  In this study, we explore the synergy of deep learning and financial market
applications, focusing on pair trading. This market-neutral strategy is
integral to quantitative finance and is apt for advanced deep-learning
techniques. A pivotal challenge in pair trading is discerning temporal
correlations among entities, necessitating the integration of diverse data
modalities. Addressing this, we introduce a novel framework, Multi-modal
Temporal Relation Graph Learning (MTRGL). MTRGL combines time series data and
discrete features into a temporal graph and employs a memory-based temporal
graph neural network. This approach reframes temporal correlation
identification as a temporal graph link prediction task, which has shown
empirical success. Our experiments on real-world datasets confirm the superior
performance of MTRGL, emphasizing its promise in refining automated pair
trading strategies.
