Aim/purpose – This article aims to explore the network topology of the stock market in Poland during the COVID-19 pandemic.
Design/methodology/approach – Kruskal’s algorithm was used to find the minimum spanning trees (MST) of three undirected correlation networks: MST1 (December 2019 – August 2021), MST2 (February 2020 – April 2020), and MST3 (June 2021 – August 2021). There were123 firms included in all three networks representing three key indexes (WIG20, mWIG40, and sWIG80).
Findings – The comovements of stock prices varied between various periods of the pandemic. The most central firms in Poland were PEO, UNT, SPL, PKO, KGH, CCC, and PZU. WIG20 was the most influential stock index for all networks. During the turbulent period represented by MST2, many of Poland’s largest companies have clustered around KGH at the center of the network. In contrast, MST3 is the least compact of the three networks and is characterized by the absence of a single strongly influential node.
Research implications/limitations – Correlation networks are efficient at quantitatively describing the degree of interdependence of a stock. MST finding algorithms are a crucial method of analysis for correlation networks. However, a limitation of the study, inherent to undirected correlation networks, is the inability to determine the direction of influence that stocks have on each other.
Originality/value/contribution – The results of the article contribute to the economic analysis of stock markets in several ways. First, it expands on Gałązka (2011) by including additional centralities and the dynamic aspect of changes in the topology during the COVID-19 pandemic. Second, it broadens the MST-based empirical research of stock markets by showing the emergence of the star topology during the period of high uncertainty in Poland. Third, it has practical applications for systemic risk assessment and portfolio diversification.
