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

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  • 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
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    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).
    4. Felix Drinkall & Janet B. Pierrehumbert & Stefan Zohren, 2024. "Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMs," Papers 2407.17624, arXiv.org, revised Jan 2025.
    5. Chew Lian Chua & Sarantis Tsiaplias & Ruining Zhou, 2024. "Constructing a high‐frequency World Economic Gauge using a mixed‐frequency dynamic factor model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2212-2227, September.

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