Thomas Renault
Title
Cited by
Cited by
Year
Intraday online investor sentiment and return patterns in the US stock market
T Renault
Journal of Banking & Finance 84, 25-40, 2017
972017
Words are not all created equal: A new measure of ECB communication
M Picault, Renault, Thomas
Journal of International Money and Finance 79, 136-156, 2017
332017
Economic uncertainty before and during the COVID-19 pandemic
D Altig, S Baker, JM Barrero, N Bloom, P Bunn, S Chen, SJ Davis, ...
Journal of Public Economics, 104274, 2020
122020
Market manipulation and suspicious stock recommendations on social media
T Renault
Available at SSRN 3010850, 2017
92017
What makes cryptocurrencies special? investor sentiment and return predictability during the bubble
CYH Chen, R Despres, L Guo, T Renault
Investor Sentiment and Return Predictability During the Bubble (June 3, 2019), 2019
62019
Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages
T Renault
Digital Finance, 1-13, 2019
42019
Comment prévoir l’emploi en lisant le journal
C Bortoli, S Combes, T Renault
Note de conjoncture, INSEE, mars, 35-43, 2017
22017
Does investor sentiment on social media provide robust information for Bitcoin returns predictability?
D Guégan, T Renault
Finance Research Letters, 101494, 2020
12020
When machines read the web: Market efficiency and costly information acquisition at the intraday level
R Gillet, T Renault
Finance 40 (2), 7-49, 2019
12019
Nowcasting GDP Growth by Reading Newspapers
C Bortoli, S Combes, T Renault
Economie et Statistique 505 (1), 17-33, 2018
12018
Social Distancing Beliefs and Human Mobility: Evidence from Twitter
S Porcher, T Renault
arXiv preprint arXiv:2008.04826, 2020
2020
Media sentiment on monetary policy: determinants and relevance for inflation expectations
M Picault, J Pinter, T Renault
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