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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.676.pdf
Code
 midas-research/man-sf-emnlp
Data
StockNet