Convolutional Neural Networks for Financial Text Regression

Neşat Dereli, Murat Saraclar


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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P19-2046.pdf