IDEAS home Printed from https://ideas.repec.org/a/eme/jpifpp/v34y2016i3p249-262.html
   My bibliography  Save this article

Google search volume sentiment and its impact on REIT market movements

Author

Listed:
  • Nicole Braun

Abstract

Purpose - The purpose of this paper is to analyze the effect of investor sentiment, measured with Google internet search data, on volatility forecasts of the US REIT market. Design/methodology/approach - The author uses the S&P US REIT index and collects search volume data from Google Trends for all US REIT. Two different Generalized Autoregressive Conditional Heteroskedastic models are then estimated, namely, the baseline model and the Google augmented model. Using these models, one-step-ahead forecasts are conducted and the forecast accuracies of both models are subsequently compared. Findings - The empirical results reveal that search volume data can be used to predict volatility on the REIT market. Especially in periods of high volatility, Google augmented models outperform the baseline model. Practical implications - The results imply that Google data can be used on the REIT market as a market indicator. Investors could use Google as an early warning system, especially in periods of high volatility. Originality/value - This is the first paper to use Google search query data for volatility forecasts of the REIT market.

Suggested Citation

  • Nicole Braun, 2016. "Google search volume sentiment and its impact on REIT market movements," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 34(3), pages 249-262, April.
    • Handle: RePEc:eme:jpifpp:v:34:y:2016:i:3:p:249-262
    DOI: 10.1108/JPIF-12-2015-0083
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/JPIF-12-2015-0083/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/JPIF-12-2015-0083/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/JPIF-12-2015-0083?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    Citations

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


    Cited by:

    1. Wendi Zhang & Bin Li & Alan Wee-Chung Liew & Eduardo Roca & Tarlok Singh, 2023. "Predicting the returns of the US real estate investment trust market: evidence from the group method of data handling neural network," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-33, December.
    2. Chaiyuth Padungsaksawasdi & Sirimon Treepongkaruna & Robert Brooks, 2019. "Investor Attention and Stock Market Activities: New Evidence from Panel Data," IJFS, MDPI, vol. 7(2), pages 1-19, June.

    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:eme:jpifpp:v:34:y:2016:i:3:p:249-262. 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: Emerald Support (email available below). General contact details of provider: .

    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.