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When Behavioral Portfolio Theory Meets Markowitz Theory

Author

Listed:
  • Marie Pfiffelmann

    (EM Strasbourg - École de Management de Strasbourg = EM Strasbourg Business School - UNISTRA - Université de Strasbourg)

  • Tristan Roger

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Olga Bourachnikova

    (EM Strasbourg - École de Management de Strasbourg = EM Strasbourg Business School - UNISTRA - Université de Strasbourg)

Abstract

The Behavioral Portfolio Theory (BPT) developed by Shefrin and Statman is often confronted to the Markowitz's Mean Variance Theory (MVT). Although the BPT optimal portfolio is theoretically not mean variance efficient, some recent studies show that under the assumption of normally distributed returns, MVT and some models incorporating features of BPT can generate similar asset allocations. In this paper, we compare the asset allocations generated by BPT and MVT without restrictions. Using US stock prices from the CRSP database for the 1995-2011 period, we empirically determine the BPT optimal portfolio. We show that the Shefrin and Statman's optimal portfolio is MV efficient in more than 70% of cases. However, our results also indicates that MV investors will typically not select the BPT portfolio as this portfolio is always associated with a high return and an important level of risk. We show that the risk aversion coefficient of the BPT portfolio is up to 60 times smaller than the risk aversion degree of usual MV investors.

Suggested Citation

  • Marie Pfiffelmann & Tristan Roger & Olga Bourachnikova, 2016. "When Behavioral Portfolio Theory Meets Markowitz Theory," Post-Print hal-01483831, HAL.
    • Handle: RePEc:hal:journl:hal-01483831
    DOI: 10.1016/j.econmod.2015.10.041
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    Cited by:

    1. Chen, Xingjiang & Ruan, Xinfeng & Zhang, Wenjun, 2021. "Dynamic portfolio choice and information trading with recursive utility," Economic Modelling, Elsevier, vol. 98(C), pages 154-167.
    2. Marija Kuzmanovic & Dragana Makajic-Nikolic & Nebojsa Nikolic, 2019. "Preference Based Portfolio for Private Investors: Discrete Choice Analysis Approach," Mathematics, MDPI, vol. 8(1), pages 1-20, December.
    3. Sandisele JAFFAR & Thomas HABANBAKIZE & Fabian MOODLEY & Paul-Francois MUZINDUTSI, 2025. "The Influence of Political, Economic, and Financial Risks on the South African Global Equity Portfolio Returns under Changing Market Conditions," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 9(1), pages 48-71.
    4. Hübner, Georges & Lejeune, Thomas, 2021. "Mental accounts with horizon and asymmetry preferences," Economic Modelling, Elsevier, vol. 103(C).
    5. Amen Aissi Harzallah & Mouna Boujelbene Abbes, 2020. "The Impact of Financial Crises on the Asset Allocation: Classical Theory Versus Behavioral Theory," Journal of Interdisciplinary Economics, , vol. 32(2), pages 218-236, July.
    6. Xi Zhang & Xu Wu & Linlin Zhang & Zhonglu Chen, 2022. "The Evaluation of Mean-Detrended Cross-Correlation Analysis Portfolio Strategy for Multiple risk Assets," Evaluation Review, , vol. 46(2), pages 138-164, April.
    7. Lehlohonolo Letho & Grieve Chelwa & Abdul Latif Alhassan, 2022. "Cryptocurrencies and portfolio diversification in an emerging market," China Finance Review International, Emerald Group Publishing Limited, vol. 12(1), pages 20-50, January.
    8. Kuo-Hwa Chang & Michael Nayat Young, 2019. "Portfolios Optimizations of Behavioral Stocks with Perception Probability Weightings," Annals of Economics and Finance, Society for AEF, vol. 20(2), pages 817-845, November.
    9. Maxime MERLI & Antoine PARENT, 2022. "Portfolio Diversification During the Belle Époque: When the Actual Portfolios of French Individual Investors Met Behavioral Finance," Working Papers of LaRGE Research Center 2022-01, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.

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