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Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach

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  • Shi, Yanlin
  • Ho, Kin-Yip
  • Liu, Wai-Man

Abstract

Using computational linguistic analysis of intraday firm-level news releases, this study models the relation between public information flows and stock volatility under different regimes. We analyze how the hourly return volatility of S&P100 stocks from 2000 to 2010 are linked to the various linguistics-based sentiment scores of the news releases, which are obtained from the RavenPack News Analytics Database. Results from the Markov Regime-Switching GARCH (MRS-GARCH) model indicate that firm-specific news sentiment is more significant in quantifying intraday volatility persistence in the calm (low-volatility) state than the turbulent (high-volatility) state. Furthermore, the impact of news sentiment differs across industries and firm size.

Suggested Citation

  • Shi, Yanlin & Ho, Kin-Yip & Liu, Wai-Man, 2016. "Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 291-312.
    • Handle: RePEc:eee:reveco:v:42:y:2016:i:c:p:291-312
    DOI: 10.1016/j.iref.2015.12.003
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    3. Roland Fuess & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia in the Cross-Section of Global Equity and Currency Returns," BAFFI CAREFIN Working Papers 19116, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    4. Feng, Lingbing & Fu, Tong & Shi, Yanlin, 2022. "How does news sentiment affect the states of Japanese stock return volatility?," International Review of Financial Analysis, Elsevier, vol. 84(C).
    5. Chou, Ke-Hsin & Day, Min-Yuh & Chiu, Chien-Liang, 2023. "Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 365-385.
    6. Roland Füss & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia In The Cross-Section of Global Equity," Working Papers on Finance 1913, University of St. Gallen, School of Finance, revised May 2020.
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    8. Chuliá, Helena & Guillén, Montserrat & Uribe, Jorge M., 2017. "Measuring uncertainty in the stock market," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 18-33.
    9. Gong, Yuting & Li, Kevin X. & Chen, Shu-Ling & Shi, Wenming, 2020. "Contagion risk between the shipping freight and stock markets: Evidence from the recent US-China trade war," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    10. Ho, Kin-Yip & An, Jiyoun, 2020. "Decomposing the value premium: The role of intangible information in the Chinese stock market," Emerging Markets Review, Elsevier, vol. 44(C).
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    12. Peng Ye & Yong Li & Abu Bakkar Siddik, 2023. "Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm," Energies, MDPI, vol. 16(11), pages 1-39, June.
    13. Zhou, Xinmiao & Zhang, Junru & Zhang, Zhaoyong, 2021. "How does news flow affect cross-market volatility spillovers? Evidence from China’s stock index futures and spot markets," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 196-213.
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    More about this item

    Keywords

    Public information arrival; Stock return volatility; News sentiment; Markov Regime-Switching GARCH;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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