IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v38y2019i6p504-518.html
   My bibliography  Save this article

Forecasting economic indicators using a consumer sentiment index: Survey‐based versus text‐based data

Author

Listed:
  • Minchae Song
  • Kyung‐shik Shin

Abstract

Given the confirmed effectiveness of the survey‐based consumer sentiment index (CSI) as a leading indicator of real economic conditions, the CSI is actively used in making policy judgments and decisions in many countries. However, although the CSI offers qualitative information for presenting current conditions and predicting a household's future economic activity, the survey‐based method has several limitations. In this context, we extracted sentiment information from online economic news articles and demonstrated that the Korean cases are a good illustration of applying a text mining technique when generating a CSI using sentiment analysis. By applying a simple sentiment analysis based on the lexicon approach, this paper confirmed that news articles can be an effective source for generating an economic indicator in Korea. Even though cross‐national comparative research results are suited better than national‐level data to generalize and verify the method used in this study, international comparisons are quite challenging to draw due to the necessary linguistic preprocessing. We hope to encourage further cross‐national comparative research to apply the approach proposed in this study.

Suggested Citation

  • Minchae Song & Kyung‐shik Shin, 2019. "Forecasting economic indicators using a consumer sentiment index: Survey‐based versus text‐based data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 504-518, September.
  • Handle: RePEc:wly:jforec:v:38:y:2019:i:6:p:504-518
    DOI: 10.1002/for.2584
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2584
    Download Restriction: no

    File URL: https://libkey.io/10.1002/for.2584?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
    ---><---

    Citations

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


    Cited by:

    1. Assaf, Ata & Charif, Husni & Mokni, Khaled, 2021. "Dynamic connectedness between uncertainty and energy markets: Do investor sentiments matter?," Resources Policy, Elsevier, vol. 72(C).
    2. Petrova, Diana & Trunin, Pavel, 2020. "Revealing the mood of economic agents based on search queries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 71-87.
    3. Chen, Yangyang & Goyal, Abhinav & Veeraraghavan, Madhu & Zolotoy, Leon, 2020. "Terrorist attacks, investor sentiment, and the pricing of initial public offerings," Journal of Corporate Finance, Elsevier, vol. 65(C).

    More about this item

    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:wly:jforec:v:38:y:2019:i:6:p:504-518. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.