NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction

• URL: http://arxiv.org/abs/2507.02018v1
• Date: Wed, 02 Jul 2025 13:59:46 GMT
• Title: NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction
• Authors: Yingjie Niu, Mingchuan Zhao, Valerio Poti, Ruihai Dong,
• Abstract summary: Graph representation methods have been widely adopted in financial applications to enhance company representations by leveraging inter-firm relationships.<n>Existing graph models specifically designed for stock prediction often suffer from excessive complexity and poor generalization.<n>We propose a long-term stock prediction task and develop a Node-level Graph Attention Network (NGAT) specifically tailored for corporate relationship graphs.
• Score: 4.743574336827573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph representation learning methods have been widely adopted in financial applications to enhance company representations by leveraging inter-firm relationships. However, current approaches face three key challenges: (1) The advantages of relational information are obscured by limitations in downstream task designs; (2) Existing graph models specifically designed for stock prediction often suffer from excessive complexity and poor generalization; (3) Experience-based construction of corporate relationship graphs lacks effective comparison of different graph structures. To address these limitations, we propose a long-term stock prediction task and develop a Node-level Graph Attention Network (NGAT) specifically tailored for corporate relationship graphs. Furthermore, we experimentally demonstrate the limitations of existing graph comparison methods based on model downstream task performance. Experimental results across two datasets consistently demonstrate the effectiveness of our proposed task and model. The project is publicly available on GitHub to encourage reproducibility and future research.

Related papers

• Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks [37.020118015110086]
  Machine learning on graphs has demonstrated promise in drug design and molecular property prediction.<n>This position paper calls for a paradigm shift toward more meaningful benchmarks, rigorous evaluation protocols, and stronger collaboration with domain experts.
  arXiv  Detail & Related papers  (2025-02-20T13:21:47Z)
• Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
  We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs)<n>This framework provides a standardized setting to evaluate GNNs across diverse datasets.<n>We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-01T08:48:53Z)
• Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis [7.309233340654514]
  This paper introduces a theoretical framework that rigorously analyzes graph prompting from a data operation perspective.<n>We provide a formal guarantee theorem, demonstrating graph prompts capacity to approximate graph transformation operators.<n>We derive upper bounds on the error of these data operations by graph prompts for a single graph and extend this discussion to batches of graphs.
  arXiv  Detail & Related papers  (2024-10-02T15:07:13Z)
• Deep Prompt Tuning for Graph Transformers [55.2480439325792]
  Fine-tuning is resource-intensive and requires storing multiple copies of large models. We propose a novel approach called deep graph prompt tuning as an alternative to fine-tuning. By freezing the pre-trained parameters and only updating the added tokens, our approach reduces the number of free parameters and eliminates the need for multiple model copies.
  arXiv  Detail & Related papers  (2023-09-18T20:12:17Z)
• GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks [16.455234748896157]
  GraphPrompt is a novel pre-training and prompting framework on graphs. It unifies pre-training and downstream tasks into a common task template. It also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-train model.
  arXiv  Detail & Related papers  (2023-02-16T02:51:38Z)
• Robust Causal Graph Representation Learning against Confounding Effects [21.380907101361643]
  We propose Robust Causal Graph Representation Learning (RCGRL) to learn robust graph representations against confounding effects. RCGRL introduces an active approach to generate instrumental variables under unconditional moment restrictions, which empowers the graph representation learning model to eliminate confounders.
  arXiv  Detail & Related papers  (2022-08-18T01:31:25Z)
• Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities [128.55790219377315]
  Graph neural networks have emerged as a leading architecture for many graph-level tasks. graph pooling is indispensable for obtaining a holistic graph-level representation of the whole graph.
  arXiv  Detail & Related papers  (2022-04-15T04:02:06Z)
• Data Augmentation for Deep Graph Learning: A Survey [66.04015540536027]
  We first propose a taxonomy for graph data augmentation and then provide a structured review by categorizing the related work based on the augmented information modalities. Focusing on the two challenging problems in DGL (i.e., optimal graph learning and low-resource graph learning), we also discuss and review the existing learning paradigms which are based on graph data augmentation.
  arXiv  Detail & Related papers  (2022-02-16T18:30:33Z)
• Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
  We present a comprehensive study on graph embedded few-shot learning. We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels. Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
  arXiv  Detail & Related papers  (2021-02-14T05:28:13Z)
• Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction [69.1473775184952]
  We introduce a realistic problem of few-shot out-of-graph link prediction. We tackle this problem with a novel transductive meta-learning framework. We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction.
  arXiv  Detail & Related papers  (2020-06-11T17:42:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.