Abstract
Forecasting financial volatility of a publicly-traded company from its annual reports has been previously defined as a text regression problem. Recent studies use a manually labeled lexicon to filter the annual reports by keeping sentiment words only. In order to remove the lexicon dependency without decreasing the performance, we replace bag-of-words model word features by word embedding vectors. Using word vectors increases the number of parameters. Considering the increase in number of parameters and excessive lengths of annual reports, a convolutional neural network model is proposed and transfer learning is applied. Experimental results show that the convolutional neural network model provides more accurate volatility predictions than lexicon based models.- Anthology ID:
- P19-2046
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 331–337
- Language:
- URL:
- https://aclanthology.org/P19-2046
- DOI:
- 10.18653/v1/P19-2046
- Cite (ACL):
- Neşat Dereli and Murat Saraclar. 2019. Convolutional Neural Networks for Financial Text Regression. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 331–337, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Convolutional Neural Networks for Financial Text Regression (Dereli & Saraclar, ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-2046.pdf