IDEAS home Printed from https://ideas.repec.org/a/eee/intfin/v42y2016icp139-154.html
   My bibliography  Save this article

Language, news and volatility

Author

Listed:
  • Byström, Hans

Abstract

I use Google News to study the relation between news volumes and stock market volatilities. More than nine million stock market-related news stories in English and Chinese are collected and the dynamics of the news volume and the stock market volatility is compared. I find that the stock market volatility and the number of publicly available global news stories are strongly linked in both languages. Furthermore, the directional link between news and volatility rather is from news to volatility than vice versa. In out-of-sample evaluations of volatility forecasts I find news volumes to improve forecasts, regardless of language.

Suggested Citation

  • Byström, Hans, 2016. "Language, news and volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 42(C), pages 139-154.
    • Handle: RePEc:eee:intfin:v:42:y:2016:i:c:p:139-154
    DOI: 10.1016/j.intfin.2016.03.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1042443116300063
    Download Restriction: Full text for ScienceDirect subscribers only

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Su, Dongwei & Fleisher, Belton M., 1999. "Why does return volatility differ in Chinese stock markets?," Pacific-Basin Finance Journal, Elsevier, vol. 7(5), pages 557-586, December.
    2. Berry, Thomas D & Howe, Keith M, 1994. "Public Information Arrival," Journal of Finance, American Finance Association, vol. 49(4), pages 1331-1346, September.
    3. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    4. Poon, Winnie P. H. & Fung, Hung-Gay, 2000. "Red chips or H shares: which China-backed securities process information the fastest?," Journal of Multinational Financial Management, Elsevier, vol. 10(3-4), pages 315-343, December.
    5. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Jian Yang, 2003. "Market Segmentation and Information Asymmetry in Chinese Stock Markets: A VAR Analysis," The Financial Review, Eastern Finance Association, vol. 38(4), pages 591-609, November.
    8. 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.
    9. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    10. Ederington, Louis H & Lee, Jae Ha, 1993. "How Markets Process Information: News Releases and Volatility," Journal of Finance, American Finance Association, vol. 48(4), pages 1161-1191, September.
    11. Hans Byström, 2009. "News aggregators, volatility and the stock market," Economics Bulletin, AccessEcon, vol. 29(4), pages 2673-2682.
    12. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    News aggregator; Language; Volatility; Stock market; Chinese;

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

    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:eee:intfin:v:42:y:2016:i:c:p:139-154. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: http://www.elsevier.com/locate/intfin .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.