# Trading Graph Neural Network

arXiv: https://arxiv.org/abs/2504.07923 · HTML: https://mlsanity.com/paper/2504.07923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Graph Neural Networks for Causal Inference Under Network Confounding](https://mlsanity.com/paper/2211.07823/) (2022)

## Abstract (arXiv)

This paper proposes a new algorithm -- Trading Graph Neural Network (TGNN) that can structurally estimate the impact of asset features, dealer features and relationship features on asset prices in trading networks. It combines the strength of the traditional simulated method of moments (SMM) and recent machine learning techniques -- Graph Neural Network (GNN). It outperforms existing reduced-form methods with network centrality measures in prediction accuracy. The method can be used on networks with any structure, allowing for heterogeneity among both traders and assets.