Inferring financial stock returns correlation from complex network analysis

TL;DR

This paper uses complex network analysis to distinguish meaningful stock return correlations from noise, revealing that central stocks influence market behavior and improving portfolio performance over traditional methods.

Contribution

It introduces a novel approach combining network analysis with simulated market data to better interpret correlation matrices and optimize portfolios.

Findings

High eigenvector centrality stocks dominate correlation structure

Network clustering correlates with market modes

Portfolio based on simulated market outperforms mean-variance approach by up to 50%

Abstract

Financial stock returns correlations have been studied in the prism of random matrix theory, to distinguish the signal from the "noise". Eigenvalues of the matrix that are above the rescaled Marchenko Pastur distribution can be interpreted as collective modes behavior while the modes under are usually considered as noise. In this analysis we use complex network analysis to simulate the "noise" and the "market" component of the return correlations, by introducing some meaningful correlations in simulated geometric Brownian motion for the stocks. We find that the returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering found in the network. We then use simulated "market" random walks to build an optimal portfolio and find that the overall return performs better than using the historical mean-variance data, up to 50% on short time scale.