Artificial Bee Colony Algorithm for Portfolio Optimization Problems

Wang, Zhen; Liu, Sanyang; Kong, Xiangyu

doi:10.4156/ijact.vol4.issue4.2

Cited by 14 publications

(3 citation statements)

References 18 publications

(23 reference statements)

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“…SIA involves the collective study of the individuals behavior of the population interact with one another locally. The study has been done to solve the portfolio optimization problem using SIA (Deng et al , 2012; Koshino et al , 2007; Zhu et al , 2011; Ni et al , 2017; Haqiqi and Kazemi, 2012; Li et al , 2012; Wang et al , 2012; Tuba and Bacanin, 2014a, b; Kalayci et al , 2017; Gao et al , 2018; Suthiwong et al , 2019). PSO (Koshino et al ., 2007) proposed a combination of the inertia weights approach and the constriction factor approach to solve the portfolio selection problem with the aim of ranking individuals in the population.…”

Section: Related Workmentioning

confidence: 99%

“…The authors have been presented a cluster-based ACO algorithm for portfolio selection using an iterative k -means method to optimize the Sharpe ratio. In the domain, implementations of an artificial bee colony (ABC) for portfolio problems can be found (Wang et al , 2012; Tuba and Bacanin, 2014a, b). The authors have proposed an integrated method-based ABC to solve the portfolio section problem with cardinality constrained.…”

Section: Related Workmentioning

confidence: 99%

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Solving constrained portfolio optimization model using stochastic fractal search approach

Shahid

Ashraf²,

Shamim³

et al. 2022

IJICC

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PurposeOptimum utilization of investments has always been considered one of the most crucial aspects of capital markets. Investment into various securities is the subject of portfolio optimization intent to maximize return at minimum risk. In this series, a population-based evolutionary approach, stochastic fractal search (SFS), is derived from the natural growth phenomenon. This study aims to develop portfolio selection model using SFS approach to construct an efficient portfolio by optimizing the Sharpe ratio with risk budgeting constraints.Design/methodology/approachThis paper proposes a constrained portfolio optimization model using the SFS approach with risk-budgeting constraints. SFS is an evolutionary method inspired by the natural growth process which has been modeled using the fractal theory. Experimental analysis has been conducted to determine the effectiveness of the proposed model by making comparisons with state-of-the-art from domain such as genetic algorithm, particle swarm optimization, simulated annealing and differential evolution. The real datasets of the Indian stock exchanges and datasets of global stock exchanges such as Nikkei 225, DAX 100, FTSE 100, Hang Seng31 and S&P 100 have been taken in the study.FindingsThe study confirms the better performance of the SFS model among its peers. Also, statistical analysis has been done using SPSS 20 to confirm the hypothesis developed in the experimental analysis.Originality/valueIn the recent past, researchers have already proposed a significant number of models to solve portfolio selection problems using the meta-heuristic approach. However, this is the first attempt to apply the SFS optimization approach to the problem.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Solving constrained portfolio optimization model using stochastic fractal search approach

Shahid

Ashraf²,

Shamim³

et al. 2022

IJICC

View full text Add to dashboard Cite

show abstract

“…The results show that the average return of a portfolio in the trading bubble space at a certain level of risk is greater than that of a non-bubble portfolio, based on subjective accounting. Wang et al . (2012) compared the bee colony algorithm (ABC) with the GA, tabu search (TS), simulated repayment (SA), and PSO algorithms and reported that the bee colony algorithm performed well in solving the portfolio optimization problem and obtained better solutions than other heuristic algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Portfolio optimization in the capital market bubble space, an application of meta-heuristic algorithms

Mohammadi

Khoshouei²,

Chadegani³

2022

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PurposeIn this study, to maximize returns and minimize investment risk, an attempt was made to form an optimal portfolio under conditions where the capital market has a price bubble. According to the purpose, the research was of the applied type, in terms of data, quantitative and postevent, and in terms of the type of analysis, it was of the descriptive-correlation type. Sequence, skewness and kurtosis tests were used to identify the months with bubbles from 2015 to 2021 in the Tehran Stock Exchange. After identifying the bubble courses, artificial bee colony meta-heuristic and invasive weed algorithms were used to optimize the portfolio. The purpose of this paper is to address these issues.Design/methodology/approachThe existence of bubbles in the market, especially in the capital market, can prevent the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. However, due to the goal of investors to achieve a portfolio of high returns with the least amount of risk, there is need to pay attention to these markets increases.FindingsThe results identify 14 periods of price bubbles during the study period. Additionally, stock portfolios with maximum returns and minimum risk were selected for portfolio optimization. According to the results of using meta-heuristic algorithms to optimize the portfolio, in relation to the obtained returns and risk, no significant difference was observed between the returns and risk of periods with price bubbles in each of the two meta-heuristic algorithms. This study can guide investors in identifying bubble courses and forming an optimal portfolio under these conditions.Research limitations/implicationsOne of the limitations of this research is the non-generalizability of the findings to stock exchanges of other countries and other time periods due to the condition of the price bubble, as well as other companies in the stock market due to the restrictions considered for selecting the statistical sample.Originality/valueThis study intends to form an optimal stock portfolio in a situation where the capital market suffers from a price bubble. This study provides an effective and practical solution for investors in the field of stock portfolio optimization.

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Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature

Zhang

Guo

2017

Fuzzy Optim Decis Making

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Artificial Bee Colony Algorithm for Portfolio Optimization Problems

Cited by 14 publications

References 18 publications

Solving constrained portfolio optimization model using stochastic fractal search approach

Solving constrained portfolio optimization model using stochastic fractal search approach

Portfolio optimization in the capital market bubble space, an application of meta-heuristic algorithms

Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature

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