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
- Editors:
- Iryna Gurevych, Yusuke Miyao
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/P18-1183.pdf
- Code
- yumoxu/stocknet-dataset
- Data
- StockNet, Astock