MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning

• URL: http://arxiv.org/abs/2401.14199v2
• Date: Tue, 6 Feb 2024 01:43:58 GMT
• Title: MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning
• Authors: Junwei Su, Shan Wu, Jinhui Li
• Abstract summary: 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. Our experiments on real-world datasets confirm the superior performance of MTRGL.
• Score: 2.879827542529434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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