Trading Graph Neural Network

Dealers' position in the trading network is shown to have a significant impact on asset prices. 1 However, it remains challenging to account for the structure of trading networks during the estimation of dealer and asset features' impact on asset prices. Structural approaches usually rely on specific network structures to reduce complexity in estimation (e.g. Pint er and Usl u, 2022; Eisfeldt et al., 2023; Cohen et al., 2024), which limits the accuracy and generalizability of the estimation method. Reduced-form approach uses centrality measures to capture dealers' position in the network(e.g. Di Maggio et al., 2017; Hollifield et al., 2017; Li and Sch urhoff, 2019), but recent papers point out linear regressions with centrality measures can lead to biased estimation when the network is sparse (Cai, 2022).