Portfolio Rebalancing under Uncertainty Using Meta-heuristic Algorithm

TL;DR

This paper introduces a meta-heuristic algorithm approach to solve the portfolio rebalancing problem under uncertainty, considering transaction costs and using CPPI strategy, demonstrating its effectiveness over traditional solvers.

Contribution

It proposes a novel meta-heuristic algorithm for portfolio rebalancing under uncertainty, outperforming global optimization solvers in larger problem instances.

Findings

Uncertain parameters influence efficient frontiers and model performance.

CPPI strategy limits downside risk in bear markets.

Meta-heuristic algorithms yield better results for larger problems.

Abstract

In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain parameters and different belief degrees will produce different efficient frontiers, and affect the performance of the proposed model. Moreover, CPPI strategy performs as an insurance mechanism and limits downside risk in bear markets while it allows potential benefit in bull markets. Finally, using a globally optimization solver and genetic algorithm (GA) for solving the model, we concluded that the problem size is an important factor in solving portfolio rebalancing problem with uncertain parameters and to gain better results, it is recommended to use a meta-heuristic algorithm rather than a global solver.