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News sentiment and states of stock return volatility: Evidence from long memory and discrete choice models

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  • Shi, Yanlin
  • Ho, Kin-Yip

Abstract

This paper examines the impact of public news sentiment on volatility states of firm-level returns. We firstly propose a Markov regime switching fractionally integrated exponential GARCH (MRS-FIEGARCH) model, which is employed to estimate the latent volatility states of intraday stock return. By using the new RavenPack Dow Jones News Analytics database, we fit discrete choice models to investigate the impact of news sentiment on changes of volatility states of the constituent stocks in the Dow Jones Composite Average. Our results demonstrate that sentiments of macroeconomic and firm-specific news can significantly influence the likelihoods of volatility states of intraday stock returns.

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  • Shi, Yanlin & Ho, Kin-Yip, 2021. "News sentiment and states of stock return volatility: Evidence from long memory and discrete choice models," Finance Research Letters, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319309961
    DOI: 10.1016/j.frl.2020.101446
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    Cited by:

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    2. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    3. Kao, Yu-Sheng & Zhao, Kai & Chuang, Hwei-Lin & Ku, Yu-Cheng, 2024. "The asymmetric relationships between the Bitcoin futures’ return, volatility, and trading volume," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 524-542.
    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. Zhang, Hongwei & Hong, Huojun & Guo, Yaoqi & Yang, Cai, 2022. "Information spillover effects from media coverage to the crude oil, gold, and Bitcoin markets during the COVID-19 pandemic: Evidence from the time and frequency domains," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 267-285.
    6. Luo, Yi & Li, Xiaoming & Yu, Wei & Huang, Kun & Yang, Yihe & Huang, Yao, 2024. "Research on human dynamics characteristics under large-scale stock data perturbation," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    7. Petar Sorić & Ivana Lolić & Marina Matošec, 2023. "The persistence of economic sentiment: a trip down memory lane," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(2), pages 371-395, April.
    8. Ştefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2023. "Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-58, December.
    9. Kao, Yu-Sheng & Day, Min-Yuh & Chou, Ke-Hsin, 2024. "A comparison of bitcoin futures return and return volatility based on news sentiment contemporaneously or lead-lag," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    10. Niu, Zibo & Liu, Yuanyuan & Gao, Wang & Zhang, Hongwei, 2021. "The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China," Resources Policy, Elsevier, vol. 73(C).

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