RT Journal Article
SR Electronic
T1 Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP 152
OP 166
DO 10.3905/jfds.2022.1.104
VO 4
IS 4
A1 Qinkai Chen
A1 Christian-Yann Robert
YR 2022
UL https://pm-research.com/content/4/4/152.abstract
AB Predicting stock prices from textual information is a challenging task due to the uncertainty of the market and the difficulty in understanding the natural language from a machine’s perspective. Previous research has mostly focused on sentiment extraction based on news associated with a single stock; however, the stocks on the financial market can be highly correlated, as news regarding one stock can quickly impact the prices of other stocks. To take this effect into account, the authors propose a new stock movement prediction framework: multigraph recurrent network for stock forecasting (MGRN). This architecture allows combining the textual sentiment from financial news and multiple relational information extracted from other types of financial data. Through an accuracy test and a trading simulation on the stocks of the STOXX Europe 600 index, the authors demonstrate a better performance of their model compared with other benchmarks.