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When Machines Read the Web: Market Efficiency and Costly Information Acquisition at the Intraday Level

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When Machines Read the Web: 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 Gilleta,b and Thomas Renault∗c aUniversit´eParis 1 Panth´eon-Sorbonne, PRISM bUniversit´eLibre de Bruxelles, Solvay Brussels School of Management, Centre Emile Berheim cUniversit´eParis 1 Panth´eon-Sorbonne, CES & LabEx R´eFi March 2019 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 GrossmanStiglitz 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 JEL classification: G12, G14. ∗Electronic address: [email protected]; Corresponding author: Thomas Renault. Mai- son des Sciences Economiques.´ 106-112, boulevard de l'H^opital 75013 Paris. 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 infor- mation 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 (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 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 GrossmanStiglitz paradox (Grossman and Stiglitz, 1980). While theoret- ical 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 develop- ments. 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. 2 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." 1 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 demon- strating why a stock is overvalued. Regulators are naturally concerned about the possibility that activist short sellers manipulate the market by creating panic (Zhao, 2018), but this strategy is not illegal as long as the information published is not fraudulent or deliberately misleading.2 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 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. Analyzing the HTML source code of the Citron Research website, we find that a report 1 https://www.nytimes.com/2017/06/08/magazine/the-bounty-hunter-of-wall-street.html 2 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. 3 Fig. 1. Citron Research Tweet: Impact on Alliance Data System's Stock Price and Trading Volume Notes: 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. 4 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.3 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 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).4 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. The fact that Andrew Left uses Twitter to disseminate new information to the market- 3 We identify the following lines of code on the HTML source code of the website: <span class='av-structured-data' itemprop="datePublished" datetime="2016-08-19T14:57:43+00:00">2016- 08-19 14:57:43</span> 4 "World Acceptance: What Happens if Credit Insurance Disappears" - Citron Research 5 Fig. 2. Citron Research Report and Tweet: Impact on World Acceptance's Stock Price and Trading Volume Notes: 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?".
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