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 journalArticle

9 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 languageEnglish
Pages (from-to)291-312
Number of pages22
JournalInternational Review of Economics and Finance
Volume42
DOIs
Publication statusPublished - 1 Mar 2016
Externally publishedYes

Keywords

  • public information arrival
  • stock return volatility
  • news sentiment
  • Markov Regime-Switching GARCH

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