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Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures?
[Regression Models with Mixed Sampling Frequencies]

Author

Listed:
  • Steven Lehrer
  • Tian Xie
  • Tao Zeng

Abstract

Social media data present challenges for forecasters since one must convert text into data and deal with issues related to these measures being collected at different frequencies and volumes than traditional financial data. In this article, we use a deep learning algorithm to measure sentiment within Twitter messages on an hourly basis and introduce a new method to undertake mixed data sampling (MIDAS) that allows for a weaker discounting of historical data that is well-suited for this new data source. To evaluate the performance of approach relative to alternative MIDAS strategies, we conduct an out of sample forecasting exercise for the consumer confidence index with both traditional econometric strategies and machine learning algorithms. Irrespective of the estimator used to conduct forecasts, our results show that (i) including consumer sentiment measures from Twitter greatly improves forecast accuracy and (ii) there are substantial gains from our proposed MIDAS procedure relative to common alternatives.

Suggested Citation

  • Steven Lehrer & Tian Xie & Tao Zeng, 2021. "Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures? [Regression Models with Mixed Sampling Frequencies]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 910-933.
    • Handle: RePEc:oup:jfinec:v:19:y:2021:i:5:p:910-933.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbz037
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    Citations

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    Cited by:

    1. Steven F. Lehrer & Tian Xie, 2022. "The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success," Management Science, INFORMS, vol. 68(1), pages 189-210, January.
    2. Menghan Zhang & Xue Qi & Xinyan Liu & Ke Zhang, 2024. "RETRACTED: Who Leads? Who Follows? Exploring Agenda Setting by Media, Social Bots and Public in the Discussion of the 2022 South Korean Presidential Election," SAGE Open, , vol. 14(2), pages 21582440241, May.
    3. Lahiri, Kajal & Yang, Cheng, 2022. "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, vol. 38(2), pages 545-566.
    4. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
    5. Andranik Tumasjan, 2024. "The many faces of social media in business and economics research: Taking stock of the literature and looking into the future," Journal of Economic Surveys, Wiley Blackwell, vol. 38(2), pages 389-426, April.
    6. Algaba, Andres & Borms, Samuel & Boudt, Kris & Verbeken, Brecht, 2023. "Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 266-278.
    7. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
    8. 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.

    More about this item

    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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