@InProceedings{chang-EtAl:2016:COLING,
  author    = {Chang, Ching-Yun  and  Zhang, Yue  and  Teng, Zhiyang  and  Bozanic, Zahn  and  Ke, Bin},
  title     = {Measuring the Information Content of Financial News},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  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.},
  url       = {http://aclweb.org/anthology/C16-1303}
}