RiskFinder: A Sentence-level Risk Detector for Financial Reports

Yu-Wen Liu, Liang-Chih Liu, Chuan-Ju Wang, Ming-Feng Tsai


Abstract
This paper presents a web-based information system, RiskFinder, for facilitating the analyses of soft and hard information in financial reports. In particular, the system broadens the analyses from the word level to sentence level, which makes the system useful for practitioner communities and unprecedented among financial academics. The proposed system has four main components: 1) a Form 10-K risk-sentiment dataset, consisting of a set of risk-labeled financial sentences and pre-trained sentence embeddings; 2) metadata, including basic information on each company that published the Form 10-K financial report as well as several relevant financial measures; 3) an interface that highlights risk-related sentences in the financial reports based on the latest sentence embedding techniques; 4) a visualization of financial time-series data for a corresponding company. This paper also conducts some case studies to showcase that the system can be of great help in capturing valuable insight within large amounts of textual information. The system is now online available at https://cfda.csie.org/RiskFinder/.
Anthology ID:
N18-5017
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
81–85
Language:
URL:
https://www.aclweb.org/anthology/N18-5017
DOI:
10.18653/v1/N18-5017
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-5017.pdf