The Network Of Causal Relationships In The U.S. Stock Market

Keywords

Big data; Causal market graph; Granger causality; Network analysis; Stock market

Abstract

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 2001–2017 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.

Publication Date

1-1-2018

Publication Title

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

Volume

11280 LNCS

Number of Pages

541-542

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85059056152 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85059056152

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