News-sentiment networks as a company risk indicator

Thomas Forss and Peter Sarlin

Journal of Network Theory in Finance

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.

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Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ8:5515601

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