Inter-graph and Intra-graph: Utilizing global financial markets and constituent stocks for stock index prediction

Abstract

Stock index prediction is a significant yet difficult undertaking due to its incorporation of complex and diverse information. Following the implementation of Graph Neural Networks in financial data analysis, numerous researchers have focused on the node-level task of forecasting individual stock movements by analyzing the relationships between stocks. However, two key challenges remain: first, realizing different speeds of feature propagation among nodes in graph representation learning; second, predicting stock indices by extracting and aggregating fluctuations from constituent stocks through graph-level tasks remains unaddressed. To tackle these challenges, this paper proposes a novel spatio-temporal prediction framework combining both node-level and graph-level tasks. The framework includes two types of graphs: inter-graph and intra-graph, which combine information from the micro, meso, and macro dimensions. For the inter-graph at the node level, we introduce the Granger causality test as an innovative node filtering method, which realizes the propagation of features between nodes with different strengths and speeds in the process of graph representation learning. For the intra-graph at the graph level, we examine various graph pooling methods and pooling proportions of stock index constituents to enhance the interpretability of the results and to provide new theoretical insights for stock index prediction. In conclusion, we develop the Graph Representation Learning-based Long Short-Term Memory (GRL-LSTM) model for forecasting stock index movements, and demonstrate the superiority of our approach on four major Chinese stock markets.