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Social media sentiment, investor herding and informational efficiency

Ni Yang et al.

Journal of Financial Research2026https://doi.org/10.1111/jfir.70039article
AJG 3ABDC A
Weight
0.50

Abstract

We investigate the impact of social media sentiment on the informational efficiency of financial markets. In particular, we examine the relationship between sentiment extracted from Twitter posts and two widely used efficiency measures: return autocorrelation and variance ratio. Our results show that more positive sentiment is associated with increased return autocorrelation and variance ratio the following day, suggesting a decline in informational efficiency. Furthermore, we demonstrate that the effect of social media sentiment on informational efficiency is driven by the herding behavior among traders, with more positive sentiment leading to more pronounced herding activity. These findings suggest that elevated social media sentiment contributes to a reduction in the quality of the information environment, ultimately leading to informationally inefficient equity prices.

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https://doi.org/https://doi.org/10.1111/jfir.70039

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@article{ni2026,
  title        = {{Social media sentiment, investor herding and informational efficiency}},
  author       = {Ni Yang et al.},
  journal      = {Journal of Financial Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/jfir.70039},
}

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Social media sentiment, investor herding and informational efficiency

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.