Didem Kurt & John Hulland
Aggressive Marketing Strategy
Following Equity Offerings and Firm
Value: The Role of Relative Strategic
Flexibility
Firms raise a significant amount of funds and gain competitive advantage over their rivals through equity financing,
namely through initial public offerings and seasoned equity offerings. The authors find that both initial public offering
firms and seasoned equity offering firms adopt a more aggressive marketing strategy during the two years following
their offering. However, not all equity issuers benefit equally from increased marketing spending, which can help
signal companies’ growth prospects to investors. A key moderator of the link between marketing investment and
firm value is the strategic flexibility of rivals with respect to issuers. In particular, the stock market reacts favorably
to an aggressive marketing strategy initiated by issuers competing against rivals with relatively less flexibility,
whereas increased marketing expenditures do not translate into higher firm value when rivals have greater
flexibility. Furthermore, the authors show that marketing expenditures create value within context: the role of
marketing in enhancing shareholder value and the moderating effect of rivals’ strategic flexibility are more
pronounced in the two-year window immediately following an equity offering than at any other time. The authors
conclude with a discussion of implications for theory and practice.
Keywords: marketing–finance interface, marketing resources, financial market performance, equity offerings,
strategic flexibility, signaling
equity offerings (SEOs) is critical to longer-term success;
yet very little is known about how strategic marketing
investments are related to these financing decisions. Such
an understanding is needed to help marketers achieve a
more equal footing with other members in the boardroom
(Nath and Mahajan 2008; Verhoef and Leeflang 2009).
Initial public offerings take place when firms seek
equity investment from the public for the first time. In contrast, SEOs involve the issuance of additional equity by
public companies to raise capital. Both IPOs and SEOs play
a critical role in enhancing firms’ competitive positions in
product markets because issuers can raise significant
amounts of cash to finance new projects (Akhigbe, Borde,
and White 2003; Hsu, Reed, and Rocholl 2010). However,
the following key questions of interest for marketers have
not been addressed to date: (1) To what extent do firms
change their marketing efforts as a result of raising significant amounts of capital through the issue of equity? (2)
How does this change in marketing investment affect subsequent firm value? and (3) Are returns to firms’ marketing
efforts during the immediate postoffering period different
than during other time periods? The current study addresses
all three questions.
Researchers have begun to examine how changes in the
marketing strategies of equity-issuing firms made before
the equity offering affect financial outcomes. For example,
Luo (2008) investigates how IPO firms can use marketing
investments to achieve a higher level of raised capital by
General Motors Co., which raised more than $20 billion
in an initial public offering yesterday, will be more
aggressive marketing its brands.
—bloomberg.com, November 18, 2010
The successful completion of our $550 million common
stock offering this week provides us with the strategic
flexibility to continue the momentum we created and
compete aggressively.
A
—Radian Group Inc., Business Wire, May 12, 2010
lthough the extant literature on the marketing–finance
interface links investments in marketing to subsequent financial outcomes (for a review, see Srinivasan
and Hanssens 2009), researchers have paid surprisingly little
attention to the effect of corporate financial policy on marketing strategy and subsequent firm value. As Garmaise (2009,
p. 325) notes, “Marketing assets are typically financed with
equity.” Thus, a better understanding of the connection
between marketing strategy and firms’ acquisition of equity
capital through initial public offerings (IPOs) and seasoned
Didem Kurt is Assistant Professor of Marketing, School of Management,
Boston University (e-mail: [email protected]). John Hulland is Emily H. and
Charles M. Tanner Jr. Chair in Sales Management, Terry College of Business, University of Georgia (e-mail: [email protected]). The authors
thank Mei Feng, Ahmet Kurt, Natalie Mizik, and the JM review team for
their comments on earlier versions of this article. Leigh McAlister served
as area editor for this article.
© 2013, American Marketing Association
ISSN: 0022-2429 (print), 1547-7185 (electronic)
57
Journal of Marketing
Vol. 77 (September 2013), 57–74
increasing demand for their stocks. Mizik and Jacobson
(2007) examine firms’ marketing expenditures before SEO
issuance and find that the long-term stock performance of
firms that decrease their marketing spending in the preoffering period is inferior to that of other issuing firms that do
not reduce their pre-SEO spending. However, these studies
focus exclusively on the preoffering marketing strategies of
issuing firms and do not examine how issuing firms’ marketing efforts change in the postoffering period or how
investors respond to these changes.
Using data sets of IPOs and SEOs issued in the United
States between 1970 and 2004, we find that both IPO and
SEO firms adopt a more aggressive marketing strategy during the two-year period following their offering.1 We argue
that increased marketing expenditures enable issuers to signal their growth prospects to the stock market and communicate a willingness to make major investments in marketbased assets. In that sense, these postoffering marketing
investments can help managers mitigate investor concerns
that the equity issuance is nothing more than an attempt to
“cash in” on market overvaluation (Myers and Majluf
1984) but instead a legitimate initiative to take advantage of
growth opportunities. However, we then contend that not all
firms benefit equally from an increase in postoffering marketing spending and identify a boundary condition for the
link between marketing expenditures and firm value: strategic flexibility of rivals with respect to issuers.
Drawing on previous research (e.g., Aaker and Mascarenhas 1984; Ansoff 1965), we use financial leverage
(i.e., debt-to-asset ratio) as a proxy for strategic flexibility.
This measure fits well with the context of our study and is
rooted in both theoretical and empirical literature examining the link between firms’ financial strength and product
market behavior (e.g., Bolton and Scharfstein 1990; Chevalier 1995; Gielens et al. 2008; Telser 1966). The main finding emerging from this stream of literature is that firms with
low financial leverage (i.e., higher strategic flexibility) are
better able to respond to an aggressive product market strategy initiated by a competitor, whereas highly leveraged
firms often lack the resources needed to counteract an
aggressive strategy. Moreover, highly leveraged firms,
which have a higher probability of experiencing financial
distress and bankruptcy, may be reluctant to invest in marketing resources due to their intangible and nontransferable
nature (Grullon, Kanastas, and Kumar 2006; Srivastava,
Shervani, and Fahey 1998).
Our results suggest that the stock market reacts favorably to an aggressive marketing strategy initiated by equity
issuers competing against rivals with relatively less strategic flexibility, whereas increased marketing expenditures do
not translate into higher firm value when the rivals have
more strategic flexibility than the issuers (i.e., they have a
lower percentage of debt vs. equity financing in their capital
structures). Furthermore, we address whether the significant
interactive effect of marketing resources and relative strate-
1An aggressive marketing strategy can involve either the tone or
intensity of a firm’s marketing campaign. In the present study, due
to data limitations, we restrict our analysis to changes in issuers’
marketing intensity (i.e., marketing expenditures).
58 / Journal of Marketing, September 2013
gic flexibility on firm value is only observed in the periods
immediately following equity issuance or whether this effect
also manifests itself during other periods. The postoffering
period is unique for several reasons, making it a worthwhile
period in which to examine the marketing–finance interface.
First, equity offerings may convey the unfavorable message that the firm’s shares are overvalued (Myers and
Majluf 1984). In addition, equity issuance (especially IPOs)
can signal a change in the outlook of an industry as a whole,
leading to increased investor attention not only for issuers
but also for their rivals (Akhigbe, Borde, and White 2003).
Therefore, during the postoffering period, there is greater
need for marketing spending, which can help firms communicate their growth prospects to investors. Second, an
equity offering recapitalizes the firm in a way that significantly decreases its debt-to-equity ratio, leading to a major
and rapid shift in its strategic flexibility against rivals.
Third, because equity-issuing firms raise significant
amounts of cash that can be used to finance new projects,
these firms gain a temporary competitive advantage over
rivals (Hsu, Reed, and Rocholl 2010). Thus, we contend
that marketing investments in this postoffering period can
have a particularly profound impact on shareholder wealth.
Consistent with our prediction, we find that although strategic flexibility of rivals moderates the relationship between
marketing expenditures and firm value in all periods, this
effect is more significant in the years immediately following equity issuance than in other periods.
Our research makes at least two important contributions
to the literature. First, to the best of our knowledge, our
study is the first to introduce, operationalize, and empirically
test the concept of strategic flexibility in the marketing–
finance literature. Johnson et al. (2010, p. 85) point out that
“[strategic flexibility] has not been considered from strategically crucial marketing perspectives.” Specifically, we
examine how enhanced flexibility is associated with aggressive marketing strategy and in turn how stock return performance reflects this relationship. It is important to study
strategic flexibility because it is a key factor determining
firms’ willingness and ability to adapt to changing competitive environments. Theories of market-based assets (Srivastava et al. 1998) and customer equity (Rust et al. 2004) suggest that increased marketing efforts can help firms enhance
their market values. Our study demonstrates that this impact
is likely to be heterogeneous not only across firms but also
across market situations. In particular, investors’ assessments of the strategic flexibility of industry rivals moderate
how they respond to changes in firms’ marketing investments (shown as a moderating effect in Figure 1), and this
effect is more salient during the postoffering period.
Second, we contribute to the nascent literature on the
marketing–finance interface around equity offerings. Whereas
Luo (2008) and Mizik and Jacobson (2007) show that issuing firms either use or sacrifice preoffering marketing
resources to raise more capital, we examine whether issuers
use raised funds to finance further marketing investments
and how investors react to changes in their marketing
spending. Our contribution beyond these studies is critical
because managers, who command significantly greater
resources during the postoffering versus preoffering period,
FIGURE 1
Marketing Expenditures, Relative Strategic Flexibility, and Firm Value: A Framework
•Srinivasan et al. (2009)
•Joshi and Hanssens (2010)
Marketing Expenditures
Firm Value
•The current study
Path A
Path B
Strategic Flexibility: Own vs. Rivals
•Financial leverage (i.e., debt/total assets)
•Ansoff (1965)
•Aaker and
Mascarenhas (1984)
•Mizik and Jacobson (2007)
•Luo (2008)
•The current study
Corporate Financial
Policy
•Equity (e.g., IPOs, SEOs, share
repurchases)
•Debt (e.g., bonds, leveraged buyouts)
need to better understand the impact of postoffering investments in marketing on firm value to make appropriate and
timely spending decisions. Moreover, our study is fundamentally different from Luo’s (2008) and Mizik and Jacobson’s (2007) in that we examine the impact of corporate
financial policy on marketing spending (which in turn
affects shareholder wealth), whereas they focus solely on
the effect of marketing spending on financial metrics. We
illustrate this distinction in Figure 1 as Path A versus Path
B. Relatedly, our study offers practical implications that are
relevant for the postoffering period, when firms have
greater flexibility to undertake major investments, whereas
Luo (2008) and Mizik and Jacobson (2007) offer implications
that are applicable in the period leading up to the offering.
The remainder of the article is organized as follows.
First, we present our conceptual framework and derive
testable hypotheses. Next, we describe the marketing and
financial data sources we use in our empirical study, explain
our methodology, and present our results. Finally, we discuss the implications of our findings.
Conceptual Background
Most firms must routinely consider various forms of financing to be able to make appropriate marketing decisions.
Although firms’ capital structures exhibit considerable
cross-sectional variation, many firms find it necessary at
•Ritter (1991)
•Loughran and Ritter
(1995)
•Spiess and Affleck-Graves
(1995)
•Lyandres, Sun, and Zhang
(2008)
some point to access public equity. Through IPOs, private
firms raise a significant amount of funds with which to
finance new projects. Indeed, IPOs play an important role in
the growth of the U.S. economy, representing $285 billion
raised through more than 1,000 offerings during the first
decade of the twenty-first century.2 Successful IPOs provide
issuing firms with additional benefits, including increased
public recognition and heightened financial analyst interest.
In addition, IPOs not only satisfy the immediate capital
requirements of the firm but also enable firms to make subsequent equity offerings to raise additional capital, typically
referred to as SEOs. These offerings are an important source
of external funding for public companies in that the number
of SEOs issued during the first decade of the twenty-first
century is more than double the number of IPOs.
Although equity offerings provide firms with various
benefits, they do not guarantee superior subsequent firm
performance. Indeed, most extant work shows the opposite.
For example, previous research (e.g., Jain and Kini 1994;
Loughran and Ritter 1997) has shown that both IPO and
SEO firms significantly increase their investment in physical assets such as new machines, equipment, plants, and
stores in the postoffering period. However, excessive capital expenditures by issuing firms can result in the deterioration of operational performance. Indeed, although IPO
2We
retrieved these data from Jay Ritter’s website (Ritter 2011).
The Role of Relative Strategic Flexibility / 59
firms often significantly increase their postoffering capital
expenditures, their profitability and productivity typically
decline substantially (Chemmanur, He, and Nandy 2010).
In parallel, SEO firms have higher ratios of capital expenditure and research and development (R&D) to assets than
nonissuers in the years following equity issuance, and yet
their operating performance—as measured by operating
income relative to sales, profit margin, and return on
assets—decreases significantly following the offering (Fu
2010; Loughran and Ritter 1997). Relatedly, both IPO and
SEO firms, on average, experience poor postoffering stock
return performance (for a review, see Ritter 2003).
Recent IPOs with disappointing postoffering stock performance, such as Facebook, Groupon, and Zynga, have
fueled the long-lasting debate on whether firms sell shares
to the public to take advantage of growth opportunities or to
convert overvalued equity into cash. In their seminal article,
Myers and Majluf (1984) develop an adverse selection
model, proposing that managers—who are better informed
than investors about the value of the firm’s assets in place
and growth opportunities—prefer to issue equity when their
private valuation is lower than that of investors. Thus, equity
offerings may be interpreted as a signal that the issuer’s
stock is overvalued (Asquith and Mullins 1986; Masulis
and Korwar 1986). It is therefore critical for managers of
equity-issuing firms to communicate their firms’ prospects
to investors clearly and take actions highlighting their commitment to undertake profitable investment opportunities.
Luo (2008) and Chemmanur and Yan (2009) examine
whether issuers change their marketing spending before
issuance to reduce information asymmetries and how such
spending changes affect investors’ reactions to offering
announcements. On the one hand, Luo (2008) argues that
managers can better convey their firm’s intrinsic value and
future prospects to potential investors by using productmarket advertising, enabling them to enhance trading volumes, reduce IPO underpricing, and thereby raise more capital. Consistent with this view, Chemmanur and Yan (2009)
find that, on average, IPO firms spend more on advertising
in the IPO year than the pre-IPO years. On the other hand,
Mizik and Jacobson (2007) find that some SEO firms
engage in “myopic management,” cutting their marketing
spending before issuance so they can report higher earnings
and sell shares at a higher price. All three studies focus
solely on changes in the preoffering marketing expenditures
of issuing firms (i.e., Path B in Figure 1). However, analysis
of postoffering changes in the marketing spending of IPO
and SEO firms and the valuation impact of these changes is
also needed; this analysis is the focus of our study. In the
postoffering period, issuers have more resources and greater
flexibility to alter their marketing efforts and better communicate their growth prospects to investors.
We contend that increased marketing efforts during the
postoffering period can enable managers to reveal their favorable private information about their firms’ future profitability
and productivity, helping investors better assess the issuers’
growth opportunities as well as the return on their current
investments in physical assets. Previous research provides
the basis for this argument. Erickson and Jacobson (1992),
for example, conduct one of the earliest studies pointing out
60 / Journal of Marketing, September 2013
the signaling role of marketing spending. They maintain
that increased advertising expenditures are associated with
greater stock returns because investors interpret them as
signals of higher future profitability. Other studies have
highlighted that marketing data can help investors form
more accurate expectations about the size, timing, and risk
of a company’s future cash flows (e.g., Joshi and Hanssens
2010; Srivastava et al. 1999). This stream of literature proposes that greater investment in marketing resources
enhances cash flow, accelerates the receipt of cash flow, and
reduces the firm’s vulnerability to cash flow variability.
In particular, theories of market-based assets (Srivastava et al. 1998) and customer equity (Rust et al. 2004) suggest that increased marketing spending helps firms generate
valuable assets such as strong brands and loyal customers,
thus creating entry and switching barriers. Consequently,
high levels of marketing spending can be an indication of
greater future profitability. For example, a firm that builds
new facilities and buys new machinery to increase its production capacity can signal a greater return on these investments through higher marketing expenditures, which will
help the firm boost its sales and utilize its higher capacity.
Indeed, Rao and Bharadwaj (2008) demonstrate that marketing activities boost productivity by reducing a firm’s
total working capital requirement. In parallel, a company
that raises equity can open stores in a new region but will
benefit more fully by using some of its capital on higher
advertising spending to attract new customers and increase
sales to existing customers. Furthermore, McAlister, Srinivasan, and Kim (2007) show that more advertising lowers a
company’s systematic risk (and thus cost of capital) by
insulating it from the negative impact of market downturns.
Overall, recent literature has suggested that marketing
investments can increase firm value by leading investors to
revise their future cash flow expectations upward as well as
to discount those cash flows at a lower rate. However, we
argue that not all firms can be expected to benefit from pursuing an aggressive marketing strategy following equity
issuance. We propose the strategic flexibility of rivals as a
significant moderator of how investors respond to firms’
increased marketing efforts.
Strategic Flexibility
Strategic flexibility is broadly defined as having the organizational capability and resources that enable a firm to
respond quickly and effectively to changing competitive
conditions (e.g., Harrigan 1985; Sanchez 1995; Zhou and Wu
2010). However, as Grewal and Tansuhaj (2001, p. 72) note,
“It is best to consider strategic flexibility a polymorphous
construct; that is, the exact meaning and conceptualization
of strategic flexibility varies from one context to another.”
For example, Aaker and Mascarenhas (1984) propose that
flexibility can be linked to several functional areas such as
operations (e.g., maintaining excess capacity), management
(e.g., having a decentralized decision-making system), marketing (e.g., participating in multiple product markets), and
finance (e.g., having emergency borrowing and stock-issuing
power). In our study, we focus on the financial flexibility
dimension and discuss how it relates to firms’ strategic marketing decisions.
Liquid assets, in addition to providing the capacity to
raise additional financial resources when necessary, allow
firms to both absorb and react to unfavorable developments
in their competitive environments. In particular, maintaining an adequate level of flexibility in other functional areas
such as operations and marketing depends critically on
whether firms have sufficient financial flexibility. Previous
studies (e.g., Aaker and Macarenhas 1984; Ansoff 1965)
have suggested that financial leverage (i.e., debt-to-asset
ratio) is a critical determinant of flexibility. The key advantage of financial leverage is that it is an objective measure
that is publicly available for a large number of firms. In line
with previous research, as we discuss in detail subsequently,
we maintain that highly leveraged firms have less strategic
flexibility and are less likely to respond to the aggressive
marketing tactics of their equity-issuing competitors.
Other possible financial measures of strategic flexibility
include such variables as cash and current ratio (i.e., current
assets divided by current liabilities). We contend that leverage is a more appropriate measure of flexibility in our context for two reasons. First, contrary to popular belief, previous research has indicated that financially constrained (vs.
unconstrained) firms tend to hold more cash due to precautionary motives (e.g., Whited and Wu 2006). Thus, larger
cash reserves alone do not necessarily indicate a higher level
of strategic flexibility. Second, in contrast to financial leverage, cash and current ratio neither measure the difficulty of
obtaining additional capital nor capture the degree of managers’ reluctance to make investments in intangible assets.
In addition, note that financial leverage is a more forwardlooking measure in that firms using more debt must pay out
a larger part of their current and future cash flows as interest payments, reducing their future cash holdings. To shed
some light on how financial leverage limits firms’ strategic
flexibility, we next review the finance literature focusing on
the link between leverage and product-market behavior.
Financial Leverage and Flexibility
The finance literature has extensively studied the connection between financial markets and product markets (e.g.,
Bolton and Scharfstein 1990; Campello 2006; Chevalier
1995; Kovenock and Phillips 1997; Phillips 1995). Telser’s
(1966) so-called long-purse theory of predation describes
how cash-rich firms drive their financially weak rivals out
of the market by adopting aggressive product market strategies that reduce rivals’ cash flows. Relatedly, Bolton and
Scharstein (1990) argue that financial constraints imposed
on rival firms (by their external financiers) encourage deeppocketed firms to engage in predatory behavior and force
these financially weak rivals out of business.
Several studies provide empirical support for the longpurse argument. For example, Opler and Titman (1994) find
that highly leveraged firms in distressed industries lose
market share to their less leveraged rivals. In parallel,
Chevalier (1995) documents that highly leveraged supermarkets compete less aggressively and are subject to
aggressive competitive behavior (e.g., market expansion)
by less leveraged firms. Similarly, European retailers with
higher leverage have been shown to experience poorer per-
formance than their less leveraged counterparts following
Wal-Mart’s 1999 entry into the United Kingdom (Gielens et
al. 2008). Kovenock and Phillips (1997) find that a firm is
more likely to close plants and less likely to make major
new investments following leveraged recapitalization (i.e.,
changing capital structure by significantly increasing debt).
In a related study, Zingales (1998) finds that highly leveraged trucking firms were less likely to survive following
the Carter deregulation because high levels of debt limited
carriers’ ability to invest and affected the price they could
charge during the price wars after the deregulation.
This limited flexibility and weakened competitive
response ability of highly leveraged firms is partly because
they must use cash generated from operations to service
their debt loads. As a result, highly leveraged firms can find
themselves in a situation in which they must limit their
investment, cut discretionary expenses, and reduce the quality of their products and services. For example, Matsa
(2011) finds that high leverage and limited corporate liquidity are associated with more frequent stockouts and lower
product quality in the supermarket industry. Even when
expenses do not need to be cut, high-leverage firms are
often constrained in their ability to raise additional debt
capital because they have used most of the credit lines
available to them. In addition, their cost of equity is high
due to the increased riskiness associated with large debt on
their balance sheet. Finally, research has shown highly
leveraged firms to be reluctant to make major marketing
investments such as advertising due to the intangible and
nontransferable nature of marketing assets (Grullon,
Kanatas, and Kumar 2006). That is, because leverage
increases the probability of financial distress and because
marketing assets cannot be liquidated in the case of financial distress (or bankruptcy), highly leveraged firms tend to
limit their marketing expenditures.
In light of the previous discussion, we propose that an IPO
or SEO firm competing against rivals with less strategic flexibility is more likely to benefit from increasing its marketing
efforts after an equity offering than a firm operating in an
industry composed of rivals with relatively higher strategic
flexibility. This is because the value of growth opportunities
communicated through increased marketing spending
depends critically on the extent to which industry rivals would
react to take advantage of similar opportunities. Indeed,
investors may negatively view an aggressive marketing
strategy initiated by an issuer with relatively flexible rivals
due to the timely and effective responses that those rivals
can make, which results in lower stock returns. Formally:
H1: The relative strategic flexibility of industry rivals with
respect to IPO- and SEO-issuing firms moderates the relationship between issuers’ postoffering marketing expenditures and firm value. Specifically, increased marketing
expenditures are associated with higher firm value for
issuers facing rivals with relatively less flexibility but not
for issuers facing rivals with relatively more flexibility.
Postoffering Period Versus Nonoffering Period
We further propose that the postoffering period, which we
define as the first and second years after the offering year, is
different from other periods in that firms facing rivals with
The Role of Relative Strategic Flexibility / 61
less flexibility are more likely to benefit from increased
marketing efforts during this period. We build our argument
on the basis of two streams of literature: investor reaction
around offerings (e.g., Asquith and Mullins 1986; Bayless
and Chaplinsky 1996; Masulis and Korwar 1986) and
competitive effects of offerings (e.g., Akhigbe, Borde, and
White 2003; Hsu, Reed, and Rocholl 2010).
An extensive stream of literature in finance has emphasized that managers sell their companies’ shares to the public to exploit equity mispricing rather than to raise capital
for profitable investment opportunities. Although investors
are already generally concerned with holding or buying
overvalued stocks, these concerns are heightened when
companies announce equity offerings. This is because the
pecking order theory of Myers and Majluf (1984) suggests
that managers prefer to finance investments with debt and
retained earnings and will only issue equity when their private valuation of their company’s stock is lower than that of
the market. An equity issuance therefore may convey the
message that the company’s stock is overvalued, and issuing firms that fail to communicate their prospects to
investors can expect poor stock returns. Accordingly, the
role of marketing spending in mitigating investors’ concerns that a particular stock is overvalued is expected to be
more pronounced during the period that immediately follows an offering as compared with nonoffering periods.
Another important reason the postoffering period is
unique is that a firm’s equity issuance leads to a change in
the industry’s competitive environment. Equity-issuing
firms raise a significant amount of funds and thus gain a
temporary edge over their rivals. Firms can use proceeds
from the offering to initiate new competitive tactics, which
alter the industry dynamics. Furthermore, equity issuance
recapitalizes the issuing firm in a way that results in a significant and immediate decline in its debt-to-capital ratio,
enhancing its relative strategic flexibility versus rivals.
Such a leverage-decreasing shift in a firm’s capital structure, a rapid improvement in flexibility, is unlikely to occur
during periods other than those immediately following an
equity offering. In addition, equity issuance—particularly
an IPO—may signal a change in industry outlook and
increase investor attention toward not only issuers but also
their rivals. Relatedly, Hsu, Reed, and Rocholl (2010, p.
495) recommend that “firms that compete with IPO candidates need to understand how new issuance affects their
competitive environment and how they can strategically
respond to it.” However, it would be unrealistic to assume
that the competitive advantages of issuers, including heightened investor attention, will last forever. As the offering
proceeds are used up, marketing strategies are implemented, and growth opportunities become fewer, the temporary relative advantage of issuers will dissipate.
The previous discussion suggests that the role of marketing in enhancing firm value and the moderating role of
relative strategic flexibility are expected to be stronger in
the immediate postoffering period versus other periods for
three main reasons: (1) increased investor attention for both
issuers and rivals, (2) a significant shift in the strategic
flexibility of issuers relative to their rivals, and (3) intensified competition among industry members. Formally:
62 / Journal of Marketing, September 2013
H2: The interactive effect of firms’ marketing expenditures and
their rivals’ relative strategic flexibility on firm value is more
pronounced in the years immediately following an equity
offering (i.e., year +1 and year +2) than in other periods.
Sample
Data and Measures
We obtained our sample of IPO and SEO firms from the
Securities Data Corporation’s New Issues database for the
period 1970–2004. Consistent with previous work, we
exclude closed-end funds, American Depository Receipts,
real estate investment trusts, unit offerings, spin-offs,
reverse leveraged buyouts, regulated utilities, and financial
firms. In addition, we include only those offerings that
involve at least some newly issued (i.e., primary) shares (to
avoid including offerings that solely involve the insiders’
resale of existing shares). Finally, for our IPO sample, we
eliminate any IPO followed by an SEO issuance within the
next two years to ensure that our results are not confounded
by the SEO effect. Similarly, for our SEO sample, we discard any SEOs issued within the two years following an
IPO to avoid confounding IPO effect. We gathered monthly
stock returns data from the University of Chicago’s Center
for Research in Security Prices and accounting information
from the Compustat Fundamentals Annual File.
To ensure that our results are not driven by small firms,
we exclude those issuers with total assets less than $10 million at the end of the offering year. This filtration criterion
is commonly used in studies that examine capital structure
changes and equity issuance (e.g., Baker and Wurgler 2002;
Leary and Roberts 2005). Furthermore, after eliminating
offerings with insufficient stock return and accounting/
marketing data to enter into our regression analyses, the final
IPO sample represents 1,581 offerings, and the final SEO
sample represents 1,729 offerings by 1,200 unique firms.3
Table 1 summarizes the sample statistics for the final
sets of IPO and SEO firms. Panel A reports the distribution
of offerings by industry, and Panel B reports the distribution
by year.4 Panels C and D summarize the mean and standard
deviation for five characteristics of the IPO and SEO firms
as reported in the year of the offering (i.e., year 0): total
assets, market value, annual sales growth, marketing intensity (the firm’s marketing spending in the issue year scaled
by its total assets), and financial leverage (total debt divided
by total assets).
3We cannot directly compare the size of our SEO sample with
that reported in Mizik and Jacobson (2007) due to differences in
data filtration (e.g., we exclude SEOs issued within two years following the IPO) and sample period between the two studies. However, we obtain a number (i.e., 2,281) close to what they report
(i.e., 2,238) if we restrict our sample to the period 1970–2001 and
do not exclude SEOs issued within two years following an IPO.
4In our sample, there is only one IPO issued in 1975 and no
IPOs issued in 1974. Jay Ritter (2008) refers this period as “the
IPO drought of 1974–1975.” On his website, he notes that he was
able to locate only four IPOs in 1974 (after excluding unit offerings and best effort deals). However, due to missing accounting
and marketing data, we excluded those IPOs from our final IPO
sample.
TABLE 1
Characteristics of IPOs and SEOs Included in the Sample over 1970–2004
Industry
Oil and gas
Food products
Apparel and other textile products
Paper and paper products
Chemical products
Manufacturing
Computer equipment and services
Electronic equipment
Transportation
Scientific instruments
Communications
Durable goods
Retail
Automotive dealers and services
Eating and drinking establishments
Entertainment services
Health
All others
Year
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
A: SIC Distribution of IPOs and SEOs
SIC Code(s)
13
20
23
24, 25, 26, 27
28
30, 32, 33, 34
35, 73
36
37, 39
38
48
50
53, 54, 56, 57, 59
55
58
70, 79
80
22, 29, 51, 52, 87, 99
B: Time Distribution of IPOs and SEOs
IPO
Frequency
5
17
46
6
0
1
11
6
2
5
19
20
14
102
27
41
69
59
20
28
16
46
81
111
74
105
156
106
53
115
133
20
15
11
41
IPO Cumulative
Frequency
5
22
68
74
74
75
86
92
94
99
118
138
152
254
281
322
391
450
470
498
514
560
641
752
826
931
1,087
1,193
1,246
1,361
1,494
1,514
1,529
1,540
1,581
IPO
SEO
SEO
Frequency
SEO Cumulative
Frequency
6
15
2
18
67
61
592
215
41
200
9
49
149
11
33
23
23
67
8
24
23
8
4
18
32
15
31
19
65
50
40
140
23
39
52
43
17
25
23
80
65
69
56
95
106
65
33
74
86
65
64
78
94
35
17
1
34
171
74
367
319
95
239
15
57
147
6
40
20
25
67
8
32
55
63
67
85
117
132
163
182
247
297
337
477
500
539
591
634
651
676
699
779
844
913
969
1,064
1,170
1,235
1,268
1,342
1,428
1,493
1,557
1,635
1,729
The Role of Relative Strategic Flexibility / 63
TABLE 1
Continued
C: IPO Sample Size, Marketing Resources, and Financial Leverage Characteristics
M
SD
M
SD
Total
Assets
121.96
245.15
Market
Value
332.16
778.12
Sales
Growth
1.106
2.222
Marketing
Intensity
.297
.199
D: SEO Sample Size, Marketing Resources, and Financial Leverage Characteristics
Total
Assets
888.08
2,273.80
Market
Value
949.41
2,100.21
Sales
Growth
.412
.513
Marketing
Intensity
.223
.156
Financial
Leverage
.115
.157
Financial
Leverage
.172
.158
Notes: Total assets and market value are reported in millions of dollars. Market value is the number of shares outstanding multiplied by the
stock price at the end of year 0 (i.e., the offering year). Sales growth is the change in sales in year 0 deflated by lagged sales. Marketing intensity is marketing expenditures in year 0 scaled by total assets. Financial leverage is total debt divided by total assets and measured for year 0. To mitigate outlier effects, we Winsorized all the variables at the 1st and 99th percentile levels.
These statistics reveal that IPO firms tend to be smaller
than SEO firms, as measured by total assets and market
value. Specifically, the average SEO firm is almost eight
times larger than the average IPO firm in terms of asset
size. In addition, IPO firms experience larger sales growth
in the year of the offering and have lower financial leverage
and higher marketing intensity than SEO firms. Notably,
whereas SEO firms, on average, experience a 41% growth
in sales during the offering year, IPO firms report an average sales growth of 111%. Given that the IPO and SEO
samples consist of firms with significantly different characteristics, we test our first hypothesis separately for each
group. However, we use a combined sample of two groups
when testing H2 because it compares the postoffering and
nonoffering periods.
Measures
Stock returns. We measure changes in firm value using
stock returns. Drawing from previous research (e.g., Currim, Lim, and Kim 2012; Srinivasan, Lilien, and Sridhar
2011; Teoh, Welch, and Wong 1998a, b), we calculate the
annual stock return for a given fiscal year following the
offering by compounding the monthly stock returns for 12
months (starting with the first month of the fiscal year following the IPO or SEO year). To control for the size and
value risk (Fama and French 1993), we include lagged market value and book-to-market ratio of issuers in our regression analyses. Mizik and Jacobson (2003, 2008) and Mizik
(2010) use a similar approach.5
5We do not use Fama–French (1993) three-factor or Carhart
(1997) four-factor models to calculate stock returns, because we
have only 12 observations in each year with which to estimate
these models for each firm. As Teoh, Welch, and Wong (1998a)
state, 12 observations are too few to reliably estimate regression
betas and alpha, with the latter representing the average monthly
abnormal stock return for the firm. However, we test the robustness of our results by using the calendar-time portfolio approach
with a four-factor model (Carhart 1997). The results are available
upon request. We do not present these results in the article,
because the calendar-time portfolio approach is intended to test
market mispricing, which is not the goal of the present study.
64 / Journal of Marketing, September 2013
Marketing expenditures. Following Mizik and Jacobson
(2007) and Luo (2008), we use selling and general administrative expenditures (SG&A; Compustat item XSGA)
minus R&D expenditures (Compustat item XRD) scaled by
lagged total assets (Compustat item AT) as a measure of the
firm’s marketing expenditure intensity (MKT). We do not
use advertising expenditure (also available on Compustat)
as a measure of marketing intensity for two main reasons.
First, under generally accepted accounting principles, companies are not required to disclose their advertising spending in the income statement as a separate line item or in the
notes to it, whereas this is not the case for SG&A and R&D.
Thus, limiting our analysis to those firms with available
advertising data would restrict our sample and potentially
introduce selection bias. Second, the advertising expenditure
item excludes several major marketing expenditures, such
as salaries of the sales and marketing staff, sales commissions, and operating expenses of company-owned stores,
which are a significant part of total marketing spending.6
Because the investors react only to unexpected information (Fama 1970, 1991), explanatory variables should
reflect unanticipated changes. We calculate unexpected
changes in marketing spending as deviations from industry
forecasts using the cross-sectional model as implemented
by Roychowdhury (2006), Cohen, Dey, and Lys (2008), and
6We acknowledge that our proxy for marketing expenditures
includes nonmarketing expenses such as administrative personnel
salaries. However, we use the unexpected component of the marketing expenditure intensity (rather than its level) in our analysis.
Thus, we believe that our measure captures mainly the unexpected
increase in marketing spending because an abnormal increase in
administrative personnel salaries or other similar items following
the offering would not be expected (in particular not after controlling for firm size).
Cohen and Zarowin (2010) in the accounting literature.7
Following this estimation approach, the “normal” level of
marketing expenditures can be expressed as a linear function
of lagged sales (Compustat item SALE) and an asset-scaled
constant (as shown in Equation 1). The intuition behind this
approach is that within an industry, greater sales are associated with greater marketing spending. Thus, variation in
sales can account for a significant amount of variation in
firms’ marketing intensity. This is consistent with recent
studies that have used the change in a company’s sales as
the sole factor in predicting the change in its marketing
expenditures (e.g., Kim and McAlister 2011). A key advantage of our approach is that it holds economy- and industrywide factors constant by estimating industry-level regressions separately for each year in the sample period.
Furthermore, the model includes an asset-scaled constant to
avoid a spurious correlation between scaled marketing
expenditures and scaled sales due to variation in the scaling
variable (i.e., total assets).
(1)
MKTit = β 0 + β1
Salesi, t − 1
1
+ β2
+ ε it .
Assetsi, t − 1
Assetsi, t − 1
For each year, we estimate Equation 1 for every industry, classified according to its two-digit Standard Industrial
Classification (SIC) code, by using all the firms (with available data) in that industry. These estimations generate
industry-specific betas that vary across time. We require at
least ten observations for each industry-year grouping to
obtain reliable estimates of the coefficients. Table 2 presents the regression results averaged across all industryyear estimations. The average R-square is 33%, which suggests that this cross-sectional model has reasonable
predictive power.
After we obtained the estimated coefficients for each
industry-year group (b̂0, b̂1, b̂2), we calculate the “normal”
level of marketing expenditure (NMKT) for each firm as
follows:
(2)
Salesi, t − 1
1
,
NMKTit = βˆ 0 + βˆ 1
+ βˆ 2
Assetsi, t − 1
Assetsi, t − 1
where our measure of unanticipated change in marketing
expenditures is the difference between actual marketing
expenditure in year t and the fitted normal marketing
7Another way to obtain unanticipated changes in marketing
expenditure is to estimate a first-order autoregressive model where
the dependent variable is MKTit and the independent variable is
MKTi,t – 1. However, as Mizik (2010) suggests, estimating a firstorder autoregressive model using panel data raises econometric
issues. That is, in the presence of fixed effects, the estimated
coefficient on MKTi,t – 1 will be biased. She suggests that applying
the Anderson and Hsiao (1982) procedure, which involves firstdifferencing demeaned variables and then using instruments for the
first-differenced MKTt – 1 measure, results in consistent estimates.
However, this procedure requires using MKTt – 2 and MKTt – 3 to
create an instrument for the first-differenced MKTt – 1. In the case of
IPOs, we will not be able to apply this procedure in year +1 because
accounting/marketing data for most of the IPO firms are not available in Compustat for the years preceding the pre-IPO year. Thus,
we prefer the cross-sectional estimation method, which is robust to
the aforementioned problem and has reasonable predictive power.
TABLE 2
Parameters of the Cross-Sectional Regression
Model Used to Estimate “Normal” Level of
Marketing Expenditures as a Function of Lagged
Sales and Lagged Scaled Total Assets
Intercept
1/Assetst – 1
Salest – 1/Assetst – 1
R-square
Number of industry-year
Coefficient (t)
Coefficient (t)
Coefficient (t)
groups
DV = MKTt
.099* (26.08)
2.765* (18.89)
.129* (49.74)
32.99%
1,225
*p < .01.
Notes: Parameters are averaged across industry-year groupings.
The regressions are estimated for every industry every year.
The dependent variable is marketing expenditures (Compustat item XSGA – item XRD) scaled by the firm’s lagged total
assets. Industries are defined at two-digit SIC level. We eliminated from the sample industry-years with fewer than ten
observations. The table reports the mean coefficients across
all industry-years. We calculated t-statistics using the standard error of the mean across industry-years. The table also
reports the mean R-squares (across industry-years).
expenditure for the same year, defined as UMKTit =
MKTit – NMKTit.
We acknowledge that managers use various inputs in
determining their companies’ marketing spending. However, we try to forecast rather than explain issuers’ marketing intensity. Thus, omitted variables do not pose a significant threat to the predictive ability of the model as long as
they are correlated with the independent variables included
in the model (for a detailed discussion about forecasting vs.
explaining, see Mizik 2012; Mizik and Jacobson 2009). Given
that our model has a respectable R-square (comparable with
the 38% reported in Roychowdhury 2006), we believe we
can measure the unexpected portion of issuers’ marketing
expenditure intensity with a certain degree of confidence.
Strategic flexibility of rivals. Our proxy for flexibility is
financial leverage. In line with previous studies (e.g., Gielens et al. 2008; Rego, Billett, and Morgan 2009), we define
financial leverage as total debt [long-term debt (Compustat
item DLTT) + debt in current liabilities (Compustat item
DLC)], divided by total assets. To calculate relative strategic flexibility of rivals with respect to issuers, we follow the
standardization approach Campello (2006) suggests. That
is, we first calculate the mean leverage of the remaining
companies in the same two-digit industry as issuers. Then,
we calculate the difference between the leverage of the
issuer and the mean leverage of rivals. However, Campello
(2006) points out that it is the relative size of the difference
rather than the absolute size that matters. Therefore, we
divide the difference between the leverage of the issuer and
the mean leverage of rivals by the standard deviation of
leverage of all firms in an industry. As a result, we obtain
our measure of rivals’ relative flexibility (RIVALFLEX).
The standardization enables us to use the same metric
across different industries. Positive values of RIVALFLEX
indicate that rivals have greater strategic flexibility than
issuers, whereas negative values indicate the opposite.
The Role of Relative Strategic Flexibility / 65
Analysis and Results
a larger industry-adjusted change than the SEO firms in
year +1 (IPO: 4.678% vs. SEO: 1.73%, t = 9.70, p < .01)
but not in year +2 (IPO: 1.08% vs. SEO: .79%, t = .85, n.s.).
Overall, it seems that IPO and SEO firms typically allocate
additional funds to marketing and follow a more aggressive
strategy than their rivals in the two years following equity
issue.10
IPOs/SEOs and Postoffering Marketing
Expenditures
We first examine whether IPO and SEO firms significantly
increase their marketing spending after an equity
offering.8 In addition, we analyze whether they adopt a
more aggressive marketing strategy than their rivals subsequent to the offering. Thus, in Table 3 we report both raw
(unadjusted) changes and industry-adjusted changes in the
marketing expenditures of issuing firms. We determine raw
changes by calculating the yearly difference in marketing
expenditures and scaling it by total assets. To obtain industry-adjusted values, we first calculate the mean of the raw
changes in marketing expenditures of rival firms and subtract it from the raw change in marketing expenditures of
the issuer.9
The results show that both the IPO and SEO samples,
on average, exhibit a significantly positive change in marketing expenditures in the two years following an offering.
The raw changes for both groups are significant at the 1%
level. However, the increase in marketing expenditures for
the IPO firms is much higher than that for the SEO firms in
year +1 (IPO: 7.73% vs. SEO: 4.02%, t = 11.79, p < .01).
Both groups also significantly increase their marketing
expenditures in year +2 (vs. the baseline year), but there is
no difference between the two groups (IPO: 3.256% vs.
SEO: 2.96%, t = .80, n.s.). Furthermore, industry-adjusted
values for both samples are significantly positive at the 1%
level in the two years following issue, indicating that
issuers adopt a more aggressive marketing strategy than
their rivals during those years. Again, the IPO firms exhibit
Unanticipated Change in Marketing, Strategic
Flexibility, and Firm Value
Although the preceding analysis shows that some of the
capital raised from equity offerings is, on average, allocated
to marketing, it does not confirm that such efforts have a
positive impact on firm value, nor does it examine the moderating effect of strategic flexibility. To test H1, we estimate
the following stock-return response model separately for
our IPO and SEO samples by using ordinary least squares
(OLS) with heteroskedasticity-robust standard errors clustered at the firm level:
(3) STRit = b0 + b1 ¥ UMKTit + b2 ¥ RIVALFLEXit
+ b3 ¥ UMKTit ¥ RIVALFLEXit + b4 ¥ UROAit
+ b5 ¥ USalesit + b6 ¥ HHIit + b7 ¥ MRETit
+ b8 ¥ BMit – 1 + b9 ¥ MVit – 1 + Year Dummiesit
+ Industry Dummiesit (two-digit SIC) + it.
The dependent variable in this model is the annual stock
return. We estimated the model separately for year +1 and
year +2 following an offering because the number of firms
entering the model differs for each year on the basis of the
availability of stock return and marketing data. Specifically,
in year +1 regressions, we regress the stock return calculated for the fiscal year following the offering year on the
8Although IPOs and SEOs both involve issuance of equity, they
are two separate corporate financing events. Unlike SEO firms,
IPO firms are private firms with no publicly traded stock before
the offering. Therefore, key articles examining equity offerings
analyze the two events separately (e.g., Chemmanur and Yan
2009; Loughran and Ritter 1995; Lyandres, Sun, and Zhang 2008).
We follow this tradition.
9We Winsorized both raw and industry-adjusted changes at the
1st and 99th percentile levels to reduce the impact of outliers.
10We find that the industry-adjusted changes for both samples
are close to zero and nonsignificant in years +3 and +4 (IPO year
+3: .13%, t = .44, n.s.; IPO year +4: .23%, t = .93, n.s.; SEO year
+3: –.12%, t = –.84, n.s.; SEO year +4: .03%, t = .24, n.s.). These
findings suggest that enhanced marketing expenditures by issuers
are observed only for the two years immediately following an
equity offering.
TABLE 3
Changes in Marketing Expenditures
Changes in Marketing Expenditures (% of Total Assets)
Raw (i.e., Unadjusted) Values
M
N
Industry-Adjusted Values
M
N
Year +1
7.73*
1,416
4.67*
1,416
IPO
Year +2
3.25*
1,314
1.08*
1,314
Year +1
4.03*
1,616
1.73*
1,616
SEO
Year +2
2.96*
1,536
1,536
.79*
*p < .01.
Notes: The raw (i.e., unadjusted) value is based on the yearly change in marketing expenditures (Compustat item XSGA – item XRD), scaled
by the firm’s total assets. The industry-adjusted value is based on the difference between the unadjusted value of the sample firm and
the mean unadjusted value of all remaining firms in the same two-digit SIC code. To mitigate outlier effects, we Winsorized all the
variables at the 1st and 99th percentile levels.
66 / Journal of Marketing, September 2013
independent variables measured for the fiscal year following
the offering year (except for book-to-market ratio [BM] and
market value [MV], for which we use the lagged values as
is done in prior studies). Similarly, in year +2 regressions,
we regress the stock return calculated for the fiscal year following the first postoffering fiscal year on the independent
variables measured contemporaneously as well as lagged
book-to-market ratio and market value. In addition, note
that both UMKT and RIVALFLEX are mean-centered to
facilitate the interpretation of the main effects (Aiken and
West 1991). All variance inflation factor values are less
than 3, suggesting that multicollinearity is not a problem
(for the correlation matrix, see Table 4).
We use several control variables. Because previous
research has shown that unanticipated changes in accounting performance measures such as return on assets (ROA)
and sales can predict stock returns (e.g., Kothari 2001), we
include measures of both as control variables in our model.
We calculate ROA by dividing income before extraordinary
items (Compustat item IB) by total assets (e.g., Rego, Billet, and Morgan 2009). We then estimate the following firstorder autoregressive model with firm fixed effects11:
11We do not apply the Anderson and Hsiao (1982) approach as
Mizik (2010) suggests while estimating the first-order autoregressive regression because the three-year lagged ROA data required to
apply the procedure in year +1 are not available for most of the IPO
firms in Compustat. In addition, because we are not aware of a
commonly used cross-sectional estimation method for ROA in the
literature, the only option is to augment the first-order autoregressive model with firm fixed effects (Flannery and Rangan 2006).
(4)
ROAit = b0 + b1ROAi, t – 1 + Firm Fixed Effects
+ Year Dummiesit + it.
The model includes firm fixed effects because we estimate a pooled regression model using all firm-year observations available for issuers included in each sample. By
doing so, we try to avoid using biased coefficient estimates
stemming from omitted firm-specific factors. We use the
residuals from this model as the estimates of unanticipated
change in ROA (UROA) of the issuing firm. Following
Mizik and Jacobson (2008), we calculate unanticipated
change in sales as USalesit = log(Salesit) – log(Salesit – 1).
We also control for industry concentration. Drawing
from prior studies (e.g., Luo, Homburg, and Wieseke 2010),
we measure industry concentration using the Herfindahl–
Hirschman Index (HHI), calculated using all Compustat
firms with the available data. The index is defined as HHIj =
Sir = 1s2ij, where sij is the market share of firm i in industry j
(defined by two-digit SIC code). Thus, HHI ranges between
0 and 1. Lower HHI suggests that the industry is shared by
many competitors, whereas higher HHI implies that it is
concentrated in the hands of a small number of large firms.
Furthermore, to control for the general market conditions,
we include the compounded annual return for the market
(MRET) using the Center for Research in Security Prices
value-weighted index. Finally, we include lagged market
value and book-to-market ratio to capture firm-specific size
and value risk factors, respectively (Fama and French
1993). Market value is the natural log of end-of-year stock
price (Compustat item PRCC_F) multiplied by number of
TABLE 4
Correlation Matrix
STR
UMKT
RFLEX
UROA
USales
HHI
MRET
BM
MV
STR
UMKT
RFLEX
UROA
USales
HHI
MRET
BM
MV
1
STR
.148
–.044
.255
.149
.014
.299
.063
–.049
1
STR
.107
–.104
.330
.372
–.019
.313
.037
–.081
UMKT
.009
1
–.036
.022
.231
.039
.008
–.185
.079
UMKT
.024
1
–.060
.028
.265
.022
–.003
–.220
.102
A: Year +1 After the Offering
RFLEX
–.062
–.073
1
–.163
–.025
.067
.041
.025
–.068
UROA
.273
.027
–.228
1
.128
.037
.118
–.051
–.021
USales
.160
.178
–.077
.276
1
–.028
–.042
–.351
.239
B: Year +2 After the Offering
RFLEX
–.086
–.081
1
–.117
.045
.066
.045
.016
–.078
UROA
.290
.018
–.120
1
.293
.029
.162
–.074
–.051
USales
.226
.213
.020
.383
1
.047
.139
–.280
.080
HHI
.031
.031
–.043
.022
–.026
1
.077
.080
–.185
HHI
–.043
–.005
–.022
.036
–.001
1
.085
.067
–.183
MRET
.364
.040
.010
–.001
.012
.041
1
.086
–.174
MRET
.343
.013
.002
.070
.068
.008
1
–.057
–.067
BM
MV
.121
–.113
.137
–.145
–.344
.132
.162
1
–.643
–.005
–.102
.045
.040
.101
–.176
–.127
–.306
1
BM
MV
.134
–.127
.064
–.174
–.307
.104
.124
1
–.622
–.067
–.110
.044
.053
.071
–.159
–.120
–.378
1
Notes: Correlation coefficients for the IPO (SEO) sample are reported below (above) the diagonal. In Panel A, for the IPO sample,  greater than
|.051| is significant at the 5% level, and for the SEO sample,  greater than |.048| is significant at the 5% level. In Panel B, for the IPO
sample,  greater than .054 is significant at the 5% level, and for the SEO sample,  greater than .049 is significant at the 5% level.
The Role of Relative Strategic Flexibility / 67
shares outstanding (Compustat item CHSO). Book-to-market
ratio is the natural log of book value of common stock
(Compustat item CEQ) divided by market value. Note that
to mitigate the impact of outliers, we Winsorized all the
continuous variables entered into the model at the 1st and
99th percentile levels. Table 5 reports the results.12
For IPO firms, there is a positive and significant effect
of unanticipated marketing expenditure on stock returns for
the year +1 (b1 = .373, p < .01). Furthermore, there is a
negative and significant interaction between UMKT and
RIVALFLEX (b3 = –.313, p < .05), in support of H1. That
is, the greater the rivals’ strategic flexibility, the lesser the
positive impact of marketing on issuers’ firm value.
Remember that we estimate these coefficients using meancentered values (i.e., mean RIVALFLEX = 0) and that
RIVALFLEX is a standardized measure. As a result, post
hoc analysis reveals that when rivals’ strategic flexibility as
compared with issuers is one unit lower, the impact of
unanticipated change in marketing expenditures on firm
value is almost doubled (bUMKT, Low RIVALFLEX = .687, t =
4.77, p < .01). In contrast, in the situations of rivals’ high
flexibility by one unit of relative strategic flexibility, the
12We estimate the model separately for each of the two years
following an offering and report the results in separate columns. In
the first column, the dependent variable is stock return calculated
for the year immediately after the offering. We estimate the regression results presented in the next column by using the variables
calculated specifically for the second year following an offering.
In other words, there is no overlap among the variables used in the
models.
positive impact of increased marketing is canceled out
(bUMKT, High RIVALFLEX = .060, t = .43, n.s.). In other
words, an unanticipated increase in marketing expenditures
in the year following an IPO results in higher firm value
only for issuers facing rivals with limited strategic flexibility. Although the results are qualitatively similar in year +2,
neither the main effect of marketing (b1 = .140, n.s.) nor the
interaction effect is significant (b3 = –.077, n.s.). This finding may largely be the result of the fact that IPO firms
undertake large increases in marketing spending in year +1,
as noted previously.
In contrast to IPO firms, the main effect of unanticipated marketing for SEO firms is not significant in year +1
(b1 = –.066, n.s.). We also observed this result for year +2
(b1 = –.060, n.s.). However, consistent with H1, there is a
significant negative interaction between UMKT and
RIVALFLEX in both year +1 (b3 = –.346, p < .01) and year
+2 (b3 = –.328, p < .01). These results again suggest that the
relative strategic flexibility of rivals with respect to issuers
play an important role in determining the impact of marketing on firm value during the period immediately following
an SEO. Specifically, post hoc analyses reveal that when
RIVALFLEX is one unit below (above) the mean, there is a
positive (negative) link between unanticipated marketing
expenditures and stock returns (Year +1: bUMKT,
LowRIVALFLEX = .280, t = 1.90, p < .10; bUMKT, HighRIVALFLEX = –.411, t = –2.82, p < .01; Year +2: bUMKT,
LowRIVALFLEX = .268, t = 1.65, p < .10; bUMKT, HighRIVALFLEX = –.389, t = –2.31, p < .05). From these results, we
can conclude that the stock market reacts favorably when
TABLE 5
OLS Regressions of Stock Returns for Years +1 and +2 Following the Offering on Unanticipated Change
in Marketing Expenditures, Strategic Flexibility, and Controls
IPO Sample
Main Variables
UMKTt
RIVALFLEXt
UMKTt ¥ RIVALFLEXt
Control Variables
UROAt
USalest
HHIt
MRETt
BMt – 1
MVt – 1
Industry dummies (two-digit SICs)?
Year dummies?
Cluster-robust SEs?
N
R-square
Year +1
STRt
b1
b2
b3
b4
b5
b6
b7
b8
b9
.373** (3.73)
–.005 (–.20)
–.313* (–2.06)
1.090** (7.48)
.282** (5.68)
–.567 (–1.02)
1.711** (8.93)
.094** (2.65)
.024 (1.14)
Yes
Yes
Yes
1,468
28.12%
Year +2
STRt
.140 (1.20)
–.081** (–3.90)
–.077 (–.54)
1.167** (7.02)
.788** (11.20)
–.126 (–.24)
1.622** (9.15)
.124** (4.41)
–.010 (–.59)
Yes
Yes
Yes
1,355
36.97%
SEO Sample
Year +1
STRt
–.066 (–.68)
–.024 (–1.45)
–.346** (–3.21)
1.631** (9.04)
.279** (5.31)
–.104 (–.34)
1.410** (10.59)
.131** (5.79)
.014 (1.46)
Yes
Yes
Yes
1,666
32.27%
Year +2
STRt
–.060 (–.52)
–.087** (–4.88)
–.328** (–2.86)
1.632** (9.13)
.424** (6.70)
–.271 (–.78)
1.471** (10.04)
.173** (6.67)
–.015 (–1.48)
Yes
Yes
Yes
1,569
34.44%
*p < .05.
**p < .01.
Notes: The dependent variable in each column is the annual stock returns. The independent variables are measured contemporaneously with
stock returns, except BM and MV, for which we use lagged values. IPOSEO dummy equals 1 if a fiscal year corresponds to the first or
second year following an equity offering and 0 otherwise. The first column presents the results estimated using OLS and robust standard errors clustered at the firm level. The second column reports the results obtained using the Fama–MacBeth method and Newey–
West correction for standard errors. To mitigate outlier effects, we Winsorized all the variables at the 1st and 99th percentile levels.
68 / Journal of Marketing, September 2013
issuers facing relatively less flexible rivals increase their
marketing efforts beyond anticipated levels following an
SEO, whereas we observe the opposite effect (i.e., negative
abnormal stock return) for issuers with relatively more flexible rivals.
It is worth noting that the main effect for the relative
strategic flexibility of rivals is negative and significant in
the second-year regressions for both IPOs and SEOs.13 This
is consistent with the extensive body of literature that documents that firms with higher financial leverage fail to
respond to aggressive strategies initiated by their less leveraged rivals and thereby experience poor operating and stock
return performance. At first glance, this evidence may seem
inconsistent with Modigliani and Miller’s (1958) proposition that capital structure has no impact on firm value.
However, this irrelevance proposition relies on a set of
strong assumptions (e.g., that there are no taxes and information asymmetries). More relevant to our study, they
assume that finance leverage does not affect the probability
distribution of earnings (i.e., there are no risks associated with
using debt). On the contrary, a significant number of studies
have shown that this assumption does not hold, because
assuming larger debt makes a company more vulnerable to
competition.
Postoffering Period Versus Nonoffering Period
To test H2, we augment our main regression model (Equation 4) with a new dummy variable (IPOSEO) that we allow
to interact with UMKT and RIVALFLEX and use all the
Compustat firm-year observations during 1970–2008 with
available stock return, accounting, and marketing data.14 By
combining our previous IPO and SEO data sets with the
Compustat data, we are able to identify postoffering and
nonoffering periods. IPOSEO is a dummy variable that is
set equal to 1 for the two years immediately following any
IPO or SEO issue and equal to 0 for all other periods.15
Although years +3, +4, and so on are technically part of the
postoffering period, we define our postoffering event window as year +1 and year +2. In addition, as we noted previously, we find that the significant increase in industryadjusted marketing expenditures of IPO and SEO firms
shown for years +1 and +2 are not observed in years +3 and
+4. Indeed, previous research (e.g., DeAngelo, DeAngelo,
13Due to its significant main effect on stock returns as well,
relative strategic flexibility is more correctly referred to as a “quasimoderator” than as a pure moderator (see Sharma, Durand, and
Gur-Arie 1981).
14In the sample we used to test H , the last firm-year observa1
tion belongs to 2006 (i.e., year +2 for IPOs/SEOs issued in 2004).
To test H2, we expanded our sample to include firm-year observations through 2008. As a result, we have two additional years of
observations (i.e., nonoffering period) for IPOs issued in 2004.
15In contrast to the previous section, we do not exclude those
IPOs followed by an SEO in the two-year period or those SEOs
issued within two years after the IPO. This is because we are now
comparing the postoffering period with the nonoffering period
rather than testing a unique IPO or SEO effect. If we do not
include those previously excluded IPOs or SEOs in our sample,
the periods immediately following those offerings will be falsely
coded as a nonoffering period, biasing our results.
and Stulz 2010) has shown that most SEO firms use a large
amount of the cash raised through the offering in the year
immediately following an offering. Thus, it seems reasonable to limit our attention to the two years after the offering
and label this period the (immediate) postoffering period.
The modified model includes the following variables:
(5) STRit = b0 + b1 ¥ UMKTit + b2 ¥ RIVALFLEXit
+ b3 ¥ IPOSEOit + b4 ¥ UMKTit ¥ RIVALFLEXit
+ b5 ¥ UMKTit ¥ IPOSEOit
+ b6 ¥ RIVALFLEXit ¥ IPOSEOit
+ b7 ¥ UMKTit ¥ RIVALFLEXit ¥ IPOSEOit
+ b8 ¥ UROAit + b9 ¥ USalesit + b10 ¥ HHIit
+ b11 ¥ MRETit + b12 ¥ BMit – 1 + b13 ¥ MVit – 1
+ Year Dummiesit + Industry Dummiesit (two-digit SIC)
+ it.
All the variables are as defined previously. We calculate
annual stock returns (for each fiscal year) for all the firms
on Compustat with available accounting and marketing data
and regress them on contemporaneously measured independent variables, except BM and MV, for which we use the
lagged values. That is, unlike Equation 3, the dependent
variable is not just issuers’ postoffering stock returns;
rather, we employ panel data with all the annual observations available for an issuer. However, to distinguish the
postoffering and nonoffering periods, we use the IPOSEO
dummy, which turns on if a fiscal year corresponds to the
first or second year following an equity offering. In addition, the coefficient of interest to test our second hypothesis
is b7. Finding a negative and significant coefficient on this
three-way interaction term will provide support for our
argument that the interactive influence of unanticipated
marketing expenditures and relative strategic flexibility of
rivals is more pronounced in the period immediately following an IPO or SEO.
The final sample we use to estimate our model includes
58,236 firm-year observations. Because we have more than
one yearly observation per firm in our pooled data set, we
need to control for both cross-sectional correlation and serial correlation across observations while estimating our
OLS model. Otherwise, standard errors of the estimated
coefficients will be biased. Clustering standard errors at the
firm level has been shown to produce unbiased standard
errors unless the number of observations in each cluster is
too small (Petersen 2009). This should not be a concern for
our sample because it covers a sufficiently long period of
time (average number of observations per firm = 8.4). Furthermore, Petersen (2009) shows that the Fama–MacBeth
(1973) method also generates unbiased standard errors in
the absence of serial correlation. The Fama–MacBeth
method involves estimating the OLS regression separately
in each year and then calculating estimated coefficients/
standard errors on the basis of the time-series averages of
parameters obtained from each yearly regression. Drawing
on previous literature, we estimate our model by using both
methods: cluster-robust standard errors (i.e., firm level) and
The Role of Relative Strategic Flexibility / 69
Fama–MacBeth estimation with the Newey–West correction
for serial correlation. Although we focus our discussion on the
pooled OLS results, the Fama–MacBeth results are similar.
The first column of Table 6 presents results obtained
from pooled OLS with cluster-robust standard errors. The
TABLE 6
OLS Regressions of Stock Returns on
Unanticipated Change in Marketing Expenditures,
Strategic Flexibility, Postoffering Indicator, and
Controls
All Compustat
Firms
Main Variables
UMKTt
RIVALFLEXt
IPOSEOt
UMKTt
¥ RIVALFLEXt
UMKTt ¥ IPOSEOt
RIVALFLEXt
¥ IPOSEOt
UMKTt
¥ RIVALFLEXt
¥ IPOSEOt
Control Variables
UROAt
USalest
Pooled Fama–
OLS
MacBeth
STRt
STRt
b1
b2
b3
b4
b5
b6
b7
b8
b9
HHIt
b10
BMt – 1
b12
MRETt
MVt – 1
Industry dummies
(two-digit SICs)?
Year dummies?
Standard errors
N
R-square
b11
b13
.090**
(4.59)
–.056**
(–13.70)
–.136**
(–18.83)
–.090**
(–3.10)
.010
(.18)
–.004
(–.39)
–.175*
(–2.31)
.056*
(2.10)
–.055**
(–5.65)
–.105**
(–6.50)
–.106**
(–3.43)
–.003
(–.03)
–.015
(–.82)
–.222*
(–2.20)
1.554**
(48.33)
.469**
(33.62)
–.029
(.52)
1.422**
(55.09)
.134**
(31.87)
–.010**
(–6.69)
1.719**
(14.46)
.538**
(13.12)
(Dropped)
1.323**
(10.43)
.123**
(10.23)
–.008
(–1.32)
Yes
Yes
Yes
No
Clustered Newey–
firm level
West
58,236
58,236
28.85% 28.05%
Notes: The dependent variable in each column is the annual stock
returns. The independent variables are measured contemporaneously with stock returns, except BM and MV, for which
we use lagged values. IPOSEO dummy equals 1 if a fiscal
year corresponds to the first or second year following an
equity offering and 0 otherwise. The first column presents
the results estimated using OLS and robust standard errors
clustered at the firm level. The second column reports the
results obtained using the Fama–MacBeth method and
Newey–West correction for standard errors. To mitigate outlier effects, we Winsorized all the variables at the 1st and
99th percentile levels.
70 / Journal of Marketing, September 2013
main effect for UMKT (b1 = .090, p < .01) is positive and
significant, and the interaction between UMKT and
RIVALFLEX (b4 = –.090, p < .01) is again negative and
significant. Furthermore, there is a significant three-way
interaction between UMKT, RIVALFLEX, and IPOSEO
(b7 = –.175, p < .05), in support of H2. That is, the interactive influence of marketing and relative strategic flexibility
reported previously is significantly magnified in the period
immediately following the equity offering. As the second
column of Table 6 reports, we also obtain the significant
three-way interaction when we use the Fama–MacBeth
method (b7 = –.222, p < .05).
Post hoc analyses of the OLS results reveal that in the
situations of high relative flexibility of rivals by one unit of
strategic flexibility, the impact of marketing on firm value
doubles during nonoffering periods (bUMKT, LowRIVALFLEX,
Nonoffering = .181, t = 6.76, p < .01) and quadruples during
the two years following an offering (bUMKT, LowRIVALFLEX,
Postoffering = .356, t = 5.85, p < .01). This is consistent with our
argument that the interaction between marketing and strategic flexibility is magnified during the postoffering period
versus other periods. Moreover, when the strategic flexibility
of rivals compared with issuers is one unit lower, the positive
main effect of marketing is canceled out during nonoffering
periods (bUMKT, HighRIVALFLEX, Nonoffering = .000, t = .00,
n.s.) and becomes negative during the postoffering period
(bUMKT, HighRIVALFLEX, Postoffering = –.175, t = –2.88, p < .01).
Discussion
Both IPOs and SEOs play an important role in enhancing
the competitive position of issuing firms. Issuers not only
raise large amounts of cash that can be used to finance new
projects but also significantly enhance their strategic flexibility by deleveraging their capital structures. Our research
examines whether issuers use these funds to follow a more
aggressive marketing strategy during the postoffering
period and, more important, whether such investments are
associated with increases in firm value. We find that both
IPO and SEO firms significantly increase their marketing
spending in the two years following an offering. However,
we also demonstrate that not all issuers benefit from adopting an aggressive marketing strategy, and we examine a
critical moderating variable that affects the link between
marketing spending and firm value: relative strategic flexibility of industry rivals. Specifically, the stock market reacts
favorably when issuing firms pursue an aggressive marketing strategy against rivals with relatively less flexibility, but
the opposite is true for issuers facing more flexible rivals.
Furthermore, we extend our analysis to examine how the
valuation impact of marketing spending differs between the
postoffering period and other periods. We find that the
interactive influence of marketing expenditures and relative
flexibility of rivals on firm value is more pronounced in the
two-year period immediately following an offering than in
other periods. We summarize these findings, along with
their implications for managers (which we discuss subsequently), in Table 7.
Findings
TABLE 7
Summary of Findings and Managerial Implications
Equity-issuing firms adopt a more aggressive
marketing strategy by significantly increasing
their marketing budgets.
Aggressive postoffering marketing spending
results in higher firm value when issuers face
rivals with relatively lower strategic flexibility.
Managerial Implications
Issuers: Enhanced flexibility triggered by equity offerings supports major
marketing investments.
Rivals: Maintain enough financing capacity to counteract issuers’ strategic
marketing actions.
Issuers: Enhanced postoffering marketing efforts help communicate
growth opportunities to investors. Limited strategic flexibility of
rivals makes the signaling effect of aggressive marketing
spending more impactful.
Aggressive postoffering marketing spending does Issuers: Avoid initiating costly postoffering marketing “fights” when rivals
not translate into higher firm value (and can even
have both the ability and motivation to counteract. Instead, keep
destroy firm value) when issuers compete against
marketing spending within the traditional, expected range.
rivals with relatively greater strategic flexibility.
Aggressive marketing spending has a more
pronounced impact on firm value during the twoyear postoffering period than any other period.
Theoretical Implications
Issuers: Timely execution of the postoffering marketing plan is necessary
to get the most benefit from the offer proceeds.
Our research helps extend the theory of market-based assets
(Srivastava, Shervani, and Fahey 1998) by examining how
industry dynamics and market conditions affect value
enhancement through marketing. In particular, our current
knowledge of the marketing–finance interface around
equity offerings is limited to the findings of a few studies
examining the valuation impact of changes in the preoffering marketing expenditures of issuing firms (e.g., Luo
2008; Mizik and Jacobson 2007). We extend this research
by showing the valuation impact of changes in postoffering
marketing expenditures. Our research is not limited to just
an examination of postoffering period changes, however,
because we contrast these changes with the role of
increased marketing spending on firm value in other periods and find a significant difference between the two.
Therefore, our findings also extend recent research examining the direct effect of marketing on stock returns (Joshi
and Hanssens 2010; Srinivasan et al. 2009) by using a large
sample of firms operating in a wide range of industries over
an extensive time period.
Furthermore, although previous research highlights
strategic flexibility as a valuable organizational capability,
there is little evidence regarding how increased flexibility
contributes to shareholder wealth. In our study, we show
that investors’ valuation of equity-issuing firms’ marketing
efforts depends critically on whether those firms have more
flexibility than rivals.
Finally, we extend the scope of previous work investigating the marketing–finance interface by including elements of firms’ capital structure decisions. As Garmaise
(2009) notes, this is an underexplored aspect of corporate
finance in marketing research that needs further attention.
Our work is directly related to prior research in the finance
literature examining postoffering stock return underperformance (e.g., Loughran and Ritter 1995; Ritter 1991; Spiess
and Affleck-Graves 1995). This stream of literature suggests that simply raising capital through IPOs and SEOs
does not guarantee superior subsequent performance.
Indeed, issuing firms often experience poor returns because
they tend to overinvest in physical assets (e.g., Lyandres,
Sun, and Zhang 2008). We show that investment in marketing assets can help issuers earn higher stock returns, thus
mitigating the postoffering underperformance often
observed in the finance literature.
Managerial Implications
Managers know more about the current performance and
future prospects of their firms than outsiders. Recent literature suggests that increased marketing spending can help
managers reduce informational asymmetries before equity
offerings and thereby increase the demand for their companies’ stock (e.g., Luo 2008). However, it cannot be overlooked that equity-offering firms, especially IPOs (which
are private and often small firms), have limited resources
during the preoffering period, restricting their ability to
undertake major marketing campaigns. Indeed, the desire to
raise more capital leads some managers to cut preoffering
marketing expenditures in the hopes of boosting offer price
through reporting higher earnings (Mizik and Jacobson
2007). The postoffering period is therefore different than
the preoffering period (and nonoffering periods in general)
in that managers have the resources and flexibility to make
major investments in marketing assets. We propose that
postoffering marketing spending plays two important roles.
First, investors are likely to interpret aggressive marketing
expenditures as a signal of management’s opinion that
future profitability will be higher. Second, adopting an
aggressive marketing strategy can mitigate investors’ concern that a firm issued new shares to convert overvalued
equity into cash rather than to take advantage of growth
opportunities.
In light of our findings, we suggest a two-step plan for
equity-offering firms. The first step involves crafting a solid
marketing plan before the equity issuance and revealing
some details of the plan in the offering prospectus, which
often uses boilerplate language when mentioning the use of
funds (e.g., “the company intends to use the net proceeds
The Role of Relative Strategic Flexibility / 71
from the offering for working capital and other general corporate purposes”). Providing hints about the extent to which
proceeds will be used for marketing investments can not
only enable issuers to raise higher capital (Hanley and
Hoberg 2010) but also garner a more positive investor reaction when the firm actually undertakes those investments.
This is because a well-thought-out postoffering marketing
strategy—rather than a campaign financed with residual
funds after covering other expenditures—communicates a
stronger message regarding the managers’ optimism. There
is certainly a trade-off between attempting to reduce information asymmetries by sharing the details of postoffering
marketing plans with investors and revealing valuable
information to competitors, which is the very reason some
companies do not disclose their marketing expenditures as a
separate item on the income statement. In addition, given
that enhanced marketing spending does not translate into
higher postoffering firm value in the case of issuers that
face rivals with greater strategic flexibility, such firms
would be better off if they refrained from initiating an
aggressive marketing strategy and communicated this message clearly in the prospectus. Indeed, our results suggest
that unwise use of marketing resources can erode or even
destroy shareholder value when rivals have greater capability and a motivation to respond to issuers’ aggressive marketing efforts.
The second step involves a timely execution of the proposed marketing strategy. At this step, managers must recognize that the impact of marketing spending depends on
market conditions. That is, the positive effects of increased
marketing spending are greater in the period immediately
following equity issuance than at other times, for several
reasons. First, equity offerings (especially IPOs) result in
heightened investor attention. Second, because equity offerings significantly deleverage issuers and generate a large
amount of funds in a short period of time, they enhance the
competitive position and strategic flexibility of issuers over
rivals. However, this competitive advantage of issuers does
not last forever, dissipating relatively quickly as the offering
proceeds are depleted. Accordingly, a failure to abide by the
proposed marketing plan would diminish the manager’s reputation and credibility with market participants. Managers
must also realize that there are decreasing returns to marketing expenditures. In particular, unusually high marketing
spending by IPO firms in the first year after the offering is
notable. One immediate implication evident in our results is
that additional marketing spending in the following period
does not enhance shareholder wealth. Therefore, allocating
marketing funds in a balanced fashion year to year can help
managers realize higher returns on their investments.
Finally, our results also offer implications for the managers of rival firms operating in the same industry as
issuers. They should be aware that equity issuing is followed by a period in which issuers enhance their marketing
budgets, resulting in intense marketing-based competition.
Limited strategic flexibility stemming from high financial
leverage may prevent rival firms from initiating a timely
and effective countermarketing campaign.
72 / Journal of Marketing, September 2013
Limitations and Further Research
Our research is not without its limitations. Although previous work (e.g., Luo 2008; Mizik 2010; Mizik and Jacobson
2007) has commonly used our measure of marketing spending (SG&A expenditure minus R&D expenditure), we
acknowledge that it includes nonmarketing expenses, such
as administrative overhead and legal expenditures. On the
one hand, it is not possible to isolate marketing expenditures using Compustat data. On the other hand, a key
advantage of using Compustat is that it provides comprehensive data on SG&A and R&D expenditures for many
firms across a variety of industries.
Another limitation of our study is that our measure of
strategic flexibility (i.e., financial leverage) does not fully
reflect the extent to which a company has a flexible organizational structure. Operational and managerial flexibility
are also important determinants of a company’s overall
strategic flexibility (Aaker and Mascarenhas 1984). However, the data on those dimensions of flexibility are not
readily available. Future studies could survey industry
experts or use statements in the companies’ annual reports
to operationalize strategic flexibility on a broader scale.
Although this alternative approach would allow for a more
comprehensive and complete examination of the role of
strategic flexibility on firm performance, it relies less on
objective measures. Given that the strategic flexibility is
closely linked to marketing and firm value, further research
should explore a broader conceptualization of flexibility
and alternative ways to measure it.
Moreover, although we note that increased marketing
spending can signal both higher and less risky future cash
flows, we do not distinguish the two channels of value creation in our tests. It would be worthwhile to examine the
potential risk reduction implications of enhanced postoffering
marketing budgets, particularly during turbulent economic
conditions. Note that Carlson, Fisher, and Giammarino
(2010) reveal that systematic risk declines during the postSEO period. However, they do not examine how marketing
spending relates to changes in postoffering systematic risk.
Future studies could explore the role of marketing spending
and strategic flexibility in changing risk dynamics around
equity offerings.
It is also worth noting that our study does not provide
any direct evidence about the extent to which marketing
managers are involved in the process of equity offerings
and in determining the use of funds. Our results, however,
suggest that marketing managers can be instrumental in helping issuers use offer proceeds in a value-enhancing manner.
Therefore, it would be fruitful to examine issuing firms’
practices by conducting surveys or in-depth interviews with
marketing and finance executives. Finally, we only focus on
IPOs and SEOs, which are leverage-decreasing financing
events that enhance firms’ strategic flexibility. However, it
would be worthwhile to examine how leverage-increasing
events such as debt offerings (capital inflow) or share repurchases (capital outflow) affect marketing strategy and how
investors respond to changes in firms’ marketing expenditures around these events.
Conclusion
To enhance their marketing expenditures, IPO and SEO
firms allocate significant amounts of capital raised through
their equity offerings. However, the link between these marketing expenditures and firm value is heterogenous across
firms and market conditions. Only those issuers competing
against rivals with relatively less strategic flexibility benefit
from the adoption of an aggressive marketing strategy. In
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