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

Yanlin Shi; Kin Yip Ho

doi:10.1016/j.frl.2020.101446

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

Yanlin Shi^*, Kin Yip Ho

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

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.

Original language	English
Article number	101446
Journal	Finance Research Letters
Volume	38
DOIs	https://doi.org/10.1016/j.frl.2020.101446
Publication status	Published - Jan 2021

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

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.",

keywords = "Asset volatility, Discrete choice model, Markov regime-switching FIEGARCH, News sentiment, Public information arrival",

author = "Yanlin Shi and Ho, {Kin Yip}",

note = "Publisher Copyright: {\textcopyright} 2020 Elsevier Inc.",

year = "2021",

month = jan,

doi = "10.1016/j.frl.2020.101446",

language = "English",

volume = "38",

journal = "Finance Research Letters",

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T2 - Evidence from long memory and discrete choice models

AU - Shi, Yanlin

AU - Ho, Kin Yip

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AB - 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.

KW - Asset volatility

KW - Discrete choice model

KW - Markov regime-switching FIEGARCH

KW - News sentiment

KW - Public information arrival

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VL - 38

JO - Finance Research Letters

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ER -

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

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