An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization

Kalaycı, Can Berk; Polat, Olcay; Akbay, Mehmet Anıl

doi:10.1016/j.swevo.2020.100662

Cited by 53 publications

(25 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model is similar to the classical formulation of Markowitz for investment portfolios, and is an optimization problem defined by Equation (20). This model aims to minimize the variance of the portfolio [22]; they use the classical Threshold Accepting algorithm [44].…”

Section: Masese Modelmentioning

confidence: 99%

“…On the other hand, the process behind the Gilli model [21] shown in Equations ( 18) and ( 19) is described in lines 9-14. Finally, the function of the Masese model [22], shown in Equation (20), is presented in lines 15-17. The remainder of the process is the same as in the GENPO algorithm.…”

Section: Hybrid Pseudocodesmentioning

confidence: 99%

“…A Firefly Algorithm (FA) and an Imperialist Competitive Algorithm (ICA) are used to select the best set of assets using the mean semi-variance approach as the objective function [19]. In [20] a hybrid algorithm was proposed that combines Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) optimization, and GA for solving the cardinality constrained portfolio optimization problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SAIPO-TAIPO and Genetic Algorithms for Investment Portfolios

Frausto–Solís

Aldaz

Angel

et al. 2022

Axioms

View full text Add to dashboard Cite

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.

show abstract

Section: Masese Modelmentioning

confidence: 99%

Section: Hybrid Pseudocodesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SAIPO-TAIPO and Genetic Algorithms for Investment Portfolios

Frausto–Solís

Aldaz

Angel

et al. 2022

Axioms

View full text Add to dashboard Cite

show abstract

“…As opined by Harrison et al [16], in the case of a defined set of projects, selecting and scheduling the optimum subset of projects are challenging issues recognized as nondeterministic polynomial time (NP)-Hard and addressed as project portfolio selection and scheduling problem. It was demonstrated by Kalayci et al [17] that an effective mixed metaheuristic algorithm blends the significant elements of an artificial bee colony (ABC), consistent optimization of an ant colony, and GAs to resolve the issue of cardinality restricted portfolio optimization. Escobar-Anel et al [18] prescribed two approaches: First, the optimization problem is reduced to an associate problem with constraints independent of wealth and a different utility function.…”

Section: Introductionmentioning

confidence: 99%

An adaptive metaheuristic approach for risk-budgeted portfolio optimization

Gandikota

Hasan²,

Jaafar³

2023

IJ-AI

View full text Add to dashboard Cite

<div align="left"><span lang="EN-US">An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns complex when risk budgeting and other investor preferential constraints are weighed in, rendering it difficult for direct solving via conventional approaches. As such, this present study proposes a novel technique that addresses the problem of constrained risk budgeted optimization with multiple crossovers (binomial, exponential & arithmetic) together with the hall of fame via differential evolution (DE) strategies. The proposed automated solution facilitates portfolio managers to adopt the best possible portfolio that yields the most lucrative returns. In addition, the outcome coherence is verified by monitoring the best blend of evolution strategies. As a result, imminent outcomes were selected based on the best mixture of portfolio returns and Sharpe ratio. The monthly stock prices of Nifty50 were included in this study.</span></div>

show abstract

“…Arriaga and Valenzuela-Rendó, (2012) proposed a simple hill climbing algorithm called steepest ascent hill climbing algorithm which gave similar results as the complex evolutionary algorithm. Moreover, Kalayc et al (2020) present an efficient hybrid metaheuristic algorithm that combines critical components from continuous ant colony optimization, artificial bee colony optimization and genetic algorithms for solving cardinality constrained portfolio optimization problem. Other researchers who applied heuristic algorithms to deal with portfolio problem are (Aranha and Iba 2009;Maringer, 2008;Aranha and Iba 2008;Crama and Schyns, 2003;Schaerf, 2002;Gilli and Këllezi, 2000).…”

Section: Introductionmentioning

confidence: 99%

Efficient and Simple Heuristic Algorithm for Portfolio Optimization

John

2022

TJET

View full text Add to dashboard Cite

Markowitz model considers what is termed as standard portfolio optimization. The portfolio optimization problem is a problem which based on asset allocation and diversification for maximum return with minimum risk. Thus, the standard portfolio optimization problem happens when the constraints considered are budget and no-short selling. In reality however, portfolio optimization has realistic constraints to be incorporated such as holding sizes, cardinality and transaction cost. When realistic constraints are added into portfolio optimization problem, it becomes too complex to be solved by standard optimization methods which in this case turns to be an extended portfolio optimization problem. Markowitz solution and the standard methods like quadratic programming become inapplicable. With such limitation, heuristic methods are usually used to deal with this extended portfolio optimization problem. Therefore, this paper proposes a heuristic algorithm for the extended portfolio optimization problem. It is a hill climbing algorithm named Hill Climbing Simple (HC-S) which is then validated by solving the standard Markowitz model. In fact, the proposed algorithm is benchmarked with the quadratic programming (QP), which is a standard method. By benchmarking HC-S with QP, it showed that HC-S can attains similar accurate solutions. Also, HC-S demonstrated to be more effective and efficient than threshold accepting (TA), an established algorithm for portfolio optimization since HC-S find solutions with significant higher objective value and require less computing time as compared to standard methods.

show abstract

An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization

Cited by 53 publications

References 16 publications

SAIPO-TAIPO and Genetic Algorithms for Investment Portfolios

SAIPO-TAIPO and Genetic Algorithms for Investment Portfolios

An adaptive metaheuristic approach for risk-budgeted portfolio optimization

Efficient and Simple Heuristic Algorithm for Portfolio Optimization

Contact Info

Product

Resources

About