# HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

arXiv: https://arxiv.org/abs/1908.07999 · HTML: https://mlsanity.com/paper/1908.07999/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 30 of 37)

- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [REST: Relational Event-driven Stock Trend Forecasting](https://mlsanity.com/paper/2102.07372/) (2021)
- [Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction](https://mlsanity.com/paper/2305.08740/) (2023)
- [MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model](https://mlsanity.com/paper/2409.07486/) (2024)
- [Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs](https://mlsanity.com/paper/2106.06218/) (2021)
- [Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction](https://mlsanity.com/paper/2211.07400/) (2022)
- [Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks](https://mlsanity.com/paper/2201.04965/) (2022)
- [Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions](https://mlsanity.com/paper/2401.02143/) (2024)
- [Multi-relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends Classification](https://mlsanity.com/paper/2401.05430/) (2024)
- [Ploutos: Towards interpretable stock movement prediction with financial large language model](https://mlsanity.com/paper/2403.00782/) (2024)
- [From Deep Learning to LLMs: A survey of AI in Quantitative Investment](https://mlsanity.com/paper/2503.21422/) (2025)
- [Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data](https://mlsanity.com/paper/2107.10941/) (2021)
- [Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification](https://mlsanity.com/paper/2208.06651/) (2022)
- [DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction](https://mlsanity.com/paper/2401.01846/) (2024)
- [An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions](https://mlsanity.com/paper/2204.09501/) (2022)
- [RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets](https://mlsanity.com/paper/2510.14986/) (2025)
- [GCNET: graph-based prediction of stock price movement using graph convolutional network](https://mlsanity.com/paper/2203.11091/) (2022)
- [FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks](https://mlsanity.com/paper/2106.10159/) (2021)
- [Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction](https://mlsanity.com/paper/2107.14033/) (2021)
- [GraphCNNpred: A stock market indices prediction using a Graph based deep learning system](https://mlsanity.com/paper/2407.03760/) (2024)
- [Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations](https://mlsanity.com/paper/2412.04034/) (2024)
- [OATS: Online Data Augmentation for Time Series Foundation Models](https://mlsanity.com/paper/2601.19040/) (2026)
- [Enterprise Analytics using Graph Database and Graph-based Deep Learning](https://mlsanity.com/paper/2108.02867/) (2021)
- [CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction](https://mlsanity.com/paper/2411.06391/) (2024)
- [NETpred: Network-based modeling and prediction of multiple connected market indices](https://mlsanity.com/paper/2212.05916/) (2022)
- [Graph Attention with Hierarchies for Multi-hop Question Answering](https://mlsanity.com/paper/2301.11792/) (2023)
- [Discovering Predictable Latent Factors for Time Series Forecasting](https://mlsanity.com/paper/2303.10426/) (2023)
- [Higher-order Graph Attention Network for Stock Selection with Joint Analysis](https://mlsanity.com/paper/2306.15526/) (2023)
- [Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data](https://mlsanity.com/paper/2412.10540/) (2024)
- [TRACE: Temporal Rule-Anchored Chain-of-Evidence on Knowledge Graphs for Interpretable Stock Movement Prediction](https://mlsanity.com/paper/2603.12500/) (2026)

## Abstract (arXiv)

Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy. First, the quality of collected information from different types of relations can vary considerably. No existing work has focused on the effect of using different types of relations on stock market prediction or finding an effective way to selectively aggregate information on different relation types. Furthermore, existing works have focused on only individual stock prediction which is similar to the node classification task. To address this, we propose a hierarchical attention network for stock prediction (HATS) which uses relational data for stock market prediction. Our HATS method selectively aggregates information on different relation types and adds the information to the representations of each company. Specifically, node representations are initialized with features extracted from a feature extraction module. HATS is used as a relational modeling module with initialized node representations. Then, node representations with the added information are fed into a task-specific layer. Our method is used for predicting not only individual stock prices but also market index movements, which is similar to the graph classification task. The experimental results show that performance can change depending on the relational data used. HATS which can automatically select information outperformed all the existing methods.