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Updated: Mar 29, 2026
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Comparing Ising and Spin Glass Dynamics in Financial Markets: A Complex Systems Approach to Asset Interdependence
Irina Georgescu1, Jani Kinnunen2
1Department of Economic Informatics and Cybernetics, Bucharest University of Economics, Calea Dorobanți, 15-7, Sector 1, 010552 Bucharest, Romania.
Entropy (Basel, Switzerland)
|March 28, 2026
Summary
This study compares Ising and spin glass models for financial markets. The spin glass model better captures complex, heterogeneous interactions and nonlinear dependencies in asset behavior.
Area of Science:
- Statistical physics applied to financial markets
- Complex systems analysis
- Network theory in economics
Background:
- Financial markets exhibit complex collective behavior driven by dynamic interactions.
- Understanding asset interdependence is crucial for market analysis.
- Existing models may oversimplify the intricate relationships within financial systems.
Purpose of the Study:
- To compare Ising and spin glass models for analyzing financial market interdependence.
- To investigate the structural heterogeneity of asset interactions.
- To assess the ability of different frameworks to capture nonlinear dependencies.
Main Methods:
- Utilizing daily data from a 15-asset commodity system (2020-2024).
- Constructing rolling coupling matrices using linear correlations and nonlinear mutual information.
- Embedding interaction matrices into Ising and Sherrington-Kirkpatrick (spin glass) frameworks.
Main Results:
- Both Ising and spin glass models show similar aggregate synchronization dynamics.
- The spin glass framework reveals significantly richer structural heterogeneity.
- Preserving interaction sign structure in spin glass models yields wider dispersion and nontrivial network configurations suppressed in the Ising model.
Conclusions:
- The Ising model serves as a benchmark for market coherence.
- The spin glass model is essential for capturing heterogeneous interactions and nonlinear dependencies in financial markets.
- Statistical physics models offer valuable insights into complex financial market dynamics.
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