IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v13y2025i2p107-d1674580.html
   My bibliography  Save this article

Highlighting the Role of Morality in News Framing and Its Short-Term Effects on Stock Market Fluctuations

Author

Listed:
  • Paula T. Wang

    (Media Neuroscience Lab, Department of Communication, University of California Santa Barbara, Santa Barbara, CA 93106, USA)

  • Musa Malik

    (Media Neuroscience Lab, Department of Communication, University of California Santa Barbara, Santa Barbara, CA 93106, USA)

  • René Weber

    (Media Neuroscience Lab, Department of Communication, University of California Santa Barbara, Santa Barbara, CA 93106, USA
    Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA 93106, USA
    Division of Communication and Media, Ewha Woman’s University, Seoul 03760, Republic of Korea)

Abstract

The Model of Intuitive Morality and Exemplars (MIME) suggests that news audiences, including investors, evaluate news based on their moral frames, and that these moral evaluations shape behavior. We extracted moral signals from 382,185 news articles across an 8-month period and examined their predictive effect on stock market movement. Results indicate that morality is a strong predictor during low economic periods and is driven by subversion and sanctity. Overall, our study suggests that moral framing and its foundations are important considerations for research on news effects, especially during periods of economic instability. The study provides an additional theoretical perspective on stock market fluctuations as well as practical implications for stakeholders with an interest in dampening collective panics and stabilizing investor sentiment.

Suggested Citation

  • Paula T. Wang & Musa Malik & René Weber, 2025. "Highlighting the Role of Morality in News Framing and Its Short-Term Effects on Stock Market Fluctuations," IJFS, MDPI, vol. 13(2), pages 1-19, June.
  • Handle: RePEc:gam:jijfss:v:13:y:2025:i:2:p:107-:d:1674580
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/13/2/107/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7072/13/2/107/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nyman, Rickard & Kapadia, Sujit & Tuckett, David, 2021. "News and narratives in financial systems: Exploiting big data for systemic risk assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    2. Harris, Colin & Myers, Andrew & Kaiser, Adam, 2023. "The humanizing effect of market interaction," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 489-507.
    3. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    4. 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. Nyman, Rickard & Kapadia, Sujit & Tuckett, David, 2021. "News and narratives in financial systems: Exploiting big data for systemic risk assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    2. Amer Demirovic & Ali Kabiri & David Tuckett & Rickard Nyman, 2020. "A common risk factor and the correlation between equity and corporate bond returns," Journal of Asset Management, Palgrave Macmillan, vol. 21(2), pages 119-134, March.
    3. Gao, Xin & Xu, Weidong & Li, Donghui, 2025. "Media coverage and managerial investment learning from stock markets: International evidence," Research in International Business and Finance, Elsevier, vol. 76(C).
    4. David E. Allen & Michael McAleer & Abhay K. Singh, 2019. "Daily market news sentiment and stock prices," Applied Economics, Taylor & Francis Journals, vol. 51(30), pages 3212-3235, June.
    5. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    6. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    7. Eryka Probierz & Adam Galuszka & Katarzyna Klimczak & Karol Jedrasiak & Tomasz Wisniewski & Tomasz Dzida, 2021. "Financial Sentiment on Twitter's Community and it's Connection to Polish Stock Market Movements in Context of Behavior Modelling," European Research Studies Journal, European Research Studies Journal, vol. 0(4 - Part ), pages 56-65.
    8. Michael J. Seiler & David M. Harrison, 2011. "Perceived Versus Actual Susceptibility to Normative Influence in the Presence of Defaulting Landlords," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 3(2), pages 55-77, September.
    9. Caporale, Guglielmo Maria & Spagnolo, Fabio & Spagnolo, Nicola, 2016. "Macro news and stock returns in the Euro area: A VAR-GARCH-in-mean analysis," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 180-188.
    10. Leilane de Freitas Rocha Cambara & Roberto Meurer, 2023. "News sentiment and foreign portfolio investment in Brazil," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3332-3348, July.
    11. repec:hal:spmain:info:hdl:2441/3mgbd73vkp9f9oje7utooe7vpg is not listed on IDEAS
    12. Yang-Cheng Lu & Yu-Chen Wei, 2013. "The Chinese News Sentiment around Earnings Announcements," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 44-58, October.
    13. Ahmed, Yousry & Elshandidy, Tamer, 2016. "The effect of bidder conservatism on M&A decisions: Text-based evidence from US 10-K filings," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 176-190.
    14. Leif Anders Thorsrud, 2016. "Nowcasting using news topics Big Data versus big bank," Working Papers No 6/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    15. Liebmann, Michael & Orlov, Alexei G. & Neumann, Dirk, 2016. "The tone of financial news and the perceptions of stock and CDS traders," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 159-175.
    16. Shams, Syed & Bose, Sudipta & Sheikhbahaei, Ali, 2024. "Pricing media sentiment: Evidence from global mergers and acquisitions," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
    17. Ferdinand Graf, 2011. "Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets," Working Paper Series of the Department of Economics, University of Konstanz 2011-18, Department of Economics, University of Konstanz.
    18. Juanjuan Wang & Shujie Zhou & Wentong Liu & Lin Jiang, 2024. "An ensemble model for stock index prediction based on media attention and emotional causal inference," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1998-2020, September.
    19. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    20. Marlene Amstad & Leonardo Gambacorta & Chao He & Dora Xia, 2021. "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," BIS Working Papers 917, Bank for International Settlements.
    21. Nicholas Guest & Jaewoo Kim, 2024. "The media response to a loss of analyst coverage," Review of Accounting Studies, Springer, vol. 29(4), pages 3752-3787, December.

    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:jijfss:v:13:y:2025:i:2:p:107-:d:1674580. 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 (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.