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Twitter ( en anglais), rebaptisé progressivement X depuis le , est un réseau social de microblogage. Il permet à un utilisateur d’envoyer gratuitement des micromessages limités à , appelés tweets ou gazouillis par messagerie instantanée ou par SMS. Twitter est créé le par Jack Dorsey, Evan Williams, Biz Stone et Noah Glass. Le service en ligne est rapidement devenu populaire. Le , il compte d’utilisateurs actifs par mois, de tweets envoyés par jour et est disponible en plus de quarante langues. En 2018, Twitter annonce pour la première fois avoir fait du profit. Le , Twitter accepte la proposition de rachat d'Elon Musk pour la somme de de dollars, c'est cependant le 27 octobre 2022 qu'Elon Musk confirme le rachat des actions de Twitter et devient le nouveau propriétaire. Les mesures qu'il prend et les déclarations qu'il fait après son arrivée entraînent une forte réduction du nombre des salariés de la société. En avril 2023, la société X Corp., propriété d'Elon Musk créée en 20

Essays in Monetary Policy and Asset Pricing

Benoit Vincent Sylvain Cornet

This thesis uses machine learning techniques and text data to investigate the relationships that arise between the Fed and financial markets, and their consequences for asset prices.The first chapter, entitled Market Expectations and the Impact of Unconventional Monetary Policy: An Application to Twitter Data, is an answer to (Greenlaw et al., 2018), who show, by looking at a large set of monetary policy announcements made between November 2008 and December 2017 - FOMC meetings, release of minutes and speeches of the Fed Chair - that long term yields tended to increase following these events. By doing so, the authors challenge common wisdom according to which the central bank intervention in the aftermath of the financial crisis lowered long term rates (Gagnon, 2016). Using machine learning and twitter data, this chapter develops a novel measure of market expectations of monetary policy, and shows that the increase in yields was simply due to a marginal adjustment of market expectations following announcements being less dovish than expected.The second chapter, entitled Informational Feedback Loop, Monetary Policy Decisions and Asset Prices Dynamics, investigates the consequences of a Fed that uses (1) its own private signal and (2) fed funds futures to take its monetary policy decision. Fed funds futures aggre- gate private information received by financial markets participants - traders - but they also depend on traders' expectations about the Fed's behavior, which makes futures endogenous in the central bank decision. The theoretical model shows that the surprise generated by monetary policy announcements and the subsequent adjustment in short term U.S. treasury yields depend on the precision of the signals received by each agent. When the signal received by traders is more precise than the central bank's, the latter relies more on fed funds futures to take its decision, and the surprise and adjustment of short term yields are smaller. By contrast, long term yields adjust only because the announcement provides traders with new information about the state of the economy, by revealing the central bank's private signal. Finally, when the Fed is averse to financial markets volatility, it tends to put some weight on fed funds futures even if they are not informative about the state of the economy. The empirical part of the paper provides some evidence supporting these channels, by using a topic and tone approach (Hansen and McMahon, 2016) to extract the precision of the signals received by the central bank and traders from FOMC minutes and tweets respectively.

EPFL2022

Predicting behavior of Twitter Users

Adela Maznikar

Social networks today are great source of data which can be used and analyzed in different ways. In our project the main goal is to predict the behavior of the users, more accurately said: we try to predict what will a particular user tweet in the future, when given a training set of existing URL mentions. The topic has been previously explored in the paper Outtweeting the Twitterers-Predicting Information Cascades in Microblogs by Wojciech Galuba, Karl Aberer, Dipanjan Chakraborty, Zoran Despotovic, Wolfgang Kellerer and our work is the extension of their research. As mentioned in their paper, accurate predictions can be used for many purposes. Some of them are: personalized recommendation, ranking and filtering of the stream of tweets that the user sees etc. Also. it can be used in predicting in virality of a certain URL which can help in marketing campaigns for predicting the success of that campaign. One of the main tasks on this project is: Build a framework which can rank, evaluate different models of predictions with online data. We made a tool/framework in Java that can work with different models of predictions, the evaluator just need to specify the model of prediction with very little code and the framework offers a lot of support for calculating usually used parameters in the model so the prediction is made really easier once the model is known. However in order to test if our framework works correctly, we implemented two models which will be described further in the report and we were working with offline data.

2011

Essays in Monetary Policy and Asset Pricing

Benoit Vincent Sylvain Cornet

EPFL2022