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.

Throughout time, operational laws and concepts from complex systems have been employed to quantitatively model important aspects and interactions in nature and society. Nevertheless, it remains enigmatic and challenging, yet inspiring, to predict the actual interdependencies that comprise the structure of such systems, particularly when the causal interactions observed in real-world phenomena might be persistently hidden. In this article, we propose a robust methodology for detecting the latent and elusive structure of dynamic complex systems. Our treatment utilizes short-term predictions from information embedded in reconstructed state space. In this regard, using a broad class of real-world applications from ecology, neurology, and finance, we explore and are able to demonstrate our method’s power and accuracy to reconstruct the fundamental structure of these complex systems, and simultaneously highlight their most fundamental operations.