Community analysis of International stock markets
Complex networks made by using financial data have always been of high importance. Time-series analysis of graphs can often reveal patterns and bahaviour which help can investors and policy makers determine the effect of multiple assets on each other. This study uses international stock market data to analyze such effects.
Additionally, the currency exchange rate of each of the countries under analysis is multiplied over the complete dataset to study the changes due to exchange rates. Communities are formed using the greedy maximization algorithm, which maximizes stability of the communities. The study also calculates the betweenness centrality to understand the influence of nodes within a community. Timescale of the study was split into two. The first analysis was focused on the changes in linkages due to the business cycle movement over the period of 2008-2019. The second analysis was a microscopic analysis of the change in communities, and their roles both before and during the Covid-19 pandemic, over the timescale of September 2019 - April 2020.
Results found several patterns among the Asian countries over the period of 2008-2019. Along with this the study found change in dynamics of economies with regards to China during the Covid-19 pandemic due to change in market sentiment.