Talks and Poster Presentations (with Proceedings-Entry):

N. Rekabsaz, M. Lupu, A. Baklanov, A. Duer, L. Andersson, A. Hanbury:
"Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models";
Talk: Association for Computational Linguistics, Vancouver, Canada; 2017-07-30 - 2017-08-04; in: "Annual Meeting of the Association for Computational Linguistics", Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Volume 1 (2017), 1712 - 1721.

English 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 Informa- tion Retrieval (IR) term weighting mod- els that are effectively extended by related terms using word embeddings. In parallel to textual information, factual market data have been widely used as the main-stream approach to forecast market risk. We therefore study different fusion methods to combine text and market data re- sources. 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.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.