Customer Satisfaction and Stock Prices: High Returns, Low Risk

Abstract

Do investments in customer satisfaction lead to excess returns? If so, are these returns associated with higher stock market risk? The empirical evidence presented in this article suggests that the answer to the first question is yes, but equally remarkable, the answer to the second question is no, suggesting that satisfied customers are economic assets with high returns/low risk. Although these results demonstrate stock market imperfections with respect to the time it takes for share prices to adjust, they are consistent with previous studies in marketing in that a firm's satisfied customers are likely to improve both the level and the stability of net cash flows. The implication, implausible as it may seem in other contexts, is high return/low risk. Specifically, the authors find that customer satisfaction, as measured by the American Customer Satisfaction Index (ACSI), is significantly related to market value of equity. Yet news about ACSI results does not move share prices. This apparent inconsistency is the catalyst for examining whether excess stock returns might be generated as a result. The authors present two stock portfolios: The first is a paper portfolio that is back-tested, and the second is an actual case. At low systematic risk, both outperform the market by considerable margins. In other words, it is possible to beat the market consistently by investing in firms that do well on the ACSI.

Full text

3
Journal of Marketing
Vol. 70 (January 2006), 3–14
©2006, American Marketing Association
ISSN: 0022-2429 (print), 1547-7185 (electronic)
Claes Fornell, Sunil Mithas, Forrest V. Morgeson III, & M.S. Krishnan
Customer Satisfaction and Stock
Prices: High Returns, Low Risk
Do investments in customer satisfaction lead to excess returns? If so, are these returns associated with higher
stock market risk? The empirical evidence presented in this article suggests that the answer to the first question is
yes, but equally remarkable, the answer to the second question is no, suggesting that satisfied customers are eco-
nomic assets with high returns/low risk. Although these results demonstrate stock market imperfections with
respect to the time it takes for share prices to adjust, they are consistent with previous studies in marketing in that
a firm’s satisfied customers are likely to improve both the level and the stability of net cash flows. The implication,
implausible as it may seem in other contexts, is high return/low risk. Specifically, the authors find that customer sat-
isfaction, as measured by the American Customer Satisfaction Index (ACSI), is significantly related to market value
of equity. Yet news about ACSI results does not move share prices. This apparent inconsistency is the catalyst for
examining whether excess stock returns might be generated as a result. The authors present two stock portfolios:
The first is a paper portfolio that is back tested, and the second is an actual case. At low systematic risk, both out-
perform the market by considerable margins. In other words, it is possible to beat the market consistently by invest-
ing in firms that do well on the ACSI.
Claes Fornell is Donald C. Cook Professor of Business Administration and
Director of the National Quality Research Center (e-mail: cfornell@umich.
edu), Forrest V. Morgeson III is a research scientist, National Quality
Research Center (e-mail: morgeson@umich.edu), and M.S. Krishnan is
Professor of Business Information Technology, Area Chair, and Mary and
Mike Hallman Fellow (e-mail: mskrish@umich.edu), Stephen M. Ross
School of Business, University of Michigan. Sunil Mithas is an assistant
professor, R.H. Smith School of Business, University of Maryland (e-mail:
smithas@umd.edu). The authors thank John Cattier, Stefan Dragolov,
Janice Kwan, Eli Dragolov, and Steven Erisch for providing research
assistance in collecting necessary data for this study. They also acknowl-
edge useful comments and help from David VanAmburg and Jaesung
Cha of National Quality Research Center at University of Michigan during
the progress of the study. The authors are grateful to Ramanath Subra-
manyam for his help and comments during early stages of this research.
Financial support for this study was provided in part by the Michael R. and
Mary Kay Hallman Fellowship at the Stephen M. Ross School of Busi-
ness, University of Michigan.
Does the satisfaction of a company’s customers have
anything to do with the company’s stock price? In a
way, it must. Otherwise, it would be necessary to go
back to the drawing board of how capitalistic market theory
is supposed to work. The empirical literature on the nature
of the relationship is growing, but it is still in its infancy in
many respects. Empirical results (Anderson 1996; Ander-
son, Fornell, and Lehmann 1994; Anderson, Fornell, and
Mazvancheryl 2004; Bolton 1998; Fornell 2001; Ittner and
Larcker 1996, 1998; Rust, Moorman, and Dickson 2002)
point toward a significant relationship between customer
satisfaction and economic performance in general, but less
is known about how the satisfaction of companies’ cus-
tomers translates into securities pricing and investment
returns, and virtually nothing is known about the associated
risks. The tacit link between buyer utility and the allocation
of investment capital is a fundamental principle on which
the economic system of free market capitalism rests. The
degree to which capital flows from investors actually move
in tandem with consumer utility is a matter of significant
importance because it is an indication of how well (or
poorly) markets truly work. A parallel, but narrower, issue
is the extent to which equity markets themselves are effi-
cient. The assertion in efficient markets theory is that finan-
cial prices accurately reflect all public information at all
times, but financial assets can be correctly priced in equity
markets even if consumer sovereignty is restricted in prod-
uct markets. In a monopoly, for example, dissatisfied buyers
cannot punish the seller by taking their business elsewhere,
and capital flows are not likely to correspond with con-
sumer utility, but that does not in itself imply that capital
markets are inefficient. However, efficient allocation of
resources in the overall economy and consumer sovereignty
depend on the joint ability of product and capital markets to
reward and punish companies such that firms that fail to sat-
isfy customers are doubly punished by both customer
defection and capital withdrawal. Similarly, firms that do
well by their customers would be doubly rewarded by more
business from customers and more capital from investors.
We begin by estimating the relationship between cus-
tomer satisfaction and market value of equity. As expected,
we find a strong relationship. This prompts the question,
How do investors react to news about changes in customer
satisfaction? Despite the significant relationship between
customer satisfaction and market value, we find no evidence
that they react in a timely manner. Is this because the news
is somehow factored into share prices already, or is it due to
market imperfections? The evidence suggests the latter.
Two stock portfolios, one paper strategy that is back tested
and another describing an actual case, lead to similar con-
clusions: It is possible to beat the market consistently with
investment decisions based on customer satisfaction. Per-
haps even more remarkable is that these results are not
associated with a risk premium. Indeed, the opposite is true;

4/ Journal of Marketing, January 2006
the systematic risk is low relative to the market. Although
these results may appear too good to be true and puzzling
from a conventional financial perspective, we present argu-
ments suggesting that they are in line with empirical find-
ings both in marketing and in the literature on customer
satisfaction.
Customer Satisfaction: Economic
Effects
We now turn to the fundamentals behind the relationship
between customer satisfaction and stock prices. Both basic
neoclassical economics and marketing theory provide a
general case for a positive association. Conversely, several
market factors may contribute to a negative relationship. In
addition to market factors, there are other circumstances in
which the association could actually be negative. We dis-
cuss both categories and empirically analyze stock market
reaction to quarterly news releases from the American Cus-
tomer Satisfaction Index (ACSI) (Fornell et al. 1996). By
controlling for other firm-specific news, we test the effect
of news announcements about customer satisfaction on
stock prices.
Real economic growth depends on the productivity of
economic resources and the quality of the output (as experi-
enced by the user) that those resources generate. In the final
analysis, expanding economic activity per se is not what is
essential. Both marketing and neoclassical economics view
consumer utility, or satisfaction, as the real standard for
economic growth. The extent to which buyers financially
reward sellers that satisfy them and punish those that do not
and the degree to which investment capital reinforces the
power of the consumer are fundamental to how markets
function. A well-functioning market allocates resources,
including capital, to create the greatest possible consumer
satisfaction as efficiently as possible. A dissatisfied buyer
will not remain a customer unless there is nowhere else to
go or it is too expensive to get there.
Restrictions on consumer choice may be good for the
monopolist but, in general, are considered harmful to the
economy. In a competitive marketplace that offers meaning-
ful consumer choice alternatives, firms that do well by their
customers are rewarded by repeat business, lower price
elasticity, higher reservation prices, more cross-selling
opportunities, greater marketing efficiency, and a host of
other things that usually lead to earnings growth (Fornell et
al. 1996).
To what degree do markets actually reflect the norma-
tive tenets of market theory? Under what circumstances
would news about increasing customer satisfaction con-
tribute to higher stock prices? Under what circumstances
would it have the opposite effect? From a cursory glance at
the literature, it is tempting to assume that news about rising
customer satisfaction would have an immediate and positive
effect on stock prices. Beginning with the early contribu-
tions of Bursk (1966) and Jackson (1985), there is substan-
tial conceptual logic and empirical evidence to suggest that
the health of a firm’s customer relationships is a relevant
indicator of firm performance (Ambler et al. 2002; Bell et
al. 2002; Berger et al. 2002; Blattberg and Deighton 1996;
1For example, in a study of a catalog retailer, Reinartz and
Kumar (2000, 2002) find a weaker-than-expected (but significant)
relationship between customer retention and profitability. The
strength of any customer retention–profitability relationship
depends on the cost of creating and maintaining repeat customers.
If repeat business is created through price discounts or other
means that do not cause an upward shift in the firm’s demand
curve, the relationship will be weaker. Thus, repeat business pro-
duced by higher customer satisfaction will be more profitable in
general than repeat business generated by price discounts.
Bolton, Lemon, and Verhoef 2004; Fornell 1995, 2001; For-
nell et al. 1996; Hogan, Lemon, and Rust 2002; Rust et al.
2004). For example, it has been found that customer satis-
faction has a negative impact on customer complaints and a
positive impact on customer loyalty and usage behavior
(Bolton 1998; Fornell 1992). Increased customer loyalty
may increase usage levels (Bolton, Kannan, and Bramlett
2000), secure future revenues (Rust and Keiningham 1994;
Rust, Moorman, and Dickson 2002), reduce the cost of
future transactions (Reichheld and Sasser 1990), lower
price elasticity (Anderson 1996), and minimize the likeli-
hood of customer defection (Anderson and Sullivan 1993;
Mithas, Jones, and Mitchell 2004). Customer satisfaction
may also reduce costs related to warranties, complaints,
defective goods, and field service costs (Anderson, Fornell,
and Lehmann 1994; Fornell 1992; Garvin 1988). Empirical
evidence also suggests that customer perceptions of supe-
rior quality are associated with higher economic returns
(Aaker and Jacobson 1994; Capon, Farley, and Hoenig
1990; Fornell 2001). Naumann and Hoisington (2001)
report positive associations among employee satisfaction,
customer satisfaction, market share, and productivity mea-
sures at IBM Rochester. Several case-based research studies
also find that customer satisfaction is positively associated
with employee loyalty, cost competitiveness, profitable per-
formance, and long-term growth (Heskett, Sasser, and
Schlesinger 1997; Reichheld and Teal 1996). Finally, in a
recent study of the relationship between customer satisfac-
tion and shareholder return, Anderson, Fornell, and Maz-
vancheryl (2004) find a strong relationship between cus-
tomer satisfaction and Tobin’s q (as a measure of
shareholder value) after controlling for fixed, random, and
unobservable factors.
If, as the empirical evidence suggests, customer satis-
faction tends to improve repeat business, usage levels,
future revenues, positive word of mouth, reservation prices,
market share, productivity, cross-buying, cost competitive-
ness, and long-term growth and if it tends to reduce cus-
tomer complaints, transaction costs, price elasticity, war-
ranty costs, field service costs, defective goods, customer
defection, and employee turnover, it seems logical to expect
that these effects will eventually affect stock prices and
company valuations. Even if a substantial portion of the
empirical findings were dismissed,1it would be difficult not
to take seriously the notion of customer satisfaction as a
real, albeit intangible, economic asset. Putting the argument
in the context of economic assets allows for a logical link to
conventional capital asset pricing (see Fornell and Werner-
felt’s [1987, 1988] economic analysis of the value of keep-
ing customers and the more recent work by Wayland and

Customer Satisfaction and Stock Prices / 5
Cole [1997], Srivastava, Shervani, and Fahey [1998], Gruca
and Rego [2005], Gupta, Lehmann, and Stuart [2004],
Anderson, Fornell, and Mazvancheryl [2004], Hogan,
Lemon, and Rust [2002], Venkatesan and Kumar [2004],
and Rust, Lemon, and Narayandas [2005]). Srivastava,
Shervani, and Fahey (1998) provide the conceptual logic for
more categorically drawing the link from the empirical
findings on customer satisfaction to stock returns and share-
holder value. They identify four major determinants of a
company’s market value: (1) acceleration of cash flows, (2)
increase in cash flows, (3) reduction of risk associated with
cash flows, and (4) increase in the residual value of the
business. Not only does the customer asset, as defined by
the expected future discounted net cash flow from current
and future customers, influence all four determinants, but it
can also be expressed as the sum of all other economic
assets of the firm (Wayland and Cole 1997); Gupta,
Lehmann, and Stuart (2004) also demonstrate this.
First, the acceleration of cash flows is affected by the
speed of buyer response to marketing efforts. Because it
takes less effort to persuade a satisfied customer (Keller
1993) and because it is more likely that receivables turnover
is better for firms with satisfied customers, speed of cash
flow is positively affected. Second, there is also a relation-
ship between customer satisfaction and levels of net cash
flows. Marginal costs of sales and marketing are lower, as
are the requirements for working capital and fixed invest-
ments (Srivastava, Shervani, and Fahey 1998). In addition,
revenue growth benefits from more repeat business. Gruca
and Rego (2005) provide empirical evidence for this. They
show that increases in customer satisfaction lead to signifi-
cant cash flow growth. Specifically, one point in customer
satisfaction (measured by ACSI on a 0–100 scale) was asso-
ciated with a 7% increase in cash flow. Third, Gruca and
Rego also find that the risk associated with future cash
flows is reduced for firms with high customer satisfaction.
If the variability in cash flows is reduced, the cost of capital
goes down as well, thus producing yet another source for
stock price growth. Finally, the residual value of business is
a function of the size, loyalty, and quality of the customer
base, all of which are obviously related to the satisfaction of
this customer base (Srivastava, Shervani, and Fahey 1998).
Customer Satisfaction and Market
Value of Equity
In light of the evidence reported in the literature, it might be
expected that a significant relationship exists between mar-
ket value of equity and ACSI. If customer satisfaction truly
is an economic asset that is not only left off the balance
sheet but also not fully reflected by the recorded assets,
there should be a relationship between ACSI scores and
market capitalization. Following standard practice (Barth
and McNichols 1994; Ittner and Larcker 1998; Landsman
1986), the model we define in Equation 1 estimates the
effect of an identifiable, but intangible, asset on market cap-
italization in a combined longitudinal cross-sectional set-
ting, while controlling for accounting book values. In this
case, we are interested in (1) whether customer satisfaction
(ACSI) has a significant effect on market value of equity
and (2) the size of this effect when we control for the influ-
ence of the recorded assets and liabilities. Thus:
(1) lnMVE = α+ β1lnBVA + β2lnBVL + β3lnACSI,
where MVE is the market value of equity, BVA is the book
value of total assets, BVL is the book value of liabilities,
and ACSI is the respective company score on the American
Customer Satisfaction Index.
We obtained the data from 1994–2002 ACSI scores and
COMPUSTAT company book values for the same years;
there were a total of 601 observations. The mean value for
market value is $26 billion. For book value of assets and
liabilities, the respective means are $23 billion and $15
billion.
Table 1 reports the results of fitting Equation 1 by ordi-
nary least squares. The regression is highly significant with
an R-square of .70. The ACSI coefficient is significant and
large. A 1% change in ACSI is associated with a 4.6%
change in market value. The coefficients for assets and lia-
bilities are also significant with the expected signs. Table 1
also includes estimates without the ACSI variable: R-square
drops to .61, and the negative coefficient for liabilities
increases. Thus, by omitting customer satisfaction in the
estimation of company market value, the negative impact of
liabilities appears to be greater than it should be. This is
also consistent with the previous discussion that customer
satisfaction alleviates cash flow volatility and reduces the
cost of capital.
Investor Reaction
We offer the preceding results as empirical evidence in
favor of the argument that customer satisfaction should be
considered an economic asset beyond what is recognized on
the balance sheet. The results are also consistent with the
finding that customer satisfaction is associated with long-
term shareholder value. However, they do not address
TABLE 1
The Effect of ACSI on Market Value of Equity
Ln(Market Ln(Market
Value) Value)
ACSI (lnACSI) β14.592
(.000)
Total assets (lnBVA) β21.943 2.039
(.000) (.000)
Total liabilities (lnBVL) β3–1.020 –1.157
(.003) (.003)
Constant α–20.056 .235
(.000) (.704)
N601 601
R2.70 .61
F(.000) (.000)
Notes: We estimated the models by including dummy variables (not
shown) for years 1995 through 2002 (1994 was the refer-
ence year); pvalues are in parentheses.

6/ Journal of Marketing, January 2006
whether ACSI scores provide information to investors (that
is not already known to them). How then do investors react
to news about customer satisfaction? Do they take advan-
tage of customer satisfaction as a leading indicator of finan-
cial performance, or do they fail to realize the potential of
the information? Is the information already factored in
share prices? If, indeed, customer satisfaction is relevant to
market valuation, according to efficient markets theory, new
information about customer satisfaction fluctuations should
be instantly reflected in stock prices. That is, all else being
equal, investors should reward firms with increased market
capitalization as news about the improved value of firms’
customer relationships becomes available. Conversely, news
about deteriorating customer relationships should have the
opposite effect.
Such reasoning is in concert with financial valuation
theory in that customer relationships can be expressed in
terms of net present value (Blattberg and Deighton 1996;
Hogan et al. 2002; Rust and Keiningham 1994; Venkatesan
and Kumar 2004). In competitive markets, the value of sat-
isfied customers should be worth more than the value of
dissatisfied customers.
However, the picture is more complicated. There are
reasons for suggesting that investors may not react in a pre-
dictable fashion, and there are circumstances in which stock
prices drop as a result of news about increasing customer
satisfaction. Although such a reaction might be at odds with
how capitalistic markets should function, consumer market
imperfections (which may be unknown) make it difficult to
anticipate capital market reactions to customer satisfaction
news. First, investors may react negatively to news about
rising customer satisfaction if they believe that the firm is
giving away too much surplus to the buyers. The aggregate
surplus that consumers receive from goods and services is a
measure of consumer welfare, but the difference between
the maximum price a buyer is willing to pay and the actual
price is also something that investors may want to capture.
Investors would be more likely to consider an increase in
consumer surplus in a negative light if the buyer’s switching
costs were significant, if there was a high degree of product
differentiation, or if there was some level of monopoly
power.
Second, investors may also take an unkind view of
improvements in customer satisfaction for firms that
already have substantial leads over their competition. If cus-
tomers of competing firms were much less satisfied, the
marginal return of improving customer satisfaction further
might be called into question. Certainly, it would be diffi-
cult to ignore economic laws of diminishing returns for the
investment in any economic asset, regardless of its status on
the balance sheet.
Third, and related to the preceding two points, the mar-
ginal cost of improving customer satisfaction may be too
high. This is particularly relevant for services, especially
those that are labor intensive. For example, it has been
shown (Anderson, Fornell, and Rust 1997) that productivity
and customer satisfaction are not always compatible in the
service sector. Improved customer satisfaction may come at
the expense of productivity, and vice versa. Such a trade-off
between productivity and customer satisfaction may also
contribute to unfavorable market reactions to news about
customer satisfaction improvements.
Fourth, there is the possibility of a “reverse causality
effect.” Customer defection can have a positive effect on
average customer satisfaction simply because the departing
customers were the most dissatisfied. Customers who
remain are less likely to be as discontented. Instead of ben-
efiting from improved satisfaction, the company simply
retains a smaller group of (somewhat more satisfied) cus-
tomers, but often with reduced sales and profits as a result.
Note that this effect is not the result of consumer market
imperfections. Indeed, it is more likely to occur in fluid
markets with low switching barriers and no monopoly
power.
Fifth, issues of timing and expectations complicate the
matter further. The ACSI measures each company once a
year. Although the measurement is cumulative in the sense
that it aims to capture all relevant customer experience
(with the product or service) to date, it is possible for
changes in customer satisfaction to occur and to have an
effect on buyer behavior (with implications on revenues and
profits) in periods between measurements. It is also difficult
to ascertain market expectations about customer satisfac-
tion. A firm’s improved ACSI scores may be viewed as neg-
ative if the firm was expected to show a greater degree of
improvement, and vice versa. Sources of such expectations
may be difficult to identify; they may include corporate
announcements about additional investments in consumer
service, the hiring of more frontline personnel, investments
in information technology and customer relationship man-
agement systems (Krishnan et al. 1999; Mithas, Krishnan,
and Fornell 2005a, b; Prahalad, Krishnan, and Mithas 2002;
Rai, Patnayakuni, and Patnayakuni 2006; Sambamurthy,
Bharadwaj, and Grover 2003; Srinivasan and Moorman
2005), and a host of things that may affect customer
satisfaction.
For all these reasons, it is not easy to predict stock mar-
ket reactions to news about customer satisfaction for indi-
vidual companies. It would not be enough to appeal to cap-
italistic market theory without consideration of an
assortment of market conditions. Even if the relevant char-
acteristics were observed and properly assessed, the effect
may be confounded by the shape of the satisfaction
response curve, by the firm’s location on the curve, or by
the reverse causation phenomenon. Thus, even if investors
do not react to news about customer satisfaction, share
prices may still be unbiased. Only if it is possible to use
ACSI information to discover mispriced stocks consistently
over time and invest accordingly (and thus beat the market)
can the question about possible pricing bias be addressed.
Accordingly, we conduct both an event study and portfolio
studies. We begin with the event study.
We estimate the rate of return on stock price of firm j on
day t with the following market model:
(2) Rjt = αj+ βjRmt + εjt,
where Rjt is the rate of return on the common stock of the
jth firm on day t, Rmt is the market rate of return using the
Standard & Poor’s (S&P) 500 composite index on day t, αj
is an intercept, βjis a slope parameter that measures the

Customer Satisfaction and Stock Prices / 7
2Although ACSI has measured customer satisfaction since
1994, before the second quarter of 1999, the results were pub-
lished once a year in Fortune magazine, making it difficult to pin-
point the event date because readers received the magazine on dif-
ferent dates. For example, Ittner and Larcker’s (1998) study used a
much longer event window (five and ten days) in their analysis of
the effect of the Fortune publication.
sensitivity of Rjt to the market index, and εjt is a disturbance
term with the usual ordinary least squares properties. The
S&P 500 is a capitalization weighted index based on a
broad cross-section of the market and has been used in sev-
eral previous event studies (MacKinlay 1997).
We use the market model (Equation 2) to estimate the
abnormal return for the common stock of firm j on day t,
such that
(3) ARjt = Rjt – (αj+ βjRmt).
On the basis of recommended guidelines and commonly
used practice, we specified the length of the estimation
period as 255 days, ending 46 days before the event date
(Cowan 2003; McWilliams and Siegel 1997).
We averaged daily abnormal returns using the closing
price at the day of the release over the sample of N firms to
yield cumulative abnormal returns:
where T1and T2correspond to the first and last days of the
event period.
Because the sampling distribution of abnormal returns
tends to be skewed and leptokurtic (Brown and Warner
1985; Corrado 1989; Lyon, Barber, and Tsai 1999;
McWilliams and Siegel 1997), we used bootstrapping for
all significance tests. Use of bootstrapped test statistics pro-
vide a more robust assessment of the statistical significance
of the results of the event study and is becoming increas-
ingly common in academic studies (Chatterjee, Richardson,
and Zmud 2001).
It is well known that event studies benefit considerably
from a precise definition of the event period (Barclay and
Litzenberger 1988; Brown and Warner 1985; Dyckman,
Philbrick, and Jens 1984). As for the ACSI, there is an exact
announcement day. Thus, we specify a one-day event
period. A short event period leads to more efficient estima-
tion because it reduces the possibility of other factors
affecting the returns. It also increases the power of the sta-
tistical tests.
As we indicated previously, we defined the event as the
ACSI announcement for a firm as (simultaneously) pub-
lished in The Wall Street Journal and on the ACSI Web site.
Because the The Wall Street Journal is a daily publication, it
seems reasonable to consider the day the ACSI announce-
ment is published in The Wall Street Journal as the day
when “the event” occurs. The included events range from
the second quarter of 1999 to the third quarter of 2002.2
Following recommended practice (MacKinlay 1997),
we set up controls so that the customer satisfaction news
() ,412
1
2
1
CAR
AR
N
TT
tT
T
jt
j
N
===
∑∑
3Although it seems unlikely that the ACSI information would
trickle out slowly or that investors would simply wait before acting
on the information, we also calculated cumulative abnormal
returns for a 5-day window following the release date. Again, the
null hypothesis of zero abnormal returns cannot be rejected. A
final test checks for the possibility of prior leakage of information.
The ACSI results were routinely provided under embargo to the
public relations and market research units of corporate subscribers
would not be confounded with other news items over the
same period. We collected all firm-related news items five
calendar days before and after the event date from leading
sources: PR Newswire, Dow Jones, and Business Wire. We
excluded from the analysis events with announcements
regarding mergers and acquisitions, spin-offs, stock splits,
chief executive officer and chief financial officer changes,
layoffs, restructurings, earnings announcements, and law-
suits. In total, after we eliminated the events with con-
founded news items during the event windows, the data
consisted of 161 events for 89 companies for which stock-
trading data were available for the study period. For manu-
facturing firms, there were 48 events of rising ACSI scores;
for service firms, the corresponding number was 32. There
were 41 events with decline in ACSI for manufacturing and
40 for services. The results appear in Table 2.
Although the combined samples for manufacturing and
services changed in the direction of higher stock prices for
firms with an increase in ACSI and lower prices for firms
with a decrease in ACSI, these effects are too weak to be
distinguished from chance variation. The results for service
firms are also contradictory; that is, share prices increased
regardless of the direction of the change in ACSI.
These results are fairly consistent with the findings of
Ittner and Larcker (1998). They used a longer event window
of five days and found no significant effects of ACSI news.
For a ten-day window, they reported close to significant (at
the 10% level) abnormal returns but only after they deleted
outliers (still 97%–98% of the variance in the abnormal
returns remained unexplained). Note that Ittner and Larcker
were forced to use cross-sectional analysis (because of the
limited amount of data available at the time), and thus their
results cannot be used to test the effect of ACSI on changes
in stock prices (Lambert 1998).3
TABLE 2
Abnormal Returns For ACSI News
Announcements
Event Day
Type of Abnormal
News Firm Type N Returns
Increase Manufacturing 80 .34
in ACSI and services
Manufacturing 48 .01
Services 32 .85
Decrease Manufacturing 81 –.17
in ACSI and services
Manufacturing 41 –.54
Services 40 .20
Notes: None of the abnormal returns are significant at the 5% level
based on nonparametric bootstrapped tests (two tailed).

8/ Journal of Marketing, January 2006
and to The Wall Street Journal about two weeks before the release.
That corresponds to 10 additional trading days before the event.
Again, the results are not significant. The results of 5-day and 15-
day analyses appear in the Appendix.
Portfolio Studies
The finding that news about customer satisfaction does not
move stock prices, even though customer satisfaction is sta-
tistically significant with respect to market valuation, is not
necessarily contradictory to efficient markets theory. It
would still be possible that stock prices fully and quickly
reflect all available information. It is also possible that firms
with highly satisfied customers generate higher profits.
Under efficient markets theory, however, it would not be
possible to develop trading strategies based on ACSI infor-
mation that consistently earn excess returns.
As a way to test this, we consider two stock portfolios
based on ACSI data. The first is a hypothetical paper portfo-
lio with simple trading rules. The second is a real-world
portfolio. Both tests have pros and cons. However, their
combination leads to more powerful analyses. The major
advantage of the paper portfolio is that the trading rules are
clear and explicit. In addition to there being no theoretical
basis for simulating various permutations of the trading
strategy, its major disadvantage is that it is hypothetical and
back tested with perfect hindsight.
For the real-world portfolio, these advantages and dis-
advantages are reversed. Actual trading involves many
details regarding timing, size of investments, number of
holdings, rebalancing, and so forth, that are not always pre-
cisely determined by static and explicit decision rules. In
many ways, the situation is similar to case studies of orga-
nizations for which theoretical specificity must give way to
realism. Yet this is also the benefit of a real-world portfolio:
It presents authentic investments and actual results.
Most important, however, paper portfolio and the actual
portfolio share a critical aspect: Both rely solely on ACSI
data. No other type of data or additional information was
considered in any of the investment decisions. Although
details may vary between the portfolios, and assuming an
absence of contemporaneous underlying share price deter-
minants that covary with ACSI (we discuss this further sub-
sequently), it would be difficult to attribute the results to
sources other than customer satisfaction.
Because the market does not immediately respond to
changes in ACSI, it seems reasonable to base the strategy
on both levels (because they do not appear to be fully
impounded in the market price) and changes (because they
do not appear at all to be impounded in the market price).
To have a diversified portfolio of reasonable size, we
selected firms in the top 20% of ACSI (relative to their
competition). In line with Jones and Sasser (1995) and
Ittner and Larcker (1998), who find that low levels of cus-
tomer satisfaction appear to have less of an impact, we also
made the selection conditional on the requirement of being
above the ACSI national average.
Because ACSI scores for each individual firm are
updated once a year, we purchased shares (using the closing
price) the day the ACSI results were announced and held
them for a year or longer. If we picked a stock the first year,
we examined its inclusion in the portfolio again the follow-
ing year. If it met the criteria of being in the top 20% and
above the national (average) ACSI level, we held it for
another year and then subjected it to the same test. If it
failed the criteria, we sold it. We applied the same principle
for all stocks. If we did not pick a stock the first year, we
reexamined it for inclusion the next year, and so on. This
trading strategy led to a portfolio of 20 companies in 1997
and 20 in 1998, and there were 26 companies at the conclu-
sion of the test on May 21, 2003.
Between February 18, 1997, and May 21, 2003, a
period when the stock market had both ups and downs, the
portfolio generated a cumulative return of 40% (with divi-
dends and transaction costs excluded but after stock splits,
if any, were adjusted). It outperformed the Dow Jones
Industrial Average (DJIA) by 93%, the S&P 500 by 201%,
and NASDAQ by 335%. Figures 1–3 show the cumulative
returns over time.
The results suggest that customer satisfaction pays off
in up-markets and down-markets. When the stock market
grew, the stock prices of many firms with highly satisfied
customers grew even more. The only exception occurred at
the peak of the stock market bubble in 1999, when NAS-
DAQ and the S&P 500 generated short-lived, but higher,
returns. When the stock market dropped in value, the stock
prices of firms with highly satisfied customers seemed to
have benefited from some degree of insulation.
Although the trading rules barred all information
beyond ACSI, could there be competing explanations for
the findings? We examine the possibilities. First, and
regardless of whether efficient markets theory holds, it is
difficult to beat the market. Most professional stock pickers
do not, and the chance probability of consistent excess
returns is minute. Second, there are theoretical underpin-
nings behind these returns that are bolstered by empirical
–10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
ACSI
DJIA
20032002200120001999199819971996
DJIA = 21%
Total
cumulative
return
ACSI = 40%
aFebruary 18, 1997, through May 21, 2003.
FIGURE 1
Cumulative Returns: ACSI Top 20% Versus DJIA
(1997–2003)a

Customer Satisfaction and Stock Prices / 9
–
10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
ACSI
S&P 500
20032002200120001999199819971996
S&P 500 =
13%
Total
cumulative
return
ACSI = 40%
aFebruary 18, 1997, through May 21, 2003.
FIGURE 2
Cumulative Returns: ACSI Top 20% Versus S&P
500 (1997–2003)a
–10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
110%
120%
130%
140%
150%
160%
170%
180%
190%
200%
ACSI
NASDAQ
20032002200120001999199819971996
NASDAQ =
9%
Total
cumulative
return
ACSI = 40%
aFebruary 18, 1997, through May 21, 2003.
FIGURE 3
Cumulative Returns: ACSI Top 20% Versus
NASDAQ (1997–2003)a
findings in the marketing literature. Third, the abnormal
returns are not due to compensation for risk. The beta risk
associated with the portfolio is .78 and thus is substantially
less than market.4Fourth, there is no known ACSI covariate
that accounts for the results. Firms are included in ACSI on
the basis of size (Fornell et al. 1996), and the ACSI aggre-
gate should approximately mirror large cap indexes (e.g.,
DJIA). There is no reason to suspect that the top 20% of
ACSI firms would have the same performance as the bot-
tom 80%. To verify this empirically, we collected stock
price data for all ACSI firms. The cumulative return for the
bottom 80% portfolio was 20.4%, which is virtually identi-
cal to that of the DJIA (21%), thus ruling out selection cri-
teria as a covariate of the stock performance. Because book-
to-market ratios are sometimes mentioned as possible
predictors of long-term abnormal stock returns (Fama and
French 1992), we compared these ratios as well. For the top
20%, the book-to-market ratio was .41 for the full time
period. The corresponding ratio for the bottom 80% was
.42. Finally, we examined the “size effect.” It has been
shown that smaller firms’ stocks tend to produce higher
returns (Banz 1981; Bernard and Thomas 1989; Reinganum
1981). Are the top 20% of ACSI firms smaller than the bot-
tom 80%? Consistent with the results in Table 1, the oppo-
site is true. The average revenue for the top 20% of firms is
$37.8 billion, compared with $26.4 billion for the bottom
80%.
Overall, these results are noteworthy because they sug-
gest a trading strategy with high returns and low risk, based
on theory and findings not in finance or economics but
4The beta values are relative to the S&P 500 over the entire life
of the portfolio. On a year-by-year basis, the betas are .72 (1997),
.75 (1998), .74 (1999), .75 (2000), .76 (2001), .81 (2002), and .93
(2003).
rather in the marketing literature. Because of the magnitude
of the abnormal returns, the findings are perhaps even more
remarkable considering that most brokerage firms had neg-
ative abnormal returns on their stock recommendations
between 1997 and 2001 (McGeehan 2001). Nonetheless, a
word of caution is warranted. The results refer to a paper
portfolio that is back tested. As with all such testing, there
are obvious limitations. In hindsight, it may not be that dif-
ficult to find a successful trading strategy. In defense of the
results, however, we note that the trading strategy used was
extraordinarily simple and straightforward.
However, not all individual trades were good ones.
Gateway computers is a case in point. Stock was purchased
on August 21, 2000, because the company had an ACSI
score of 78, which was in the top 20% of its industry and
above the national average. Stock was sold on August 20,
2001, because the company dropped out of the top 20%.
Over that period of time, the company’s stock price fell by
84% because consumer demand for personal computers
declined sharply. As we discussed previously, there may be
many reasons that the trading strategy may not work for
each individual company all the time. In addition, there is
no reason to believe that high customer satisfaction will
provide full stock market insulation if primary consumer
demand falters. It may dampen the fall to some degree, but
industry demand is affected by many other factors. How-
ever, from a diversified portfolio, negative industry distur-
bances should be counterbalanced by positive ones. To

10 / Journal of Marketing, January 2006
–
30%
–
20%
–
10%
0%
10%
20%
30%
40%
S&P 500
ACSI Portfolio
20042003200220012000
–8%
–12%
6%
–13%
–5%
–23%
36%
26%
32%
9%
aApril 11, 2000, through December 31, 2004.
FIGURE 4
Yearly Performance of ACSI Portfolio and S&P 500
(2000–2004)a
5For discussions on this type of elasticity, see Fornell (1992)
and Anderson, Fornell, and Mazvancheryl (2004).
6The ACSI includes many utilities. To compensate, each utility
in the portfolio received only half of the dollar amount invested in
nonutility companies.
7Accordingly, some trades were executed subsequent to the
ACSI release date (approximately 70%), and some were executed
before that date. Because there was no announcement event effect,
the positive returns were not helped by this timing. On the con-
trary, they were somewhat handicapped. The cumulative return for
the portfolio for all the event days (using the closing price of the
day of the ACSI release), through February 2003, was –1.6%. The
DJIA and S&P 500 did somewhat better at +.5% and +.6%,
respectively. All trades subsequent to February 2003 were exe-
cuted after the ACSI release.
8The 2000 results do not represent the full year. They reflect the
starting date of the portfolio (April 11, 2000).
some extent, the large losses made on Gateway in one
period were compensated by a 47% gain in another period
(1999–2000), as well as Dell Computer’s return of 50% in
1998–1999 and the very large rise of the Wal-Mart (96% in
1998–1999) and Southwest Airlines (96% in 1998–1999)
stocks.
Our final analysis goes from hypothetical paper trading
to an actual stock portfolio of ACSI companies. Thus, it
avoids the usual limitations of back testing and provides
additional evidence about the strength of the relationship
between customer satisfaction and stock price. As before,
we considered no other data but ACSI data in the invest-
ment decisions. We took both long and short positions and
based trading decisions on ACSI levels, changes, and satis-
faction elasticity of demand.5The portfolio took long posi-
tions in firms with high (above competition) and increasing
(by two points or more) ACSI scores and also when the
ACSI–customer retention (elasticity) estimate was above
average. It took short positions when scores were low
(lower than any competitor) and deteriorating (by two
points or more). The short positions were generally small
and ranged over time from 0% to 20% of the portfolio. We
held these positions for a one-year period or more. We bal-
anced the portfolio weighting with approximately the same
dollar amount for each company at the starting point but
also with consideration given to industry representation.6
We periodically rebalanced to dollar equalize the positions,
and we executed trades throughout the year without atten-
tion to the date of the ACSI release.7We reinvested divi-
dends and included transaction costs. Trading began in
April 2000, at a time just before the burst of the stock bub-
ble from which the market, as of the end of 2004, has not
yet recovered.
The portfolio generated impressive positive returns. As
we show in Figure 4, it outperformed the S&P 500 each and
every year during the five years since its inception, often by
a considerable margin. The smallest gain relative to market
was in 2000 when the S&P dropped by 12% and the ACSI
portfolio dropped by 8%.8In the following year, the portfo-
lio gained 6%, whereas the S&P continued to fall by 13%.
With the exception of 2002, when the S&P fell by 23% and
the ACSI portfolio fell by 5%, there have been large gains:
36% in 2003 (versus 26% for the S&P) and 32% in 2004
(versus 9% for the S&P). On a cumulative basis (Figure 5),
–50%
–40%
–30%
–20%
–10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
2001 2002 2003 2004
ACSI
+75%
DJIA
–5%
S&P
500
–19%
aApril 11, 2000, through December 31, 2004.
FIGURE 5
ACSI Portfolio Versus DJIA and S&P 500
(2000–2004)a

Customer Satisfaction and Stock Prices / 11
9Because the portfolio includes reinvested dividends and trans-
action costs, the comparison with market indexes is not exactly
“apples to apples,” but the gains relative to market are much
greater than the net effects from dividends and transaction costs.
For example, with dividends and transaction costs included, the
average diversified stock portfolio rose only 1% according to Lip-
per Research over the same time period.
10The year-to-year ACSI portfolio betas relative to the DJIA are
.87 (2000), .67 (2001), .74 (2002), .82 (2002), and .84 (2004). The
year-to-year ACSI portfolio betas relative to the S&P 500 are .83
(2000), .73 (2001), .72 (2002), .82 (2003), and .87 (2004).
the ACSI portfolio gained 75%, compared with a loss of
19% for the S&P 500. The DJIA did slightly better at –4%.9
As with the previous portfolio study, these returns are
not associated with higher risk. The betas for the real-world
portfolio are equivalent to the paper portfolio. The mean
beta risk for all years was .77 relative to the S&P and .76
relative to the DJIA. On an annual basis, the beta risk of the
portfolio was consistently below market.10
Similar to the hypothetical paper version, share prices
for firms that did well on ACSI rose faster than the market
and dropped at a slower rate. However, short selling pro-
duced weaker returns (less than 3% of the total), implying
asymmetric impacts from high versus low customer satis-
faction. However, it would be premature to conclude on the
basis of this finding alone that the market rewards high cus-
tomer satisfaction more than it punishes low satisfaction.
Additional studies would be necessary to draw such a
conclusion.
Discussion
By any standard, the results of the studies we report herein
are extraordinary. Not only do they show that investments
based on customer satisfaction produce sizable excess
returns, but they also upset the basic financial principle that
assets producing high returns carry high risk. The price of
any financial asset is determined by the current value of the
future cash payments it generates, discounted to compen-
sate for risk and cost of capital. The economic value of sat-
isfied customers seems to be systematically undervalued,
even though these customers generate substantial net cash
flows with low volatility. Firms that do better than their
competition in terms of satisfying customers (as measured
by ACSI) generate superior returns at lower systematic risk.
Similar security mispricings, but on a much lower scale,
have been found in related situations with respect to quality
awards announcements (Hendricks and Singhal 2001) and
marketing investments (Penman and Zhang 2001), but most
discoveries of security mispricings point to “overpricing”
(Shiller 2000). Under the assumption of efficient markets
theory, if information about customer satisfaction is rele-
vant to a company’s future economic performance, it should
be factored into the company’s stock price such that it
would not be possible to earn economic profits by trading
on this information. Yet we show that this is indeed possi-
ble. However, this does not imply a wholesale rejection of
efficient markets theory. There have been many documented
anomalies over the years (e.g., the January effect, the small-
11This is not to suggest that all securities analysts completely
ignore customer satisfaction. For example, Merrill Lynch and
Morgan Stanley have written reports on it, and in general, analysts
attribute the surge in Apple’s stock price in late 2004 to an
increase in customer satisfaction.
firm effect, the Monday effect, the post-earnings-
announcement drift). Most of them have since disappeared.
Whether that will be the case with the customer satisfaction
effect is yet to be determined, but it is less likely because,
with the exception of the public release of ACSI and con-
trary to the January effect and others, the information is not
costless and requires more sophisticated measurement tech-
nology than firms typically use today (Ittner and Larcker
2003). Because investments based on customer satisfaction
may not be publicly known, it would also be much more
difficult to verify a possible demise. According to Gupta,
Lehmann, and Stuart (2004), financial analysts have yet to
give more than scant attention to off-balance-sheet assets,
even though these assets may be key determinants of a
firm’s market value. By using a discounted cash flow analy-
sis for estimating the value of customer relationships,
Gupta, Lehmann, and Stuart find that some companies were
potentially mispriced, whereas others were not.
Taken as a whole, the conclusion from these analyses is
that though firms with highly satisfied customers usually
generate positive abnormal returns, news about changes in
customer satisfaction does not have an effect on stock
prices. In other words, the primary thesis behind capitalist
markets and marketing theory is borne out, but not without
reservations about efficient (equity) markets theory. Specif-
ically, there seem to be imperfections with respect to the
time it takes for stock markets to reward firms that do well
by their customers and to punish firms that do not. In the
wake of accounting scandals, the bursting of the stock mar-
ket bubble, and the continued weakening of the relationship
between balance sheet assets and future income, it would be
in the interest of securities research to pay closer attention
to customer satisfaction and the strength of customer rela-
tionships. For marketing managers, it is clear that the cost
of managing customer relationships and the cash flows they
produce is fundamental to value creation.
The principal short-term impact of the findings may be
to boost security analysts’ demand for customer satisfaction
information. If history is a guide, however, it will be diffi-
cult for analysts to separate the “good” from the “bad” and
the relevant from the irrelevant when it comes to this type
of information. This will be a serious challenge because
recent experience with valuing intangible assets did not turn
out well. Just before the stock market bubble burst, Wall
Street seemed enamored with customer metrics, most of
which had dubious connections to economic value creation
and large losses in shareholder wealth as a result. Thus, a
more prudent approach might be expected this time, but
appropriate prudence does not suggest eschewing customer
metrics altogether, which more or less seems to be the case
for securities research today.11 Rather, the quality of mea-
surement and economic relevance should dictate how these
metrics should best be used. For example, although cus-
tomer satisfaction is strongly related to economic value

12 / Journal of Marketing, January 2006
creation and the (collective) customer may have informa-
tion relevant to the financial prospects of the firm before
investors do, this cannot be leveraged if measurement is
flawed and unable to extract the information. Ittner and Lar-
cker (2003) find that most companies’ measurement
methodologies for customer satisfaction are “mindless,”
misleading, and too primitive to be useful. This state of
affairs is unlikely to change unless shareholders, corporate
boards, and investors put more pressure on companies to
account for intangible assets more effectively. If accounting
were to include information about the health of the firm’s
customer relationships, investors would have a better under-
standing of the link between the firm’s assets and its capac-
ity to generate shareholder wealth, and managers would be
compensated accordingly. Corporate governance would
benefit as well.
If investors were to use capital more expediently to
encourage firms to strive for higher levels of customer satis-
faction and better quality of measurement, there would be
many beneficiaries. Consumers would be better off. As con-
sumer and equity markets become more synchronized, their
joint influence may contribute to less misallocation of capi-
tal, quicker deflation of stock market bubbles (see Figures
1–3), fewer cases of security mispricings, and a better func-
tioning of markets in general.
Although investors fail to realize the relevance of ACSI
news and equity markets exhibit imperfections in this
regard, the results of this study affirm the workings of free
capitalistic markets with respect to the relationship between
consumer utility and the flow of investment capital.
Although it takes a while for equity markets and consumer
markets to exercise their joint power, the finding that cus-
tomer satisfaction and stock prices eventually move
together suggests that most markets get it right in the long
run. They apparently offer meaningful consumer choice,
and short-term fluctuations and other exceptions notwith-
standing, sellers may take comfort that they will (eventu-
ally) be rewarded for treating customers well and that they
risk punishment for treating customers poorly.
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Five and Fifteen Days of Cumulative Abnormal
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Type of 5-Day 15-Day
News Firm Type N Window Window
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in ACSI and services
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