@inproceedings{sawhney-etal-2020-deep,
title = "Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations",
author = "Sawhney, Ramit and
Agarwal, Shivam and
Wadhwa, Arnav and
Shah, Rajiv Ratn",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.676",
doi = "10.18653/v1/2020.emnlp-main.676",
pages = "8415--8426",
abstract = "In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market. Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks. The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting. We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. Through experiments on real-world S{\&}P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.",
}
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<abstract>In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market. Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks. The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting. We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. Through experiments on real-world S&P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.</abstract>
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%0 Conference Proceedings
%T Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations
%A Sawhney, Ramit
%A Agarwal, Shivam
%A Wadhwa, Arnav
%A Shah, Rajiv Ratn
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sawhney-etal-2020-deep
%X In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market. Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks. The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting. We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. Through experiments on real-world S&P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.
%R 10.18653/v1/2020.emnlp-main.676
%U https://aclanthology.org/2020.emnlp-main.676
%U https://doi.org/10.18653/v1/2020.emnlp-main.676
%P 8415-8426
Markdown (Informal)
[Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations](https://aclanthology.org/2020.emnlp-main.676) (Sawhney et al., EMNLP 2020)
ACL