Longitudinal market structure detection using a dynamic modularity-spectral algorithm

TL;DR

This paper presents DynMSA, a novel spectral clustering algorithm combining Random Matrix Theory and modularity optimization to detect market structures and improve portfolio diversification.

Contribution

The paper introduces DynMSA, a new dynamic clustering method that outperforms existing models in identifying stable market clusters and regime changes.

Findings

DynMSA outperforms baseline models in correlation separation.

It detects stable clusters and regime shifts effectively.

Portfolios based on DynMSA clusters show improved risk-adjusted returns.

Abstract

In this paper, we introduce the Dynamic Modularity-Spectral Algorithm (DynMSA), a novel approach to identify clusters of stocks with high intra-cluster correlations and low inter-cluster correlations by combining Random Matrix Theory with modularity optimisation and spectral clustering. The primary objective is to uncover hidden market structures and find diversifiers based on return correlations, thereby achieving a more effective risk-reducing portfolio allocation. We applied DynMSA to constituents of the S&P 500 and compared the results to sector- and market-based benchmarks. Besides the conception of this algorithm, our contributions further include implementing a sector-based calibration for modularity optimisation and a correlation-based distance function for spectral clustering. Testing revealed that DynMSA outperforms baseline models in intra- and inter-cluster correlation differences, particularly over medium-term correlation look-backs. It also identifies stable clusters and detects regime changes due to exogenous shocks, such as the COVID-19 pandemic. Portfolios constructed using our clusters showed higher Sortino and Sharpe ratios, lower downside volatility, reduced maximum drawdown and higher annualised returns compared to an equally weighted market benchmark.