From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations
This addresses the need for automated financial news relevance assessment without manual annotations, though it is incremental as it adapts existing attention mechanisms to a new domain.
The paper tackles the problem of automatically identifying financially relevant news by repurposing attention weights from a neural network trained on stock price prediction, achieving high ranking of relevant news correlated with prediction accuracy on 1.5M headlines and four US stock indices.
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.