Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends

Doering, Jana; Kizys, Renatas; Juan, Ángel A.; Fitó, Àngels; Polat, Onur

doi:10.1016/j.orp.2019.100121

Cited by 42 publications

(17 citation statements)

References 126 publications

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“…Among them, the excellent performance of WOA in portfolio optimization is also reported by [47]. In addition, referring to [76,77], we select three other competitive algorithms that demonstrate their excellent performance in optimizing the same problem in the literature, namely, Adaptive Particle Swarm Optimization (APSO) [44], Fireworks Algorithm (FA) [45] and Harmony Search Algorithm (HSA) [46]. Due to a large dimension of some datasets, the MaxFEs is set to × 10 5 for each algorithm to achieve a trade-off between computational cost and performance.…”

Section: B the Experimental Setupmentioning

confidence: 81%

An Adaptive Multi-Population Optimization Algorithm for Global Continuous Optimization

Tam

Yeung

2021

IEEE Access

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Nowadays, there are various optimization problems that exact mathematical methods are not applicable. Metaheuristics are considered as efficient approaches for finding the solutions. Yet there are many real-world problems that consist of different properties. For instance, financial portfolio optimization may contain many dimensions for different sets of assets, which suggests the need of a more adaptive metaheuristic method for tackling such problems. However, few existing metaheuristics can achieve robust performance across these variable problems even though they may obtain impressive results in specific benchmark problems. In this paper, a metaheuristic named the Adaptive Multi-Population Optimization (AMPO) is proposed for continuous optimization. The algorithm hybridizes yet modifies several useful operations like mutation and memory retention from evolutionary algorithms and swarm intelligence (SI) techniques in a multi-population manner. Furthermore, the diverse control on multiple populations, solution cloning and reset operation are designed. Compared with other metaheuristics, the AMPO can attain an adaptive balance between the capabilities of exploration and exploitation for various optimization problems. To demonstrate its effectiveness, the AMPO is evaluated on 28 well-known benchmark functions. Also, the parameter sensitivity analysis and search behavior study are conducted. Finally, the AMPO is validated on its applicability through a portfolio optimization problem as a challenging example of real-world applications. The benchmark results show that the AMPO achieves a better performance than those of nine state-ofthe-art metaheuristics including the IEEE CEC winning algorithms, recent SI and multi-population/hybrid metaheuristics. Besides, the AMPO can consistently produce a good performance in portfolio optimization.

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Section: B the Experimental Setupmentioning

confidence: 81%

An Adaptive Multi-Population Optimization Algorithm for Global Continuous Optimization

Tam

Yeung

2021

IEEE Access

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“…Some investigations have expanded the work of the Markowitz model, such as the mean semi-variance model [3], mean objective model [4], and portfolio optimization models with fuzzy logic [5]. Different metaheuristics algorithms have been applied to solve difficult problems, and recently important overviews have been published regarding these types of problems [6][7][8]. The reason for employing these algorithms is that they obtain suitable solutions within reasonable execution times [9].…”

Section: Introductionmentioning

confidence: 99%

SAIPO-TAIPO and Genetic Algorithms for Investment Portfolios

Frausto–Solís

Aldaz

Angel

et al. 2022

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The classic model of Markowitz for designing investment portfolios is an optimization problem with two objectives: maximize returns and minimize risk. Various alternatives and improvements have been proposed by different authors, who have contributed to the theory of portfolio selection. One of the most important contributions is the Sharpe Ratio, which allows comparison of the expected return of portfolios. Another important concept for investors is diversification, measured through the average correlation. In this measure, a high correlation indicates a low level of diversification, while a low correlation represents a high degree of diversification. In this work, three algorithms developed to solve the portfolio problem are presented. These algorithms used the Sharpe Ratio as the main metric to solve the problem of the aforementioned two objectives into only one objective: maximization of the Sharpe Ratio. The first, GENPO, used a Genetic Algorithm (GA). In contrast, the second and third algorithms, SAIPO and TAIPO used Simulated Annealing and Threshold Accepting algorithms, respectively. We tested these algorithms using datasets taken from the Mexican Stock Exchange. The findings were compared with other mathematical models of related works, and obtained the best results with the proposed algorithms.

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“…Therefore, new optimization approaches are needed in order to take into account all the relevant factors while supporting dynamic decision making. In addition, the need to add realistic constraints to some portfolio optimization problems has made them become NP‐hard (Doering et al., 2019). For this reason, metaheuristics are becoming increasingly popular for solving these project portfolio problems (Beasley, 2013).…”

Section: Introductionmentioning

confidence: 99%

A clustering‐based review on project portfolio optimization methods

Saiz

Lostumbo

Juan

et al. 2021

Int Trans Operational Res

Self Cite

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Project portfolio management and optimization constitutes a critical activity for organizations in different industrial sectors and business. The scientific literature in this subject is extremely vast, which makes it difficult to understand the connections among the existing approaches and perspectives. This paper provides a clustering map of the existing work on the subject, thus identifying the main trends and approaches from different scientific communities. After analyzing each of the identified clusters, the paper provides insights and emerging trends that can be useful both for researchers and practitioners in the area.

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Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends

Cited by 42 publications

References 126 publications

An Adaptive Multi-Population Optimization Algorithm for Global Continuous Optimization

An Adaptive Multi-Population Optimization Algorithm for Global Continuous Optimization

SAIPO-TAIPO and Genetic Algorithms for Investment Portfolios

A clustering‐based review on project portfolio optimization methods

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