Graph Portfolio: High-Frequency Factor Predictors via Heterogeneous Continual GNNs

Abstract

This study aims to address the challenges of financial price prediction in high-frequency trading (HFT) by introducing a novel continual learning framework based on factor predictors via graph neural networks. The model integrates multi-factor pricing theory with real-time market dynamics, effectively bypassing the limitations of conventional time series forecasting methods, which often lack financial theory guidance and ignore market correlations. We propose three heterogeneous tasks, including price gap regression, changepoint detection, and price moving average regression to trace the short-, intermediate-, and long-term trend factors present in the data. We also account for the cross-sectional correlations inherent in the financial market, where prices of different assets show strong dynamic correlations. To accurately capture these dynamic relationships, we resort to spatio-temporal graph neural network (STGNN) to enhance the predictive power of the model. Our model allows a continual learning strategy to simultaneously consider these tasks (factors). To tackle the catastrophic forgetting in continual learning while considering the heterogeneity of tasks, we propose to calculate parameter importance with mutual information between original observations and the extracted features. Empirical studies on the Chinese futures data and U.S. equity data demonstrate the superior performance of the proposed model compared to other state-of-the-art approaches.