The Financial Connectome: A Brain-Inspired Framework for Modeling Latent Market Dynamics

TL;DR

This paper introduces the Financial Connectome, a brain-inspired framework that models financial markets as dynamic, modular systems revealing hidden structures and early warning signals without relying on predictive labels.

Contribution

It pioneers the field of financial connectomics by applying neuroscience-inspired methods to uncover latent market modules and their dynamics.

Findings

Markets exhibit modular, self-organizing architectures similar to brains.

The framework captures regime shifts and systemic early warning signals.

Reveals persistent market subnetworks and their evolution over time.

Abstract

We propose the Financial Connectome, a new scientific discipline that models financial markets through the lens of brain functional architecture. Inspired by the foundational work of group independent component analysis (groupICA) in neuroscience, we reimagine markets not as collections of assets, but as high-dimensional dynamic systems composed of latent market modules. Treating stocks as functional nodes and their co-fluctuations as expressions of collective cognition, we introduce dynamic Market Network Connectivity (dMNC), the financial analogue of dynamic functional connectivity (dFNC). This biologically inspired framework reveals structurally persistent market subnetworks, captures regime shifts, and uncovers systemic early warning signals all without reliance on predictive labels. Our results suggest that markets, like brains, exhibit modular, self-organizing, and temporally evolving architectures. This work inaugurates the field of financial connectomics, a principled synthesis of systems neuroscience and quantitative finance aimed at uncovering the hidden logic of complex economies.