A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariances

Kizys, Renatas; Doering, Jana; Juan, Ángel A.; Polat, Onur; Calvet, Laura; Panadero, Javier

doi:10.1016/j.cor.2021.105631

Cited by 16 publications

(14 citation statements)

References 52 publications

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“…Table 1 shows the solution methodologies sorted in a chronological order (according to year). The most recent solution methodologies are; Non-Linear Activated Beetle Antennae Search [15], Multi-Objective Genetic Algorithm [16], Improved Genetic Algorithm [17], Quadratic Programming [18], and a combination of Variable Neighborhood Search and Monte Carlo Simulation [19].…”

Section: Categorisation Of Solution Methodologiesmentioning

confidence: 99%

“…Kizys et al proposed a simulation-optimisation approach by integrating Variable Neighbourhood Search (VNS) metaheuristic with Monte Carlo simulation in addressing the benchmark OR-Library dataset [19]. A mathematical formulation based on stochastic portfolio model was formulated, where risk was treated as an objective function while return was treated as a constraint.…”

Section: Hybrid Approachesmentioning

confidence: 99%

“…Figure 4 displays the solution methodologies for the POP arranged according to datasets. The state-of-the art meth- ods for the OR-Library dataset are; a hybrid of Variable Neighbourhood Search and Monte Carlo Search from [19], Modified Squirrel Search Algorithm from [43], and Artificial Bee Colony from [50]. Population-based metaheuristic is a favoured approach in addressing the OR-Library datasets.…”

Section: A Benchmark Datasetsmentioning

confidence: 99%

“…Khan et al believed that the efficiency and convergence rate of their NABAS algorithm could be further improved [15]. Kizys et al hoped to hybridize a statistical learning technique with a metaheuristic-based approach (known as learnheuristics) for the dynamic stochastic POP [19]. Dhaini and Mansour suggested to hybridize their MSSA algorithm with features from other metaheuristic techniques to improve its exploration and exploitation capabilities [43].…”

Section: Research Opportunitiesmentioning

confidence: 99%

“…[17] Quadratic Programming (QP) [18] Variable Neighborhood Search(VNS), Monte Carlo Simulation (MCS) [19] Mathematical Optimization [15]. A gradient estimate measure was used as an update criteria in a BAS algorithm.…”

mentioning

confidence: 99%

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Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

et al. 2023

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This survey paper provides an overview of current developments for the Portfolio Optimisation Problem (POP) based on articles published from 2018 to 2022. It reviews the latest solution methodologies utilised in addressing POPs in terms of mechanisms and performance. The methodologies are categorised as Metaheuristic, Mathematical Optimisation, Hybrid Approaches, Matheuristic and Machine Learning. The datasets (benchmark, real-world, and hypothetical) utilised in portfolio optimisation research are provided. The state-of-the-art methodologies for benchmark datasets are presented accordingly. Population-based metaheuristics are the most preferred techniques among researchers in addressing the POP. Hybrid approaches is an emerging trend (2018 onwards). The OR-Library is the most widely used benchmark dataset for researchers to compare their methodologies in addressing POP. The research challenges and opportunities are discussed. The summarisation of the published papers in this survey provides an insight to researchers in identifying emerging trends and gaps in this research area.

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