Corporate Use of Social Media
Michael J. Jung,* James P. Naughton,†
Ahmed Tahoun,‡ and Clare Wang†
January 2014
Abstract
We examine corporate adoption of social media and provide the first large-sample evidence on
the determinants and market consequences of the decision to disseminate quarterly earnings
news through social media. We find that social media usage for earnings news is distinct from
other forms of voluntary disclosure and document a number of interesting attributes of this
disclosure mechanism. Social media usage for earnings news is inversely related to the number
of social media followers, suggesting that firms with large social media followings are hesitant to
use social media for financial information. However, we find that earnings news is more likely to
be communicated when the news is positive, suggesting that some firms are opportunistic in their
use of social media. Moreover, when we examine the market response to social media
communications, we find that trading volume increases and that the primary driver is increases in
large rather than small trades. This is inconsistent with the notion that social media primarily
benefits small investors. Lastly, we find that the market reaction is stronger for firms that follow
a consistent rather than ad hoc social media disclosure policy.
Keywords: Commitment to Disclosure, Voluntary Disclosure, Social Media, Facebook, Twitter
* Corresponding author. Leonard N. Stern School of Business, New York University, 44 West 4th St., New York,
NY 10012, 212-998-0193, mjung@stern.nyu.edu; †Northwestern University; ‡London Business School.
Acknowledgements. We thank workshop participants at New York University and the 2013 UNC/Duke Fall Camp
for their comments and suggestions. Naughton and Wang are grateful for the funding of this research by The
Kellogg School of Management and the Lawrence Revsine Research Fellowship. We thank Blake Disiere, Sam
Faycurry, Michael Licata, Stacy Ni, Alice Yujia Qiu, Borui Xiao, Melody Xu, Rena Xin Xu, David Zeyu Wang and
Martin Ying for providing excellent research assistance.
1. Introduction
Social media has transformed communications in many sectors of the U.S. economy. It is
now used for disaster preparation and emergency response (FEMA, 2013), security at major
events (PERF, 2011), and public agencies are researching new uses in geolocation (DARPA,
2010), law enforcement,1 court decisions,2 and military intelligence (DARPA, 2011).
Internationally, social media is credited for organizing political protests across the Middle East
(Stone and Cohen, 2009) and a revolution in Egypt (WSJ, 2011; Vargas, 2012). In the business
world, social media is commonly considered a revolutionary sales and marketing platform
(Forbes, 2013; HBR, 2010; Larcker et al., 2012) and a powerful recruiting and networking
channel (Li, 2013).
In contrast, there is little to no research on how many firms use social media to
communicate financial information to investors and how investors process information provided
through social media channels.3 This omission is notable because, ex ante, it is not clear why a
company would adopt a social media platform, nor how investors would respond to financial
information disseminated through social media. Furthermore, the need for research is
underscored by the recent SEC announcement that firms may use social media to announce key
financial information in compliance with Regulation Fair Disclosure (SEC, 2013). Motivated by
this omission, we examine a broad set of questions that provide insight into social media as a
voluntary disclosure mechanism. More specifically, we evaluate the determinants of and the
market reaction to social media usage for reporting quarterly earnings.
1
The State Department sponsored a simulated law enforcement search called the ―Tag Challenge‖ in March 2012
(www.tag-challenge.com) to find five suspects in five cities across North America and Europe using social media.
2
US courts use information posted on social media to determine the appropriate time in jail required for the case
(http://dailynexus.com/2007-02-28/court-case-decision-reveals-dangers-of-networking-sites/).
3
One exception is Blankespoor et al (2013), who examine the use of social media by 85 small technology firms.
1
Our initial analysis focuses on both Facebook and Twitter, as they are the two most
prevalent social media platforms. We construct a dataset on the use of both platforms by
S&P1500 firms for 2010 through the first quarter of 2013. Our dataset identifies which firms
have a social media presence, the size of the social media audience in terms of ―likes‖ and
―followers‖, and when the firms used social media to disseminate quarterly earnings news. We
first document that, as of July 2013, 47% of firms have adopted a corporate Twitter account and
44% have a Facebook page. However, only about half of the firms with a Twitter account and
about a third of the firms with a Facebook page have ever tweeted or posted information about
quarterly earnings announcements.4 Given the choice between the two social media platforms,
firms have a stronger preference for Twitter—of the firms that disseminate earnings news via
social media, 91% use Twitter and 52% use Facebook.
Our analysis of the determinants of Twitter adoption reveals that larger firms are more
likely to have corporate Twitter accounts and are more likely to use Twitter to disseminate
earnings news, contrary to the notion that smaller firms benefit more from using social media
(Blankespoor et al., 2013). Surprisingly, we also find that firms with a larger social media
audience are less likely to use Twitter for earnings news, which we believe reflects the fact that
such firms tend to be retail firms with millions of followers who are primarily customers rather
than investors (e.g. McDonald‘s Corp). We find consistent but statistically weaker results for
Facebook. We do not find that factors such as firm performance, growth and leverage, which
have been shown to be related to firms‘ traditional disclosure outlets (e.g., conference calls, press
4
Terminology differs slightly between Facebook and Twitter. Firms ―post‖ information to their Facebook page but
―tweet‖ information over their Twitter accounts. For brevity, we occasionally use the terms interchangeably.
2
releases, etc.), are significant predictors of Twitter (or Facebook) adoption for financial reporting
purposes.5
Drawing on the voluntary disclosure literature, we also consider whether the use of social
media to disseminate earnings is partially driven by the direction of the earnings news. We find
evidence that firms are more likely to disseminate earnings news through Twitter when the news
is good, contrary to the small sample evidence in Blankespoor et al. (2013).6 We also find that
there is significant variation in the consistency (or frequency) of earnings tweets, with about 16%
of firms tweeting earnings almost every quarter and 40% of firms doing so rarely. We do not find
similar results for Facebook, partially because our Facebook specifications have low power due
to the limited number of firms that use Facebook exclusively for reporting quarterly earnings.
Overall, these findings suggests that while some firms have made a strong commitment to use
social media to disclose or highlight earnings news every quarter, and thus meet the spirit of the
SEC‘s recent announcement,7 other firms may be more opportunistic in their decision to
highlight earnings news on social media.
When we examine the capital market consequences of social media usage, we conduct
event study tests using daily and intra-day trading data. Using several different market-based
measures including abnormal returns, absolute abnormal returns, abnormal volume, abnormal
5
It is beyond the scope of this paper to examine the determinants of general corporate social media usage. Such a
model likely requires numerous non-accounting factors such as marketing strategies, social responsibility initiatives,
online sales, employee relations, and managers‘ preferences and characteristics. For example, in an interview with a
mid-sized commercial bank (which is one of the firms in our sample), managers from the public relations, investor
relations, and legal department stated that the bank adopted Facebook and Twitter because its customers, partners,
and employees in the field were avid users of social media and preferred that method of communication. This factor
would not be captured by traditional accounting measures.
6
Throughout this paper, we compare and contrast several of our results to those found in Blankespoor et al. (2013)
because it was one of the first studies to examine the firms‘ use of social media. Any noted differences are likely due
to vastly different sample sizes (1500 vs. 85 firms), number of industries, and time periods examined in each study.
7
The Securities and Exchange Commission issued a report on April 2, 2013 that makes clear that companies can use
social media outlets like Facebook and Twitter to announce key information in compliance with Regulation Fair
Disclosure (Regulation FD) so long as investors have been alerted about which social media will be used to
disseminate such information. See SEC release 2013-51.
3
bid-ask spreads, and average trade sizes, we highlight several findings. First, we find
corroborating evidence that firms tend to disseminate earnings news over social media when the
news is good, as the three-day signed returns are higher and the absolute returns are lower for the
quarterly earnings announcements that are disseminated over social media. Second, the absolute
market reactions are higher for firms with larger social media audiences, consistent with the
notion that a firm‘s followers do include investors and other capital market participants. Third,
in contrast to the early evidence in Blankespoor et al. (2013), we find that the three-day average
bid-ask spread is actually higher for firms that announce earnings news via social media,
suggesting that such communications increase information asymmetry.
For the firms in our sample that tweet during market hours, we use intra-day trading data
and hand-collected time stamps of tweets to examine the market reaction associated with three
different types of earnings-related tweets: earnings announcement (―EA‖), preview and rehash.
We categorize a tweet as an EA tweet if it is the first tweet mentioning the firm‘s earnings
announcement and it occurred on the earnings announcement date. A tweet is categorized as a
preview tweet if it only mentions the date of the upcoming earnings announcement and a rehash
tweet if it mentions highlights from the prior earnings announcement.8 On average, EA tweets
occur several hours after the earnings announcement9, preview tweets occur 13 days before the
earnings announcement date and rehash tweets occur 1 to 2 days after the earnings
announcement date.
Our intra-day analyses reveal that trading volume increases in response to EA tweets, but
not for rehash tweets. We also find higher trading volume following a preview tweet, which is
8
The fact that we routinely observe three types of earnings-related tweets suggests that Twitter, as a disclosure
channel, provides unique features relative to other channels. For example, it is less common to observe firms issuing
press releases reminding investors of upcoming earnings announcements or rehashing prior earnings highlights.
9
The exact time of the earnings announcement is based on data from I/B/E/S.
4
surprising considering a preview tweet only contains a mere reminder of an upcoming earnings
announcement.10 When we partition our data into large and small trades, we find that the
primary driver of the increased trading volume at the time of the tweet is larger trades. We find
corroborating evidence from examining average trade sizes, which increases during the time of
the EA tweets. Therefore, while social media is commonly viewed as a disclosure channel that
provides timely access to information for all investors, and thus ―levels the playing field‖ for
small investors, our results suggest that larger investors react quicker to earnings-related tweets.
In our final set of analyses, we identify firms that use social media for earnings news
consistently. We classify this group of approximately 100 firms as ―committers‖ because once
they post earnings news to social media for a particular quarter, they do so again in all
subsequent quarters. Our intention is to examine if market reactions differ significantly for
committers versus firms that use social media on an ad hoc basis. While we do not find
differences across all market reaction variables, we do find that abnormal turnover is higher in
the three-day earnings announcement window for committers that post earnings to Facebook and
that absolute abnormal returns are higher for committers that tweet earnings over Twitter,
relative to the market reactions for uncommitted firms. These results provide some evidence that
a commitment to social media usage for earnings news is associated with a greater market
reaction.
The findings presented in this paper are relevant to firms that have adopted social media,
or are considering adoption, and to regulators debating the costs and benefits of firms' use of
10
We conjecture that this increase in volume could be due to investors interpreting a preview tweet as a positive
sign for the upcoming earnings announcement. In untabulated tests, we do find that the mean three-day abnormal
return is higher for earnings announcements that were previewed versus not previewed (0.4% vs. 0.2%), but the
difference is not statistically significant. We also find that the mean abnormal return for earnings announcements
that were rehashed is significantly higher than earnings announcements that were not rehashed (1.6% vs. 0.1%),
indicating that firms tend to rehash only good earnings news several days after the earnings announcement.
5
social media for capital market communications. We document that: 1) adoption of the two most
prevalent social media platforms by the largest corporations has exceeded 50%, 2) the propensity
to disseminate financial news over social media depends on the direction of the news, 3) there is
a market reaction to earnings tweets that is separate from the market reaction to the actual
earnings news, and 4) the market reaction differs for firms that use social media on a consistent
versus ad hoc basis.
In addition, our study reveals how disclosure choices evolve during a time before the
SEC approved the use of social media as an official disclosure venue (i.e., April 2013). This
setting is interesting because, a priori, it is not clear to what extent firms and investors rely on
disclosure venues not yet approved by the SEC, nor it is clear how the market reacts to such
disclosures. We show that a subset of firms adopt early, make a commitment, and that the
market reaction differs for these firms. Our findings extend prior studies that examined
commitments to increased disclosure in international settings (e.g., Leuz and Verrecchia, 2000)
by highlighting a setting within the U.S. where there remains cross-sectional variation in firms‘
commitment to increased disclosure using the newest technology.
The paper proceeds as follows. In Section 2, we review the literature and develop our
hypotheses. In Section 3, we describe the construction of our database and summarize
descriptive statistics. We outline the research design in Section 4, describe the empirical results
in Section 5 and summarize our conclusions in Section 6.
2. Background and Hypothesis Development
Social media provides a unique and revolutionary approach for firms to communicate
directly with their investors and interested stakeholders. Conventionally, firms publicize
6
earnings announcements by sending a press release to newswire services, to equity research
databases, and to individual brokerage firms and financial institutions (Frankel et al., 1999). In
this manner, firms send the news once and do not know how many people receive the news. In
contrast, social media allows a firm to send multiple messages over time directly to a known
number of followers. Typically, this is accomplished by tweeting short messages and including a
link to a complete news release found on the corporate website (Blankespoor et al., 2013). The
result is a reduction in information dissemination costs, an increase in speed and flexibility for
the news dissemination, and a reduction in information acquisition costs for the firm‘s investors.
Despite the difference in how information is provided through social media compared
with past alternatives, there is very little research on why some firms use social media to
disseminate quarterly earnings news and whether this choice has capital market consequences.
Blankespoor et al. (2013) find that firms‘ decision to tweet earnings news is not dependent on the
direction (good or bad) of the news, suggesting that firms are not opportunistic in this respect.
They also find that that Twitter usage reduces information asymmetry for a sample of 85
technology firms, with the reductions in information asymmetry concentrated in the smaller, less
visible firms. Both findings are relevant to the current policy debate on corporate social media
usage, highlighted by the SEC‘s April 2013 statement (SEC, 2013) that firms may use Twitter
and Facebook to announce key financial information in compliance with Regulation Fair
Disclosure so long as investors have been alerted to look for such announcements. However, the
lack of evidence for a broader sample of firms underscores the need for additional research.
To develop a prediction about when firms disseminate earnings over social media, we
draw from prior studies that have shown that news can be selectively disclosed to benefit either
the firm or its managers. For example, Healy and Palepu (1995) find that firms are more likely to
7
disclose good news in advance of raising capital or engaging in merger activity. Conversely,
Skinner (1994) suggests that firms voluntarily disclose bad news to mitigate litigation risk.
Similarly, disclosure choices could affect stock compensation (Aboody and Kasznik, 2000) or
the signalling of managerial talent (Trueman, 1986). While these studies have focused on
various types of news, including earnings announcements, earnings pre-announcements, and
management forecasts, the general finding is that the direction of the news could affect the
decision to disseminate each quarter‘s earnings news via social media. Blankespoor et al. (2013)
test this prediction but find that the direction of the earnings news does not affect Twitter usage
for a small sample of technology firms. However, this result may not generalize to a large
sample of firms; therefore, the first hypothesis we test is whether the decision to disseminate
earnings news through social media is related to the direction of the news.
H1: Firms’ decision to disseminate earnings news each quarter on social media is related to the
direction of the news.
Our remaining predictions focus on the market reactions of news disseminated versus not
disseminated over social media. Knowing whether financial disclosures over social media
provide information to capital market participants is fundamental to understanding the role of
social media in the corporate disclosure process. In fact, understanding which firms provide such
disclosures is not valuable if the disclosures themselves do not have capital markets
consequences. Analytical work by Holthausen and Verrecchia (1990) demonstrate that returnand volume-based measures are equally valid measures of information content of financial
disclosures. Accordingly, empirical studies such as Frankel et al. (1999) conclude that earnings
conference calls are informative to stock market participants because of higher levels of return
volatility and trading volume during the time of the conference call. Similarly, Bushee et al.
8
(2011) find that managerial presentations at investor conferences are informative because they
are positively associated with abnormal absolute stock returns and abnormal trading volume.
Following the above studies, we also examine market reactions using abnormal return and
volume measures.
The association with return and volume measures in our social media setting, in which
firms may or may not publicize earnings news that is also disseminated over traditional channels,
is unclear ex ante. We start with case in which a firm decides to publicize its earnings news over
social media, which we assume results in more capital market participants knowing about the
news than if the news were not disseminated over social media. Then, depending on the content
of the earnings announcement, there could be more or less of a market reaction. Holthausen and
Verrecchia (1990) present a model in which the return and volume reactions to an information
disclosure depend on both the extent to which investors become more knowledgeable and find
consensus (i.e., the ―informedness‖ and consensus effects). Their work suggests that if an
earnings announcement leads to greater informedness and consensus, and dissemination over
social media amplifies these effects, then there should be greater return volatility associated with
earnings news disseminated over social media. However, greater informedness leads to higher
volume reactions while greater consensus leads to lower volume, thus, the ultimate effect on
volume depends on which effect dominates.
Next, we consider the case in which the firm does not publicize its earnings news over
social media. If the reason is that the news is bad, and bad news tends to be associated with
larger return and volume reactions (consistent with greater informedness and less consensus in
the Holthausen and Verrecchia model and the ―torpedo effect‖ documented in Skinner and Sloan
(2002)), then we should find lower market reactions for the earnings announcement disseminated
9
over social media because the news tends to be good. Such a finding would also be consistent
with our first hypothesis. In either case, we would expect differential market reactions for
earnings announcements publicized over social media relative to earnings announcements not
publicized over social media. We state our second hypothesis as follows.
H2a: The market reaction to earnings announcements disseminated over social media differs
from the market reaction to earnings announcements not disseminated over social media.
In addition to examining measures of information content, prior studies have examined
measures of information asymmetry associated with financial disclosures. Analytical work by
Diamond and Verrecchia (1991) show that information disclosures can reduce information
asymmetry and lead to greater liquidity in the stock, while Kim and Verrecchia (1994) consider
how an earnings announcement may allow certain traders to make superior judgements over
other traders that lead to greater information asymmetry and less liquidity. In their study of 85
technology firms that used Twitter from March 2007 to September 2009, Blankespoor et al
(2013) find increased stock market liquidity (lower abnormal bid-ask spreads and greater
abnormal depths) when smaller, less visible firms disseminate earnings news via Twitter,
consistent with a reduction in information asymmetry. But the authors also note that their results
may not generalize to other firms, industries, or time periods. Therefore, we also test the
hypothesis that earnings news publicized over social media for a broad set of firms is associated
with a reduction in information asymmetry.
H2b: Information asymmetry is reduced when firms disseminate earnings news over social
media.
A unique aspect of disseminating earnings news over Twitter is that firms may tweet
multiple earnings-related messages over time. We indeed find not only tweets about a firm‘s
10
earnings announcements, which we refer to as ―EA tweets,‖ but also tweets reminding followers
of upcoming earnings announcements and tweets rehashing highlights from past earnings
announcements. We refer to these types of tweets as ―preview tweets‖ and ―rehash tweets,‖
respectively, and we examine all three types of tweet separately in our intra-day analyses. We
believe that examining the reactions to preview and rehash tweets is important to understanding
the overall disclosure strategy that firms employ to communicate with investors via social media
and how those communications are interpreted by the market.
While one may not expect preview and rehash tweets to generate any market reaction
because they do not contain any new information, it is plausible that a preview tweet could
prompt some investors to trade in advance of the actual earnings announcement and rehash
tweets could prompt some investors to trade after the earnings announcement period. For
example, in the case of preview tweets, some investors may believe that firms are more likely to
tweet reminders of upcoming earnings announcements if the earnings news is expected to be
positive. Investors who take this view will purchase the firm‘s stock after receiving the preview
tweet. Therefore, we test whether the market reactions of preview and rehash tweets differ from
earnings announcement tweets. We state our next hypothesis in the null form.
H3: Preview and rehash tweets generate the same market reaction as EA tweets.
Lastly, we examine variation in firms‘ consistency, or level of commitment, to use social
media for financial disclosures. Specifically, we investigate whether the determinants and
capital market consequences are different between firms that disseminate earnings news over
social media every quarter and firms that do so on an ad hoc basis. The consequences of a
commitment to a social media disclosure policy is especially interesting since some firms
presumably made this commitment before the SEC endorsed the use of social media as an
11
official disclosure venue. We define firms to be ―committers‖ if, after they have disseminated
earnings news over social media in one quarter, they do so again each and every subsequent
quarter. 11 A firm must have disclosed earnings news on social media for at least two consecutive
quarters before we designate it as a committer.
We conjecture that a firm‘s decision to commit to disseminate earnings news over social
media every quarter is related to the market reaction to the firm‘s earnings announcements prior
to the commitment. That is, a firm that perceives the market does not react to its earnings
announcements is more likely to make a commitment to disseminate earnings announcements
over social media in an effort to increase the market reaction. There are a number of reasons
why market participants may not react to a given firm‘s earnings announcement. First, the firm
may be ―neglected‖ in the sense that very few investors and analysts follow the firm. Second,
investors may deem the firm‘s earnings news as less than credible. Third, the issue with
credibility may be exacerbated if the market perceives that the firm will only disseminate
earnings news over social media when the news is good and suppress the news when it is bad.
We expect these issues to be mitigated for firms that have shown a commitment to disclose
earnings news over social media on a consistent basis.
H4: There is a greater market reaction for firms that disseminate earnings news over social
media every quarter than for firms that do so on an ad hoc basis.
3. Data
We begin with all firms included in the S&P1500 index as of January 2013, based on
data from Compustat. We then collect data from each firm‘s Facebook and Twitter sites (if they
11
Leuz and Verrecchia (2000) note that ―…the distinction between a commitment and a voluntary disclosure is that
the former is a decision by the firm about what it will disclose before it knows the content of the information (i.e., ex
ante), whereas the latter is a decision by the firm made after it observes the content (i.e., ex post).‖ In our setting, we
assume that committed firms disseminate earnings news over social regardless of whether their earnings
announcements reveal good or bad news.
12
exist) using the following procedure.12 First, we visit each firm‘s corporate website and look for
icons or links to its social media sites. This step ensures that we find the firm‘s true corporate
Facebook and Twitter sites, as opposed to sites that may be managed by communities or user
groups associated with the firm. If we do not find social media links on the corporate website,
then we manually search for the firm‘s Facebook and Twitter pages on the respective social
media sites, taking care to use only the official corporate pages if they exist.
Once we have found the corporate Facebook and Twitter sites, we proceed as follows. If
the firm has a Facebook page, then we collect information on when the firm joined Facebook, the
number of ―Likes‖ (as of July 2013), and whether the firm created any posts concerning earnings
announcements for any quarter from the first quarter of 2010 through the first quarter of 2013.
We accomplish this last step by scrolling through the entire timeline and searching for terms
such as ―quarter,‖ ―fiscal,‖ ―earnings,‖ ―results,‖ and their variants. If a firm has a Twitter page,
then we collect information on the number of tweets and followers. We then use a web utility at
www.allmytweets.net to retrieve all the firm‘s tweets (up to a maximum of 3,200 tweets), record
the date of the first tweet, and search for tweets about earnings announcements for any quarter
from the first quarter of 2010 through the first quarter of 2013. For firms that had more than
3,200 tweets (214 out of the 708 firms that use Twitter), we used Twitter‘s advanced search
feature to manually retrieve all tweets containing the earnings-related terms. Appendix B
provides examples of earnings news posted to Facebook and tweeted over Twitter.
We collected data on the first 250 firms and then hired four research assistants (RAs) to
collect data on the remaining 1,250 firms. To ensure the accuracy of the data, each RA was
12
All of our empirical tests focus on the corporate use of Facebook and Twitter because those were the only outlets
explicitly identified by the SEC in its April 2013 announcement concerning social media usage to comply with
Regulation Fair Disclosure. We explored the possibility that other social media platforms such as LinkedIn,
Pinterest, YouTube, and Google+ could be used by firms, but these other platforms are not conducive to
disseminating earnings news.
13
responsible for collecting data for 625 firms, resulting in two RAs collecting data for each firm.
We then cross-checked the data from each pair of RAs for consistency and manually checked
firms‘ Facebook and Twitter sites to correct any inconsistencies in the data.
For the subsample of firms that have ever used Twitter to disseminate earnings-related
news during market trading hours (9:30AM to 4:00PM Eastern Standard Time), we record the
time stamp of all earnings-related tweets, consisting of EA tweets, preview tweets, and rehash
tweets. We employ six additional research assistants and again ensure accuracy by assigning
pairs of RAs to collect time stamps for approximately 400 firms each and cross-checking any
inconsistencies ourselves. Tweets are categorized as EA tweets if they were the first tweet
mentioning the firm‘s earnings announcement and they occurred on the same date as the earnings
announcement date. Tweets are categorized as preview tweets if they only mention the date of
the next earnings announcement and are categorized as rehash tweets if they mention highlights
from the most recent earnings announcement. On average, preview tweets occur 13 days before
the earnings announcement date and rehash tweets occur 1 to 2 days after the earnings
announcement date. To examine the intra-day market reactions to earnings-related tweets, we
only use the tweets that occur between 9:45AM and 3:45PM to insure 15 minutes of trading
before and after the tweet. Trading data comes from the Trade and Quote (TAQ) database
through Wharton Research Data Services (WRDS).
3.1 Descriptive Statistics
An overview of the corporate use of social media is illustrated in Figure 1, Panel A.
Slightly over half (52%) of S&P1500 firms have adopted social media as of July 2013. The
majority of these firms have both a Facebook page and a Twitter account; the remainder is split
14
between firms that have only one or the other. Among firms that have ever disseminated earnings
announcements over social media, there is a stronger preference to do so using Twitter rather
than Facebook. There are 214 firms that have ever tweeted earnings news on Twitter (but not
Facebook), 192 firms that have used both Twitter and Facebook, and only 40 firms that have
posted earnings to Facebook (but not Twitter). The data suggests that Twitter is now the
preferred platform for firms that choose to disseminate earnings news over social media. One
reason may be that, despite Twitter‘s 140 character limit per tweet, a firm can send multiple
tweets about different aspects of the earnings news. As illustrated in Panel B of Appendix B,
Alcoa sent 24 tweets regarding its 2013 first quarter earnings.
The time trend in corporate social media adoption is illustrated in Figure 1, Panel B. The
earliest adopters of Facebook joined in November 2007 and the first set of firms to create a
Twitter account did so in May 2008. By early 2013, the corporate adoption rate of Twitter
surpassed the rate for Facebook. By the end of our data collection period, approximately 47% of
S&P1500 firms had a Twitter account, 44% had a Facebook page, and 52% had adopted one or
the other. The time trend data also suggests that Twitter is becoming the preferred social media
platform for companies.
The breakdown of our sample of 1,500 firms across industries (Fama-French 30), and
their adoption of social media, is provided in Table 1. Facebook and Twitter adoption is highest
for customer facing industries such as Meals, Retail, Books and Services (each over 60%), while
adoption is lowest for industrial sectors such as Oil and Steel (roughly 20%). This evidence is
consistent with surveys indicating the potential of social media as a sales and marketing platform
(e.g. Larcker et al, 2012). However, a high percentage of social media adoption within an
industry does not translate well into social media usage to disseminate earnings news. For
15
example, the Meals industry contains 26 firms, of which 19 have a Facebook site and 18 have a
Twitter account. However, only 1 firm uses either Facebook or Twitter to disseminate quarterly
earnings. The pattern is similar for the Retail industry, in which only 5 out of 65 firms that use
Facebook and 10 out of 61 firms that use Twitter, ever disseminate earnings news over the
respective channels. In contrast, the industries with the highest percentage of firms that use social
media to disseminate earnings, conditional on having a social media presence, are Oil and Steel.
Descriptive statistics of the variables used in our empirical tests, spanning 1,452 firms
with requisite Compustat, CRSP, and IBES data over a maximum of 13 quarters (Q1 2010 to Q1
2013), are provided in Table 2. Continuous variables are winsorized at the 1st and 99th
percentiles. Of the 18,820 firm-quarters in our sample, a corporate Facebook page exists 35.5%
of the time, compared with only 5.7% of firm-quarters in which quarterly earnings are posted on
Facebook. This gap highlights the significant difference between identifying Facebook usage and
identifying Facebook usage for financial information. A similar, but not as disparate, pattern
holds for Twitter. A corporate Twitter account exists for 31.7% of the sample firm-quarters,
while earnings are tweeted in 11.7% of those firm-quarters. Even when the units of analyses are
firm-quarters, the data suggests that Twitter is the more prevalent social media platform to
disseminate earnings news.
4. Research Design
This section proceeds by first outlining how we identify the firm attributes that are
correlated with a firm‘s choice to disseminate earnings news via social media. We use the results
of this estimation to not only provide insights into the drivers of social media usage to announce
16
earnings, but also to provide a basis for the propensity score approach to selecting control firms
for our tests of the market consequences of social media usage.
4.1 Determinants of Social Media Usage for Earnings Announcements
Our determinants tests proceed in two steps. First, we estimate a firm-level, crosssectional regression using firms‘ attributes for the last fiscal quarter on or before March 31,
2013. We use a set of probit regressions that estimate the determinants of: 1) whether the firm
disseminated earnings news on Facebook (Twitter) in any quarter during the sample period, and
2) whether the firm committed to using Facebook (Twitter) to disseminate earnings news.13 As
noted in Section 2, a committed firm is one that uses Facebook (Twitter) to disseminate earnings
news each and every quarter once it starts to disseminate earnings news on Facebook (Twitter).
The specifications are as follows:
FB_EAi = α0a + α1a LOG_FB_LIKESi + α2a TW_EAi
+ ∑αia Disclosure Factorsi + ϵ1a
(1a)
TW_EAi = α0b + α1b LOG_TW_FOLWRSi + α2b FB_EAi
+ ∑αib Disclosure Factorsi + ϵ1b
(1b)
All variables are defined in Appendix A. All specifications include industry fixed effects (Fama
French 10). The dependent variables FB_EAi and TW_EAi take the value of 1 (0 otherwise) if
firm i posted earnings news to Facebook and Twitter, respectively, at least once during the
sample period. We include LOG_FB_LIKESi (LOG_TW_FOLWRSi) to proxy for the size of
firm i‘s Facebook (Twitter) audience, as it could be related to the propensity of the firm to
13
We do not model the decision to use social media for non-financial purposes because doing so would require a
model that incorporates numerous non-accounting factors. For example, one of the primary benefits of social media
highlighted in the popular press is as a sales and marketing tool. Therefore, it is likely that marketing expense,
corporate social responsibility performance, online retail presence and several other non-accounting factors would
drive social media usage. Other than through changing the incentives to use social media, there is little prior
research to suggest that these variables would affect a firm‘s voluntary disclosure choice.
17
disseminate earnings news over social media. Similarly, we include TW_EAi (FB_EAi) in
equation 1a (1b), to control for the fact that there is a competing social media platform. We first
run the regression using all firms in the sample, including those that do not use either social
media platform, and then we re-estimate the regression including only those firms that have a
Facebook Page (Twitter Account). This procedure allows us to identify more precisely whether
the firms that do not use social media are good controls for the firms that use social media.
The Disclosure Factors we use follow related research that has examined other voluntary
disclosure outlets, including conference calls (Frankel et al. 1999), corporate websites (Ettredge
et al., 2002), and conference presentations (Bushee et al. 2011). More specifically, we include a
set of variables that reflect the size, analyst coverage, performance, and risk of the firm. These
variables are defined in Panel D of Appendix A. We also re-estimate (1a) and (1b) focusing on
firms that have committed to use social media for earnings news each and every quarter. In that
specification, the independent variables are unchanged, and the dependant variables are
FB_EA_COMMIT and TW_EA_COMMIT. FB_EA_COMMIT (TW_EA_COMMIT) takes the
value of 1 (zero otherwise) if firm i announces its earnings consistently each and every quarter
on Facebook (Twitter) once it starts using that social media platform. A firm must have disclosed
earnings news on social media for at least two consecutive quarters before it is designated as a
committer. In other words, no firm will be designated as committed for the quarter in which it
disseminates earnings news through social media for the first time.
In the second step of our determinants tests, we estimate a panel regression using firmquarter observations. This increases the number of observations from 1,452 firms in equation (1)
to as many as 18,820 firm-quarters. More importantly, it allows us to test whether there are timevarying firm characteristics that are correlated with social media disclosures. We test our first
18
hypothesis (H1) by expanding the set of independent variables in equation (1) to include a
variable MEETBEATi,q, which takes the value of 1 if firm i meets or beats the consensus analyst
forecast in quarter q. Including this variable allows us to examine whether firms
opportunistically disseminate earnings news using social media on a quarter-by-quarter basis.
The specifications we employ are as follows:
FB_EA_Qi,q = γ0a + γ1a MEETBEATi,q + γ2a FB_PAGE_Qi,q-1 + γ3a LOG_FB_LIKESi
+ γ4a TW_EA_Qi,q + ∑ γia Disclosure Factorsi,q + ϵ2a
(2a)
TW_EA_Qi,q = γ0b + γ1b MEETBEATi,q + γ2b TW_ACCOUNT_Qi,q-1
+ γ3b LOG_TW_FOLWRSi + γ4b FB_EA_Qi,q + ∑ γib Disclosure Factorsi,q + ϵ2b
(2b)
All variables are defined in Appendix A. All specifications include industry fixed effects (Fama
French 10) and quarter fixed effects.14 The dependent variables FB_EA_Qi,q and TW_EA_Qi,q,
take the value of 1 (0 otherwise) if firm i posted earnings news to Facebook and Twitter,
respectively, for fiscal quarter q. If firms only post good earnings news to social media, then the
coefficient on MEETBEAT will be positive (i.e. γ1>0). But if firms do not distinguish good
news from bad when deciding to disseminate earnings over social media, then the coefficient
will not be significantly different from zero (i.e. γ1=0).
We include FB_PAGE_Q (TW_ACCOUNT_Q), an indicator variable set to 1 if firm i
has a Facebook page (Twitter account) as of the end of quarter q-1 and zero otherwise, because
the likelihood that a firm releases earnings on Facebook (Twitter) is dependent on whether the
firm has an existing Facebook page (Twitter account). We use lagged values for these variables
because we are capturing whether the firm has an existing social media presence at the time it
14
In robustness tests, we include firm fixed effects because it is possible that there is an unobserved firm-specific
factor whose omission from our multivariate analysis is material. This is a very conservative approach in our setting,
since our dataset only covers 13 quarters. However, none of our inferences are affected by this change.
19
decides to disseminate earnings. We include LOG_FB_LIKES and LOG_TW_FOLWRS to
proxy for the size of the social media presence. We do not have historical data on the number of
likes on Facebook or the number of followers on Twitter, so the value we use is based on an
examination of each social media platform as of the end of our sample period. As with equation
(1), we control for the alternate social media platform, and as an additional specification, we reestimate equation (2) by dropping firms that do not have a social media presence.
4.2 Capital Market Consequences of Social Media Usage
When we examine the capital market consequences of social media usage, we conduct
event study tests using daily and intra-day trading data. Using daily data provided by CRSP, we
test for a market reaction using three proxies measured over a three-day window around the date
of the earnings announcement: absolute abnormal returns (ABS_CAR), abnormal turnover
(ABN_TURN), and abnormal bid-ask spread (ABN_SPREAD). Because the choice to
disseminate financial information via social media is endogenous, we employ a propensity score
approach (Rosenbaum, 2005; Rosenbaum and Rubin, 1983) where we match two firms that are
equally likely to disseminate earnings via social media based on equation (2), but where only one
firm actually disseminates earnings via social media. The advantage of this approach is that it
allows us to match firms along all the observable disclosure factors used in our determinants
model discussed in Section 4.1. The specifications we employ are as follows:
EA_RESPONSEi,q = ρ0a + ρ1aMEETBEATi,q+ ρ2aFB_EA_Qi,q+ρ3a LOG_FB_LIKESi+
ρ4aTW_EA_Qi,q + ∑ρiaDisclosure Factorsi,q + ϵ3a
(3a)
EA_RESPONSEi,q = ρ0b + ρ1bMEETBEATi,q+ ρ2bTW_EA_Qi,q+ρ3b LOG_TW_FOLWRSi+
ρ4bFB_EA_Qi,q + ∑ρibDisclosure Factorsi,q + ϵ3b
20
(3b)
Where EA_RESPONSE is either the three-day abnormal absolute returns (ABS_CAR),
abnormal trading turnover (ABN_TURN), or abnormal bid-ask spread (ABN_SPREAD)
surrounding the release of quarterly earnings on Facebook in equation 3a and Twitter in 3b. All
variables are defined in Appendix A. Our second hypothesis (H2a and H2b) is that there is a
different market reaction when earnings news is disseminated over social media (i.e., ρ2≠0).
A potential issue with equation (3) is that any documented market reaction may be driven
by another event that occurs around the same time as the dissemination of the earnings news over
social media. We address this concern by performing a set of tests that utilize intra-day data. We
focus on tweets because we can identify the specific time of the tweet. We measure the market
reaction during five 5-minute intervals: t0 is the first 5 minutes after the tweet, t1 is between 5 to
10 minutes after the tweet, t2 is between 10 to 15 minutes after the tweet, t-1 is 5 minutes before
the tweet, and t-2 is between 5 and 10 minutes before the tweet.
We compute four volume-based measures to examine the market response during the
minutes surrounding the tweets. Abnormal volume (ABN_VOL) is defined as the trading
volume during the 5-minute time interval scaled by a measure of ―normal trading volume.‖
Since each time interval is relatively short, our abnormal volume measure may be distorted for
firms with normally low trading volume. Therefore, we choose the scalar to be the average
trading volume for a 5-minute time interval from the trading day one week prior (i.e., the entire
day‘s volume divided by 78 5-minute intervals within a 6.5 hour trading day). In addition, to
examine potential differences in the market response due to large and small investors, we
compute ABN_VOL_LG and ABN_VOL_SM as the abnormal volume from large and small
trades, respectively, where large trades are defined as those for $50,000 or more (Lee, 1992;
Bushee et al., 2003). Finally, we compute the change in the average trade size (TRADE_SIZE)
21
as the mean size of all trades during the 5-minute time interval divided by the mean size of all
trades from the trading day one week prior.
Lastly, we extend the specifications in equation (3) to investigate whether a commitment
to social media affects the capital market response. We include two additional variables—
FB_EA_COMMIT is an indicator set to 1 (0 otherwise) for firms that have committed to
disseminate earnings news over Facebook and FB_EA_COMMIT_Q is an indicator set to 1 (0
otherwise) for the quarters in which the committed firm has used Facebook for earnings news.
We define similar variables for firms that have committed to using Twitter for earnings news
(TW_EA_COMMIT and TW_EA_COMMIT_Q). We note that for each committed firm, there
are quarters in which the firm has not yet used social media for earnings news, which we refer to
as the pre-commitment period. We expect that the coefficient on FB_EA_COMMIT
(TW_EA_COMMIT) to be negative, indicating that there was relatively less of a market reaction
in the pre-period for firms who became committers. Our fourth hypothesis (H4) states that the
coefficient on FB_EA_COMMIT_Q (TW_EA_COMMIT_Q) should be positive, indicating that
committed firms experienced a larger capital market response in the post-period.
5. Results
This section proceeds in two parts. In the first subsection, we summarize our findings on
the determinants of social media usage. Next, we provide evidence on the capital market
response to social media usage.
5.1 Determinants of Social Media Usage for Earnings Announcements
The results of estimating firm-level regressions using equation (1) are shown in Table 3,
Panel A. The Facebook specifications are shown in columns (1) – (3) and the Twitter
22
specifications in columns (4) – (6). A comparison of column (1) and (4) highlights the
differences between the firms that use Facebook to disseminate earnings announcements (at least
once) versus those that use Twitter. As noted earlier, there is a significant overlap in Facebook
and Twitter usage. However, these specifications include controls for the alternative social media
outlet, and therefore the coefficients can be interpreted as the incremental effect of Facebook and
Twitter, respectively. In terms of the differences, we find that firm size is negatively associated
with Facebook usage but positively associated with Twitter usage. In other words, we find that
large firms are more likely to use Twitter and less likely to use Facebook to disseminate earnings
news. It is somewhat surprising that large firms are more likely to use Twitter for disseminating
earnings announcements since the benefits of improved dissemination have been argued to be
greater for smaller firms (Blankespoor et al. 2013).
We also find that analyst coverage is negatively associated with Twitter and Facebook.
This finding is consistent with both social media platforms being used by firms with low analyst
coverage to disseminate information to investors. In both specifications, the size of the firms‘
social media presence (proxied by either ―likes‖ on Facebook or ―followers‖ on Twitter) is
positively associated with whether earnings are released via social media. In addition, the
statistical significance of this relationship is very strong. We do not find that other traditional
determinants of voluntary disclosure, such as market-to-book (MTB), firm performance (ROA)
or growth (GROWTH), are associated with the decision to use social media to disseminate
earnings news. Despite the lack of significance on these variables, the explanatory power of our
model is relatively strong. The pseudo-R2 is 40% in column (1) and 48% in column (4).
When we restrict the sample to the firms that have a Facebook page or Twitter account,
the results change somewhat, as shown in columns (2) and (5). Because these specifications only
23
include firms that use social media, the coefficients are capturing the difference between firms
that disseminate and do not disseminate earnings through social media. For the Facebook
specifications, there is no longer a statistically significant association between firm size and
Facebook usage for earnings news. In addition, there is now a negative association between the
size of the firm‘s Facebook audience (LOG_FB_LIKES) and Facebook usage for earnings news,
in contrast with the results in column (1). For the Twitter specifications, the positive association
between firm size and Twitter usage for earnings dissemination is stronger. However, as with the
Facebook specifications, there is now a negative association between Twitter usage and the size
of the firm‘s Twitter audience (LOG_TW_FOLWRS). Overall, these results suggest while larger
firms are more likely to use Twitter to disseminate earnings news, firms with more followers are
not are more reluctant to use Twitter for earnings news. While we have not fully investigated the
reasons for this apparent inconsistency, it is plausible that firms with more followers are using
social media primarily for reaching customers rather than investors. In our summary statistics,
we found that retail firms are more likely to use social media and have more followers.
We next focus on firms that have committed to disseminating earnings news on Facebook
and Twitter in columns (3) and (6), respectively. For these specifications, we only include firms
that use social media. As a result, the coefficients capture the difference between a committed
and non-committed firm, conditional on the firm using social media during our sample period.
There are 406 (232) firms who have used Twitter (Facebook) to disseminate earnings news; 108
firms commit to disclose their quarterly earnings on social media (either on Twitter or
Facebook), 90 firms commit to disclose on Twitter, 43 firms commit to disclose on Facebook,
and 25 firms commit to disclose on both Facebook and Twitter.
24
None of the traditional measures of the incentives for voluntary disclosure are statistically
significant in the Facebook specifications. Similarly, only size is statistically significant in the
Twitter specifications. This result suggests that other than size, traditional voluntary disclosure
models do not explain the choice to commit to social media. We do find a significantly negative
coefficient for LOG_FB_LIKES, indicating that firms with a larger Facebook following are less
likely to commit to posting earnings news consistently. Alternatively, the result could indicate
that firms with a small Facebook following are trying to attract a larger audience by
demonstrating a commitment to use social media for earnings news.
The results of estimating firm-quarter-level regressions using equation (2) are shown in
Table 3, Panel B. The Facebook specifications are shown in columns (1) – (3) and the Twitter
specifications in columns (4) – (6). The first specification for both Facebook and Twitter use our
entire sample of 18,820 firm-quarters. Under this specification, we find that book leverage
(LEVERAGE) is positively associated with disseminating earnings news on Facebook, and that
firm size (SIZE) has a strong positive association with disseminating earnings news on Twitter.
These specifications include a single indicator variable for lagged social media presence. As
noted earlier, we expected the decision to disseminate earnings news through social media to be
dependent on having a social media presence. This expectation is clearly satisfied by our data,
based on the extremely high levels of significance on the lagged values of FB_PAGE_Q and
TW_ACCOUNT_Q.
In column (2) and (5) we focus on the subset of the firm-quarters in which firms have a
social media presence. We do this because it is possible that non-social media firms are not good
controls due to unobserved differences. Under this approach, we find that several of the
traditional disclosure factors are associated with social media usage. We find that Facebook
25
usage is positively associated with SIZE and ROA, indicating that larger and more profitable
firms are more likely to use Facebook to disseminate earnings news. Similarly, we find that
Twitter usage is positively associated with SIZE and MTB. These results suggest that Twitter
usage is also more prevalent in larger and better performing firms.
Our first hypothesis (H1) is that the decision to disseminate earnings news each quarter
on social media is related to the direction of the news. We test H1 by including the variable
MEETBEAT, as shown in columns (3) and (6). We find that firms are more likely to announce
quarterly earnings through social media when they meet or beat the consensus analyst forecast
for the quarter. However, this result only holds for Twitter—the coefficient on MEETBEAT in
column (6) is positive and highly significant. For Facebook, the coefficient in column (3) is
positive but insignificant. This lack of a result for Facebook may, in part, be due to the statistical
power of our tests. As noted earlier, there are only 40 firms that disseminate earnings news on
Facebook exclusively, compared with 214 firms that use Twitter exclusively. Overall, our
findings suggest that firms are more likely to tweet earnings when the news is positive.
5.2 Capital Market Consequences of Social Media Usage
We first present univariate evidence in Table 4 on the market reaction of quarterly
earnings announcements that are disseminated versus not disseminated over social media,
conditional on social media usage. We include market-based measures Size Adjusted Return
(SAR), Cumulative Abnormal Return (CAR), the absolute value of CAR (ABS_CAR), abnormal
turnover (ABN_TURN) and abnormal spread (ABN_SPREAD). Each variable is defined in
Panel B of Appendix A. For each variable, we calculate the mean for quarters in which earnings
news is on social media and not on social media (i.e., the ―Yes‖ and ―No‖ columns), and then we
test for differences in these means. Hypothesis H2a is that there is a significant difference in the
26
market reactions. The results for earnings on Facebook are provided in Panel A and the results
for earnings on Twitter are provided in Panel B.
Panel A reveals that the mean ABS_CAR and ABN_TURN are both significantly lower
for earnings announcements that are posted to Facebook, indicating that the average market
reaction is lower in absolute terms. There is no significant difference for SAR, CAR or
ABN_SPREAD. The results for earnings that are tweeted on Twitter (Panel B) show a similar
pattern in direction, but with higher levels of statistical significance. The mean ABS_CAR,
ABN_TURN and ABN_SPREAD are all significantly lower for earnings announcements
tweeted on Twitter. In addition, both SAR and CAR are significantly higher for earnings
announcements tweeted on Twitter. Together, these results indicate that firms tend to tweet more
good than bad news earnings, consistent with the evidence from our determinants tests from the
prior section. Assuming that this market reaction is primarily driven by the information content
of the earnings release itself, these results indicate that firms are not disseminating negative
earnings surprises on Twitter. Our results using abnormal spreads (ABN_SPREAD) indicate that
the average bid-ask spread is lower when earnings announcements are tweeted via Twitter,
consistent with hypothesis H2b and the findings from Blankespoor et al. (2013) for small
technology firms.
We selected the sample used in our subsequent analyses using the propensity scores (i.e.,
the predicted probability of FB_EA_Qi,q = 1 or TW_EA_Qi,q = 1) computed from our
determinants model. We matched each of the 1,038 (2,071) treatment observations15 (i.e., firms
who disseminated earnings news on Facebook (Twitter)) to a control observation (i.e., firms that
have a Facebook (Twitter) presence but did not disseminate earnings news on Facebook
15
The number of Facebook (Twitter) earnings news treatment observations (i.e., 1,038 (2,071)) is less than the total
in Table 4 (i.e., 1,066 (2,136) ) because at the time we collected data some firms in our sample had not yet
announced first quarter 2013 earnings.
27
(Twitter)), using the same fiscal quarter and with the smallest propensity score difference. To
assess the effectiveness of the matching procedure, we evaluate the covariate balance between
the two samples; i.e., whether the treatment and control samples are similar along the
determinants variables included in our model. In untabulated results, we find only a few
statistically significant differences between the two groups. Firms using Facebook to disseminate
earnings news are smaller; firms using Twitter to disseminate earnings have less analyst
coverage, lower ROA, lower sales growth and higher leverage.
The results of estimating equation (3) are provided in Table 5. The coefficients on
FB_EA_Q and TW_EA_Q are insignificant for the ABS_CAR and ABN_TURN variables, but
positive and significant for the ABN_SPREAD. This result indicates that the endogenous nature
of a firm‘s choice of social media communications has a significant effect on the regression
results. Our multivariate analysis suggests that the average bid-ask spread is actually higher for
firms that announce earnings via social media, inconsistent with H2b. Under the Kim and
Verrecchia (1994) model, this result suggests that the wide-spread dissemination of an earnings
announcement over social media allows some investors to make judgements about the firm‘s
performance that are superior to the judgments of other investors, resulting in greater information
asymmetry and less stock liquidity.
The coefficients on both measures of social media audience size—LOG_FB_LIKES for
Facebook and LOG_TW_FOLWRS for Twitter—are positive and highly significant for the
ABS_CAR and ABN_TURN specifications (columns (1), (2), (4) and (5)). These results
indicate that the larger the social media audience, the larger the market reaction for earnings
news disseminated over social media, suggesting that a firm‘s followers includes many capital
market participants. Moreover, the negative and significant coefficients on MEETBEAT suggest
28
that there is a greater market response to firms that report negative earnings surprises, consistent
with the ―torpedo effect‖ (Sloan and Skinner, 2002).
The results of our intra-day analyses are illustrated in Figure 2 and Table 6. Among the
firms that have a Twitter account and use it for disseminating earnings news, only a subset sends
earnings-related tweets during market hours and only for some quarters. Therefore, our intra-day
analyses are not without limitations and possible self-selection bias. There are 292 firm-quarters
for which we observe an EA tweet during market hours, 380 firm-quarters for rehash tweets, and
607 firm quarters for preview tweets. In each panel of Figure 2, we plot the mean market
response for EA tweets, preview tweets, and rehash tweets for each time interval. Interval t0 is
the first 5 minutes after the tweet, t1 is between 5 to 10 minutes after the tweet, t2 is between 10
to 15 minutes after the tweet, t-1 is 5 minutes before the tweet, and t-2 is between 5 and 10
minutes before the tweet. In each case, we scale by the average 5-minute trading volume from
the same day one week prior to the tweet. We note that we do not solely compare volume
around the tweet relative to volume during a control period, but rather, we compare volume
across the five time intervals.
The scaling produces values in excess of 100% for both the EA and rehash tweets,
suggesting that these tweets are generally disseminated during periods of unusually high trading
volume. In contrast, the preview tweets are typically disseminated when trading volume is low.
Figure 2, Panel A shows that volume increases most for EA tweets during interval t0, increases
slightly for preview tweets during interval t1, but does not change much for rehash tweets across
the five time periods. Subject to the above mentioned limitations of our intra-day analyses, we
conclude that EA tweets, preview tweets and rehash tweets generate different market reactions.
29
Figure 2, Panel B and Panel C partition the volume data from Panel A into large and
small trades, respectively. The existing literature has noted that large traders should be analysed
separately from small traders (e.g. Lee, 1992; Bushee, Matsumoto, Miller, 2003). We follow this
literature and identify large trades as those that involved in excess of $50,000. These panels
show that the increases in trading volume for EA tweets at t0 and preview tweets at t1 is due to
increases in volume from large trades. Therefore, while social media is commonly viewed as a
disclosure channel that provides timely access to information for all investors, and thus ―level the
playing field‖ for small investors, our results suggest that larger investors react quicker to
earnings-related tweets.
This finding is further illustrated in Figure 2, Panel D, which shows how the average
trade size changes over the testing period. The average trade size increases substantially in
response to EA tweets. The 6% increase in the average trade size is a result of the average trade
size increasing from 160 shares in the control period to 170 shares in the testing period.16 The
results in Table 6 show that the trading volume associated with large trades increased in a
statistically significant way relative to small trades for each type of tweet. These results suggest
that larger traders are more likely to follow tweets. Moreover, it shows that larger trades respond
not only to earnings announcements tweets, but also preview and rehash tweets as well.
The final set of results focuses on firms that commit to using social media for earnings
news. The results in Table 7 add two additional variables that allow us to identify whether there
is a differential capital market reaction for firms that are committed to social media disclosures.
Column (2) shows that the coefficient on FB_EA_COMMIT is significantly negative when the
16
While an increase in average trade size from 160 to 170 shares may appear nominal, the vast majority of trades are
for one round lot (100 shares). Thus, it requires a substantial increase in large lot trades to raise the average to 170.
For example, if there are 1,000 trades at 100 shares and 71 trades at 1,000 shares, the average trade size would be
160 shares (171,000/1,071). Holding the number of one lot trades constant, there would need to be 84 ten-lot trades
(an 18% increase) in order to raise the average trade size to 170 shares (184,000/1,084).
30
dependent variable is abnormal turnover (ABN_TURN), indicating that there was relatively less
trading volume in the pre-period for firms who became committers on Facebook. Similarly,
column (4) shows that the coefficient on TW_EA_COMMIT is significantly negative when the
dependent variable is absolute abnormal return (ABS_CAR), indicating less market reaction
prior to the commitment to use Twitter. For the post-commitment period, we find results
consistent with our fourth hypothesis (H4). The coefficients on FB_EA_COMMIT_Q and
TW_EA_COMMIT_Q are positive and significant in columns (2) and (4), indicating that
committed firms experienced a larger capital market response in the post-commitment period.
Collectively, these results indicate that even though the overall market reaction is modest across
all firms in our sample, the market reaction is relatively strong for firms that committed to
disseminating earnings news over social media consistently.
6. Conclusion
This study is the first to document the adoption of social media by the largest publiclytraded companies in the U.S. and their specific use of social media to disseminate financial
information. Using hand-collected data on the use of social media by S&P1500 firms from 2010
to early 2013, we conclude that corporate adoption of social media has surpassed 50% and that
Twitter is the preferred platform to ―tweet‖ quarterly earnings news. However, our evidence also
indicates that firms are more likely to tweet only the good news and not the bad earnings news,
suggesting some opportunism in the decision to use social media for financial information. This
finding is relevant to policy debates concerning emerging disclosure technologies and their
impact on firms, investors, and capital markets.
31
This study also provides evidence on how the market responds to earnings news provided
through social media. Using intra-day data, we find that trading volume increases in response to
the initial announcement of earnings on Twitter and that the primary driver of the increased
trading volume is larger rather than smaller trades. In addition, for firms that have shown a
commitment to using social media for financial information by disseminating earnings news each
and every quarter regardless of the direction of the news, there is a larger market reaction as
reflected in greater information content and trading volume during the earnings announcement
window. This finding is relevant for firms, their managers, and their board of directors that may
be considering or establishing their social media disclosure policies.
32
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35
Appendix A: Variable Description and Data Sources
Specifications of variables used throughout the paper. Table A1 Panels A, B, C and D describe the social media
variables, the market reaction variables, the intra-day market reaction variables and the control variables,
respectively.
Variable
Description
Data Source
Panel A: Social Media Variables
FB_PAGE_Q
FB_EA
FB_EA_Q
FB_EA_COMMIT
FB_EA_COMMIT_Q
FB_LIKES
LOG_FB_LIKES
TW_ACCOUNT_Q
TW_EA
TW_EA_Q
TW_EA_COMMIT
TW_EA_COMMIT_Q
TW_FOLWRS
LOG_TW_FOLWRS
Indicator variable set to 1 if the firm has a Facebook page
at the end of the quarter
Indicator variable set to 1 if the firm posted news of its
earnings on Facebook (i.e., FB_EA_Q = 1) at least once
during our sample period
Indicator variable set to 1 if the firm posted news of its
earnings on Facebook on the actual date of its earnings
announcement or one day afterwards for the quarter
Indicator variable set to 1 if the firm posted news of its
earnings on Facebook (i.e., FB_EA_Q = 1) each and every
quarter after the first time
Indicator variable set to 1 (0 otherwise) for the quarters in
which the committed firm (i.e., FB_EA_COMMIT = 1) has
used Facebook for earnings news
The number of Facebook likes that a firm had at the end of
July 2013
The natural logarithm of the number of Facebook likes
Indicator variable set to 1 if the firm has a Twitter account
at the end of the quarter
Indicator variable set to 1 if the firm tweeted news of its
earnings (i.e., TW_EA_Q = 1) at least once during our
sample period
Indicator variable set to 1 if the firm tweeted news of its
earnings on the actual date of its earnings announcement
or one day afterwards for the quarter
Indicator variable set to 1 if the firm tweeted news of its
earnings (i.e., TW_EA_Q = 1) each and every quarter after
the first time
Indicator variable set to 1 (0 otherwise) for the quarters in
which the committed firm (i.e., TW_EA_COMMIT = 1)
has used Twitter for earnings news
The number of Twitter followers that a firm had at the end
of July 2013
The natural logarithm of the number of Twitter followers
36
Facebook
Facebook
Facebook
Facebook
Facebook
Facebook
Facebook
Twitter
Twitter
Twitter
Twitter
Twitter
Twitter
Twitter
Panel B: Market Reaction Variables
SAR
CAR
ABS_CAR
ABN_TURN
ABN_SPREAD
Three-day raw return minus the return of the corresponding
size-decile index centered at the dates of the quarterly
earnings announcement (―QEA‖)
Three-day cumulative abnormal return CAR measured as
the residual from a market model. The market model
parameters are estimated over the period from 11 to 265
days before the QEA using returns from a value-weighted
market portfolio
Absolute value of CAR
CRSP
CRSP
CRSP
Three-day average volume divided by shares outstanding,
less the average turnover in the estimation period. The
estimation period beings 61 days prior to the QEA and ends
2 days prior to the QEA
Constructed as above using the bid-ask spread, defined as
the difference between the bid and ask price divided by the
average of the bid and ask price, multiplied by 100
CRSP
CRSP
Panel C: Intra-day Market Reaction Variables
ABN_VOL
ABN_VOL_LG
ABN_VOL_SM
TRADE_SIZE
Trading volume during the 5-minute time interval divided
by the trading volume during a control period. The control
period is the average trading volume for a 5-minute time
interval from the trading day one week prior.
Constructed as above for trades that were for $50,000 or
more
Constructed as above for trades that were for less than
$50,000
The mean size of all trades during the 5-minute time
interval divided by the mean size of all trades from the
trading day one week prior
TAQ
TAQ
TAQ
TAQ
Panel D: Control Variables
SIZE
MTB
LOGANALYST
ROA
GROWTH
LEVERAGE
MEETBEAT
Natural logarithm of total assets, measured at the end of the
quarter
Market value of equity divided by common equity,
measured at the end of the quarter
Natural logarithm of number of analysts with an EPS
forecast for the quarter
Income before extraordinary items divided by total assets,
measured at the end of the quarter
Year-over-year percentage change in quarterly sales
Sum of long-term debt and debt in current liabilities
divided by total assets, measured at the end of the quarter
Indicator variable set to 1 if the firm‘s actual EPS meet or
beat the consensus analyst forecast for the quarter
37
Compustat
Compustat
IBES
Compustat
Compustat
Compustat
IBES
Appendix B: Examples of Earnings News Posted to Facebook and Tweeted over Twitter
Panel A: An Earnings Post from Alcoa on April 8, 2013
38
Panel B: Multiple Earnings Tweets from Alcoa on April 8, 2013
























$AA Listen to Replay of Alcoa's 1Q13 Earnings Available From 04/08/2013 07:00 PM ET To: 04/15/2013
11:59 PM ET http://t.co/scx8SnDorh Apr 08, 2013
Alcoa Reports First Quarter Net Income of $0.13 Per Share; Income of $0.11 Per Share Excluding Special
Items http://t.co/L1TzM3BeVS Apr 08, 2013
$AA Reports 1Q13: Alcoa‗s aluminum helps airplanes and autos increase energy efficiency
http://t.co/Wm7Xk3PnZj Apr 08, 2013
*HAPPENING NOW* Tune-in now for $AA Alcoa's 1Q13 earnings webcast. http://t.co/Xm36qEkBdx Get the
slides, listen-in, and get replay info. Apr 08, 2013
$AA: Global end market growth strong in Aero, Auto, Truck, Packaging, Building & Construction, Industrial
Gas Turbine http://t.co/o1H7fybJob Apr 08, 2013
$AA Download Alcoa's 1Q13 earnings presentation on @slideshare: http://t.co/6bdxjBXCFY Apr 08, 2013
$AA CEO Kleinfeld: Alcoa achieved these results by pressing our ―innovation edge, scale and strength in end
markets‖ http://t.co/WeMrH6jkSZ Apr 08, 2013
$AA Alcoa's 1Q13 earnings presentation webcast about to begin at 5:00pm ET. Tune in at
http://t.co/bvvPonQGjx Apr 08, 2013
(2/2) $AA CEO Kleinfeld: ―…while our upstream business continues to move down the cost curve.‖
http://t.co/F7ZareRGez Apr 08, 2013
(1/2) CEO Kleinfeld: ―Our mid & downstream businesses now account for 72% of our total after-tax operating
income…‖ http://t.co/WINpIMr5El Apr 08, 2013
(3/3) CEO Kleinfeld: ―…and remarkable upstream performance in the face of weak metal prices.‖
http://t.co/udoQzMNluk Apr 08, 2013
(2/3) $AA CEO Kleinfeld: ―…improved results in our midstream business…‖ http://t.co/OexNBwCinl Apr 08,
2013
(1/3) $AA CEO Kleinfeld: ―This was a strong quarter led by record profitability in our downstream business…‖
http://t.co/iOzRIZkvop Apr 08, 2013
$AA Reports 1Q13: Alcoa‘s 1Q13 net income excluding special items was the best since the third quarter of
2011 http://t.co/mo3xTwD8n3 Apr 08, 2013
$AA Reports 1Q13: Alcoa delivered solid first quarter results across all business segments
http://t.co/qN2mIkd34n Apr 08, 2013
$AA Reports 1Q13: Value-added businesses now account for 72% of total after tax operating income
http://t.co/hv7TqkAmAj Apr 08, 2013
$AA Reports: Global end market growth remains solid, forecast of 7% global aluminum demand growth in
2013 reaffirmed http://t.co/HSB75cTU4o Apr 08, 2013
$AA Reports 1Q13: Debt-to-capital ratio 35 percent http://t.co/PgnsoYyqzW Apr 08, 2013
$AA Reports 1Q13: Strong liquidity with cash on hand of $1.6 billion http://t.co/eGNBoyV2nf Apr 08, 2013
$AA Reports 1Q13: Record low first quarter days working capital http://t.co/Ib4Ztxwq8N Apr 08, 2013
$AA Reports 1Q13: Improved performance in Alumina and Primary Metals year-over-year, despite lower metal
prices http://t.co/8UtStSPpEu Apr 08, 2013
$AA Reminder: Tune in to Alcoa's Webcast of 1Q13 Results Beginning at 5:00pm ET at
http://t.co/9HTm56HHYO. Release: http://t.co/jtrtVlNGkj Apr 08, 2013
$AA Reports 1Q13: Record after-tax operating income in Engineered Products and Solutions
http://t.co/8mAKTC3PfW Apr 08, 2013
$AA Reports 1Q13: Net income $0.13 per share http://t.co/0BYmhamFJS Apr 08, 2013
39
Figure 1: Social Media Usage by S&P1500 Firms
Panel A: Corporate Use of Social Media
Panel B: Adoption of Social Media over Time
55%
Either 52%
50%
Twitter 47%
45%
Facebook 44%
40%
35%
30%
25%
20%
15%
10%
5%
40
May-13
Feb-13
Nov-12
Aug-12
May-12
Feb-12
Nov-11
Aug-11
May-11
Feb-11
Nov-10
Aug-10
May-10
Feb-10
Nov-09
Aug-09
May-09
Feb-09
Nov-08
Aug-08
May-08
Feb-08
Nov-07
0%
Figure 2: Market Reactions Associated with Earnings-Related Tweets
Panel A: Mean Abnormal Volume (ABN_VOL) Associated with Earnings-Related Tweets
Mean Volume Relative to Control
Period
ABN_VOL
250%
200%
150%
EA Tweets
100%
Rehash EA Tweets
50%
Preview EA Tweets
0%
t-2
t-1
t0
t1
t2
5-minute Intervals Around the Tweet
Panel B: Mean Abnormal Volume (ABN_VOL) from Large Trades Associated with Earnings-Related Tweets
Mean Volume Relative to Control Period
ABN_VOL_LG (from Large Trades)
1200%
1000%
800%
600%
EA Tweets
Rehash EA Tweets
400%
Preview EA Tweets
200%
0%
t-2
t-1
t0
t1
t2
5-minute Intervals Around the Tweet
41
Figure 2 (continued)
Panel C: Mean Abnormal Volume (ABN_VOL) from Small Trades Associated with Earnings-Related Tweets
Mean Volume Relative to Control Period
ABN_VOL_SM (from Small Trades)
250%
200%
150%
EA Tweets
100%
Rehash EA Tweets
Preview EA Tweets
50%
0%
t-2
t-1
t0
t1
t2
5-minute Intervals Around the Tweet
Panel D: Mean Change in Trade Size (TRADE_SIZE) Associated with Earnings-Related Tweets
Mean Size Relative to Control Period
TRADE_SIZE
8.0%
6.0%
4.0%
2.0%
0.0%
-2.0%
-4.0%
-6.0%
-8.0%
-10.0%
-12.0%
EA Tweets
-2
-1
0
1
2
Rehash EA Tweets
Preview EA Tweet
5-minute Intervals Around Tweet
The figure reports the intra-day market reactions to earnings news related tweets. We use four intra-day market reaction
proxies: 1) ABN_VOL is the trading volume during the 5-minute time interval divided by the trading volume during a
control period. The control period is the average trading volume for a 5-minute time interval from the trading day one
week prior. 2) ABN_VOL_LG represents ABN_VOL for trades that were for $50,000 or more. 3) ABN_VOL_SM represents
ABN_VOL for trades that were for less than $50,000. 4) TRADE_SIZE is the mean size of all trades during the 5-minute
time interval divided by the mean size of all trades from the trading day one week prior. We report results for the four
proxies in Panels A-D, respectively. In each panel, we compare intra-day markets reactions for five 5-minute intervals
surrounding earnings-related tweets. We categorize earnings related tweets into three categories: 1) an earnings
announcement tweet if it is the first tweet mentioning the firm‘s earnings announcement and it occurred on the earnings
announcement date; 2) an earnings rehash tweet if it mentions highlights from the prior earnings announcement; and 3) an
earnings preview tweet if it only mentions the date of the upcoming earnings announcement.
42
Table 1: Sample Composition and Social Media Usage by Industry
FF30
Industry
Autos
Beer
Books
Bus. Equip.
Carry
Chemicals
Clothing
Construction
Coal
Electronics
Fab. Prods.
Fin
Food
Games
Health
Household
Meals
Mines
Oil
Other
Paper
Retail
Services
Smoke
Steel
Telecom
Trans
Textiles
Utilities
Wholesale
Total
Unique
Firms
19
7
9
170
13
35
25
45
5
18
56
283
41
18
103
24
26
9
64
35
27
92
161
4
22
34
36
4
68
47
1,500
Facebook
Users
(FB_PAGE)
N
%
7
3
6
92
4
13
13
15
2
7
20
103
16
9
26
11
19
3
12
16
9
65
102
5
20
21
23
21
663
36.8
42.9
66.7
54.1
30.8
37.1
52.0
33.3
40.0
38.9
35.7
36.4
39.0
50.0
25.2
45.8
73.1
33.3
18.8
45.7
33.3
70.7
63.4
22.7
58.8
58.3
33.8
44.7
44.2
Earnings News on Facebook
At Least Once
Committed
(FB_EA)
(FB_EA_COMMIT)
N
%
N
2
2
2
44
2
6
4
4
1
3
12
30
6
11
2
1
2
8
7
5
5
41
4
6
8
9
5
232
28.6
66.7
33.3
48.4
50.0
50.0
30.8
26.7
50.0
42.9
60.0
29.4
37.5
42.3
18.2
5.3
66.7
66.7
43.8
55.6
7.7
40.2
80.0
30.0
38.1
39.1
23.8
35.2
1
1
8
4
1
2
5
1
3
1
1
3
1
5
1
1
2
1
1
43
43
Twitter
Users
(TW_ACCOUNT)
N
%
6
4
6
103
5
15
13
17
3
7
22
110
16
12
37
10
18
3
14
14
10
61
106
1
3
25
20
28
19
708
31.6
57.1
66.7
60.6
38.5
42.9
52.0
37.8
60.0
38.9
39.3
38.9
39.0
66.7
35.9
41.7
69.2
33.3
21.9
40.0
37.0
66.3
65.8
25.0
13.6
73.5
55.6.
41.2
40.4
47.2
Earnings News on Twitter
At Least Once
Committed
(TW_EA)
(TW_EA_COMMIT)
N
%
N
2
3
6
69
4
8
3
8
2
5
20
71
8
3
27
3
1
3
13
8
5
10
66
1
3
12
11
20
11
406
33.3
75.0
100.0
67.0
80.0
53.3
23.1
47.1
66.7
71.4
90.9
64.5
50.0
25.0
73.0
30.0
5.6
100.0
92.9
57.1
50.0
16.4
62.3
100.0
100.0
48.0
55.0
71.4
57.9
57.3
3
1
12
1
2
1
2
13
3
9
1
6
3
17
1
2
4
6
3
90
The sample is comprised of all firms included in the S&P1500 index as of January 2013. The table reports the total number of unique firms by the Fama-French 30
industry as well as the firm‘s social media usage: 1) FB_PAGE (TW_ACCOUNT) indicates firms with a Facebook page (Twitter account) at the end of July 2013; 2)
FB_EA (TW_EA) indicates firms that posted (tweeted) news of its earnings of Facebook (Twitter) at least once during our sample period; and 3) FB_EA_COMMIT
(TW_EA_COMMIT) indicates firms that the posted (tweeted) news of its earnings on Facebook (Twitter) each and every quarter after the first time. A firm must have
posted (tweeted) earnings news on Facebook (Twitter) for at least two consecutive quarters before it is designated as a committer.
44
Table 2: Descriptive Statistics
Variable
Social Media Variables:
FB_PAGE_Q (Indicator)
FB_EA_Q (Indicator)
FB_LIKES
TW_ACCOUNT_Q (Indicator)
TW_EA_Q (Indicator)
TW_FOLWRS
Market Reaction Variables:
SAR
CAR
ABS_CAR
ABN_TURN
ABN_SPREAD
Control Variables:
SIZE
MTB
LOGANALYST
ROA
GROWTH
LEVERAGE
MEETBEAT (Indicator)
N
Mean
Std.Dev.
P1
P25
Median
P75
P99
18,820
18,820
18,820
18,820
18,820
18,820
0.355
0.057
329,001
0.317
0.117
30,590
0.479
0.232
2,661,207
0.465
0.322
253,599
0
0
0
4,008
5,984,756
0
0
0
3,224
507,068
18,820
18,820
18,820
18,820
18,820
0.002
0.002
0.049
1.892
0.008
0.065
0.065
0.047
0.965
0.037
-0.187
-0.188
0.001
0.588
-0.081
-0.033
-0.033
0.015
1.249
-0.007
0.000
0.000
0.034
1.649
0.000
0.035
0.036
0.067
2.265
0.015
0.199
0.198
0.232
6.034
0.197
18,820
18,820
18,820
18,820
18,820
18,820
18,820
8.046
2.722
2.326
0.014
0.062
0.202
0.735
1.701
2.516
0.664
0.019
0.180
0.170
0.441
4.949
0.534
0.693
-0.052
-0.573
0.000
6.786
1.299
1.792
0.004
-0.010
0.050
7.917
1.969
2.398
0.012
0.066
0.180
9.098
3.115
2.833
0.023
0.147
0.311
12.618
15.690
3.526
0.075
0.539
0.655
The sample comprises a maximum of 18,820 firm-quarter observations for the S&P1500 firms between 1Q2010 and 1Q2013 for which sufficient Compustat financial
data, CRSP stock price data and IBES analyst forecasts data exist. We eliminate firm-quarters with negative shareholders‘ equity. The table presents descriptive
statistics for the variables used in the firm-quarter level regression analyses. We employ the following social media variables: FB_PAGE_Q (TW_ACCOUNT_Q) is an
indicator variable set to 1 if the firm has a Facebook page (Twitter account) at the end of the quarter. FB_EA_Q (TW_EA_Q) is an indicator variable set to 1 if the firm
posted (tweeted) news of its earnings on Facebook (Twitter) on the actual date of its earnings announcement or one day afterwards for the quarter. FB_LIKES
(TW_FOLWRS) is the number of Facebook likes (Twitter followers) that a firm had at the end of July 2013. We use the following market reaction variables: SAR is the
three-day raw return minus the return of the corresponding size-decile index centered at the dates of the quarterly earnings announcement (―QEA‖). CAR is the three-day
cumulative abnormal return measured as the residual from a market model. The market model parameters are estimated over the period from 11 to 265 days before the
QEA using returns from a value-weighted market portfolio. ABS_CAR represents the absolute value of CAR. ABN_TURN is the three-day average volume divided by
shares outstanding, less the average turnover in the estimation period. The estimation period beings 61 days prior to the QEA and ends 2 days prior to the QEA.
Similarly, we construct ABN_SPREAD using the bid-ask spread, defined as the difference between the bid and ask price divided by the average of the bid and ask price,
multiplied by 100. We use the following control variables: Size is natural logarithm of total assets. MTB is the ratio of market value of equity divided by book value of
common equity. LOGANALYST is the natural logarithm of the number of analysts with an EPS forecast for the quarter. ROA is income before extraordinary items
divided by total assets. Growth is the year-over-year percentage change in quarterly sales. Leverage is the sum of long-term debt and debt in current liabilities divided
by total assets. MEETBEAT is an indicator variable set to 1 if the firm‘s actual EPS meet or beat the consensus analyst forecast for the quarter. Accounting data and
market values are measured as of the fiscal-quarter end. Except for variables with natural lower or upper bounds, variables are winsorized at the 1st and 99th percentile.
45
Table 3: Determinants of Firm’s Social Media Usage for Earnings News
Panel A: Firm Level Regression
Earnings News on Facebook
SIZE
MTB
LOGANALYST
ROA
GROWTH
LEVERAGE
LOG_FB_LIKES
Earnings News on Twitter
(1)
(2)
(3)
(4)
(5)
(6)
At Least Once
At Least Once
Committed
At Least Once
At Least Once
Committed
(Full Sample)
(User Sample)
(User Sample)
(Full Sample)
(User Sample)
(User Sample)
-0.085*
0.037
0.037
0.105***
0.196***
0.254***
(-1.92)
(0.71)
(0.40)
(2.68)
(4.30)
(4.28)
-0.005
0.028
0.017
-0.007
0.020
0.042
(-0.21)
(1.04)
(0.41)
(-0.36)
(0.79)
(1.45)
-0.182*
-0.018
-0.031
-0.200**
-0.035
0.031
(-1.69)
(-0.14)
(-0.15)
(-1.97)
(-0.29)
(0.19)
0.792
3.030
5.760
1.271
1.187
6.474
(0.30)
(0.93)
(0.96)
(0.54)
(0.44)
(1.48)
-0.061
-0.161
0.082
-0.351
-0.576
-0.424
(-0.15)
(-0.33)
(0.11)
(-0.97)
(-1.28)
(-0.81)
0.986***
0.695*
-0.151
-0.306
-0.730*
-0.581
(2.76)
(1.66)
(-0.21)
(-0.94)
(-1.94)
(-1.13)
0.121***
-0.132***
-0.288***
(8.93)
(-4.72)
(-4.17)
0.221***
-0.080**
-0.038
(15.86)
(-2.54)
(-0.83)
1.676***
1.441***
(13.15)
(9.98)
LOG_TW_FOLWRS
TW_EA
1.608***
1.382***
(14.32)
(10.61)
TW_EA_COMMIT
1.891***
(7.95)
FB_EA
FB_EA_COMMIT
1.788***
(7.77)
Industry Fixed Effects
Included
Included
Included
Included
Included
Included
N
1,452
649
649
1,452
691
691
Pseudo R2
40.3%
28.5%
36.3%
48.3%
25.9%
22.7%
46
Panel B: Firm-Quarter Level Regression
Earnings News on Facebook
(1)
(2)
Earnings News on Twitter
(3)
(4)
(5)
(6)
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
(Full Sample)
(User Sample)
(User Sample)
(Full Sample)
(User Sample)
(User Sample)
SIZE
0.016
0.133***
0.133***
0.146***
0.225***
0.223***
(0.47)
(3.21)
(3.21)
(5.50)
(6.57)
(6.50)
MTB
-0.012
0.013
0.013
0.027*
0.030*
0.030*
LOGANALYST
ROA
GROWTH
LEVERAGE
(-0.73)
(0.67)
(0.66)
(1.71)
(1.65)
(1.65)
-0.116
-0.033
-0.034
-0.094
-0.005
-0.008
(-1.44)
(-0.34)
(-0.35)
(-1.42)
(-0.06)
(-0.10)
2.010
4.071*
3.993*
1.900
2.776
2.327
(1.21)
(1.83)
(1.79)
(1.33)
(1.55)
(1.29)
0.148
0.187
0.183
0.051
0.228
0.197
(0.80)
(0.89)
(0.88)
(0.34)
(1.29)
(1.11)
0.514*
0.311
0.311
-0.010
-0.114
-0.114
(1.78)
(0.93)
(0.94)
(-0.04)
(-0.43)
(-0.43)
MEETBEAT
LOG_FB_LIKES
0.021
0.143***
(0.36)
(2.85)
-0.035***
-0.195***
-0.195***
(-2.73)
(-9.15)
(-9.16)
LOG_TW_FOLWRS
TW_EA_Q
1.612***
1.554***
1.554***
(17.91)
(16.47)
(16.46)
FB_PAGE_Q_LAG
1.772***
0.083***
-0.047*
-0.046
(7.57)
(-1.66)
(-1.64)
1.781***
1.885***
1.887***
(20.00)
(18.23)
(18.20)
(13.39)
FB_EA_Q
TW_ACCOUNT_Q_LAG
1.278***
(19.90)
Industry and Quarter
Fixed Effects
N
Pseudo R
2
Included
Included
Included
Included
Included
Included
18,820
6,687
6,687
18,820
5,968
5,968
44.7%
34.2%
34.2%
44.7%
23.7%
23.9%
The table reports the determinants of firm‘s social media usage for earnings news. We report results based on a firmlevel regression (Panel A) and a firm-quarter level regression (Panel B). In Panel A, we report probit coefficient
estimates and (in parentheses) z-statistics from regressing FB_EA (TW_EA) on the firm‘s social media audience size,
alternative social media outlets and other economic and institutional variables. In Columns 3 (6), we examine the
determinants for committing to social media usage by using FB_EA_COMMIT (TW_EA_COMMIT) as the
dependent variables. In Panel B, we report probit coefficient estimates and (in parentheses) z-statistics based on
standard errors clustered by firm from regressing FB_EA_Q (TW_EA_Q) on the firm‘s social media audience size,
alternative social media outlets and other economic and institutional variables. For details on the variables see
Tables 1 and 2. We use the natural log of the raw values and lag the variables by one quarter where indicated. We
include industry- and quarter-fixed effects in the regressions, but do not report the coefficients. ***, **, and *
indicate statistical significance at the 1%, 5%, and 10% levels (two-tailed).
47
Table 4: Changes in Capital Markets Reaction by Social Media Usage
Panel A: Facebook Users
Earnings News on Facebook
(1)
Yes
(2)
No
(1) - (2)
Diff.
F-stat
p-value
N
1,066
5,621
SAR
0.003
0.002
0.000
0.03
0.865
CAR
0.003
0.002
0.001
0.14
0.710
ABS_CAR
0.049
0.053
(0.004) **
5.13
0.024
ABN_TURN
1.963
2.028
(0.065) *
3.63
0.057
ABN_SPREAD
0.005
0.007
(0.001)
1.43
0.232
F-stat
p-value
Panel B: Twitter Users
Earnings News on Twitter
(1)
Yes
(2)
No
(1) - (2)
Diff.
N
2,136
3,832
SAR
0.004
0.001
0.003 *
3.66
0.056
CAR
0.004
0.001
0.004 **
4.3
0.038
ABS_CAR
0.047
0.053
(0.007) ***
28.02
0.000
ABN_TURN
1.898
2.022
(0.124) ***
21.23
0.000
ABN_SPREAD
0.005
0.007
(0.002) ***
6.67
0.010
The table reports changes in capital market reactions for earnings news disseminated through Facebook (Panel A)
and Twitter (Panel B), conditional on social media usage. We report the average of the various proxies of capital
market reactions in the three day window from -1 to +1 around quarterly earnings announcements for earnings news
disseminated through social media (Column 1) and earnings news not disseminated through social media (Column
2). We indicate statistical significance of differences across the columns with t-tests. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels (two-tailed). SAR is the three-day raw return minus the return
of the corresponding size-decile index centered at the dates of the quarterly earnings announcement (―QEA‖). CAR
is the three-day cumulative abnormal return measured as the residual from a market model. The market model
parameters are estimated over the period from 11 to 265 days before the QEA using returns from a value-weighted
market portfolio. ABS_CAR represents the absolute value of CAR. ABN_TURN is the three-day average volume
divided by shares outstanding, less the average turnover in the estimation period. The estimation period beings 61
days prior to the QEA and ends 2 days prior to the QEA. Similarly, we construct ABN_SPREAD using the bid-ask
spread, defined as the difference between the bid and ask price divided by the average of the bid and ask price,
multiplied by 100.
48
Table 5: Capital Markets Consequences of Social Media Usage
Earnings News on Facebook
Earnings News on Twitter
Dependent
(1)
(2)
(3)
(4)
(5)
(6)
Variable:
ABS_CAR
ABN_TURN
ABN_SPREAD
ABS_CAR
ABN_TURN
ABN_SPREAD
0.002
0.054
0.002*
(0.77)
(0.93)
(1.84)
0.002**
FB_EA_Q
TW_EA_Q
SIZE
MTB
LOGANALYST
ROA
GROWTH
LEVERAGE
MEETBEAT
LOG_FB_LIKES
N
R
2
(2.09)
-0.165***
-0.000
-0.009***
-0.157***
-0.001
(-5.83)
(-5.86)
(-0.55)
(-8.41)
(-6.51)
(-1.26)
-0.001
0.002
0.000
-0.000
-0.002
0.000
(-0.76)
(0.18)
(0.11)
(-0.65)
(-0.10)
(1.21)
0.004
0.281***
-0.002
0.005*
0.198***
-0.003*
(-1.76)
(1.33)
(5.45)
(-1.04)
(1.84)
(3.71)
-0.315***
-4.685**
-0.010
-0.151
2.168
0.023
(-3.19)
(-2.25)
(-0.21)
(-1.64)
(0.88)
(0.67)
0.026**
0.517**
0.003
0.013
0.190
-0.003
(-0.97)
(2.58)
(2.20)
(0.73)
(1.60)
(1.00)
0.000
-0.080
-0.003
0.002
0.189
-0.003
(0.00)
(-0.39)
(-0.55)
(0.25)
(0.86)
(-1.06)
-0.006**
-0.272***
0.000
-0.001
-0.071
0.000
(-2.16)
(-3.55)
(0.01)
(-0.60)
(-1.30)
(0.08)
0.003***
0.053***
0.000
(2.86)
(2.64)
(0.69)
0.004***
0.047**
0.000
(3.99)
(2.27)
(0.25)
0.002
0.010
0.002
(0.77)
(0.17)
(0.99)
FB_EA_Q
Industry and Quarter
Fixed Effects
0.037
(0.73)
-0.008***
LOG_TW_FOLWRS
TW_EA_Q
0.003
(1.55)
0.001
0.019
0.000
(0.28)
(0.32)
(0.05)
Included
Included
Included
Included
Included
Included
2,076
2,076
2,076
4,142
4,142
4,142
16.8%
19.3%
4.0%
15.6%
15.8%
2.9%
The table reports the capital market consequences of using social media to disseminate earnings news. We use a
propensity score framework to identify the sample where we match two firms that are equally likely to disseminate
earnings via social media based on the determinants in Table 3 Panel B, but where only one firm actually
disseminates earnings via social media. The table reports OLS coefficient estimates and (in parentheses) t-statistics
based on standard errors clustered by firm from regressing the various market reaction variables on a social media
usage indicator (FB_EA_Q or TW_EA_Q) plus controls. For details on the variables see Tables 1 and 2. We use the
natural log of the raw values indicated. We include industry- and quarter-fixed effects in the regressions, but do not
report the coefficients. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels (two-tailed).
49
Table 6: Intra-day Market Reactions to Earnings News Related Tweets
Panel A: Earnings Announcement Tweets
Mean
Period N ABN_VOL
N
t-2
292
181% *** 184
t-1
292
212% *** 184
t0
292
225% *** 184
t1
292
181% *** 184
t2
292
166% *** 184
Mean
ABN_VOL_LG
444%
611%
981%
510%
367%
Panel B: Earnings Rehash Tweets
Mean
Period N ABN_VOL
t-2
380
162% ***
t-1
380
161% ***
t0
380
159% ***
t1
380
168% ***
t2
380
137% ***
N
262
262
262
262
262
Mean
ABN_VOL_LG
502%
463%
461%
454%
400%
Panel C: Earnings Preview Tweets
Mean
Period N ABN_VOL
t-2
607
5%
t-1
607
13% *
t0
607
21% ***
t1
607
34% ***
t2
607
26% ***
N
350
350
350
350
350
Mean
ABN_VOL_LG
160%
107%
170%
414%
165%
***
***
***
***
***
***
***
***
***
***
*
**
**
**
***
‡
‡‡
‡‡‡
‡
‡
N
292
292
292
292
292
Mean
ABN_VOL_SM
192%
219%
206%
180%
181%
‡‡‡
‡‡
‡‡‡
‡‡‡
‡‡‡
N
380
380
380
380
380
Mean
ABN_VOL_SM
153%
159%
155%
149%
133%
‡‡
‡‡
‡
‡‡‡
N
607
607
607
607
607
Mean
ABN_VOL_SM
6%
15%
19%
20%
23%
***
***
***
***
***
N
292
292
292
292
292
Mean
TRADE_SIZE
-3%
-3%
6%
0%
-5%
***
***
***
***
***
N
380
380
380
380
380
Mean
TRADE_SIZE
-2%
-5%
-2%
0%
-3%
**
**
***
***
***
N
607
607
607
607
607
Mean
TRADE_SIZE
-9%
-10%
-8%
1%
-7%
***
***
***
**
*
***
The table reports the intra-day market reactions to earnings news related tweets. We categorize earnings related tweets into three categories: 1) an earnings
announcement tweet if it is the first tweet mentioning the firm‘s earnings announcement and it occurred on the earnings announcement date; 2) an earnings
rehash tweet if it mentions highlights from the prior earnings announcement; and 3) an earnings preview tweet if it only mentions the date of the upcoming
earnings announcement. We report results for the three categories in Panels A-C, respectively. In each panel, we compare the various proxies for intra-day
markets reactions for five 5-minute intervals surrounding earnings-related tweets. For details on the variables see Figure 2. ***, **, * indicate significantly
different from zero at the 1%, 5%, and 10% level, respectively, using a two-tailed t-test. ‡‡‡, ‡‡, ‡ indicate ABNVOL_LG significantly different from
ABNVOL_SM at the 1%, 5%, and 10% level, respectively, using a two-tailed t-test.
50
Table 7: The Differential Capital Markets Consequences for Committed Social Media Usage
Earnings News on Facebook
Earnings News on Twitter
Dependent
(1)
(2)
(3)
(4)
(5)
(6)
Variable:
ABS_CAR
ABN_TURN
ABN_SPREAD
ABS_CAR
ABN_TURN
ABN_SPREAD
FB_EA_Q
0.000
0.051
-0.000
(0.08)
(1.16)
(-0.30)
TW_EA_Q
FB_EA_COMMIT
FB_EA_COMMIT_Q
-0.020
-1.180***
(-0.73)
(-4.40)
(1.34)
0.026
1.148***
-0.006
(0.91)
(4.10)
(-0.79)
TW_EA_COMMIT_Q
MTB
LOGANALYST
ROA
GROWTH
-0.016
-0.001
(-0.42)
(-1.46)
0.010
TW_EA_COMMIT
SIZE
0.000
(0.00)
-0.017***
-0.259
0.011
(-3.01)
(-1.56)
(0.69)
0.017***
0.252
-0.009
(2.99)
(1.49)
(-0.55)
-0.008***
-0.164***
-0.002***
-0.008***
-0.158***
-0.002***
(-11.79)
(-10.66)
(-4.08)
(-11.19)
(-9.76)
(-4.12)
-0.001*
0.002
0.000
-0.001**
-0.005
0.000
(-1.92)
(0.24)
(1.08)
(-2.27)
(-0.58)
(0.40)
0.006***
0.271***
-0.002*
0.005***
0.283***
-0.002
(3.06)
(7.42)
(-1.80)
(2.73)
(7.47)
(-1.54)
-0.216***
-0.757
-0.056*
-0.166***
0.070
-0.042
(-4.45)
(-0.74)
(-1.93)
(-3.14)
(0.07)
(-1.44)
0.025***
0.459***
0.002
0.022***
0.426***
0.001
(4.47)
(4.47)
(0.71)
(4.32)
(4.06)
(0.36)
LEVERAGE
0.002
-0.044
-0.009***
0.003
0.013
-0.008***
(0.25)
(-0.37)
(-3.19)
(0.44)
(0.10)
(-2.73)
MEETBEAT
-0.007***
-0.261***
-0.001
-0.007***
-0.217***
-0.001
(-5.08)
(-7.93)
(-0.86)
(-4.64)
(-6.39)
(-0.71)
0.002***
0.041***
0.000
(5.76)
(5.09)
(1.41)
0.003***
0.046***
0.000
(4.61)
(3.49)
(1.39)
LOG_FB_LIKES
LOG_TW_FOLWRS
TW_EA_Q
-0.001
-0.003
-0.001
(-0.79)
(-0.09)
(-1.21)
FB_EA_Q
Industry and Quarter
Fixed Effects
N
R
2
0.002
0.049
0.001
(0.68)
(1.10)
(1.22)
Included
Included
Included
Included
Included
Included
6,687
6,687
6,687
5,968
5,968
5,968
12.2%
17.2%
3.1%
13.6%
15.6%
3.2%
The table reports the differential capital market consequences of committed social media usage to disseminate earnings
news. The table reports OLS coefficient estimates and (in parentheses) t-statistics based on standard errors clustered by firm
from regressing the various market reaction variables on a committed social media usage indicator (FB_EA_COMMIT or
TW_EA_COMMIT), a social media usage indicator (FB_EA_Q or TW_EA_Q), the interaction (FB_EA_COMMIT_Q or
TW_EA_COMMIT_Q) plus controls. For details on the variables see Tables 1 and 2. We use the natural log of the raw
values indicated. We include industry- and quarter-fixed effects in the regressions, but do not report the coefficients. ***,
**, and * indicate statistical significance at the 1%, 5%, and 10% levels (two-tailed).
51