RENAULT Thomas

We construct a novel database containing hundreds of thousands geotagged messages related to the COVID-19 pandemic sent on Twitter. We create a daily index of social distancing—at the state level—to capture social distancing beliefs by analyzing the number of tweets containing keywords such as “stay home”, “stay safe”, “wear mask”, “wash hands” and “social distancing”. We find that an increase in the Twitter index of social distancing on day t-1 is associated with a decrease in mobility on day t. We also find that state orders, an increase in the number of COVID-19 cases, precipitation and temperature contribute to reducing human mobility. Republican states are also less likely to enforce social distancing. Beliefs shared on social networks could both reveal the behavior of individuals and influence the behavior of others. Our findings suggest that policy makers can use geotagged Twitter data—in conjunction with mobility data—to better understand individual voluntary social distancing actions.

Immigration and crime are two first-order issues that are often considered jointly in people's minds. This paper analyzes how media reporting policies on crime impact natives' attitudes towards immigration. We depart from most studies by investigating the content of crime-related articles instead of their coverage. Specifically, we use a radical change in local media reporting on crime in Germany as a natural experiment. This unique framework allows us to estimate whether systematically disclosing the places of origin of criminals affects natives' attitudes towards immigration. We combine individual survey data collected between January 2014 and December 2018 from the German SocioEconomic Panel with data from more than 545,000 crime-related articles in German newspapers and data on their diffusion across the country. Our results indicate that systematically mentioning the origins of criminals, especially when offenders are natives, significantly reduces natives' concerns about immigration.

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In the last 10 years, new forms of currencies have emerged: crypto-currencies, the best known of which is bitcoin. However, they are neither the first form of alternative currency, nor the first form of digital currency. But they have their own characteristics. Explanation and comparison with other forms of currencies, fiduciary or electronic.

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The massive increase in the volume of data generated every day by individuals on the Internet offers researchers the opportunity to approach the question of the predictability of financial markets from a new angle. Without claiming to provide a definitive answer to the debate between the proponents of market efficiency and behavioral finance researchers, this thesis aims to improve our understanding of the price formation process in financial markets through a Big Data approach. Specifically, this thesis focuses on (1) measuring investor sentiment at intraday frequency, and the relationship between investor sentiment and aggregate market returns,(2) measuring investor attention to economic and financial information in real time, and the relationship between investor attention and the dynamics of large-cap stock prices, and finally, (3) the detection of suspicious behaviors that may diminish the informational role of financial markets, and the relationship between the volume of activity on social networks and the stock prices of small-cap firms. The first essay proposes a methodology to construct a new indicator of investor sentiment by analyzing the content of messages posted on the social network Stock-Twits. By examining the specific characteristics of each user (level of experience, investment approach, holding period), this essay provides empirical evidence that the behavior of naive investors, subject to periods of excessive optimism or pessimism, has an impact on stock market valuation, in line with theories of behavioral finance. The second essay proposes a methodology to measure investors' attention to news in real time by combining data from traditional media with the content of messages sent by a list of experts on the Twitter platform. This test shows that when news attracts investors' attention, market movements are characterized by a sharp increase in traded volumes, increased volatility and price jumps. This essay also demonstrates that there is no significant information leakage when information sources are combined to correct a potential timestamp problem. The third essay investigates the risk of informational manipulation by examining a new dataset of Twitter posts about small-cap companies. This essay proposes a new methodology to identify anomalous behavior in an automated manner by analyzing user interactions. Given the large number of suspicious buy recommendations sent by certain groups of users, the empirical analysis and findings of this essay underscore the need for greater regulatory oversight of information posted on social networks as well as the value of better education of individual investors.

The massive increase in the availability of data generated everyday by individuals on the Internet has made it possible to address the predictability of financial markets from a different perspective. Without making the claim of offering a definitive answer to a debate that has persisted for forty years between partisans of the efficient market hypothesis and behavioral finance academics, this dissertation aims to improve our understanding of the price formation process in financial markets through the use of Big Data analytics. More precisely, it analyzes: (1) how to measure intraday investor sentiment and determine the relation between investor sentiment and aggregate market returns, (2) how to measure investor attention to news in real time, and identify the relation between investor attention and the price dynamics of large capitalization stocks, and (3) how to detect suspicious behaviors that could undermine the in-formational role of financial markets, and determine the relation between the level of posting activity on social media and small-capitalization stock returns. The first essay proposes a methodology to construct a novel indicator of investor sentiment by analyzing an extensive dataset of user-generated content published on the social media platform Stock-Twits. Examining users’ self-reported trading characteristics, the essay provides empirical evidence of sentiment-driven noise trading at the intraday level, consistent with behavioral finance theories. The second essay proposes a methodology to measure investor attention to news in real-time by combining data from traditional newswires with the content published by experts on the social media platform Twitter. The essay demonstrates that news that garners high attention leads to large and persistent change in trading activity, volatility, and price jumps. It also demonstrates that the pre-announcement effect is reduced when corrected newswire timestamps are considered. The third essay provides new insights into the empirical literature on small capitalization stocks market manipulation by examining a novel dataset of messages published on the social media plat-form Twitter. The essay proposes a novel methodology to identify suspicious behaviors by analyzing interactions between users and provide empirical evidence of suspicious stock recommendations on social media that could be related to market manipulation. The conclusion of the essay should rein-force regulators’ efforts to better control social media and highlights the need for a better education of individual investors.

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