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  • 华中科技大学

沈淑琳 讲师(高校)

沈淑琳,女,华中科技大学经济学院讲师。美国雪城大学经济学博士,研究方向为金融计量,房地产金融,城市与区域经济学。近年来主持国家自然科学(青年)项目,华中科技大学自主创新研究基金(人文社科)项目等。研究成果发表于Journal of Applied Econometrics,Canadian Journal of Economics, Journal of Real Estate Finance and Economics, Journal of Population Economics, Pacific-Basin Finance Journal, Resource and Energy Economics, Travel Beviour and Society等国际权威期刊。


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