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

ArXiv ID: 2401.14199 “View on arXiv”

Authors: Unknown

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.

Keywords: Multi-modal Temporal Relation Graph Learning (MTRGL), temporal graph neural network, pair trading, link prediction, market-neutral strategy, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper introduces a novel graph neural network architecture with memory modules and temporal graph attention, requiring advanced mathematical concepts; it also reports experiments on real-world datasets and includes an ablation study, indicating strong empirical validation.

  flowchart TD
    A["Research Goal:<br/>Discern Temporal Correlations<br/>in Pair Trading"] --> B["Key Methodology: MTRGL Framework<br/>Multi-modal Temporal Relation Graph Learning"]
    B --> C["Computational Process:<br/>Temporal Graph Link Prediction<br/>Memory-based Temporal GNN"]
    C --> D{"Key Data Inputs"}
    D --> D1["Time Series Data<br/>Price/Returns"]
    D --> D2["Discrete Features<br/>Financial Attributes"]
    D1 & D2 --> E["Key Finding:<br/>Superior Performance on<br/>Real-world Equities Datasets"]
    E --> F["Outcome:<br/>Refined Automated<br/>Pair Trading Strategies"]