Measuring news media sentiment using big data for Chinese stock markets

发布时间：2022-07-16 点击次数：

• 论文类型：期刊论文
• 论文编号：101810
• 第一作者：沈淑琳
• 发表刊物：Pacific-Basin Finance Journal
• 收录刊物：SSCI
• 学科门类：经济学
• 一级学科：应用经济学
• 文献类型：J
• 期号：74
• ISSN号：0927-538X
• 关键字：China；News sentiment；Big data；Stock market；GDELT
• DOI码：10.1016/j.pacfin.2022.101810
• 发表时间：2022-07-16
• 影响因子：3.239
• 摘要：We construct and assess new time series measures of news media sentiment based on Global Data on Events, Location, and Tone (GDELT) using Data Science techniques. Five sentiment measures representing the news media Tone, Optimism, Attention, Tone Dispersion, and Emotional Polarity of Chinese stock markets are constructed based on article tone scores and media coverages from GDELT. All these news media sentiment measures are shown to have significant predictive power for Chinese stock market returns and volatilities. We also document substantial asymmetric sentiment effects on the Chinese stock market returns and volatilities. Sentiment extended EGARCH models are shown to improve market return and volatility forecasting accuracy significantly.
• 发布期刊链接：https://doi.org/10.1016/j.pacfin.2022.101810