mlsanityData to 7 Oct 2026

MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction

arXiv 2402.06633 ↗First posted 19 Jan 2024q-fin.ST cs.IR cs.LGcited by 18

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Abstract (arXiv)

The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from various aspects such as economic indicators, financial reports, global news, and investor sentiment. Traditional sequential methods and graph-based models have been applied in stock movement prediction, but they have limitations in capturing the multifaceted and temporal influences in stock price movements. To address these challenges, the Multi-relational Dynamic Graph Neural Network (MDGNN) framework is proposed, which utilizes a discrete dynamic graph to comprehensively capture multifaceted relations among stocks and their evolution over time. The representation generated from the graph offers a complete perspective on the interrelationships among stocks and associated entities. Additionally, the power of the Transformer structure is leveraged to encode the temporal evolution of multiplex relations, providing a dynamic and effective approach to predicting stock investment. Further, our proposed MDGNN framework achieves the best performance in public datasets compared with state-of-the-art (SOTA) stock investment methods.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 19 Jan 2024.

Builds on

12 of 25 references.

References · 12

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  7. 2020Heterogeneous Graph Transformercited by 581
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  9. 2018Temporal Relational Ranking for Stock Predictioncited by 100
  10. 2021HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Informationcited by 43
  11. 2021Heterogeneous Temporal Graph Neural Networkcited by 23
  12. 2021Graph-Based Learning for Stock Movement Prediction with Textual and Relational Datacited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 10.

18 papers.

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  1. 2024MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRUcited by 4
  2. 2025A Distillation-based Future-aware Graph Neural Network for Stock Trend Predictioncited by 3
  3. 2025Unlocking Multi-Modal Potentials for Link Prediction on Dynamic Text-Attributed Graphscited by 3
  4. 2024Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiencycited by 2
  5. 2025Gated Fusion Enhanced Multi-Scale Hierarchical Graph Convolutional Network for Stock Movement Predictioncited by 2
  6. 2026MEME: Modeling the Evolutionary Modes of Financial Marketscited by 2
  7. 2024GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Networkcited by 0
  8. 2025Variational Graph Convolutional Neural Networkscited by 0
  9. 2025EP-GAT: Energy-based Parallel Graph Attention Neural Network for Stock Trend Classificationcited by 0
  10. 2026GAPNet: Plug-in Jointly Learning Task-Specific Graph for Dynamic Stock Relationcited by 0
  11. 2026AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagationcited by 0
  12. 2026Relational Probing: LM-to-Graph Adaptation for Financial Predictioncited by 0
6 more
  1. 2026TopFeaRe: Locating Critical State of Adversarial Resilience for Graphs Regarding Topology-Feature Entanglementcited by 0
  2. 2026ACT: Anti-Crosstalk Learning for Cross-Sectional Stock Ranking via Temporal Disentanglement and Structural Purificationcited by 0
  3. 2026Game-Theoretic Modeling of Heterogeneous Investor Interactions for Stock Price Forecastingcited by 0
  4. 2026Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphscited by 0
  5. 2026AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Miningcited by 0
  6. 2026RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecastingcited by 0