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Market Efficiency and Costly Information Acquisition at the Intraday Level

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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 ISSN 0752-6180 ISBN 9782706145872 Article disponible en ligne à l'adresse : -------------------------------------------------------------------------------------------------------------------- https://www.cairn.inforevue-finance-2019-2-page-7.htm -------------------------------------------------------------------------------------------------------------------- Distribution électronique Cairn.info pour Presses universitaires de Grenoble. © Presses universitaires de Grenoble. Tous droits réservés pour tous pays. La reproduction ou représentation de cet article, notamment par photocopie, n'est autorisée que dans les limites des conditions générales d'utilisation du site ou, le cas échéant, des conditions générales de la licence souscrite par votre établissement. Toute autre reproduction ou représentation, en tout ou partie, sous quelque forme et de quelque manière que ce soit, est interdite sauf accord préalable et écrit de l'éditeur, en dehors des cas prévus par la législation en vigueur en France. Il est précisé que son stockage dans une base de données est également interdit. Document téléchargé depuis www.cairn.info - 109.133.138.144 13/09/2019 15:02 © Presses universitaires de Grenoble 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 40-2_RevueFinance.indd 7 22/08/2019 09:47:12 8 Finance Vol. 40 N° 2 2019 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 demon- strate 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 tech- nological 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 40-2_RevueFinance.indd 8 22/08/2019 09:47:12 When Machines Read the Web 9 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 40-2_RevueFinance.indd 9 22/08/2019 09:47:12 10 Finance Vol. 40 N° 2 2019 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).
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