Portfolio Cuts: A Graph-Theoretic Framework to Diversification

TL;DR

This paper introduces a graph-theoretic portfolio partitioning method called portfolio cuts, enabling more robust and computationally feasible asset allocation by leveraging graph structures and avoiding covariance matrix inversion.

Contribution

The paper proposes a novel graph-based portfolio cut framework that incorporates domain knowledge and physical intuition for improved diversification and tractability.

Findings

Allows portfolio construction without covariance matrix inversion

Demonstrates robustness and economic meaningfulness of asset clusters

Shows advantages over traditional methods through real-world data simulations

Abstract

Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis, by means of graphs. Next, to relax the assumption of the completeness of graph topology and to equip the graph model with practically relevant physical intuition, we introduce the portfolio cut paradigm. Such a graph-theoretic portfolio partitioning technique is shown to allow the investor to devise robust and tractable asset allocation schemes, by virtue of a rigorous graph framework for considering smaller, computationally feasible, and economically meaningful clusters of assets, based on graph cuts. In turn, this makes it possible to fully utilize the asset returns covariance matrix for constructing the portfolio, even without the requirement for its inversion. The advantages of the proposed framework over traditional methods are demonstrated through numerical simulations based on real-world price data.