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        <title>Incorporating Fine-grained Events in Stock Movement Prediction</title>
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    <name type="personal">
        <namePart type="given">Xuancheng</namePart>
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    <abstract>Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5105</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Incorporating Fine-grained Events in Stock Movement Prediction
%A Chen, Deli
%A Zou, Yanyan
%A Harimoto, Keiko
%A Bao, Ruihan
%A Ren, Xuancheng
%A Sun, Xu
%Y Hahn, Udo
%Y Hoste, Véronique
%Y Zhang, Zhu
%S Proceedings of the Second Workshop on Economics and Natural Language Processing
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F chen-etal-2019-incorporating
%X Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.
%R 10.18653/v1/D19-5105
%U https://aclanthology.org/D19-5105
%U https://doi.org/10.18653/v1/D19-5105
%P 31-40

Markdown (Informal)

[Incorporating Fine-grained Events in Stock Movement Prediction](https://aclanthology.org/D19-5105) (Chen et al., 2019)

• Incorporating Fine-grained Events in Stock Movement Prediction (Chen et al., 2019)

ACL

• Deli Chen, Yanyan Zou, Keiko Harimoto, Ruihan Bao, Xuancheng Ren, and Xu Sun. 2019. Incorporating Fine-grained Events in Stock Movement Prediction. In Proceedings of the Second Workshop on Economics and Natural Language Processing, pages 31–40, Hong Kong. Association for Computational Linguistics.