Market Efficiency and Costly Information Acquisition at the Intraday Level

WHEN MACHINES READ THE WEB: MARKET EFFICIENCY AND COSTLY INFORMATION ACQUISITION AT THE INTRADAY LEVEL

Roland Gillet et Thomas Renault

Presses universitaires de Grenoble | « Finance »

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble 2019/2 Vol. 40 | pages 7 à 49

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Powered by TCPDF (www.tcpdf.org) When Machines Read the Web: Market Efficiency and Costly Information Acquisition at the Intraday Level Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

Roland Gillet1,2, Thomas Renault3

AbstrAct We investigate the efficient market hypothesis at the intraday level by analyzing market reactions to negative tweets and reports published on the Internet by an activist short seller. Conducting event studies, we find that fast-moving traders can generate small, albeit significant, abnormal profit by trading on public information published on social media. The market reaction to tweets is stronger when a company is mentioned for the first time on Twitter, showing that investors can disentangle new information from noise in real time. We also find that traders who manage to identify the information on the short seller’s website before the dissemination of the same news on Twitter can generate much greater abnormal returns. As acquiring information on a website is more costly and difficult than acquiring the same information on Twitter, our findings provide empirical evidence supporting the Grossman–Stiglitz paradox at the intraday level. Very short-lived market anomalies do exist in the stock market to compensate investors who spent time and money in setting up bots and algorithms to trade on new information before the crowd.

Keywords: Market efficiency, intraday analysis, costly information acquisition, event study, Twitter, short seller.

1. Introduction

According to the semi-strong form of the efficient market hypothesis, publicly available information is fully reflected into stock prices (Malkiel and Fama, 1970). When new information comes into the market, it should therefore be instantaneously integrated into the price in such a way that making risk-adjusted economic profit by trading on public news is impossible

1 Université Paris 1 Panthéon-Sorbonne, PRISM. 2 Université Libre de Bruxelles, Solvay Brussels School of Management, Centre Emile Berheim. 3 Université Paris 1 Panthéon-Sorbonne, CES & LabEx RéFi. Electronic address: [email protected]; Corresponding author: Thomas Renault. Maison des Sciences Économiques. 106-112, boulevard de l’Hôpital 75013 Paris. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Jensen, 1978). In practice, however, how can the market react instantaneously to new information? Indeed, the adjustment of market prices to information supposes that a sufficient number of investors have access to the news to integrate it into stock prices. When trading was executed by human traders and given the time needed to gather new information, read it, and trade on it, it was not surprising to find a lag of at least a few minutes between the

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble first release of a news and its integration into prices (Patell and Wolfson, 1984; Chordia et al., 2005). Yet, the recent technological revolution and the use of computers for trading purposes have changed the way markets work (Riordan et al., 2013). In a world driven by algo traders and machines reading the news (Groß-Klußmann and Hautsch, 2011), there is technically no such thing as limits to instantaneous integration. However, setting up bots and algorithms to gather and analyze new information is costly. In this regard, short-lived market anomalies might still exist to compensate for the cost of information acquisition, consistent with the Grossman-Stiglitz paradox (Grossman and Stiglitz, 1980). While theoretical models demonstrate that trading automatically on public information faster than other investors is one of the keys to maximize expected profit (Foucault et al., 2016), empirical evidence on the importance of the speed and impact of costly information acquisition at the intraday level remains limited. In this study, we re-investigate market efficiency in light of recent technological developments. To do so, we use a novel dataset of messages and reports published on Twitter and on a website by Andrew Left, a famous activist short seller and the founder of Citron Research. In a recent article, the New York Times magazine describes Andrew Left as “the Bounty Hunter of Wall Street [...] sniffing out corporate fraud and gets rich doing it.”4 One of the specificities of Andrew Left is that he uses his own channel of communication to disseminate his reports. According to Left, “if you build enough of a reputation, all you need are some Twitter followers and a website.” The standard strategy of Left consists of shorting a stock before publishing a negative research report about the shorted company on his website or on Twitter. The report usually contains some accusations of fraud and/or information demonstrating why a stock is overvalued. Regulators are naturally concerned about the possibility that activist short sellers manip- ulate the market by creating panic (Zhao, 2018), but this strategy is not

4 https://www.nytimes.com/2017/06/08/magazine/the-bounty-hunter-of-wall-street.html Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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illegal as long as the information published is not fraudulent or deliberately misleading.5 Anecdotal evidence, often covered by the financial press, suggests that tweets and reports from Andrew Left lead to large market reactions. For example, on August 19, 2016, at 10:58:07, Left published a message on Twitter about a company called Alliance Data System ($ADS), criticizing Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble the business model of the company and highlighting the risks associated with the financialization of the company: “Citron exposes $ADS for who they REALLY are. CSFB got ball rolling. tgt $100 https://t.co/RL3GQgd05g. Gotta love the shopping cart trick.” One minute before the tweet, at 10:57 a.m., ADS’s stock was traded at $195.96. One minute after the tweet, it was traded at $192.14 (1.95%). This sharp decrease in stock price was also associated with a very high trading volume; at the minute of the release of the tweet, the trading volume was 75 times higher than that five minutes before. Figure 1 illustrates this example.

Figure 1. Citron Research Tweet: Impact on Alliance Data System’s Stock Price and Trading Volume The figure shows the large decrease in ADS’s stock price and the large increase in trading volume around the publication of a tweet from Citron Research on August 19, 2016. The dashed black line represents the time of the tweet release (10:58 a.m.) and is contemporaneous to the very large increase in trading volume (in red) and the large decrease in stock price (in blue). In this example, the stock price reverses to its level before the tweet in less than two hours. The trading volume returns to normal in approximately two hours.

175,000 197 150,000

196 Price 125,000 100,000 195 75,000 194 50,000 193 25,000 Volume 192 0

08-19 10 08-19 11 08-19 12 08-19 13 08-19 14 08-19 15 Date

5 Left has been sued many times by companies and shareholders for the reports he has published, but he claims to never have lost a case in the United States. However, Left was found guilty and barred from trading on Chinese markets for five years after being sued for “false and misleading claims” in 2016. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Analyzing the HTML source code of the Citron Research website, we find that a report entitled “Alliance Data Systems: If you don’t like the answer, just change the question!” was available online 24 seconds before the mention on Twitter.6 Fast-moving traders use sophisticated methods to detect the release of a report on the Internet and to trade on it before the crowd might be able to generate abnormal profit. Examining all reports

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble published by Andrew Left between 2013 and 2017, we find several cases in which a lag of at least one minute exists between the exact minute at which a news report was published on the Citron Research website (http:// www.citronresearch.com) and the publication of the same information on Citron Research’s Twitter account (@CitronResearch). For example, on May 14 2013, at 12:22:01, Citron Research published on its website a negative report relative to the company World Acceptance ($WRLD).7 Five minutes later, at 12:27:32, the Twitter account of Citron Research released a tweet related to the report, with exactly the same content as the report published at 12:22:01. Analyzing the one-minute price and trading volume, we find a strong increase in trading volume and a sharp decrease in stock price at the exact minute of the release of the report on the Citron Research website. The stock price decreased by 1.17% in one minute, from $90.54 at 12:22 p.m. to $89.48 at 12:23 p.m. We also identify a sharp decrease in stock price the minute just after the tweet was released, from $89.64 to $88.77 (0.97%). This pattern is interesting, as the information disseminated on Twitter was stale and does not contain any new information compared with the report published five minutes earlier. The price and volume patterns in this anecdotal evidence are consistent with the presence of algorithmic traders using textual analysis to automatically identify stocks targeted by Citron Research. Figure 2 illustrates World Acceptance’s stock price and trading volume from open to close on May 14, 2013.

6 We identify the following lines of code on the HTML source code of the website: 2016-08-19 14:57:43. 7 “World Acceptance: What Happens if Credit Insurance Disappears” – Citron Research. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Figure 2. Citron Research Report and Tweet: Impact on World Acceptance’s Stock Price and Trading Volume The figure shows the large decrease in World Acceptance’s stock price and the large increase in trading volume following the release of a negative report published on the Citron Research website at 12:22:01 on May 14, 2013: “World Acceptance: What Happens if credit insurance disappears?”. The figure also illustrates the increase in trading volume and the decrease in stock price following the tweet from Citron Research published 5 minutes after on Twitter “$WRLD what happens if company loses credit insurance? Citron Research tells you.” In this example, while the trading volume

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble returned to normal very quickly, the stock price continued to decrease until the end of the day.

92 50,000 Price 91 40,000

90 30,000

89 20,000

88 10,000 Volume 87 0

05-14 10 05-14 11 05-14 12 05-14 13 05-14 14 05-14 15 Date

The fact that Andrew Left uses Twitter to disseminate new information to the marketplace and that a short lag exists between the publication of a news on the Citron Research website and the same news on its Twitter account provides a unique opportunity to examine the speed of the price adjustment to news and the impact of costly information acquisition. To do so, we examine if the price and volume patterns presented in the anecdotal examples can be generalized by analyzing market reaction, at the intraday level, to the content published on Twitter and on Left’s website by Citron Research. Then, we compare the abnormal return of different trading strategies to assess if fast-moving traders can make an abnormal return by exploiting the publication lag between the release of a report on the web and its dissemination on Twitter. Conducting event studies and removing events on days with corporate announcements or macroeconomic announcements, we find that the negative tweets and reports by Andrew Left have a strong impact on the stock market (significant at the 1% level). On average, the stock prices of companies targeted by Citron Research decrease by 1.56% on the 10 minutes following Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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the publication of negative reports. Investigating the cross-sectional variation in abnormal returns by regressing abnormal returns on tweets’ characteristics and on firm-specific information, we find that the market reaction is stronger when a company is mentioned for the first time by Andrew Left on Twitter. This result is consistent with the findings in the analyst recommendations literature that the market responds more positively to the first report of sell-

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble side analysts than to subsequent recommendations (Irvine, 2003).

We also find that a trading strategy based on content published on Twitter generates very short-lived abnormal returns at the intraday level. The market reacts nearly instantaneously to public information disseminated on a platform with a wide audience, such as Twitter, but fast-moving traders can still earn a profit of 0.25% by shorting the targeted stocks within the end of the minute the news was released. While statistically significant, incorporating reasonable transaction costs mostly eliminates the anomaly, consistent with the efficient market hypothesis at the intraday level. However, we find that fast-moving traders who manage to identify the information on the website before its publication on Twitter can generate higher abnormal returns. On average, at the exact minute of the news release on the Citron Research website, abnormal returns decrease by 1.29%. The minute after the event, returns continue to decrease by 0.72%. Abnormal returns on minutes 0 and 1 are statistically significant at the 1% level, and the magnitude of the price movement seems to be large enough to cover reasonable transaction costs.

Because of the availability of a Twitter programming application (API) that allows any developer to gather in real time and for free all messages sent by a list of predefined users (up to 5,000 users), the cost of information acquisition on Twitter is small. Furthermore, the short length of messages and the availability of the cashtag function (i.e., the use of a “$” sign followed by the ticker of a company to identify a stock precisely) facilitate the identification of the targeted stocks on Twitter. However, identifying anomalies on a less-known channel of communication and developing specific tools and algorithms to continuously monitor the information published on an external website entail costs. In this regard, we find that very short-lived market anomalies do exist in the stock market to compensate investors who spent time and money in setting up bots and algorithms to extract information and trade on it before the crowd. Our empirical results are also in line with the theoretical model of fast-versus slow-moving traders of Foucault et al. (2016). Fast-moving traders using textual analysis and Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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automatic trading algorithms to buy or sell a stock at the exact minute of the first mention of a news on a website can generate significant abnormal returns. Slow-moving traders cannot. Even if the main focus of this study is the market reaction to information at the intraday level, we also analyze the impact of Citron Research tweets and reports on a longer event window. We find that, on average, the impact Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble on the targeted company stock price is permanent (i.e., we do not find any significant price reversal). While the risk of market manipulation should not be understated, activist short seller activity seems to improve market efficiency by avoiding the over-valuation of fraudulent or risky companies, consistent with the results of Zhao (2018). As long as the information is credible, negative reports from an activist short seller can accelerate price discovery and might reduce noise trader risk (Ljungqvist and Qian, 2016). Overall, our findings shed light on market efficiency at the intraday level and on the speed adjustment of price to information. Recent advances in natural language processing and the rise of high-frequency trading (Brogaard et al., 2014) have accelerated the speed at which textual information is integrated into stock prices. To borrow an old joke widely told among economists, if a $100 bill is lying on the floor and according to the efficient market hypothesis, it is not even necessary to bend down to pick it up because if it were genuine, someone would have already taken it (Lo, 2008). In this study, we provide evidence that although a $100 bill lying on the floor of a stock exchange might not be there for long, a few sophisticated investors processing information quickly and searching for information everywhere on the Internet might still be able to pick up the $100 bill before it disappears. But speed matters! This paper is organized as follows. Section 2 describes the data. Section 3 presents the methodology and discusses the hypotheses. Section 4 shows the results of the event studies and cross-sectional regressions. Section 5 discusses our findings. Section 6 concludes.

2. Data To analyze market efficiency and the impact of costly information acquisition at the intraday level, we collect the data sent by a notorious short seller on two different platforms: a widely known social media platform (Twitter) and a less-known website (CitronResearch.com). Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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2.1. Twitter Data Twitter is a microblogging platform in which users can send short messages up to 280 characters (140 characters before November 2017). With approximately 500 million messages sent every day and 100 million daily active users, Twitter is one of the most visited websites in the world. Because of its wide audience and the possibility of extracting data for free Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble using the Twitter API, Twitter is also a very valuable source of information for researchers working on the efficient market hypothesis. In the literature, the vast majority of studies that use data from Twitter focus on the relation between investor sentiment and stock return at a daily frequency (Sprenger et al., 2014b; Ranco et al., 2015). However, as highlighted by Sprenger et al. (2014a), another promising path for future research is to use Twitter for analyzing how news items are incorporated into stock markets’ prices at the intraday level. To shed light on market efficiency at the intraday level, we extract all messages sent on Twitter by the official account of Citron Research: @CitronResearch8. Contrary to the European market (see Jones et al., (2016) for an analysis of the impact of the new regulation on the disclosure of large short positions in France, Spain, and the UK), US securities laws do not mandate the disclosure of short positions (Appel et al., 2018). As Andrew Left is only investing his own money and as he mostly holds short positions, the regulatory disclosure obligation does not apply to him, making the framework unique for analyzing market efficiency and the impact of costly information acquisition. According to Citron Research’s disclaimer, “The principals of Citron Research most always hold a position in any of the securities profiled on the site. Citron Research will not report when a position is initiated or covered. Each investor must make that decision based on his/her judgment of the market.” Furthermore, focusing on the messages sent by a specific user allows us to carefully examine the context and content of each tweet. In this regard, our study follows an approach similar to that used by Ge et al. (2018); they focus on all tweets sent by the President of the United States. However, contrary to Ge et al. (2018) who use daily data because tweets from Donald Trump (@Potus) were sent mostly outside market trading hours, we use intraday data because tweets from @CitronResearch were sent mostly during market trading hours.

8 In the remainder of the study, we use @CitronResearch to designate the Twitter account of Citron Research https://twitter.com/CitronResearch Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Other famous activists, such as Carl Icahn, Bill Ackman, and David Einhorn, manage public hedge funds and thus face a different regulatory framework and do not have the same short selling constraints. While it could have been interesting to study a greater number of activists to facilitate the generalization of our results, @CitronResearch is the only notorious individual investor we can find that uses both its website and Twitter as primary

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble channels of communication for voluntary disclosures of short positions. Before conducting our analysis, we simply verify that CitronResearch’s publications are independent of that of other activists. To do so and following Greenwood and Schor (2009), we download all Schedule 13D from the SEC’s EDGAR database during our sample period.9 Our sample includes 49,888 filings. Using the Central Index Key from the SEC’s computer system and sorting entities by the number of 13D filings filed over the period, we identify a list of 10 activists investors: Gamco Investors (847 filings over the period), Bulldog Investors (258), Carl Icahn (211), Joseph Stillwell (193), Starboard Value (171), Elliott Associates (116), Raging Capital Management (91), Lone Star Value (88), Esl Partners (88), and Orbimed Advisors (87). We also consider a list of top activist investors from ActivistInsight.10 We then follow the methodology proposed by Brav et al. (2010) and used by Appel et al. (2018) to look for any type of information disclosure from those two lists of activists around the publication of reports by Citron Research. We use the News Monitor of Thomson Reuters Eikon to search for all news containing the name of the targeted company (or the ticker) and a keyword related to short selling/activism (“short,” “activist,” “activism”) or the name of other activist investors (“Carl Icahn,” “Gamco Investors,” “Joseph Stillwell,” “Starboard Value,” “Jana Partners”). Overall, we do not find any evidence of clustering of publications by other activist investors around the publication of Citron Research’s reports. As of April 2018, @CitronResearch has a total of 79,600 followers and has sent a total of 348 tweets.11 Using the Twitter API, we extract all tweets sent by @CitronResearch, and we add the exact timestamp of the message,

9 An entity is required to fill out a Schedule 13D when it acquires beneficial ownership of more than 5% of a voting class of a company’s equity securities. 10 From “The Activist Investing Annual Review 2018,” produced by Activist Insight in association with Schulte Roth Zabel (SRZ). The list includes Elliott Management, Trian Partners, Third Point Partners, Amber Capital, Carl Icahn, Starboard Value, Mercato Capital Management, Jana Partners, Oasis Management, and Allan Gray. 11 To the best of our knowledge, the activist investor with the highest number of followers on Twitter is Carl Icahn (@Carl_C_Icahn), who has more than 300,000 followers. David Einhorn (@davidein), another famous activist on Twitter cited in Appel et al. (2018), has 38,000 followers. With now more than 100,000 followers (as of January 1st, 2019), Citron Research is one of the most followed activist short sellers in the world. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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up to a one-second precision, and the tweet content into a database. We manually analyze each message to determine if the tweet includes the name of a publicly traded company and to define the associated sentiment (positive, neutral, or negative). As discussed by Ge et al. (2018), analyzing manually each tweet is preferable when the number of tweets is limited, as it allows taking into account the context of each tweet and avoiding classification

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble errors. For example, the message “@clairecmc Sen Claire McCaskill Before you comment on Mylan, maybe you Should look in your backyard at $MNK” is negative when we consider the context, but an algorithm, such as the one developed by Renault (2017), would not be able to identify the sentiment of the message properly.12 We remove duplicate tweets, neutral tweets, and messages sent outside of market trading hours. We also remove events contemporaneous to corporate announcements (earnings, dividends) or macroeconomic announcements (FOMC, GDP). The decontamination process resulted in a clean sample of 151 negative messages sent about 46 companies between January 1, 2013 and September 1, 2017.13 Table 1 presents a sample of tweets sent by @CitronResearch. Companies targeted by Citron Research have a median market capitalization of 5 billion USD (mid-cap companies). The sample also includes a few very large companies, such as Facebook, Nvidia, and Valeant Pharmaceuticals (with a market capitalization greater than 50 billion USD), and a few small-capital companies, such as Uni-Pixel (with a market capitalization of a few hundred millions at the time of the event).

12 This tweet from @CitronResarch comes as an answer to a statement from Senator McCaskill about the “obscene profit” of some drug makers, including Mylan. See https://www.mccaskill.senate.gov/media-center/news-releases/ mccaskill-reacts-to-mylan-announcement-on-move-to-increase-epipen-rebates 13 We also find 20 positive messages sent about 13 companies during the same period. However, given the low number of positive events, we choose to focus on negative events. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

40-2_RevueFinance.indd 16 22/08/2019 09:47:13 When Machines Read the Web 17 –1.32 –0.18 –3.18 –0.63 –0.62 –1.96 –0.62 –1.92 –0.13 –1.34 Market reaction (%) IOC NUS BRLI ISRG ISRG ISRG ISRG DDD DDD Ticker UNXL Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Tweet $ISRG State of Mass. issuing warnings on robotic surgery. It is all about the training. http://t.co/ iyM3syS2m6 Citron not done $unxl smells real fishy. Invest with caution. Stories that seem too good to be true normally are. We have seen this before. Breaking!! $ISRG loses summary judgement ruling. Trial in 3 weeks. All the docs does not want you to see posted on Citron. $300 pt $BRLI looks to be in a lot of trouble. We think this is the first bit series bad news come http://t.co/kpZ3MBll4W $ISRG exposed in new lawsuit along with Journal of AMA Editorial MUST READ. Citron affirms $ISRG exposed in new lawsuit along with Journal of AMA Editorial MUST $300 tgt. Go to site and read amazing story $IOC maybe the end is finally near for this promo...have to give them credit lasted a lot longer than we ever imagined $ISRG http://t.co/EXKVzyfjbP Hospitals getting cautious on robotic surgery. More to come. $NUS Problems in China seem imminent. On major problem $HLF does not have http://t.co/ xeQJCjulGt Buuble Stock of the Year $DDD Read full report A Justin Bieber Sex Toy.....Really? The race to the bottom is on in 3dprinting has begun. http://t.co/mhJrbcNB Be careful before you buy $DDD without all of the facts. Sample of Negative Tweets Sent by @CitronResearch 1.

09:41:00 14:40:00 11:24:00 11:33:00 13:37:00 11:22:00 13:16:00 10:23:00 13:55:00 13:56:00 Timestamp 2013-03-20 2013-03-21 2013-03-26 2013-04-10 2013-02-21 2013-03-01 2013-03-04 2013-03-13 2013-02-14 2013-01-08 Table This table presents a sample of tweets sent by @CitronResearch during our period. Timestamps and tweet content are extracted using the Twitter API. “Market reaction” is the return of targeted stock on [5:+5] minutes around report release. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

40-2_RevueFinance.indd 17 22/08/2019 09:47:13 18 Finance Vol. 40 N° 2 2019 –0.56 –1.53 –1.59 –1.36 0.354 –1.51 –1.09 –0.96 0.210 –0.13 –2.77 Market reaction (%) IOC CHE ISRG ISRG DDD ANGI ANGI ANGI Ticker UNXL UNXL WRLD Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Tweet $UNXL great job in hurting your clients by Cowen and Craig Hallum. Why let the truth get way of banking fees? What a joke Citron dissects $ANGI showing how they are defrauding customers while deceiving Wall Street. http://t.co/Fn9MwJO3Xn and well $ISRG Citron shows that good sound fundamental investigative research is still alive Citron comments on Interoil $IOC – does Belesis downfall put eggs benedict johnpaulson face? $Angi shareholders should read last paragraph http://t.co/iLS9vm8en6 no background checks- Great idea Angi $DDD your Cubify can be as dangerous a cigarette. Be careful when you make Yoda head. http://t.co/Rtc3rchyM4 $WRLD what happens if company loses credit insurance? Citron Research tells you $ISRG the lawsuits keep rolling in. More bad news http://t.co/cHewoC7gEe Citron will post follow up with status of Taylor case it is happens $UNXL time to tell the truth shareholders. Citron updates report at http://t.co/Fn9MwJO3Xn Citron Research on $ANGI The accounting is so bad that people forgot how much worse the business model is. $CHE Game Over! Government joins qui tam lawsuit on top of last weeks suit. Will be $AMED part deux. http://t.co/dVIjVPakNf 11:12:00 14:02:00 09:56:00 11:51:00 09:57:00 10:15:00 12:27:00 13:25:00 11:45:00 14:05:00 12:54:00 Timestamp 2013-05-31 2013-06-12 2013-07-09 2013-07-11 2013-07-16 2013-07-24 2013-05-14 2013-04-12 2013-04-23 2013-05-01 2013-05-10 Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

40-2_RevueFinance.indd 18 22/08/2019 09:47:13 When Machines Read the Web 19 –2.24 0.369 –0.77 –0.06 –0.80 –0.23 0.794 –0.81 Market reaction (%) TSLA TSLA TSLA ANGI ANGI ANGI Ticker XONE WRLD Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Tweet $TSLA Citron adds some thoughtful commentary. http://t.co/Fn9MwJO3Xn $XONE http://t.co/Rwa0DNJpeT good job exposing this me too bs co. but they left out one key component that will be discussed at later date Citron has stayed away til now short $tsla Car of the future is today. Valuation has no respect for Citron has stayed away til now short $tsla Car of the future is today. Valuation real competition http://t.co/5aH4np4kOV $tsla getting juicy. Citron adding to short. Incremental buyers should dry up soon $ANGI- The hidden danger of already an unprofitable biz. No background check http://t. co/9u6zf4EYLH financially and socially irresponsible Another competitor to $ANGI http://t.co/kHd8ESfVG6 $WRLD 5 minutes of must-see TV http://t.co/xVmlbRnP6e...about time government! If $Angi did not already have competition. Differnce is Ebay knows how to make $ http://t.co/ r2lHClQaFS and knows real verification 09:42:00 10:05:00 10:56:00 13:46:00 09:42:00 10:27:00 13:17:00 12:27:00 Timestamp 2013-08-23 2013-08-28 2013-07-30 2013-08-08 2013-08-21 2013-07-26 2013-07-25 2013-07-24 Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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2.2. Website Data When Citron Research releases a report, an article containing a few paragraphs and linking to a report in PDF format is usually published on the Citron Research website (see Appendix A for an example of an article containing a link to a report).14

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble To examine the impact of costly information acquisition, we extract all articles published on the official website of Citron Research. We insert into a database the URL, the title, and the exact publication date of all reports published between January 1, 2013 and September 1, 2017. We manage to extract the timestamp of each publication, up to a one-second precision, as a tag named “datePublished” is available on the HTML source code of the reports published on the Citron Research website. We also analyze manually the content of the report to identify the company targeted by Citron Research. Similar to the content published on Twitter (88% of the tweets were negative), all reports but four are negative (94%). Table 2 presents a sample of negative reports published on the Citron Research website. We find that all negative reports, except one, have been published during market opening hours, consistent with the short seller objective to draw the attention of other market participants. Using the same previous filtering process, we end up with a clean sample of 63 negative reports.15

14 http://www.citronresearch.com/ 15 We also find four positive reports. As for the tweets and given the low number of positive reports, we choose to focus on negative events. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

40-2_RevueFinance.indd 20 22/08/2019 09:47:13 When Machines Read the Web 21 –1.67 –1.88 –3.23 –3.47 –1.15 –6.30 –0.68 –1.45 0.140 –3.71 Market reaction (%) Z W VRX Ticker TXTR MBLY MBLY AXDX GPRO AMBA AMBA Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Article Title WHY A CONGRESSIONAL SUBPOENA TO VALEANT ABOUT PRICE GOUGING ON DRUGS SHOULD BE GRANTED HOW THE VOLKSWAGEN SCANDAL PROVES CITRON S ANALYSIS OF MOBILEYE MOBILEYE: PRICE TARGET $25 NEAR TERM – ON THE WAY TO $10 JUST FOLLOW THE MONEY AXDX BIG LIE EXPOSED BY SEC – $1.00 TARGET WITHIN 18 MONTHS THE RIDICULOUSNESS OF AMBARELLA TOP 10 REASONS WHY ZILLOW BREAKS $40 WHY AMBARELLA WILL TRADE RIGHT BACK TO $90 – ON ITS WAY MUCH LOWER IN THE COMPANY S OWN WORDS WAYFAIR IS THE MOST MISPRICED STOCK CITRON RESEARCH HAS SEEN IN YEARS – FAIR VALUE UNDER $10 GOPRO TO $30 WITHIN 12 MONTHS – THE COMPS WALL STREET ISN T TALKING ABOUT THE FRAUD AT TEXTURA Sample of Negative Reports from the Citron Research Website 2.

13:47:00 11:29:00 12:54:00 10:59:00 11:32:00 10:56:00 11:09:00 11:12:00 11:54:00 12:01:00 Timestamp 2014-09-28 2014-09-24 2014-09-09 2014-03-09 2014-06-19 2014-07-24 2014-07-29 2014-08-31 2014-11-04 2014-09-29 Table Research website during our sample period. “Market reaction” is the return of This table presents a sample of reports published on the Citron targeted stock on [5:+5] minutes around the report release. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

40-2_RevueFinance.indd 21 22/08/2019 09:47:13 22 Finance Vol. 40 N° 2 2019 –1.27 –1.84 –6.28 –1.90 –2.70 –5.31 –3.43 0.651 –0.66 –1.79 –5.10 Market reaction (%) CC ADS VRX VRX VRX XON MNK Ticker MBLY JCOM MNST TWOU Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Article Title MALLINCKRODT CEO FRAUD EXPOSED BY THE NEW MEDICARE DRUG DASHBOARD ALLIANCE DATA SYSTEMS: IF YOU DON T LIKE THE ANSWER JUST CHANGE QUESTION! CITRON COMMENTS ON RECENT COVERAGE OF INTREXON CHEMOURS IS A BANKRUPTCY WAITING TO HAPPEN! WAS PURPOSELY DESIGNED FOR BANKRUPTCY! MOBILEYE IS WORTH $11 PER SHARE – INSIDERS KNOW IT GOLDMAN SACHS KNOWS IT CITRON AND NOW YOU KNOW IT! CITRON EXPOSES THE DIRTY SECRETS OF J2 GLOBAL JCOM ! THE MONSTER CREATED BY WALL STREET TWOU – FOR-PROFIT EDUCATION WITH AN F IN INVESTABILITY CITRON S LAST WORD ON VALEANT CITRON RESEARCH EXPOSES THE INFORMATION THAT CONGRESS WILL FIND IF IT SUBPOENAS VALEANT VALEANT – COULD THIS BE THE PHARMACEUTICAL ENRON ? 11:36:00 10:57:00 10:01:00 12:50:00 12:19:00 11:48:00 12:14:00 12:16:00 10:59:00 11:49:00 10:06:00 Timestamp 2014-11-16 2014-08-19 2014-04-22 2014-06-02 2014-04-13 2014-03-10 2014-01-29 2014-10-12 2014-11-02 2014-10-02 2014-10-21 Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

40-2_RevueFinance.indd 22 22/08/2019 09:47:13 When Machines Read the Web 23 –1.15 –0.11 –3.32 –1.16 –1.60 –5.75 0.468 –1.71 –0.46 –2.32 –0.54 –6.02 Market reaction (%) LCI MSI FLT FLT TDG TDG EXAS EXAS ESRX MNK AVXS Ticker NVDA Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Article Title NVIDIA TO TRADE BACK $130 IF YOU DON T BELIEVE CITRON THE SCIENCE CITRON PROVIDES NEW PROOF WHY MALLINCKRODT IS ON ITS WAY TO ZERO COULD TRANSDIGM BE THE VALEANT OF AEROSPACE INDUSTRY? EXPRESS SCRIPTS: REBATE SYSTEM IS A FINANCIAL ENGINEERING KICKBACK SCHEME CITRON EXPOSES THE VULNERABILITY OF MOTOROLA SOLUTIONS – PRICE TARGET $45 CITRON EXPOSES MORE UNDISCLOSED RELATIONSHIPS AT TRANSDIGM FLEETCOR – OR IS IT FEECOR FLEECECOR? CITRON EXPOSES THE DIRTY ILLEGAL SECRETS OF FLEETCOR AND PROOF COMPANY IS ALREADY IN COVER-UP MODE CITRON RESEARCH EXPOSES EXACT SCIENCES AND PROVES BEYOND ANY DOUBT WHY THIS STOCK WILL SOON BE CUT IN HALF LANNETT: CITRON EXPOSES THE LAWSUITS THAT WILL WIPE OUT EQUITY AVEXIS – THE NEXT BIOTECH BLOWUP 10:09:00 11:38:00 10:10:00 09:45:00 10:31:00 10:13:00 14:05:00 10:18:00 12:59:00 10:44:00 11:40:00 15:01:00 Timestamp 2014-06-09 2014-05-23 2014-06-05 2014-01-20 2014-01-27 2014-02-07 2014-03-09 2014-04-04 2014-04-27 2014-05-15 2014-01-17 2014-12-14 Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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2.3. Stock Market Data We use intraday one-minute price and volume data to compute the abnormal returns and abnormal volume.16 We define an abnormal return as the difference between raw percentage price changes and S&P500 raw returns on the same minute, as follows: Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble ARit,,=, R it- Rm t (1)

where ARit, denotes the one-minute abnormal return of stock i, Rit, is the one-minute return of the individual stock, and Rmt is the one-minute return of S&P500 (SPY Exchange Traded Fund). As a robustness check, we also consider raw returns, as in Busse and Green (2002), and a market model with lagged returns, as in Groß-Klußmann and Hautsch (2011). As we are focusing on a very short event window and consistent with Fama (1998), we find that our results are not affected by the choice of the model of abnormal return. For readability, we only report our results for the market return model from Equation 1. To capture the pronounced intraday volume trading patterns (U shaped), we define an abnormal volume by standardizing the process using the weekly average of the corresponding underlying one-minute intervals, as in Groß Klußmann and Hautsch (2011):

Vit, AVit, =, (2) 1 -1 åVdit,, 5 d =5-

where AVit, denotes the abnormal trading volume on minute t for stock i, Vit, is the one-minute trading volume, and Vdit,, is the trading volume of the corresponding underlying minute t on day d.

3. Methodology and Hypothesis

In this section, we describe the methodology used to test the efficient market hypothesis at the intraday level and at the daily horizon. Then, we present the methodology used to test the costly information acquisition hypothesis.

16 As we do not have access to bid-ask spread data, we are unable to compute trade direction, as in Lee and Ready (1991), nor analyze precisely the impact on volatility and liquidity. We leave this for future research. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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3.1. Market Efficiency and Costly Information Acquisition at the Intraday Level According to the semi-strong form of the efficient market hypothesis, it should be impossible to make risk-adjusted economic profit by trading on public news. To say it otherwise, if markets are efficient, we should not find any post-event price continuation nor any post-event price reversal. As in Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Boudt and Petitjean (2014), Busse and Green (2002), and Groß Klußmann and Hautsch (2011), we use an intraday event study methodology to analyze how the market responds to information and to test market efficiency at the intraday level. We also conduct cross-sectional analyses by regressing abnormal returns on tweets’ characteristics (reports’ characteristics) and on firm-specific information. The first step in any event study is to define the event of interest (Fama et al. 1969). We first define the publication of a negative report on the Citron Research website (63 negative reports) as an event. Then, we consider the publication of a message on Twitter by @CitronResearch (151 negative tweets) as an event. Comparing both samples, we find that 56 reports out of 63 were followed by a message on Twitter mentioning the news by @CitronResearch. For 7 reports, the information was published only on the website. For 33 reports, we identify a lag of at least one minute between the publication of the report on the Citron Research website and its dissemination on Twitter. For the 23 other reports, the information was disseminated on Twitter at the exact same minute of the publication of the report on the Citron Research website. Figure 3 presents a plot of the distribution of the lag between reports and tweets. Messages published on Twitter that are not directly related to a report published on the website (95 tweets) are mostly reiterations of previous positions17 or announcements of new positions.18 While for scheduled announcements, such as earnings announcements, obtaining a cleanly dated event is easy, identifying the exact instance at which an unscheduled news announcement is made public is not always effortless. As shown by Bradley et al. (2014), timestamp delays can easily lead to incorrect inferences for intraday event studies. In this regard, analyzing the content sent by a specific short seller who uses Twitter and its website

17 See, for example, the following tweet: “Citron believes that $AXDX is Theranos part deux. We reiterate our $1 price target For those who forgot the story https://t.co/VRqdoDyOo3, January 20, 2016”. 18 See, for example, the following tweet: “$tsla getting juicy. Citron adding to short. Incremental buyers should dry up soon,” August 8, 2013. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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to release primary information is especially interesting, as it allows us to obtain the exact second at which a news report was made public.

Figure 3. Distribution of the Lag between Reports and Tweets The figure shows the lag, in minutes, between the release of a report on the Citron Research website and the dissemination of the same news on Twitter for the 58 reports that were followed by a message on Twitter. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

0 0 2 4 6 8 10 12 14

We analyze the adjustment of stock prices and trading volumes around the events. We define as minute 0 the minute at which a news was made public, and we analyze the evolution of the abnormal return and abnormal volume on an event window starting 30 minutes before the event and ending 30 minutes after the event (a 61-minute event window). We test the significance of the abnormal return and abnormal volume by conducting non-parametric Corrado (1989) rank test, as in Campbell and Wasley (1996). We use an estimation window of 390 minutes (one trading day) before the beginning of the event window. We define an average abnormal return (AAR) and an average abnormal volume (AAV) as follows: 1 n AARt =,å ARit, (3) n i=1 1 n AAVt =,å AVit, (4) n i=1 where n is the number of events. We also compute the median abnormal return (MAR) and median abnormal volume (MAV) to avoid having our Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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results driven by a few extreme events. Finally, we compute the cumulative average abnormal return (CAAR) as the cumulative sum of the AAR across the event window. Following our anecdotal observations and the previous findings in the literature, we make the hypothesis that markets should start reacting at the exact minute of the publication of new information. Negative tweets/ Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble reports should be associated with negative abnormal returns on minute 0. As in Busse and Green (2002), we also make the hypothesis that abnormal returns should exhibit post-event continuation, as the market’s reaction is not entirely instantaneous. If this hypothesis is true, we should identify negative abnormal returns in the minute(s) following the negative tweets/ reports. We expect the post-event continuation to be very short lived because if any pricing anomaly exists, rational arbitrageurs should compete to take advantage of the mispricing. We also make the hypothesis that fast-moving traders using sophisticated methods to detect the release of the report on the Internet and to trade on it before the crowd might be able to generate higher abnormal profit than those trading on public news disseminated on Twitter. In this regard, we hypothesize that abnormal returns just after the announcement should be greater when there is a lag between the publication of the report on the website and the dissemination of the news on Twitter than when there is no lag. As setting up bots and algorithms to gather and analyze new information is costly, short-lived market anomalies might exist to compensate for the cost of information acquisition. The more difficult it is to collect information, the higher the (absolute) abnormal return should be. As is standard in the event studies literature, we also investigate the cross-sectional variation of abnormal returns by regressing abnormal returns on tweets’/reports’ characteristics and on firm-specific information (firm size, industry, earnings per share, number of analysts). We expect the market reaction to be greater when a company is negatively mentioned for the first time by a short seller on Twitter. This hypothesis is based on previous findings in the analyst recommendations literature that the market reaction is stronger when an analyst starts covering a stock than when following recommendations, as analyst initiations tend to contain better information than continuation reports do. Indeed, the first mention of a company by a short seller well known for tracking frauds and hyped companies can be interpreted by market participants as a negative signal regarding future stock prices. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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3.2. Market Efficiency at the Daily Level We also examine, at a daily frequency, the evolution of abnormal returns up to two weeks after the release of tweets by @CitronResearch. Analyzing the price patterns on a longer event window allows us to assess if the information disseminated on social media by @CitronResearch only generates short-term panic or if the tweets contain value-relevant information about Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble the targeted company. If the information is noisy, we should see a price reversal on the days following the events. If the information is credible, the impact on abnormal returns should be permanent. To do so, we conduct another event study by defining the day of the publication of a message containing the name of a listed company on Twitter by @CitronResearch as an event. We consider an estimation window of 250 days before the event and an event window of [10:+10] days around the tweet release. We use a market model to compute abnormal returns at a daily frequency, and we define the abnormal volume by dividing the volume on a given day by the average volume on the estimation window. After removing stocks with less than one year of historical data and focusing on negative tweets, we end up with a total of 131 events about 35 companies.

4. Results

4.1. Intraday Event Study: Negative Reports In this subsection, we present the results of event studies, in which events are defined as negative reports published on the Citron Research website (63 reports). Then, we present the results for the 40 negative reports for which we find a lag of at least one minute before the release of the report on the Citron Research website and the release of the tweet disseminating the information on social media. We denote these events as slow dissemination reports.19 We also present the results for the 23 negative reports for which a tweet was published at the exact same minute of the report release. We denote such events as fast dissemination reports. According to costly information acquisition hypothesis, we expect to find stronger abnormal returns after slow dissemination reports than after fast dissemination reports, as the cost and complexity of extracting information in real time on a website are greater than the cost of gathering information on Twitter.

19 The results are similar when we exclude the 7 reports that were never disseminated on Twitter. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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We present the results for the AAR, MAR, CAAR, AAV, and MAV. Panel A of Table 1 shows the intraday price and volume reactions to all reports. Panel B shows the results for slow dissemination reports, and Panel C shows those for fast dissemination reports. For readability, we present in Table 3 our results on a [10:10]-minute period around the events.20 Figure 4 shows the AAR and CAAR for negative reports on the [30:+30]-minute

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble event window. Figure 5 shows the MAV and AAV. Table 3. Event Study Results: Negative Reports The table shows the intraday price and volume reactions to negative reports published on the Citron Research website. The table also presents the results by splitting events into two: events for which we find a lag between the publication of the report on the website and its dissemination on Twitter (slow dissemination reports), and events for which there is no lag (fast dissemination reports). Superscripts *, **, and *** denote an abnormal return and abnormal volume significance, respectively, at the 1%, 5%, and 10% levels using a Corrado rank test. For readability, we highlight in red all minutes for which abnormal returns are significant and greater than 20 basis points (i.e., minutes for which abnormal returns cover transaction costs).

20 We do not find any economically significant impact on the [30:–10]-minute window nor on [+10:+30] minutes. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Minute AAR MAR CAAR AAV MAV 9 –0.0736 0.0047 –1.6411*** 16.8394*** 6.7849*** 10 0.0766* 0.0731* –1.5645*** 20.3122*** 6.8531*** Panel B: Slow dissemination reports (40 events) –10 0.0382 0.0099 –0.2524 4.9873 1.1084 –9 0.0278 0.0358 –0.2246 3.2614 1.3258 Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble –8 –0.0237 –0.0105 –0.2483 2.7337 1.4105 –7 –0.058 –0.0204 –0.3063 2.3551 0.9801 –6 0.0089 0.022 –0.2974 6.7753 0.9501 –5 –0.0144 0.001 –0.3118 3.4644 1.1096 –4 –0.0121 –0.0324 –0.3239 3.9042 1.1066 –3 –0.0525 –0.0189 –0.3764 5.3867 1.4929 –1 –0.0107 –0.0052 –0.369 3.34 1.2533 0 –1.2869*** –0.8872*** –1.6559*** 125.639*** 13.6903*** 1 –0.7184*** –0.403*** –2.3743*** 116.3734*** 31.3524*** 2 0.0395 –0.0091 –2.3347*** 148.9701*** 15.2132*** 3 0.0035 0.0388 –2.3312*** 43.0251*** 15.7474*** 4 –0.0393 –0.0092 –2.3705*** 30.1271*** 13.1264*** 5 0.2818 0.0266 –2.0887*** 46.1471*** 11.1472*** 6 –0.1178 –0.0589 –2.2064*** 20.8012*** 9.1764*** 7 0.016 0.054 –2.1904*** 28.5666*** 15.2374*** 8 0.1242 0.0379 –2.0662*** 23.1298*** 12.9065*** 9 –0.0806 0.0046 –2.1469*** 21.8526*** 9.7328*** 10 0.0537** 0.0774** –2.0932*** 21.6102*** 7.9894*** Panel C: Fast dissemination reports (23 events) –10 –0.0849*** –0.089*** 0.4398 2.4839 0.8713 –9 –0.0081 –0.0083 0.4317 1.5545 0.8889 –8 0.05 0.0049 0.4817 1.3715 1.0417 –7 –0.0396 0.0081 0.4421 1.6246 0.7592 –6 0.1246* 0.0527* 0.5667 1.8448 0.7719 –5 –0.0074 –0.0313 0.5593 1.6466 1.3508 –4 0.0151 0.0178 0.5744 1.4622 1.2619 –3 –0.0149 –0.0116 0.5596 1.0463 0.6381 –2 –0.0518 –0.0775 0.5077 1.9394 1.1406 –1 –0.0503 0.0118 0.4575 4.9978 2.0223 0 –1.099*** –0.6489*** –0.6415*** 47.3037*** 14.9696*** 1 –0.1923 –0.0708 –0.8338*** 75.4884*** 9.6541*** Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Minute AAR MAR CAAR AAV MAV 2 –0.1059 –0.1451 –0.9397*** 32.4564*** 7.4173*** 3 0.1188 0.0708 –0.8209*** 15.3642*** 5.7068*** 4 0.1531 0.0387 –0.6678*** 19.2432*** 5.6723*** 5 –0.0776 –0.0288 –0.7455*** 11.4226*** 6.1411*** 6 –0.1474 0.0106 –0.8929*** 18.5544*** 5.995*** Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble 7 0.0313 0.0343 –0.8616*** 13.3808** 3.7854** 8 0.1616 0.0486 –0.7000*** 8.6669*** 4.3573*** 9 –0.0615 0.0213 –0.7615*** 8.1208*** 4.3693*** 10 0.1164 0.0487 –0.6451*** 18.0548*** 5.0224***

Figure 4. Intraday Event Study Negative Reports: Average Abnormal Return and Cumulative Average Abnormal Return The figure shows the average abnormal return and the cumulative average abnormal return (in %) on a [−30:+30]-minute event window around the publication of negative reports by Citron Research. The dashed vertical line at 0 represents the exact minute of the tweet release. The dashed horizontal line at 0 shows if the abnormal returns are negative or positive on a given minute.

–0.5

–1.0

–1.5

–2.0

Average Abnormal Return Cumulative Average Abnormal Return –30 –20 –10 0 10 20 30

We find that the abnormal return is large and significant, at the 1% level, at the exact minute of the release of negative reports. The average (median) abnormal return on minute 0 is equal to 1.22% (0.70%). The average (median) abnormal volume is also very large and significant at the minute of the report release (equal to 97.04 for the average volume and to 14.96 for the median volume). Consistent with the costly information Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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acquisition hypothesis, we find that a trading strategy based on slow dissemination reports generates strong and significant abnormal returns on the minute(s) after the event. The average (median) abnormal return on the minute after the release of a negative report not directly disseminated on Twitter is equal to 0.72% (0.40%). However, when a report is published on the website and disseminated at the exact same minute on Twitter (fast

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble dissemination reports), abnormal returns are not significantly different from zero after the event.

Figure 5. Intraday Event Study Negative Reports: Average Abnormal Volume and Median Abnormal Volume The figure shows the median abnormal volume and the average anormal volume on a [30:+30]- minute event window around the publication of negative reports by Citron Research. The dashed vertical line at 0 represents the exact minute of the tweet release. The dashed horizontal line at 1 shows if an abnormal volume is higher than the average volume during the estimation window.

Median Abnormal Volume 100 Average Abnormal Volume

0 –30 –20 –10 0 10 20 30 The very large market reaction at the exact minute of the release of slow dissemination reports strongly suggests that some investors are tracking in real time the content published on the Citron Research website to identify new reports before the dissemination of the report on other channels of communication, such as Twitter. As acquiring information on a website is costlier and difficult than acquiring the same information on Twitter, very short-lived market anomalies might exist in the stock market to compensate investors who spent time and money in setting up bots and algorithms to trade on new information before the crowd. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Furthermore, we do not find any strong evidence of informed trading just before the announcement of the reports: abnormal returns on minutes 30 to 1 are not significantly different from zero. This result suggests that Andrew Left tends to build his short position on the few days before the release of a negative report and not on the few minutes before the release. This result is also consistent with the findings of Ljungqvist and Qian (2016) that share

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble turnover begins to rise significantly around four days before the publication of reports by small short sellers, such as Citron Research. The absence of movement prior to the news release (at the intraday level) likewise seems to favor the hypothesis that the pre-announcement movements identified by Groß Klußmann and Hautsch 2011) are certainly caused by a clustering of news items in their sample, consistent with their own conjecture.

4.2. Intraday Cross-Sectional Analysis: Negative Reports

In this subsection, we investigate the cross-sectional variation of abnormal returns by regressing abnormal returns on reports’ characteristics and firm-specific information. We consider the following model:

ii i ii CAR + = ab+CAR -- + bSlowReport ++ b Biotech b Tech tt,5 1tt 30, 1 2 t 3 t4 t i i ii ++bb56Sizet TotalAnalystt + b 7 NegEPSt + b 8 Fridayt + e it ,,

(5)

i where CARtt,5+ is the cumulative abnormal return for stock i between minute 0 (minute of the publication of the report on the Citron Research i website) and minute 5 on day t. CARtt--30, 1 is the cumulative abnormal return between minute 30 and minute 1 on the same day. SlowReport is a dummy variable equal to 1 when we find a lag of at least one minute before the release of the report on the Citron Research website and the release of the tweet disseminating the information on social media. Biotech and Tech are two dummy variables equal to 1 if company operations are related to the biotechnological/technological sector, following North American Industry Classification System codes. Size is the log of the market capitalization. Analyst is the total number of analysts following the company. NegEPS is a dummy variable equal to 1 if the last earnings reported by the company were negative. Friday is a dummy equal to 1 if the tweet was sent on the last day of the trading week. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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The variables NegEPS , Tech , and Size are standard and widely used in the literature (see, for example, Busse and Green (2002)). We add the Friday variable following DellaVigna and Pollet (2009), who find that the market reaction is more delayed on days with limited attention, such as Friday. We also add the variable Analyst , as Dyck and Zingales (2003) find that the market reaction is more pronounced when investors have fewer

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble alternative sources of information to turn to. Table 4 presents the results. We find that slow dissemination reports have statistically stronger effects than fast dissemination reports have (at the 1% level). The value of the coefficient (0.99) is consistent with the results from the previous subsection, in which we find that the cumulative AARs on a [0:+5]-minute event window around the publication of negative reports are equal to 1.19% for fast dissemination reports and 1.83% for slow dissemination reports. Interestingly, we also find that the market reaction is statistically smaller (less negative) for reports about large capitalization stocks and for reports published on a Friday (at the 5% level), which is consistent with previous findings in the literature.

Table 4. Cross-Sectional Regression: Cumulative Abnormal Returns on Report Characteristics and Firm-specific Information ii i This table reports the results of the equation CARtt,5+ = ab++12CARtt--30, 1 bSlowReportt i ii i i i +b3Biotecht + b 4 Techt ++ b 56 Sizet b TotalAnalystt + b7 NegEPSt + be 8 Fridayti. Standard errors are computed using White (1980)’s heteroskedasticity robust standard errors. The superscripts ***, **, and * indicate statistical significance at the 1%, 5%, an d 10% levels, respectively. The regressions include 63 observations.

Coeff. std error a –4.60186*** 1.19707

CARtt--30, 1 –0.05964 0.09623

SlowReportt –0.99953*** 0.36087

Biotecht 0.56272 0.39953

Techt 0.10921 0.45236

Sizet 0.4194** 0.17001

TotalAnalystt –0.01252 0.03316

NegEPSt –0.61635 0.38748

Fridayt 0.73586** 0.32318 Adj R 2 (%) 23.4 Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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When a report is published on the Citron Research website and disseminated with a lag on Twitter, the market reaction is therefore stronger. This result can be considered consistent with that of Tetlock (2011) that investors overreact to stale information and create temporary pressure on the asset price. However, given the low number of observations (63), we should be cautious before extending Tetlock (2011)’s results to our frame-

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble work. In fact, the choice to disseminate a report on Twitter at the exact same minute of the publication of the report on the Citron Research website (fast dissemination report) might not be exogenous. In a strategy to maximize its market impact, Citron Research may choose to disseminate more informative reports on Twitter with a short lag, and differences in report informativeness might explain differences in market reaction. Although we cannot precisely confirm or refute this hypothesis with the data we have, we explore more extensively the market reaction to stale (original) tweets in the next subsection.

4.3. Intraday Event Study: Negative Tweets In this subsection, we present the results of event studies, in which events are defined as negative tweets published by @CitronResearch on Twitter (151 tweets). Then, we present the results for the 95 negative tweets that are not directly related to a report previously published on the Citron Research website; we denote these events as original tweets. We also present the results for the 56 negative tweets that are related to a report; we denote those events as stale tweets.21 We consider the timestamp at which each tweet was posted by @CitronResearch to simulate a trading strategy based only on the content published on social media. Panel A of Table 5 shows the intraday price and volume reactions to all tweets. Panel B shows the results for original tweets and Panel C for stale tweets. Figure 6 shows the AAR and CAAR for negative reports on the [30:+30]-minute event window. Figure 7 shows the MAV and AAV.

21 We have a total of 63 reports, of which 7 were never followed by a tweet. Thus, we end up with a total of 56 stale tweets. We denote these tweets using the word “stale,” following Tetlock (2011). Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Table 5. Event Study Results: Negative Reports The table shows the intraday price and volume reactions to negative reports published on the Citron Research website. The table also presents the results by splitting events into two: events for which we find a lag between the publication of the report on the website and its dissemination on Twitter (slow dissemination reports), and events for which there is no lag (fast dissemination reports). Superscripts *, **, and *** denote an abnormal return and abnormal volume significance, respectively, at the 1%, 5%, and 10% levels using a Corrado rank test. For readability, we highlight in red all minutes for which abnormal returns are significant and greater than 20 basis points (i.e., minutes for which abnormal returns cover transaction costs). Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

Minute AAR MAR CAAR AAV MAV Panel A: All reports (63 reports) –10 –0.0068 –0.0205 0.0003 4.0734 1.0729 –9 0.0147 0.0251 0.015 2.6382 1.0536 –8 0.0032 –0.0059 0.0182 2.2364 1.2103 –7 –0.0513 –0.005 –0.0331 2.0884 0.9708 –6 0.0511 0.0252 0.0181 4.9753 0.8568 –5 –0.0118 0.0 0.0062 2.8008 1.3145 –4 –0.0022 –0.0217 0.0041 3.0126 1.1166 –3 –0.0387 –0.0164 –0.0347 3.8021 1.1817 –2 –0.0074 –0.0065 –0.0421 2.4214 1.2253 –1 –0.0252 0.0 –0.0673 3.9452 1.5725 0 –1.2183*** –0.6988*** –1.2855*** 97.0404*** 14.9696*** 1 –0.5263*** –0.3418*** –1.8119*** 101.4471*** 22.4619*** 2 –0.0135 –0.029 –1.8254*** 106.4333*** 13.2663*** 3 0.0456 0.0578 –1.7798*** 32.9267*** 9.9237*** 4 0.031 –0.0049 –1.7489*** 26.1536*** 10.4394*** 5 0.1506 0.0099 –1.5983*** 33.4699*** 8.5906*** 6 –0.1286 –0.0425 –1.7269*** 19.9809*** 8.0895*** 7 0.0216 0.037 –1.7053*** 23.0226*** 9.5216*** 8 0.1378 0.0395 –1.5675*** 17.8497*** 7.8172*** 9 –0.0736 0.0047 –1.6411*** 16.8394*** 6.7849*** 10 0.0766* 0.0731* –1.5645*** 20.3122*** 6.8531*** Panel B: Slow dissemination reports (40 events) –10 0.0382 0.0099 –0.2524 4.9873 1.1084 –9 0.0278 0.0358 –0.2246 3.2614 1.3258 –8 –0.0237 –0.0105 –0.2483 2.7337 1.4105 –7 –0.058 –0.0204 –0.3063 2.3551 0.9801 –6 0.0089 0.022 –0.2974 6.7753 0.9501 –5 –0.0144 0.001 –0.3118 3.4644 1.1096 Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Minute AAR MAR CAAR AAV MAV –4 –0.0121 –0.0324 –0.3239 3.9042 1.1066 –3 –0.0525 –0.0189 –0.3764 5.3867 1.4929 –1 –0.0107 –0.0052 –0.369 3.34 1.2533 0 –1.2869*** –0.8872*** –1.6559*** 125.639*** 13.6903*** 1 –0.7184*** –0.403*** –2.3743*** 116.3734*** 31.3524*** Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble 2 0.0395 –0.0091 –2.3347*** 148.9701*** 15.2132*** 3 0.0035 0.0388 –2.3312*** 43.0251*** 15.7474*** 4 –0.0393 –0.0092 –2.3705*** 30.1271*** 13.1264*** 5 0.2818 0.0266 –2.0887*** 46.1471*** 11.1472*** 6 –0.1178 –0.0589 –2.2064*** 20.8012*** 9.1764*** 7 0.016 0.054 –2.1904*** 28.5666*** 15.2374*** 8 0.1242 0.0379 –2.0662*** 23.1298*** 12.9065*** 9 –0.0806 0.0046 –2.1469*** 21.8526*** 9.7328*** 10 0.0537** 0.0774** –2.0932*** 21.6102*** 7.9894*** Panel C: Fast dissemination reports (23 events) –10 –0.0849*** –0.089*** 0.4398 2.4839 0.8713 –9 –0.0081 –0.0083 0.4317 1.5545 0.8889 –8 0.05 0.0049 0.4817 1.3715 1.0417 –7 –0.0396 0.0081 0.4421 1.6246 0.7592 –6 0.1246* 0.0527* 0.5667 1.8448 0.7719 –5 –0.0074 –0.0313 0.5593 1.6466 1.3508 –4 0.0151 0.0178 0.5744 1.4622 1.2619 –3 –0.0149 –0.0116 0.5596 1.0463 0.6381 –2 –0.0518 –0.0775 0.5077 1.9394 1.1406 –1 –0.0503 0.0118 0.4575 4.9978 2.0223 0 –1.099*** –0.6489*** –0.6415*** 47.3037*** 14.9696*** 1 –0.1923 –0.0708 –0.8338*** 75.4884*** 9.6541*** 2 –0.1059 –0.1451 –0.9397*** 32.4564*** 7.4173*** 3 0.1188 0.0708 –0.8209*** 15.3642*** 5.7068*** 4 0.1531 0.0387 –0.6678*** 19.2432*** 5.6723*** 5 –0.0776 –0.0288 –0.7455*** 11.4226*** 6.1411*** 6 –0.1474 0.0106 –0.8929*** 18.5544*** 5.995*** 7 0.0313 0.0343 –0.8616*** 13.3808** 3.7854** 8 0.1616 0.0486 –0.7000*** 8.6669*** 4.3573*** 9 –0.0615 0.0213 –0.7615*** 8.1208*** 4.3693*** 10 0.1164 0.0487 –0.6451*** 18.0548*** 5.0224*** Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Figure 6. Intraday Event Study: Negative Tweets Average Abnormal Return and Cumulative Average Abnormal Return The figure shows the average abnormal return and the cumulative average abnormal return (in %) on a [30:+30]-minute event window around the publication of negative tweets by @ CitronResearch. The dashed vertical line at 0 represents the exact minute of the tweet release. The dashed horizontal line at 0 shows if abnormal returns are negative or positive on a given minute.

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble 0.0

–0.5

–1.0

–1.5

–2.0

Average Abnormal Return Cumulative Average Abnormal Return –30 –20 –10 0 10 20 30

Figure 7. Intraday Event Study: Negative Tweets Average Abnormal Volume and Median Abnormal Volume The figure shows the median abnormal volume and the average abnormal volume on a [30:+30]- minute event window around the publication of negative tweets by @CitronResearch. The dashed vertical line at 0 represents the exact minute of the tweet release. The dashed horizontal line at 1 shows if the abnormal volume is higher than the average volume during the estimation window.

Median Abnormal Volume Average Abnormal Volume 50

0 –30 –20 –10 0 10 20 30 Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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We find that an abnormal return is also large and significant, at the 1% level, at the exact minute of the release of negative tweets. The magnitude of the price decrease on minute 0 is approximately the same as that of the price decrease in the previous subsection (0.89%). When we focus on stale tweets, we find a large and significant negative abnormal return on minutes 2 and 1. This finding is consistent with that in the previous subsection: movements

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble prior to stale tweets are caused by a lag between the publication of a report and the dissemination of the report on Twitter (Panel B), leading to an imperfect timestamp identification. Consistent with the absence of informed trading (at least in the few minutes prior to the tweet release), we do not find any significant price decrease before the release of original tweets (Panel C). After the release of negative tweets, the trading volume remains abnor- mally high during the entire event window. Interestingly, we also identify a very short-lived price continuation after the release of negative tweets. AAR is negative and significant at the 1% level on the minute after the event. The average (median) abnormal return on minute 1 is equal to 0.24% (0.12%). Comparing the results for original tweets and stale tweets, we find that the price decrease on minute 1 is only significant for original tweets. After minute 1, we identify a small price reversal for stale tweets, whereas we identify a small continuation of the price decrease for original tweets. CAAR continues to decrease slowly for up to 15 minutes after the tweet release, but the price decrease after minute 1 is not significant using a multi-period Corrado rank test. This result suggests that fast-moving traders implementing automatic trading strategies shorting a stock just after the release of negative original tweets can make abnormal profit (excluding transaction costs). However, while the anomaly is very large for a one-minute period (0.30% in a minute would be equivalent to a 63% daily return), incorporating a 10 bps two-way transaction cost mostly eliminates the anomaly. This very short-lived market anomaly is thus consistent with the efficient market hypothesis, as market participants seem to compete in order to take advantage of the mispricing and to maximize the expected returns. Price movements after the release of negative tweets are significant, albeit small, and are significant only for original tweets. This finding tends to provide evidence that investors can disentangle new information from stale information in real time. Overall, the magnitude of abnormal returns following tweets by @CitronResearch is comparable to that of the price patterns documented by Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Busse and Green (2002). Analyzing the Morning Call and the Midday Call segments on CNBC TV, Busse and Green (2002) find that the abnormal return increases (decreases) by 0.5% (1.25%) following positive (negative) reports released on television. They also find that traders executing a buy order within 15 seconds after the release of a positive report on TV can generate small but significant profit (0.16%). We document similar patterns

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble for a trading strategy based on tweet releases. However, contrary to Busse and Green (2002) who find that the anomaly following negative mentions on TV is more gradual (the stock price decreases for up to 5 minutes),22 we find that the price adjustment is nearly instantaneous, even for negative reports. This finding suggests that the development of algorithmic trading and the smaller cost of trade execution accelerate the speed at which new information is integrated into stock prices.

4.4. Intraday Cross-Sectional Analysis: Twitter Messages In this subsection, we investigate the cross-sectional variation of abnormal returns by regressing abnormal returns on tweets’ characteristics and on firm-specific information. We consider the following model:

ii ii i CAR = ab++AR- b FirstMention +b Report + b Biotech tt,0, 2 1t 1 2 t 3 t 4 t ii i i i ++bbb5Techt 67 Sizet + TotalAnalystt + b 8 NegEPSt + b 9 Fridayt + e it ,, (6)

i where CARtt,0, 2 is the cumulative abnormal return for stock i between i minute 0 and minute t2 on day t. ARt-1 is the abnormal return on the day before the event. FirstMention is a dummy variable equal to 1 if a stock is mentioned for the first time on Twitter by Citron Research (coverage initiation). Report is a dummy variable equal to 1 if the tweet includes a link to a report published on the Citron Research website. Biotech , Tech , Size, Analyst , NegEPS , and Friday are the same variables as in Equation 5.23

Table 6 presents the cross-sectional results for t2 = 0, t2 = 1, and t2 = 30 (i.e., AR on the event minute, 1-minute CAR, and 30-minute CAR). The cross-sectional regressions show that the market reaction is much

22 Busse and Green (2002) argue that the difference in the speed of market reaction might be caused by the higher costs of short selling. 23 We do not include the number of times a message was shared by other users on Twitter (i.e., number of retweets), as this variable is not available in real time but only ex-post. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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more pronounced when a firm is negatively discussed by Citron Research for the first time. This result is consistent with previous findings on analyst recommendations showing that the market responds more strongly to the first report of sell-side analysts than to other recommendations Irvine (2003). Indeed, the first tweet from Citron Research about a given company often provides (indirectly) some information about the short position of Citron

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Research on the targeted stock. Tweets targeting the same company after the first mention tend to contain less information and are often used by Citron Research to repeat or reiterate the negative report published previously. We also find that the decrease in stock price is lower for large firms and, to a lesser extent, lower for firms with a large number of analysts. As less information is generally available for smaller firms, this result is consistent with previous findings in the literature: the market reaction is more pronounced when investors have fewer alternative sources of information to turn to.24 Interestingly, we do not find any tweeting strategy by Andrew Left to avoid posting messages on low-attention days, such as Friday. The percentage of tweets sent on a Friday is approximately the same as that sent on other days. Furthermore, we do not find any day-of-the-week effects nor any difference in market reaction over time.

4.5. Daily Event Study: Twitter Messages As a complementary analysis, we also present our results for a [10:10]-day event window around the negative tweets from Citron Research.25 Figure 8 shows the daily AAR and CAAR. Analyzing one-minute data, we previously find a CAAR of 2.28% on a [30:+30]-minute event window around the release of negative tweets. The daily analysis confirms this pattern. The average (median) price reaction on the event day is equal to 3.41% (2.73%) and is significant at the 1% level. Interestingly, we also find a continuation in price decrease in the following two weeks, as in Ljungqvist and Qian (2016). This finding is likewise consistent with the results of Chen and Rhee (2010) and Zhao (2018); activist short seller activity seems to improve market efficiency by avoiding the over-valuation of fraudulent or risky companies. Novice arbitrageurs with limited capital can help make prices efficient Ljungqvist and Qian (2016).

24 The variables Size and TotalAnalyst are strongly correlated (0.74). If we remove Size from the model in t2 = 0, the variable TotalAnalyst is significant at the 5% level. 25 As a robustness check, we also recompute all results from the previous section (intraday analysis) based on this subsample of stocks/events. We find that our results are robust to the sample of events considered. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

40-2_RevueFinance.indd 41 22/08/2019 09:47:14 42 Finance Vol. 40 N° 2 2019 0.6126 0.5539 1.2762 0.63451 0.02827 0.17609 0.57411 0.52829 0.48894 0.03283 std error =30 4.5 2 t Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Coeff. 0.00606 0.03408 0.17321 0.20752 –0.08716 –0.80407 –0.92623 –0.65839 –0.03335 –1.75729*** b b b b

it i t i ii t tt 0.3455 0.43156 0.49716 0.62802 0.01691 0.11872 0.36652 0.92512 0.40559 0.02238 std error = 1 2 15.5 t 1 - t

AR FirstMention Report Biotech Tech Size 30. Standard errors are computed using White (1980)’s heteroskedasticity 12 3 4 56

Coeff. 2 ++ + + ++ t 0.12309 0.02893* –0.33407 –0.74407 –0.15653 –0.53093 –0.00645 0.72086** ab b –1.89669** –1.60269*** = 1 and

,0, 2 itt = i

2 t

CAR 0,

2 t for 0.7472

0.37313 0.47663 0.61826 0.01432 0.09717 0.33954 0.31001 0.37746 0.01427 std error = 0 2 13.7 t

t ti Coeff. –0.324 0.01227 0.43867 –0.59267 –0.30156 –0.36801 –0.01011 0.19227** –1.28432*** –2.05557***

it ii

t 2 t t ,0, t

Cross-Sectional Regression: Cumulative Abnormal Returns on Tweets’ Characteristics and Firm-specific Cross-Sectional Regression: Cumulative Abnormal Returns

(%) t

t t 2 6.

1 CAR R t t - t

TotalAnalyst NegEPS Friday 7 89 AR FirstMention NegEPS Friday b b be Report Biotech Size a Tech TotalAnalyst Adj Table Information This table reports the results of equation robust standard errors. The superscripts ***, **, and * indicate statistical significance at the 1%, 5%, 10% levels, respectively. regressions include 151 observations. + + ++ Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Figure 8. Daily Event Study: Twitter Data The figure shows the average abnormal return and the cumulative average on a [10:+10]-day event window around the publication of negative tweets by @CitronResearch. The dashed vertical line at 0 represents the exact day of the tweet release.

–2

–3 30

–4 20 –5

–6 Average Abnormal Return Cumulative Average Abnormal Return –7 –10.0 –7.5 –5.0 –2.5 0.0 2.5 5.0 7.5 10

5. Discussion

Our findings provide empirical evidence showing that public information revealed by Citron Research on Twitter and its website has a strong impact on stock prices. These results raise two important questions. First, why does Andrew Left choose to publicly disclose some information to market participants (voluntary disclosure) if profitable trades can still be exploited? Second, would it be possible for an individual investor to make profit by implementing a trading strategy based on public information disclosed by Citron Research ? Regarding the first question, we can relate our findings to those of Ljungqvist and Qian (2016) that publicly disclosing a negative research report helps small investors overcome limits to arbitrage associated with short selling. Small arbitrageurs, who face short sale constraints and limits to arbitrage, may reveal their information to the market to induce long (unconstrained) investors to sell their shares.26. Despite the costs associated

26 Citron Research accounts for 106 of the 358 reports in the work of Ljungqvist and Qian (2016). Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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with the disclosure of short positions (litigation risks), Appel et al. (2018) find that the number of public short selling campaigns has increased significantly in the past decade. This could be explained by the very high return for activists, such as Citron Research, of public short selling campaigns.27 According to an investor presentation seen by Reuters, Andrew Left earned an average annualized return of 89.98% between 2007 and 2017. The results

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble of Ljungqvist and Qian (2016)’s work also suggest that the trading profit is large enough to cover analyst salaries and private investigators, even if small investors “do not have deep enough pockets to correct the mispricing on their own.” This explanation is fully consistent with our findings. It could also explain why Andrew Left has recently announced that “after investing only his own wealth for roughly two decades, [he] is speaking to potential investors about launching Citron Capital, his first-ever hedge fund, that will begin trading in weeks.”28. Regarding the profitability, net of all costs, of a short-term trading strategy for an uninformed investor who chooses to follow Citron Research’s reports/tweets, the question is more complex. Individual investors face short selling constraints and need to pay higher transaction costs than sophisticated investors do. Furthermore, setting up bots and algorithms entails cost, and complex natural language processes, such as deep neural networks (see Mahmoudi et al. (2018)), should be implemented to understand the content of a report in real time without any manual classification. Unfortunately, from the data we have, we cannot measure the liquidity of the market just after the release of a report nor measure the transaction costs (shorting fees) on high-volatility periods or the risks associated with this strategy given the evolution of the bid-ask spread at the intraday level. Furthermore, if markets are perfectly efficient and if “academic research destroy stock return predictability” (McLean and Pontiff, 2016), the pattern described in this study should now disappear, as the results are publicly available. We encourage further research in this area.

6. Conclusion

During the early days of the market efficiency hypothesis more than 50 years ago, transactions were executed by humans traders, and information

27 A short seller can also buy put options or benefit from short-term spikes in volatility to increase its trading profit. 28 “Short seller Andrew Left to seek investor money for his first hedge fund,” CNBC, October 30, 2018. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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was disseminated only once a day by a limited number of newspapers. However, over the past decades, the rise of algorithmic trading and the massive increase in the volume of information available to investors have totally changed the way the market works. However, how this revolution has affected market efficiency and the speed of price adjustment to public news remains an open question.

Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Using a novel dataset of messages sent on Twitter by a short seller and combining data from social media with a dataset of reports published on a website, we re-examine the market efficiency hypothesis and the impact of costly information acquisition at the intraday level. Using event studies, we provide empirical evidence that the speed at which public news is integrated into stock prices has largely increased over the past decades. Traders now have a maximum of one minute to buy/sell a stock after the release of public news if they want to make abnormal returns. We also provide empirical evidence that when the news is disseminated on a channel of communication with a large audience, traders only generate very small abnormal returns. However, if traders manage to identify news on a less-known website before the dissemination of the same news on social media, they can generate significant abnormal returns, even after taking into account reasonable transaction costs. Conducting cross-sectional regressions, we likewise find that the market reaction is stronger when a company is mentioned for the first time on Twitter. This finding tends to support the hypothesis that investors (or bots) can disentangle new information from noise in real time. Lastly, we demonstrate that the price impact on the event day is not reversed over the following two weeks. Activist short seller activity seems to improve market efficiency by avoiding the over-valuation of fraudulent or risky companies. Overall, our findings are consistent with the costly information acquisition hypothesis and shed light on the Grossman Stiglitz paradox at the intraday level. Very short-lived market anomalies do exist in the stock market to compensate investors who spent time and money in setting up bots and algorithms to trade on new information before the crowd. Fast-moving traders using complex textual analysis techniques and automatic trading algorithms to buy or sell a stock within the end of the minute of the first mention of the information on a website can generate significant abnormal returns. On the contrary, slow-moving traders cannot. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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Appendix A

Figure 9. Citron Research, Website Screenshot, “Could TransDigm be the Valeant of the Aerospace Industry?” The figure shows a screenshot from the webpage http://citronresearch.com/could-transdigm- be-the-valeant-of-the-aerospace-industry/. The exact second of the publication of the article is available on the HMTL source code of the webpage. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble

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