# Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction

arXiv: https://arxiv.org/abs/2107.14033 · HTML: https://mlsanity.com/paper/2107.14033/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 22 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Risk-Averse Trust Region Optimization for Reward-Volatility Reduction](https://mlsanity.com/paper/1912.03193/) (2019)
- [Applications of deep learning in stock market prediction: recent progress](https://mlsanity.com/paper/2003.01859/) (2020)
- [HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction](https://mlsanity.com/paper/1908.07999/) (2019)

## Built on by (most cited first, 3 of 3)

- [Cross-Modal Temporal Fusion for Financial Market Forecasting](https://mlsanity.com/paper/2504.13522/) (2025)
- [GAPNet: Plug-in Jointly Learning Task-Specific Graph for Dynamic Stock Relation](https://mlsanity.com/paper/2602.00888/) (2026)
- [Game-Theoretic Modeling of Heterogeneous Investor Interactions for Stock Price Forecasting](https://mlsanity.com/paper/2605.23953/) (2026)

## Abstract (arXiv)

Predicting the future price trends of stocks is a challenging yet intriguing problem given its critical role to help investors make profitable decisions. In this paper, we present a collaborative temporal-relational modeling framework for end-to-end stock trend prediction. The temporal dynamics of stocks is firstly captured with an attention-based recurrent neural network. Then, different from existing studies relying on the pairwise correlations between stocks, we argue that stocks are naturally connected as a collective group, and introduce the hypergraph structures to jointly characterize the stock group-wise relationships of industry-belonging and fund-holding. A novel hypergraph tri-attention network (HGTAN) is proposed to augment the hypergraph convolutional networks with a hierarchical organization of intra-hyperedge, inter-hyperedge, and inter-hypergraph attention modules. In this manner, HGTAN adaptively determines the importance of nodes, hyperedges, and hypergraphs during the information propagation among stocks, so that the potential synergies between stock movements can be fully exploited. Extensive experiments on real-world data demonstrate the effectiveness of our approach. Also, the results of investment simulation show that our approach can achieve a more desirable risk-adjusted return. The data and codes of our work have been released at https://github.com/lixiaojieff/HGTAN.