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

被引：32
作者：
Shi, Yanlin [1 ,2 ]
Ho, Kin-Yip [1 ]
Liu, Wai-Man [1 ]
机构：
[1] Australian Natl Univ, Res Sch Finance Actuarial Studies & Stat, Canberra, ACT 2601, Australia
[2] Australian Natl Univ, Sch Demog, Canberra, ACT 2601, Australia
来源：
INTERNATIONAL REVIEW OF ECONOMICS & FINANCE | 2016年 / 42卷
关键词：
Public information arrival; Stock return volatility; News sentiment; Markov Regime-Switching GARCH; MACROECONOMIC NEWS; TRANSACTION DATA; TIME-SERIES; BAD-NEWS; GARCH; MODEL; RATES; FIRM; HETEROSKEDASTICITY; ANNOUNCEMENTS;
D O I：
10.1016/j.iref.2015.12.003
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
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. (C) 2015 Elsevier Inc. All rights reserved.
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页码：291 / 312
页数：22
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