Macroeconomic context-aware graph topology learning for stock price forecasting using graph neural network

Stock price forecasting is a crucial challenge in FinTech industries, with implications that extend to algorithmic trading. In recent studies, Graph Neural Networks (GNNs) are employed for prediction; however, they are still limited to involving macroeconomic contexts. Hence, in this study, a novel GNN-based method for stock price forecasting is designed, with a graph building structure influenced by macroeconomic variables, namely inflation, interest rate, and GDP growth regimes. Our model captures the relationships between stocks on the basis of regime-specific, macro-driven static graphs along with an LSTM model. The proposed approach outperforms existing methods and provides a new viewpoint on stock forecasting.