Optimal Portfolio with Sustainable Attitudes under Cumulative Prospect Theory
| Autor: | Massimiliano Kaucic, Filippo Piccotto, Gabriele Sbaiz, Giorgio Valentinuz |
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| Rok vydání: | 2023 |
| Předmět: |
Economics and Econometrics
Global and Planetary Change Algebra and Number Theory Visual Arts and Performing Arts Communication Stratigraphy Biophysics Geology General Chemistry Earth and Planetary Sciences (miscellaneous) Discrete Mathematics and Combinatorics General Agricultural and Biological Sciences Finance Earth-Surface Processes |
| Zdroj: | Journal of Applied Finance & Banking. |
| DOI: | 10.47260/jafb/1344 |
| Popis: | 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. Keywords: Cumulative prospect theory, ESG scores, portfolio optimization, genetic algorithm, European stock exchange. |
| Databáze: | OpenAIRE |
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