An Evolutionary Optimization Approach to Risk Parity Portfolio Selection
This work addresses portfolio optimization for investors needing long-short strategies, but it is incremental as it applies existing evolutionary methods to a specific financial problem.
The paper tackles the risk parity portfolio selection problem for long-short portfolios, which is non-trivial compared to long-only cases, by proposing a genetic algorithm and local search heuristic, with numerical results on real-world data showing successful computation of solutions.
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.