  Predicting stock prices from textual information is a challenging task due to
the uncertainty of the market and the difficulty understanding the natural
language from a machine's perspective. Previous researches focus mostly on
sentiment extraction based on single news. However, the stocks on the financial
market can be highly correlated, one news regarding one stock can quickly
impact the prices of other stocks. To take this effect into account, we propose
a new stock movement prediction framework: Multi-Graph Recurrent Network for
Stock Forecasting (MGRN). This architecture allows to combine the textual
sentiment from financial news and multiple relational information extracted
from other financial data. Through an accuracy test and a trading simulation on
the stocks in the STOXX Europe 600 index, we demonstrate a better performance
from our model than other benchmarks.
