Congressional stock trading has raised concerns about potential information asymmetries and conflicts of interest in financial markets. We introduce a temporal graph network (TGN) framework to identify information channels through which members of Congress may possess advantageous knowledge when trading company stocks. We construct a multimodal dynamic graph integrating diverse publicly available datasets, including congressional stock transactions, lobbying relationships, campaign finance contributions, and geographical connections between legislators and corporations. Our approach formulates the detection problem as a dynamic edge classification task, where we identify trades that exhibit statistically significant outperformance relative to the S&P 500 across long time horizons. To handle the temporal nature of these relationships, we develop a two-step walk-forward validation architecture that respects information availability constraints and prevents look-ahead bias. We evaluate several labeling strategies based on risk-adjusted returns and demonstrate that the TGN successfully captures complex temporal dependencies between congressional-corporate interactions and subsequent trading performance.

翻译：国会股票交易引发了人们对金融市场中潜在信息不对称和利益冲突的担忧。我们引入了一种时序图网络（TGN）框架，旨在识别国会议员在进行公司股票交易时可能拥有优势信息的信息通道。我们构建了一个整合多种公开数据集的多模态动态图，这些数据包括国会股票交易、游说关系、竞选资金捐助，以及立法者与公司之间的地理关联。我们的方法将检测问题构建为一个动态边分类任务，旨在识别那些在长时间跨度内相对于标准普尔500指数表现出统计显著超额收益的交易。为了处理这些关系的时序特性，我们开发了一种两步向前滚动验证架构，该架构尊重信息可获取性约束并防止前瞻性偏差。我们评估了几种基于风险调整收益的标签策略，并证明TGN能够成功捕捉国会-企业互动与后续交易表现之间复杂的时序依赖关系。