Portfolio Optimization Using Particle Swarm Optimization And Genetic Algorithm

Kamali, Samira

doi:10.22436/jmcs.010.02.01

Cited by 20 publications

(9 citation statements)

References 16 publications

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“…Fourth, overseeing policy statements, capital market conditions and investment strategies to verify any changes. These steps are a continuous procedure in a portfolio (Kamali, 2014).…”

Section: Portfoliomentioning

confidence: 99%

Optimizing Stock Portfolio with Markowitz Method as a Reference for Investment Community Decisions

Balqis

Subiyanto

Supian

2021

IJRCS

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An investor who wants to invest by avoiding risk makes investors tend to choose investments with the same expected return and the smallest or lowest possible risk. Therefore, investors expect to be able to maximize profits and minimize risk at the same time in investing. In a stock portfolio, it can be done by investing the funds owned by investors into several stocks so that it can reduce the risk of losses that will occur simultaneously. In choosing the right company to invest in with consideration of expected return and risk, a multi-objective optimization with multivariate objects can be used so that it can meet the expectations of investors. The portfolio concept introduced by Markowitz is a portfolio optimization intended for standard investors because it only refers to one explanation of portfolio returns. The Markowitz method can produce an optimal stock portfolio by considering the expected return and risk simultaneously so that the maximum profit can be obtained without eliminating the existing risk.

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“…Fourth, overseeing policy statements, capital market conditions and investment strategies to verify any changes. These steps are a continuous procedure in a portfolio (Kamali, 2014).…”

Section: Portfoliomentioning

confidence: 99%

Optimizing Stock Portfolio with Markowitz Method as a Reference for Investment Community Decisions

Balqis

Subiyanto

Supian

2021

IJRCS

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“…PSO has been shown to be very competitive amongst other approaches to unconstrained portfolio optimization. Kamali showed that PSO was able to obtain higher quality portfolios in less time than a GA [88]. PSO, compared with a GA and an industry tool, proved to be a superior algorithm in nding solutions to portfolio optimization [194].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…A variety of evolutionary and swarm intelligence algorithms have been proposed to solve the portfolio optimization problem. [5], [7], [15], [17], [46], [48], [75], [76], [79], [80], [88], [129], [166], [189],…”

Section: Representation Of Algorithmsmentioning

confidence: 99%

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Set-Based Particle Swarm Optimization for Portfolio Optimization

Erwin

Engelbrecht

2020

Lecture Notes in Computer Science

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By submitting this thesis electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualication.

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“…Since in the harmony, musician seek optimum solutions at each stage using the generated rules in the previous steps, and Because harmony search algorithm does not work well in the local optimum [19] and this action increases the execution time and reduce convergence of the algorithm [20], we use a genetic algorithm. Genetic algorithm increases the search space to achieve universal or near-universal response [21].For this purpose, we use two operators of genetic algorithm, crossover and mutation; In addition search Harmony operators in the stage production rules. So we implement the two operators on generated rules.…”

Section: Improvement Of the Proposed Algorithmmentioning

confidence: 99%

Hybrid Harmony Search And Genetic For Fuzzy Classification Systems

Mahmoodi¹,

Mahmoodi²

2014

J. Math. Computer Sci.

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In this paper, a method based on Harmony Search Algorithm (HSA) is proposed for pattern classification. One of the important issues in the design of fuzzy classifier if the product of fuzzy if then rules. So that the number of incorrectly classified patterns is minimized. In the HSA-based method, every musician makes a musical note and it can be regarded as a solution vector. The algorithm uses Genetic algorithm based local search to improve the quality of fuzzy classification system. The proposed algorithm is evaluated on a breast cancer data. The results show that the algorithm based on improved genetic is able to produce a fuzzy classifier to detect breast cancer.

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Portfolio Optimization Using Particle Swarm Optimization And Genetic Algorithm

Cited by 20 publications

References 16 publications

Optimizing Stock Portfolio with Markowitz Method as a Reference for Investment Community Decisions

Optimizing Stock Portfolio with Markowitz Method as a Reference for Investment Community Decisions

Set-Based Particle Swarm Optimization for Portfolio Optimization

Hybrid Harmony Search And Genetic For Fuzzy Classification Systems

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