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Twitter-based market uncertainty and global stock volatility predictability

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  • Ma, Yong
  • Li, Shuaibing
  • Zhou, Mingtao

Abstract

This study integrates Twitter-based market uncertainty (TMU) into the predictive framework of daily volatility in twenty international equity markets. The study reveals that TMU has a strong predictive ability for stock volatility from both in- and out-of-sample perspectives. Interestingly, despite Twitter being inaccessible in China, the interconnectedness of global financial markets allows it to indirectly impact China’s stock market volatility. The research also highlights that TMU plays a particularly significant role in forecasting stock market volatility during turbulent periods, such as the COVID-19 epidemic. Furthermore, integrating TMU into the volatility prediction framework leads to an improvement in economic value. These findings are essential for policymakers to develop effective market-stabilizing policies and for investors to enhance the management of their investment portfolios.

Suggested Citation

  • Ma, Yong & Li, Shuaibing & Zhou, Mingtao, 2025. "Twitter-based market uncertainty and global stock volatility predictability," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    • Handle: RePEc:eee:ecofin:v:75:y:2025:i:pa:s1062940824001815
    DOI: 10.1016/j.najef.2024.102256
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G19 - Financial Economics - - General Financial Markets - - - Other

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