News-sentiment networks as a risk indicator

Thomas Forss and Peter Sarlin

Papers from arXiv.org

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, and input it into our risk model together with co-occurrences of companies extracted from news on a quarterly basis. We can show that the highest quarterly risk value outputted by our risk model, is correlated to a higher chance of stock price decline, up to 70 days after a risk measurement. Our results show that the highest difference in the probability of stock price decline, compared to the benchmark containing all risk values for the same period, is during the interval from 21 to 30 days after a quarterly measurement. The highest average probability of company stock price decline, is found at a delay of 28 days, after a company has reached its maximum risk value. The highest probability differences for a daily decline were calculated to be 13 percentage points.

Date: 2017-06, Revised 2018-05
New Economics Papers: this item is included in nep-rmg
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