Research on Graph Neural Network in Stock Market

The stock market is a very important part of the financial field, and the prediction of the stock market has a great relationship with the returns and risk safety of the entire financial field. With the continuous mature application of machine learning and deep learning in other fields, such as image processing and text analysis, people begin to focus on the use of different models so as to predict stock volatility. However, in view of the unique multi-source and heterogeneous characteristics of stock information, the artificial neural network relying on deep learning cannot make a good prediction on it. At this time, the graph neural network that can well analyze the graph structure data is gradually favored by scholars at home and abroad, and the research thinking is also expanding. This dissertation examines the purpose of deeply analyzing the methods of different graph neural network models on stock prediction through an inductive study of amount of relevant literature. In this paper, we not only classify the literature by various graph neural network models, but also describe objectively the models and ideas presented in each paper. By referring to literature, this paper summarizes the previous research results, analyzes the applicability and results of different methods, and lays a foundation for better stock prediction in the future.