论文标题
随着时间的推移导航财务嵌入的动力
Navigating the Dynamics of Financial Embeddings over Time
论文作者
论文摘要
财务交易构成实体之间的联系，并通过这些连接制定了大规模的异质加权图。在不断更新的相互作用的迷宫中，存在各种基于相似性的模式，可以为金融系统的动态提供见解。在当前的工作中，我们建议将图表学习在可扩展的动态设置中的应用，作为以有意义且健壮的方式捕获这些模式的一种手段。我们开始对潜在轨迹进行严格的定性分析，以从提议的代表及其演变中提取现实世界的见解，据我们所知，这是金融领域中的第一个。潜在空间的转变与已知的经济事件有关，尤其是最近的19 Covid-19大流行对消费者模式的影响。捕获此类模式表明通过结合潜在图表来表示财务建模的值。
Financial transactions constitute connections between entities and through these connections a large scale heterogeneous weighted graph is formulated. In this labyrinth of interactions that are continuously updated, there exists a variety of similarity-based patterns that can provide insights into the dynamics of the financial system. With the current work, we propose the application of Graph Representation Learning in a scalable dynamic setting as a means of capturing these patterns in a meaningful and robust way. We proceed to perform a rigorous qualitative analysis of the latent trajectories to extract real world insights from the proposed representations and their evolution over time that is to our knowledge the first of its kind in the financial sector. Shifts in the latent space are associated with known economic events and in particular the impact of the recent Covid-19 pandemic to consumer patterns. Capturing such patterns indicates the value added to financial modeling through the incorporation of latent graph representations.
