Expanding portfolio diversification through cluster analysis beyond traditional volatility

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dc.contributor.authorKuzheliev, Mykhailoen_US
dc.contributor.authorZherlitsyn, Dmytroen_US
dc.contributor.authorRekunenko, Ihoren_US
dc.contributor.authorNechyporenko, Alinaen_US
dc.contributor.authorStabias, Sergiien_US
dc.date.accessioned2025-02-17T07:20:37Z
dc.date.available2025-02-17T07:20:37Z
dc.date.issued2025
dc.description.abstractThe study reviews the application of machine learning tools in financial investment portfolio management, focusing on cluster analysis for asset allocation, diversification, and risk optimization. The paper aims to explore the use of clustering analysis to broaden the concept of portfolio diversification beyond traditional volatility metrics. An open dataset from Yahoo Finance includes a ten-year historical period (2014–2024) of 130 actively traded securities from international stock markets used. Dataset selection prioritizes top liquidity and trading activity. Python analytical tools were employed to clean, process, and analyze the data. The methodology combines classical Markowitz optimization with clustering analysis techniques, highlighting variancereturn trade-offs. Various asset characteristics, including annualized return, standard deviation, Sharpe ratio, correlation with indices, skewness, and kurtosis, were incorporated into the clustering models to reveal hidden patterns and groupings among financial assets. Results show that while clustering enhances insights into asset diversity, classical approaches remain historically superior in optimizing risk-adjusted returns. This study concludes that clustering complements, rather than replaces, classical methods by broadening the understanding of diversification and addressing many diversity factors, such as metrics of the technical, graphical, and fundamental analysis. The paper also introduces the diversity rate based on clustering, which measures the variance balance by all features within and between clusters, providing a broader perspective on diversification beyond traditional metrics. Future research should investigate dynamic clustering techniques, integrate fundamental economic indicators, and develop adaptive models for effective portfolio management in evolving financial markets.en_US
dc.identifier.citationExpanding portfolio diversification through cluster analysis beyond traditional volatility / Mykhailo Kuzheliev, Dmytro Zherlitsyn, Ihor Rekunenko, Alina Nechyporenko, Sergii Stabias // Investment Management and Financial Innovations. - 2025. - Vol. 22, Issue 1. - P. 147-159. - http://dx.doi.org/10.21511/imfi.22(1).2025.12en_US
dc.identifier.issn1810-4967
dc.identifier.issn1812-9358
dc.identifier.urihttp://dx.doi.org/10.21511/imfi.22(1).2025.12
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/33598
dc.language.isoenen_US
dc.relation.sourceInvestment Management and Financial Innovationsen_US
dc.statusfirst publisheden_US
dc.subjectfinancial investmenten_US
dc.subjectasset allocationen_US
dc.subjectfinancial securityen_US
dc.subjectportfolioen_US
dc.subjectdiversificationen_US
dc.subjectmachine learning (ML)en_US
dc.subjectcluster analysisen_US
dc.subjectfinancial marketen_US
dc.subjectPython analytical toolsen_US
dc.subjectarticleen_US
dc.titleExpanding portfolio diversification through cluster analysis beyond traditional volatilityen_US
dc.typeArticleen_US
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