PT - JOURNAL ARTICLE AU - Qinkai Chen AU - Christian-Yann Robert TI - Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data AID - 10.3905/jfds.2022.1.104 DP - 2022 Sep 05 TA - The Journal of Financial Data Science PG - jfds.2022.1.104 4099 - https://pm-research.com/content/early/2022/09/05/jfds.2022.1.104.short 4100 - https://pm-research.com/content/early/2022/09/05/jfds.2022.1.104.full 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.