RT Book, Section
SR 00
ID 10.18653/v1/P17-1157
A1 Rekabsaz, N.
A1 Lupu, M.
A1 Baklanov, A.
A1 Hanbury, A.
A1 Duer, A.
A1 Anderson, L.
T1 Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models
YR 2017
FD 2017
VO 1
SP 1712
OP 1721
AB 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.
T2 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics
PB Association for Computational Linguistics
PP Vancouver, Canada
AV Published
LK https://pure.iiasa.ac.at/id/eprint/14384/