Risk management in multi-objective portfolio optimization under uncertainty

TL;DR

This paper introduces a robust multi-objective optimization framework for portfolio management that incorporates benchmark comparisons to better handle uncertainties in real-world financial markets.

Contribution

It extends the min-regret robustness concept by integrating benchmark comparisons, bridging theoretical models with practical investment decision-making.

Findings

Enhanced portfolio robustness against market uncertainties

Improved decision-making reliability in multi-objective optimization

Bridging theoretical models with real-world investment scenarios

Abstract

In portfolio optimization, decision makers face difficulties from uncertainties inherent in real-world scenarios. These uncertainties significantly influence portfolio outcomes in both classical and multi-objective Markowitz models. To address these challenges, our research explores the power of robust multi-objective optimization. Since portfolio managers frequently measure their solutions against benchmarks, we enhance the multi-objective min-regret robustness concept by incorporating these benchmark comparisons. This approach bridges the gap between theoretical models and real-world investment scenarios, offering portfolio managers more reliable and adaptable strategies for navigating market uncertainties. Our framework provides a more nuanced and practical approach to portfolio optimization under real-world conditions.