
stock market reacts to
public information.
Single-equation models
and, thus, no temporal
chain leading to stock
returns.
Aaker and Jacobson
(1994) (across
industries)
Aaker and Jacobson
(2001) (within industry)
Mizik and Jacobson
(2003) (across
industries)
Srinivasan et al. (2009)
(within industry)
Stock returns/ perceived
quality
Stock returns/brand
attitude
Stock returns/shifts in
strategic emphasis
Stock returns/marketing
actions
Ta ble 1
OVERVIEW OF RESEARCH APPROACHES
5. Persistence
modeling
These models use a system’s representation in
which each equation tracks the behavior of an
important agent: the consumer (demand equa-
tion), the manager (decision rule equation),
competition (competitive reaction equation), and
the investor (stock price equation).
A vector autoregressive model provides a
flexible treatment of both short-term and long-
term effects.
Robust to deviations from stationarity.
Provides a forecasted, expected baseline for
each performance variable.
Allows for various dynamic feedback loops
among marketing and stock performance
variables.
Requires detailed
marketing data at the brand
or strategic business unit
level.
Requires time-series over a
long horizon.
Inherently reduced-form
models.
Pauwels et al. (2004)
(within industry)
Joshi and Hanssens
(2008) (within two
industries)
Firm valuation/new product
introductions, sales
promotions
Stock returns/advertising
296 JOURNAL OF MARKETING RESEARCH, JUNE 2009
impact of these exogenous variables provides the ultimate
evidence of marketing’s contribution to shareholder value.
Metrics. Stock returns have unexpected components due
to financial and nonfinancial results and actions/signals. On
the results side, the most straightforward are top-line (reve-
nue) and bottom-line (earnings) surprises. These are typi-
cally modeled with time-series extrapolations.
2
In addition,
earnings surprises can be modeled as the difference
between analysts’ consensus forecasts and the realized
value of earnings. Unexpected stock return components that
are the result of actions or signals include changes in mar-
keting strategy (e.g., price hikes, price cuts), partnership
announcements, top-management changes, advertising
campaigns, new product introductions, and the like. In this
way, virtually all aspects of marketing strategy can be
examined in terms of the extent to which investors recog-
nize them. In addition, it is possible to incorporate surprises
in nonfinancial metrics that are generally believed to have a
long-term impact on business performance, including cus-
tomer satisfaction, customer attrition, brand equity, and
customer equity. If desired, competitive results and
competitive signals can be modeled in the same way as own
results and signals.
Methods. The Carhart four-factor financial model is
based on cross-sectional inferences. The model is simple to
estimate, and under the null of the factor model, firm-
specific attributes should not matter. In practice, however,
the model may be subject to omitted variables as well as the
temporal chain leading to stock returns. Depending on the
research hypotheses and the data at hand, different methods
are used to complement the four-factor financial model. For
example, when firm actions take on the form of discrete
interventions with information release at known time
stamps, an event study (Table 1, Row 2) is necessary (e.g.,
Ball and Brown 1968; Chaney, Devinney, and Winer 1991).
Such events may be recurring throughout the year (e.g.,
earnings announcements) or may be intermittent (e.g., new
product introductions). When the actions are continuous
rather than discrete, stock return models (Table 1, Row 4)
can be used (e.g., Aaker and Jacobson 1994; Lev 1989).
Such stock return models are single-equation models, and
as such, they are limited in their ability to represent the
temporal chain leading to stock returns. Persistence model-
ing (e.g., vector autoregressive models), which involves a
system of equations (Table 1, Row 5), can be used for this
purpose (e.g., Eun and Shim 1989; Pauwels et al. 2004).
Such models generate impulse–response functions that can
be used to assess the speed with which stock returns react
to new information.
Capturing the long-term impact of marketing on valua-
tion is more difficult because investors react to “news”
quickly, and thus any extended horizon is subject to several
3
Exceptions include investments in retail warehouses, retail outlets, and
so on, that are marketing investments accounted for (in part) in the book
value of the firm.
intermediate events that cloud the relationship the
researcher is seeking. The abnormal returns can be summed
across the horizon to obtain models of cumulative abnormal
return (CAR) or buy-and-hold returns (BHAR). Cumulative
abnormal return measures abnormal returns relative to a
model such as the capital asset pricing model (CAPM)
(Fama 1998) or the Carhart four-factor financial model and
is preferred for short horizons (e.g., several days). Buy-and-
hold return reflects the abnormal return an investor would
earn from holding the stock for an extended period, using
compounded interest, and therefore is preferred for longer
horizons (e.g., several months or more) (e.g., Barber and
Lyon 1997). Using either metric, the preferred solution is to
build a test versus control portfolio—“test” refers to a mar-
keting condition that did not exist in “control”—and track
its performance over extended periods. Two such
approaches are the calendar portfolio method (e.g., Fama
1998; Sorescu, Shankar, and Kushwaha 2007) and the
matched-pair return model (Barber and Lyon 1997). A
calendar-time portfolio includes all stocks of firms with the
event as the unit of analysis (e.g., a new product announce-
ment) and then measures the long-term abnormal returns of
that portfolio (see Table 1, Row 3). In contrast, a matched-
pair return model includes only the stocks of the focal firm
and a matched firm.
Figure 1 is a schematic representation of these metrics,
modeling steps, and choices. Table 1 summarizes the char-
acteristics and limitations of each method, as well as the
nature of the samples used in the study (i.e., a firm over
time only, firms within an industry, and firms across indus-
tries). We now discuss the metrics and the modeling
approaches in more detail.
Metrics on Marketing and Firm Value
Market capitalization and stock returns. The ultimate
metric of shareholder value is firm value or market capitali-
zation, the share price multiplied by the number of out-
standing shares. To operationalize firm value for empirical
work, we need to take two factors into account.
First, we need to isolate the book value of the firm,
which is typically not related to marketing activity.
3
This is
achieved by either Tobin’s q, the ratio of market value to
the replacement cost of the firm’s assets, or the market-to-
book ratio. Of these, Tobin’s q is a preferred metric because
the use of replacement costs of assets avoids accounting
complications associated with book value, which rarely
reflects the actual value of assets (McFarland 1988). How-
ever, replacement costs of intangible assets are not easy to
discern in most cases (ibid). Furthermore, Tobin’s q data
are typically available only on a quarterly or annual basis.
Second, we need to incorporate the random-walk behav-
ior in stock prices (Fama 1965). Unlike the typical time-
2
Typically, this involves estimating an autoregressive model of the
variable (e.g., earnings) on its past lags and using the residuals as the
unanticipated component of the variable.
Marketing and Firm Value 297
Persistence
Model
(e.g., Pauwels et
al. 2004)
Stock Return
Model
(e.g., Mizik and
Jacobson 2004)
Calendar
Portfolio
(e.g., Sorescu,
Shankar, and
Kushwaha 2007)
Event Study
(e.g., Chaney,
Devinney, and
Winer 1991)
Four-Factor Model
(e.g., McAlister,
Srinivasan, and Kim
2007)
Firm Actions/Signals (see Table 4)
•The part of unexpected components of stock that is
explained by firm managerial actions/signals.
•Changes in marketing strategy, such as price hikes or
reductions, partnership announcements, top
management changes, advertising campaigns, and
new product introductions (e.g., Chaney, Devinney,
and Winer 1991).
Firm Results (see Table 3)
•The part of unexpected components of stock that is
explained by top-line (revenue) and bottom-line
(earnings) surprises (e.g., Kothari 2001).
•Surprises in nonfinancial metrics, including customer
satisfaction, brand equity, and customer equity (e.g.,
Barth et al. 1998; Madden, Fehle, and Fournier 2006).
•Analyst earnings expectations or time-series
extrapolations.
Systematic Market Risk (see Table 2)
•The part of risk explained by changes in
average market portfolio returns.
•β in CAPM models (Lintner 1965).
•Fama and French (2006) generates better
estimates of stock returns than simple β
alone.
Unsystematic Risk (see Table 2)
•The part of risk that cannot be explained
by changes in average market portfolio
returns.
•Idiosyncratic volatility/residual risk (e.g.,
Aaker and Jacobson 1987; Luo 2007).
Abnormal Returns
Business results, marketing signals
(Aaker and Jacobson 1994; Lev 2004;
Pauwels et al. 2004)
Expected Returns (see Table 2)
β market risk, size, book-to-market,
momentum factors (Carhart 1997; Fama
and French 1996, 2006) + residual returns
(Campbell et al. 2001)
+=
Research Approaches (see Table 1)
Residual ββ Risk
Total Returns
Figure 1
FLOW CHART OF RETURN AND RISK
series behavior of consumer sales or product prices, the
permanent component in stock price fluctuations dominates
(i.e., the series are in a constant state of evolution). Taking
logarithms of stock prices, followed by first differences to
account for the random-walk behavior, results in stationary
(mean-reverting) time series of stock returns as a dependent
variable.
As Figure 1 (first row) shows, the total stock returns of a
firm have two parts: expected returns and abnormal returns.
Fama and French (1992, 1996) propose a three-factor
explanatory model for expected stock returns, including the
size risk factor, the value risk factor, and the market risk
factor (i.e., β). In particular, investors can be expected to
receive additional returns by investing in stocks of compa-
nies with smaller market capitalization and with lower
market-to-book ratios. Both of these effects imply that
riskier stocks are characterized by higher returns. Carhart
(1997) extends this model to a four-factor model by includ-
ing a momentum factor. Specifically, the extended Carhart
four-factor explanatory financial model for stock returns is
estimated as follows:
4
To construct momentum, we used six value-weighted portfolios,
including NYSE, AMEX, and NASDAQ stocks, formed on size and
monthly prior (2–12) returns. The monthly portfolios are the intersections
of two portfolios formed on size and three portfolios formed on prior (2–
12) return. The monthly size breakpoint is the median NYSE market
equity, and the monthly prior (2–12) return breakpoints are the 30th and
where R
it
is the stock return for firm i at time t, R
rf,t
is the
risk-free rate of return in period t, R
mt
is the average market
rate of return in period t, SMB
t
is the return on a value-
weighted portfolio of small stocks less the return of big
stocks, HML
t
is the return on a value-weighted portfolio of
high book-to-market stocks less the return on a value-
weighted portfolio of low book-to-market stocks, and
UMD
t
is the average return on two high prior-return portfo-
lios less the average return on two low prior-return portfo-
lios.
4
These are referred to as the market factor, size factor,
() ( )
,,
1RR R R sSMBhHML
it rf t i i mt rf t i t i t
−=+ − + +αβ
+++uUMD
itit
ε ,
298 JOURNAL OF MARKETING RESEARCH, JUNE 2009
70th NYSE percentiles. For further details, we refer readers to http://
mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_
mom_factor.html.
5
Related finance literature (e.g., Daniel and Titman 1997) has proposed
characteristics-based models, which argue that firm characteristics rather
than the sensitivity to the four risk factors drive stock returns. As an exam-
ple, it is firm size and not the sensitivity to the size factor (SMB) that
drives stock returns. However, Davis, Fama, and French (2000) subse-
quently argue that such characteristics-based effects are confined to the
shorter sample used in the former study.
6
Empirical evidence suggests that under certain sampling conditions,
such as using samples with only large firms versus small caps (Loughran
1997) or samples trimmed for extreme observations (Knez and Ready
1997), the size factor and the value factor become insignificant. Bollerslev
and Zhang (2003) use high-frequency data (e.g., five-minute intervals) to
find a sign reversal in these two factors. As for momentum, its effect sign
depends on the period considered as follows (see, e.g., Subrahmanyam
2005): It is negative for 1 week up to 1 month, positive for 3- to 12-month
periods (Jegadeesh and Titman 1993), and negative for long horizons, such
as 3 to 5 years (DeBondt and Thaler 1985). Robustness checks of these
factor effects are an area of ongoing research in empirical finance.
value factor, and momentum factor, respectively. The data
source for the four-factor financial model is Kenneth
French’s Web site at Dartmouth, which provides details on
all factors at the daily and weekly levels (see http://mba.
tuck.dartmouth.edu/pages/faculty/ken.french/data_library.
html). In addition, ε
it
is the error term; α
i
is the model inter-
cept; and β
i
, s
i
, h
i
, and u
i
are parameter estimates of the fac-
tors used in the model. If the stock’s performance is “nor-
mal,” the four-factor model captures the variation in R
it
,
and α
i
is zero.
5
Therefore, α
i
is the abnormal return associ-
ated with firm i, and ε
it
captures additional abnormal
(excess) returns associated with period t.
The empirical evidence around the three Fama–French
factors is typically positive, while the evidence on the
Carhart fourth factor (momentum) is ambiguous.
6
Momen-
tum captures the notion that a stock that has performed well
in the recent past continues to do so, and vice versa
(Jegadeesh and Titman 1993). Among others, Fama and
French (1996) question whether the momentum effect is
real and call for further empirical verification. As such, we
recommend that marketing researchers tackling the investor
valuation question use the Carhart four-factor model as the
starting point but be prepared for ambiguous results on the
momentum factor.
Systematic risk and idiosyncratic risk. A second funda-
mental metric in finance is firm stock risk (Hamilton 1994).
Greater risk, as reflected in higher stock price volatility,
may suggest vulnerable and uncertain cash flows in the
future, which induces higher costs of capital financing, thus
damaging firm valuation in the long run. Total risk has two
components: systematic risk (related to variability through
the four factors) and idiosyncratic (firm-specific) risk (see
Figure 1, second row). Risk stems from several factors,
including market volatility (through β in Equation 1) at
macroeconomic levels (e.g., exchange rate, interest risk) or
at the sector level (some industries by their very nature are
more or less stable), product-market competition (competi-
tion may be stronger or weaker than anticipated), and
project-level outcomes (projects such as new product
launches may fare better or worse than expected).
Systematic market risk is the part of the total risk that is
explained by changes in overall market portfolio returns
7
Although our focus here is on outcomes in the real stock market, the
application of Internet-based virtual stock markets is an emerging empiri-
cal approach that can be used to predict market valuation. Its basic idea is
to bring a group of participants together through the Internet to trade
shares of virtual stocks. These stocks represent a bet on the outcome of
future market situations, and their value depends on the realization of
these market situations (e.g., Elberse 2007).
due to inflation, interest rate changes, and so on, that are
common to all stocks; it is measured by the β
i
in Equation
1. By construction, the stock market as a whole has a β of
1.0. A stock whose return falls more than a drop in market
return has a β greater than 1.0, and vice versa. Thus, β, a
measure of the stock’s sensitivity to market changes, is an
important metric for publicly listed firms.
Unsystematic or idiosyncratic risk is the part of the risk
that cannot be explained by changes in average market
portfolio returns; this is measured by the variance of the
residuals in Equation 1. Idiosyncratic risk constitutes
approximately 80% of total risk on average (Goyal and
Santa-Clara 2003). Recent finance literature has shown its
relevance in firm value determination for several reasons
(Brown and Kapadia 2007). First, all else being equal,
investors favor stable earnings over volatile earnings (e.g.,
Ang, Chen, and Xing 2006; Goyal and Santa-Clara 2003;
Graham, Harvey, and Rajgopal 2005). Insofar as marketing
contributes to the stability or volatility of earnings, this
becomes an important area for marketing researchers to
address—in particular, the impact of marketing on the
firm’s required level of working capital (Rao and Bharad-
waj 2008). Indeed, the higher the volatility, the more work-
ing capital is required to prevent insolvency. Second, high
levels of idiosyncratic risk increase the number of securities
required to generate a well-diversified portfolio (see Camp-
bell et al. 2001). Similarly, some investors cannot diversify
(e.g., participants in employee stock option plans) and must
bear idiosyncratic risk. Third, stock option prices depend
on the total volatility of the underlying stock, of which
idiosyncratic volatility is the largest component. In sum-
mary, an emerging body of literature is incorporating mar-
ket realities that firm value depends on both systematic and
idiosyncratic risk, with each component affecting value
negatively. Table 2 provides an overview of different
investor response metrics, each with its own characteristics
and limitations.
7
Marketing asset and marketing action metrics. On the
independent variable side, marketing is represented by one
or more asset metrics or by direct marketing actions
(investments) (see Figure 1, third row). The asset metrics
include intermediate performance metrics, such as brand
equity, and customer metrics, such as customer satisfaction,
customer equity, and perceived product quality. Marketing
action metrics include new products, advertising, promo-
tions, channels of distribution, and so on. Several empirical
generalizations about the customer response effects of these
actions have been derived (see, e.g., Hanssens, Parsons, and
Schultz 2001).
Of recent interest to marketing researchers is the ques-
tion whether investors react differently to movements in
asset metrics (e.g., movements in customer satisfaction)
versus directly observable marketing investments (e.g.,
marketing spending movements). In answering this ques-
tion, several empirical issues arise.
Marketing and Firm Value 299
Dependent Financial Metric Characteristics Limitations
Illustrative Studies (Data
Interval Used)
A. Returns/Levels Metrics
1. Firm valuation (stock
price × number of shares
outstanding)
(a) Forward-looking measure, providing
market-based views of investor
expectations of the firm’s future profit
potential.
(a) Need to incorporate the random-walk
behavior in stock prices.
(b) Estimated model should be robust to
deviations from stationarity—in particular,
the presence of random walks in stock
prices, which can lead to spurious
regression problems (Granger and
Newbold 1986).
Fornell et al. (2006)
(annual)
2. Relative importance of
tangibles to intangibles:
(a) Tobin’s q (ratio of
market value of the
firm to the
replacement cost of
the firm’s assets)
Characteristic (a) applies here.
(b) Values greater than unity signal a
contribution of intangible assets on
valuation.
(c) Accepted paradigms of research (e.g.,
event study, vector autoregressive
modeling, stock return response models)
can be used to assess firm value effects.
(d) Directly comparable across industries,
whereas accounting measures may not be
easily compared (Mittal et al. 2005).
(e) Monte Carlo experiments show that
Tobin’s q estimates have smaller average
errors and greater correlation with true
measures (McFarland 1988) as compared
with accounting rates of return.
Limitations (a) and (b) apply here.
(c) Replacement cost of tangible assets is
difficult to compute and that of intangible
assets is usually ignored (Mittal et al.
2005).
Simon and Sullivan (1993)
(annual)
Rao, Agarwal, and
Dahlhoff (2004) (annual)
(b) Market-to-book ratio
(ratio of market value
to book value of
common equity)
Characteristics (a), (b), (c), and (d) apply
here.
Limitations (a) and (b) apply here. Pauwels et al. (2004)
(weekly)
3. Stock returns (change in
the total value of an
investment in a common
stock over some period
per dollar of initial
investment and defined
as (Price
t
+ Dividend
t
–
Price
t– 1
)/(Price
t– 1
)
A stationary time series of stock returns is
obtained as a dependent variable.
No obvious limitations. Srinivasan et al. (2009)
(weekly)
Ta ble 2
DEPENDENT FINANCIAL METRICS FOR ASSESSING INVESTOR RESPONSE
First, there is the issue of temporal aggregation of the
data, which may be different for both the dependent
variable (e.g., daily price changes) and the independent
variables (e.g., monthly changes in marketing actions).
Although marketing actions can theoretically be traced
back to daily or even five-minute intervals (e.g., a firm’s
announcement of an innovation launch), they are typically
examined at weekly or longer intervals (e.g., weekly in
Pauwels et al. 2004; annually in McAlister, Srinivasan, and
Kim 2007). New econometric methods are available to deal
with such differences in temporal aggregation (e.g., Ghy-
sels, Santa-Clara, and Valkanov 2006).
Second, cross-sectional studies have sometimes linked
stock prices directly to levels of marketing (e.g., Rao, Agar-
wal, and Dahlhoff 2004). However, models based on the
EMH must recognize that investors react only to new infor-
mation, which is operationalized as the difference between
the actual and the expected level of the independent
variable. As such, models based solely on these levels
ignore the distinction between unexpected changes and
expected levels of marketing actions and thus have limited
value, from both a theoretical and a methodological
perspective.
Finally, stock return is typically measured at the firm or
corporate level, while marketing actions often take place at
the brand or product level. As such, the level of aggregation
differs between the dependent variable (firm stock returns)
and the independent variables, such as brand metrics and
brand extension announcements (e.g., Barth et al. 1998;
Geyskens, Gielens, and Dekimpe 2002; Lane and Jacobson
1995; Pauwels et al. 2004). A modeling solution is to
aggregate the brand-level marketing variables. However,
such aggregation would involve a substantial loss of infor-
mation and, thus, managerial insight. For one, managers
would no longer be able to pinpoint which brands (e.g.,
those with more versus less advertising support, innovation
level, and quality) and/or targeted categories contribute
more or less to the firm’s stock returns. For another, the
300 JOURNAL OF MARKETING RESEARCH, JUNE 2009
2. Systematic market
volatility (the part of
stock volatility that is
explained by changes in
average market portfolio
returns)
It is the market risk common to all firms
and is easily compared across industries.
Based on the CAPM and dependent on the
market portfolio returns; a stock whose
return falls (or rises) more than the fall (or
rise) in market return has a β > 1.0, and
vice versa.
Has received considerable attention in the
literature.
Can be extended with finer-grained analyses
for upside and downside βs (Ang, Chen,
and Xing 2006).
Accounts for only approximately 20% of
the total risk.
Can be measured but cannot be eliminated.
Inferences are sensitive to the
choice/definition of the market portfolio.
McAlister, Srinivasan, and
Kim (2007) (monthly)
Fornell et al. (2006) (daily)
3. Idiosyncratic volatility
(the variability that is not
explained by changes in
average market portfolio
return but instead by
firm-specific events)
It is independent of the economy but is firm
idiosyncratic.
Assumption is that unsystematic risk could
be eliminated in a well-diversified portfolio
since unique risks could cancel each other
out.
Accounts for 80% of the total risk.
Theoretically, it is not related to a firm’s
long-term stock price, but there is
increasing empirical support for the role of
idiosyncratic volatility (e.g., Brown and
Kapadia 2007).
Luo (2007) (daily
aggregated to monthly)
Osinga et al. (2009)
(monthly)
Ta ble 2
CONTINUED
8
It would be ideal to run the regression RET
BRAND
= bX + μ, where
RET
BRAND
is the return associated exclusively with the particular brand
information X. However, given the corporate nature of stock returns, the
estimated regression is RET = βX+ ε, where RET is the total corporate
stock return, which is composed of RET
BRAND
and RET
NOT-BRAND
(i.e.,
the stock return that is not associated with the brand). Because RET =
(RET
BRAND
+ RET
NOT-BRAND
), it can be shown that the least squares esti-
mate E[β] = E[(X′X)
–1
X′(RET
BRAND
+ RET
NOT-BRAND
)] = b (see
Geyskens, Gielens, and Dekimpe 2002; Lane and Jacobson 1995), leading
to an unbiased estimate, under the reasonable assumption that
RET
NOT-BRAND
and X are uncorrelated.
estimated aggregate effects may be fully driven by one or
two brands.
8
Stock return impact assessment typically
works well for major events associated with large brands
(i.e., with a high signal-to-noise ratio). It also works well
for smaller brands focused on one or a few lines of business
or on tracking stocks created by large parent companies to
value the financial results of specific subsidiaries. For
example, Gupta, Lehmann, and Stuart (2004) study the
relationship between customer equity and stock price for
some Internet firms. Whether such assessment pertains to
large or small brands, we believe that it is preferable to link
stock returns to brand-level variables, even though that aug-
ments the size of the data matrix.
In summary, we offer three specific recommendations to
marketing researchers tackling the investor valuation ques-
tion: (1) Start with the Carhart four-factor model; (2) assess
the impact of unanticipated changes, recognizing that
investors react only to new information; and (3) preferably
use Tobin’s q as the metric of firm valuation.
Measuring Investor Response to Marketing Using Four-
Factor Finance Models
Several recent studies have examined the relationship
between marketing and firm value starting with the four-
factor financial or CAPM models (see Figure 1, first item in
the last row). Such studies assume that financial markets
are efficient and have focused either (1) on the level of
financial performance (e.g., Barth et al. 1998; Madden,
Fehle, and Fournier 2006) or (2) on the variability in finan-
cial performance (e.g., Gruca and Rego 2005; McAlister,
Srinivasan, and Kim 2007).
One approach is to start with the four-factor model in
Equation 1 to compare the performance of firms that have a
proven emphasis on a particular marketing characteristic
(e.g., branding) with a relevant benchmark set of firms. The
null hypotheses are that, in Equation 1, α
i
= 0 and the β
i
coefficients of the two portfolios are equal; that is, there is
neither a significant abnormal return for the portfolio of
focal firms nor a significant difference in the variability (β)
for the portfolio of focal firms versus the benchmark port-
folio of firms. The alternate hypotheses are that α
i
> 0 and
the β
i
coefficients of the two portfolios are not equal. Posi-
tive α
i
relative to the benchmark indicates superior per-
formance in returns, and β
i
< 1 suggests below-average
market risk, and vice versa.
B. Risk/Volatility Metrics
1. Cash flow volatility
(firm’s cash flow
coefficient of variation
divided by the market’s
cash flow coefficient of
variation)
Coefficient of variability equal to one
indicates that the firm’s cash flows are as
volatile as those of the overall market.
A coefficient of variability greater than one
indicates higher volatility than the market,
and vice versa.
Cash flow volatility can explain as much as
80% of the variation in systematic market
risk.
It is not based on a financial model such as
CAPM.
Brand-level data are needed for all or most
of the firm’s divisions.
Gruca and Rego (2005)
(annual)
Fischer, Shin, and Hanssens
(2009) (quarterly)
Dependent Financial Metric Characteristics Limitations
Illustrative Studies (Data
Interval Used)
Marketing and Firm Value 301
9
Table 4 summarizes the substantive findings of this and other market-
ing studies.
Studies of marketing’s impact on returns include Mad-
den, Fahle, and Fournier (2006), who compare an ex ante
portfolio of 111 company brands that appeared on the Inter-
brand list of World’s Most Value Brands at least once
between 1994 and 2001 with a benchmark.
9
Along
comparable lines, Rao, Agarwal, and Dahlhoff (2004) esti-
mate the relationship between brand strategy and firm value
(as measured by Tobin’s q) using a cross-sectional, time-
series panel model that controls for firm-specific variables
reflecting either previous operations or future growth
opportunities.
Studies of marketing’s impact on volatility include
McAlister, Srinivasan, and Kim (2007), who examine the
relationship between firms’ advertising and R&D expendi-
tures and their systematic market risk. First, they estimate a
firm’s systematic market risk, β, starting with the CAPM,
using both the equal-weighted and value-weighted portfo-
lios. In a second step, they assess the effect of advertising/
sales and R&D/sales on systematic market risk, incorporat-
ing unobserved firm heterogeneity and serial correlation in
the errors by estimating a model with the systematic market
risk, β
it
, as the dependent variable.
The four-factor financial model is relatively straightfor-
ward to estimate and is useful in assessing cross-sectional
variation in investor response. However, existing applica-
tions rarely control for all four factors. For example, the
branding study of Rao, Agarwal, and Dahlhoff (2004)
accounts for two factors (the size factor and the relative
importance of intangibles), and the systematic-risk study of
McAlister, Srinivasan, and Kim (2007) controls for three of
the four factors (excluding the momentum factor). In addi-
tion, the inferences from the portfolio approach are sensi-
tive to the choice of the benchmark portfolio (Barber and
Lyon 1997). For example, a benchmark portfolio based on
strong brands runs the risk of omitted-variable bias because
brand strength may be associated with other characteristics
that are not represented in the portfolio. As such, the selec-
tion of the benchmark is important, and it is advised to con-
duct robustness checks using samples matched on several
characteristics (e.g., industry, market share). Finally, the
four-factor model assumes that markets are efficient. The
persistence model we discuss subsequently allows the
researcher to test for deviations from market efficiency.
Measuring Investor Response Using Event Studies
When firm actions take on the form of interventions with
known time stamps, an event study is necessary. Event
studies eliminate the dependence on accounting informa-
tion—again assuming that markets are efficient—and allow
for an inference of cause and effect in a quasi-experimental
setting (see Figure 1, second item in the last row). Indeed,
all event studies are joint tests of the hypothesis under con-
sideration as well as the efficiency of capital markets (Fama
et al. 1969). The intuition behind the event study methodol-
ogy is that given market efficiency, perfect information, and
rationality of investors (Fama 1991), the effect of a relevant
event should be immediately reflected in stock prices.
Event studies require that the share-price reaction to the
event of interest can be clearly isolated while controlling
for other relevant information and that an appropriate
10
Event leakage can be investigated by including preevent periods in the
event window (e.g., Chaney, Devinney, and Winer 1991).
benchmark can be used to compute normal and abnormal
returns. Event studies have been used to measure investor
impact of new product announcements (Chaney, Devinney,
and Winer 1991), corporate name changes (Horsky and
Swyngedouw 1987), brand extensions (Lane and Jacobson
1995), celebrity endorsements (Agarwal and Kamakura
1995), joint ventures (Johnson and Houston 2000), Internet
channel additions (Geyskens, Gielens, and Dekimpe 2002),
new product quality reports (Tellis and Johnson 2007), mar-
ket entry of a retailer (Gielens et al. 2008), and motion pic-
ture advertising (Joshi and Hanssens 2009).
The abnormal return for a stock is the ex post return of
the stock during the course of the event window less the
normal expected return, assuming that the event had not
taken place (Srinivasan and Bharadwaj 2004). Starting with
the Carhart four-factor financial model, the abnormal return
for a stock is calculated as follows:
In Equation 2, ε
it
, the measure of abnormal return (risk
adjusted) for firm i in period t, provides an unbiased esti-
mate of the future earnings generated by the event (Fama
1970). This abnormal return is then aggregated over the
length of the window after the event of interest to arrive at
the CAR.
10
The statistical significance of the abnormal
return is calculated by dividing the CAR by its standard
error. When the test period is short (e.g., a day, a week), the
CAR measures are not too sensitive to the financial model
used to adjust for risk. For longer test periods, event studies
are sensitive to the return metrics used (Fama 1998). Con-
sequently, it is advisable for researchers to use multiple
measures of abnormal returns, such as continuously com-
pounded abnormal return or BHAR, and to assess the sensi-
tivity of findings to these alternative return metrics (Jacob-
son and Mizik 2009a, b; Lyon, Barber, and Tsai 1999).
Note also that for applications outside the United States,
data on some of the four factors are available at Kenneth
French’s Web site for a set of 20 major countries, including
the United Kingdom, Germany, and Japan. For inter-
national applications beyond this set of countries, Equation
2 may not include SMB, HML, and UMD (e.g., Gielens et
al. 2008). Gielens and colleagues (2008) find that their sub-
stantive results are insensitive to such omissions in their
empirical setting. Overall, we recommend that investor val-
uation applications in markets with incomplete factor data
conduct robustness checks for such omissions.
Measuring Investor Response Using Calendar Portfolio
Theory
The event study methodology has a limitation that makes
it inappropriate for measuring long-term abnormal returns
to events that are clustered in time; namely, it cannot prop-
erly account for cross-sectional dependency (or overlap)
among events, which could lead to misleading statistical
inferences (Barber and Lyon 1997; Kothari and Warner
2006; Mitchell and Stafford 2000). One way to account for
such cross-sectional dependency is to compute “one-to-one
matched-pair returns” by matching firms that are closest in
() ( ) ( )
,,
2 εαβ
it it rf t i i mt rf t i t
RR R R sSMB=− −− − −
− hhHML uUMD
iti
− .
302 JOURNAL OF MARKETING RESEARCH, JUNE 2009
size and market-to-book ratio to the target firm (Barber and
Lyon 1997; Joshi and Hanssens 2008).
Another approach is the calendar-time portfolio method
(Fama 1998; Mitchell and Stafford 2000), which has
recently been applied in marketing (Sorescu, Shankar, and
Kushwaha 2007) (see Figure 1, third item in the last row).
This method begins with the construction of a single port-
folio (called a calendar-time portfolio) to include all stocks
of firms with the event as the unit of analysis (e.g., a new
product announcement) and then measures the long-term
abnormal returns to that portfolio using the four-factor
model in Equation 1. Unlike the matched-pair approach, the
calendar portfolio method is based on a large comparison
sample, so the potential omitted-variable bias resulting
from industry characteristics variables is smaller (Barber
and Lyon 1997).
The calendar-time method automatically accounts for
cross-sectional correlation of returns (Lyon, Barber, and
Tsai 1999; Mitchell and Stafford 2000). This is because the
standard error of the abnormal return estimates of the port-
folio, α
p
, is not computed from the cross-sectional variance
(as is the case with the event study method) but rather from
the intertemporal variation of portfolio returns. Given
rational investors, monthly stock returns are serially uncor-
related (Kothari and Warner 2006), so the methodology is
well specified, and statistical inferences are likely to be
more accurate than those obtained with event studies in
which the standard error is computed within the cross-
section. However, the calendar-time portfolio approach
has less power to detect abnormal performance because it
averages over months of “hot” and “cold” event activity
(Loughran and Ritter 2000). For example, the calendar-time
portfolio approach may fail to identify significant abnormal
returns if abnormal performance primarily exists in months
of heavy event activity. Because stocks are grouped into a
portfolio and a single measure of returns is obtained for the
entire group, it is not possible to use a cross-section regres-
sion model to analyze the relationship between financial
performance and marketing drivers (e.g., marketing
actions). When the actions are continuous or repetitive
rather than discrete, stock return models are better suited
for that purpose.
Measuring Investor Response Using Stock Return
Response Models
Stock return response models (e.g., Brennan 1991; Lev
1989) are similar to event studies, except the inputs are
continuous rather than discrete in nature (see Figure 1,
fourth item in the last row). Marketing examples include
price movements, advertising spending, and distribution
outlets. Both approaches build on the EMH, and both
assess the stock return reaction to unanticipated events (i.e.,
the effect of new information on investors’ expectations of
discounted future cash flows). Stock return models may be
specified on whatever data interval is appropriate for the
marketing resources being deployed, such as weekly data
for advertising or monthly data for major new product
innovations.
Stock return response models establish whether investors
perceive information on change in marketing activity, such
as advertising spending, as contributing to a change in the
projection of future cash flows (Mizik and Jacobson 2004).
The causal inference in stock return models is not as
straightforward as in event studies. Indeed, event studies
are designed as controlled quasi experiments, in which the
postevent behavior of the stock price is tested relative to the
expected preevent behavior, so the causal inference is
direct. In contrast, stock return models may lead to signal-
ing interpretations as well. For example, suppose an auto-
mobile manufacturer announces a significant increase in its
promotional incentives, and its stock price goes down. One
interpretation is that investors anticipate that these promo-
tions will reduce the firm’s future profit margins and, there-
fore, cash flows, indicative of a causal linkage from promo-
tions to cash flows and, thus, to firm valuation. Another
interpretation is that the market views the increase in pro-
motional spending as a signal of weakening consumer
demand for the firm’s products and adjusts its valuation of
the firm accordingly, indicative of a signaling linkage from
promotional spending to firm valuation.
More broadly, both event studies and stock return
response models may be subject to omitted-variable bias.
For example, forecasts of downturns in demand or
increases in commodity prices may lead to (1) more aggres-
sive firm innovation spending and (2) decreased sales of
existing products. If the latter is greater than the former, a
study of innovation spending could show a negative rather
than a positive effect on stock returns.
In a stock return response model, the four-factor finan-
cial model (Equation 1) is augmented with firm results and
actions to test hypotheses on their impact on future cash
flows. These are expressed in unanticipated changes (i.e.,
deviations from past behaviors that are already incorporated
in investor expectations). The stock return response model
is defined as follows:
where R
it
is the stock return for firm i at time t and ER
it
is
the expected return from the four-factor model in Equation
1. A test of “value relevance” of unexpected changes to
firm and competitive results and actions is a test for signifi-
cance of the β coefficients in Equation 3; significant values
imply that these variables provide incremental information
in explaining stock returns.
The components of stock returns that are, to some extent,
under managerial control are of three kinds: financial
results, customer asset metrics (nonfinancial results), and
marketing actions. Financial results include unanticipated
revenues (UΔREV) and earnings (UΔINC), and non-
financial results include metrics such as customer satisfac-
tion and brand equity (UΔCUST). Specific marketing
actions are the unanticipated changes to marketing
variables or strategies (UΔOMKT). In addition, competitive
actions or signals in the model reflect the unanticipated
changes to competitive results, marketing actions, strategy,
and intermediate metrics (UΔCOMP), and ε
i2t
is the error
term. As an illustrative example, Srinivasan and colleagues
(2009) investigate the impact of product innovations, adver-
tising, promotions, customer quality perceptions, and
competitive actions on stock returns for automobile
manufacturers.
The unanticipated components can be modeled as the
difference between analysts’ consensus forecasts and the
realized value (in the case of earnings) or with time-series
()3
123
R ER U REV U INC U CUST
it it it it it
=+ + +
+
βββΔΔΔ
βββε
45 2
U OMKT U COMP
it it i t
ΔΔ++,
Marketing and Firm Value 303
11
The long-term behavior of each endogenous variable is obtained from
a shock-initiated chain reaction across the equations. For example, a suc-
cessful new product introduction will generate higher revenue, which may
prompt the manufacturer to reduce sales promotions in subsequent peri-
ods. The combination of increased sales and higher margins may improve
earnings and ultimately stock price. Because of such chains of events, the
full performance implications of the initial product introduction may
extend well beyond its immediate effects.
extrapolations using the residuals from a time-series model
(e.g., Lev 1989). A few studies argue that analysts’ fore-
casts could be more accurate predictors of earnings expec-
tations than time-series models because analysts have
access to broader and more current information sets (e.g.,
advance knowledge of firm actions), leading to improved
quantitative models (Brown and Rozeff 1978; Brown et al.
1987).
Recent research in finance has relaxed the EMH assump-
tion of investors’ structural knowledge while maintaining
the rationality assumption in decision making (e.g., Brav
and Heaton 2002; Brennan and Xia 2001). This literature
suggests that with rational learning, stock prices move not
only when new information becomes available but also
when investors improve their understanding of the various
economic relationships that shape the market equilibrium.
Thus, the short-term investor reaction to marketing “news”
may be adjusted over time until it stabilizes in the long run
and loses its ability to adjust stock prices further. Under the
EMH, there would not be any time-adjusted effects because
the impact of marketing actions would be fully contained in
the next period’s stock price. This perspective motivates the
use of persistence models instead of event windows to
study marketing’s impact on firm value, which we turn to
next.
Measuring Investor Response Using Persistence Modeling
Persistence models (see Figure 1, fifth item in the last
row) use a system’s representation (e.g., Dekimpe and
Hanssens 1995; Pauwels et al. 2002), in which each equa-
tion tracks the behavior of an important agent—for exam-
ple, the consumer (demand equation), the manager (deci-
sion rule equations), competition (competitive reaction
equation), and, finally, the investor (stock price equation).
11
As an example, a persistence model estimated as a vector
autoregressive model can be specified for each brand (two
in the illustration) of firm i as follows:
()4
1
2
Δ
Δ
Δ
MBR
INC
REV
MKT
MKT
it
it
it
it
it
⎡
⎣
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎤⎤
⎦
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
=+ ×
−
−
−
CB
MBR
INC
REV
MK
n
it n
it n
it n
Δ
Δ
Δ
TT
MKT
it n
it n
n
N
1
2
1
−
−
=
⎡
⎣
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎤
⎦
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
+
∑
Γ××
⎡
⎣
⎢
⎢
⎢
⎢
⎤
⎦
⎥
⎥
⎥
⎥
+
X
X
X
u
u
u
u
t
t
t
MBRit
INCit
REVit
1
2
3MMKT it
MKT it
u
1
2
⎡
⎣
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎤
⎦
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
,
where B
n
and Γ are vectors of coefficients, [u
MBRit
, u
INCit
,
u
REVit
, u
MKT1,t
, u
MKT2t
]′∼N(0, Σ
u
), N is the order of the
system based on Schwartz’s Bayesian information crite-
rion, and all variables are expressed in logarithms or their
changes (Δ). In this system, the first equation is an
expanded version of the stock return response model in
Equation 3. The second and third equations explain the
changes in, respectively, bottom-line (INC) and top-line
(REV) financial performance of firm i. The fourth and fifth
equations represent firm i’s marketing actions (e.g., for
each brand)—that is, MKT
1t
and MKT
2t
. For example,
Pauwels and colleagues (2004) consider a brand’s new
product introductions and sales promotions. The exogenous
variables in this dynamic system (X
1t
, X
2t
, X
3t
...) could
include controls, such as the Carhart four factors and the
impact of stock market analyst earnings expectations (Ittner
and Larcker 1998). The impact of contemporaneous shocks
is incorporated through the elements of Σ
u
. Such models
provide baseline forecasts of each endogenous variable,
along with estimates of the shock or surprise component in
each variable. If the EMH holds and all relevant new infor-
mation is incorporated immediately into stock returns, the
lagged terms in the investor equation of Equation 4 will be
zero. In contrast, lagged effects indicate that information is
incorporated gradually. For example, Pauwels and col-
leagues (2004) show that investors in the automotive indus-
try need about six weeks to fully incorporate the impact of
a new product introduction on stock returns.
Although the system’s representation makes these mod-
els more comprehensive than the single-equation
approaches (see Figure 1, first four items in the last row),
vector autoregressive models have some limitations. First,
persistence models are inherently reduced-form models,
unless structural restrictions are imposed on the contempo-
raneous causal ordering. Second, the data requirements are
substantial, and the data-generating process is assumed to
be constant over time. To alleviate this concern, the stability
of results over time needs to be tested, which may lead to
moving-window estimation to capture response shifts (e.g.,
Pauwels and Hanssens 2007). Finally, vector autoregressive
models can result in overparameterization, which may
affect the quality of individual parameter estimates.
MARKETING AND FIRM VALUE: FINDINGS
The models reviewed have been used in several studies
on the marketing–finance interface that enable us to formu-
late some empirical patterns. Table 3 summarizes the
results for market asset metrics, and Table 4 focuses on
marketing actions. We present these as propositions rather
than empirical generalizations at this juncture because the
studies are recent and, in many cases, still need replication
across industries. In turn, we discuss propositions on brand
equity, customer equity, customer satisfaction, R&D and
product quality, and specific marketing-mix actions. We
conclude with a discussion of the emerging evidence on
biases in investor response.
Marketing Assets and Investor Response
Brand equity effects. Over the past decade, there has
been significant interest among academics and practitioners
in understanding the importance of brand equity (Keller
and Lehmann 2006). Brands are viewed as assets that gen-
erate future cash flows (Aaker and Jacobson 1994; Rao,
304 JOURNAL OF MARKETING RESEARCH, JUNE 2009
Marketing Metric Illustrative Metrics Characteristics Illustrative Studies Empirical Findings
1. Brand equity
Financial World’s
measure of brand
equity
Young &
Rubicam’s Brand
Asset Valuator
Brand’s strength is
determined by five
components.
Based on consumer self-
reports on five brand asset
pillars—relevance, vitality,
esteem, knowledge, and
differentiation.
Available only for large
firms.
Not always publicly
available to investors (e.g.,
Young & Rubicam).
Barth et al. (1998)
Simon and Sullivan (1993)
Madden, Fehle, and
Fournier (2006)
Rao, Agarwal, and
Dahlhoff (2004); Joshi and
Hanssens (2008)
Mizik and Jacobson (2007)
Stock returns are positively related to brand
valuation.
A substantial fraction of the valuation of
consumer goods companies and even some
high-technology firms is based on brand
equity.
Strong brands deliver greater stock returns and
do so with lower risk.
Impact of branding on firm valuation is
moderated by type of branding strategy:
corporate branding, house of brands, or mixed
branding.
Changes in a firm’s brand assets are associated
with changes in financial market valuation.
2. Customer
satisfaction
American
Customer
Satisfaction Index
(ACSI)
Publicly available ACSI
data but not at the firm
level.
ACSI scores are updated
only annually.
Disaggregate firm/product
data available for certain
industries (e.g., auto from
J.D. Power and
Associates).
Ittner and Larcker (1998)
Anderson, Fornell, and
Mazvancheryl (2004)
Gruca and Rego (2005)
Fornell et al. (2006); Mittal
et al. (2005)
Gupta and Zeithaml (2006)
Luo and Bhattacharya
(2006)
A 5-unit increase on a 0–100 scale (roughly
one standard deviation from its mean) in the
ACSI was associated with a 1% increase in
CARs.
A 1% change in ACSI is associated with a
1.016% change in Tobin’s q.
A 1-point increase in the ACSI generates an
additional growth in cash flows and a decrease
in cash flow variability.
Highly satisfied customers generate positive
returns.
There is a strong link among customer
satisfaction, firm profitability, and market
value.
Customer satisfaction partially mediates the
relationship between corporate social
responsibility and firm market value.
3. Customer metrics
Customer lifetime
value
Customer equity
Customer metrics data tend
to be proprietary.
Gupta, Lehmann, and
Stuart (2004)
Valuing customers makes it feasible to value
firms because customer equity moves in
parallel with market value for three of the five
companies.
Retention is more important than margin or
acquisition cost because a 1% improvement in
retention can improve profitability by
approximately 5%, while a similar
improvement in margin and acquisition cost
improves profits by 1.1% and .1%,
respectively.
4. Product quality
Equitrend
Perceived Quality
J.D. Power and
Associates
Perceived Appeal
and Quality
Product Review
(e.g., LexisNexis)
Customer-driven measures.
Amenable to event study
analysis.
Time-intensive data
collection (e.g., product
review data).
Aaker and Jacobson
(1994); Mizik and
Jacobson (2003)
Srinivasan et al. (2009)
Tellis and Johnson (2007)
Perceived quality is associated with changes in
stock returns, and thus investors view quality
signals as providing useful information about
future prospects of the firm.
New product introductions that enjoy more
positive consumer perceptions of quality and
product appeal lead to systematically higher
returns.
Ratings of quality in published reviews
influence investors’ evaluation of the quality of
the firm’s products. Firms with good-quality
reviews enjoy a gain of 10% in stock returns
over the same period, while firms with poor-
quality reviews suffer a drop of returns of
approximately 5%.
Ta ble 3
MARKETING ASSETS (AS PREDICTORS) AND INVESTOR RESPONSE: METRICS AND FINDINGS
Marketing and Firm Value 305
Marketing Metric Illustrative Metrics Characteristics Illustrative Studies Empirical Findings
1. Advertising Advertising dollars
(e.g.,
COMPUSTAT)
Advertising dollars
(e.g., TNS Media)
COMPUSTAT provides
aggregate firm-level
quarterly data, but it is
widely available.
TNS Media provides
disaggregate data at the
brand/category level, and
data interval is monthly.
Data are expensive.
Frieder and Subrahmanyam
(2005); Grullon, Kanatas,
and Weston (2004); Joshi
and Hanssens (2008); Barth
et al. (1998); Rao, Agarwal,
and Dahlhoff (2004)
Advertising directly affects stock returns
beyond the indirect effect of advertising
through lifting sales revenues and profits.
Advertising has a direct effect on firm value
through two mechanisms: spillover and
signaling.
Investors cognizant of the benefits of
increased advertising through enhanced
brand equity may look beyond a firm’s
current cash flows and translate the long-
term effects of advertising into firm
valuation.
Mathur and Mathur (2000);
Mathur, Mathur, and Rangan
(1997)
Advertising may act as a signal of the firm’s
financial well-being or competitive viability.
Grullon, Kanatas, and
Weston (2006)
Firms that raise significant amounts of equity
capital increase their advertising significantly
more than firms with higher financial leverage
(i.e., higher levels of debt relative to equity
capital).
McAlister, Srinivasan, and
Kim (2007)
Advertising lowers its systematic market risk.
Srinivasan et al. (2009) Communicating the differentiated added value
created by product innovation yields higher
firm value effects of these innovations,
especially for pioneering innovations.
2. Price promotions Promotional
expenditures
(e.g., J.D. Power
and Associates)
Disaggregate, weekly,
brand-/category-level data,
but data tend to be
proprietary.
Pauwels et al. (2004) Price promotions diminish long-term firm
value, even though they have positive effects
on revenues and, in the short run, on profits.
A policy of aggressive new product
introductions acts as an antidote for
excessive reliance on consumer incentives.
3. Distribution
channels
Channel additions
(e.g., newspaper
search of Internet
channel additions)
Amenable to event study
analysis.
Internet data collection is
time intensive.
Geyskens, Gielens, and
Dekimpe (2002)
Gielens et al. (2008)
Investors perceive the expected gains of the
added channel as outweighing its costs.
However, the negative stock returns are
observed for established firms that may be
hurt by Internet channel cannibalization.
Entry of large retailers can have negative
and positive effects on firm value of other
retailers.
4. New products Product
preannouncements
(e.g., LexisNexis)
New product
introductions
(e.g., J.D. Power
and Associates)
Amenable to event study
analysis.
Researcher needs to control
for considerable delay in
preannouncement date and
introduction.
Chaney, Devinney, and
Winer (1991)
Sorescu, Shankar, and
Kushwaha (2007)
Kelm, Narayanan, and
Pinches (1995)
Pauwels et al. (2004)
Srinivasan et al. (2009)
New product announcements generate small
excess stock market returns for a few days.
Financial returns from preannouncements
are significantly positive in the long run.
Additional excess returns can be created
when the new product is subsequently
launched.
New product introductions increase long-
term financial performance and firm value,
but promotions do not. Moreover, investor
reaction to new product introduction occurs
over time, indicating that useful information
unfolds in the first two months after product
launch.
Pioneering (new-to-the-world) innovations
have a higher stock return impact than
nonpioneering innovations.
Ta ble 4
MARKETING ACTIONS (AS PREDICTORS) AND INVESTOR RESPONSE: METRICS AND FINDINGS
306 JOURNAL OF MARKETING RESEARCH, JUNE 2009
Agarwal, and Dahlhoff 2004), and investors appear to con-
sider brand value in their stock evaluation (Barth et al.
1998; Simon and Sullivan 1993). Marketing research on the
link between brand-related intangible assets and firm value
has assessed stock market reaction to the changing of a
company’s name (Horsky and Swyngedouw 1987), new
product announcements (Chaney, Devinney, and Winer
1991), perceived quality (Aaker and Jacobson 1994), brand
extensions (Lane and Jacobson 1995), brand attitude
(Aaker and Jacobson 2001), and customer mind-set brand
metrics (Mizik and Jacobson 2008).
Research using a commercial brand equity metric, Inter-
brand, has indicated that strong brands not only deliver
greater stock returns than a relevant benchmark portfolio
but also do so with lower risk (Madden, Fehle, and Fournier
2006). In addition, research has implied that the impact of
marketing variables on Tobin’s q may be moderated by the
type of branding strategy a firm adopts (Rao, Agarwal, and
Dahlhoff 2004; Joshi and Hanssens 2008). A corporate
branding strategy was found to offer higher returns than
either a house-of-brands strategy or a mixed-branding strat-
egy. Although there is intense discussion about the admis-
sion of brands into financial accounts in the accounting
community (Barth et al. 1998; Lev and Sougiannis 1996),
there is little disagreement that brands are intangible assets
of a firm. In summary, improvements in brand equity have a
significant, positive impact on firm valuation.
Customer satisfaction effects. Several recent studies have
shown a strong link among customer satisfaction, firm prof-
itability, and market value (for a review, see Gupta and
Zeithaml 2006). Changes in customer satisfaction are asso-
ciated with increases in abnormal returns (Ittner and Lar-
cker 1998), increases in Tobin’s q (Anderson, Fornell, and
Mazvancheryl 2004), increases in cash flows, and decreases
in cash flow variability (Gruca and Rego 2005). Using
comprehensive historical data, Luo and Bhattacharya
(2006) show that customer satisfaction partially mediates
the relationship between corporate social responsibility and
firm market value. Furthermore, higher levels of customer
dissatisfaction harm a firm’s future idiosyncratic stock
returns (Luo 2007). Because cash flow volatility affects the
firm’s cost of capital, this effect provides yet another source
for stock price appreciation.
In cross-sectional analyses, Fornell and colleagues
(2006) and Mittal and colleagues (2005) report that firms
with highly satisfied customers usually generate positive
returns. In addition, Fornell and colleagues report that
changes in the American Customer Satisfaction Index are
not immediately or fully incorporated into stock returns.
This situation creates an arbitrage opportunity for alert
investors, which the authors find to be sizable over a five-
year horizon. Conversely, Anderson, Fornell, and Maz-
vancheryl (2004) report that satisfaction growth is posi-
tively related to Tobin’s q growth. A possible explanation
for these different findings may stem from the different
periods used (1994–1997 versus 1994–2002). The differ-
ence may also stem from a failure to include an appropriate
measure of unanticipated customer satisfaction in the stock
return model (for a discussion, see Jacobson and Mizik
2009a, b). Yet another possible explanation is that some
studies do not control for financial and accounting informa-
tion that is likely to affect investor expectations (i.e., an
omitted variables problem). For example, Fornell and col-
leagues do not consider two of the four factors—namely,
the size and the value risk factors. In summary, levels of
customer satisfaction are significantly related to firm value,
while news about changes in customer satisfaction may not
result in an immediate change in firm valuation.
Customer equity effects. Customer equity and market val-
uation are intrinsically related because they are two ver-
sions of the principle of the present value of a stream of
expected future cash flows. This connection helps make
marketing more financially relevant and accountable. As an
illustration, in a small-sample study of five companies,
Gupta, Lehmann, and Stuart (2004) demonstrate how valu-
ing customers makes it feasible to value firms because cus-
tomer equity moves in parallel with market value for three
of the five companies. Notably, they find that the remaining
two companies are potentially mispriced. Their key find-
ings are in Column 5 of Table 3. However, customer equity
maximization can imply a narrowing of the customer base
because the firm concentrates its efforts on the most prof-
itable customers. This practice may increase the firm’s risk
in the long run, which is an area in need of further research.
In summary, improvements in customer equity are signifi-
cantly related to firm value.
R&D and product quality effects. Several studies have
linked firm value to R&D expenditures (Doukas and
Switzer 1992; Chan, Lakonishok, and Sougiannis 2001);
discretionary expenditures, such as R&D and advertising
(Erickson and Jacobson 1992; Griliches 1981; Jaffe 1986;
Pakes 1985); and innovation (Bayus, Erickson, and Jacob-
son 2003; Pauwels et al. 2004). Most notably, value crea-
tion (e.g., through investments in R&D), in combination
with value appropriation (e.g., through investments in
advertising), has been found to enhance firm value (Mizik
and Jacobson 2003). As for product quality, its relationship
to market valuation is a relatively new research area. The
research is sparse because there are varying definitions for
quality, and there are significant differences between objec-
tive quality and perceived quality (Mitra and Golder 2006).
Changes in perceived quality are associated with changes in
stock returns, and thus investors view the quality signal as
providing useful information about the future-term
prospects of the firm (Aaker and Jacobson 1994; Mizik and
Jacobson 2004). Moreover, two recent studies suggest that
it takes innovation and quality assessment to improve stock
performance. Srinivasan and colleagues (2009) assess the
impact of unanticipated consumer quality scores in product
innovations, and Tellis and Johnson (2007) focus on the
impact of expert ratings of quality; we note their key find-
ings in Table 3. In summary, it takes more than merely
introducing new products to improve stock performance.
Improvements in consumer appraisal in terms of perceived
quality, particularly for new products, are significantly
related to firm value.
Overall, research supports the proposition that brand
equity, customer satisfaction, customer equity, and R&D
and product quality are all linked to firm value. These are
slow-moving performance metrics that are not immediately
visible. In contrast, marketing initiatives are typically
immediately observable, but because they are not outcome
variables, their impact on firm value is more ambiguous.
Marketing and Firm Value 307
Marketing Mix and Investor Response
Advertising effects. Several recent studies have suggested
that a firm’s advertising (Frieder and Subrahmanyam 2005;
Grullon, Kanatas, and Weston 2004; Joshi and Hanssens
2008) directly affects stock returns, beyond the indirect
effect of advertising through lifting sales revenues and prof-
its. The intangible equity that advertising attempts to create,
ostensibly for customer marketing purposes, can spill over
onto investors and increase the firm’s salience with individ-
ual investors, who typically prefer holding stocks that are
well known or familiar to them (Frieder and Subrah-
manyam 2005; Grullon, Kanatas, and Weston 2004). Luo
and Donthu (2006) report a positive influence of marketing
communication productivity on shareholder value. These
findings help explain why several firms advertise at levels
beyond those justified by sales response alone. Indeed,
recent studies have confirmed that advertising expenditures
create an intangible asset (Barth et al. 1998; Rao, Agarwal,
and Dahlhoff 2004). After controlling for other factors,
Grullon, Kanatas, and Kumar (2006) find that firms that
decrease their leverage through increased equity financing
advertise more aggressively than firms whose debt financ-
ing has increased. Their rationale for this increased
advertising spending is that it creates more assets that are
intangible and nontransferable. In addition, McAlister,
Srinivasan, and Kim (2007) report that a firm’s advertising
lowers its systematic market risk. Srinivasan and colleagues
(2009) find that communicating the added value created by
product innovation to consumers yields higher firm value
effects of these innovations, especially for pioneering inno-
vations. Our conclusion is that advertising affects intangible
firm value and lowers systematic market risk. Moreover,
product innovation affects firm value more when it is
accompanied by higher advertising support.
New product introduction effects. It has been reported
that new product announcements generate small excess
stock market returns for a few days (Chaney, Devinney, and
Winer 1991; Eddy and Saunders 1980; Kelm, Narayanan,
and Pinches 1995). Although these studies have focused on
the short-term effect, recent evidence indicates that the
financial returns from preannouncements are significantly
positive in the long run as well, with annual abnormal
returns of approximately 13% (Sorescu, Shankar, and
Kushwaha 2007). Similarly, Pauwels and colleagues (2004)
find that new product introductions increase long-term
financial performance and firm value, but promotions do
not. Moreover, investor reaction to new product introduc-
tion occurs over time, indicating that financially useful
information unfolds in the first two months after product
launch. Finally, the stock performance impact shows a U-
shaped relationship to innovation level, which is predomi-
nantly in the positive zone, but with a preference for new
market entries over minor innovations (Pauwels et al.
2004). However, this positive impact of innovation is not
without error; recent empirical evidence suggests that
investor reaction is a poor predictor of the eventual com-
mercial success of new product introductions (Markovitch
and Steckel 2006). We conclude that firm innovativeness is
predominantly positively related to firm value and poten-
tially unfolds over time.
Price promotion effects. Although many studies have
examined the impact of price promotions on revenues and
firms, their impact on firm valuation is relatively under-
researched. An exception is Pauwels and colleagues (2004),
who find that investor reaction mirrors consumer reaction
to incentive programs, which is strong, immediate, and
positive (Blattberg, Briesch, and Fox 1995; Srinivasan et al.
2004). However, these beneficial effects are short-lived for
all but firm top-line performance because both long-term
bottom-line and firm value elasticities are negative. Price
promotions may also signal desperation, foretelling
decreased earnings. Another plausible explanation for these
sign switches is price inertia or habit formation in sales pro-
motions: The short-term success of promotions makes it
attractive for managers to continue using them (Nijs, Srini-
vasan, and Pauwels 2007). However, this practice eventu-
ally erodes profit margins, and bottom-line performance
and firm value suffer in the long run. In summary, price
promotions are negatively related to firm value in the long
run.
Channels of distribution effects. The relationship
between channel strategy and market valuation is also
underresearched. In a study of the net impact of an addi-
tional Internet channel on a firm’s stock return, Geyskens,
Gielens, and Dekimpe (2002) show that, on average,
investors perceive that the expected gains of the added
channel will outweigh its costs. However, negative stock
returns are observed for established firms that may be hurt
by Internet channel cannibalization. More recently, Gielens
and colleagues (2008) assessed the effect of Wal-Mart’s
entry in the United Kingdom on the stock prices of Euro-
pean retailers. They find that the shareholder value of
incumbent retailers is negatively affected by the degree of
overlap with Wal-Mart in assortment, positioning, and
country of entry. Conversely, the shift in retail power can
also lead to positive effects in the form of channelwide pro-
ductivity increases for all retailers. Although these studies
examine the market valuation impact of channel additions,
research is needed on channel deletions as well. We con-
clude that, on average, the opening of new distribution
channels is positively related to firm value.
How Does Stock Price Influence Marketing Actions?
The previously stated propositions establish that
investors interpret many marketing initiatives, and therefore
marketers may want to incorporate investor behavior in
their actions. For example, Rappaport (1987, p. 62) notes
that “sophisticated managers have found that they can learn
a lot if they analyze what the stock price tells about the
market’s expectations about their company’s perform-
ance;… managers who ignore important signals from stock
price do so at their peril.” The central premise in this
research is that managers look to stock returns for informa-
tion, actively respond to that information, and do so differ-
ently depending on whether the information is “good news”
or “bad news.” Specifically, managers of firms with under-
performing stocks react more aggressively with changes to
their product portfolio and distribution than managers of
firms with high-performing stocks (Markovitch, Steckel,
and Yeung 2005).
Recent evidence also suggests that in a myopic effort to
inflate current-term earnings to give the appearance of
improved long-term business prospects (and thus enhance
stock price), managers tend to reduce marketing expendi-
308 JOURNAL OF MARKETING RESEARCH, JUNE 2009
tures at the time of seasoned equity offerings (Mizik and
Jacobson 2007). Furthermore, an unexpected decline in a
firm’s stock price has been shown to lower managers’ sub-
sequent marketing and R&D spending (Shin, Sakakibara,
and Hanssens 2008). In summary, preliminary evidence
supports reverse causality; that is, changes in firm value
may drive some marketing actions.
Biases in Investor Response to Marketing Actions
Given stock market reaction to marketing changes, there
are several reasons investors may find it difficult to evaluate
the impact of marketing actions, leading to deviations from
the standard EMH model (e.g., Thaler 2005). First, investor
overconfidence bias is well documented (e.g., Daniel and
Titman 1999) and is hypothesized to stem from illusions of
control and knowledge. Second, investor familiarity bias
occurs because investors are cognitively unable to apply the
same level of expertise across an entire universe of stocks
(Freider and Subrahmanyam 2005; Shiller 2003). In this
context, advertising can help attract a disproportionate
number of investors who, at least in part, make their invest-
ments based on familiarity rather than fundamental infor-
mation (Grullon, Kanatas, and Weston 2004). Third,
investors are subject to loss-aversion bias (Benartzi and
Thaler 1995). Even those with long-term investment hori-
zons are tempted to change course at the prospect of short-
term losses.
Finally, investors may be influenced by persuasive com-
munication, either by companies themselves or by stock
analysts. Companies spend substantial resources in dealing
with capital markets through press releases, corporate
advertising, chief executive officer appearances, and the
like. Stock analysts specialize in certain sectors and com-
pete with one another for influence over investors when
they make stock recommendations. Recent work shows that
investor portfolio choices for mutual funds are affected by
fund advertising (Cronqvist 2006; Gallaher, Kaniel, and
Starks 2005; Sirri and Tufano 1998), even though such
advertising provides little direct informational content (e.g.,
Nelson 1974). In other words, investors are biased toward
investing more in mutual funds with higher levels of adver-
tising, even though these funds are not associated with
higher postadvertising excess returns (Jain and Wu 2000;
Mullainathan and Shleifer 2005). Similarly, analysts may
have a biasing influence on investors as well. Specifically,
analyst forecasts could be positively biased because of
client relationships (e.g., Kothari 2001) or herding behavior
(e.g., Trueman 1994). In summary, preliminary evidence
indicates that there are biases in investor response to mar-
keting actions.
FUTURE RESEARCH DIRECTIONS
Our review has emphasized the importance of the
investor community in the design and execution of market-
ing plans. Investors react to changes in important marketing
assets and actions that they perceive as changing the out-
look of firms’ cash flows. Several econometric models have
been developed to parameterize these relationships, and
several empirical propositions have been generated to date.
These lead to the formulation of an important agenda for
further research in the following areas:
1. Comparing the different measures of brand equity: We
know that investors react to movements in brand value, but
are these brand metrics reliable and consistent with each
other? In general, what is the best approach to quantify the
value of intangibles (e.g., brands, intellectual property) and
to assess their impact on cash flows, growth, and risk?
2. Understanding the stock market impact of various metrics
of return on marketing investment: Given that the benefits
of sound marketing and branding strategy are typically
materialized over multiple periods, are these measures of
return on marketing investment shortsighted?
3. Understanding the stock market impact of known marketing
phenomena such as diffusion of innovation, which can gen-
erate momentum in sales and stock returns. More generally,
assessing how marketing may create the momentum factor
in the Carhart four-factor financial model.
4. Understanding the stock market impact of corporate social
responsibility initiatives, such as environmental sustainabil-
ity. In particular, do higher levels of social responsibility
investments hurt or benefit firms from a firm valuation
perspective?
5. Assessing the influence of public relations efforts on the
investor community.
6. Prescribing the critical marketing information elements that
should be made available to investors: As an example,
should firm revenue be broken down between existing and
new customers? In addition, how should the value of
market-based assets (e.g., customer lifetime value, brand
equity, channel equity) and firms’ marketing strategies be
communicated? What is the role of intermediate perform-
ance metrics, such as customer satisfaction, and how do
they affect valuation? Why are movements in customer sat-
isfaction not immediately reflected in stock returns, even
though a long-term relationship exists between customer
satisfaction and investor valuation?
7. Understanding the volatility component of firm value: In
particular, do higher levels of brand equity, customer equity,
and product variety reduce the vulnerability of companies
to competitive inroads, thus reducing risk and volatility of
cash flows? Does this result in favorable risk profiles (lower
βs)? Furthermore, what is the relationship between volatil-
ity in cash flows (or volatility in earnings) and the firm’s
systematic market risk (i.e., β)?
8. Dealing with short-term revenue pressures: To date, the
empirical evidence supports the notion that the stock mar-
ket is not myopic. Thus, companies that engage in effective
strategic marketing spending should feel justified in their
actions. However, many corporate executives are concerned
about their quarterly performance metrics, which motivates
some of their actions. How can these two seemingly contra-
dictory behaviors be reconciled?
9. Identifying the conditions under which investor reaction is
accurate and how long it takes for such investor reaction to
materialize: Given the mixed evidence on the quality of
investor reaction, it is important to understand when biases
occur and how they can be corrected.
10. Understanding the potential biases introduced by persuasive
communication of analysts and company representatives:
How do analysts’ interpretations of marketing activities,
such as product-price changes, affect stock returns? Can
corporate lobbying efforts influence analyst reports? In
turn, how do these reports influence subsequent movements
in firm value? Is there a difference in the behavior of stock
returns of firms that are tracked by analysts versus those
that are not? How long does it take for investors to account
for such biases?
Marketing and Firm Value 309
Overall, given the increasing pressures on marketing
executives to demonstrate the financial accountability of
their firms’ marketing initiatives, the studies we have
reviewed clearly point to the link between marketing
actions and investor response. Lev (2004) notes that mar-
keting managers need to generate better information about
their intangibles (e.g., investments in brand building, prod-
uct and service innovations, R&D) and the benefits that
flow from them and then disclose that information to the
capital markets to give investors a sharper picture of the
company’s performance outlook. As a step in that direction,
we hope that the collective findings in this article generate a
much-needed discussion among senior management,
finance and marketing executives, and academics on the
important role of marketing actions in determining firm
valuation.
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