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

Shirokikh Oleg (), Pastukhov Grigory (), Semenov Alexander (), Butenko Sergiy (), Veremyev Alexander (), Pasiliao Eduardo L. () and Boginski Vladimir ()
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Shirokikh Oleg: Frontline Solver, Reno, NV, USA
Pastukhov Grigory: CSX Transportation, Jacksonville, FL, USA
Semenov Alexander: Department of Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA
Butenko Sergiy: Department of Industrial & Systems Engineering, Texas A&M University, College Station, TX, USA
Veremyev Alexander: Department of Industrial Engineering & Management Systems, University of Central Florida, Orlando, FL, USA
Pasiliao Eduardo L.: Munitions Directorate, Air Force Research Laboratory, Eglin AFB, FL, USA
Boginski Vladimir: Department of Industrial Engineering & Management Systems, University of Central Florida, Orlando, FL, USA

Dependence Modeling, 2022, vol. 10, issue 1, 177-190

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.

Keywords: network analysis; graph theory; causal market graph; Granger causality; k-core; PageRank (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:10:y:2022:i:1:p:177-190:n:6

DOI: 10.1515/demo-2022-0110

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