Innovation in Portfolio Optimization through the Use of Genetic Algorithms for Sustainable Entrepreneurship in Volatile Markets

Abstract

Context. In the global financial landscape, marked by heightened economic volatility and constant transformations, entrepreneurs face the challenge of identifying sustainable investment strategies to ensure effective long-term risk management.

Purpose. This study aims to develop a model for optimizing investment portfolios through financial and technological tools to maximize returns in highly volatile environments. Moreover, it aligns with the principles outlined in the Oslo Manual and the United Nations Sustainable Development Goals (SDGs).

Methodology. Markowitz portfolio optimization using a classic genetic algorithm applied to data from 10 companies selected from the technology, health, and finance sectors. This data, obtained from Yahoo Finance, covers the period from 2020 to 2023. The reliability of the models was rigorously validated through internal consistency analysis, ensuring their robustness.

Theoretical and Practical Findings. Theoretical results confirm the applicability of genetic algorithms in optimizing diversified portfolios. In practice, their potential to encourage investments in sustainable companies is evident, aligning with the SDGs by fostering key areas such as industrial innovation.

Originality. This study adopts a multidisciplinary approach by integrating finance and technology in the selection of investment portfolios. The literature review highlights how the synergy between these two fields promotes sustainable development.

Conclusions and limitations. Findings underscore the potential of genetic algorithms to perform in highly volatile contexts. However, the reliance on historical data analysis alone highlights the need for additional studies in real-world environments. These could focus on comparing other optimization models and exploring their impact in regions with diverse market structures.