Modern and Post-Modern Portfolio Formation in the Cryptocurrency Market and Optimal Component Allocation Using Genetic Algorithm

Document Type : Original Article

Authors

1 MSc., Department of Business Administration, Faculty of Finance, Management and Entrepreneurship, University of Kashan, Kashan, Iran.

2 Assistant Prof., Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.

3 Associate Prof., Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.

Abstract

Intoduction: The cryptocurrency market, known for its high volatility, has attracted significant attention. Some investors refrain from entering this market due to the fear of losses, while others take on high risks in hopes of achieving substantial profits. Forming an optimal portfolio in this market aims to balance risk and return, requiring careful selection and weighting of assets. Analytical tools and modern methods assist investors in choosing a combination of assets that reduces the risk of market fluctuations while providing appropriate returns.
Methods: This research is thematically situated in the field of finance and investment, specifically focusing on the optimization of cryptocurrency portfolios. In terms of timing, the historical data analyzed in this study pertains to the period between 2021 and 2023. This study aims to compare various constructed portfolios and identify the most efficient portfolio selection model among the portfolios formed in this study by analyzing the cryptocurrency market index and selected cryptocurrencies. It utilizes valid performance measurement criteria and genetic algorithms. To achieve this, a sample portfolio is first created based on the cryptocurrency market index over a suitable time frame. This time frame is chosen to ensure that despite market fluctuations, the number, type, and weight of the cryptocurrencies constituting the market index portfolio remain as stable as possible. Subsequently, for this portfolio, the values of return, variance, standard deviation, Sharpe ratio, Sortino ratio, Calmar ratio, and relative risk measure (coefficient of variation) are calculated. In the next step, using models and genetic algorithms, optimal weights for each of the cryptocurrencies in the market portfolio are computed, and the results are compared.
Results and discussion: The results reveal that the portfolio of cryptocurrencies with the highest market value will experience less risk than other selected portfolios due to greater stability. Also, the cryptocurrency market index with the Sharpe optimization model strategy has less risk than other models, and portfolios formed with this model show better performance in terms of risk-return balance. Moreover, portfolio selection based on the return-to-risk ratio (standard deviation) yields superior results when optimized using the Sortino and Calmar models, particularly with long-term historical data. Cryptocurrencies that exhibit higher predictability can contribute to the selection of more efficient portfolios and reduce downside risk.
Conclusions: The findings indicate that portfolios composed of cryptocurrencies with the highest market capitalization perform better than other portfolios. These assets, due to their greater stability and lower volatility, entail less risk for investors. Therefore, it is recommended that investors, especially in unstable market conditions, focus on high-market-cap assets to mitigate downward fluctuations and achieve reasonable returns. Utilizing metrics such as the return-to-risk ratio based on downside deviation, the return-to-risk ratio of maximum drawdown, and value at risk (VaR) can enhance diversification in portfolio selection methods and enable more precise comparisons. This approach not only improves accuracy in evaluations but also helps construct portfolios that are optimized and efficient in terms of risk and return.

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References

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Journal of Industrial Management Perspective
Volume 15, Issue 4 - Serial Number 60
December 2025
Pages 173-202

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