Evaluating the Risk-Return Profile of a Portfolio of ESG and Traditional Assets Using a Hybrid Optimisation Model

Attila Banyai (), Tibor Tatay (), Gergo Thalmeiner () and Laszlo Pataki ()
Additional contact information
Attila Banyai: Doctoral School of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences, Hungary
Tibor Tatay: Department of Statistics, Finances and Controlling, Széchenyi István University, Hungary
Gergo Thalmeiner: Department of Investment, Finance and Accounting, Hungarian University of Agriculture and Life Sciences, Hungary
Laszlo Pataki: Doctoral School of Management and Business Administration, John von Neumann University, Hungary

Virtual Economics, 2025, vol. 8, issue 1, 16-39

Abstract: This article examines the risk-return dynamics of portfolios combining environmental, social, and governance (ESG) assets with traditional investment instruments. A hybrid optimisation framework is applied, uniting mean-variance principles with combinatorial selection and machine learning techniques. The study addresses two central questions: whether ESG funds provide diversification benefits, and whether they mitigate downside risk in periods of financial stress. The analysis draws on a dataset of five ESG and five non-ESG funds, spanning varied sectors and risk profiles, observed over a five-year horizon marked by diverse macroeconomic conditions. Portfolio performance is evaluated using the Sharpe ratio, with differential evolution and support vector machine algorithms employed to capture linear and non-linear aspects of risk-adjusted returns. The findings reveal a consistent positive association between ESG allocation and portfolio performance. Optimised portfolios frequently allocated 80-90 per cent of their weight to ESG assets, particularly GRID and ESGV. ESG holdings were shown to strengthen diversification, improve upside potential, and reduce downside exposure, especially during volatile market phases. Traditional assets contributed stability but played a weaker role in enhancing risk-adjusted returns. Statistical analysis confirmed both research hypotheses: portfolios integrating ESG investments achieved higher Sharpe ratios without excessive risk, and ESG funds demonstrated resilience under adverse conditions. Machine learning models further underscored the significance of non-linear patterns, which enhanced the explanatory power of ESG exposure in the optimisation process. In sum, the study contributes to growing evidence that ESG assets not only advance sustainability objectives but also deliver measurable financial benefits. The hybrid methodological approach illustrates the importance of balanced allocation constraints and robust optimisation in portfolio design. These results suggest that incorporating ESG assets can simultaneously reinforce financial stability and support long-term sustainable development.

Keywords: portfolio rebalancing; sustainable investment; risk-adjusted returns; ESG and Non-ESG assets allocation; shape ratio; support vector machine algorithms (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.virtual-economics.eu/index.php/VE/article/download/462/182 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aid:journl:v:8:y:2025:i:1:p:16-39

DOI: 10.34021/ve.2025.08.01(2)

Access Statistics for this article

Virtual Economics is currently edited by Aleksy Kwilinski

More articles in Virtual Economics from The London Academy of Science and Business
Bibliographic data for series maintained by Aleksy Kwilinski ().