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Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios

  • Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to approximations, as fractional stocks are typically not tradeable. While these approximations are effective for large investment budgets,Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to approximations, as fractional stocks are typically not tradeable. While these approximations are effective for large investment budgets, they deteriorate as budgets decrease. To alleviate this issue, a discrete Markowitz portfolio theory (DMPT) with finite budgets and integer stock weights can be formulated, but results in a non-polynomial (NP)-hard problem. Recent progress in quantum processing units (QPUs), including quantum annealers, makes solving DMPT problems feasible. Our study explores portfolio optimization on quantum annealers, establishing a mapping between continuous and discrete Markowitz portfolio theories. We find that correctly normalized discrete portfolios converge to continuous solutions as budgets increase. Our DMPT implementation provides efficient frontier solutions, outperforming traditional rounding methods, even for moderate budgets. Responding to the demand for environmentally and socially responsible investments, we enhance our discrete portfolio optimization with ESG (environmental, social, governance) ratings for EURO STOXX 50 index stocks. We introduce a utility function incorporating ESG ratings to balance risk, return and ESG friendliness, and discuss implications for ESG-aware investors.show moreshow less

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Metadaten
Author:Francesco Catalano, Laura Nasello, Daniel Guterding
URL:http://dx.doi.org/10.3390/risks12040066
DOI:https://doi.org/10.3390/risks12040066
ISSN:2227-9091
Parent Title (English):Risks
Publisher:MDPI
Document Type:Article
Language:English
Year of Publishing:2024
Date of Publication (online):2024/05/13
Publishing Institution:Technische Hochschule Brandenburg
Release Date:2024/05/13
Volume:12
Issue:4
Article Number:66
First Page:1
Last Page:12
Institutes:Fachbereich Wirtschaft
University Bibliography:Hochschulbibliografie
Licence (German):Creative Commons - CC BY - Namensnennung 4.0
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