Sentiment Correlation in Financial News Networks and Associated Market Movements

Abstract

In an increasingly connected global market, news sentiment towards one company may not only indicate its own market performance, but can also be associated with a broader movement on the sentiment and performance of other companies from the same or even different sectors. In this paper, we apply NLP techniques to understand news sentiment of 87 companies among the most reported on Reuters for a period of 7 years. We investigate the propagation of such sentiment in company networks and evaluate the associated market movements in terms of stock price and volatility. Our results suggest that, in certain sectors, strong media sentiment towards one company may indicate a significant change in media sentiment towards related companies measured as neighbours in a financial network constructed from news co-occurrence. Furthermore, there exists a weak but statistically significant association between strong media sentiment and abnormal market return as well as volatility. Such an association is more significant at the level of individual companies, but nevertheless remains visible at the level of sectors or groups of companies.

Contribution highlights

• Our results suggest that, in certain sectors, strong media sentiment towards one company may indicate a significant change in media sentiment towards related companies measured as neighbours in a financial network constructed from news co-occurrence.

Extracted from the public abstract; no claims have been added.

Code and data

Publication metadata

DOI

10.1038/s41598-021-82338-6

PMID

33542292

PMCID

PMC7862280

Volume

11

Issue

1

Pages / article

3062

Version relationship

No separate preprint-to-publication relationship is currently recorded.

BibTeX

@article{Wan2021Sentimentcorrelation,
  title = {Sentiment Correlation in Financial News Networks and Associated Market Movements},
  author = {Xingchen Wan and Jie Yang and Slavi Marinov and Jan-Peter Calliess and Stefan Zohren and Xiaowen Dong},
  journal = {Scientific Reports},
  year = {2021},
  url = {https://www.nature.com/articles/s41598-021-82338-3},
  doi = {10.1038/s41598-021-82338-6},
  pmid = {33542292},
  volume = {11},
  number = {1},
  pages = {3062}
}