IDEAS home Printed from https://ideas.repec.org/p/zbw/cfswop/492.html
   My bibliography  Save this paper

News media sentiment and investor behavior

Author

Listed:
  • Kräussl, Roman
  • Mirgorodskaya, Elizaveta

Abstract

This paper investigates the impact of news media sentiment on financial market returns and volatility in the long-term. We hypothesize that the way the media formulate and present news to the public produces different perceptions and, thus, incurs different investor behavior. To analyze such framing effects we distinguish between optimistic and pessimistic news frames. We construct a monthly media sentiment indicator by taking the ratio of the number of newspaper articles that contain predetermined negative words to the number of newspaper articles that contain predetermined positive words in the headline and/or the lead paragraph. Our results indicate that pessimistic news media sentiment is positively related to global market volatility and negatively related to global market returns 12 to 24 months in advance. We show that our media sentiment indicator reflects very well the financial market crises and pricing bubbles over the past 20 years.

Suggested Citation

  • Kräussl, Roman & Mirgorodskaya, Elizaveta, 2014. "News media sentiment and investor behavior," CFS Working Paper Series 492, Center for Financial Studies (CFS).
    • Handle: RePEc:zbw:cfswop:492
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/103755/1/803538200.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    2. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    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.
    4. Werner Antweiler & Murray Z. Frank, 2004. "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards," Journal of Finance, American Finance Association, vol. 59(3), pages 1259-1294, June.
    5. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. William N. Goetzmann & Dasol Kim & Robert J. Shiller, 2016. "Crash Beliefs From Investor Surveys," NBER Working Papers 22143, National Bureau of Economic Research, Inc.

    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. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    2. N. Banholzer & S. Heiden & D. Schneller, 2019. "Exploiting investor sentiment for portfolio optimization," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 671-702, December.
    3. Sun, Licheng & Najand, Mohammad & Shen, Jiancheng, 2016. "Stock return predictability and investor sentiment: A high-frequency perspective," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 147-164.
    4. Vegard Høghaug Larsen & Leif Anders Thorsrud, 2022. "Asset returns, news topics, and media effects," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 838-868, July.
    5. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    6. Santi, Caterina, 2023. "Investor climate sentiment and financial markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    7. Christina Bannier & Thomas Pauls & Andreas Walter, 2019. "Content analysis of business communication: introducing a German dictionary," Journal of Business Economics, Springer, vol. 89(1), pages 79-123, February.
    8. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2018. "LASSO-driven inference in time and space," CeMMAP working papers CWP36/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Karapandza, Rasa, 2016. "Stock returns and future tense language in 10-K reports," Journal of Banking & Finance, Elsevier, vol. 71(C), pages 50-61.
    10. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2018. "Textual Sentiment, Option Characteristics, and Stock Return Predictability," IRTG 1792 Discussion Papers 2018-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Fengler, Matthias & Phan, Minh Tri, 2023. "A Topic Model for 10-K Management Disclosures," Economics Working Paper Series 2307, University of St. Gallen, School of Economics and Political Science.
    12. Daniele Ballinari & Simon Behrendt, 2021. "How to gauge investor behavior? A comparison of online investor sentiment measures," Digital Finance, Springer, vol. 3(2), pages 169-204, June.
    13. Gu, Chen & Kurov, Alexander, 2020. "Informational role of social media: Evidence from Twitter sentiment," Journal of Banking & Finance, Elsevier, vol. 121(C).
    14. 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.
    15. Duan, Yuejiao & Liu, Lanbiao & Wang, Zhuo, 2021. "COVID-19 Sentiment and the Chinese Stock Market: Evidence from the Official News Media and Sina Weibo," Research in International Business and Finance, Elsevier, vol. 58(C).
    16. John Garcia, 2021. "Analyst herding and firm-level investor sentiment," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 461-494, December.
    17. Liao, Rose & Wang, Xinjie & Wu, Ge, 2021. "The role of media in mergers and acquisitions," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    18. Avramov, Doron & Li, Minwen & Wang, Hao, 2021. "Predicting corporate policies using downside risk: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 1-26.
    19. Anand, Abhinav & Basu, Sankarshan & Pathak, Jalaj & Thampy, Ashok, 2021. "The impact of sentiment on emerging stock markets," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 161-177.
    20. Zongwu Cai & Pixiong Chen, 2022. "New Online Investor Sentiment and Asset Returns," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202216, University of Kansas, Department of Economics, revised Nov 2022.

    More about this item

    Keywords

    Investor behavior; News media sentiment; Financial market crises; Pricing bubbles; Framing effects; MMFs; SEC; securities; net asset value; financial crisis; shadow banking; systemic risk; financial crisis;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:cfswop:492. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/ifkcfde.html .

    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.