Projects per year
Abstract / Description of output
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.11https://github.com/yumoxu/stocknet-dataset
Original language | English |
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Title of host publication | Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |
Place of Publication | Melbourne, Australia |
Publisher | Association for Computational Linguistics |
Pages | 1970-1979 |
Number of pages | 10 |
Publication status | Published - Jul 2018 |
Event | 56th Annual Meeting of the Association for Computational Linguistics - Melbourne Convention and Exhibition Centre, Melbourne, Australia Duration: 15 Jul 2018 → 20 Jul 2018 http://acl2018.org/ |
Conference
Conference | 56th Annual Meeting of the Association for Computational Linguistics |
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Abbreviated title | ACL 2018 |
Country/Territory | Australia |
City | Melbourne |
Period | 15/07/18 → 20/07/18 |
Internet address |
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Dive into the research topics of 'Stock Movement Prediction from Tweets and Historical Prices'. Together they form a unique fingerprint.Projects
- 1 Finished
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SUMMA - Scalable Understanding of Mulitingual Media
Renals, S., Birch-Mayne, A. & Cohen, S.
1/02/16 → 31/01/19
Project: Research