A Systematic Review on Graph Neural Network-based Methods for Stock Market Forecasting

Abstract

Financial technology (FinTech) is a field that uses artificial intelligence to automate financial services. One area of FinTech is stock analysis, which aims to predict future stock prices in order to develop investment strategies that maximize profits. Traditional methods of stock market prediction, such as time series analysis and machine learning, struggle to handle the non-linear, chaotic, and sudden changes in stock data and may not consider the interdependence between stocks. Recently, graph neural networks (GNNs) have been used in stock market forecasting to improve prediction accuracy by incorporating the interconnectedness of the market. GNNs can process non-Euclidean data in the form of a knowledge graph. However, financial knowledge graphs can have dynamic and complex interactions, which can be challenging for graph modeling technologies. This work presents a systematic review of graph based approaches for stock market forecasting. This review covers different types of stock analysis tasks (classification, regression, and stock recommendation), a generalized framework for solving these tasks, and a review of various features, datasets, graph models, and evaluation metrics used in the stock market. The results of various studies are analyzed, and future directions for research are highlighted.