IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-2-38476-585-0_24.html

ESG Scores Predict Stock Performance in the Technology Sector

In: Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)

Author

Listed:
  • Jinglin Pei

    (Durham University)

Abstract

Recently, Environmental, Social, and Governance (ESG) has become increasingly important in investment decisions. As a result, many investors are questioning whether ESG scores can help predict stock performance, especially in the technology sector. This study investigates whether ESG scores can enhance short-term return prediction for technology firms by using Bloomberg data from 2020 to 2025. This study applied Extreme Gradient Boosting (XGBoost) and Random Forest models to predict 20-day returns by combining ESG data with technical indicators. The results showed weak predictive performance with low accuracy and negative R2 values. Ordinary Least Squares (OLS) regression revealed some significant ESG components, but their effects were inconsistent. Shapley Additive Explanations (SHAP) analysis confirmed ESG features had minimal impact compared to past return data. These findings suggest that traditional ESG scores may lack short-term predictive value. Future research should explore sentiment-based ESG measures, avoid inconsistency between different rating agencies, and improve model design to capture more meaningful insights.

Suggested Citation

  • Jinglin Pei, 2026. "ESG Scores Predict Stock Performance in the Technology Sector," Advances in Economics, Business and Management Research, in: Ata Jahangir Moshayedi (ed.), Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025), pages 203-212, Springer.
  • Handle: RePEc:spr:advbcp:978-2-38476-585-0_24
    DOI: 10.2991/978-2-38476-585-0_24
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:advbcp:978-2-38476-585-0_24. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.