Forecasting Firm Material Events from 8-K Reports

Shuang (Sophie) Zhai, Zhu (Drew) Zhang


Abstract
In this paper, we show deep learning models can be used to forecast firm material event sequences based on the contents in the company’s 8-K Current Reports. Specifically, we exploit state-of-the-art neural architectures, including sequence-to-sequence (Seq2Seq) architecture and attention mechanisms, in the model. Our 8K-powered deep learning model demonstrates promising performance in forecasting firm future event sequences. The model is poised to benefit various stakeholders, including management and investors, by facilitating risk management and decision making.
Anthology ID:
D19-5104
Volume:
Proceedings of the Second Workshop on Economics and Natural Language Processing
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
22–30
Language:
URL:
https://www.aclweb.org/anthology/D19-5104
DOI:
10.18653/v1/D19-5104
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5104.pdf