Network methods can extract the structure of financial correlation matrices, and market geometry reconstructs the correlation relationship by constructing a vector set in the Euclidean space. This study uses a geometric perspective to analyse financial networks and examine the relationship between correlation structures and geometric conditions. Based on the concept of Euclidean space, we can naturally define geometric concepts such as stock vector and inner product between stocks. The analysis reveals that the structure of the financial correlation network is significantly affected by geometric conditions. We use stock market data to construct networks with different structures, such as a network with a hub node. We find that some stocks with small vector norms have an important effect on changes in network structure. In addition, we define a dimension to describe the correlation information included in the subspace of the market space and find that the dynamics of the dimension are related to the market state. This paper establishes a way to study network structure through market geometry, thereby providing a new method of correlation analysis.

网络方法可以提取金融相关矩阵的结构，市场几何结构通过在欧几里得空间中构建向量集来重建相关关系。这项研究使用几何视角来分析金融网络并检查相关结构和几何条件之间的关系。基于欧几里得空间的概念，我们可以自然地定义几何概念，例如股票向量和股票之间的内积。分析表明，财务关联网络的结构受几何条件的影响很大。我们使用股票市场数据来构建具有不同结构的网络，例如具有中心节点的网络。我们发现，某些具有较小向量范数的股票对网络结构的变化具有重要影响。此外，我们定义了一个维数来描述包含在市场空间子空间中的相关信息，并发现维数的动态与市场状态有关。本文建立了一种通过市场几何学研究网络结构的方法，从而提供了一种新的相关分析方法。