Paper: arXiv 2401.14199

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]