A mixture transition distribution approach to portfolio optimization

TL;DR

This paper introduces a novel portfolio optimization method using Mixture Transition Distribution (MTD) networks to better capture asset dependencies, leading to improved risk-adjusted returns over traditional models.

Contribution

It presents a new approach applying MTD-based directed networks and local assortativity measures for portfolio optimization, outperforming classical mean-variance methods.

Findings

MTD networks effectively model complex asset relationships.

Portfolios using network-based measures outperform classical methods.

Enhanced diversification and Sharpe ratios achieved.

Abstract

Understanding the dependencies among financial assets is critical for portfolio optimization. Traditional approaches based on correlation networks often fail to capture the nonlinear and directional relationships that exist in financial markets. In this study, we construct directed and weighted financial networks using the Mixture Transition Distribution (MTD) model, offering a richer representation of asset interdependencies. We apply local assortativity measures--metrics that evaluate how assets connect based on similarities or differences--to guide portfolio selection and allocation. Using data from the Dow Jones 30, Euro Stoxx 50, and FTSE 100 indices constituents, we show that portfolios optimized with network-based assortativity measures consistently outperform the classical mean-variance framework. Notably, modalities in which assets with differing characteristics connect enhance diversification and improve Sharpe ratios. The directed nature of MTD-based networks effectively captures complex relationships, yielding portfolios with superior risk-adjusted returns. Our findings highlight the utility of network-based methodologies in financial decision-making, demonstrating their ability to refine portfolio optimization strategies. This work thus underscores the potential of leveraging advanced financial networks to achieve enhanced performance, offering valuable insights for practitioners and setting a foundation for future research.