IDEAS home Printed from https://ideas.repec.org/a/taf/tjmaxx/v11y2024i2p161-181.html
   My bibliography  Save this article

Dividend announcement and the value of sentiment analysis

Author

Listed:
  • Susana Álvarez-Díez
  • J. Samuel Baixauli-Soler
  • Anna Kondratenko
  • Gabriel Lozano-Reina

Abstract

Payout policy constitutes one of the most important corporate financial decisions since dividends are essential factors in determining a firm’s value. A dividend announcement generates a market signal which translates into changes in stock returns, impacting short-term price fluctuations and producing abnormal returns. The sample consists of 394 companies listed on the S&P500 index, from which 1574 dividend announcements and 7222 news items are derived during the years 2022–2023. News pieces are obtained from 58 specialized sources, and ChatGPT is used to automate the sentiment extracted from them. Using sentiment analysis, this paper shows the key role played by sentiments derived from financial news posted just after dividend announcements in predicting market reaction and helping investors to select optimal investment strategies. This paper contributes to the current literature, highlighting the influence that sentiments have on determining stock market returns.

Suggested Citation

  • Susana Álvarez-Díez & J. Samuel Baixauli-Soler & Anna Kondratenko & Gabriel Lozano-Reina, 2024. "Dividend announcement and the value of sentiment analysis," Journal of Management Analytics, Taylor & Francis Journals, vol. 11(2), pages 161-181, April.
    • Handle: RePEc:taf:tjmaxx:v:11:y:2024:i:2:p:161-181
    DOI: 10.1080/23270012.2024.2306929
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23270012.2024.2306929
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/23270012.2024.2306929?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 search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).

    More about this item

    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:taf:tjmaxx:v:11:y:2024:i:2:p:161-181. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjma .

    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.