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Prospect theory–based portfolio optimization: an empirical study and analysis using intelligent algorithms

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  • N. Grishina
  • C. A. Lucas
  • P. Date

Abstract

The behaviourally based portfolio selection problem with investor’s loss aversion and risk aversion biases in portfolio choice under uncertainty is studied. The main results of this work are: developed heuristic approaches for the prospect theory model proposed by Kahneman and Tversky in 1979 as well as an empirical comparative analysis of this model and the index tracking model. The crucial assumption is that behavioural features of the prospect theory model provide better downside protection than traditional approaches to the portfolio selection problem. In this research the large-scale computational results for the prospect theory model have been obtained for real financial market data with up to 225 assets. Previously, as far as we are aware, only small laboratory tests (2–3 artificial assets) have been presented in the literature. In order to investigate empirically the performance of the behaviourally based model, a differential evolution algorithm and a genetic algorithm which are capable of dealing with a large universe of assets have been developed. Specific breeding and mutation, as well as normalization, have been implemented in the algorithms. A tabulated comparative analysis of the algorithms’ parameter choice is presented. The prospect theory model with the reference point being the index is compared to the index tracking model. A cardinality constraint has been implemented to the basic index tracking and the prospect theory models. The portfolio diversification benefit has been found. The aggressive behaviour in terms of returns of the prospect theory model with the reference point being the index leads to better performance of this model in a bullish market. However, it performed worse in a bearish market than the index tracking model. A tabulated comparative analysis of the performance of the two studied models is provided in this paper for in-sample and out-of-sample tests. The performance of the studied models has been tested out-of-sample in different conditions using simulation of the distribution of a growing market and simulation of the t-distribution with fat tails which characterises the dynamics of a decreasing or crisis market.

Suggested Citation

  • N. Grishina & C. A. Lucas & P. Date, 2017. "Prospect theory–based portfolio optimization: an empirical study and analysis using intelligent algorithms," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 353-367, March.
    • Handle: RePEc:taf:quantf:v:17:y:2017:i:3:p:353-367
    DOI: 10.1080/14697688.2016.1149611
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    References listed on IDEAS

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    1. Martin Vlcek, 2006. "Portfolio Choice with Loss Aversion, Asymmetric Risk-Taking Behavior and Segregation of Riskless Opportunities," Swiss Finance Institute Research Paper Series 06-27, Swiss Finance Institute.
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    Cited by:

    1. Francesco Cesarone & Massimiliano Corradini & Lorenzo Lampariello & Jessica Riccioni, 2023. "A new behavioral model for portfolio selection using the Half-Full/Half-Empty approach," Papers 2312.10749, arXiv.org.
    2. Wencheng Yu & Shaobo Liu & Lili Ding, 2021. "Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    3. Fortin, Ines & Hlouskova, Jaroslava, 2024. "Prospect theory and asset allocation," The Quarterly Review of Economics and Finance, Elsevier, vol. 94(C), pages 214-240.
    4. Sweksha Srivastava & Abha Aggarwal & Pooja Bansal, 2024. "Efficiency Evaluation of Assets and Optimal Portfolio Generation by Cross Efficiency and Cumulative Prospect Theory," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 129-158, January.
    5. 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.
    6. 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.
    7. Julio Cezar Soares Silva & Adiel Teixeira de Almeida Filho, 2023. "A systematic literature review on solution approaches for the index tracking problem in the last decade," Papers 2306.01660, arXiv.org, revised Jun 2023.
    8. Peter P. Wakker, 2023. "The correct formula of 1979 prospect theory for multiple outcomes," Theory and Decision, Springer, vol. 94(2), pages 183-187, February.

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