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RESEARCH CENTER FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS of Ukraine (KHARKIV, UKRAINE)

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ID R30-03156

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Liburkina L. M.

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Financial Asset Portfolio Management in the Context of Financial Innovation Development
Rysin V. V., Kormushyn Y. K.

Rysin, Vitalii V., and Kormushyn, Yaroslav K. (2026) “Financial Asset Portfolio Management in the Context of Financial Innovation Development.” Business Inform 7:463–471.
https://doi.org/10.32983/2222-4459-2026-7-463-471

Section: Finance, Money Circulation and Credit

Article is written in Ukrainian
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UDC 336.761:004.8:004.62

Abstract:
The rapid development of financial innovations, digital transformation of financial markets, and the widespread adoption of artificial intelligence and big data technologies are fundamentally changing approaches to financial asset portfolio management. These trends necessitate the development of new analytical approaches capable of improving investment decision-making under conditions of increasing market complexity and uncertainty. The aim of the article is to examine the transformation of financial asset portfolio management under the influence of financial innovations and to substantiate the application of machine learning methods and big data analytics for enhancing portfolio diversification and optimisation. The study employs a systematic approach, methods of scientific generalisation, comparative, structural, and logical analysis, synthesis, classification, and theoretical modelling. The research is based on the analysis of contemporary scientific literature on portfolio investment theory, financial innovations, artificial intelligence, machine learning, and big data technologies. The research results indicate that integrating machine learning methods with traditional portfolio management approaches improves the accuracy of forecasting expected returns and risks, enables the identification of nonlinear relationships among financial assets, supports the dynamic assessment of market conditions, and enhances investment decision-making. The study identifies the main directions of big data application in financial services, including financial risk assessment, fraud detection, customer segmentation, market forecasting, regulatory compliance, and automated portfolio management. It has been found that clustering techniques, dimensionality reduction methods, deep and recurrent neural networks, reinforcement learning, and natural language processing significantly enhance portfolio diversification and optimisation by expanding the analytical capabilities of investment management systems. At the same time, the study identifies key limitations of artificial intelligence applications in portfolio management, including model overfitting, dependence on data quality, interpretability issues, substantial computational requirements, and financial market instability. The authors propose practical recommendations for incorporating machine learning techniques, big data analytics, and digital asset indicators into portfolio management systems to improve investment efficiency and support decision-making within the context of the digital transformation of financial markets.

Keywords: financial assets; financial innovations; big data; machine learning; diversification; digital assets; investment portfolio.

Fig.: 1. Bibl.: 20.

Rysin Vitalii V. – Doctor of Sciences (Economics), Professor, Professor, Department of Finance, Lviv Polytechnic National University (12 Stepana Bandery Str., Lvіv, 79013, Ukraine)
Email: [email protected]
Kormushyn Yaroslav K. – Postgraduate Student, Department of Finance, Lviv Polytechnic National University (12 Stepana Bandery Str., Lvіv, 79013, Ukraine)
Email: [email protected]

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