Thomas Renault

Thomas Renault
Université de Paris 1 Panthéon-Sorbonne | UNiVPARIS1 · Interdisciplinary Centre for Research in Management Science

PhD

About

24
Publications
3,716
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
825
Citations
Additional affiliations
September 2012 - present
IESEG School of Management
Position
  • Research Assistant

Publications

Publications (24)
Article
We construct a new indicator to capture media sentiment about the European Central Bank monetary policy and its relevant environment by analyzing 25,000 articles from five major international newspapers. Using named entity recognition and part-of-speech tagging, we propose a methodology to dissociate the dissemination of official communications of...
Article
Full-text available
We construct a new indicator to capture media sentiment about the European Central Bank monetary policy and its relevant environment by analyzing 25,000 articles from five major international newspapers. Using named entity recognition and part-of-speech tagging, we propose a methodology to dissociate the dissemination of official communications of...
Article
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...
Article
Full-text available
We consider several economic uncertainty indicators for the US and UK before and during the COVID-19 pandemic: implied stock market volatility, newspaper-based policy uncertainty, twitter chatter about economic uncertainty, subjective uncertainty about business growth, forecaster disagreement about future GDP growth, and a model-based measure of ma...
Article
Full-text available
We use a large dataset of one million messages sent on the microblogging platform StockTwits to evaluate the performance of a wide range of preprocessing methods and machine learning algorithms for sentiment analysis in finance. We find that adding bigrams and emojis significantly improve sentiment classification performance. However, more complex...
Preprint
Full-text available
We construct a new indicator to capture media sentiment about the European Central Bank monetary policy and its relevant environment by analyzing 25,000 articles from five major international newspapers. Using named entity recognition and part-of-speech tagging, we propose a methodology to dissociate the dissemination of the central bank's official...
Preprint
Full-text available
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 hand...
Article
We use a dataset of approximately one million messages sent on StockTwits to explore the relationship between investor sentiment on social media and intraday Bitcoin returns. We find a statistically significant relationship between investor sentiment and Bitcoin returns for frequencies of up to 15 minutes. For lower frequencies, the relation disapp...
Article
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 soci...
Article
Big data and economics: Evolution or Revolution? Researchers in economics now have access to a substantial amount of data. Big data contributes to improving the quality and timing of macroeconomic indicators, and to answering major economic questions. However, it comes with new biases and limitations, which require researchers to develop new theori...
Article
The massive increase in the volume of data generated daily on the Internet offers researchers the opportunity to approach the issue of financial market predictability from a new perspective. In particular, the analysis of content published on social networks now makes it possible to quantify investors’ opinions in an automated way. In this article,...
Thesis
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 hyp...
Article
Full-text available
We develop a field-specific dictionary to measure the stance of the European Central Bank (ECB) monetary policy (dovish, neutral, hawkish) and the state of the Eurozone economy (positive, neutral, negative) through the content of ECB press conferences. In contrast with traditional textual analysis, we propose a novel approach using term-weighting a...
Article
We implement a novel approach to derive investor sentiment from messages posted on social media before we explore the relation between online investor sentiment and intraday stock returns. Using an extensive dataset of messages posted on the microblogging platform StockTwits, we construct a lexicon of words used by online investors when they share...

Network

Cited By