IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v18y2025i8p412-d1710464.html

News Sentiment and Stock Market Dynamics: A Machine Learning Investigation

Author

Listed:
  • Milivoje Davidovic

    (Finance Academic Group, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA)

  • Jacqueline McCleary

    (College of Science, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA)

Abstract

The study relies on an extensive dataset (≈1.86 million news headlines) to investigate the heterogeneity and predictive power of explicit sentiment signals (TextBlob, VADER, and FinBERT) and implied sentiment (VIX) for stock market trends. We find that news content predominantly consists of objective or neutral information, with only a small portion carrying subjective or emotive weight. There is a structural market bias toward upswings (bullish market states). Market behavior appears anticipatory rather than reactive: forward-looking implied sentiment captures a substantial share (≈45–50%) of the variation in stock returns. By contrast, sentiment scores, even when disaggregated into firm- and non-firm-specific subscores, lack robust predictive power. However, weekend and holiday sentiment contains modest yet valuable market signals. Algorithm-wise, Gradient Boosting Machine (GBM) stands out in both classification (bullish vs. bearish) and regression tasks. Neither FinBERT news sentiment, historical returns, nor implied volatility offer a consistently exploitable edge over market efficiency. Thus, our findings lend empirical support to both the weak-form and semi-strong forms of the Efficient Market Hypothesis. In the realm of exploitable trading strategies, markets remain an enigma against systematic alpha.

Suggested Citation

  • Milivoje Davidovic & Jacqueline McCleary, 2025. "News Sentiment and Stock Market Dynamics: A Machine Learning Investigation," JRFM, MDPI, vol. 18(8), pages 1-54, July.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:8:p:412-:d:1710464
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/18/8/412/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/18/8/412/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shleifer, Andrei, 2000. "Inefficient Markets: An Introduction to Behavioral Finance," OUP Catalogue, Oxford University Press, number 9780198292272.
    2. Ball, R & Brown, P, 1968. "Empirical Evaluation Of Accounting Income Numbers," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 6(2), pages 159-178.
    3. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John L. Campbell & Hsinchun Chen & Dan S. Dhaliwal & Hsin-min Lu & Logan B. Steele, 2014. "The information content of mandatory risk factor disclosures in corporate filings," Review of Accounting Studies, Springer, vol. 19(1), pages 396-455, March.
    2. Wang, Wenzhao & Duxbury, Darren, 2021. "Institutional investor sentiment and the mean-variance relationship: Global evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 415-441.
    3. Lu Zhang, 2017. "The Investment CAPM," European Financial Management, European Financial Management Association, vol. 23(4), pages 545-603, September.
    4. James P. Ryans, 2021. "Textual classification of SEC comment letters," Review of Accounting Studies, Springer, vol. 26(1), pages 37-80, March.
    5. Pedersen, Lasse Heje, 2022. "Game on: Social networks and markets," Journal of Financial Economics, Elsevier, vol. 146(3), pages 1097-1119.
    6. Antonio Sánchez Serrano, 2018. "EU banks after the crisis: sinners in the hands of angry markets," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 1(9), pages 24-51, May.
    7. Jacob Boudoukh & Ronen Feldman & Shimon Kogan & Matthew Richardson, 2013. "Which News Moves Stock Prices? A Textual Analysis," NBER Working Papers 18725, National Bureau of Economic Research, Inc.
    8. Souza, Thársis T.P. & Aste, Tomaso, 2019. "Predicting future stock market structure by combining social and financial network information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    9. Wu, Chen-Hui, 2022. "The informativeness of brokerage reports: Privately-circulated versus publicly-disseminated news," International Review of Financial Analysis, Elsevier, vol. 83(C).
    10. Sabet, Amir H. & Agha, Mahmoud & Heaney, Richard, 2018. "Value of investment: Evidence from the oil and gas industry," Energy Economics, Elsevier, vol. 70(C), pages 190-204.
    11. Roman Frydman & Søren Johansen & Anders Rahbek & Morten Nyboe Tabor, 2017. "The Qualitative Expectations Hypothesis: Model Ambiguity, Consistent Representations of Market Forecasts, and Sentiment," CREATES Research Papers 2017-23, Department of Economics and Business Economics, Aarhus University.
    12. Liu, Yahui & Zhao, Wenxuan & Gao, Di & Chen, Zhaohui, 2026. "From chain waves to market moves: Untangling price efficiency in the supply chain network," Journal of Banking & Finance, Elsevier, vol. 185(C).
    13. Seok, Sang Ik & Cho, Hoon & Ryu, Doojin, 2019. "Firm-specific investor sentiment and the stock market response to earnings news," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 221-240.
    14. Karmanpartap Singh Sidhu & Junyi Fan & Maryam Pishgar, 2026. "Which Voices Move Markets? Speaker Identity and the Cross-Section of Post-Earnings Returns," Papers 2604.13260, arXiv.org.
    15. Price, S. McKay & Doran, James S. & Peterson, David R. & Bliss, Barbara A., 2012. "Earnings conference calls and stock returns: The incremental informativeness of textual tone," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 992-1011.
    16. Alberto Barroso Del Toro & Laura Vivas Crisol & Xavier Tort-Martorell, 2022. "The Sustainability Narrative: A Multi Study Using Event Studies to Analyse the American Energy Companies Shareholder’s Reaction to Sustainability News," IJERPH, MDPI, vol. 19(23), pages 1-17, November.
    17. Eierle, Brigitte & Klamer, Sebastian & Muck, Matthias, 2022. "Does it really pay off for investors to consider information from social media?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    18. Shin, Heejeong & Park, Sorah, 2018. "Do foreign investors mitigate anchoring bias in stock market? Evidence based on post-earnings announcement drift," Pacific-Basin Finance Journal, Elsevier, vol. 48(C), pages 224-240.
    19. Yu-Chen WEI & Yang-Cheng LU & I-Chi LIN, 2015. "The Impact Of Financial News And Press Freedom On Abnormal Returns Around Earnings Announcement Periods In The Shanghai, Shenzhen And Taiwan Stock Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 39-59, September.
    20. Sebastian Lehner & Alejandro Lopez-Lira, 2026. "ChatGPT as a Time Capsule: The Limits of Price Discovery," Papers 2604.21433, arXiv.org.

    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:gam:jjrfmx:v:18:y:2025:i:8:p:412-:d:1710464. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.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.