FinSense: An Assistant System for Financial Journalists and Investors

Yi-Ting Liou, Chung-Chi Chen, Tsun-Hsien Tang, Hen-Hsen Huang, Hsin-Hsi Chen

Abstract: This paper demonstrates FinSense, a system that improves the working efficiency of financial information processing. Given the draft of a financial news story, FinSense extracts the explicit-mentioned stocks and further infers the implicit stocks, providing insightful information for decision making. We propose a novel graph convolutional network model that performs implicit financial instrument inference toward the in-domain data. In addition, FinSense generates candidate headlines for the draft, reducing a significant amount of time in journalism production. The proposed system also provides assistance to investors to sort out the information in the financial news articles.