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The Impact of Managerial Dissemination of Firm Disclosure
Elizabeth BlankespoorRoss School of BusinessUniversity of Michigan
Gregory S. MillerRoss School of BusinessUniversity of Michigan
Hal D. WhiteRoss School of BusinessUniversity of [email protected]
July 2010
ABSTRACT
Firms have traditionally relied on information intermediaries, such as the press, to disseminate firm-initiated disclosures. However, intermediaries face constraints on the amount of news they can disseminate to investors, and thus tend to focus on highly visible firms, for which there is more readership interest. This paper examines whether firms can complement traditional dissemination channels by using new information technology that provides firms direct access to a broad set of investors on a real-time basis. Using a sample of technology firms with active Twitter accounts, we find that postings (tweets) increase around firm-initiated news events. This increase is primarily driven by tweets containing hyperlinks, which is consistent with firms using this innovative technology to disseminate firm news. We also find that greater tweeting during news event windows is associated with lower bid-ask spreads and greater depths. These relations are stronger for tweets with hyperlinks. Our results hold mainly for firms with lower visibility—that is, firms that are smaller, have lower analyst coverage and have fewer shareholders. These findings suggest that managers use this new direct-access information technology to reduce information asymmetry, particularly for those firms that are arguably most in need.
We would like to thank Michelle Hanlon, Lian Fen Lee, Xi Li, Jeff Ng, Scott Richardson, Nemit Shroff, Hollis Skaife, Ewa Sletten, Rodrigo Verdi, Terry Warfield, Joe Weber, and workshop
participants at the 2010 London Business School Accounting Symposium, MIT and the University of Wisconsin for helpful comments and suggestions. We are also grateful to Stephen Gao, Arkisha Howard, Mitch Meyle, and Cathy Twu for their excellent research assistance, and Paul Michaud for programming advice and assistance.
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“We [recognize] the vital role of the Internet and electronic communications in modernizing the disclosure system under the federal securities laws and in promoting transparency, liquidity and efficiency in our trading markets. Central to the effective operation of our trading markets is the ongoing dissemination of information by companies about themselves and their securities.” (Emphasis added)
- SEC Release No. 34-58288, August 2008
1. Introduction
A large number of studies find that disclosure reduces information asymmetry.1 However,
recent research argues that dissemination plays a critical role in disclosure’s ability to effectively
reduce information asymmetry (Bushee, Core, Guay, and Hamm, 2010; Soltes, 2010). That is,
disclosure becomes less effective at reducing asymmetry if information is delayed or does not
reach a subset of intended recipients. Firms have traditionally relied on information
intermediaries, such as the press, to disseminate firm-initiated information (Miller, 2006).
Unfortunately, constraints on resources, airtime, and space limit the amount of firm news that the
press can distribute to investors (Soltes, 2010). Moreover, the press’ bias towards covering
highly visible firms (Miller, 2006) further reduces coverage of average and low visibility firms.
Combined, this suggests that many firms face impaired dissemination.
This paper examines whether firms are able to mitigate these dissemination issues using
recent innovations in information technology. In the last few years, there has been a major shift
in communication technology, particularly online communications. Most importantly for our
study, several technologies have emerged that allow managers to directly access investors on a
frequent and real-time basis. We refer to these technologies as direct-access information
technologies (DAITs), and they include Twitter, Really Simple Syndication (RSS) feeds and
corporate email alerts.2 The fundamental characteristic of DAITs is their ‘push’ technology (as
referenced by the SEC in its August 2008 “Guidance on the Use of Company Websites”). ‘Push’
technology refers to electronic communication in which the sender (e.g., firm) transmits
information to the user (e.g., investor) rather than waiting until the user specifically requests the
1 See Healy and Palepu (2001) for a review of the disclosure literature.2 Technically, social media sites, such as Facebook, are also DAITs; however, as we discuss later in the paper, for several reasons, Twitter is widely viewed as the appropriate site to use for dissemination.
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information from the sender (i.e., pull technology). For example, a website is a ‘pull’ technology,
since investors must go to the website to retrieve the desired information. In contrast, DAITs
send the website posting, or hyperlink to the posting, directly to investors when the information
is posted.3
The typical disclosure process for a firm begins when management creates a formal press
release with guidance from its lawyers and investor relations advisors.4 The firm then sends the
release to a newswire service (e.g., Business Wire, PR Newswire), which in turn distributes the
news to various information intermediaries, such as the press or analysts, via newswire.
Information intermediaries then select some subset of the press releases to deliver to the public,
either fully intact or adapted by the intermediary (Miller, 2006). DAITs allow firms to
complement these traditional dissemination channels by pushing the information to investors
simultaneously via Twitter postings, RSS feeds and email alerts.5
Firms can use DAITs either to more widely disseminate information publicly released via
traditional mechanisms, such as a press release, or as the sole mechanism for disclosing new
information. However, given the potentially severe costs associated with disclosing information
solely through nontraditional channels (e.g., running afoul of the SEC’s Regulation Fair
Disclosure rules or inadvertently providing valuable proprietary information), most firms are
reluctant to disclose information via DAITs without extensive controls in place.6 In contrast,
3 As an indication of the growing relevance these new technologies hold in the capital markets, a July 2009 online survey by the corporate communications firm, Brunswick Group, of 455 analysts and institutional investors (both buy-side and sell-side) finds that almost two-thirds of the respondents believe that new electronic media, such as corporate blogs and social media sites, will become increasingly important in helping them make investment decisions in the future.4 Note that, although firms are required to submit SEC filings, such as 8-Ks, 10-Qs and 10-Ks, these filings are typically preceded by (or simultaneously released with) public disclosures via press releases. 5 Dissemination via DAITs generally entails a very brief description of the news—often times a single sentence or less—and the hyperlink to the full press release, which is typically stored on the corporate (IR) website. As a sign of the times, some firms, such as Expedia, BGC Partners, and most recently, Google, have started releasing their earnings announcements on their website, foregoing the traditional newswire release. 6 To prevent disclosure problems, firms like Dell, Cisco and AMD implemented a certification program and social
media training (e.g., Twitter policies) for their employees (NIRI Conference, June 2009). In August 2008, the SEC provided guidance on the use of websites to communicate information, particularly with respect to compliance with Reg FD. Although the SEC release mentions DAIT (which it refers to as “push” technology) in a dissemination context, it has not yet provided detailed disclosure guidance for DAIT.
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firms can use the new technology for dissemination (as opposed to disclosure) with much less
concern for these issues, since the information has already been publicly disclosed through
traditional channels, and therefore has presumably been evaluated by the firm’s management,
investor relations team and disclosure lawyers. As a result, DAITs provide firms a mechanism to
complement traditional dissemination channels in an attempt to mitigate news coverage
deficiencies.
Firms that utilize DAITs often integrate these technologies in various ways. For example,
some firms use technology, such as Twitterfeed, to update their Twitter accounts via RSS feeds
whenever news is released through the company website or blogs or both, while other firms have
Twitter pages within their Facebook accounts. Further, firms often use DAITs simultaneously as
independent channels to disseminate information. From a practical standpoint, this makes it very
difficult to isolate the impact of any one technology. Further, external researchers cannot observe
the historical usage of RSS feeds and email alert data, making them difficult to measure.
Accordingly, we focus our analyses on the popular microblogging site Twitter.
Twitter was created in October 2006 as a free service that allows users to communicate via
‘tweets,’ which are text-based messages of up to 140 characters, to subscribers (known as
followers) of the user’s Twitter page. According to Nielsen Online, Twitter grew to 75 million
unique users by the end of 2009, making it both the fastest growing and one of the largest social
networks in the U.S.7 Moreover, anecdotal evidence suggests that Twitter is IR professionals’
new technology of choice for breaking firm news (Q4 Web Systems, 2009). Finally, start-up
costs are generally trivial for both firms and investors.
Despite its enormous growth, Twitter is still in its infancy. As such, our analyses focus on
information technology (IT) firms because we expect these firms to be early adopters of
technology, and thus most likely to be actively engaged in the use of DAITs.8 Specifically, we
7 Twitter’s growth and consistent media attention in the last couple years has resulted in Twitter being declared “The most popular English word” in 2009 by the Global Language Monitor. 8 In support of this contention, a Q4 Web Systems report provides evidence that the largest two business sectors that engage in Twitter are the technology sector—29% of activity—and the telecommunications sector—15% of activity
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identify a group of firms recognized as top IT firms by BusinessWeek, Computer Business
Review, Fortune, and Net Valley, and retain only those with active Twitter accounts during the
2007 to 2009 period.
As previously mentioned, Twitter can be used to disseminate information or to disclose
information. However, disclosure via nontraditional channels has potential costs. Accordingly,
we expect that firms are likely to use Twitter predominantly to disseminate information. Using a
sample of 102 IT firms, we partition the tweets into those with hyperlinks and those without
hyperlinks. The average (median) firm provides hyperlinks in 75% (78%) of their tweets. Since
the purpose of hyperlink tweets is to direct followers’ attention to online information and tweets
are restricted to 140 characters, our finding suggests that firms are using Twitter primarily for
dissemination.
We examine tweeting activity around firm-initiated news events (i.e., press releases). We
find that, although firms tweet throughout the year, tweeting increases significantly around firm-
initiated news events, and the increase is strongly driven by the tweets containing hyperlinks.
This finding supports the contention that managers use Twitter as a tool to further disseminate
firm-initiated news.
We next test whether greater tweeting around firm-initiated news events changes the
information environment for these firms. As Bushee et al. (2010) point out, dissemination can
reduce information asymmetry to the extent it provides information to a broad set of investors
who would not otherwise have the information, thus reducing the information advantage of
informed traders. Since Twitter is a free service and easy to use, firms can greatly reduce
information acquisition costs for investors while incurring only trivial transaction costs
themselves. Accordingly, we expect that Twitter can help reduce information asymmetry by
providing managers direct access to a broader set of their investor base.
Using a firm’s bid-ask spread and depth as proxies for information asymmetry, we find
(Q4 Web Systems, 2009).
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evidence consistent with this prediction. That is, we find that greater tweeting during the three-
day news event window is associated with lower bid-ask spreads and greater depths. These
relations continue to hold after controlling for the information content of firm-initiated
disclosure, the presence of other information intermediaries (i.e., press coverage, analyst
coverage, institutional holdings), market conditions (e.g., volatility) and firm-specific
characteristics, such as growth opportunities and size. We also find that the relations are
strongest for those tweets with hyperlinks. These findings indicate that managers are able to help
reduce information asymmetry by disseminating firm-initiated information via DAITs.
We then narrow our analyses to a subset of news events—earnings announcements—for two
reasons. First, earnings announcements are one of the most common and arguably the most
important news events to investors. Second, examining earnings announcements allows us to
provide additional controls for the information content of the news (i.e., earnings surprise). It is
not clear ex ante whether DAITs will have an impact in this setting. On one hand, DAITs should
play a larger economic role, since investors generally find the information more useful. On the
other hand, investors are likely to anticipate earnings news and make an effort to obtain it via
alternate sources, thereby reducing the benefits of DAITs. Although our earnings announcement
sample restriction reduces the sample size from 3,693 to 157 observations, we find that the
relation between tweeting and our information asymmetry measures continues to hold at
conventional statistical significance levels.
Finally, we test whether the relation between tweeting and information asymmetry is
strongest for firms that are not highly visible. Since the press and analysts have constraints on
resources (Bushee and Miller, 2009; Soltes, 2010) and are biased toward coverage of high
visibility firms (Bhushan, 1989; Miller, 2006), we contend that DAITs should play a larger role
in reducing information asymmetry for less visible firms. Using market value, analyst following
and number of shareholders as proxies for visibility, we find evidence consistent with our
conjecture.
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Our paper makes several contributions to the information dissemination literature, and
thereby the broader disclosure literature. First, dissemination channels have traditionally been
controlled by information intermediaries who make dissemination choices consistent with their
own incentives (e.g., disseminate information that attracts readership); however, many firms lack
the characteristics that encourage information intermediaries to disseminate the information. This
paper shows that managers can use DAITs to ‘bypass the middleman’ and reach a broader set of
investors.
Second, we provide greater insight into the role of dissemination in financial markets. The
current literature in the area provides important insights, but the study of dissemination is still
nascent. In particular, Bushee et al. (2010) show the impact of press-based dissemination on
information asymmetry. In a concurrent working paper, Soltes (2010) also examines the impact
of press-driven dissemination by investigating spreads, trading volume and idiosyncratic
volatility. However, both of these studies examine press-initiated dissemination of firm-initiated
disclosure. We add to this emerging literature by providing further evidence of the effect of
dissemination on a firm’s information environment by looking at firm-initiated dissemination of
firm-initiated disclosure. Our focus on firm-initiated, as opposed to press-initiated, dissemination
provides a useful setting to examine the impact of dissemination independent of the related
benefits supplied by the media (e.g. credibility, information interpretation, etc.). Our results offer
additional support that increased dissemination of information – without additional processing or
validation – can improve the information environment.
Third, in August 2008, the SEC provided guidance on the use of corporate websites and
Internet ‘push’ technologies (i.e., DAITs) to disseminate information to investors, and strongly
encouraged managers to use this new technology in an effort to make markets more transparent,
efficient and liquid. It is not clear, however, whether these technological innovations have a
significant effect on the market given both the coverage of traditional channels and the novelty
of the technology. We address this question by providing initial evidence that managers can
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indeed increase market liquidity through reductions in information asymmetry using these new
technologies.
Finally, these technologies have become an integral part of firm communications in recent
years, and we provide initial insight into at least one way that firms incorporate this new
communication technology into their disclosure strategies. Companies have not only additional
flexibility, but also more control over the effectiveness of their disclosures, thereby reducing
their dependence on third parties to disseminate firm-initiated news.
The remainder of the paper proceeds as follows. The next section discusses the background,
motivation and related literature. Section 3 describes the sample. Section 4 presents the main
empirical findings, and results from additional tests and sensitivity analyses are reported in
Section 5. Finally, Section 6 summarizes and concludes.
2. Background, motivation and related literature
2.1. DISCLOSURE, INFORMATION ASYMMETRY AND DISSEMINATION
The disclosure literature has explored various aspects of firm communications. For example,
studies show that disclosure decisions are based on such factors as litigation (e.g., Skinner, 1994;
Field et al. 2005), proprietary costs (e.g., Hayes and Lundholm, 1996; Harris, 1998, Piotroski,
2003), agency costs (e.g., Berger and Hann, 2007), performance (e.g., Schrand and Walther,
2000; Miller, 2002,), and investor base (e.g., Bushee et al., 2003). In addition, disclosure has
been shown to impact such items as a firm’s cost of capital (Botosan, 1997), analyst following
(Lang and Lundholm, 1996), institutional investor following (Bushee and Noe, 2000), and stock
price volume and volatility (Healy, Hutton, and Palepu, 1999; Bushee and Noe, 2000). In
general, the overarching theme in this literature is that disclosure reduces information
asymmetry, and managers adjust their disclosure in a manner consistent with this belief.
An important assumption in the disclosure literature is that once information is disclosed, it is
readily available to all investors. Until recently, however, firms have not had direct
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communication channels to a broad set of investors on a frequent and real-time basis. Firms have
generally relied on information intermediaries, such as the press, to disseminate firm-initiated
information (Miller, 2006; Bushee et al., 2010). Unfortunately, constraints on resources, airtime,
and/or space limit the amount of firm news that journalists and analysts can distribute to
investors, so firm disclosures do not always reach the public in a broad and efficient manner.
Dissemination, therefore, plays a critical role in enhancing the effectiveness of firm
disclosures, particularly with respect to information asymmetry. Information asymmetry creates
transaction costs by introducing adverse selection. In particular, adverse selection induces a
reduction in liquidity as uninformed investors are less willing to trade for fear of incurring
trading losses to informed investors (see Copeland and Galai, 1983; Kyle, 1985; Glosten and
Milgrom, 1985; Diamond and Verrecchia, 1991; Leuz and Verrecchia, 2000). Greater
dissemination can mitigate this issue by allowing information to reach a broader set of investors,
thereby reducing information asymmetry and increasing liquidity (Bushee et al., 2010). To the
extent firms can complement traditional dissemination channels with DAITs, firms can benefit
from further reductions in information asymmetry.
From a firm’s perspective, the use of a DAIT as a dissemination mechanism offers several
advantages over traditional information intermediaries, such as the press or analysts. First, firms
have more control over dissemination of information. This can be particularly beneficial to firms
that are not highly visible and may receive less coverage by the news media and analysts.
Second, DAITs can potentially reach a wider audience by making the news more accessible,
particularly for investors with relatively higher search costs given that costs to set up and receive
information via DAITs are generally trivial. Finally, new information can be disseminated
instantly, which allows information to get to investors faster than using the traditional news
media route. These advantages are not meant to justify DAITs as a substitute for information
intermediaries, but rather a complement to traditional dissemination channels.
2.2. DISSEMINATION AND FIRM VISIBILITY
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As Merton (1987) points out, information acquisition costs related to searching, gathering
and processing firm news constrain the amount of information that can be assimilated by
investors. Firms typically use information intermediaries, such as the press, to disseminate firm
news in an attempt to reduce investor information acquisition costs. The press, however, have
constraints on resources, which limit their ability to disseminate all firm news (Soltes, 2010).
Further, Miller (2006) finds that the business press is biased toward coverage of high visibility
firms, as news on these firms is in higher demand. Combined, this indicates that, although high
visibility firms are likely to receive broad coverage via traditional channels, most firms are likely
to receive limited press coverage, resulting in a larger proportion of investors not receiving firm-
initiated disclosures.
Analysts also serve as valuable information intermediaries that help reduce information
asymmetry (Dempsey, 1989; Brennan, Jegadeesh, and Swaminathan, 1993; Brennan and
Tamarowski, 2000). However, like the press, analysts tend to follow larger, more visible firms to
maximize the usefulness of their reports (Bhushan, 1989). Moreover, these firms are likely to
generate more lucrative underwriting opportunities for the investment banks. This suggests that
high visibility firms receive a disproportionate share of analyst coverage, which can create
dissemination deficiencies for other firms.
We predict that DAITs should play a larger role in reducing information asymmetry for firms
that are not highly visible by allowing these firms to reach a broader set of investors. That is,
investors generally have relatively low-cost access to information for highly visible firms, since
these firms tend to receive broad news coverage. Thus, the value of a new dissemination channel
is limited for highly visible firms. In contrast, lower visibility firms tend to receive less coverage
through traditional channels, which results in increased investor information acquisition costs,
and thus information asymmetry between investors. As such, a new channel can have much
greater value for lower visibility firms.
2.3 INFORMATION TECHNOLOGY AND COMMUNICATION
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2.3.1. Websites and Webcasts
Our study also relates to the burgeoning literature examining the relation between
information technology and communication. For example, studies, such as Ashbaugh et al.
(1999) and Ettredge et al. (2002), examine determinants and firm characteristics associated with
the posting of financial information on corporate websites. Bushee et al. (2003) argue that
innovations in Internet webcasting allow firms to communicate with a broad set of investors both
inexpensively and directly via webcast conference calls, thereby reducing information
asymmetry between informed and uninformed investors. Although websites and conference calls
are valuable communication mechanisms, websites generally require investors to actively seek
out, or pull, information from managers and conference calls are infrequent and restricted to
small periods of time. In contrast, our study adds to this literature by examining new technology
that allows firms to push information directly to investors on a regular basis as newsworthy
events occur.
2.3.2. Stock Message Boards
Other studies examine investors’ ability to access information via non-firm sources, such as
stock message boards. For example, Wysocki (1998) examines firm-related determinants of
message-posting volume on stock message boards, and Antweiler and Frank (2004) find that
board messages have information content that can help predict market volatility. Das and Chen
(2007) develop a methodology for extracting small investor sentiment from stock message
boards. Concurrent research by Lerman (2010) examines accounting-related discussions on
Internet message boards, and finds these discussions increase when there is greater information
uncertainty. Given information on stock message boards is almost exclusively driven by
individual investors (Wysocki, 1998; Lerman, 2010), our study adds to this literature by
examining firms’ use of DAITs to disseminate information directly to investors.
2.3.3. Direct-Access Information Technologies
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Within the last decade, there has been a major shift in communication technology,
particularly online communications, which has the potential to fundamentally change the way
firms communicate with investors (SEC, 2008). In particular, several technologies have been
created that allow managers to directly access investors on a frequent and real-time basis. We
refer to these technologies as direct-access information technologies (DAITs), and they include
Twitter, Really Simple Syndication (RSS) feeds and corporate email alerts. The fundamental
characteristic of DAITs is their ‘push’ technology, whereby the sender pushes information via
the Internet to the user rather than requiring the user to specifically request the information from
the sender. Thus, the use of DAITs allows for a more timely and efficient transmission of
information, thereby reducing investors’ information acquisition costs.
With regard to investors’ DAIT access and implementation, corporate IR websites generally
have various DAIT icons on which investors can click to subscribe to firm news updates via the
particular mechanism.9 RSS feeds and email alerts are similar in that they require the investor to
subscribe to the service and obtain the needed ‘receivers’ (i.e., feed reader or email account) to
receive the information.10 For Twitter, the investor can either sign up through Twitter as a
‘follower’ or subscribe to an RSS feed from the corporate Twitter page, whereby the RSS feed
sends tweets to the investor’s feed reader. Investors can also direct tweets to their email accounts
via services, such as Feedmyinbox.com or other similar technology.
Given the difficulty both in disentangling DAITs from one another and in obtaining RSS feed
and email alert data, we chose to examine Twitter as the ‘representative’ DAIT, or proxy for the
suite of DAITs. Twitter captures the essence of DAIT for three reasons: (1) Twitter was
explicitly created to transmit news directly to followers in real-time. As Evan Williams, co-
founder of Twitter, explains, “What we have to do is deliver to people the best and freshest most
9 Appendices A and B provide the IR website and Twitter page, respectively, for Ingram Micro, Inc., a low visibility firm in our sample that uses email alerts and Twitter to disseminate information directly to investors.10 RSS feeds and corporate email alerts are also extremely similar services in that they both consist of electronic transfers of information that are put together by the firm, typically in a formal way, to reach investors via their feed reader or email account. User interfaces for feed readers are typically even set up to look like familiar email interfaces with inboxes and sorted feeds based on the sender’s preferences.
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relevant information possible. We think of Twitter as it's not a social network, but it's an
information network. It tells people what they care about as it is happening in the world”
(Williams, 2009). Consistent with this mission, Twitter was designed to restrict messages to 140
characters to be compatible with SMS (Short Message Service). This allows tweets to be sent as
mobile text messages, which allows for greater accessibility; (2) Twitter is currently the fastest
growing and one of the largest social networks in the U.S., so it is both a relevant and potentially
powerful dissemination mechanism; and (3) investor relation departments appear to be adopting
Twitter as their social medium of choice.11
3. Sample Selection and Variable Definitions
3.1 SAMPLE SELECTION
Twitter is a new technology. As such, to increase the probability of capturing active Twitter
users, we limit our sample to IT firms because these firms tend to be early adopters of
technology. Although this sampling procedure creates a more powerful setting from which to
conduct our analyses, our findings might not generalize to other firms or industries. However,
we believe this trade off is worthwhile as our sample of early adopters can provide insights into
the future potential of DAITs.
We first compile a list of 141 technology firms from four industry lists: Business Week’s
2009 InfoTech 100, Fortune’s 20 Most Profitable Technology Companies, Computer Business
Review’s Top 50 Global Technology Firms, and Net Valley’s Top 100 IT Companies. For each
of these 141 firms, we search for their official Twitter account (or official investor relations /
news Twitter account) using a variety of techniques, including searching corporate websites,
reviewing internet directories of companies’ Twitter accounts (e.g. Social Brand Index,
Twarketing, Twibs.com, etc.), and using the Google search engine. We find 102 firms with
11 http://www.corporate-eye.com/blog/2009/07/twitter-and-investor-relations-why-so-difficult/ http://bx.businessweek.com/digital-public-relations/view?url=http://socialmediatoday.com/SMC/194321 http://www.marketwire.com/press-release/Q4-Web-Systems-Report-Reveals-Early-Adopters-Using-Twitter-for- Investor-Relations-1036195.htm
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active Twitter accounts as of September 30, 2009.12 For each of these firms, we use PERL
programming language to gather all tweets since the account’s inception.
We gather potential news event days for each firm, where a news event is defined as a firm-
initiated press release. For earnings announcement press releases, we use the report date as given
by Compustat, starting with fiscal year 2006 since our earliest Twitter account opened in March
2007. As described in Table 1, we start with an initial sample of 89 firms and 266 earnings
announcements occurring after the related firm’s first tweet on its official Twitter account.
Excluding observations without the necessary Compustat, CRSP, and Trade and Quote (TAQ)
variables results in a final earnings announcement sample of 157 earnings announcements (60
firms).
To identify the remaining firm-initiated press release dates, we search the press release wires
(PR Newswire, Business Wire, FD Newswire, and M2 Presswire) in Factiva during each firm’s
tweeting period. We use Factiva’s company identifier to locate press releases associated with
each firm.13 To ensure the press releases are firm-initiated, we remove articles that do not include
a variant of the firm’s name in the Contact field (the portion of the article that identifies who
initiated the press release). We begin with an initial sample of 5,122 press release dates within
firm tweeting periods. After excluding observations without the necessary Compustat, CRSP, or
TAQ variables and those with press release dates that are also earnings announcement dates, the
final press release sample has 3,536 observations (70 firms). The combined sample (i.e., press
releases and earnings announcements) is comprised of 3,693 observations (73 firms).
3.2 VARIABLE DEFINITIONS
12 To determine whether each Twitter account was the company’s official Twitter account, we searched for links to the Twitter account from the company’s webpage, specific mentions of the Twitter account in the firm’s press releases, and company-specific information and links in the ‘Biography’ section of the related Twitter account. 13 The choice of press release wires follows extant disclosure research (e.g., Bushee and Miller, 2009; Core et al., 2008; Bushee et al., 2010), with the exception that we also use M2 Presswire, which is the third most widely used press release wire after PR Newswire and Business Wire. M2 Presswire was launched in 1994 as a comprehensive global distribution service for press releases and became the major press wire for technology news. Several of the companies in our sample release disclosures via M2 Presswire, with at least one firm releasing exclusively through M2 Presswire.
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To examine the effect of DAIT dissemination on information asymmetry, we focus on
abnormal levels of tweeting, bid-ask spreads and market depth during specific news events,
where abnormal is defined using the firm as its own control. This allows us to remove any firm
fixed effects that are correlated with our information asymmetry measures. Similar to prior
literature, we define the event period, EVENT, as the 3-day window around the news event
(trading day -1 to trading day +1). The pre-period, PRE, is defined as the period of 60 trading
days prior to the event period. We truncate all variables at 1% and 99% to remove the effect of
outliers.
3.2.1. Information Asymmetry Measure
As Leuz and Verrecchia (2000, page 92) point out, “[i]nformation asymmetries create costs
by introducing adverse selection into transactions between buyers and sellers of firm shares. In
real institutional settings, adverse selection is typically manifest in reduced levels of liquidity.”
That is, information asymmetry plays a critical role in the liquidity of capital markets. Following
this intuition, we use the bid-ask spread as our main proxy for information asymmetry, as it
captures market makers’ and other liquidity suppliers’ (i.e., public limit order traders)
willingness to trade (Cohen, Maier, Schwartz and Whitcomb, 1986; Harris, 1990; Lee and
Ready, 1991; Lee, Mucklow and Ready, 1993).14 However, a stock quote includes the bid and
ask prices as well as the number of shares available at each price—i.e., depth. Thus, our
understanding of shifts in market liquidity can be further enhanced by examining depth (Lee,
Mucklow and Ready, 1993; Kavajecz, 1999; Bushee et al., 2010). Accordingly, we use depth as
an additional proxy for information asymmetry.15 As information asymmetry decreases, bid-ask
14 Leuz and Verrecchia (2000, page 99) detail the relation between spread and information asymmetry as follows: “The bid-ask spread is commonly thought to measure information asymmetry explicitly. The reason for this is that the bid-ask spread addresses the adverse selection problem that arises from transacting in firm shares in the presence of asymmetrically informed investors. Less information asymmetry implies less adverse selection, which, in turn, implies a smaller bid-ask spread.”15 An alternative approach sometimes used to measure liquidity is to examine trading volume. However, liquidity relates to the notion that investors can “quickly buy or sell large numbers of shares when they want and at low transaction costs” (Harris, 1990). Spreads (and depths) represent the transaction costs associated with trading quickly, and thus more fully capture this notion. Volume, on the other hand, can be thought of as an outcome that is a function of these transaction costs—that is, lower spreads and greater depths generally lead to more trading.
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spreads decrease and/or depth increases. Because both mechanisms can be used to address
information asymmetries, it is not necessary that both be used simultaneously. In other words,
reductions in spread or greater depth alone can indicate reductions in information asymmetry,
provided the alternate measure does not counteract the effect (e.g., lower spread, but reduced
depth as well), which would suggest a substitution effect by liquidity suppliers (Lee, Mucklow
and Ready, 1993).
Using data from Trade and Quote (TAQ), we measure abnormal bid-ask spread
(Spread_abn) as the EVENT period average daily percent spread minus the PRE period average
daily percent spread, where daily percent spread is the daily average of each quote’s spread,
calculated as the difference between the offer price and bid price, divided by the midpoint of the
offer and bid price, all multiplied by 100.16 We measure abnormal depth, Depth_abn, during the
event period as the log of the average daily depth during the EVENT period minus the log of the
average daily depth during PRE period, where the daily depth is the daily average of each
quote’s depth, calculated as the sum of the dollar offer size and the dollar bid size.
3.2.2. Abnormal Tweet Measure
To measure abnormal tweeting, we divide the EVENT period average daily number of tweets
by the PRE period average daily number of tweets.17 We follow this procedure using total tweets
(Tweets_abn), tweets with hyperlinks (LinkTweets_abn), and tweets without hyperlinks
(NonLinkTweets_abn).18
However, this need not be the case. As Kim and Verrecchia (1994) show, volume can increase as a result of increased information asymmetry. Although the relation between liquidity and volume is ambiguous, for completeness, we examine a volume-based measure of liquidity, i.e., Amivest measure, in section 5. 16 Similar to Bushee et al. (2010), we only use quotes with a positive spread given between 9:30 a.m. and 4:00 p.m. and captured during trading modes (as recommended by WRDS documentation). We also remove quotes with spreads greater than 90% of the mid-point price.17 As discussed earlier, “daily” refers to trading days. However, since tweets can occur on non-trading days as well, we first re-assign tweets to the closest trading day on or after the actual tweeting day. We find most tweeting in official firm Twitter accounts occurs during business hours and thus on trading days.18 Note that in cases where the firm has zero tweets during the EVENT period and zero tweets during the PRE period, we set the related abnormal tweet variable equal to one. Significance levels in the results remain the same if these observations are dropped rather than set equal to one. If the PRE period has zero tweets but the EVENT period has a positive number of tweets, we code the abnormal tweet variable to missing. We attempt to address this issue by using several alternate measures for abnormal tweeting discussed in section 5.
16
3.2.3. Control Variables
We control for the information content of the news event using several variables. First, we
control for the amount of firm-initiated disclosure (FirmDisc) using the average daily word count
for firm-initiated press releases in Factiva over the period, as this has been shown to be
correlated with lower spread and greater depth (Van Buskirk, 2006).19 We measure abnormal
firm disclosure (FirmDisc_abn) as the percentage change in disclosure from the PRE period to
the EVENT period: FirmDisc_abn = log((1 + FirmDiscEVENT)/(1 + FirmDiscPRE)).
Second, we control for the market reaction to the news event, Abs_abn_ret, measured as the
absolute value of the difference between a firm’s cumulative return and the cumulative value-
weighted return of all firms in the regression sample during the three-day EVENT period.20
Third, we control for the volume reaction to the news event, Abn_turn, measured as the
difference between the EVENT period market-adjusted turnover and the PRE period market-
adjusted turnover, where turnover is defined as the average daily dollar volume divided by the
average market value of shares outstanding. We calculate market turnover as the average total
daily dollar volume for all firms in the regression sample divided by the average total market
value of shares outstanding for all firms in the regression sample.
Finally, for our sample of earnings announcement press releases, we control for the
information content of the news event using the absolute earnings surprise (abs_esurp),
calculated as the forecast error for the most recent IBES consensus forecast prior to the event
date. If IBES data is unavailable, we use the forecast error for the most recent First Call
consensus forecast prior to the event date. If neither is available, we use the seasonal random
19 We use the maximum word count when there are multiple press releases on one day. Similar to Bushee at al. (2010), if there is no press release wire on an earnings announcement date, we substitute the maximum word count for press-initiated articles. Our results are qualitatively similar, and even slightly stronger, without implementing this assumption.20 Results using alternate market adjustments to returns, such as equal-weighted return for the regression sample and equal-weighted and value-weighted returns for CRSP firms, are qualitatively similar. In sensitivity tests discussed in section 5, we also allow the return coefficient to vary based on the sign of the news and our results continue to hold at similar statistically significant levels.
17
walk earnings surprise using Compustat data. For all three measures, we divide the earnings
surprise by price as of the end of the same fiscal quarter in the prior year.
We also control for the presence of other information intermediaries – press coverage,
analyst coverage, and institutional holdings. Following Bushee et al. (2010), we focus on the
dissemination role of press coverage, proxying for press-initiated dissemination (Press) through
traditional channels (e.g., newspapers, magazines, etc.) using the average daily number of unique
news sources from all news sources other than press release wires in Factiva. Similar to the firm-
initiated disclosure variable, we measure abnormal press-initiated dissemination (Press_abn) as
the EVENT dissemination relative to the PRE dissemination: Press_abn = log((1 + PressEVENT)/(1
+ PressPRE)). Analyst coverage (Lnanalyst) is defined as the log of one plus the number of
analysts issuing a forecast in the most recent consensus date prior to the event date (but no more
than 90 days prior) as captured by IBES, or FirstCall if there is no information in IBES. 21
Institutional holdings (Inst_hold) is calculated as the percent of outstanding shares held by
institutions as of the most recent measurement date prior to the event date (but no more than 100
days prior) using data from Thomson Reuters Institutional Holdings database (Spectrum). For
both Lnanalyst and Inst_hold, we set missing values equal to zero.
We also control for firm-specific and market-related characteristics. In particular, we include
firm size (Lnmv), measured as the log of the market value of equity as of the fiscal quarter end
date, and growth opportunities (Btm), measured as the book value of assets divided by the market
value of assets. We control for the business model of the firm (i.e., capital intensive versus labor
intensive) using assets (Lnatq), measured as the log of total assets as of the fiscal quarter end
date. We include the log of quarter-end stock price (Lnprc), as well as prior quarter stock-return
21 For press release observations, we match each press release to the most recent prior fiscal period using the earnings announcement date, or the fiscal period end date if the earnings announcement date is missing, and assign the related firm-specific variables to the observation. For example, for a press release issued on April 5, the observation is matched to the fiscal period with the most recent earnings announcement prior to April 5, and the firm-specific variables relevant to that fiscal period (such as Lnanalyst, Inst_hold, Btm, Lnmv, Lnprc, etc.) are assigned to that press release observation. Results are very similar if we match using only fiscal period end date rather than earnings announcement date.
18
volatility (Qt1_volat), prior quarter turnover (Qt1_turn), and prior quarter depth (Qt1_depth),
calculated as the annualized standard deviation of daily stock returns, the daily average share
turnover, and the average of daily depth, respectively, where daily depth is defined as the daily
average of each TAQ quote’s depth (the sum of the dollar offer size and the dollar bid size).
Finally, we include a control for the number of shareholders (Lnown), measured as the weighted
average of the log of the prior and current fiscal year-end number of shareholders.
4. Descriptive Statistics and Results
4.1 TWITTER ACTIVITY
4.1.1. Descriptive Statistics
Panel A of Table 2 provides descriptive statistics related to Twitter activity for our sample of
firms. The initial adoption of Twitter for our sample is March 4, 2007 and the latest adoption is
September 26, 2009 indicating that firms are progressively adopting Twitter. Figure 1 shows the
steady increase in the number of firms that tweeted over the course of our sample period. The
mean number of tweets for a firm is 306, but varies from 5 to 1,660. We find that the mean
number of followers for our sample of firms is 24,414 whereas the median firm has 1,435
followers. These statistics indicate considerable variation across firms.
We also find that the mean (median) firm tweets 43 (25) times per month, with some firms
tweeting as little as once or twice and others close to 400 times per month. To assess the time
trend in tweeting activity, we examine the average daily number of tweets per firm over time.
Figure 2 indicates that, in addition to an increase in the number of active Twitter using firms, the
Twitter activity per firm has increased.
We then focus on the type of tweeting activity occurring in our sample. For example, firms
can use Twitter to disseminate information, to respond to inquiry or concerns (i.e., manage their
brand), or to provide new information. We find that 75.1% (77.8%) of the mean (median) firm’s
tweets contain hyperlinks, which is consistent with the majority of tweets being used to
19
disseminate information, such as press releases or website announcements. It also appears that a
small number of tweets are ‘reply’ tweets, which indicates that firms are also using Twitter to
respond to other firm stakeholders’ comments and/or questions. This is consistent with the
intention of social media to share information between users in a collaborative environment. An
even smaller percentage of tweets relate to ‘retweeting’ information, which is consistent with
firms attempting to increase exposure to relevant tweets.
4.1.2. Tweeting Around News Events
We next examine whether communication via Twitter is associated with firm-initiated
disclosure. Panel B of Table 2 partitions the sample into event periods and non-event periods,
where event periods are days that contain news events (i.e., earnings announcements and firm-
initiated press releases). Tweeting totals are provided for each period. We find that, although
event days make up 28% of the sample period, 39% of tweeting occurs on those days. Moreover,
41% of the tweets with hyperlinks are posted on days with a news event. The proportional
increases in tweeting are statistically significant at the <.0001 level.
Figure 3 presents the average daily number of tweets in the 30-day trading window (-15,
+15) around news events, where we remove other event days from the comparison period (i.e., -
15 to -1, +1 to +5), as we are interested in comparing event days to non-event days. As shown in
the figure, firms increase tweeting activity on news event days, which is driven by the tweets
with hyperlinks. We compare the tweet total on the event date to the tweet total for each day in
the 30-day window. T-tests indicate that the tweeting increase is statistically significant at the
<.001 level for all tweets and the <.0001 level for tweets with hyperlinks. Tweets without
hyperlinks and ‘reply’ tweets remain relatively constant across the period, which indicates that
there is a certain amount of Twitter interactions taking place that is unrelated to formal firm-
initiated news events.
4.1.3. Tweeting and Type of News
Prior research shows that some disclosure choices are associated with the type of news being
20
provided (Skinner, 1994; Kasznik and Lev, 1995; Hutton, Miller and Skinner, 2003).
Accordingly, we test whether abnormal tweeting is associated with the type of news—i.e.,
direction and/or magnitude of the news. In Panel C of Table 2, we partition both the full sample
of press releases and the earnings announcement subsample into (1) positive and negative news,
based on either abnormal returns in the EVENT period or earnings surprises for the quarter, (2)
extreme and non-extreme news, based on whether the abnormal return in the EVENT period or
earnings surprise for the quarter is in the 75th percentile of the abnormal returns or earnings
surprises, respectively, and (3) a combination of the two partitions.22 We calculate the percentage
of event days where LinkTweet_abn is greater than one (indicating that EVENT period link
tweeting is greater than average PRE period link tweeting).
Across both samples and news proxies, we find that there are no statistical differences
between the proportions of firms that tweet additional links during positive news periods and
those that tweet additional links during negative news periods. That is, firms are no more likely
to tweet positive news than they are to tweet negative news. In contrast, we find weak evidence
that managers consider the magnitude of the news when deciding whether to tweet. In particular,
for the full sample of press releases (using abnormal returns as the proxy for news), we find that
there is a slightly larger probability (Chi-square p-value = 0.078) that firms tweet more extreme
news than non-extreme news. However, there is no statistically significant relation for the
earnings announcement subsample, regardless of the news proxy. Lastly, we examine whether
managers consider the direction of the news when it is extreme versus non-extreme, and we find
no statistical difference across partitions.23
Overall, the evidence in Table 2 suggests that firms are using Twitter to disseminate firm-
22 We also try alternate definitions of extreme news for the press release observations. In particular, extreme news is defined as absolute abnormal returns that are (1) greater than the mean absolute abnormal returns around earnings announcements, (2) greater than the firm-specific mean magnitude of the absolute abnormal returns around earnings announcements, (3) in the upper tercile of the absolute return magnitude, and (4) in the upper decile of the absolute return magnitude. Results are qualitatively similar.23 Although we do not find strong evidence that tweeting is based on sign and/or magnitude of the news, we control for these factors in our sensitivity analyses in section 5.2.
21
initiated information. Moreover, there is weak evidence that the choice to tweet is based on the
magnitude of the news, but no evidence that the choice is based on the direction of the news. In
the next sections, we use univariate and multivariate analyses to examine whether dissemination
has an effect on the firms’ information environment. In particular, we predict that greater
dissemination via Twitter reduces information asymmetry.
4.2 DESCRIPTIVE STATISTICS FOR MAIN ANALYSES
Panel A of Table 3 provides descriptive statistics for the variables used in our main analyses.
We first report the abnormal press coverage and tweet variables. Consistent with Bushee et al.
(2010), the mean and median abnormal press coverage variables are positive, indicating that the
press provides greater dissemination during the event period. The mean and median abnormal
tweet measures are greater than one, which indicates an increase in tweeting during the event
period, particularly for those tweets with hyperlinks. The positive mean and negative median
values on the abnormal firm disclosure variables suggests that firm disclosures are highly
variable around news events. Although the mean and median spreads are insignificantly different
from zero, the mean and median depth values are positive, indicating that news events reduce
information asymmetry, on average.
As expected, the mean and median abnormal stock return and volume variables are positive,
as the market responds to releases of firm news. The average firm in the sample appears to be
relatively visible—mean (median) assets is around $43.7 billion ($12.4 billion) and mean
(median) analyst following is 18 (21). However, there is great variation in both variables. For
example, market values range from $474 million to $284 billion, and analyst following ranges
from 2 to 31 analysts. Institutional holdings and shareholder ownership have considerable
variation as well. This allows us to examine cross-sectional predictions related to visibility to
further substantiate our findings.
Panel B of Table 3 provides Pearson univariate correlations between our abnormal tweet
variables and the abnormal measures of spread (Spread_abn), depth (Depth_abn), press coverage
22
(Press_abn), and firm disclosures (FirmDisc_abn). As predicted, our tweet variables are
negatively associated with spread and positively associated with depth, and tweets with
hyperlinks appear to have the strongest negative association with respect to spreads. Consistent
with Bushee et al. (2010), we find that firm disclosures are positively associated with bid-ask
spread and negatively associated with depth, albeit not statistically significant. The correlation
between press coverage and both spread and depth is positive, but statistically significant only
for the spread relation.
4.3 RESULTS
In this section, we examine whether firm-initiated tweets have an impact on a firm’s
information environment, particularly with respect to information asymmetry. We also assess
whether this effect is greater for firms that have lower visibility.
4.3.1. Firm-initiated tweeting and information asymmetry
We test whether firm-initiated tweeting has an impact on a firm’s information environment
by estimating a pooled OLS regression for firm i and news event t using robust standard errors
clustered by firm:
Info_Asymit = β0 + β1Abn_tweetsit + Controls (1)
where Info_Asym represents one of the two information asymmetry variables, Spread_abn and
Depth_abn, and Abn_tweets represents one of the three abnormal tweet measures: total tweets
(Tweets_abn), tweets with hyperlinks (LinkTweets_abn), and tweets without hyperlinks
(NonLinkTweets_abn). Controls represents the control variables as described in section 3.2. We
include all firm-initiated press releases, including earnings announcements, as our news event
periods.
Although the univariate analyses suggest that tweeting is primarily related to dissemination,
it is very likely that firms tweet new information as well. We argue that either could affect a
23
firm’s information environment by reducing information asymmetry. Thus, we predict that there
is a negative relation between Abn_tweets and Info_Asym (i.e., β1 < 0).
Panel A of Table 4 provides the coefficient estimates and p-values when estimating the
model using Spread_abn as the proxy for information asymmetry. As predicted, the coefficient
for β1 (-0.003) in Column 1 for the combined tweet measure, Tweets_abn, is negative and
significant at the 0.010 level, after controlling for the level of firm-initiated disclosure, the
presence of other information intermediaries, market conditions and firm-specific
characteristics.24 We also estimate the model using LinkTweets_abn and NonLinkTweets_abn to
disentangle whether the reduction in the bid-ask spread is a result of dissemination or potentially
new information.25 Columns 2 and 3 show the results using LinkTweets_abn and
NonLinkTweets_abn, respectively, and Column 4 presents the results where both tweet measures
are included. The results in column 1 for Tweets_abn appear to be driven by dissemination.
Columns 2 and 4 show the coefficient estimate on LinkTweets_abn is negative and statistically
significant at the 0.002 level. The coefficient estimate on NonLinkTweets_abn is insignificant
whether included separately or in the combined model. An F-test conducted on the difference
between the two tweet measures in column 4 indicate that they are statistically different at the
<0.01 level.
Panel B of Table 4 provides the coefficient estimates and p-values when estimating the
model using Depth_abn as the proxy for information asymmetry. Consistent with expectations,
the coefficient on Tweets_abn (0.014) is positive and significant at the 0.010 level. Moreover, the
coefficient estimate on LinkTweets_abn is positive and statistically significant at the 0.015 level,
24 Note that, unlike Bushee et al. (2010), our press coverage variable is not statistically significant. This is likely due to differences in the sample selection process. For example, our sample was selected to include firms in the technology industry, which includes firms that substantively vary along dimensions of size, analyst following, and institutional ownership. Bushee et al. (2010) eliminate firms with extremely high and extremely low press coverage and concentrate on those with high expected information asymmetry. We do not make predictions on other control variables.25 To the extent LinkTweets_abn contains links unrelated to the news event or NonLinkTweets_abn contains news-related summary information already posted online, our measures contain error. We attempt to address this issue in section 5.
24
suggesting that dissemination reduces information asymmetry. Interestingly, the coefficient on
NonLinkTweets_abn is also positive and statistically significant both when included separately
(column 3) and in the combined model (column 4). This suggests that managers are either
tweeting additional information that might be helpful to investors during news events or
disseminating information in a concise manner without hyperlinking to additional information.
We then narrow our analyses to a subset of press releases—earnings announcements.
Earnings announcements are one of the most common and arguably most important news events
related to a firm. Using only this type of news event allows us to control for the information
content of the news (i.e., earnings surprise). This restriction, however, greatly reduces the sample
size from 3,693 to 157 observations. It is not clear ex ante whether DAITs will have a greater or
lesser impact in this setting. Although DAITs should play a larger economic role given the
usefulness of earnings information, investors are more likely to anticipate this news and obtain it
in real-time via alternate sources, such as conference calls. Thus, it is an empirical question
whether the additional power gained from looking solely at earnings announcements will be
enough to outweigh the loss in power related to the large reduction in sample size and investor
anticipation of, and access to, alternative sources for the information.
Panel A (B) of Table 5 provides coefficient estimates and p-values for the three tweet
measures when estimating the model using Spread_abn (Depth_abn) and only the earnings
announcement subset. Consistent with the findings in Table 4 for the full press release sample,
columns 1 through 3 in Panel A indicate that the coefficient estimates on the three tweet
measures remain negative, but only LinkTweets_abn is statistically significant (0.057 level)
while Tweets_abn is marginally significant at the 0.104 level. NonLinkTweets_abn remains
insignificant, but the p-value comes closer to conventional significance levels (0.129 level).
Column 4 provides results when estimating the model including both LinkTweets_abn and
NonLinkTweets_abn variables. The coefficient estimate on LinkTweets_abn remains negative
and statistically significant.
25
Panel B of Table 5 reports positive and statistically significant coefficient estimates on
Tweets_abn and LinkTweets_abn as well as a positive and statistically significant coefficient on
NonLinkTweets_abn, consistent with our findings in Table 4 using the full press release sample.
Overall, the findings in Panel A (B) of Table 5 are consistent with those in Panel A (B) of Table
4, suggesting that DAITs can be used as a dissemination mechanism to reduce information
asymmetry during firm-initiated news events.
4.3.2. Dissemination and firm visibility
We then test whether the negative relation between dissemination (i.e., LinkTweets_abn) and
our information asymmetry measures is strongest for firms that are not highly visible. Given
analysts and the press have constraints on resources (Soltes, 2010; Bushee and Miller, 2009) and
are biased toward coverage of high visibility firms (Bhushan, 1989; Miller, 2006), less visible
firms are likely to receive less coverage than high visibility firms. Consequently, we predict that
DAITs should play a larger role in reducing information asymmetry for less visible firms.
We proxy for firm visibility using two variables commonly used in the visibility literature:
firm size and analyst following (Bushee and Miller, 2009). In addition, Merton (1987) suggests
that visibility is ultimately a function of the number of investors that are aware of the stock—the
greater the visibility, the greater the number of investors trading in the stock. To empirically
capture this notion, we also proxy for visibility using the number of investors holding a firm’s
shares.26
We first partition the sample of news events into quartiles based on the market value of
equity of the firms. The same procedure is then conducted using the number of analysts
following the firm and the number of shareholders holding the stock for the period. For all
variables, the top quartile represents high visibility firms and the bottom three quartiles represent
low visibility firms.
Table 6 provides results from estimating a stacked regression of equation 1, where both the
26 To the best of our knowledge, this variable has not been used as a proxy for visibility in prior studies.
26
full sample and earnings announcement subsample are partitioned into high and low visibility
firms based on our three visibility proxies. Panel A reports the results using bid-ask spread as the
information asymmetry proxy for both the full sample of press releases and the earnings
announcement subsample. For the full sample of firm-initiated press releases, the
LinkTweets_abn coefficient estimates for the high visibility firms are insignificantly different
from zero across all three visibility proxies. In contrast, the coefficient estimates for the low
visibility firms are negative and statistically significant at the 0.002 level or better for all three
visibility measures. Comparing the estimates between high and low visibility groups, we find
that the estimates for the low visibility firms are lower than those for the high visibility firms at
the 0.002, 0.001 and 0.033 level using size, analyst following and the number of shareholders as
visibility proxies, respectively. This suggests that DAIT dissemination helps reduce information
asymmetry for low visibility firms, but does not appear to provide significant benefit to high
visibility firms, which typically garner broader coverage via traditional dissemination channels.
For completeness, we also examine the earnings announcement subsample. We continue to
find that dissemination during the news event window is associated with lower spreads for low
visibility firms. Each of the coefficient estimates on the visibility proxies is negative and
statistically significant at conventional levels. We also find that LinkTweets_abn is negatively
associated with spreads for large firms and those with a large number of shareholders, although
only the former relation is statistically significant.27 Comparing the estimates between high and
low visibility groups, we find that the estimates for low visibility firms are lower than those for
the high visibility firms for all three visibility proxies, although the difference is statistically
significant only when using the analyst following proxy for visibility. Overall, the results in
Panel A of Table 6 are consistent with our predictions and suggest that dissemination via Twitter
27 Our prediction for the relation between dissemination and bid-ask spreads is one-sided, and p-values in Table 6 are reported accordingly. To the extent one expects dissemination to have no effect for high visibility firms, two-tailed p-values should be used for inferences. Two-tailed p-values indicate that the positive relation is statistically significant at the 0.01 level when using analyst following as the visibility proxy. We do not have an explanation for this result.
27
is more effective at reducing information asymmetry for low visibility firms.
Panel B reports the results using market depth as the proxy for information asymmetry. For
the full sample of press releases, we find that the coefficient estimates for the low visibility firms
are positive and statistically significant at the 0.02 level or better for all three visibility measures.
The coefficient estimates for the high visibility firms are also positive, but insignificantly
different from zero. The difference between the coefficient estimates for the high visibility group
and those for the low visibility group is positive for all three measures, as predicted, but not
statistically significant.
For the earnings announcement subsample, we find that the coefficient estimates for the low
visibility firms are positive for all three measures; however, only the coefficient using the
number of shareholders is statistically significant. For the high visibility firms, the coefficient on
LinkTweets_abn is positive, and significant only when using size as the information asymmetry
proxy. F-tests across visibility groups indicate that there is no difference between groups
regardless of the visibility proxy used. Overall, the evidence in Table 6 suggests that firms with
low visibility benefit more from the use of DAIT dissemination of firm-initiated news.
5. Additional tests and sensitivity analyses
5.1. ADDITIONAL TESTS
We conduct several additional tests related to our analyses. First, in our tabulated analyses,
we proxy for dissemination using tweets containing hyperlinks. We argue that these tweets
represent dissemination of information related to the relevant news event, and tweets without
hyperlinks provide new information. However, this does not have to be the case. LinkTweets_abn
could contain hyperlinks unrelated to the news event and/or NonLinkTweets_abn could contain
information already posted online elsewhere, although perhaps in a more condensed format.
To address this issue, we examine the earnings announcement subset. We look through each
tweet to determine whether the hyperlink tweets direct investors to earnings announcement
28
related information and the non-hyperlink tweets relate to non-event related information.28 We
group the tweets into four categories: (1) earnings-related hyperlink; (2) earnings-related, but no
hyperlink; (3) non-earnings related hyperlink; and (4) non-earnings related, but no hyperlink.
We rerun our main analysis from Tables 4 and 5 estimating the effect of these measures on
spread and depth. Using the bid-ask spread as our proxy of information asymmetry, we find that
the coefficient on the earnings-related hyperlink tweet variable is negative and statistically
significant at the 0.027 level. In fact, this is the only tweet group that is statistically significant.
This finding is consistent with firms using Twitter to disseminate firm-initiated disclosures
around news events, which provides support for the use of our abnormal tweet measures. Using
depth as our asymmetry proxy, we find that none of the tweet groupings are significant at
conventional levels. Combined, this suggests that liquidity suppliers for our sample of firms tend
to address information asymmetries using spread rather than depth.29
Second, we assess whether the existence of an earnings announcement related tweet with a
hyperlink reduces information asymmetry. Specifically, we regress our information asymmetry
measures (i.e., Spread_abn and Depth_abn) on an indicator variable that takes on the value of
one if there is an earnings announcement related tweet with a hyperlink during the EVENT
period, and zero otherwise, and the control variables used in the main analyses.
It is important to note that our abnormal information asymmetry measures represent the
difference in the measures between the EVENT period and the PRE period. Thus, the model
intercept represents the average change in the asymmetry measures across periods for those firms
that do not tweet a hyperlink to earnings announcement related information, and the coefficient
28 We do not examine tweets around our press release sample because the determination of whether the information relates to the news press release is much more subjective and imprecise.29 We further examine the hyperlinks to observe where they are directing followers. We find that, of the firm-earnings announcement observations with an earnings-related tweet, 94% link to a firm-initiated disclosure (e.g. earnings announcement press release, conference call press release or transcript) and 6% link to media-initiated disclosure about the firm’s earnings release. We rerun the analyses using only tweets with hyperlinks to firm disclosures, and the results are consistent with our earlier findings—the coefficient on the firm-initiated hyperlink indicator variable is negative and significant at the 0.0115 level for abnormal spread and is positive but insignificant at conventional statistical levels for abnormal depth.
29
on the indicator variable represents the difference in asymmetry measures across periods for
those firms that tweet a link as compared to those that do not. This is effectively a difference in
difference design. Using the bid-ask spread as our proxy for information asymmetry, we find that
the hyperlink tweet indicator variable is negative and significant at the 0.009 level, indicating
that DAIT dissemination reduces information asymmetry. In comparison, we find that, as
expected, the depth indicator variable is positive; however, it is not significant at conventional
statistical levels.
Third, we examine whether dissemination via DAIT is associated with greater trading
activity per unit change in price. Following Cooper, Groth, and Avera (1985), Khan and Baker
(1993), Amihud, Mendelson, and Lauterbach (1997), and Berkman and Eleswarapu (1998), we
use the Amivest liquidity ratio, which is calculated as the sum of the daily volume divided by the
sum of the absolute daily return for the firm over a particular period. A relatively higher ratio
means that larger amounts of stock are traded with less effect on prices—that is, the stock is
more liquid. As with our information asymmetry measures, we compute an abnormal liquidity
ratio by taking the log of the EVENT period ratio divided by the PRE period ratio. We then
regress this measure on our DAIT dissemination proxy, LinkTweets_abn, contemporaneous
abnormal spread and depth and the control variables used in our main analyses. We find that, for
both the full sample of press releases and the earnings announcement only subsample,
LinkTweets_abn is positive; however, it is statistically significant (p-value = 0.025) only for the
full sample of press releases. These results provide further evidence that dissemination via DAIT
increases liquidity.
Finally, we examine whether the composition of traders changes as a result of DAIT
dissemination of firm news. Investors that are more strongly impacted by dissemination
deficiencies (via increased information acquisition costs) are more likely to benefit from DAIT
dissemination. Although this increases their willingness to trade, it is unclear whether more
trading by this group (relative to the less impacted group) actually takes place. Since retail
30
investors typically have fewer resources than institutional investors, we argue that deficiencies in
dissemination would have the largest impact on this group of investors.
We proxy for retail investor trades (small trades) using the number of trades less than
$10,000 and institutional investor trades (large trades) as those greater than $50,000.30 We then
determine abnormal levels of large and small trades using two approaches. Following Bushee et
al. (2003), our first measure (Trade_Number) is calculated as the difference between the number
of large (small) trades in the EVENT period and the number of large (small) trades in the three
days prior to the EVENT period, divided by the number of large (small) trades in the EVENT
period. Our second measure (Trade_Proportion) is the difference between the proportion of
large (small) trades in the EVENT period and the PRE period, where the proportion is calculated
by dividing the number of large (small) trades by the total number of trades. We find that, when
we use Trade_Proportion on the earnings announcement subsample, the proportion of small
trades increases in the EVENT period (1-sided p-value = 0.07). However, we do not find
statistically significant increases during the full sample of press releases or using the first
measure, making it difficult to draw conclusions about the effect of tweeting on investor
composition.
5.2. SENSITIVITY ANALYSES
We also run several robustness tests. First, we examine multiple alternative abnormal tweet
measures. Our tabulated analyses do not include those observations for which there is tweeting in
the EVENT period only (which is arguably the most powerful setting) because the denominator
of our abnormal tweeting measures are the number of tweets in the PRE period, and dividing by
zero is undefined. To address this issue, we add 1, 0.1, and 0.01 individually to both numerator
and denominator. We also take the log of each approach to address any potential skewness
issues. Second, we allow the sign of the abnormal return control variable to vary in our analyses
30 We classify trade size using the firm-specific trade size method recommended by Lee and Radhakrishna (2000), using the firm’s stock price at the beginning of each day to determine the largest round-lot trade size less than or equal to the specified dollar threshold.
31
by including an interaction with an indicator for negative returns. Third, we control for the
magnitude of the earnings news in our analyses by including an indicator variable for firms with
absolute abnormal returns in the top quartile. Fourth, we bootstrap standard errors. Finally, in an
attempt to maintain sample size, we rerun our analyses winsorizing (rather than truncating) at the
1% level. Collectively, our results remain qualitatively similar.
6. Conclusion
The disclosure literature typically assumes that once information is disclosed, it is readily
available to all investors. Until recently, however, firms have not had direct communication
channels to investors on a regular basis. Firms have typically relied on information
intermediaries, such as the press, to disseminate firm-initiated information (Miller, 2006; Bushee
and Miller, 2009; Bushee et al., 2010). Unfortunately, constraints on resources, airtime, and
space limit the amount of firm news that journalists can distribute to investors (Soltes, 2010).
Thus, firm disclosures do not always reach the public on a timely basis.
This paper examines whether firms use recently developed technology that allows firms to
directly access investors—direct-access information technology (DAIT)—as a complementary
communication channel to improve dissemination of firm-initiated information. These
technologies include Twitter, RSS feeds and email alerts. Given the difficulty both in
disentangling DAITs and in obtaining RSS feed and email alert data, we focus our analyses on
Twitter.
Using a sample of information technology firms with active Twitter accounts, we find that
tweeting increases significantly around firm-initiated news events, and this increase is strongly
driven by tweets with hyperlinks. We also find that greater tweeting during news event windows
is associated with lower bid-ask spreads and greater market depth, after controlling for
information content, the level of firm-initiated disclosure, the presence of other information
intermediaries, market conditions and firm-specific characteristics. This relation is strongest for
32
firms with relatively lower visibility. Overall, these findings indicate that firms use Twitter to
disseminate firm-initiated disclosures, and this dissemination helps reduce information
asymmetry, particularly for those firms that are arguably most in need.
The August 2008 SEC guidance on the use of corporate websites encourages firms to engage
in push technologies to convey information to a broader set of investors on a timely basis. We
provide initial insights into at least one way that firms incorporate this new communication
technology into their disclosure strategies and the impact of that technology on firms’
information environments. Given our focus on early adopter firms, our findings might not
generalize to other firms or industries; however, as more firms begin to use these technologies,
we encourage future research to explore both alternate uses and further economic consequences.
33
APPENDIX A – Sample Investor Relations Website Pages (Ingram Micro, Inc.)
Email alerts
Twitter icon
34
APPENDIX B – Sample Twitter Page (Ingram Micro, Inc.)
35
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Figure 1: Number of Tweeting FirmsFor the 102 sample firms used in the study
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20080102 20080310 20080514 20080721 20080924 20081128 20090205 20090414 20090618 200908240
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Figure 2: Average Tweets Per Firm Over TimeTrendline is moving average over 40 trading
days
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Figure 3: Mean Number of TweetsNon-Event Trading Days
tweetslinktweetsnonlinktweetsreplies
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