Robust conditional expectation reward-risk performance measures

In this paper, we develop robust portfolio optimization models for conditional expectation type reward-risk performance measures that significantly improve upon conventional portfolio selection techniques. In particular, we directly address estimation error in the portfolio optimization process by adopting a robust optimization method that is typically used with conventional robust statistical estimation techniques. Alongside this robust optimization, we propose the use of an early-warning system based on moving averages to predict market crises. Empirical analyses based on the US stock market validate the proposed robust approaches and highlight the implications of financial crises for portfolio selection problems. The results confirm that the proposed robust portfolio optimization models substantially improve upon their conventional counterparts for out-of-sample portfolios, providing valuable managerial insights. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper presents robust portfolio optimization models that significantly improve upon conventional portfolio selection techniques by addressing estimation error and using an early-warning system. Empirical analyses based on the US stock market validate the proposed robust approaches and show their superiority in out-of-sample portfolios.