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Public Information Arrival and Stock Return Volatility: Evidence from News Sentiment and Markov Regime-Switching Approach

Public Information Arrival and Stock Return Volatility

Kin-Yip Ho, Raymond (Wai-Man) Liu and Yanlin Shi

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 2000-2010 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.

Updated:   8 March 2019 / Responsible Officer:  CBE Communications and Outreach / Page Contact:  College Web Team