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Measuring news media sentiment using big data for Chinese stock markets

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Listed:
  • Shen, Shulin
  • Xia, Le
  • Shuai, Yulin
  • Gao, Da

Abstract

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.

Suggested Citation

  • Shen, Shulin & Xia, Le & Shuai, Yulin & Gao, Da, 2022. "Measuring news media sentiment using big data for Chinese stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:pacfin:v:74:y:2022:i:c:s0927538x22001056
    DOI: 10.1016/j.pacfin.2022.101810
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    2. Loan Thi Vu & Dong Ngoc Pham & Hang Thu Kieu & Thuy Thi Thanh Pham, 2023. "Sentiments Extracted from News and Stock Market Reactions in Vietnam," IJFS, MDPI, vol. 11(3), pages 1-16, August.

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    More about this item

    Keywords

    China; News sentiment; Big data; Stock market; GDELT;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G40 - Financial Economics - - Behavioral Finance - - - General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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