IDEAS home Printed from https://ideas.repec.org/f/pre517.html
   My authors  Follow this author

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

Centre d'Économie de la Sorbonne
Université Paris 1 (Panthéon-Sorbonne)

Paris, France
https://centredeconomiesorbonne.cnrs.fr/
RePEc:edi:cenp1fr (more details at EDIRC)

Research output

as
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. Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Klochkov, Yegor, 2019. "SONIC: SOcial Network with Influencers and Communities," IRTG 1792 Discussion Papers 2019-025, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. 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.
    4. 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.
    5. 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.
    6. Sergey Nasekin & Cathy Yi-Hsuan Chen, 2020. "Deep learning-based cryptocurrency sentiment construction," Digital Finance, Springer, vol. 2(1), pages 39-67, September.
    7. Marco Caiffa & Vincenzo Farina & Lucrezia Fattobene, 2020. "All that glitters is not gold: CEOs' celebrity beyond media content," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(3), pages 444-460, July.
    8. 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.
    9. Di, Li & Shaiban, Mohammed Sharaf & Hasanov, Akram Shavkatovich, 2021. "The power of investor sentiment in explaining bank stock performance: Listed conventional vs. Islamic banks," Pacific-Basin Finance Journal, Elsevier, vol. 66(C).
    10. Guégan, Dominique & Renault, Thomas, 2021. "Does investor sentiment on social media provide robust information for Bitcoin returns predictability?," Finance Research Letters, Elsevier, vol. 38(C).
    11. Chen, Cathy Yi-Hsuan & Després, Roméo & Guo, Li & Renault, Thomas, 2019. "What makes cryptocurrencies special? Investor sentiment and return predictability during the bubble," IRTG 1792 Discussion Papers 2019-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    12. Wen, Zhuzhu & Gong, Xu & Ma, Diandian & Xu, Yahua, 2021. "Intraday momentum and return predictability: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 95(C), pages 374-384.
    13. 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).
    14. Ballinari, Daniele & Behrendt, Simon, 2020. "Structural breaks in online investor sentiment: A note on the nonstationarity of financial chatter," Finance Research Letters, Elsevier, vol. 35(C).
    15. 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.
    16. 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.
    17. Emna Mnif & Anis Jarboui & M. Kabir Hassan & Khaireddine Mouakhar, 2020. "Big data tools for Islamic financial analysis," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 10-21, January.
    18. Rossouw, Stephanie & Greyling, Talita, 2020. "Big Data and Happiness," GLO Discussion Paper Series 634, Global Labor Organization (GLO).
    19. Saurabh, Samant & Dey, Kushankur, 2020. "Unraveling the relationship between social moods and the stock market: Evidence from the United Kingdom," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    20. Kelley Bergsma & Andy Fodor & Vijay Singal & Jitendra Tayal, 2020. "Option trading after the opening bell and intraday stock return predictability," Financial Management, Financial Management Association International, vol. 49(3), pages 769-804, September.
    21. Long, Wen & Zhao, Manyi & Tang, Yeran, 2021. "Can the Chinese volatility index reflect investor sentiment?," International Review of Financial Analysis, Elsevier, vol. 73(C).
    22. 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".
    23. Karam KIM & Doojin RYU, 2020. "Predictive ability of investor sentiment for the stock market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 33-46, December.
    24. Rui Fan & Oleksandr Talavera & Vu Tran, 2018. "Social media bots and stock markets," Working Papers 2018-30, Swansea University, School of Management.
    25. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    26. 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.
    27. Lan, Yueqin & Huang, Yong & Yan, Chao, 2021. "Investor sentiment and stock price: Empirical evidence from Chinese SEOs," Economic Modelling, Elsevier, vol. 94(C), pages 703-714.
    28. Dong, Hang & Gil-Bazo, Javier, 2020. "Sentiment stocks," International Review of Financial Analysis, Elsevier, vol. 72(C).
    29. Simon Porcher & Thomas Renault, 2021. "Social distancing beliefs and human mobility: Evidence from Twitter," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-12, March.
    30. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    31. 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.
    32. 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.
    33. Florian Röder & Andreas Walter, 2019. "What Drives Investment Flows Into Social Trading Portfolios?," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 42(2), pages 383-411, July.
    34. Zachary McGurk & Adam Nowak & Joshua C. Hall, 2020. "Stock returns and investor sentiment: textual analysis and social media," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(3), pages 458-485, July.
    35. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    36. Fu, Junhui & Wu, Xiang & Liu, Yufang & Chen, Rongda, 2021. "Firm-specific investor sentiment and stock price crash risk," Finance Research Letters, Elsevier, vol. 38(C).
    37. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Post-Print halshs-02181597, HAL.
    38. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    39. 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.
    40. Yanhui Chen & Hanhui Zhao & Ziyu Li & Jinrong Lu, 2020. "A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from China," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-18, December.

  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. Paloviita, Maritta & Haavio, Markus & Jalasjoki, Pirkka & Kilponen, Juha & Vänni, Ilona, 2020. "Reading between the lines - Using text analysis to estimate the loss function of the ECB," Research Discussion Papers 12/2020, Bank of Finland.
    3. Hubert, Paul & Labondance, Fabien, 2021. "The signaling effects of central bank tone," European Economic Review, Elsevier, vol. 133(C).
    4. Cour-Thimann, Philippine & Jung, Alexander, 2020. "Interest rate setting and communication at the ECB," Working Paper Series 2443, European Central Bank.
    5. 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.
    6. Paul Hubert & Fabien Labondance, 2020. "Central Bank Tone and the Dispersion of Views within Monetary Policy Committees," Sciences Po publications 02/2020, Sciences Po.
    7. Hamza Bennani & Pavel Gertler & Roman Horvath & Nicolas Fanta, 2020. "Does Central Bank Communication Signal Future Monetary Policy in a (post)-Crisis Era? The Case of the ECB," Post-Print hal-02486315, HAL.
    8. Renaud Beaupain & Alexandre Girard, 2020. "The value of understanding central bank communication," Post-Print hal-02509297, HAL.
    9. Baranowski, Paweł & Doryń, Wirginia & Łyziak, Tomasz & Stanisławska, Ewa, 2021. "Words and deeds in managing expectations: Empirical evidence from an inflation targeting economy," Economic Modelling, Elsevier, vol. 95(C), pages 49-67.
    10. 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.
    11. 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).
    12. 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.
    13. Hüning, Hendrik, 2020. "Swiss National Bank communication and investors’ uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    14. Peter Tillmann, 2020. "Financial Markets and Dissent in the ECB’s Governing Council," MAGKS Papers on Economics 202048, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    15. Paweł Baranowski & Wirginia Doryń & Tomasz Łyziak & Ewa Stanisławska, 2020. "Words and deeds in managing expectations: empirical evidence on an inflation targeting economy," NBP Working Papers 326, Narodowy Bank Polski, Economic Research Department.
    16. 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.
    17. 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.
    18. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    19. Paweł Baranowski & Hamza Bennani & Wirginia Doryń, 2020. "Do ECB introductory statements help to predict monetary policy: evidence from tone analysis," NBP Working Papers 323, Narodowy Bank Polski, Economic Research Department.
    20. Firrell, Alastair & Reinold, Kate, 2020. "Uncertainty and voting on the Bank of England’s Monetary Policy Committee," Bank of England working papers 898, Bank of England.
    21. 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.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Thomas Renault should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.