# Higher-order Graph Attention Network for Stock Selection with Joint Analysis

arXiv: https://arxiv.org/abs/2306.15526 · HTML: https://mlsanity.com/paper/2306.15526/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 67 references that are in the map.
- First posted on arXiv 27 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [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)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Higher-order organization of complex networks](https://mlsanity.com/paper/1612.08447/) (2016)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks](https://mlsanity.com/paper/1702.05870/) (2017)
- [HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction](https://mlsanity.com/paper/1908.07999/) (2019)
- [Classification-based Financial Markets Prediction using Deep Neural Networks](https://mlsanity.com/paper/1603.08604/) (2016)
- [Tensor Spectral Clustering for Partitioning Higher-order Network Structures](https://mlsanity.com/paper/1502.05058/) (2015)
- [Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction](https://mlsanity.com/paper/2005.04955/) (2020)
- [MotifExplainer: a Motif-based Graph Neural Network Explainer](https://mlsanity.com/paper/2202.00519/) (2022)
- [Motif-Based Spectral Clustering of Weighted Directed Networks](https://mlsanity.com/paper/2004.01293/) (2020)
- [Ranking Users in Social Networks with Motif-based PageRank](https://mlsanity.com/paper/1912.11817/) (2019)
- [Unifying Homophily and Heterophily Network Transformation via Motifs](https://mlsanity.com/paper/2012.11400/) (2020)

## Built on by (most cited first, 1 of 1)

- [MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU](https://mlsanity.com/paper/2410.20679/) (2024)

## Abstract (arXiv)

Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasets