  We present a method to automatically identify financially relevant news using
stock price movements and news headlines as input. The method repurposes the
attention weights of a neural network initially trained to predict stock prices
to assign a relevance score to each headline, eliminating the need for manually
labeled training data. Our experiments on the four most relevant US stock
indices and 1.5M news headlines show that the method ranks relevant news
highly, positively correlated with the accuracy of the initial stock price
prediction task.
