Sector Neutral Portfolios: Long memory motifs persistence in market structure dynamics

TL;DR

This paper investigates the long-memory persistence of motif structures in market correlation networks, demonstrating their usefulness in forecasting, portfolio diversification, and outperforming traditional volatility-based methods.

Contribution

It introduces an unsupervised technique to identify persistently correlated stock sets and applies persistence analysis to improve portfolio diversification and risk management.

Findings

Persistent motifs follow power-law decay patterns.

Identified sectors driven by strong fundamentals.

Persistence-based measures outperform volatility weighting.

Abstract

We study soft persistence (existence in subsequent temporal layers of motifs from the initial layer) of motif structures in Triangulated Maximally Filtered Graphs (TMFG) generated from time-varying Kendall correlation matrices computed from stock prices log-returns over rolling windows with exponential smoothing. We observe long-memory processes in these structures in the form of power law decays in the number of persistent motifs. The decays then transition to a plateau regime with a power-law decay with smaller exponent. We demonstrate that identifying persistent motifs allows for forecasting and applications to portfolio diversification. Balanced portfolios are often constructed from the analysis of historic correlations, however not all past correlations are persistently reflected into the future. Sector neutrality has also been a central theme in portfolio diversification and systemic risk. We present an unsupervised technique to identify persistently correlated sets of stocks. These are empirically found to identify sectors driven by strong fundamentals. Applications of these findings are tested in two distinct ways on four different markets, resulting in significant reduction in portfolio volatility. A persistence-based measure for portfolio allocation is proposed and shown to outperform volatility weighting when tested out of sample.