IDEAS home Printed from https://ideas.repec.org/a/ids/ijicbm/v34y2025i3p389-407.html
   My bibliography  Save this article

Financial sentiment analysis of news articles with long text corpus for equity portfolio construction

Author

Listed:
  • Senthil Arasu Balasubramanian
  • J. Nancy Christina
  • P. Sridevi

Abstract

Forecasting stock performance is a well-researched area. In recent times, textual data related to stock market are considered to have more meaningful insights and various natural language processing (NLP) techniques are employed to process it. Several studies have used news headlines to predict stock market performance and most of the studies focus on short-term forecasting which considers lags of days or less and pose a higher risk than investing over a long-term. In this study, long text corpus of news articles of non-financial stocks from Nifty 50 is analysed with an objective to construct an equity portfolio. A pre-trained NLP model FinBERT was used to analyse the sentiment of the financial text. The resulting portfolio was found to outperform the market. However, few stocks with extraordinary performance were missed as the stocks for the portfolio were selected using news articles as the only source.

Suggested Citation

  • Senthil Arasu Balasubramanian & J. Nancy Christina & P. Sridevi, 2025. "Financial sentiment analysis of news articles with long text corpus for equity portfolio construction," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 34(3), pages 389-407.
    • Handle: RePEc:ids:ijicbm:v:34:y:2025:i:3:p:389-407
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=144909
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijicbm:v:34:y:2025:i:3:p:389-407. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=235 .

    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.