News-sentiment networks as a company risk indicator

Thomas Forss and Peter Sarlin

Abstract

To understand the relationship between news sentiment and company stock price movements, and to better understand connectivity among companies, we define an algorithm for measuring sentiment-based network risk. The algorithm ranks companies in networks of co-occurrences and measures sentiment-based risk by calculating both individual risks and aggregated network risks. We extract relative sentiment for companies to get a measure of individual company risk. We then input this into our risk model together with co-occurrences of companies extracted from news on a quarterly basis. We show that the highest quarterly risk value outputted by our risk model is correlated to a higher chance of stock price decline up to seventy days after a quarterly risk measurement. Our results show that the highest difference in the probability of stock price decline is found during the interval from twenty-one to thirty days after a quarterly measurement. The highest average probability of company stock price decline is seen twenty-eight days after a company has reached the maximum risk value using our model, with a 13 percentage points increased chance of stock price decline.