Structure Estimation of Financial Networks and Its Correlation with Mobility Data

During the COVID-19 pandemic, the human mobility was restricted and significantly impacted, which also had a serious effect on financial markets. Specifically, industries such as energy, utilities, travel, retail, and hospitality were negatively affected by the decrease in population movement, leading to reduced consumer spending and lower demand for services in these sectors, while other sectors, such as healthcare and technology, saw significant growth due to increased demand during the pandemic. Additionally, the challenging market environment led to multiple anomalies in financial markets. When financial markets are abnormal, signs of distress in the market structure often appear before a crisis fully erupts. This study proposes a model based on the Stochastic Block Model (SBM) to detect anomalies by analyzing market changes and underlying spatial structure. We then extended the model to create a multi-layer model, treating the blocks in the lower layer as nodes in the upper layer, using a two-layer approach for cause analysis. We analyzed the structure of the S&P 100 and TOPIX 100 indices during the pandemic and compared it with mobility data from the same period. The results indicate a correlation between population mobility and changes in market structure during the pandemic, offering a new perspective for analyzing financial markets.