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

Abstract

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.

Forecasting Stock Price Trends by Analyzing Economic Reports With Analyst Profiles

“…Araci showed that the BERT model achieves a higher F1 score than other models such as LSTM. Del Corro and Hoffart ( 2021 ) proposed a method to automatically identify financially relevant news by applying BERT. They showed that the method ranks relevant news highly and positively correlated with the accuracy of the initial stock price prediction task.…”