Sentiment Correlation in Financial News Networks and Associated Market Movements

TL;DR

This study uses NLP to analyze how news sentiment about companies influences market movements and sentiment propagation across financial networks over seven years.

Contribution

It introduces a network-based approach to link news sentiment with market dynamics and demonstrates the propagation of sentiment among related companies.

Findings

Strong media sentiment can predict changes in related companies' sentiment.

Media sentiment correlates with abnormal stock returns and volatility.

Sentiment effects are more pronounced at the individual company level.

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 seven 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.