Simheuristic and learnheuristic for solving stochastic and / or dynamic portfolio optimization problems

Li, Yuda, Polat, Onur, Juan, Angel A., Calvet, Laura and Kizys, Renatas (2023) Simheuristic and learnheuristic for solving stochastic and / or dynamic portfolio optimization problems. Currie, Christine and Rhodes-Leader, Luke (eds.) In Proceedings of the Operational Research Society Simulation Workshop 2023 (SW23). Operational Research Society. pp. 172-181 . (doi:10.36819/SW23.020).

Abstract

Constructing portfolio by proper asset selection to maximize return and minimize risk has been considered an essential task for investment activities. Rich portfolio optimizations with realistic constraints are NP-hard problems and are commonly solved using metaheuristics. However, financial markets are characterized by their high volatility and uncertainty, and metaheuristics do not fully account for these random and/or dynamic components, which renders them unrealistic in the presence of heightened uncertainty and dynamism in financial markets. Therefore, this paper proposes a simulationoptimization approach specifically, a simheuristic algorithm to deal with the stochastic version of the problem and a learnheuristic algorithm for solving the dynamic version of the problem. Computational experiments are performed on a benchmark instance to illustrate the advantages of the proposed methodologies and analyze how the solutions change in response to a different degree of stochasticity, dynamism, and minimum required return.

This record has no associated files available for download.

Contributors

Author: Renatas Kizys

Download statistics