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Comparison of the Effectiveness of Traditional and AI-Based Methods of Investment Portfolio Optimization
Yatsenko R. M., Porokhnavets A. A.

Yatsenko, Roman M., and Porokhnavets, Andrii A. (2024) “Comparison of the Effectiveness of Traditional and AI-Based Methods of Investment Portfolio Optimization.” Business Inform 9:211–217.
https://doi.org/10.32983/2222-4459-2024-9-211-217

Section: Investment Processes

UDC 330.322:004.8

Abstract:
The aim of the article is to conduct a comparative analysis of the effectiveness of traditional and AI-based methods of investment portfolio optimization. The object of the research is the investment portfolio. The subject of the research is traditional and AI-based optimization methods. The article is a study of the effectiveness of the use of traditional and AI-based methods of optimizing the investment portfolio on the grounds of the contemporary theory of the portfolio of investment resources, the use of existing mathematical methods and models that can provide reasonable calculations of portfolio profitability according to the logical sequence of calculating the weighted average cost of capital. The carried out analysis allowed to identify the main constituent elements that ensure the profitability of various sources of funds, in particular own, credit and capital of preferred shares. In the course of the study, attention was focused on the fact that the use of different models for sources of funds allows the use of fundamentally different elements and affects the overall profitability of the portfolio in different ways. However, the main difficulty in the application of classical methods of investment portfolio optimization is the active mobility of financial markets, which is due to the general state of the external macroeconomic environment. In accordance with this, capital investors, both investors and creditors, seek to minimize possible risks, and therefore require the use of those financial instruments that will provide higher returns with less risk. Applied studies using AI methods have shown that they require the use of precise mathematical tools to calculate specific quantitative indicators, in contrast to the general recommendations for optimizing the investment portfolio.

Keywords: investment portfolio, artificial intelligence (AI), weighted average cost of capital, risk, return on investment.

Fig.: 1. Tabl.: 2. Formulae: 2. Bibl.: 9.

Yatsenko Roman M. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Economic Cybernetics and System Analysis, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkiv, 61166, Ukraine)
Email: [email protected]
Porokhnavets Andrii A. – Postgraduate Student, Department of Economic Cybernetics and System Analysis, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkiv, 61166, Ukraine)
Email: [email protected]

List of references in article

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Bashynska, I., Niekrasova, L., and Malynovska, Y. “Bayesian Network as a Decision Support System in the Company's Risk Management System of Emergency Situations“. IEEE 4th KhPI Week on Advanced Technology. Kharkiv: NTU «KhPI», 2023. DOI: 10.1109/KhPIWeek61412.2023.10312911
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Markowitz, H. M. “Portfolio Selection“. The Journal of Finance. 1952. https://www.math.ust.hk/~maykwok/courses/ma362/07F/markowitz_JF.pdf
Mestikou, M. A., Smeti, K. E., and Hachaichi, Y. “Artificial intelligence and machine learning in financial services. Market developments and financial stability implications“. Financial Stability Board (FSB). 2023. DOI: https://doi.org/10.13140/RG.2.2.14528.40967
Qiao, Q., and Beling, P. A. “Decision analytics and machine learning in economic and financial systems“. Environment Systems and Decisions, vol. 36 (2016): 109-113. DOI: https://doi.org/10.1007/s10669-016-9601-x

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