Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex...

Area of Science:

• * Economic Complexity and Network Theory
• * Quantitative Finance and Systemic Risk Analysis

Background:

• * Mathematical frameworks are crucial for understanding economic systems and crisis emergence.
• * The relationship between system structure and crisis development is of significant importance.
• * Existing methods may not fully capture the dynamic structural changes preceding economic crises.

Purpose of the Study:

• * To propose a novel method for inferring economic system structures using complex networks theory.
• * To analyze the relationship between network structure, community dynamics, and systemic risk.
• * To demonstrate the method's ability to identify risks and measure the impact of regulatory interventions.

Main Methods:

• * Inference of economic networks from company price time series using correlation matrices.
• * Application of a thresholding method on correlation coefficients to define network structure.
• * Determination of optimal threshold by comparing spectral properties of thresholded networks and correlation matrices.
• * Analysis of community structure and inter/intra-connectivity as systemic risk indicators.

Main Results:

• * Economic system behavior is demonstrably linked to its underlying network structure.
• * Economic crises are reflected as significant changes in the system's network structure.
• * The study quantifies the effects of regulation and deregulation on the economic system's structure.
• * The proposed method successfully identifies high systemic risk scenarios.

Conclusions:

• * Complex networks theory provides a powerful lens for understanding economic system dynamics.
• * Structural changes in economic networks serve as early indicators of potential crises.
• * The developed method offers a quantitative approach to assess systemic risk and policy impacts.