  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.
