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It Pays to Pay Attention:
How Firm's and Competitor's Marketing Levers affect Investor Attention and Firm Value
Abstract
Investors' attention to a firm's stock has been demonstrated to influence stock returns (Da et al.,
2011). But does a firm's marketing information draw attention to a firm's stock? Research in
finance, accounting, and marketing has investigated advertising as one potential driver of
investors' attention to a firm's stock. How about other potential marketing drivers? The authors
develop hypotheses related to the impact of the changes in four marketing levers: advertising,
product development announcements, WOM, and customer satisfaction on the change in investor
attention to a firm's stock. Furthermore, they investigate the moderating role of competitors'
marketing levers in these relationships.
To test the hypotheses, they compile a panel dataset with 349 firms covering the 2007-2017
period. The results suggest that the changes in the focal firm's advertising and WOM have a
positive and significant impact on the changes in investor attention to the focal firm.
Furthermore, these effects are amplified when there is an increase in competitors' advertising
spending and WOM, respectively. For the customer satisfaction lever, the results suggest that the
change in competitors' customer satisfaction enhances the impact of the change in focal firm's
customer satisfaction on investor attention. Collectively, the results suggest that investors attend
to the firm's and its competitors' marketing information in a much more nuanced manner than
previously thought.
Keywords: Marketing Levers, Investor Search, Competitors, Firm Value, Marketing-Finance
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INTRODUCTION
In the last two decades, the marketing-finance interface scholarship has significantly
advanced our understanding of how marketing affects firm value (Edeling et al., 2021;
Srinivasan & Hanssens, 2009). In this stream, studies have found that marketing levers such as
advertising, product announcements, word of mouth (WOM), and customer satisfaction have a
positive effect on firm's stock price (Fornell & Mithas, 2006; Joshi & Hanssens, 2010; Tirunillai
& Tellis, 2012; Warren & Sorescu, 2017a). The core argument in this research is that changes in
marketing levers affect investors' expectations about firms' operating performance, which are
reflected in the firm's stock market value (Srinivasan & Hanssens, 2009; Srivastava et al., 1998).
For instance, advertising affects the interest in a firm's stock from a wider pool of investors as it
signals higher future product market performance (Grullon et al., 2004; Srinivasan et al., 2009).
Similarly, positive changes in customer satisfaction lead to future cash flow expectations that
result in higher firm valuation (Gruca & Rego, 2005; Lim et al., 2020; Tuli & Bharadwaj, 2009).
Yet, research in marketing has only started to examine the process by which marketing
levers influence firm value (see Table 1). For instance, studies have shown that investor behavior
and analysts' recommendations are important routes through which innovation, social media
chatter, and customer satisfaction, affect firm value (Cillo et al., 2018; Luo et al., 2014; Nguyen
et al., 2020). Advancing this research, we propose that investor attention to a firm's stock is an
important route through which marketing levers affect firm value.
Investor attention, which we define as "the process that encompasses the noticing,
encoding, interpreting, and focusing time and effort by investors on information relevant to the
price of the stock" based on the work by Ocasio (1997), has been shown to have a positive
impact on abnormal returns (Da et al. 2011). More recently, several empirical studies report that
advertising expenditures are positively related to investor attention, which affects firm value
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(Focke et al., 2019; Liaukonyte & Zaldokas, 2020) (see Table 1 Panel B). However, investors
also pay more attention to a firm's stock because of the firm's other marketing levers such as
product development announcements, customer satisfaction, and WOM. Across firms in our
sample, we find instances where news reports about a firm's marketing levers lead to investor
reactions. Apple's announcement of its new iPad in 2013 led investors reacting optimistically
about its potential
1
. Pepsi's ad spend during Super Bowl led to an 0.8% increase in its stock price
one day after the ad aired
2
. Nokia's increase in customer satisfaction swayed investors resulting
in higher stock market performance
3
. United Airlines' stock market cap tanked by $770 million
to $21.5 billion after negative WOM went viral due to its mishandling of a passenger
4
. In
response to the increasing importance of WOM, VanEck Securities Corporation launched the
Exchange Traded Fund called “BUZZ” which tracks the performance of the 75 large cap U.S.
stocks based on the content aggregated from “online sources including social media, news
articles, blog posts and other alternative datasets.”
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[Insert Table 1 here]
Motivated by the above evidence and the findings from the overview of the marketing-
finance interface (Edeling et al., 2021), we test the unexplored relationships between four
marketing levers (advertising expenditures, product development announcements, customer
satisfaction, and WOM) and investor attention. As studies have shown that marketing activities
of a firm spillover to rivals (Sorescu et al., 2007), we also test the moderating role of
competitors' marketing levers. Specifically, we study the following questions:
1
https://www.redherring.com/mobile/apple-product-launch-preview-how-did-investors-react-to-the-last-five-events/
2
https://www.thestreet.com/lifestyle/sports/super-bowl-ads-stock-prices-14854700
3
https://phys.org/news/2013-08-customer-satisfaction-company-investors.html
4
https://money.com/united-airlines-fiasco-overbooked-passenger-dragged-stock-price-value/
5
https://www.vaneck.com/us/en/investments/social-sentiment-etf-buzz/
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1. Do focal firm's advertising, product development announcements, WOM, and customer
satisfaction affect investor attention to the focal firm?
2. Do a focal firm's competitors' advertising, product development announcements, WOM,
and customer satisfaction moderate the above relationships?
3. Does investor attention mediate the effect of the focal firm's advertising, product
development announcements, WOM, and customer satisfaction on focal firm value?
To address these questions, we collect focal and competitor firms' advertising
expenditures from Kantar Media AdSpender, product development announcements from
Standard & Poor's Capital IQ database, and customer satisfaction, and WOM from YouGov. To
proxy investor attention, we collect data on investors' search of financial documents from the
Securities and Exchange Commission's (SEC) EDGAR website (Madsen & Niessner, 2019;
Ryans, 2018). We test our hypotheses on a sample of 349 firms across ten years of quarterly data
from 2007 to 2017. We analyze these data using a system of equations with cross-correlated
errors, selection correction, and a fixed-effects specification.
We make the following contributions to the marketing literature. First, beyond
advertising, we examine the effect of three more marketing levers on investor attention. We find
that the changes in WOM and advertising of the focal firm have a positive impact on the changes
in investor attention. Second, we investigate the moderating role of competitive marketing levers
and show that competitor's advertising, WOM, and customer satisfaction moderate the effect of
changes in focal brand's respective marketing levers on investor attention. Thus, we contribute to
the nascent field of how competitor actions affect focal firms' stock market performance. We
show that competitor's advertising, WOM, and customer satisfaction moderate the effect of focal
brand's advertising, WOM, and customer satisfaction on investor attention, respectively. Third,
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we find that investor attention partially mediates the relationship between the focal firm's
advertising, WOM, and firm value. Our findings suggest that investor attention also mediates the
relationship between WOM and firm value apart from the advertising path, which further
emphasizes the need to consider a broad set of marketing levers as drivers of investor attention.
CONCEPTUAL BACKGROUND
We begin by providing the background for the investor attention construct and then develop
hypotheses on how advertising, product development announcements, customer satisfaction, and
WOM affect investor attention and how competitors' marketing levers moderate these effects.
We conclude with the hypothesis on the mediating role of investor attention in the focal firm's
marketing levers – abnormal returns link. We present our conceptual framework in Figure 1.
[Insert Figure 1 here]
Investor Attention
The concept of investor attention is rooted in the theory of bounded rationality (Simon, 1947).
The basic premise of the bounded rationality concept is that human beings cannot process all the
available information in the environment. Therefore attention is a scarce resource for decision-
makers (Kahneman, 1973). In organizational settings, Ocasio (1997) argues that managers can
pay attention only to a subset of all the available information and thus have bounded rationality
in their decision making. Consequently, “what managers do depends on what issues and answers
they focus on (Focus of Attention)” (Ocasio, 1997). In the finance literature, researchers have
proposed that investors operate with limited cognitive resources when making investment
decisions (Odean, 1999). Both managerial attention and investor attention literatures use the
same theoretical background that relies on bounded rationality and limited information
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processing capacity of decision-makers (Kahneman, 1973; Simon, 1947). Therefore, we rely on
the definition proposed by Ocasio 1997 and adapt it to the investment context (p.189, 1997).
Investor attention is defined as a “process that encompasses the noticing, encoding,
interpreting, and focusing time and effort by investors on information relevant to the price of the
stock”. As researchers cannot fully observe the process behind investor decision-making,
previous studies have used direct and indirect proxies for investor attention. Traditionally studies
relied on indirect proxies such as analyst following (Bushman, 1989) and business press
coverage (Bushee et al., 2010; Engelberg & Parsons, 2011). Recently, researchers have used
more direct proxies of investor attention such as searches of stock tickers in Google (Da et al.,
2011), downloads and/or requests of financial statements from SEC's EDGAR website (Drake et
al., 2016), and news searching and reading activity on Bloomberg terminals (Ben-Rephael et al.,
2017). We use the number of downloads from the EDGAR platform as the investor attention
proxy, which aligns well with our hypotheses.
Antecedents of Investor Attention
Research on the antecedents of investor attention to a firm's stock focuses on an array of
factors such as advertising spending, earnings announcements, and analyst following (Da et al.,
2011; Madsen & Niessner, 2019). There are two processes through which these antecedents may
influence investor attention. First, investors may pay more attention to a firm's stock because
they are more often exposed to the firm (e.g., Starbucks, Apple). For example, heavy advertising
can create frequent exposure of the firm to investors (Joshi & Hanssens, 2010). We label this
effect as mere awareness effect. Second, new information about the firm can help investors
deduce a firm's future cash flows. For example, investors pay greater attention to a firm's stock
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during earnings announcements, acquisitions, and earnings forecasts as these events have
upshots for a firm's future value (Drake et al., 2016). We term this effect as information effect
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.
We propose that a firm's marketing levers influence investor attention through both the
awareness and information effects. We focus on four levers that firms use to create value. We
rely on the overview of the marketing-finance interface since 1985 (Edeling et al., 2021) to select
the most relevant and important marketing levers. We use advertising spending and product
development announcements as two key budgetary levers related to value appropriation and
value creation (Mizik & Jacobson, 2003). These two marketing levers have a long history of
being examined in the marketing-finance interface literature (Edeling et al., 2021; Srinivasan &
Hanssens, 2009). We use WOM and customer satisfaction as the two key customer-based
marketing levers that have attracted increased attention from marketing-finance scholars (Babić
Rosario et al., 2016; Edeling et al., 2021). Finally, as firm advertising, product development
announcements, WOM, and customer satisfaction information is often benchmarked against the
competition, we examine the contingent effect of competitors' marketing levers in affecting
investor attention.
Marketing Levers and Investor Attention
Focal Firm's Advertising Spending. Advertising enables firms to enhance the awareness
of their brands and increase sales. Whether a firm pursues a cost-leadership or differentiation
strategy to achieve a competitive advantage, prior research finds strong support for the positive
and significant relationship between advertising and sales (McAlister et al., 2016). In their meta-
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Firms pursue various corporate branding strategies such as corporate-brand strategy or house-of-brands strategy.
For the information effect, we think that the conceptual processes are not different for firms that purse corporate
brand strategy and the firms that pursue other branding strategies for two reasons: (i) baseline knowledge of
investors about brands, (ii) low search cost of finding out the corporate owner of a brand. Also, we empirically
address this issue which we discuss in the “Robustness Checks” section.
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analysis, Edeling & Fischer (2016) report that 77% of 296 advertising elasticities are positive
and significant. Such an effect is of interest to investors as the change in sales can lead to higher
cash flows and firm value. The value-relevance of advertising expenditures to firm value
motivates investors to pay considerable attention to changes in advertising. Moreover,
advertising draws investor attention both through the exposure effect of a firm's marketing
strategies and the new information effect of increased advertising expenditures (Focke et al.,
2019; Liaukonyte & Zaldokas, 2020; Lou, 2014). In other words, when firms increase their
advertising expenditures, they are more likely to be noticed by investors.
H1a: Positive (negative) changes in the focal firm's advertising spending are positively
(negatively) associated with investor attention to the focal firm.
Focal Firm's Product Development Announcements. To reach as many customers as
possible and to create buoyant anticipation for a new product, firms organize product launch
events (Lee & O'Connor, 2003). Apple's new product launch events, such as the "Time Flies"
event in 2020, have become influential in drawing investor attention as analysts from leading
investment banks follow such events (Peterson, 2020). Also, movie producers and game
developers work with social media companies to create interest for their latest movies or games
(Gelper et al., 2018). Firms use a mix of online (social media) and offline (launch events)
channels to create multiple customer touchpoints. Simultaneously, the investor's exposure to the
new product and the firm increases, which increases the investor's awareness of the firm's stock.
Investors also attend to a firm's product development information because of the
relationship between new products and future cash flows. Rubera & Kirca (2012) conduct a
meta-analysis of innovation and firm performance and find a positive effect of innovation on
market share and sales growth. Several other studies report a positive relationship between
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product development announcements and stock returns (Pauwels et al., 2004; Sorescu & Spanjol,
2008). Furthermore, investors could react to product development announcements because they
may also signal an increase in uncertainty around a firm's future performance. Sood & Tellis
(2009) report that investors react to not only the commercialization of new products but also to
earlier phases of the product development process (i.e., initiation and development). Typically,
all phases of the product development process are defined by uncertainty. There is no guarantee
that any initiated project will be commercialized and whether it will be profitable. Thus,
investors are more likely to seek additional information about the firm as a result of new product
development announcements.
H1b: Positive (negative) changes in focal firm’s product development announcements are
positively (negatively) associated with investor attention to the focal firm.
Focal Brand's WOM. Everyday consumers engage in conversations about brands (Keller
& Fay, 2012). As a brand becomes more popular, there is greater word-of-mouth (WOM) about
the brand (Lovett et al., 2013). When people engage in positive or mixed-valenced conversations
about brands, they are more likely to retransmit the information about the brand to others (Baker
et al., 2016). Hewett et al. (2016) report a positive carryover effect of the volume of WOM. In
other words, increases in WOM in the current period translates into higher WOM in the ensuing
periods. Such steady growth in conversations about the brand increases the investors' exposure to
the brand, which in turn elevates the investors' awareness of the firm’s stock.
WOM can also signal important information about firm performance (Hewett et al., 2016;
You et al., 2015). For instance, studies have shown that the volume and valence of WOM are
significantly related to customer-acquisition metrics (De Vries et al., 2017) and stock market
reactions (McAlister et al., 2012; Tirunillai & Tellis, 2012). Empirical results suggest that the
changes in WOM reflect the current and future shifts in a firm's cash flows. Overall, as investors
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are increasingly relying on WOM as a leading indicator of brand performance (Hewett et al.,
2016), we postulate that:
H1c: Positive (negative) changes in the focal firm’s WOM are positively (negatively)
associated with investor attention to the focal firm.
Focal Firm's Customer Satisfaction. The mere awareness impact of customer satisfaction
on investor attention can operate in several different ways. Marketplaces such as Amazon.com
highlight the brands that receive the highest consumer ratings. YouGov, a prominent market
research company, updates customer satisfaction performance of the brands they monitor on a
daily basis and they publicize the brands that consumers are most satisfied with. In some
industries, such as the auto industry and financial services, firms receive awards based on their
customer satisfaction performance. J.D. Power, a market research company with expertise on the
auto industry and the recipients of customer satisfaction awards regularly publicize these
accomplishments
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. Thus, brands with high levels of customer satisfaction are likely to be more
popular, which increases investors' exposure and enhances investors' awareness of the brand.
Customer satisfaction also affects firm performance outcomes such as firm profitability,
cost of selling, and stock returns (Anderson et al., 2004; Lim et al., 2020; ). In a recent meta-
analysis, Otto et al. (2020) report a statistically significant positive association between customer
satisfaction and revenue (average correlation = .104) and a positive association between
customer satisfaction and profit (average correlation = .134). Thus, we argue that changes in
customer satisfaction performance of a firm should be of interest to investors.
H1d: Positive (negative) changes in the focal firm’s customer satisfaction are positively
(negatively) associated with investor attention to the focal firm
Moderating Role of Competitors' Marketing Information
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https://www.jdpower.com/awards
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Ocasio (1997) argues (p.190): “The principle of situated attention indicates that what
decision-makers focus on, and what they do depends on the particular context they are located
in.” In paying attention to a firm's stock, a key contextual factor for investors is competition
(e.g., Szymanski et al. 1993). Recent studies report that competitive marketing actions can affect
a focal firms' firm value (Warren & Sorescu, 2017a). These studies suggest that investors trade a
firm's stock based on competitors' actions. Thus, the conjecture of our research is that the impact
of the focal firm's own marketing levers on investor attention to its stock is contingent upon its
competitor's marketing levers.
The Moderating Role of Competitors' Advertising. Findings from field data on product-
markets suggest that firms may benefit from competitors' advertising in terms of brand
recognition and consideration (Anderson & Simester, 2013; Lewis & Nguyen, 2015). This is
because advertising may contain a form of comparison or differentiation between a set of
competing firms. Thus, when investors observe a firm's advertising, they may also be exposed to
a broader set of competing firms. In other words, when competitors increase their advertising
spend, investors may also be exposed to the focal firm.
Similar to managers who rely on competitive heuristics when setting advertising budgets
(Kolsarici et al., 2020), investors also assess competitive investments in advertising for
benchmarking purposes. Competitive dynamics of firms’ advertising spending motivates such
comparisons. Gijsenberg & Nijs (2019) report that it is common to observe a simultaneous rise in
both focal and competitor's brand advertisement incidence and spending. Furthermore, the focal
firm's sales can benefit from competitors' advertising activity (Sahni, 2016). The increase in
competitors' advertising spending should motivate investors to better understand the impact of
focal firm's advertising on future cash flows.
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H2a: The positive impact of an increase in the focal firm's advertising spending on
investor attention to the focal firm is higher (lower) when the increase in competitors'
advertising spending is high (low).
The Moderating Role of Competitors' Product Development Announcement. Like
advertising, a product development announcement can contain comparisons of several competing
firms. For instance, in consumer reports, new car seats for kids are evaluated according to their
safety features and performance (Simonsohn, 2011). Likewise, the new generation of competing
video game consoles are compared in detail on websites and magazines (PS5 vs. Xbox X). For
high-technology products such as microprocessors, similar comparisons are made not only in the
consumer space but also in the business press and analyst reports (Arya, 2020; King & Bass,
2020). Thus, simultaneous new product development activities in a product category increase
investor's exposure to the new product activities of the focal firm because of the comparative
coverage of firms' innovative activities by third parties (e.g., websites, magazines).
While the cash flow effect of the focal firm's investments in products partially depends
on its capabilities and resources, it also depends on the innovation activities of its competitors.
For example, when a firm announces a product, the stock market reaction is lower in industries
with higher product announcement activity (Warren & Sorescu, 2017a). In contrast, horizontal
product line expansions can increase the rival firm's profitability if the focal firm increases the
price of its incumbent product to avoid cannibalization (Thomadsen, 2013). When both the focal
firm and its competitors increase their product development announcements, investors search for
the focal firm's information as they get curious regarding the repercussions of these events. If
competitors increase product development, is the focal firm's product development sufficient?
Would the investments pay off? For example, an increase in industry-level innovation activity
may mean moving towards value-creation from value-appropriation, which signals a shift in the
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cash flows of firms operating in the industry (Mizik & Jacobson, 2003). In that case, investors
would be interested in understanding whether the focal firm is keeping up with the trend of
investing in innovation. To address such questions, the investors would need to obtain more
information on the firm and its product development strategy. Therefore, we hypothesize that:
H2b: The positive impact of an increase in focal firm's product development
announcements on investor attention to the focal firm is higher (lower) when the increase
in competitors' product development announcements is high (low).
The Moderating Role of Competitors' WOM. Empirical studies suggest that WOM about
competitors may enhance the salience of the focal firm's WOM (Lovett et al., 2013; Tirunillai &
Tellis, 2012). This process is enhanced by the presentation of information on popular firms in
social media rankings (e.g., trending topics on Twitter). Such comparative presentation of
information exposes the investors to focal firms along with the competitors, which in turn
increases the investors' awareness of the firm's stock.
Investors can also relate the competition's WOM to the focal firm's performance. Indeed,
studies show that WOM spills over to competitors and vice-versa (Libai et al., 2009; Lovett et
al., 2013). However, the spillover from competitors to the focal brand depends on the
characteristics of the content (e.g., diagnosticity) and brand associations (e.g., typicality)
(Sanchez et al., 2020). Thus, an increase in competitor's WOM implies that investors would need
to gather further information about the focal firm and its products to ascertain the competitive
impact of WOM on the focal firm. Thus, we hypothesize,
H2c: The positive impact of an increase in the focal firm's WOM on investor attention to
the focal firm is higher (lower) when the increase in competitors' WOM is high (low).
The Moderating Role of Competitors' Customer Satisfaction. Customer satisfaction
information often becomes available to investors in comparative form and categorized based on
the industry. For example, in the financial services sector, the customer satisfaction ranking is
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announced as "Capital One tops J.D. Power rankings for customer satisfaction"
(Americanbanker.com, December 17, 2020). In this announcement, it is also discussed how
Capital One's competitors such as Chase Bank and PNC performed. Similarly, ACSI releases the
customer satisfaction scores by industry. Thus, investors can simultaneously observe changes in
customer satisfaction of a set of competing firms. In other words, investors can compare the
customer satisfaction of several firms and then further scrutinize the firms' financials. Overall,
because of the established practice of benchmarking firms' customer satisfaction performance
with competitors in the same industry, the impact of a change in focal firm's customer
satisfaction on investor attention will be greater when there is a greater change in competitors'
customer satisfaction.
A firm’s customer satisfaction can predict its future market share when it is benchmarked
against that of its nearest rival (Rego et al., 2013). For example, Mittal and Kamakura 2001, (p.
134) find that evaluating customers’ “true” satisfaction ratings (i.e., those that will affect their
actual repurchase behaviors) requires the value of customers’ next-best alternative, which is
“based on not only the satisfaction from the brand but also the expected satisfaction from
competing brands.” When the competitors' customer satisfaction increases, it is not clear to
investors whether the competitors whose customer satisfaction performance improved can
actually influence the focal firm’s customers to switch to their offerings. Therefore, investors
would need to compare the competitor firm’s customer satisfaction, focal firm’s customer
satisfaction and respective cash flows of these firms, which enhances their motivation to learn
more about the focal firm’s stock. Thus, we hypothesize,
H2d: The positive impact of an increase in focal firm's customer satisfaction on investor
attention to the focal firm is higher (lower) when the increase in competitors' customer
satisfaction is high (low).
Investor Attention as a Mediator
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Prior research on the consequences of investor attention reports a significant positive
relationship between investor attention and a stock's ownership and liquidity (Grullon et al.,
2004), abnormal trading volume (Ben-Rephael et al., 2017), and abnormal returns (Da et al.,
2011; Lou, 2014). Given the importance of the link between marketing levers and firm value, we
focus on the mediating role of investor attention in this relationship.
Prior research in finance literature suggests that investors are more likely to invest in
stocks that they are familiar with (Frieder & Subrahmanyam, 2009; Grullon et al., 2004). Thus,
from the mere awareness perspective, familiarity with a stock leads an investor to perceive that
she has an information advantage over other stocks (Aspara, 2013), which increases the
likelihood of investing in that stock. From the information effect perspective, paying attention to
a firm's stock and learning about it because of the changes in the marketing levers increases the
investor's confidence in her knowledge of the firm. An investor is more likely to invest in a stock
when the investor's confidence in her knowledge of the firm is higher (Allgood & Walstad, 2016;
Aspara, 2013). Both mechanisms explain how the changes in marketing levers lead to investing
in stocks through investor attention. But how does this affect stock price movements?
In the finance literature, the relationship between investor attention and abnormal returns
is explained by the buy-sell imbalance theory (Chemmanur & Yan, 2019). According to buy-sell
imbalance theory, investor attention affects the imbalance between buyers and sellers because
buyers have to search from a large set of options, while sellers only have to consider the limited
number of stocks they own in their portfolios (Barber & Odean 2008). Thus, buyers consider
investing in only those stocks that they pay attention to (and ignore the others). Prior research
finds strong evidence on the contemporaneous and one-quarter increases in prices of stocks that
the investors pay greater attention to (Da et al., 2011). Furthermore, Madsen & Niessner (2019)
16
report a significant mediating effect of investor attention between print advertising and abnormal
returns, while Liaukonyte & Zaldokas (2020) find similar results using TV advertising data.
Based on the buy-sell imbalance theory, we propose that investor attention is triggered by the
changes in the focal firm's four marketing levers. This attention then increases the odds of
inclusion of the stock in the investor's consideration set, which increases stock prices. Thus, we
hypothesize,
H3: Investor attention mediates the effect of changes in focal firm's marketing levers and
abnormal stock returns
DATA AND METHODS
To answer our research questions, we merge multiple datasets that have observations
collected at different time frequencies. We obtain advertising data from Kantar Media's
AdSpender database, which is available at a monthly frequency. We obtain customer satisfaction
and brand WOM data from YouGov Group, which is available daily. We obtain product
development announcements from the Standard & Poor's Capital IQ database that is available
daily. We obtain investor attention data from Edgar SEC (Securities and Exchange Commission)
that is at a daily frequency. We retrieve stock market data available at a daily frequency from the
Center for Research in Security Prices (CRSP). Finally, we obtain our control variables from
quarterly financial statements data from S&P COMPUSTAT. Table 2 provides the description of
these variables, the source of the specific data items, and representative research that used these
variables. Table A1 and A2 in Web Appendix show the summary statistics and correlation
coefficients, while Table A3 shows that multicollinearity is not an issue in our analysis.
[Insert Table 2 about here]
Because the focal investor search and abnormal returns measures as well as control
variables are at the company level, our unit of analysis is the company-quarter. Our main starting
17
point is the YouGov brand universe, which consists of 1800 brands (for a similar sample
selection strategy, see Stäbler & Fischer, 2020). We manually identify the corporate owners of
brands by following a precise procedure that combines the search of the brand name on Google
and Wikipedia, reading brand history on the website, and checking other relevant sources (e.g.,
company reports). For each brand, we find the brand owner and possible change of ownership
throughout our sample period. Out of 1800 brands, on manual inspection, 1500 brands belong to
405 publicly traded corporate owners. Some of these companies have only one brand (mono-
brand firms such as Nike) while others have multiple brands (e.g., P&G). We check the overlap
of these 405 firms with the rest of the data. For all 405 firms we obtain financial information
from COMPUSTAT, investor search and product development announcements. Of the 405
firms, advertising expenditure data were available for 349 firms in Kantar Media database which
constituted our final sample. We observe a large number of industries represented in the final
sample, including 40 different 2-digit SIC codes (e.g., 13=" Oil & Gas Extraction", 23=" Apparel
& Other Textile Products”).
After aggregating the datasets at a quarterly frequency, we compute the 1-quarter changes
in the variables as our model specification uses a changes approach. Besides, we create temporal
separation among our key variables and for every firm in quarter t. Abnormal returns are from
the same quarter t while the other variables are from quarter t-1. The final, merged dataset covers
over a decade from 30th June (Q2) of 2007 to 30th June (Q2)-2017, yielding an unbalanced panel
of 9,105 firm-quarter observations for 349 firms.
Description of Focal Variables
Advertising Expenditures
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We measure a firm's advertising by the dollar spend on advertising (e.g., Du et al., 2018;
Wilbur, 2008) which we obtain from Kantar Media. Kantar Media is a leading database for
advertising data that tracks brand advertising activity across print, broadcast, radio, internet, and
outdoor media channels and translates this information to monetary amounts by surveying
agency and media rates (Cheong et al., 2021; Wies et al., 2019). Kantar Media's Ad$pender
database tracks and records advertising expenditures at the parent firm (e.g. The Coca-Cola
Company), brand (e.g. Coca-Cola) and even product (e.g. Diet Coke) levels. As described above,
our starting point is the YouGov brand universe and YouGov collects data at the brand-level.
Thus, to be able to examine the key relationship in our framework, we first match the brand
names as reported in Ad$pender to the brand names as used in YouGov. In other words, the
preferred matching procedure is the lowest possible aggregation level which is available in the
dataset. We then aggregate the brand-level data from Ad$pender at the parent-firm level that we
use for all of our financial information. For instance, if a parent firm in our data owns two
brands, we sum the advertising expenses for these two brands to come up with the parent firm-
level advertising expense.
There are multiple firms in our sample with missing observations in the Kantar database.
Overall, we have Kantar advertising expenditures for 210 firms. We tackle this issue by
replacing the missing values with the advertising expenses obtained from COMPUSTAT for the
remaining 139 firms. As COMPUSTAT only reports annual advertising expenditures (item
XAQ), we divide it by four to derive the quarterly metric
8
. We believe this approach yields a
complete advertising expenditure variable rather than discarding firms with missing values. To
8
There is a data item listed as quarterly advertising spending (XADQ) in the COMPUSTAT online manual.
However, this data item is not available anymore in the WRDS COMPUSTAT database and only annual advertising
expenses (XAD) are reported (Luo et al., 2010; Minton et al., 2002).
19
ensure that our approach is fairly valid, we find the correlation between the annual
COMPUSTAT and Kantar advertising is 0.793. Very similarly, a study by Focke et al. (2019, p.
12) reports a 0.79 correlation between Kantar and COMPUSTAT advertising. Also, when we
estimate our model by using only the sample of firms for which there is advertising data in
Kantar, thereby reducing the sample size, we find results that are consistent with our main
findings.
Product development announcements
In their review of research on marketing-finance interface, Srinivasan & Hanssens (2009)
argue that it is theoretically possible to obtain daily level information on innovation activities by
tracing the firm's announcement of product development events. Prior researchers have used
event studies to evaluate the effect of innovation announcements on stock returns (Sorescu et al.,
2007; Warren & Sorescu, 2017a). Following this research, we obtain every product development
announcement for each firm in our sample. To do so, we use S&P's Capital IQ database, which
has a Key Developments feature that provides categorized news and corporate event data. The
Key Developments feature categorizes a firm's developments into different types. Key
Developments types include categories of announcements such as Alliances, Business
Expansions, Client Announcements, Product-Related Announcements, Mergers & Acquisitions,
among others. For determining if an announcement can be classified as product development, we
carry out a text mining analysis within the text of all "product announcements" and "business
expansions" Key Development types. For the text mining analysis, we create a dictionary of
terms related to product development announcements (launch, patent, shipping, new product,
new process, etc.) and code an instance as 1 if the text contains these terms. To validate this
approach, we independently check the accuracy of our classification with the help of two
20
research assistants. We randomly select 500 product development announcements from the
Capital IQ database. Two research assistants independently read each announcement and classify
the announcement as a product development announcement. The inter-rater agreement is 88%.
We find a classification accuracy of 84%, i.e., 84% of the announcements classified as product
development announcements by the text algorithm are also classified as product development
announcements as per both research assistants. Our measure is a count measure for the number
of product development announcements in each quarter.
WOM
We obtain brand WOM from YouGov (Hewett et al., 2016). Respondents are prompted
for anything they have heard in the media—news, advertising, social media, or any other sources
of information—to determine whether they have noticed good or bad news, advertising, public
relations campaigns, product launches, and/or whether there is any "word on the street." The
positive WOM score on a given day represents the number of people who answered "Yes" to the
question, "Have you heard anything positive about a brand X?" Similarly, the negative WOM on
a given day is the number of people who have answered "Yes" to the question, "Have you heard
anything negative about a brand X?" Therefore, for each brand on a given day, we have a
separate score of whether people have heard something (1) positive or (2) negative about a
brand. The volume of WOM thus captures the sum of positive and negative WOM around the
brand in each quarter.
As YouGov also accounts for social media chatter in their WOM measure, we also check
the robustness of YouGov's WOM measure with social media data, which we obtain for 19 firms
for the 2012 year. The correlation between the YouGov WOM measure and Facebook's "People
Talking About This" ("PTAT") measure, which measures the extent to which users voluntarily
engage in telling a story about a brand (from the Facebook Insights tool) is .3207. We also
21
compute the correlation with the user posts on Facebook (users posting on a brand's Facebook
wall) and find a similar result (0.317). Thus, we can moderately be sure that our WOM measure
from YouGov also captures social media WOM.
Customer Satisfaction
We collect customer satisfaction measure from YouGov, which collects the number of
satisfied customers and dissatisfied customers for brands on a daily basis
9
. Customer satisfaction
measures the number of customers who have answered yes to the question "Of which of the
following brands would you say that you are a "SATISFIED CUSTOMER"? Similarly, customer
dissatisfaction measures the number of customers who have answered yes to the question "Of
which of the following brands would you say that you are a "DISSATISFIED CUSTOMER"?
We subtract the number of dissatisfied customers from the number of satisfied customers to
obtain our measure of customer satisfaction. We then take the quarterly average score across the
daily measure of customer satisfaction. We provide a detailed overview of YouGov panel
characteristics and data collection in Web Appendix.
Previous research has used the American Customer Satisfaction Index (ACSI) as a
measure of customer satisfaction. Importantly, Malshe et al. (2020) compare the ACSI's
measurement scale, YouGov's measurement scale, and a single item overall satisfaction measure
used by Mittal & Kamakura (2001). They report a 0.9 correlation between the ACSI and
YouGov satisfaction measure, and furthermore, they also load on a single principal component.
In summary, this analysis shows that the ACSI and YouGov metrics are measuring the same
underlying construct.
9
Studies have often used the ACSI as a measure of customer satisfaction which provides information on a limited
number of firms in selected industries. In addition, the ACSI scores are reported only once a year. Yet, customer
satisfaction can change from quarter to quarter and even at higher frequencies (Colicev et al., 2018).
22
Competitors’ Advertising, Product development announcements, WOM, Customer Satisfaction
We rely on Standard Industry Classification (SIC) codes from COMPUSTAT to compute
the competitor metrics for each of our focal measures of advertising, product development
announcements, customer satisfaction, and WOM. SIC codes are 4-digit codes based on the
principal end-product of the firm. They are chosen so that, as the lowest order digits are
removed, the companies are aggregated into broader but still similar groups (Fertuck, 1975).
Research on stock prices indicates that the estimates of the importance of industry are insensitive
to the level of aggregation (Wernerfelt & Montgomery, 1988). Thus, we separately compute the
variables for the competitors at 2, 3, and 4-digit codes excluding the focal firm. Notwithstanding
the findings that the level of aggregation of SIC codes does not matter for stock market return
models, some papers advocate the use of two-digit codes (Servaes, 1996; Wernerfelt &
Montgomery, 1988). Thus, while for the main results, we report the two-digit SIC codes, we also
confirm the previous research findings that the results are not sensitive to the level of
aggregation. We report the robustness results at three and 4-digit SIC codes in Web Appendix.
Abnormal Returns
We calculate the abnormal stock returns using the Fama-French four-factor model, which
along with the three-factor model that includes the market, size, and value factors (Fama &
French, 1993), also comprises the Carhart's momentum factor (Carhart, 1997). We use the CRSP
(Center for Research in Security Prices) database to obtain the data for stock prices for the firms
in our studies and obtain the Fama-French 4 factors from Kenneth French's website.
(1)
Here i stands for firm, t stands for time, Rit denotes the returns for firm i on day t, RFt is the risk-
free rate of return (thirty-day treasury bill), RMt is the return on the value-weighted (VW) market
23
portfolio, SMBt denotes the returns on a portfolio of small stocks minus returns on large stocks,
HMLt stands for returns on a portfolio of stocks with high book-to-market ratio minus the returns
on a portfolio of stocks with low book-to-market ratio, MOMt is the momentum factor calculated
as the difference between the returns of firms with rising stock returns and declining stock
returns. We estimate the firm's expected monthly stock returns using the prior 36 months' stock
returns. The level residual from the regression in equation (1) is the metric of abnormal stock
returns that we use in the paper. This metric has eliminated the part of stock returns, which could
be explained by the four factors. We calculate abnormal returns (AR) for firm i in quarter t by
using a compounding formula: ARit = {(1+ARi, j)} – 1, where ARi, j is the abnormal return of
firm i in month j of quarter t and j [1, 2, 3].
Investor search
We use a measure of investor attention that captures search by investors who are familiar
with and motivated to search for financial information and who can make investment decisions
based on objective information than rely on heuristics. Thus, we use the breadth of investor
interest in a firm's regulatory filings on the Securities and Exchange Commission (SEC)
Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) as our measure of search
by investors. Starting from February 2003, the SEC has been tracking such search traffic via the
EDGAR access log files. These log files contain detailed information about the users' IPs,
corporations and filings, and the detailed time stamp (nearest to the second). Recently, the SEC
released these log files to the public, and a growing number of academic studies have utilized
this dataset for topics relating to investor attention and information acquisition (Drake et al.,
2016; Ryans, 2018). The SEC EDGAR system hosts all mandatory filings by public companies.
The goal of EDGAR is to "increase the efficiency and fairness of the securities market……by
accelerating the receipt, acceptance, dissemination, and analysis of time-sensitive corporate
24
information filed with the agency." By creating and maintaining the EDGAR system, the SEC
enables interested parties to locate necessary financial information. It is mandatory that all public
firms submit important financial information to the SEC.
We obtain our SEC EDGAR search data from Ryans (2018). This dataset is taken from
the online EDGAR system, which maintains a log file for all activity performed by system
servers. Thus, for each request by an interested user, the log file locates the firm that investors
are inquiring about, the request time, and the type of filing being requested. There are about 451
unique filing types in the EDGAR system. These diverse set (e.g., annual (10-K), quarterly (10-
Q) reports of firm's financial position, large corporate events (8-K), 424, S, SC, 4, DEF, and
Other), of regulatory filings, are more likely accessed by knowledgeable investors, who have the
wherewithal to make stock investments, as these reports are complex, intricate, and lengthy.
To correctly compute investors' use of financial information, we discard downloads made
by robots (vs. humans). Ryans (2018) assumes humans download no more than 25 items or three
different firms' items in a single minute, and humans download no more than 500 items in a
single day. Other downloads are labeled as "robots" and removed. For robustness, we also use
the measure of investor search from Drake et al., (2016), which assumes that humans do not
download more than 1,000 items during a day or more than five items per minute (see Table
A12). To construct our quarterly measure of investor search, we count unique IP addresses to
make a request for a firm i's total regulatory filings on EDGAR on day t. Accordingly, we sum
the number of requests across the quarter
.
Control Variables
We include profit margin and R&D expenditures to control for firms' accounting
performance that can affect both investor search and abnormal returns (Edeling & Fischer, 2016;
25
Srinivasan & Hanssens, 2009) and competitive intensity to control for competition effects. We
include total assets to control for firm size effects (Warren & Sorescu, 2017a). Larger firms are
usually well-covered by analysts, news media, and security research firms, making them less
opaque compared to small firms. We also control for institutional ownership (Cillo et al., 2018)
and include a recession dummy (Q2 2008 to Q4 2010) to control for the effects of trading and
recession. Furthermore, we include the number of analysts covering the stock, which we collect
from the Institutional Brokers Estimate System (IBES) to control for the exposure to the stock
due to the analyst's coverage. Finally, research has shown that investors react to key corporate
events (Sood & Tellis, 2009). Thus, we include earnings and dividend announcements along with
other financial, organizational, and negative key developments from the Capital IQ database (see
Web Appendix for further details).
MODEL
System of Equations with Abnormal Returns, Investor Search, and Selection Equations
In line with our conceptual framework, we follow Tuli and Bharadwaj (2009) and
estimate the model in changes for all variables. As highlighted in Wooldridge (2010), this
specification strategy lowers the potential problems associated with autocorrelation (as it
removes the first-order autocorrelation) and removes the impact of time-invariant unobservable
factors (i.e., removes fixed effects). Note that we also create temporal separation between
abnormal returns and other model variables in order to capture the mediating process correctly
(see Pieters, 2017) and partially address the reverse causality between abnormal returns and other
key variables in the model. We estimate the following system of equations with cross-correlated
errors:
(2) Selectionit= π0 + π1lnSalesit + CTRL1it + ϵ1it
(3) it= α0+ α1Advit +αAdv_compit+ αCSit +α4CS_compit+
26
αit+α6WOM_compit+ αProdit +α8Prod_compit+ α9Advit Adv_compit + αCSit
CS_compit α11it WOM_compit α12Prodit Prod_compit α CTRL1it+
αYDUMMY + β QDUMMY +ϵ2it; when Selectionit = 1
(4) ARit+1= β0+ β1Searchit +β2Advit+βAdv_compit + β4CSit+βCS_compit +
βit+βWOM_compit+βProdit +βProd_compit+β9Advit Adv_compit + βCSit
CS_compit β11it WOM_compit β12Prodit Prod_compit + α CTRL2it +
αYDUMMY + β QDUMMY +ϵ3it+1;
when Selectionit = 1 where, for each firm i and quarter t, Searchit is the changes in investor
search, ARit+1 are the changes in abnormal returns in quarter t+1, Advit is the changes in
advertising expenditures, CSit is the changes in customer satisfaction, WOMit is the changes in
brand WOM, Prodit is the changes in product development announcements for the focal firm
while the subscript _comp denotes the changes in competitors' variables. CTRL1it represents all
the changes in control variables (see Table 2). Salesit is the quarterly revenues, and YDUMMY
and QDUMMY are a set of year and quarter dummy variables, which control for time variation.
We control for selection issues that are pertinent to YouGov covered and non-covered
firms. We obtain customer satisfaction and WOM data from YouGov, which does not collect
scores for all the firms available in Compustat database. Most likely, the firms covered by
YouGov are systematically different from firms that are not covered by YouGov. We include
one more equation in our model that accounts for the differences between YouGov-covered and
not covered firms. We create a dummy variable "Selection" that equals 1 for the firms with
YouGov coverage and 0 for the firms without YouGov coverage. Next, we model "Selection" as
a function of all the control variables that we use in equations 3 and 4. We include the natural
logarithm of revenue that serves as the excluded variable from equations 3 and 4 for three
27
reasons. First, in our private communications with YouGov, we learned that they select brands
based on firm sales as coverage of large firms makes YouGov more visible to all stakeholders.
Also, it becomes easier for YouGov to sell their data to large firms who can afford to pay for
such insights. Second, revenue, measured in levels, is unlikely to be theoretically and empirically
correlated to the changes in investor search as we already control for firm size when computing
the changes in these variables. This is confirmed by the very low correlation between the natural
logarithm of sales and changes in investor search (0.01). We model Selectionit as an indicator
variable such that:
Selectionit 0; Firmit YouGov
1; Firmit YouGov
Model Estimation
In Equation 4, we create time separation between abnormal returns and other model
variables to capture the mediating process. We use a time window prior to the mediating variable
and the abnormal returns variable for calculating the explanatory variables. This helps us tackle
any reverse causality issues (Boulding & Staelin, 1995). Equation 4 includes controls for all the
variables from Equation 3 and the dummy for recession. The empirical model requires that the
main equations be modeled conditionally on the selection equation to correct for selection bias.
While this can be achieved with a two-step estimation method (Heckman, 1979), we use a
simultaneous estimator as it has been shown to have higher efficiency (Breen, 1996).
To accommodate the model's many features, we use the Conditional Mixed Process
estimator (Roodman, 2011). CMP uses a seemingly unrelated regression (SUR) estimator to
solve a recursive set of equations simultaneously (Antia et al., 2017; Mallapragada et al., 2016).
CMP relies on a simulated maximum likelihood algorithm (Geweke, 1989) to directly estimate
the cumulative higher-order likelihood function yielding more efficient estimates of the
28
coefficients of interest relative to the more traditional two-stage least squares technique (or the
separate stages in the Heckman procedure). We use CMP's limited-information (LIML)
estimator, given that we do not have structural equations to satisfy the FIML assumptions. Note
that CMP does need to satisfy all the maximum likelihood assumptions. Overall, we use CMP to
account for sample selection and endogeneity of the sample selection mechanism with a first-
stage regression, robust standard errors, and correlated errors across equations.
Addressing Endogeneity Concerns
We correct for possible endogeneity bias with an statistical, instrument-free approach
used in previous research - Gaussian Copula (Papies et al., 2017; Park & Gupta, 2012). Because
of the constraints of using instruments, which have issues in defending the assumption of
“exclusion restriction” and the peril of weak instruments, this method absolves the researcher
from such challenges. This method directly models the correlation between the endogenous
regressor and the error utilizing Gaussian copulas (Papies et al., 2017). The copula links the
marginal distributions of two (or more) variables that follow any possible distribution (e.g.,
normal, non-normal). Then, the method involves adding a copula term to the equation that
represents the correlation between the endogenous variable and the error term. Hence, the
method treats the endogenous variable as a random variable from any (non-normal) marginal
population distribution, which is correlated with the normal error term of the main equation
through a copula. By including this term (i.e., the copula), the effect of the endogenous regressor
can be estimated consistently. Importantly, Gaussian copulas exploit nonnormality in the
endogenous regressor and normality of the error term(s). The Gaussian copula is implemented
through a control function approach (Datta et al., 2015; Park and Gupta 2012; Vomberg et al.,
2020). Specifically, we incorporate the following variables as additional regressors in our model:
29
(5a) Adv=
(5b) =
(5c) =
(5d) =
Where, is the inverse of the cumulative distribution function, and , ,
, , represent the empirical cumulative distribution functions of advertising,
customer satisfaction, WOM, and product development announcements, respectively. For correct
identification, all variables must be nonnormally distributed. We use Kolmogorov–Smirnov, and
Shapiro–Wilk tests to check for nonnormal distribution. We reject the null hypothesis of
normality in both tests for all four variables.
Additionally, we use an instrumental variable approach with the two-stage control
function to address potential endogeneity of the focal variables (Wooldridge, 2010). We model
the potentially endogenous variables (advertising, product development, WOM, and customer
satisfaction) as a function of exogenous and instrumental variables. We follow Madsen &
Niessner (2019) and use lagged differences in advertising, product development, WOM, and
customer satisfaction. Specifically, we use the quarterly difference in the advertising, product
development, WOM, and customer satisfaction between three and four quarters prior as
instruments. These differences capture trends in the firm’s spend in advertising, volume of
WOM, levels of satisfaction and product development indicative of the firm’s overall activity
level for each variable (Meire et al., 2019). Our selection of the instruments takes advantage of
the sequential nature of the key variables, and using lagged message types alleviates the concern
for the firm’s current competitive response and strategic intent. Practically, the older advertising,
product development, WOM, and customer satisfaction is not very visible for investors.
30
Investors are more likely to pay attention to the most recent information. Thus, it is unlikely that
the previous advertising, product development, WOM and customer satisfaction independent of
unmeasured confounders from three to four quarters earlier will have a direct influence on
current investor attention; rather, any possible influence will go through the latest advertising,
product development, WOM and customer satisfaction. In other words, our instruments are
theoretically linked to endogenous independent variables
10
.
More formally, we examined the Cragg–Donald Wald F-statistics on our instrumental
variables with Stock-Yogo F test critical values. We found that most of the values were above
the rule-of-thumb threshold of 10 (Staiger & Stock, 1997; Stock & Yogo, 2005), with one value
of 8.84, which should not cause major concern. This suggests that our instruments are not weak,
and thus the instruments satisfy the requirement for relevance (i.e., strongly correlated with the
endogenous variables). The Hansen test does not reject the null hypothesis that the instruments
are exogenous in our estimation results (Hansen’s J = 0.242, p = .6225). In the first step, we
regress potentially endogenous focal variables (advertising, product development, WOM, and
customer satisfaction) on a set of predetermined variables (as in the main equation) and lagged
values as instruments. Specifically, we estimate the following four auxiliary regressions:
(6a) Advit= θ0 + Θ
CTRL1it + θ1 _it + CF1it
(6b) it= 0 +
CTRL1it + 1_it + CFit
(6c) WOMit= π0 + Π
CTRL1it + π1_WOMit + CF3it
10
Previous research has used the information about peers or peers-of-peers as instrumental variables (Lim et al.,
2020; Malshe et al., 2020; Shi et al., 2019). We cannot use peers’ marketing information as instruments because the
objective of our research is to investigate the role of competitors’ marketing information in investors’ attention to
the focal firm. In other words, we cannot use competitors’ marketing information as an excluded variable from the
main model. Thus, we rely on lagged values of focal variables as instruments. We thank the anonymous reviewer for
bringing this issue to our attention.
31
(6d) it= ψ0+ Ψ
CTRL1it + ψ1_CSit + CF2it
Where, for every focal firm i and quarter t, Lag3_Advit is the lagged difference of advertising
between quarter 3 and quarter 4 prior to our focal period, Lag3_CSit is the lagged difference of
satisfaction between quarter 3 and quarter 4 prior to our focal period, Lag3_WOMit is the lagged
difference of WOM between quarter 3 and quarter 4 prior to our focal period, and Lag3_Prodit is
the lagged difference of product development between quarter 3 and quarter 4 prior to our focal
period. We use the estimated error terms, CF1it, CF2it , CF3it and CF4it as control function
corrections for potential endogeneity of the focal variables.
RESULTS
We present the estimation results for Equation (2)-(4) in Table 3. The baseline model is the
model without the endogeneity correction. We then add the instrumental variable correction as
the secondary model. Finally, we present the results for the model with Gaussian copulas. The
majority of the substantive results remains the same across three models. For the purposes of
brevity, we present the results based on the model with Gaussian copulas. To ease interpretation,
we multiply the coefficients (and standard errors) by 1000. Our models show a good overall fit
with respect to alternative models (see A9 in Web Appendix). Note that though our model is in
changes form, for expositional purposes, our presentation of the results is indifferent to the
language used for a levels or changes specification.
[Insert Table 3]
Selection equation. We find that the natural log of sales is statistically significant (361.78, p <
.01), indicating that firms with higher sales have a higher probability of YouGov coverage. Thus,
addressing the YouGov’s coverage of firms is important to the reliability of estimation results.
Test of Hypotheses
32
We calculate the marginal effects of changes in the focal firm's marketing levers on
investor attention and plot it against the changes in competitors' marketing levers in Figure 2.
[Insert Figure 2 here]
First, supporting H1a, changes in focal firm's advertising are positively associated with
changes in investor search (.31, p < .01). As expected, the changes in competitors' advertising
have a direct positive impact on investors' attention to the focal firm (.83, p<.01). Also, we find
that the impact of changes in advertising spending on investor attention is amplified with a
positive change in competitive advertising spending (.01, p<.05), supporting H2a. Figure 2A
illustrates that the positive effect of the change in the focal firm's advertising spending on
investor attention is higher when the change in competitors' advertising spending increases.
We do not find support for H1b. The changes in product development announcements for
the focal firm are not positively associated with changes in investor search (.34, p>.1). Similarly,
we do not find support for H2b. That is, the impact of the changes in the focal firm's product
announcements on investor attention does not depend on the changes in the competitors' product
development announcements (-.44, p>.1). Another result that is relevant to these findings is the
significant positive impact of the changes in competitors' product development announcements
on investor attention (7.20, p<.01). This result suggests that investors' attention to product
development announcements is primarily driven by the competitors' actions. Although the effects
are statistically insignificant, Figure 2B shows the positive impact of the change in competitors'
product development announcements on the positive impact of the change in focal firm's product
development announcements on investor attention.
We find that an increase in WOM related to the focal firm has a positive impact on
investor attention, which provides support for H1c (7.16, p<.01). The impact of the increase in
33
WOM related to the focal firm on investors' attention to the firm's stock increases with positive
changes in competitors' WOM (1.18, p<.1). Thus, we find support for H2c. Figure 2C shows the
positive effect of the increase in the focal firm's WOM on the change in investor attention, which
becomes stronger as the change in competitors' WOM increases. This result is striking in the
presence of the negative and significant main effect of the increases in competitors' WOM on
investor attention to the focal firm's stock (-7.19, p<.05). While an increase in competitors'
WOM draws investor attention away from the focal firm's stock, the effect of the focal firm's
WOM on investor attention becomes larger when there is an increase in competitors' WOM.
Finally, the results suggest that the increase in focal firm's customer satisfaction is not
associated with a change in investor attention, which does not provide support for H1d (.16,
p>.1). However, we find that the impact of the change in focal firm's customer satisfaction on the
change in investor attention increases with a positive change in competitors' customer
satisfaction (6.16, p<.05), which provides support for H2d. Figure 2D illustrates how the
increase in the change of competitors' customer satisfaction has a positive impact on the effect of
the change in the focal firm's customer satisfaction on the changes in investor attention.
Furthermore, the increase in competitors' customer satisfaction has a significant impact on the
investors' attention to the focal firm's stock (20.26, p<.01). These results suggest that the impact
of the change in the focal firm's customer satisfaction on investor attention is primarily
dependent on the change in the competitors' customer satisfaction.
Mediating Role of Investor Attention
We hypothesize that investor search mediates the link between the focal firm's marketing
levers and abnormal returns
11
. We find that investor search has a significant positive impact on
11
Prior studies test for moderated mediation with a “spotlight analysis” for cross-sectional data (Aiken et al., 1991).
This method is not appropriate for our sample because of the panel structure of the data.
34
abnormal returns (43.75, p < .01). We then test the significance of the products of the
coefficients of our independent variables in eq. 3, with the coefficient of investor search in eq. 4
(Baron & Kenny, 1986; Preacher & Hayes, 2004). We use 1,000 bootstrapped samples with
replacement from our data to obtain the standard error of the product of the regression
coefficients. First, advertising has a significant positive indirect impact on abnormal returns
(.000013, 95% CI = [.000004, .000310]). Second, product development announcements has no
significant indirect impact on abnormal returns (.000020, 95% bootstrap CI = [-.00004, .00088]).
Third, WOM has a significant positive indirect impact on abnormal returns (.000310, 95% CI =
[.00001, .00064]). Finally, satisfaction has no significant indirect impact on abnormal returns
(.000007, 95% CI = [-.00021, .00022]).
In terms of economic significance, we focus on the indirect effects of the mediation
analysis between our focal variables and abnormal returns through investor search. Considering
the market capitalization of 100 million dollars, a 1 million dollar change in advertising expenses
from quarter to quarter leads to a positive quarterly change of firm value of 1.3 million dollars
(1.3% of 100 million). A one unit change in WOM from quarter to quarter (out of 100 units)
leads to a positive quarterly change of firm value of 31 million (31% of 100 million). Finally, for
product development and satisfaction, as we do not observe significant effects, we do not report
the dollar estimates.
Robustness Checks
Model without competitor variables. To show the justification for the inclusion of the competitor
variables in our empirical setup, we estimate models without them and present the results in
Table A7 and Table A8 in the Web Appendix. We note that the significant results for the focal
brand variables remain unchanged. In Table A9, we report the results of the three log-likelihoods
35
of the models. The log-likelihood improves as we move from the model without competitor
variables (loglikelihood = -34822.15) to the model with competitive variables but without
interactions (loglikelihood = -34740.08) to the full model (-34729.03).
Different definitions of competition: To show the robustness of our results to how we define
competition, we compute competitors' variables at 3 and 4 SIC digits codes. Our main results
hold across these competitive definitions (See Tables A10 and A11 in the Web Appendix).
Different measure of investor search. In our main analysis, we used Ryans' (2018) measure of
investor search that controls robot IP accesses. Alternatively, Drake et al. (2016) developed a
measure that assigns a different rule for robots to be removed from the data. Specifically, this
measure removes requests from IP addresses accessing more than five filings in each minute or
more than 1,000 filings during a day. Thus, we also test the robustness of our results by using
this alternative measure. As shown in Table A12 in Web Appendix, our substantive results
remain the same.
Google Search as an additional control variable. As an alternative proxy for investor attention,
we use Google search of tickers (Da et al., 2011). We collect the data using the Google Trends
API and obtain weekly (or monthly because of data sparsity) searches of the firm's ticker symbol
on Google from June 3rd, 2007 to June 30th, 2017 using the Python programming language. Our
results remain unchanged (see Table A13).
Mono vs. Multi-brands. There are 258 mono-brand firms (e.g., Nike) and 89 multi-brand firms
(e.g., P&G) in the estimation sample. When run our model only for 258 mono-brand firms, we
confirm that our substantive results remain the same (see Table A14).
DISCUSSION
Summary of Findings
36
In the last two decades, research in the marketing-finance interface has shown that marketing
levers can affect the stock market value of the firm (Edeling et al., 2021; Srinivasan & Hanssens,
2009). In this study, we posit that investor attention to the firm stock is a key route through
which marketing information affects financial outcomes. We contribute to the marketing-finance
literature by developing and testing hypotheses related to the impact of changes in the focal
firm's marketing levers (advertising, product development announcements, WOM, and customer
satisfaction) on investor attention. We also test the moderating role of competitors' marketing
levers in these relationships and whether investor attention mediates the changes in the focal
firm's marketing levers on abnormal stock returns. The key findings are the following:
• Focal firm's advertising and WOM have a positive and significant effect on investor
attention while customer satisfaction and product development announcements do not.
• Competitor's advertising, WOM, and customer satisfaction moderate the relationship
between the focal firm's advertising, WOM, customer satisfaction, and investor attention.
• Changes in investor attention mediate the relationship between the changes in the focal
firm's advertising and WOM, and firm value.
Focal Firm's Marketing Levers and Investor Attention
The differences among the effects of the various marketing levers on investor attention
are noteworthy. These differences might stem from the scope of transmission channels of
marketing levers and the speed of transmission of marketing levers to firm performance (Cillo et
al., 2018). From the mere awareness perspective of investor attention, advertising and WOM
may have a greater impact on investor attention compared to product development
announcements and customer satisfaction. Investors are exposed to firm's advertisements via
many channels ranging from ads in investment magazines to TV and online ads (Liaukonyte &
37
Zaldokas, 2020; Madsen & Niessner, 2019). Similarly, investors get exposed to WOM about
firms on many online platforms such as Reddit, as has been recently the case with the Gamestop
stock. On such platforms, many investors or individuals produce content about the firm's stock
(Blankespoor et al., 2014; Tirunillai & Tellis, 2012). Product development announcements and
customer satisfaction may not be present in a similar range of outlets as they are primarily
publicized through the business press. The results suggest that product development
announcements and customer satisfaction may not draw investor attention due to the awareness
disadvantages compared to advertising and WOM.
From the information perspective, there may be differences among the marketing levers
with respect to their speed of impact on firm performance. Advertising and WOM have the
potential to impact the focal firm's sales faster than product development announcements and
customer satisfaction (Sethuraman et al., 2011; You et al., 2015). In contrast, for product
development and customer satisfaction, there are several conditions that need to be satisfied to
achieve a meaningful increase in a firm's future cash flows. Investments in a new product may
prove fruitful only years later, and thus investors may not be interested in product development
announcements as its impact on the firm's future performance is quite uncertain. For instance,
Warren and Sorescu (2017b) report an insignificant relationship between new product
introduction announcements and abnormal returns in part due to the innovation record of the
firm. Firms in our sample may have a history of consistently launching products. Thus, unless
there is a significant deviation from their historical product strategy, investors do not incur the
38
cost of searching for information for these firms
12
. Investors may also ignore such
announcements due to vaporware (Sorescu et al., 2007).
The results suggest that investors pay attention to a change in the focal firm’s customer
satisfaction only when there is a change in competitors’ customer satisfaction performance.
While we hypothesized the impact of the changes in competitors’ satisfaction on the relationship
between focal firm’s customer satisfaction and investor attention, we did not expect such
dominating effects of competitor information. From an information effect perspective, the
change in customer satisfaction of a firm without the competitive benchmark may not provide
information to investors to increase their attention to that firm. Investors may be implementing a
wait-see approach to receive competitive information before allocating attention to the firm.
Moderating Effect of Competitors' Marketing Levers
Our findings illustrate the nuanced dynamics between focal firm's and competitors'
marketing information in influencing investor attention to the focal firm's stock. Increases in the
focal firm's and the competitors' advertising spending have a synergistic effect on investor
attention. In addition, competitors' WOM has a dual impact on the change in investor attention.
As Figure 2C shows, the increase in competitors' WOM enhances the impact of focal firm's
WOM on investor attention to the focal firm's stock.
The moderating effect on the relationship between the change in the focal firm's customer
satisfaction and investor attention is positive (Figure 2D). As we do not find support for the main
effect of the change in focal firm's customer satisfaction, we conclude that investor attention to
customer satisfaction is primarily driven by the changes at the industry-level. This result implies
12
We test this by computing a stock variable as the cumulative sum of product development announcements. The
measure takes value of 1 if this variable > its mean + 1 std. dev, else 0. We find a positive effect of this dummy on
investor search (p<.1).
39
that investors process satisfaction performance information in a relative manner by
contextualizing a focal firm's customer satisfaction performance vis-à-vis its competitors. Prior
literature highlights the importance of studying customer satisfaction relative to competitors
when investigating its link to performance outcomes such as market share (Rego et al., 2013).
We do not find support for the moderating role of competitors' product development
activity in the relationship between the changes in the focal firm's product development and
investor attention. However, we find that the increase in competitors' product development has a
positive impact on the change in investor attention. We formally check the differential effects of
focal vs. competitor product development announcements by testing the coefficients
α3α4 in Equations 2-4. We find that the association between product development
announcements for the competitors and investor attention is stronger than the association
between the focal firm's product development announcement and investor attention (.0069, p =
.003). Prior research on new products provides support for this finding because in most industries
firms are required to invest in their products (Warren & Sorescu, 2017a). So, for investors to pay
more attention to the focal firm's product development announcements, the new information has
to incorporate novel and innovative products. Else, investors are more likely to allocate more
attention to the changes in competitors' product development announcements.
Implications
Research implications. Extant research on the drivers of investor attention has primarily
focused on advertising (see panel B, Table 1). Our findings suggest that while advertising is an
important driver of investor attention, there are other marketing levers and dynamics that affect
investor attention: (i) the change in a firm's advertising spending is not the only marketing lever
that draws investor attention, WOM marketing lever also influences investor’s attention to firm’s
40
stock (ii) the impact of the changes in firm's marketing levers on investor attention is contingent
upon competitors' levers, (iii) for product announcements and customer satisfaction, the changes
in competitors' marketing levers may be more important than the focal firm's marketing levers
(iv) investor attention partially mediates the relationship between focal firm's advertising and
WOM and abnormal stock returns.
From a theoretical perspective, the relationship between marketing levers and investor
attention is more nuanced than previously thought (e.g., Madsen & Niessner, 2019). Investors
seem to allocate their attention differently depending on the type of marketing information. The
underlying reason for this behavior is possibly related to the limited attention resources of
investors (Barber & Odeon 2008). Investors seem to consider the changes in the firm's and
competitors' marketing actions simultaneously when these changes could impact short-term
revenues and profitability (i.e., advertising or WOM). However, when there is greater
uncertainty between a firm's marketing lever and short-term performance due to a longer time
horizon of the marketing action (i.e., product development or customer satisfaction), investors
allocate their attention to these levers in the case of industry-level change. By doing so, investors
simplify their attention allocation by focusing on potentially more value-relevant information
(Hirshleifer, 2001). Consequently, marketing actions influence investors' expectation formation
process through the firm's actions as well as its competitors. The relative effect of the firm's and
competitors' actions on investor attention depends on the specific marketing lever.
Managerial implications. Our results on the mediating role of investor attention between
the changes in focal firm's marketing levers and the change in firm value underscore the
importance of taking a more holistic view of marketing actions and investor attention. While it is
important to focus on firms' advertising expenditures and WOM as the primary drivers of
41
investor attention and subsequent firm value, managers should also recognize the importance of
competitors' marketing actions to the attention of investors to their firm's stock. As demonstrated
by Figure 2A-D, the changes in competitors' marketing levers moderate the impact of a change
in firm's marketing levers on investor attention. Thus, managers need to monitor competitors'
marketing actions to draw investor attention. More specifically, managers would benefit from
monitoring competitors' product development announcements and customer satisfaction as
investors seem to pay greater attention to industry-level changes in these metrics as opposed to
firm-level information. To benefit from investors' attention to competitors' marketing
information, managers could contextualize their firm's product and customer management
strategies within an industry to better communicate how the firm's marketing strategies enable its
brands to compete more effectively vis-à-vis competitors' brands.
Next, we provide a measure of the efficacy of marketing levers by establishing the link
between the marketing levers, investor attention, and firm value. Marketing analysts can use the
EDGAR search data, which is freely available. Metrics such as the number of unique visitors to
SEC, downloads of financial statements, etc., are available at a high temporal frequency (daily).
Marketing managers can monitor the effects of marketing levers on firm value by using this data.
Investor relations play a key role in facilitating investors’ assimilation of firm
information (Chapman et al., 2019). Firms that engage in greater investor relations activity have
greater financial value than firms that do not engage (Karolyi et al., 2020). Based on the findings
related to the disclosure of customer information, Bayer et al. (2017) argue that senior marketing
executives (e.g., CMOs) should be more involved with investor relations activities. Our findings
suggest that the participation of senior marketing executives to investor relations is a necessary
condition to facilitate investors’ assimilation of marketing information as investors pay attention
42
to a wide range of marketing levers. It is the marketing executives who we believe have the best
knowledge and expertise related to the firm’s advertising or WOM as well as the competitors’
marketing strategies. They can help investors better contextualize and adapt the firm’s marketing
information in relation to its competition. Thus, investor relations functions could achieve greater
investor attention to a firm by collaborating with marketing functions within their organizations.
Limitations
This study has some limitations. First, we only use advertising expenditures and not the
advertising content. Advertising messages might contain different visual cues that can be of
interest to investors. Similarly, the information in advertising can affect how investors build
expectations on a firm’s cash flows. We call for future research to study how the content of
advertising for focal and competitor firms affects investor attention. Second, we do not capture
the investors' knowledge of stocks or their level of sophistication in our empirical model due to
lack of data on these factors. Future research could test the relationship between individual-level
investor skills, characteristics, and attention with micro-level data. Finally, investor attention
may vary at a higher frequency than quarterly changes used in this paper. While we are
interested in capturing the total effect of marketing levers on investor attention over a quarter,
future research can consider more immediate investor reactions. For instance, several studies
have shown that investors might react to daily social media sentiment (Tirunillai & Tellis, 2012)
or hourly advertising (Liaukonyte & Zaldokas 2020). We think that future research can benefit
from more fine-grained data on a few marketing levers such as WOM and customer satisfaction
(e.g., from YouGov) and relate it to investor attention.
43
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