TY - JOUR T1 - Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data JF - The Journal of Financial Data Science SP - 152 LP - 166 DO - 10.3905/jfds.2022.1.104 VL - 4 IS - 4 AU - Qinkai Chen AU - Christian-Yann Robert Y1 - 2022/10/31 UR - https://pm-research.com/content/4/4/152.abstract N2 - 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. ER -