Asset allocation: new evidence through network approaches

TL;DR

This paper introduces a network-based approach to asset allocation that leverages the dependence structure among securities, resulting in portfolios with better performance-risk trade-offs and enhanced interpretability through graphical visualization.

Contribution

It proposes three novel methods to extract dependence structures in financial networks and applies them to improve portfolio selection compared to traditional methods.

Findings

Portfolios mainly consist of peripheral, poorly connected assets.

Network-based portfolios often outperform the Global Minimum Variance portfolio in performance-risk balance.

Graphical visualization aids in understanding and interpreting the selected portfolios.

Abstract

The main contribution of the paper is to employ the financial market network as a useful tool to improve the portfolio selection process, where nodes indicate securities and edges capture the dependence structure of the system. Three different methods are proposed in order to extract the dependence structure between assets in a network context. Starting from this modified structure, we formulate and then we solve the asset allocation problem. We find that the portfolios obtained through a network-based approach are composed mainly of peripheral assets, which are poorly connected with the others. These portfolios, in the majority of cases, are characterized by an higher trade-off between performance and risk with respect to the traditional Global Minimum Variance (GMV) portfolio. Additionally, this methodology benefits of a graphical visualization of the selected portfolio directly over the graphic layout of the network, which helps in improving our understanding of the optimal strategy.