IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v254y2025ics0165176525002411.html
   My bibliography  Save this article

Predicting stock price trends using language models to extract the sentiment from analyst reports: Evidence from IBEX 35-listed companies

Author

Listed:
  • Moreno, Alejandro
  • Ordieres-Meré, Joaquín

Abstract

This study investigates the utility of large language models to extract sentiment from sell-side equity analysts’ reports and their potential ability to predict stock price trends, using the IBEX 35 index as a case study. The RoBERTa, FinBERT, and GPT natural language processing models are employed to analyze a corpus of analysts’ equity research reports over 2016–2022. The results indicate that the extracted sentiment can serve as a valuable tool for forecasting stock price movements, avoiding the potential bias in analyst reports when assigning a target price. This highlights the transformative potential of language models in the financial industry and their role in assisting investors in making informed investment decisions.

Suggested Citation

  • Moreno, Alejandro & Ordieres-Meré, Joaquín, 2025. "Predicting stock price trends using language models to extract the sentiment from analyst reports: Evidence from IBEX 35-listed companies," Economics Letters, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:ecolet:v:254:y:2025:i:c:s0165176525002411
    DOI: 10.1016/j.econlet.2025.112404
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176525002411
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2025.112404?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:ecolet:v:254:y:2025:i:c:s0165176525002411. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.