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Portfolio Selection Using Genetic Algorithm

Author

Listed:
  • Slimane Sefiane
  • Mohamed Benbouziane

Abstract

The selection of optimal portfolios is the central problem of financial investment decisions. Mathematically speaking, portfolio selection refers to the formulation of an objective function that determines the weights of the portfolio invested in each asset as to maximize return and minimize risk. This paper applies the method of genetic algorithm (GA) to obtain an optimal portfolio selection. However, the GA parameters are of great importance in the procedure of convergence of this algorithm towards the optimal solution such as crossover. While, a five stock portfolio example is used in this paper to illustrate the applicability and efficiency of genetic algorithm method, GA method can also be used however for a larger number of portfolio compositions. The results obtained confirm previous research studies about the validity and efficiency of genetic algorithm in selecting optimal portfolios.

Suggested Citation

  • Slimane Sefiane & Mohamed Benbouziane, 2012. "Portfolio Selection Using Genetic Algorithm," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 2(4), pages 1-9.
    • Handle: RePEc:spt:apfiba:v:2:y:2012:i:4:f:2_4_9
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    Cited by:

    1. Zezheng Zhang & Matloob Khushi, 2020. "GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading," Papers 2008.09471, arXiv.org.
    2. Lange Thomas & Melsom Anne May, 2024. "Tax Compliance among Managers: Evidence from Randomized Audits," Nordic Tax Journal, Sciendo, vol. 2024(1), pages 1-29.
    3. Sinha, Pankaj & Chandwani, Abhishek & Sinha, Tanmay, 2013. "Algorithm of construction of Optimum Portfolio of stocks using Genetic Algorithm," MPRA Paper 48204, University Library of Munich, Germany.
    4. Dubinskas Petras & Urbšienė Laimutė, 2017. "Investment Portfolio Optimization by Applying a Genetic Algorithm-Based Approach," Ekonomika (Economics), Sciendo, vol. 96(2), pages 66-78, February.
    5. Abdullah Asilkalkan & Asli Z. Dag & Serhat Simsek & Osman T. Aydas & Eyyub Y. Kibis & Dursun Delen, 2025. "Streamlining patients’ opioid prescription dosage: an explanatory bayesian model," Annals of Operations Research, Springer, vol. 347(2), pages 889-912, April.
    6. R. Bedoui & R. Benkraiem & K. Guesmi & I. Kedidi, 2023. "Portfolio optimization through hybrid deep learning and genetic algorithms vine Copula-GARCH-EVT-CVaR model," Post-Print hal-04631234, HAL.
    7. Massimiliano Kaucic & Filippo Piccotto & Gabriele Sbaiz & Giorgio Valentinuz, 2023. "Optimal Portfolio with Sustainable Attitudes under Cumulative Prospect Theory," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(4), pages 1-4.

    More about this item

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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