MACHINE LEARNING CLUSTERING IN FINANCIAL MARKETS: A LITERATURE REVIEW

Ștefan Rusu () and Marcel Boloș ()
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Ștefan Rusu: University of Oradea, Oradea, Romania
Marcel Boloș: University of Oradea, Oradea, Romania

Annals of Faculty of Economics, 2024, vol. 33, issue 1, 330-336

Abstract: This paper aims to present a concentrated overview of innovative research in machine learning clustering techniques as applied to to different facets of financial markets and stock market investing. Research on techniques such as K-Means Clustering or Agglomerative Hierarchical Clustering and their derivatives play a pivotal role in augmenting the stock market research and investment strategies. Wheter it is time series clustering and prediction, portfolio selection and optimization, or risk management, machine learning clustering has potential to enhance already existing processes by improving performance, reducing time spent on repetitive tasks or mitigating human errors. A truly innovative tool in the investor’s toolset, it is imperative to not overlook its limitations, such as the necessity of selecting the appropriate technique for specific datasets, or the need for human supervision to maximize its utility and insights extracted.

Keywords: artificial intelligence; machine learning; clustering; finance; investing; financial markets (search for similar items in EconPapers)
JEL-codes: C38 C53 C55 C61 C8 G10 G11 G17 (search for similar items in EconPapers)
Date: 2024
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