A note on evolutionary stochastic portfolio optimization and probabilistic constraints

TL;DR

This paper extends an evolutionary stochastic portfolio optimization framework to incorporate probabilistic constraints, demonstrating the approach's practical applicability through numerical results with financial data.

Contribution

It introduces a method to integrate probabilistic constraints into evolutionary stochastic portfolio optimization models, enhancing their flexibility and real-world relevance.

Findings

Successful integration of probabilistic constraints demonstrated

Numerical results validate the approach's applicability

Framework adaptable to various probabilistic constraints

Abstract

In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment as well as the evolutionary optimization environment are ideally suited for an integration of various types of probabilistic constraints. We show an approach on how to integrate these constraints. Numerical results using recent financial data substantiate the applicability of the presented approach.