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The cross-correlations between online sentiment proxies: Evidence from Google Trends and Twitter

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  • Zhang, Zuochao
  • Zhang, Yongjie
  • Shen, Dehua
  • Zhang, Wei

Abstract

In this paper, we explore the cross-correlations between two commonly-employed online sentiment proxies, i.e., the Financial and Economic Attitudes Revealed by Search (FEARS) from Google Trends and Daily Happiness Sentiment (DHS) from Twitter, with the methodology of MF-DCCA. The empirical results mainly show that: firstly, there exists power-law cross-correlation between the FEARS and DHS and the cross-correlation between them perform multifractality; secondly, the degree of multifractality in short term is significantly smaller than that in long term indicating a more stable cross-correlation in short term; finally, with the rolling window analysis, we further find that the evolution of the cross-correlations between FEARS and DHS is erratic.

Suggested Citation

  • Zhang, Zuochao & Zhang, Yongjie & Shen, Dehua & Zhang, Wei, 2018. "The cross-correlations between online sentiment proxies: Evidence from Google Trends and Twitter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 67-75.
    • Handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:67-75
    DOI: 10.1016/j.physa.2018.05.051
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