Computational Data and Social Networks Conference Paper uri icon


  • Springer Nature Switzerland AG 2018. We propose a network-based framework to study causal relationships in financial markets and demonstrate the proposed approach by applying it to the entire U.S. stock market. Directed networks (referred to as causal market graphs) are constructed based on stock return time series data during 20012017 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 influen-tial stocks via a PageRank algorithm. The proposed approaches offer a new angle for analyzing global characteristics and trends of the stock market using network-based techniques.

published proceedings

  • Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

altmetric score

  • 1

author list (cited authors)

  • Shirokikh, O., Pastukhov, G., Semenov, A., Butenko, S., Veremyev, A., Pasiliao, E., & Boginski, V.

citation count

  • 1

complete list of authors

  • Shirokikh, O||Pastukhov, G||Semenov, A||Butenko, S||Veremyev, A||Pasiliao, E||Boginski, V

editor list (cited editors)

  • Chen, X., Sen, A., Li, W. W., & Thai, M. T.

publication date

  • January 2018