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

ArXiv ID: 2508.02012 “View on arXiv”

Authors: Yuda Bi, Vince D Calhoun

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.

Keywords: Market Network Connectivity, Functional Connectivity, Latent Variables, Systemic Risk, Regime Shifts

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper proposes a sophisticated theoretical framework by directly importing advanced methods like groupICA and dFNC from neuroscience, indicating high mathematical complexity. However, it presents no empirical validation, backtests, or implementation details, relying purely on conceptual analogy and literature review.

  flowchart TD
    A["Research Goal:<br>Model financial markets as<br>dynamic latent systems?"] --> B{"Key Methodology:<br>Group Independent Component Analysis"}
    B --> C["Data Input:<br>Multi-asset price returns<br>co-fluctuations"]
    C --> D["Computational Process:<br>Dynamic Market Network Connectivity<br>dMNC"]
    D --> E{"Key Findings/Outcomes"}
    E --> F["Identify persistent<br>market subnetworks"]
    E --> G["Detect systemic<br>regime shifts"]
    E --> H["Reveal early warning<br>signals of risk"]