Journal of Financial Economics 105 (2012) 260–278
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`Contents lists available at SciVerse ScienceDirect
`
`Journal of Financial Economics
`
`journal homepage: www.elsevier.com/locate/jfec
`
`How are shorts informed?
`Short sellers, news, and information processing$
`
`Joseph E. Engelberg a, Adam V. Reed b,n, Matthew C. Ringgenberg c
`a Rady School of Management, University of California, San Diego, San Diego, CA 92093, USA
`b Kenan-Flagler Business School, University of North Carolina, Chapel Hill, NC 27599, USA
`c Olin Business School, Washington University in Saint Louis, St. Louis, MO 63130, USA
`
`a r t i c l e i n f o
`
`a b s t r a c t
`
`We find that a substantial portion of short sellers’ trading advantage comes from their
`ability to analyze publicly available information. Using a database of short sales combined
`with a database of news releases, we show that the well-documented negative relation
`between short sales and future returns is twice as large on news days and four times as
`large on days with negative news. Further, we find that the most informed short sales are
`not from market makers but rather from clients, and we find only weak evidence that short
`sellers anticipate news events. Overall, the evidence suggests that public news provides
`valuable trading opportunities for short sellers who are skilled information processors.
`& 2012 Elsevier B.V. All rights reserved.
`
`Article history:
`Received 15 July 2010
`Received in revised form
`8 August 2011
`Accepted 29 August 2011
`Available online 9 March 2012
`
`JEL classification:
`G12
`G14
`
`Keywords:
`Asymmetric information
`Manipulation
`News media
`Short sales
`
`1.
`
`Introduction
`
`There is now overwhelming evidence that short sellers
`are informed traders. When short interest or short volume
`are high, future returns are predictably low (see, e.g.,
`Senchack and Starks, 1993; Asquith, Pathak, and Ritter,
`2005; Boehmer, Jones, and Zhang, 2008). Return predict-
`ability, however, suggests only that short sellers have an
`information advantage over other traders. In this paper,
`we ask how short sellers obtain that advantage.
`
`$ The authors thank Dow Jones for providing access to their news
`archive and Paul Tetlock for assistance with the Dow Jones archive. We
`have benefited from comments from Greg Brown, Jennifer Conrad, Wayne
`Ferson, Charles Jones, G ¨unter Strobl, Robert Whitelaw, and an anonymous
`referee. We also thank seminar participants at the University of North
`Carolina, the 2010 Utah Winter Finance Conference, the University of
`Southern California, and the IESE Business School—IX Madrid Finance
`Workshop. This paper was previously titled, ‘‘Buy on the Rumor, [Short]
`Sell on the News: Short Sellers, News and Information Processing.’’
`n Corresponding author.
`E-mail address: adam_reed@unc.edu (A.V. Reed).
`
`0304-405X/$ - see front matter & 2012 Elsevier B.V. All rights reserved.
`doi:10.1016/j.jfineco.2012.03.001
`
`To address this question, we combine a large archive of
`all corporate news events with a large panel of daily short
`selling. This unique combination allows us to comprehen-
`sively examine the relation between short selling and
`news events. We find that a substantial portion of short
`sellers’ trading advantage comes from their ability to
`analyze publicly available information. In fact, while news
`events occur on only 22% of the days in our sample, these
`trading days account for over 45% of the total profitability
`from short selling.
`Although our evidence suggests that short sellers obtain
`an information advantage via superior information proces-
`sing, some commentators have suggested other ways that
`short sellers achieve an advantage. The Securities and
`Exchange Commission (SEC) suggested that short sellers
`spread ‘‘false rumors’’ in an effort to manipulate firms
`‘‘uniquely vulnerable to panic.’’1 If this type of manipulation
`
`1 Cox, C., 2008. What the SEC really did on short selling. The Wall
`Street Journal, 24 July.
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`were taking place, then it suggests that short sellers might
`initiate a trade and then spread rumors (see, e.g., van
`Bommel, 2003). In other words, we might expect to see
`short sellers trade before news events, even though the
`news events could turn out to contain false information.
`We find little evidence to support the claim that short
`sellers’ advantage comes from trading before information is
`released, even though short sellers have been shown to
`trade before the release of certain types of public informa-
`tion. For example, Karpoff and Lou (2010) show that short
`selling increases before the initial public revelation of firms’
`financial misrepresentation. Similarly, Christophe, Ferri, and
`Angel (2004) find evidence of informed short selling in the
`5 days before earnings announcements.
`In contrast, when we look at all corporate news events
`in the Dow Jones archive, we find that the trades of short
`sellers are similar to the trades of other market partici-
`pants in the days leading up to a news release. Uncondi-
`tionally, the ratio of short volume to total volume is 0.196
`and this ratio falls by 0.019 on negative news days and
`rises by 0.022 on positive news days. However, during the
`days leading up to the news event, the ratio is the same or
`slightly smaller than the unconditional mean, irrespective
`of the news type. Moreover, during the days after a news
`event, the ratio is the same or slightly larger than the
`unconditional mean. The result suggests that, on average,
`short sellers trade on or after news release dates and they
`do not anticipate public news announcements.
`Given that short sellers tend to trade on or after news
`events, we next ask whether these news events present
`profitable trading opportunities for short sellers. Interest-
`ingly, the extant theoretical literature provides mixed
`predictions on the role of news releases. On the one hand,
`a number of papers argue that news reduces information
`asymmetry (see, e.g., Korajczyk, Lucas, and McDonald,
`1991; Diamond and Verrecchia, 1987). For example, if a
`firm announces a merger, investors who knew that the
`merger was likely no longer have an information advan-
`tage over those who did not. The news announcement
`therefore reduces the information asymmetry between
`informed and uninformed investors. Under this view, the
`trades of informed traders (short sellers) should be less
`profitable when they are initiated immediately following
`a news announcement.
`On the other hand, several papers suggest that public
`news events can lead to differential interpretations by
`traders based on variation in the traders’ skill (see, e.g.,
`Kandel and Pearson, 1995). Rubinstein (1993) puts it
`succinctly: ‘‘In real life, differences in consumer behavior
`are often attributed to varying intelligence and ability to
`process information. Agents reading the same morning
`newspapers with the same stock price lists will interpret
`the information differently.’’ Under this view, public news
`events present profitable trading opportunities for skilled
`information processors, which can explain not only high
`volume around news events (Kandel and Pearson, 1995)
`but also evidence of return predictability from ‘‘soft’’ infor-
`mation in news announcements (see, e.g., Engelberg, 2008;
`Demers and Vega, 2008). This suggests that news announce-
`ments should make the trades of informed traders (short
`sellers) more profitable on news days.
`
`When we take both of these theories to the data, we find
`evidence in support of the second view. Several papers find
`that abnormal short selling unconditionally predicts lower
`future returns (see, e.g., Senchack and Starks, 1993; Asquith,
`Pathak, and Ritter, 2005; Boehmer, Jones, and Zhang, 2008).
`We also find that abnormal short selling leads to lower
`future returns, but we find that this effect is concentrated
`around news events. In particular, the predictability for
`future returns more than doubles on news days and quad-
`ruples on days with negative news. While a long-short
`trading strategy based on the level of short selling would
`have earned a return of 40% over our 2.5-year sample
`period, a long-short strategy that conditioned on short
`selling and news events would have earned 60%. Moreover,
`a strategy based on short selling and negative news would
`have earned an astonishing 180% during our 2.5-year
`sample period.
`An alternative explanation for this result could be that
`some buyers make systematic mistakes around news
`events (Antweiler and Frank, 2006), and that these buyers’
`mistakes are reflected in market makers’ offsetting short
`sales. To determine whether short sellers’ trades are due
`to superior information processing or to offsetting posi-
`tions, we exploit a unique feature of the short selling data,
`namely, exempt versus non-exempt trade marking, which
`allows us to distinguish market makers from non-market
`makers (clients). We find that clients’ trades are particu-
`larly well informed, and that these trades are much more
`profitable in the presence of news events. In contrast,
`market makers’ trades are not particularly well informed,
`and there is no differential impact in the presence of
`news. Thus, there appears to be little support for the claim
`that return predictability from shorts is greater on news
`days because of market makers offsetting short sales.
`Another alternative explanation for our main result is
`that short sales are profitable on news days because news
`days provide short sellers with increased liquidity. This
`explanation, however, requires that the costs of short
`selling are lower around news announcements. However,
`we find little evidence that market liquidity improves on
`news days. For example, we find that bid-ask spreads
`actually increase by nearly 5% around news announce-
`ments, which is consistent with existing models of market
`maker behavior in the presence of informed traders (see,
`e.g., Glosten and Milgrom, 1985; Kyle, 1985). When coupled
`with our finding that the trades of short sellers are more
`than twice as profitable in the presence of news, the
`evidence is consistent with the idea that public news events
`present profitable trading opportunities for skilled informa-
`tion processors and short sellers are, on average, skilled at
`processing public news.
`The remainder of this paper proceeds as follows.
`Section 2 discusses related literature. Section 3 describes
`the databases used in this study and presents our main
`hypotheses. Section 4 presents our analyses and findings.
`Finally, Section 5 concludes.
`
`2. Related literature
`
`The ideas in this paper relate to three distinct branches
`of the existing literature. First, this paper relates to an
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`extensive literature on the behavior of short sellers
`relative to other traders. Second, our paper contributes
`to a growing literature on how market participants
`respond to public news. Finally, this paper sheds light
`on an emerging debate on whether news increases or
`decreases information asymmetry. In this section, we first
`discuss prior papers that connect news to short selling.
`We then provide an overview of the relevant literature in
`each of these three branches.
`Fox, Glosten, and Tetlock (2009) use news and short
`selling data to examine the role of short sellers from a
`regulator’s perspective. Motivated by the intense scrutiny
`that short sellers receive from the press and lawmakers,
`they investigate whether short selling appears to be
`socially beneficial or harmful (and worthy of regulation).
`In addition, several extant papers look at short selling
`behavior in the context of a specific type of corporate
`news event. As such, these studies shed light on a subset
`of this paper’s sample of news events. Karpoff and Lou
`(2010), for example, examine short sellers’ positions in
`firms that are investigated for financial misconduct and find
`that short sellers generally anticipate public announcements
`of investigations. Christophe, Ferri, and Angel (2004) and
`Christophe, Ferri, and Hsieh (2010) focus on short sellers’
`trades around earnings announcements and analyst down-
`grades, respectively, and find evidence that short sellers are
`informed traders who can profit from these events. Similarly,
`Daske, Richardson, and Tuna (2005) and Boehmer, Jones, and
`Zhang (2010) look at short selling around management
`forecast announcements and earnings announcements.
`While Daske, Richardson, and Tuna (2005) find no evi-
`dence that short sale transactions concentrate prior to bad
`news events, Boehmer, Jones, and Zhang (2010) find some
`evidence of anticipation, and they show that a significant
`fraction of short sellers’ information advantage comes
`from trading around these events. Finally, Nagel (2005)
`looks at the cash flow news implied by a vector auto
`regression and finds an asymmetric effect on returns,
`indicating that short sellers help incorporate news into
`prices when short selling is not constrained.
`While the above papers identify patterns in short
`selling around a handful of specific corporate news events,
`the current paper aims to uncover patterns in short sellers’
`trades around all types of corporate news events. Doing so
`allows us to speak more generally about short sellers’
`behavior around new releases of public information. In
`particular, using a list of all corporate news events, we can
`sort the universe of trading days into those with and
`without news and examine the differential performance of
`short sellers surrounding news events.
`
`2.1. Short sellers’ trading patterns
`
`Several papers compare the trades of short sellers to
`the trades of other market participants. There are multiple
`dimensions over which trades can be compared. Much of
`the recent literature focuses on the profitability of trades,
`which, roughly speaking, can be measured using the
`performance of a stock’s price after the initiation of a
`short sale. In one of the earliest articles to empirically
`examine short sales, Seneca (1967) finds a negative relation
`
`between short interest and returns and concludes that
`short positions are indicative of bearish opinions. Similarly,
`Boehme, Danielsen, and Sorescu (2006) show that when
`short selling is constrained and there are relatively diverse
`opinions, abnormally high short interest can precede
`negative future returns. Using transaction data at a higher
`frequency, Boehmer, Jones, and Zhang (2008) find that
`heavily shorted stocks significantly underperform lightly
`shorted stocks, especially stocks heavily shorted by
`non-program institutional traders; and Diether, Lee, and
`Werner (2008) show that not only do prices follow short
`selling, but short selling also follows prices, that is, short
`sellers tend to short after price run-ups. These results
`further indicate that short sellers could have an informa-
`tion advantage.2 In sum, the above work establishes that
`the performance of short sellers’ trades indicates that
`short sellers are informed traders. Our paper contributes
`to this literature by asking how short sellers come to enjoy
`an information advantage in the first place.
`
`2.2. Public news
`
`While a large literature examines volume and return
`phenomena around specific news events (e.g., earnings
`announcements, mergers, and dividend initiations and
`omissions), a more recent literature considers such phe-
`nomena around any corporate news event. Categorizing
`all Wall Street Journal stories between 1973 and 2001,
`Antweiler and Frank (2006) find that return responses
`vary widely across news categories, although they find
`evidence of overreaction (return reversal), on average.
`Also using a database of all news events, Tetlock (2011)
`finds evidence of even stronger return reversal following
`repeated news events, consistent with the idea that
`investors overreact to ‘‘stale’’ news stories. Several studies
`using comprehensive news databases examine whether
`well-known asset pricing anomalies are related to news.
`Chan (2003) considers the momentum anomaly among
`stocks with and without recent news and finds evidence
`of price momentum only among news stocks. Similarly,
`Vega (2006) finds more earnings momentum among
`stocks with high differences of opinion on news days.
`More recently, researchers have asked whether the
`content of news stories contains value-relevant informa-
`tion. Tetlock, Saar-Tsechansky, and Macskassy (2008) and
`Engelberg (2008) show that, indeed, the qualitative con-
`tent of the information contained in news stories can
`predict both earnings surprises and short-term returns.
`These findings support the idea that there is value-
`relevant or ‘‘soft’’ information in news stories that is not
`immediately impounded into prices.
`To summarize, this literature highlights the impor-
`tance of looking at more than one news category when
`
`2 A closely related dimension of research is whether short sellers’
`trades reveal information to other market participants. In other words,
`are short sellers’ trades newsworthy in and of themselves? Senchack and
`Starks (1993) show that abnormally large short interest announcements
`have small but significant negative returns. Similarly, Aitken, Frino,
`McCorry, and Swan (1998) show that short sales are followed by price
`declines within 15 minutes on the Australian Stock Exchange.
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`assessing the behavior of short sellers. Moreover, it shows
`that the information content of news leaves room for
`traders with different information processing abilities to
`arrive at different conclusions about the value relevance
`of the news event. Our work builds on these findings by
`analyzing the universe of corporate news events in the
`U.S. over our sample period, and by asking whether, in our
`sample, information processing ability plays a role in the
`performance of short sellers’ trades.
`
`2.3. Public news and informed trading
`
`There are two views regarding the relation between
`the trading patterns of skilled investors and the release of
`public news items such as the articles contained in the
`Dow Jones archive. Under the first view, public informa-
`tion does not provide traders with an information advan-
`tage; that is, managers who rely on public information
`(rather than generate private information) are low-
`skilled. Consistent with this view, Kacperczyk and Seru
`(2007) estimate managers’ reliance on public information
`(RPI) as the R-squared of a regression of percentage
`changes in fund managers’ portfolio holdings on changes
`in analysts’ past recommendations and find that fund
`managers with low RPIs (low reliance on public informa-
`tion) perform better than fund managers with high RPIs
`(high reliance on public information).
`Under the alternative view, the public release of
`information presents trading opportunities for skilled
`processors of information; that is, when news is released,
`traders with superior information processing skills can
`convert this news into valuable information for trading
`(Kandel and Pearson, 1995). Earnings announcements, for
`example, are often accompanied by lengthy documents
`and conference calls that are scrutinized by information
`processors. Those traders who show exceptional skill in
`converting such data into value-relevant information are
`rewarded with superior returns on event-driven trades.
`Evidence consistent with this view comes from studies
`that attempt to look at the textual content of news and firm
`announcements. Specifically, Tetlock, Saar-Tsechansky, and
`Macskassy (2008), Engelberg (2008), Demers and Vega
`(2008), and Feldman, Govindaraj, Livnat, and Segal (2009)
`all show that the content of corporate news predicts returns,
`which is consistent with the view that information proces-
`sing skills can generate superior returns.
`Our paper sheds light on the above debate by finding
`additional evidence in support of the second view by
`showing that trades occurring after the release of news
`stories can be more profitable than trades in non-news
`periods.
`
`3. Hypotheses and methodology
`
`3.1. Hypothesis development
`
`hypotheses that aim to explore the source of short sellers’
`profitability.
`The timing of trades is one of the areas in which short
`sellers can differ from other traders. Prior research finds
`some evidence that short sellers trade before public
`information is released (see, e.g., Karpoff and Lou, 2010;
`Christophe, Ferri, and Angel, 2004). Similarly, the Secu-
`rities and Exchange Commission has suggested that short
`sellers spread ‘‘false rumors’’ in an effort to manipulate
`firms. Furthermore, in the popular press, there have been
`allegations of insider trading by well-known hedge funds
`such as SAC Capital Advisors and Galleon.3,4 Although
`there are many possible channels through which short
`sellers’ trades could be profitable, our first set of hypoth-
`eses seeks to empirically test whether the timing of short
`sales is different than that of other trades. We refer to this
`as the Anticipation hypothesis. Formally:
`
`H1. In the presence of news events, short sellers trade
`before other traders.
`
`This hypothesis is an alternative to the null hypothesis
`that there is no difference in timing.
`We next turn to the profitability of short sellers’ trades
`around news events. The literature is split as to whether
`news events increase or decrease asymmetric informa-
`tion, thereby increasing or decreasing the profitability of
`informed trades. On the one hand, many papers model
`news events as points in time associated with reduced
`information asymmetry (see, e.g., Korajczyk, Lucas, and
`McDonald, 1991; Diamond and Verrecchia, 1987). If news
`events do indeed reduce asymmetric information, the trades
`of informed traders (e.g., short sales) should be less profit-
`able on news days. On the other hand, other papers suggest
`that public news events are subject to differential inter-
`pretations by traders (see, e.g., Rubinstein, 1993; Kandel and
`Pearson, 1995). Under this view, public information events
`present profitable trading opportunities for skilled informa-
`tion processors, and thus, the trades of informed traders
`(e.g., short sellers) should be more profitable after news
`days. This discussion leads to the following set of hypoth-
`eses, which we call the Profitability hypotheses:
`
`H2a. Short sales are less profitable after news announ-
`cements.
`
`H2b. Short sales are more profitable after news announ-
`cements.
`
`These hypotheses rest against the backdrop of the null
`hypothesis, which states that short sales are as profitable
`after news events as they are at other times.
`Since our empirical work finds that short sales are
`more profitable after news events, we also explore why
`profitability increases. While the literature finds that
`news events create trading opportunities for informed
`
`In this section, we formalize many of the ideas introduced
`in the beginning of the paper. Our first set of hypotheses
`concerns the timing of trades, while the second set concerns
`the profitability of trades. Finally, we have two sets of
`
`3 E.g., Rothfeld, M., Pulliam, S., Bray, C., 2011. Fund titan found
`guilty—Rajaratnam convicted of insider trading; Jurors cite tapes: ‘Just a
`lot of evidence.’ The Wall Street Journal, 12 May.
`4 Our approach is not designed to detect specific instances of insider
`trading, but rather,
`it is designed to examine the average trading
`patterns of short sellers.
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`traders (see, e.g., Engelberg, 2008; Demers and Vega,
`2008), other potential explanations exist. The first alter-
`native explanation posits that some buyers make sys-
`tematic mistakes around news events (see, e.g., Antweiler
`and Frank, 2006), and that these mistakes are reflected in
`market makers’ offsetting short sales. We formalize this
`idea in our third set of hypotheses, which we call the
`Uninformed counterparty hypotheses:
`
`H3. The profitability of short sales comes from market
`makers’ offsetting trades.
`
`This hypothesis rests against the null hypothesis that
`the profitability of short sales comes equally from market
`maker and non-market maker trades.
`Another alternative explanation relates to liquidity.
`Given the increase in volume around news events, news
`events could provide a trading opportunity for those
`traders for whom liquidity is an important factor in a
`trade’s profitability. As a result, the perceived profitability
`of short sales around news events could have nothing to
`do with information; rather, short sellers could simply be
`trading around news events because news events create
`liquidity, which allows them to execute profitable trades.
`This relation between news events and liquidity is the
`basis for our fourth and final set of hypotheses, which we
`call the Liquidity hypotheses:
`
`H4. The profitability of short sales around news events is
`due to the increased liquidity that news events provide.
`
`The null hypothesis is that the profitability of short
`sales around news events is not a result of the liquidity
`that news events provide.
`
`3.2. Data
`
`To test the hypotheses developed above, we employ
`two main databases. The first database contains informa-
`tion on short sales, while the second contains news
`articles from the Dow Jones archive.
`
`3.2.1. Short sales
`Information on short sales comes from the NYSE Trade
`and Quote (TAQ) Regulation SHO database. Regulation
`SHO was adopted by the SEC in June of 2004 to establish
`new rules governing short sales in equity transactions and
`to evaluate the effectiveness of price test restrictions on
`short sales. As one consequence of Regulation SHO, transac-
`tion-level short sales data were publicly disclosed. The
`Regulation SHO database covers the period January 3, 2005
`through July 6, 2007 and contains data for all short sales that
`were reported to the NYSE for NYSE-listed and traded
`securities during this period.5 The database contains the
`stock ticker, the date and time of the transaction, the number
`of shares traded, and the execution price. While the data
`allow us to observe the opening of short positions, they do
`
`5 The vast majority of trades in the database are for NYSE-listed
`securities. Occasionally, securities that are not listed on the NYSE do
`trade on the NYSE, and these trades also appear in the Regulation SHO
`database.
`
`not contain information on the covering of these short
`positions. Thus, like other papers, we are constrained by
`the lack of information on short-covering transactions. In
`addition, the data also include an indicator that denotes
`whether a transaction was exempt from price test rules. One
`of the reasons a short sale transaction could be classified as
`exempt is that it was made by market makers engaged in
`bona fide market making activity. The exempt indicator has
`thus been used to separate trading by market makers from
`trading by non-market makers (see, e.g., Evans, Geczy, Musto,
`and Reed, 2009; Christophe, Ferri, and Angel, 2004; Boehmer,
`Jones, and Zhang, 2008; Chakrabarty and Shkilko, 2011).6
`However, when Regulation SHO was implemented, a group
`of randomly selected stocks was chosen to be part of a pilot
`study for which the exempt/non-exempt classification was
`no longer required. We exclude these pilot firms when using
`the exempt indicator variable in our analyses (i.e., Tables 6
`and 7).7
`For the purposes of our analysis, we aggregate the
`transaction data to the daily level, and we use the TAQ
`master files to add CUSIPs to the database. We then use
`the Center for Research in Security Prices (CRSP) Daily
`Stock Event file to add PERMNOs to the database. Finally,
`we add returns, closing bid price, closing ask price, total
`volume, and shares outstanding from CRSP. Using these
`data, we calculate the Amihud (2002) illiquidity measure
`defined as 107  9retit9/volumeit, where volumeit
`is the
`dollar volume, and we calculate the daily bid-ask spread
`as a percentage of the closing mid-price.
`In addition, we add information on the daily volume-
`weighted rebate rate for equity loans in each stock over
`the sample period. The rebate rate for an equity loan is
`the rate at which interest on collateral is rebated back to
`the borrower. Thus, the rate is inversely related to the cost
`of shorting a stock. Our data on rebate rates come from a
`proprietary database on equity loan transactions as
`described in Kolasinski, Reed, and Ringgenberg (2011).
`The data are compiled by a third-party provider that is
`both a market maker in the equity loan market and a data
`aggregator for major equity lenders.
`
`3.2.2. Dow Jones archive
`To compile our sample of news events, we use the Dow
`Jones archive as in Tetlock (2010). This archive contains
`all Dow Jones News Service stories and Wall Street Journal
`stories over our 2005–2007 sample period. Each observa-
`tion in the news database is a news item; each news
`item includes at least one subject code and Dow Jones’s
`designation of the corporations that are mentioned in an
`article and are the subject of the story. Table 1 displays an
`example article and the associated entry in the Dow Jones
`archive. The database contains subject codes that identify
`
`6 For example, National Association Of Securities Dealers (NASD)
`Notice to Members 06-53 notes that ‘‘Rule 5100(c)(1) provides an
`exception to the bid test for short sales by a market maker registered
`in the security in connection with bona fide market making activity.’’
`7 Details regarding the Regulation SHO pilot study, including a list of
`firms involved, are available on the SEC Web site: http://www.sec.gov/
`rules/other/34-50104.htm. Our results are robust to the inclusion of the
`Regulation SHO pilot firms.
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`Table 1
`Dow Jones archive.
`The table provides an example of the information contained in the Dow Jones archive. Panel A displays the text of an example article, while Panel B contains
`the associated entry in the Dow Jones archive. The archive entry contains the date and time the article was released as well as the stock symbols of firms
`mentioned in the article and a series of subject codes that identify the content areas of the article. For example, the code RND indicates that the article
`pertained to research and development. Articles can have more than one time stamp, indicating that the article was updated following its initial release.
`
`Panel A: Example news article
`
`GlaxoSmithKline and EPIX Pharmaceuticals
`Enter Drug Discovery and Development Alliance
`
`DOW Jones Newswires
`Epix Pharmaceuticals Inc. (EPIX) said it entered into a drug discovery and development pact with GlaxoSmithKline (GSK). As part of deal, Epix will
`receive an upfront payment of $35 million, which includes $17.5 million from the sale of 3 million shares of its common stock.
`Epix will also be eligible for up to $1.2 billion for the achievement of certain milestones, and royalties on product sales. Epix shares closed Monday
`unchanged at $5.52 and Glaxo shares fell 9 cents to $52.43. Epix said it expects to end 2006 with more than $100 million in cash and marketable
`securities. The company expects that its existing cash and marketable securities together with the expected revenue from the GlaxoSmithKline
`collaboration and other partnerships will be sufficient to fund operations through 2008.
`
`Panel B: Dow Jones archive values for the example article
`
`Story code¼20061212003980
`Date¼12/12/2006
`Time¼06:12:00:29, 06:12:02:36, 06:12:16:34, 06:12:24:59
`Stock symbols¼EPIX, GSK
`Subject codes¼CNW, DJEN, DJGP, DJGS, DJGV, DJI, DJIN, DJIV, FCTV, SPOT, WEI, RND, HDL
`
`the information content of each news article; for example,
`in Table 1 the subject code RND indicates that the article
`contains information about research and development.
`We adopt Dow Jones’s subject categorizations, which
`give us 71 different news categories. However, many of
`these subject codes are general codes that do not provide
`valuable information about the content of a news article.
`For example, nearly every article in the database has the
`code CNW, indicating that the article contains company
`news,
`in addition to a more specific news code. We
`remove these general codes from our analysis to obtain
`a final list of subject codes that contains 39 different news
`categories.8 Finally, if news is released before the market
`closes at 4:00 PM, we assign the current trading day to the
`news story; if news occurs after 4:00 PM, we assign the
`next trading day.
`The resulting news database contains the date and
`time an article was released, a unique firm identifier,
`subject codes, and a dummy variable that takes the value
`of one if a story was released in multiple pieces over the
`news day. The data also contain two dictionary-based
`sentiment score variables that indicate whether a st