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        <title>Stock Movement Prediction from Tweets and Historical Prices</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1183</identifier>
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%0 Conference Proceedings
%T Stock Movement Prediction from Tweets and Historical Prices
%A Xu, Yumo
%A Cohen, Shay B.
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F xu-cohen-2018-stock
%X 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.
%R 10.18653/v1/P18-1183
%U https://aclanthology.org/P18-1183/
%U https://doi.org/10.18653/v1/P18-1183
%P 1970-1979

Markdown (Informal)

[Stock Movement Prediction from Tweets and Historical Prices](https://aclanthology.org/P18-1183/) (Xu & Cohen, ACL 2018)

• Stock Movement Prediction from Tweets and Historical Prices (Xu & Cohen, ACL 2018)

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.