Corporate Use of Social Media

Michael J. Jung,* James P. Naughton,†

Ahmed Tahoun,‡ and Clare Wang†

April 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, the Ohio State University,

University of Miami, 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.

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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. 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

disseminating quarterly earnings.

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 from 2010 through the first quarter of 2013. Our dataset identifies which firms

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.

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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

releases, etc.), are significant predictors of Twitter (or Facebook) adoption for financial reporting

purposes.5

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. 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.

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Drawing on the voluntary disclosure literature, we also consider whether the use of social

media to disseminate earnings news 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 as their only social media platform

for disseminating 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

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

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 (1,500 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.

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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 announcement,9 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

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

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.

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.

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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

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.

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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 dissemination 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

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

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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 news over social media,

we draw from prior studies that have shown that news can be selectively disclosed based on the

type of news (Healy and Palepu 1995; Skinner 1994; Aboody and Kasznik 2000; 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

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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 return-

and 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.

(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 by considering the 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,

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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

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

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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

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

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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

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

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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.

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to the commitment. That is, a firm that perceives the market does not react to its earnings

announcements is more likely use social media for earnings news every quarter 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

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.

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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.

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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

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

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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, EA tweets occur several hours after

the earnings announcement, 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 ensure 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

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

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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

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.

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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 99

th

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

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, cross-

sectional 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

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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

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. 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.

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.

18

We run the regression on the sample of firms that adopted Facebook or Twitter (and have

all requisite data), and thus, the control firms are those that have adopted social media but do not

use it for disseminating earnings news. 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 firm-

quarter observations. This regression allows us to test whether there are time-varying firm

characteristics that are correlated with social media disclosures. We test our first 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 LOG_FB_LIKESi

+ γ3a TW_EA_Qi,q + ∑ γia Disclosure Factorsi,q + ϵ2a (2a)

TW_EA_Qi,q = γ0b + γ1b MEETBEATi,q + γ2b LOG_TW_FOLWRSi

+ γ3b FB_EA_Qi,q + ∑ γib Disclosure Factorsi,q + ϵ2b (2b)

19

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 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 the

disclosure factors used in prior research.

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

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.

20

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 + ρ1a FB_EA_Qi,q + ρ2aMEETBEATi,q +ρ3a LOG_FB_LIKESi+

ρ4aTW_EA_Qi,q + ∑ρiaDisclosure Factorsi,q + ϵ3a (3a)

EA_RESPONSEi,q = ρ0b + ρ1b TW_EA_Qi,q + ρ2b MEETBEATi,q+ρ3b LOG_TW_FOLWRSi+

ρ4bFB_EA_Qi,q + ∑ρibDisclosure Factorsi,q + ϵ3b (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., ρ1≠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

21

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)

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.

22

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) and (2) and the Twitter

specifications in columns (3) and (4). Because these specifications only include firms that use

social media, the coefficients are capturing the difference between firms that disseminate and do

not disseminate earnings through social media.

Regarding the decision to disseminate earnings news on Facebook, we find that most of

the traditional determinants of voluntary disclosure such as firm size (SIZE), market-to-book

(MTB), analyst coverage (LOGANALYST), firm performance (ROA) or growth (GROWTH)

are not significant; we find only a marginally significant positive association with firm leverage

(LEVERAGE). But we do find stronger results for the two social media variables. The

significantly positive coefficient on TW_EA indicates that firms are more likely to disseminate

earnings news on Facebook if they also do so on Twitter. The negative coefficient on

LOG_FB_LIKES indicates that firms with a larger Facebook audience are less likely to

disseminate earnings news on that platform.

For the decision to disseminate earnings news on Twitter (column 3), there is a strong

positive associate with firm size and a weaker positive association with firm leverage. 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

23

smaller firms (Blankespoor et al. 2013). Similar to the Facebook specification, firms are more

likely to disseminate earnings news on Twitter if they also disseminate it over Facebook

(FB_EA), but are less likely when if they have a larger Twitter following

(LOW_TW_FOLWRS). While we have not fully investigated the reasons for this apparent

inconsistency, it is plausible that firms with more followers (and likes) 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 (2) and (4), respectively. 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.

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.

24

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) and (2) and the Twitter

specifications in columns (3) and (4). We find that Facebook usage for earnings news 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 (2) and (4). 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 (4) is positive and highly significant. For Facebook, the coefficient in column (2) 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

25

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

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

26

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

(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

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

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

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

28

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.

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 et al, 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.

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).

29

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

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 publicly-

traded 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,

30

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.

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.

31

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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_EA

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

Facebook

FB_EA_Q

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

Facebook

FB_EA_COMMIT

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

Facebook

FB_EA_COMMIT_Q

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

Facebook

FB_LIKES The number of Facebook likes that a firm had at the end of

July 2013 Facebook

LOG_FB_LIKES The natural logarithm of the number of Facebook likes Facebook

TW_EA

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

Twitter

TW_EA_Q

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

Twitter

TW_EA_COMMIT

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

Twitter

TW_EA_COMMIT_Q

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

Twitter

TW_FOLWRS The number of Twitter followers that a firm had at the end

of July 2013 Twitter

LOG_TW_FOLWRS The natural logarithm of the number of Twitter followers Twitter

35

Panel B: Market Reaction Variables

SAR

Three-day raw return minus the return of the corresponding

size-decile index centered at the dates of the quarterly

earnings announcement (“QEA”)

CRSP

CAR

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

CRSP

ABS_CAR Absolute value of CAR CRSP

ABN_TURN

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

CRSP

ABN_SPREAD

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

Panel C: Intra-day Market Reaction Variables

ABN_VOL

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.

TAQ

ABN_VOL_LG Constructed as above for trades that were for $50,000 or

more TAQ

ABN_VOL_SM Constructed as above for trades that were for less than

$50,000 TAQ

TRADE_SIZE

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

Panel D: Control Variables

SIZE Natural logarithm of total assets, measured at the end of the

quarter Compustat

MTB Market value of equity divided by common equity,

measured at the end of the quarter Compustat

LOGANALYST Natural logarithm of number of analysts with an EPS

forecast for the quarter IBES

ROA Income before extraordinary items divided by total assets,

measured at the end of the quarter Compustat

GROWTH Year-over-year percentage change in quarterly sales Compustat

LEVERAGE Sum of long-term debt and debt in current liabilities

divided by total assets, measured at the end of the quarter Compustat

MEETBEAT Indicator variable set to 1 if the firm’s actual EPS meet or

beat the consensus analyst forecast for the quarter IBES

36

Appendix B: Examples of Earnings News Posted to Facebook and Tweeted over Twitter

Panel A: An Earnings Post from Alcoa on April 8, 2013

37

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

38

Figure 1: Social Media Usage by S&P1500 Firms

Panel A: Corporate Use of Social Media

Panel B: Adoption of Social Media over Time

Facebook 44%

Twitter 47%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

No

v-0

7

Feb

-08

Ma

y-0

8

Au

g-0

8

No

v-0

8

Feb

-09

Ma

y-0

9

Au

g-0

9

No

v-0

9

Feb

-10

Ma

y-1

0

Au

g-1

0

No

v-1

0

Feb

-11

Ma

y-1

1

Au

g-1

1

No

v-1

1

Feb

-12

Ma

y-1

2

Au

g-1

2

No

v-1

2

Feb

-13

Ma

y-1

3

Either 52%

39

Figure 2: Market Reactions Associated with Earnings-Related Tweets

Panel A: Mean Abnormal Volume (ABN_VOL) Associated with Earnings-Related Tweets

Panel B: Mean Abnormal Volume (ABN_VOL) from Large Trades Associated with Earnings-Related Tweets

0%

50%

100%

150%

200%

250%

t-2 t-1 t0 t1 t2 Me

an V

olu

me

Re

lati

ve t

o C

on

tro

l P

eri

od

5-minute Intervals Around the Tweet

ABN_VOL

EA Tweets

Rehash EA Tweets

Preview EA Tweets

0%

200%

400%

600%

800%

1000%

1200%

t-2 t-1 t0 t1 t2

Me

an V

olu

me

Re

lati

ve t

o C

on

tro

l Pe

rio

d

5-minute Intervals Around the Tweet

ABN_VOL_LG (from Large Trades)

EA Tweets

Rehash EA Tweets

Preview EA Tweets

40

Figure 2 (continued)

Panel C: Mean Abnormal Volume (ABN_VOL) from Small Trades Associated with Earnings-Related Tweets

Panel D: Mean Change in Trade Size (TRADE_SIZE) Associated with Earnings-Related Tweets

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.

0%

50%

100%

150%

200%

250%

t-2 t-1 t0 t1 t2 Me

an V

olu

me

Re

lati

ve t

o C

on

tro

l Pe

rio

d

5-minute Intervals Around the Tweet

ABN_VOL_SM (from Small Trades)

EA Tweets

Rehash EA Tweets

Preview EA Tweets

-12.0%

-10.0%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

-2 -1 0 1 2

Me

an S

ize

Re

lati

ve t

o C

on

tro

l Pe

rio

d

5-minute Intervals Around Tweet

TRADE_SIZE

EA Tweets

Rehash EA Tweets

Preview EA Tweet

41

Table 1: Sample Composition and Social Media Usage by Industry

Facebook

Earnings News on Facebook Twitter

Earnings News on Twitter

FF30 Unique Users

(FB_PAGE)

At Least Once (FB_EA)

Committed (FB_EA_COMMIT)

Users (TW_ACCOUNT)

At Least Once (TW_EA)

Committed (TW_EA_COMMIT)

Industry Firms N % N % N N % N % N

Autos 19 7 36.8

2 28.6 - 6 31.6

2 33.3 -

Beer 7 3 42.9

2 66.7 1 4 57.1

3 75.0 3

Books 9 6 66.7

2 33.3 1 6 66.7

6 100.0 1

Bus. Equip. 170 92 54.1

44 48.4 8 103 60.6

69 67.0 12

Carry 13 4 30.8

2 50.0 - 5 38.5

4 80.0 1

Chemicals 35 13 37.1

6 50.0 4 15 42.9

8 53.3 2

Clothing 25 13 52.0

4 30.8 1 13 52.0

3 23.1 -

Construction 45 15 33.3

4 26.7 - 17 37.8

8 47.1 1

Coal 5 2 40.0

1 50.0 - 3 60.0

2 66.7 -

Electronics 18 7 38.9

3 42.9 - 7 38.9

5 71.4 -

Fab. Prods. 56 20 35.7

12 60.0 2 22 39.3

20 90.9 2

Fin 283 103 36.4

30 29.4 5 110 38.9

71 64.5 13

Food 41 16 39.0

6 37.5 1 16 39.0

8 50.0 3

Games 18 9 50.0

- - - 12 66.7

3 25.0 -

Health 103 26 25.2

11 42.3 3 37 35.9

27 73.0 9

Household 24 11 45.8

2 18.2 1 10 41.7

3 30.0 1

Meals 26 19 73.1

1 5.3 - 18 69.2

1 5.6 -

Mines 9 3 33.3

2 66.7 1 3 33.3

3 100.0 -

Oil 64 12 18.8

8 66.7 3 14 21.9

13 92.9 6

Other 35 16 45.7

7 43.8 1 14 40.0

8 57.1 3

Paper 27 9 33.3

5 55.6 - 10 37.0

5 50.0 -

Retail 92 65 70.7

5 7.7 - 61 66.3

10 16.4 -

Services 161 102 63.4

41 40.2 5 106 65.8

66 62.3 17

Smoke 4 - -

- - - 1 25.0

1 100.0 -

Steel 22 5 22.7

4 80.0 1 3 13.6

3 100.0 1

Telecom 34 20 58.8

6 30.0 1 25 73.5

12 48.0 2

Trans 36 21 58.3

8 38.1 2 20 55.6.

11 55.0 4

Textiles 4 - -

- - - - -

- - -

Utilities 68 23 33.8

9 39.1 1 28 41.2

20 71.4 6

Wholesale 47 21 44.7 5 23.8 1 19 40.4 11 57.9 3

Total 1,500 663 44.2 232 35.2 43 708 47.2 406 57.3 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.

42

Table 2: Descriptive Statistics

Variable N Mean Std.Dev. P1 P25 Median P75 P99

Social Media Variables:

FB_PAGE_Q (Indicator) 18,820 0.355 0.479

FB_EA_Q (Indicator) 18,820 0.057 0.232

FB_LIKES 18,820 329,001 2,661,207 0 0 0 4,008 5,984,756

TW_ACCOUNT_Q (Indicator) 18,820 0.317 0.465

TW_EA_Q (Indicator) 18,820 0.117 0.322

TW_FOLWRS 18,820 30,590 253,599 0 0 0 3,224 507,068

Market Reaction Variables:

SAR 18,820 0.002 0.065 -0.187 -0.033 0.000 0.035 0.199

CAR 18,820 0.002 0.065 -0.188 -0.033 0.000 0.036 0.198

ABS_CAR 18,820 0.049 0.047 0.001 0.015 0.034 0.067 0.232

ABN_TURN 18,820 1.892 0.965 0.588 1.249 1.649 2.265 6.034

ABN_SPREAD 18,820 0.008 0.037 -0.081 -0.007 0.000 0.015 0.197

Control Variables:

SIZE 18,820 8.046 1.701 4.949 6.786 7.917 9.098 12.618

MTB 18,820 2.722 2.516 0.534 1.299 1.969 3.115 15.690

LOGANALYST 18,820 2.326 0.664 0.693 1.792 2.398 2.833 3.526

ROA 18,820 0.014 0.019 -0.052 0.004 0.012 0.023 0.075

GROWTH 18,820 0.062 0.180 -0.573 -0.010 0.066 0.147 0.539

LEVERAGE 18,820 0.202 0.170 0.000 0.050 0.180 0.311 0.655

MEETBEAT (Indicator) 18,820 0.735 0.441

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 99

th percentile.

43

Table 3: Determinants of Firm’s Social Media Usage for Earnings News

Panel A: Firm Level Regression

Earnings News on Facebook Earnings News on Twitter

(1) (2)

(3) (4)

At Least Once Committed

At Least Once Committed

SIZE

0.037 0.037

0.196*** 0.254***

(0.71) (0.40)

(4.30) (4.28)

MTB

0.028 0.017

0.020 0.042

(1.04) (0.41)

(0.79) (1.45)

LOGANALYST

-0.018 -0.031

-0.035 0.031

(-0.14) (-0.15)

(-0.29) (0.19)

ROA

3.030 5.760

1.187 6.474

(0.93) (0.96)

(0.44) (1.48)

GROWTH

-0.161 0.082

-0.576 -0.424

(-0.33) (0.11)

(-1.28) (-0.81)

LEVERAGE

0.695* -0.151

-0.730* -0.581

(1.66) (-0.21)

(-1.94) (-1.13)

LOG_FB_LIKES

-0.132*** -0.288***

(-4.72) (-4.17)

LOG_TW_FOLWRS

-0.080** -0.038

(-2.54) (-0.83)

TW_EA

1.382***

(10.61)

TW_EA_COMMIT

1.891***

(7.95)

FB_EA

1.441***

(9.98)

FB_EA_COMMIT

1.788***

(7.77)

Industry Fixed

Effects

Included Included

Included Included

N

649 649

691 691

Pseudo R2

28.5% 36.3%

25.9% 22.7%

44

Panel B: Firm-Quarter Level Regression

Earnings News on Facebook Earnings News on Twitter

(1) (2)

(3) (4)

SIZE

0.133*** 0.133***

0.225*** 0.223***

(3.21) (3.21)

(6.57) (6.50)

MTB

0.013 0.013

0.030* 0.030*

(0.67) (0.66)

(1.65) (1.65)

LOGANALYST

-0.033 -0.034

-0.005 -0.008

(-0.34) (-0.35)

(-0.06) (-0.10)

ROA

4.071* 3.993*

2.776 2.327

(1.83) (1.79)

(1.55) (1.29)

GROWTH

0.187 0.183

0.228 0.197

(0.89) (0.88)

(1.29) (1.11)

LEVERAGE

0.311 0.311

-0.114 -0.114

(0.93) (0.94)

(-0.43) (-0.43)

MEETBEAT

0.021

0.143***

(0.36)

(2.85)

LOG_FB_LIKES

-0.195*** -0.195***

(-9.15) (-9.16)

LOG_TW_FOLWRS

-0.047* -0.046

(-1.66) (-1.64)

TW_EA_Q

1.554*** 1.554***

(16.47) (16.46)

FB_EA_Q

1.885*** 1.887***

(18.23) (18.20)

Industry and Quarter

Fixed Effects

Included Included

Included Included

N

6,687 6,687

5,968 5,968

Pseudo R2

34.2% 34.2%

23.7% 23.9%

The table reports the determinants of firm’s social media usage for earnings news. We report results based on a firm-

level 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).

45

Table 4: Changes in Capital Markets Reaction by Social Media Usage

Panel A: Facebook Users

(1) (2) (1) - (2)

Earnings News on Facebook Yes No 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

Panel B: Twitter Users

(1) (2) (1) - (2)

Earnings News on Twitter Yes No Diff. F-stat p-value

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.

46

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

FB_EA_Q 0.002 0.054 0.002*

(0.77) (0.93) (1.84)

TW_EA_Q

0.003 0.037 0.002**

(1.55) (0.73) (2.09)

SIZE -0.008*** -0.165*** -0.000

-0.009*** -0.157*** -0.001

(-5.83) (-5.86) (-0.55)

(-8.41) (-6.51) (-1.26)

MTB -0.001 0.002 0.000

-0.000 -0.002 0.000

(-0.76) (0.18) (0.11)

(-0.65) (-0.10) (1.21)

LOGANALYST 0.004 0.281*** -0.002

0.005* 0.198*** -0.003*

(1.33) (5.45) (-1.04)

(1.84) (3.71) (-1.76)

ROA -0.315*** -4.685** -0.010

-0.151 2.168 0.023

(-3.19) (-2.25) (-0.21)

(-1.64) (0.88) (0.67)

GROWTH 0.026** 0.517** 0.003

0.013 0.190 -0.003

(2.58) (2.20) (0.73)

(1.60) (1.00) (-0.97)

LEVERAGE 0.000 -0.080 -0.003

0.002 0.189 -0.003

(0.00) (-0.39) (-0.55)

(0.25) (0.86) (-1.06)

MEETBEAT -0.006** -0.272*** 0.000

-0.001 -0.071 0.000

(-2.16) (-3.55) (0.01)

(-0.60) (-1.30) (0.08)

LOG_FB_LIKES 0.003*** 0.053*** 0.000

(2.86) (2.64) (0.69)

LOG_TW_FOLWRS

0.004*** 0.047** 0.000

(3.99) (2.27) (0.25)

TW_EA_Q 0.002 0.010 0.002

(0.77) (0.17) (0.99)

FB_EA_Q

0.001 0.019 0.000

(0.28) (0.32) (0.05)

Industry and Quarter

Fixed Effects Included Included Included

Included Included Included

N 2,076 2,076 2,076

4,142 4,142 4,142

R2 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).

47

Table 6: Intra-day Market Reactions to Earnings News Related Tweets

Panel A: Earnings Announcement Tweets

Mean Mean Mean Mean

Period N ABN_VOL N ABN_VOL_LG N ABN_VOL_SM N TRADE_SIZE

t-2 292 181% *** 184 444% *** ‡ 292 192% *** 292 -3%

t-1 292 212% *** 184 611% *** ‡‡ 292 219% *** 292 -3%

t0 292 225% *** 184 981% *** ‡‡‡ 292 206% *** 292 6%

t1 292 181% *** 184 510% *** ‡ 292 180% *** 292 0%

t2 292 166% *** 184 367% *** ‡ 292 181% *** 292 -5% **

Panel B: Earnings Rehash Tweets

Mean Mean Mean Mean

Period N ABN_VOL N ABN_VOL_LG N ABN_VOL_SM N TRADE_SIZE

t-2 380 162% *** 262 502% *** ‡‡‡ 380 153% *** 380 -2%

t-1 380 161% *** 262 463% *** ‡‡ 380 159% *** 380 -5% **

t0 380 159% *** 262 461% *** ‡‡‡ 380 155% *** 380 -2%

t1 380 168% *** 262 454% *** ‡‡‡ 380 149% *** 380 0%

t2 380 137% *** 262 400% *** ‡‡‡ 380 133% *** 380 -3% *

Panel C: Earnings Preview Tweets

Mean Mean Mean Mean

Period N ABN_VOL N ABN_VOL_LG N ABN_VOL_SM N TRADE_SIZE

t-2 607 5%

350 160% * 607 6% 607 -9% ***

t-1 607 13% * 350 107% ** ‡‡ 607 15% ** 607 -10% ***

t0 607 21% *** 350 170% ** ‡‡ 607 19% *** 607 -8% ***

t1 607 34% *** 350 414% ** ‡ 607 20% *** 607 1%

t2 607 26% *** 350 165% *** ‡‡‡ 607 23% *** 607 -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.

48

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

0.000 -0.016 -0.001

(0.00) (-0.42) (-1.46)

FB_EA_COMMIT -0.020 -1.180*** 0.010

(-0.73) (-4.40) (1.34)

FB_EA_COMMIT_Q 0.026 1.148*** -0.006

(0.91) (4.10) (-0.79)

TW_EA_COMMIT

-0.017*** -0.259 0.011

(-3.01) (-1.56) (0.69)

TW_EA_COMMIT_Q

0.017*** 0.252 -0.009

(2.99) (1.49) (-0.55)

SIZE -0.008*** -0.164*** -0.002***

-0.008*** -0.158*** -0.002***

(-11.79) (-10.66) (-4.08)

(-11.19) (-9.76) (-4.12)

MTB -0.001* 0.002 0.000

-0.001** -0.005 0.000

(-1.92) (0.24) (1.08)

(-2.27) (-0.58) (0.40)

LOGANALYST 0.006*** 0.271*** -0.002*

0.005*** 0.283*** -0.002

(3.06) (7.42) (-1.80)

(2.73) (7.47) (-1.54)

ROA -0.216*** -0.757 -0.056*

-0.166*** 0.070 -0.042

(-4.45) (-0.74) (-1.93)

(-3.14) (0.07) (-1.44)

GROWTH 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)

LOG_FB_LIKES 0.002*** 0.041*** 0.000

(5.76) (5.09) (1.41)

LOG_TW_FOLWRS

0.003*** 0.046*** 0.000

(4.61) (3.49) (1.39)

TW_EA_Q -0.001 -0.003 -0.001

(-0.79) (-0.09) (-1.21)

FB_EA_Q

0.002 0.049 0.001

(0.68) (1.10) (1.22)

Industry and Quarter

Fixed Effects Included Included Included

Included Included Included

N 6,687 6,687 6,687

5,968 5,968 5,968

R2 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).