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

Yanlin Shi; Kin Yip Ho; Wai Man Liu

doi:10.1016/j.iref.2015.12.003

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

Yanlin Shi^*, Kin Yip Ho, Wai Man Liu

^*Corresponding author for this work

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

35 Citations (Scopus)

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.

Original language	English
Pages (from-to)	291-312
Number of pages	22
Journal	International Review of Economics and Finance
Volume	42
DOIs	https://doi.org/10.1016/j.iref.2015.12.003
Publication status	Published - 1 Mar 2016

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

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.",

keywords = "Markov Regime-Switching GARCH, News sentiment, Public information arrival, Stock return volatility",

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

note = "Publisher Copyright: {\textcopyright} 2015 Elsevier Inc.",

year = "2016",

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doi = "10.1016/j.iref.2015.12.003",

language = "English",

volume = "42",

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journal = "International Review of Economics and Finance",

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T2 - Evidence from news sentiment and Markov Regime-Switching Approach

AU - Shi, Yanlin

AU - Ho, Kin Yip

AU - Liu, Wai Man

PY - 2016/3/1

Y1 - 2016/3/1

N2 - 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.

AB - 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.

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JO - International Review of Economics and Finance

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

Abstract

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