Exploring the Indian Stock Market through Minimum Spanning Trees and Planar Maximally Filtered Graphs: A Network Analysis Approach

In the ever-changing global financial environment, the Indian stock market plays a critical role, presenting unique challenges and opportunities for investors and analysts. This paper explores a network analysis methodology designed to uncover complex relationships within the Indian stock market. Our research uses a Minimum Spanning Tree (MST) and Planar Maximally Filtered Graph (PMFG) to uncover hidden patterns and gain insight into complex financial network. We use the historical log return data of the Nifty-100 stocks, a collection of 100 stocks representing major sections of the Indian economy, to extract a graphical representation of the market at a given point of time. In-order to test the efficacy of our work, we tried whether this method could extract, quantify, and represent visually the impact of the recent COVID-19 crisis that struck the Indian economy. We used various network metrics to quantify the network topology. The network metrics were in line with the economic timeline pre-COVID, COVID (decline), recovery and post-recovery.