TY  - CHAP
CY  - Vancouver, Canada
ID  - iiasa14384
UR  - https://pure.iiasa.ac.at/id/eprint/14384/
A1  - Rekabsaz, N.
A1  - Lupu, M.
A1  - Baklanov, A.
A1  - Hanbury, A.
A1  - Duer, A.
A1  - Anderson, L.
Y1  - 2017///
N2  - 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.
PB  - Association for Computational Linguistics
M1  - 1
TI  - Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models
SP  - 1712
AV  - public
EP  - 1721
T2  - Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics
ER  -