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

Luciano Del Corro, Johannes Hoffart


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.
Anthology ID:
2021.econlp-1.6
Volume:
Proceedings of the Third Workshop on Economics and Natural Language Processing
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
ECONLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
45–49
Language:
URL:
https://aclanthology.org/2021.econlp-1.6
DOI:
10.18653/v1/2021.econlp-1.6
Bibkey:
Cite (ACL):
Luciano Del Corro and Johannes Hoffart. 2021. From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations. In Proceedings of the Third Workshop on Economics and Natural Language Processing, pages 45–49, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations (Del Corro & Hoffart, ECONLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.econlp-1.6.pdf