Matrix H-theory approach to stock market fluctuations

ArXiv ID: 2503.08697 “View on arXiv”

Authors: Unknown

Abstract

We introduce matrix H theory, a framework for analyzing collective behavior arising from multivariate stochastic processes with hierarchical structure. The theory models the joint distribution of the multiple variables (the measured signal) as a compound of a large-scale multivariate distribution with the distribution of a slowly fluctuating background. The background is characterized by a hierarchical stochastic evolution of internal degrees of freedom, representing the correlations between stocks at different time scales. As in its univariate version, the matrix H-theory formalism also has two universality classes: Wishart and inverse Wishart, enabling a concise description of both the background and the signal probability distributions in terms of Meijer G-functions with matrix argument. Empirical analysis of daily returns of stocks within the S&P500 demonstrates the effectiveness of matrix H theory in describing fluctuations in stock markets. These findings contribute to a deeper understanding of multivariate hierarchical processes and offer potential for developing more informed portfolio strategies in financial markets.

Keywords: Matrix H Theory, Multivariate Stochastic Processes, Wishart/Inverse Wishart, Meijer G-functions, Hierarchical Structure, Equities (S&P 500)

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical frameworks involving matrix distributions, Meijer G-functions with matrix arguments, and hierarchical stochastic differential equations, indicating high mathematical complexity. Empirical rigor is moderate, as it applies the theory to real S&P500 data and demonstrates descriptive fit, but lacks explicit backtesting for portfolio strategies or detailed implementation steps.

  flowchart TD
    A["Research Goal: Model multivariate stochastic processes with hierarchical structure in financial markets"] --> B["Methodology: Apply Matrix H-Theory<br>Wishart/Inverse Wishart Universality Classes"]
    B --> C["Data Input: Daily Stock Returns<br>(S&P 500 Equities)"]
    C --> D["Computational Process: Estimate Background & Signal Distributions<br>using Meijer G-functions with matrix argument"]
    D --> E["Key Findings: <br>1. Captures collective market behavior<br>2. Identifies hierarchical correlations<br>3. Validated on empirical data<br>Potential for improved portfolio strategies"]