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Optimal Portfolio with Sustainable Attitudes under Cumulative Prospect Theory

Author

Listed:
  • Massimiliano Kaucic
  • Filippo Piccotto
  • Gabriele Sbaiz
  • Giorgio Valentinuz

Abstract

In the last five years, extreme events such as the COVID-19 pandemic and the Ukrainian crisis have highlighted the importance of corporate social responsibility and sustainable principles. Consequently, the investment process is changing toward more ethical choices. In this context, we extend the classical optimization framework under the cumulative prospect theory (CPT) in two directions. We first consider an agent who maximizes a financial CPT-value function preselecting the assets to be included in the portfolio based on their environmental, social, and governance (ESG) scores. Then, we develop a bi-objective model that optimizes financial and sustainable CPT-value functions at the same time. Numerical results obtained on an investable universe from the constituents of the STOXX Europe 600 show that introducing ESG information improves the portfolio’s financial performance.  JEL classification numbers: C63, G11, G17.

Suggested Citation

  • 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.
    • Handle: RePEc:spt:apfiba:v:13:y:2023:i:4:f:13_4_4
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    References listed on IDEAS

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    More about this item

    Keywords

    Cumulative prospect theory; ESG scores; portfolio optimization; genetic algorithm; European stock exchange.;
    All these keywords.

    JEL classification:

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

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