An Evolutionary Optimization Approach to Risk Parity Portfolio Selection

TL;DR

This paper introduces an evolutionary optimization method, combining genetic algorithms and local search heuristics, to effectively solve the complex risk parity portfolio selection problem, especially in long-short scenarios.

Contribution

It presents a novel evolutionary framework for risk parity portfolio optimization, addressing the non-trivial long-short case with successful numerical validation.

Findings

Effective solutions for long-short risk parity portfolios

Genetic algorithm combined with local search outperforms traditional methods

Numerical results confirm practical applicability

Abstract

In this paper we present an evolutionary optimization approach to solve the risk parity portfolio selection problem. While there exist convex optimization approaches to solve this problem when long-only portfolios are considered, the optimization problem becomes non-trivial in the long-short case. To solve this problem, we propose a genetic algorithm as well as a local search heuristic. This algorithmic framework is able to compute solutions successfully. Numerical results using real-world data substantiate the practicability of the approach presented in this paper.