IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-04739924.html
   My bibliography  Save this paper

Sentiment Analysis of Bloomberg Markets Wrap Using ChatGPT: Application to the NASDAQ
[Analyse du sentiment de l'information diffusée par Bloomberg Markets à l'aide de ChatGPT : Application au NASDAQ]

Author

Listed:
  • Baptiste Lefort

    (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay, A.I. For Alpha)

  • Eric Benhamou

    (A.I. For Alpha)

  • Jean-Jacques Ohana

    (A.I. For Alpha)

  • David Saltiel

    (A.I. For Alpha)

  • Beatrice Guez

    (A.I. For Alpha)

  • Thomas Jacquot

    (A.I. For Alpha)

Abstract

In this paper, we use a comprehensive dataset of daily Bloomberg Financial Market Summaries spanning from 2010 to 2023, published by Yahoo Finance, CNews and multiple large financial medias, to determine how global news headlines may affect stock market movements. To make this analysis more effective, we employed ChatGPT. First, from the vast pool of daily financial updates, we identify the top global news headlines that could potentially have a significant influence on stock prices. Second, for each headline, we question ChatGPT to answer whether the news might lead to a rise, fall in stock prices or is indecisive. This two-stage method proves more effective than posing a direct question to the entire text. By gathering ChatGPT's predictions day by day, we formed an overall market sentiment score. We transform this score into a practical investment strategy in the NASDAQ index, demonstrating the significance of minimizing noise in sentiment scores by initially accumulating and then detrending them. This approach showcases that ChatGPT's analysis of news headlines can provide valuable insights into future stock market behaviors and be a valuable tool to develop intuitive NLP-driven investment strategies leveraging news predictive power.

Suggested Citation

  • Baptiste Lefort & Eric Benhamou & Jean-Jacques Ohana & David Saltiel & Beatrice Guez & Thomas Jacquot, 2024. "Sentiment Analysis of Bloomberg Markets Wrap Using ChatGPT: Application to the NASDAQ [Analyse du sentiment de l'information diffusée par Bloomberg Markets à l'aide de ChatGPT : Application au NASD," Working Papers hal-04739924, HAL.
    • Handle: RePEc:hal:wpaper:hal-04739924
    Note: View the original document on HAL open archive server: https://hal.science/hal-04739924v1
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04739924v1/document
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-04739924. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.