Networks of causal relationships in the U.S. stock market

Abstract

Abstract We consider a network-based framework for studying causal relationships in financial markets and demonstrate this approach by applying it to the entire U.S. stock market. Directed networks (referred to as “causal market graphs”) are constructed based on publicly available stock prices time series data during 2001–2020, using Granger causality as a measure of pairwise causal relationships between all stocks. We consider the dynamics of structural properties of the constructed network snapshots, group stocks into network-based clusters, as well as identify the most “influential” market sectors via the PageRank algorithm. Interestingly, we observed drastic changes in the considered network characteristics in the years that corresponded to significant global-scale events, most notably, the financial crisis of 2008 and the COVID-19 pandemic of 2020.