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Portfolio Management Using Prospect Theory: Comparing Genetic Algorithms and Particle Swarm Optimization

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  • Seyedehzahra NEMATOLLAHI

    ()

  • Giancarlo MANZI

    ()

Abstract

In this work, we compare the performance of two metaheuristic optimization algorithms, namely the Genetic Algorithms (GA) and the Particle Swarm Optimization (PSO), in finding an optimized investing portfolio. This comparison is based on two performance criteria: the consistency and quality of the solution and the speed of convergence of these two algorithms. These metaheuristic algorithms will be developed further to specify the weights of assets in an optimal portfolio, which is a portfolio with a maximum level of return (or a minimum level of risk) using a portfolio optimization model. We chose the prospect theory portfolio optimization as our background model. The prospect theory model is the main behavioral alternative to the expected utility theory and is still a relatively new subject in the financial literature. A mean‐variance portfolio optimization is also considered as a benchmark to our behavioral model. The performance of these two models has been evaluated in practice using several criteria such as the CPU time and the ratio between the portfolio mean returns

Suggested Citation

  • Seyedehzahra NEMATOLLAHI & Giancarlo MANZI, 2018. "Portfolio Management Using Prospect Theory: Comparing Genetic Algorithms and Particle Swarm Optimization," Departmental Working Papers 2018-03, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  • Handle: RePEc:mil:wpdepa:2018-03
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    File URL: http://wp.demm.unimi.it/files/wp/2018/DEMM-2018_03wp.pdf
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    References listed on IDEAS

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    1. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    2. Edwin J. Elton & Martin J. Gruber, 1997. "Modern Portfolio Theory, 1950 to Date," New York University, Leonard N. Stern School Finance Department Working Paper Seires 97-3, New York University, Leonard N. Stern School of Business-.
    3. Elton, Edwin J. & Gruber, Martin J., 1997. "Modern portfolio theory, 1950 to date," Journal of Banking & Finance, Elsevier, vol. 21(11-12), pages 1743-1759, December.
    4. Wenbo Hu & Alec Kercheval, 2010. "Portfolio optimization for student t and skewed t returns," Quantitative Finance, Taylor & Francis Journals, vol. 10(1), pages 91-105.
    5. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.
    6. repec:taf:quantf:v:17:y:2017:i:3:p:353-367 is not listed on IDEAS
    7. Shefrin, Hersh & Statman, Meir, 2000. "Behavioral Portfolio Theory," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(02), pages 127-151, June.
    8. Enrico Giorgi & Thorsten Hens & János Mayer, 2007. "Computational aspects of prospect theory with asset pricing applications," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 267-281, May.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Portfolio management; Prospect theory; Optimization; Genetic algorithms; Particle swarm optimization;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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