Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations
Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah
Abstract
In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market. Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks. The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting. We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. Through experiments on real-world S&P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.- Anthology ID:
- 2020.emnlp-main.676
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8415–8426
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.676
- DOI:
- 10.18653/v1/2020.emnlp-main.676
- Cite (ACL):
- Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, and Rajiv Ratn Shah. 2020. Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8415–8426, Online. Association for Computational Linguistics.
- Cite (Informal):
- Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations (Sawhney et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.emnlp-main.676.pdf
- Code
- midas-research/man-sf-emnlp
- Data
- StockNet