Hidden Interactions in Financial Markets

Proceedings of the National Academy of Sciences (PNAS) of the United States of America, Volume x, Issue x, p. 1-6, May 2019, DOI: 10.1073/pnas.1819449116

Posted: 28 Jan 2018 Last revised: 16 May 2019

See all articles by Stavros K. Stavroglou

Stavros K. Stavroglou

Department of Mathematical Sciences and Institute for Risk and Uncertainty, University of Liverpool, UK

Athanasios A. Pantelous

Monash University - Department of Econometrics & Business Statistics

H. Eugene Stanley

Boston University - Center for Polymer Studies

Konstantin Zuev

California Institute of Technology

Date Written: May 13, 2019

Abstract

The hidden nature of causality is a puzzling, yet critical notion for effective decision-making. Financial markets are characterized by fluctuating interdependencies which seldom give rise to emergent phenomena such as bubbles or crashes. In this paper, we propose a method based on symbolic dynamics, which probes beneath the surface of abstract causality and unveils the nature of causal interactions. Our method allows distinction between positive and negative interdependencies as well as a hybrid form that we refer to as “dark causality.” We propose an algorithm which is validated by models of a priori defined causal interaction. Then, we test our method on asset pairs and on a network of sovereign credit default swaps (CDS). Our findings suggest that dark causality dominates the sovereign CDS network, indicating interdependencies which require caution from an investor’s perspective.

Keywords: financial markets; pattern causality; complex systems; sovereign CDS networks; pairs trading

JEL Classification: C00, C01, C02, C10

Suggested Citation

Stavroglou, Stavros K. and Pantelous, Athanasios A. and Stanley, H. Eugene and Zuev, Konstantin, Hidden Interactions in Financial Markets (May 13, 2019). Proceedings of the National Academy of Sciences (PNAS) of the United States of America, Volume x, Issue x, p. 1-6, May 2019, DOI: 10.1073/pnas.1819449116. Available at SSRN: https://ssrn.com/abstract=3105281 or http://dx.doi.org/10.2139/ssrn.3105281

Stavros K. Stavroglou (Contact Author)

Department of Mathematical Sciences and Institute for Risk and Uncertainty, University of Liverpool, UK ( email )

Chatham Street
Liverpool, L69 7ZA
United Kingdom

Athanasios A. Pantelous

Monash University - Department of Econometrics & Business Statistics ( email )

Wellington Road
Clayton, Victoria 3168
Australia

H. Eugene Stanley

Boston University - Center for Polymer Studies ( email )

Boston, MA 02215
United States

Konstantin Zuev

California Institute of Technology ( email )

Pasadena, CA 91125
United States

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