Anti-correlation and subsector structure in financial systems

TL;DR

This paper uses random matrix theory to analyze the eigenvector structures of financial markets, revealing anti-correlated positive and negative subsectors that vary in strength across Chinese, American, and global markets.

Contribution

It introduces a method to detect subsector structures in financial systems by considering eigenvector signs, highlighting anti-correlation patterns.

Findings

Strong subsector structure in Chinese market

Weaker subsector structure in American and global markets

Anti-correlation between positive and negative subsectors

Abstract

With the random matrix theory, we study the spatial structure of the Chinese stock market, American stock market and global market indices. After taking into account the signs of the components in the eigenvectors of the cross-correlation matrix, we detect the subsector structure of the financial systems. The positive and negative subsectors are anti-correlated each other in the corresponding eigenmode. The subsector structure is strong in the Chinese stock market, while somewhat weaker in the American stock market and global market indices. Characteristics of the subsector structures in different markets are revealed.