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

arXiv: https://arxiv.org/abs/2001.09466 · HTML: https://mlsanity.com/paper/2001.09466/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Stock Movement Prediction with Financial News using Contextualized Embedding from BERT](https://mlsanity.com/paper/2107.08721/) (2021)

## Abstract (arXiv)

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.