When Machines Read the Web: Market Efficiency and Costly Information Acquisition at the Intraday Level
Roland Gillet () and
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Thomas Renault: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne
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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 (search for similar items in EconPapers)
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Published in Finance, Presses universitaires de Grenoble, 2019, 40 (2), pp.7. ⟨10.3917/fina.402.0007⟩
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Journal Article: When Machines Read the Web: Market Efficiency and Costly Information Acquisition at the Intraday Level (2019)
Working Paper: When Machines Read the Web: Market Efficiency and Costly Information Acquisition at the Intraday Level (2019)
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