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COVID risk narratives: a computational linguistic approach to the econometric identification of narrative risk during a pandemic

Author

Listed:
  • Yuting Chen

    (Rennes SB - Rennes School of Business, UCD - University College Dublin [Dublin])

  • Don Bredin

    (UCD - University College Dublin [Dublin])

  • Valerio Potì

    (UCD - University College Dublin [Dublin])

  • Roman Matkovskyy

    (ESC Rennes School of Business - Rennes SB - Rennes School of Business)

Abstract

In this paper, we study the role of narratives in stock markets with a particular focus on the relationship with the ongoing COVID-19 pandemic. The pandemic represents a natural setting for the development of viral financial market narratives. We thus treat the pandemic as a natural experiment on the relation between prevailing narratives and financial markets. We adopt natural language processing (NLP) on financial news to characterize the evolution of important narratives. Doing so, we reduce the high-dimensional narrative information to few interpretable and important features while avoiding over-fitting.

Suggested Citation

  • Yuting Chen & Don Bredin & Valerio Potì & Roman Matkovskyy, 2022. "COVID risk narratives: a computational linguistic approach to the econometric identification of narrative risk during a pandemic," Post-Print hal-04021587, HAL.
    • Handle: RePEc:hal:journl:hal-04021587
    DOI: 10.1007/s42521-021-00045-3
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    Cited by:

    1. Foglia, Matteo & Miglietta, Federica, 2024. "Does every cloud (bubble) have a silver lining? An investigation of ESG financial markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 42(C).
    2. Mazzotta, Stefano, 2024. "Immigration Narrative and Home Prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 43(C).
    3. Mazzotta, Stefano, 2022. "Immigration narrative sentiment from TV news and the stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 34(C).
    4. Takeshi Inuduka & Akihito Yokose & Shunsuke Managi, 2024. "Influencing cryptocurrency: analyzing celebrity sentiments on X (formerly Twitter) and their impact on bitcoin prices," Digital Finance, Springer, vol. 6(3), pages 379-426, September.

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E71 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on the Macro Economy
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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