Hidden interactions in financial markets

Significance The importance of understanding the structure of financial markets has been progressively coming to a head in public discourse. With the rise and prevalence of big datasets, it is a crucial time for academics to study the interdependency outside conventional analysis. Thus, the methodology proposed in this paper does not only allow distinction between positive and negative interdependence, but additionally identifies a yet unexplored form of interaction we coin as “dark causality.” We benchmark this method on asset pairs and on a network of sovereign credit default swaps, where the dominant form of interaction is that of dark causality. Our results are relevant to finance practitioners who, by virtue of their profession, tend to engage on a regular basis with retail investors. 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.