Stock Movement Prediction from Tweets and Historical Prices

Yumo Xu, Shay B. Cohen


Abstract
Stock movement prediction is a challenging problem: the market is highly stochastic, and we make temporally-dependent predictions from chaotic data. We treat these three complexities and present a novel deep generative model jointly exploiting text and price signals for this task. Unlike the case with discriminative or topic modeling, our model introduces recurrent, continuous latent variables for a better treatment of stochasticity, and uses neural variational inference to address the intractable posterior inference. We also provide a hybrid objective with temporal auxiliary to flexibly capture predictive dependencies. We demonstrate the state-of-the-art performance of our proposed model on a new stock movement prediction dataset which we collected.
Anthology ID:
P18-1183
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1970–1979
Language:
URL:
https://aclanthology.org/P18-1183
DOI:
10.18653/v1/P18-1183
Bibkey:
Cite (ACL):
Yumo Xu and Shay B. Cohen. 2018. Stock Movement Prediction from Tweets and Historical Prices. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1970–1979, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Stock Movement Prediction from Tweets and Historical Prices (Xu & Cohen, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1183.pdf
Note:
 P18-1183.Notes.pdf
Presentation:
 P18-1183.Presentation.pdf
Video:
 https://vimeo.com/285804956
Code
 yumoxu/stocknet-dataset
Data
StockNet