@inproceedings{chang-etal-2016-measuring,
title = "Measuring the Information Content of Financial News",
author = "Chang, Ching-Yun and
Zhang, Yue and
Teng, Zhiyang and
Bozanic, Zahn and
Ke, Bin",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1303",
pages = "3216--3225",
abstract = "Measuring the information content of news text is useful for decision makers in their investments since news information can influence the intrinsic values of companies. We propose a model to automatically measure the information content given news text, trained using news and corresponding cumulative abnormal returns of listed companies. Existing methods in finance literature exploit sentiment signal features, which are limited by not considering factors such as events. We address this issue by leveraging deep neural models to extract rich semantic features from news text. In particular, a novel tree-structured LSTM is used to find target-specific representations of news text given syntax structures. Empirical results show that the neural models can outperform sentiment-based models, demonstrating the effectiveness of recent NLP technology advances for computational finance.",
}
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<abstract>Measuring the information content of news text is useful for decision makers in their investments since news information can influence the intrinsic values of companies. We propose a model to automatically measure the information content given news text, trained using news and corresponding cumulative abnormal returns of listed companies. Existing methods in finance literature exploit sentiment signal features, which are limited by not considering factors such as events. We address this issue by leveraging deep neural models to extract rich semantic features from news text. In particular, a novel tree-structured LSTM is used to find target-specific representations of news text given syntax structures. Empirical results show that the neural models can outperform sentiment-based models, demonstrating the effectiveness of recent NLP technology advances for computational finance.</abstract>
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%0 Conference Proceedings
%T Measuring the Information Content of Financial News
%A Chang, Ching-Yun
%A Zhang, Yue
%A Teng, Zhiyang
%A Bozanic, Zahn
%A Ke, Bin
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F chang-etal-2016-measuring
%X Measuring the information content of news text is useful for decision makers in their investments since news information can influence the intrinsic values of companies. We propose a model to automatically measure the information content given news text, trained using news and corresponding cumulative abnormal returns of listed companies. Existing methods in finance literature exploit sentiment signal features, which are limited by not considering factors such as events. We address this issue by leveraging deep neural models to extract rich semantic features from news text. In particular, a novel tree-structured LSTM is used to find target-specific representations of news text given syntax structures. Empirical results show that the neural models can outperform sentiment-based models, demonstrating the effectiveness of recent NLP technology advances for computational finance.
%U https://aclanthology.org/C16-1303
%P 3216-3225
Markdown (Informal)
[Measuring the Information Content of Financial News](https://aclanthology.org/C16-1303) (Chang et al., COLING 2016)
ACL
- Ching-Yun Chang, Yue Zhang, Zhiyang Teng, Zahn Bozanic, and Bin Ke. 2016. Measuring the Information Content of Financial News. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3216–3225, Osaka, Japan. The COLING 2016 Organizing Committee.