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Stock Movement Prediction from Tweets and Historical Prices

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationProceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Place of PublicationMelbourne, Australia
PublisherAssociation for Computational Linguistics
Pages1970-1979
Number of pages10
Publication statusPublished - Jul 2018
Event56th Annual Meeting of the Association for Computational Linguistics - Melbourne Convention and Exhibition Centre, Melbourne, Australia
Duration: 15 Jul 201820 Jul 2018
http://acl2018.org/

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2018
CountryAustralia
CityMelbourne
Period15/07/1820/07/18
Internet address

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.11https://github.com/yumoxu/stocknet-dataset

Event

56th Annual Meeting of the Association for Computational Linguistics

15/07/1820/07/18

Melbourne, Australia

Event: Conference

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