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.

本研究探讨深度学习与金融市场应用的协同作用，重点关注配对交易。该研究引入了一种新的框架，多模态时序关系图学习（MTRGL），将时间序列数据和离散特征组合成一个时序图，并采用基于记忆的时序图神经网络。实验结果表明MTRGL具有优异性能，在改进自动配对交易策略方面具有潜力。

MTRGL: 多模态时间关系图学习中的有效时间相关性识别