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

被引：31

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.

引用

页码：291 / 312

页数：22

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