Property of Inverse Covariance Matrix-based Financial Adjacency Matrix for Detecting Local Groups

TL;DR

This paper introduces an inverse covariance matrix-based adjacency matrix (IFAM) for better detection of local groups in financial data, addressing global factor effects and improving portfolio allocation.

Contribution

It proposes a novel IFAM method and a factor-adjusted GLASSO estimator to accurately identify local group structures in multi-level factor models.

Findings

IFAM reduces false connections between groups

Improves detection of local group memberships

Enhances portfolio allocation performance

Abstract

In financial applications, we often observe both global and local factors that are modeled by a multi-level factor model. When detecting unknown local group memberships under such a model, employing a covariance matrix as an adjacency matrix for local group memberships is inadequate due to the predominant effect of global factors. Thus, to detect a local group structure more effectively, this study introduces an inverse covariance matrix-based financial adjacency matrix (IFAM) that utilizes negative values of the inverse covariance matrix. We show that IFAM ensures that the edge density between different groups vanishes, while that within the same group remains non-vanishing. This reduces falsely detected connections and helps identify local group membership accurately. To estimate IFAM under the multi-level factor model, we introduce a factor-adjusted GLASSO estimator to address the prevalent global factor effect in the inverse covariance matrix. An empirical study using returns from international stocks across 20 financial markets demonstrates that incorporating IFAM effectively detects latent local groups, which helps improve the minimum variance portfolio allocation performance.