Metaheuristic Approach to Solve Portfolio Selection Problem

TL;DR

This paper introduces a heuristic approach combining Tabu Search and TokenRing Search to effectively solve the NP-hard portfolio optimization problem with additional constraints, outperforming traditional methods on benchmark datasets.

Contribution

It presents a novel hybrid heuristic method with a new initial solution technique for the constrained portfolio selection problem.

Findings

The proposed method effectively solves large-scale portfolio problems.

It outperforms existing heuristics on benchmark datasets.

The approach handles complex constraints efficiently.

Abstract

In this paper, a heuristic method based on TabuSearch and TokenRing Search is being used in order to solve the Portfolio Optimization Problem. The seminal mean-variance model of Markowitz is being considered with the addition of cardinality and quantity constraints to better capture the dynamics of the trading procedure, the model becomes an NP-hard problem that can not be solved using an exact method. The combination of three different neighborhood relations is being explored with Tabu Search. In addition, a new constructive method for the initial solution is proposed. Finally, I show how the proposed techniques perform on public benchmarks