Abstract
It is reported that financial news, especially financial events expressed in news, provide information to investors’ long/short decisions and influence the movements of stock markets. Motivated by this, we leverage financial event streams to train a classification neural network that detects latent event-stock linkages and stock markets’ systematic behaviours in the U.S. stock market. Our proposed pipeline includes (1) a combined event extraction method that utilizes Open Information Extraction and neural co-reference resolution, (2) a BERT/ALBERT enhanced representation of events, and (3) an extended hierarchical attention network that includes attentions on event, news and temporal levels. Our pipeline achieves significantly better accuracies and higher simulated annualized returns than state-of-the-art models when being applied to predicting Standard&Poor 500, Dow Jones, Nasdaq indices and 10 individual stocks.- Anthology ID:
- 2020.findings-emnlp.220
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2434–2444
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.220
- DOI:
- 10.18653/v1/2020.findings-emnlp.220
- Cite (ACL):
- Xianchao Wu. 2020. Event-Driven Learning of Systematic Behaviours in Stock Markets. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2434–2444, Online. Association for Computational Linguistics.
- Cite (Informal):
- Event-Driven Learning of Systematic Behaviours in Stock Markets (Wu, Findings 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.findings-emnlp.220.pdf