Measuring the Information Content of Financial News

Ching-Yun Chang, Yue Zhang, Zhiyang Teng, Zahn Bozanic, Bin Ke


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.
Anthology ID:
C16-1303
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
3216–3225
Language:
URL:
https://www.aclweb.org/anthology/C16-1303
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C16-1303.pdf