@InProceedings{rekabsaz-EtAl:2017:Long,
  author    = {Rekabsaz, Navid  and  Lupu, Mihai  and  Baklanov, Artem  and  D\"{u}r, Alexander  and  Andersson, Linda  and  Hanbury, Allan},
  title     = {Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1712--1721},
  abstract  = {Volatility prediction—an essential concept in financial markets—has
	recently been addressed using sentiment analysis methods. We investigate the
	sentiment of annual disclosures of companies in stock markets to forecast
	volatility. We specifically explore the use of recent Information Retrieval
	(IR) term weighting models that are effectively extended by related terms using
	word embeddings. In parallel to textual information, factual market data have
	been widely used as the mainstream approach to forecast market risk. We
	therefore study different fusion methods to combine text and market data
	resources. Our word embedding-based approach significantly outperforms
	state-of-the-art methods. In addition, we investigate the characteristics of
	the reports of the companies in different financial sectors.},
  url       = {http://aclweb.org/anthology/P17-1157}
}