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We present a method to automatically identify financially relevant news usingstock price movements and news headlines as input. The method repurposes theattention weights of a neural network initially trained to predict stock pricesto assign a relevance score to each headline, eliminating the need for manuallylabeled training data. Our experiments on the four most relevant US stockindices and 1.5M news headlines show that the method ranks relevant newshighly, positively correlated with the accuracy of the initial stock priceprediction task.

From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations