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Thomas Renault

Personal Details

First Name:Thomas
Middle Name:
Last Name:Renault
Suffix:
RePEc Short-ID:pre517
[This author has chosen not to make the email address public]
http://www.thomas-renault.com
Twitter: @captaineco_fr

Affiliation

Maison des Sciences Économiques
Université Paris 1 (Panthéon-Sorbonne)

Paris, France
http://mse.univ-paris1.fr/

: 01 44 07 81 00
01 44 07 81 09
106 - 112 boulevard de l'Hôpital, 75647 Paris cedex 13
RePEc:edi:msep1fr (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
  2. Picault, Matthieu & Renault, Thomas, 2017. "Words are not all created equal: A new measure of ECB communication," Journal of International Money and Finance, Elsevier, vol. 79(C), pages 136-156.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.

    Cited by:

    1. Isabelle Royer & Lionel Garreau & Thomas Roulet, 2019. "La quantification des données qualitatives : intérêts et difficultés en sciences de gestion," Post-Print hal-02303982, HAL.
    2. Broadstock, David C. & Zhang, Dayong, 2019. "Social-media and intraday stock returns: The pricing power of sentiment," Finance Research Letters, Elsevier, vol. 30(C), pages 116-123.
    3. Gao, Ya & Han, Xing & Li, Youwei & Xiong, Xiong, 2019. "Overnight Momentum, Informational Shocks, and Late-Informed Trading in China," MPRA Paper 96784, University Library of Munich, Germany.
    4. Kommel, Karl Arnold & Sillasoo, Martin & Lublóy, Ágnes, 2019. "Could crowdsourced financial analysis replace the equity research by investment banks?," Finance Research Letters, Elsevier, vol. 29(C), pages 280-284.
    5. Christina Bannier & Thomas Pauls & Andreas Walter, 2019. "Content analysis of business communication: introducing a German dictionary," Journal of Business Economics, Springer, vol. 89(1), pages 79-123, February.
    6. Steyn, Dimitri H. W. & Greyling, Talita & Rossouw, Stephanie & Mwamba, John M., 2020. "Sentiment, emotions and stock market predictability in developed and emerging markets," GLO Discussion Paper Series 502, Global Labor Organization (GLO).
    7. Chen, C. Y-H. & Härdle, W. K. & Klochkov, Y., 2019. "Influencers and Communities in Social Networks," Cambridge Working Papers in Economics 1998, Faculty of Economics, University of Cambridge.
    8. Rui Fan & Oleksandr Talavera & Vu Tran, 2020. "Social media and price discovery: the case of cross-listed firms," Discussion Papers 20-05, Department of Economics, University of Birmingham.
    9. Alexis Bogroff & Dominique Guégan, 2019. "Artificial Intelligence, Data, Ethics. An Holistic Approach for Risks and Regulation," Working Papers 2019: 19, Department of Economics, University of Venice "Ca' Foscari".
    10. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02181597, HAL.
    11. Renato Camodeca & Alex Almici & Umberto Sagliaschi, 2018. "Sustainability Disclosure in Integrated Reporting: Does It Matter to Investors? A Cheap Talk Approach," Sustainability, MDPI, Open Access Journal, vol. 10(12), pages 1-34, November.
    12. Zhang, Yaojie & Ma, Feng & Zhu, Bo, 2019. "Intraday momentum and stock return predictability: Evidence from China," Economic Modelling, Elsevier, vol. 76(C), pages 319-329.
    13. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Post-Print halshs-02181597, HAL.
    14. Kokoszka, Piotr & Miao, Hong & Petersen, Alexander & Shang, Han Lin, 2019. "Forecasting of density functions with an application to cross-sectional and intraday returns," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1304-1317.

  2. Picault, Matthieu & Renault, Thomas, 2017. "Words are not all created equal: A new measure of ECB communication," Journal of International Money and Finance, Elsevier, vol. 79(C), pages 136-156.

    Cited by:

    1. Isabelle Royer & Lionel Garreau & Thomas Roulet, 2019. "La quantification des données qualitatives : intérêts et difficultés en sciences de gestion," Post-Print hal-02303982, HAL.
    2. Youngjoon Lee & Soohyon Kim & Ki Young Park, 2018. "Deciphering Monetary Policy Committee Minutes with Text Mining Approach: A Case of South Korea," Working papers 2018rwp-132, Yonsei University, Yonsei Economics Research Institute.
    3. Beaupain, Renaud & Girard, Alexandre, 2020. "The value of understanding central bank communication," Economic Modelling, Elsevier, vol. 85(C), pages 154-165.
    4. Paul Hubert & Fabien Labondance, 2020. "Central Bank Tone and the Dispersion of Views within Monetary Policy Committees," Documents de Travail de l'OFCE 2020-02, Observatoire Francais des Conjonctures Economiques (OFCE).
    5. Aakriti Mathur & Rajeswari Sengupta, 2019. "Analysing monetary policy statements of the Reserve Bank of India," IHEID Working Papers 08-2019, Economics Section, The Graduate Institute of International Studies.
    6. Apel, Mikael & Blix Grimaldi, Marianna & Hull, Isaiah, 2019. "How Much Information Do Monetary Policy Committees Disclose? Evidence from the FOMC's Minutes and Transcripts," Working Paper Series 381, Sveriges Riksbank (Central Bank of Sweden).
    7. Young Joon Lee & Soohyon Kim & Ki Young Park, 2019. "Deciphering Monetary Policy Board Minutes with Text Mining: The Case of South Korea," Korean Economic Review, Korean Economic Association, vol. 35, pages 471-511.
    8. Ki Young Park & Youngjoon Lee & Soohyon Kim, 2019. "Deciphering Monetary Policy Board Minutes through Text Mining Approach: The Case of Korea," Working Papers 2019-1, Economic Research Institute, Bank of Korea.
    9. Youngjoon Lee & Soohyon Kim & Ki Young Park, 2019. "Measuring Monetary Policy Surprises Using Text Mining: The Case of Korea," Working Papers 2019-11, Economic Research Institute, Bank of Korea.
    10. Adam Hale Shapiro & Daniel J. Wilson, 2019. "Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives Using Text Analysis," Working Paper Series 2019-2, Federal Reserve Bank of San Francisco, revised 25 Jun 2019.

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