Signal inference in financial stock return correlations through phase-ordering kinetics in the quenched regime

ArXiv ID: 2409.19711 “View on arXiv”

Authors: Unknown

Abstract

Financial stock return correlations have been analyzed through the lens of random matrix theory to differentiate the underlying signal from spurious correlations. The continuous spectrum of the eigenvalue distribution derived from the stock return correlation matrix typically aligns with a rescaled Marchenko-Pastur distribution, indicating no detectable signal. In this study, we introduce a stochastic field theory model to establish a detection threshold for signals present in the limit where the eigenvalues are within the continuous spectrum, which itself closely resembles that of a random matrix where standard methods such as principal component analysis fail to infer a signal. We then apply our method to Standard & Poor’s 500 financial stocks’ return correlations, detecting the presence of a signal in the largest eigenvalues within the continuous spectrum.

Keywords: random matrix theory, stochastic field theory, correlation matrix, eigenvalue distribution, Marchenko-Pastur, equities

Complexity vs Empirical Score

• Math Complexity: 9.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper is highly theoretical, employing advanced statistical field theory, stochastic field theory, and phase-ordering kinetics to model signal detection in financial correlations, resulting in a very high math complexity score. While it applies the method to S&P 500 data, the empirical section is primarily a theoretical application with a stated goal of detecting signals, lacking detailed backtesting, performance metrics, or implementation-heavy data analysis, leading to a low empirical rigor score.

  flowchart TD
    A["Research Goal:<br>Signal Inference in Financial<br>Stock Return Correlations"] --> B["Data Input:<br>S&P 500 Stock Return Data"]
    B --> C["Methodology:<br>Stochastic Field Theory Model<br>Phase-ordering Kinetics"]
    C --> D["Computational Process:<br>Define Detection Threshold<br>in Quenched Regime"]
    D --> E{"Eigenvalue Analysis:<br>Standard RMT vs<br>Proposed Model"}
    E -- Standard RMT<br>Marchenko-Pastur --> F["Outcome 1:<br>No Detectable Signal<br>Continuous Spectrum Only"]
    E -- Proposed Model --> G["Outcome 2:<br>Signal Detected in<br>Largest Eigenvalues"]
    F & G --> H["Key Finding:<br>Signal Inference Possible<br>within Continuous Spectrum"]