Evaluating Modern Quantitative Methods for Investment Portfolio Management under Market Uncertainty

Article DOI: https://doi.org/10.57017/jaes.v20.3(89).05

Author(s):

Andrii FROLOV Department of Economic Theory, Kyiv National Economic University named after Vadym Hetman, Ukraine
Ruslan BOIKO Department of Accounting, Control, Analysis and Taxation, Faculty of Economics and Management, Lviv University of Trade and Economics, Ukraine
Viktoriia RUDEVSKA Finance Department, Faculty of Economics National University of Life and Environmental Sciences of Ukraine, Ukraine
Daria BUTENKO Department of Entrepreneurship, Trade and Tourism Business, NNI Economics and Law, Simon Kuznets Kharkiv National University of Economics, Ukraine
Andrii MOISIIAKHA Department of Tourism Organization PJSC "Higher Educational Institution "Interregional Academy of Personnel Management", Kyiv, Ukraine

Abstract:

This study evaluates the effectiveness of advanced quantitative techniques, Monte Carlo simulations, AI-driven models, and Genetic Algorithms in enhancing investment portfolio management beyond Traditional Modern Portfolio Theory limitations. Analysing financial data from 2014-2024, this study assessed performance using Sharpe Ratio, Value-at-Risk, and Conditional Value-at-Risk across various market scenarios including black swan events. Findings demonstrate that Genetic Algorithms achieved the highest risk-adjusted returns while minimizing volatility, AI-driven models provided superior adaptability to market fluctuations, and Monte Carlo simulations significantly improved risk assessment compared to traditional approaches. The integration of green bonds into AI-optimised portfolios successfully balanced financial performance with sustainability objectives, appealing to environmentally conscious investors. This research confirms that AI and Genetic Algorithm approaches consistently outperform traditional models in optimising risk-adjusted returns under volatile conditions. Portfolio managers should consider implementing hybrid quantitative approaches that combine AI-based decision-making with Monte Carlo stress testing to enhance investment resilience and strategic planning in dynamic financial environments.

Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Article’s history: Received 3rd of July, 2025; Revised 29th of July, 2025; Accepted 2nd of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.

How to cite:

Frolov, A., Boiko, R., Rudevska, V., Butenko, D., & Moisiiakha, A. (2025). Evaluating Modern Quantitative Methods for Investment Portfolio Management under Market Uncertainty. Journal of Applied Economic Sciences, Volume XX, Fall, 3(89), 427 – 448. https://doi.org/10.57017/jaes.v20.3(89).05

Acknowledgments: Not applicable.

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data Availability Statement: The data that support the findings of this study were obtained from Bloomberg Terminal, Yahoo Finance, Federal Reserve Economic Data (FRED), and World Bank databases. These data are publicly available at https://www.bloomberg.com, https://finance.yahoo.com, https://fred.stlouisfed.org, and https://data.worldbank.org. The processed datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

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