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

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  • Junwei Su
  • Shan Wu
  • Jinhui Li

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.

Suggested Citation

  • Junwei Su & Shan Wu & Jinhui Li, 2024. "MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning," Papers 2401.14199, arXiv.org, revised Feb 2024.
    • Handle: RePEc:arx:papers:2401.14199
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    References listed on IDEAS

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    1. Brady D. Lund & Ting Wang & Nishith Reddy Mannuru & Bing Nie & Somipam Shimray & Ziang Wang, 2023. "ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(5), pages 570-581, May.
    2. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2024.
    3. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
    4. Zihan Chen & Lei Nico Zheng & Cheng Lu & Jialu Yuan & Di Zhu, 2023. "ChatGPT Informed Graph Neural Network for Stock Movement Prediction," Papers 2306.03763, arXiv.org, revised Sep 2023.
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