Clustering is nowadays widely applied in finance, for solving portfolio selection and risk management problems. In this paper, we propose a review related to both state-of-the-art and of the recent developments of this approach. We adopt a bibliometric analysis, mapping the main issues discussed by scholars in the last 30 years with a network-based technique known as thematic analysis.

Clustering of financial time series: a bibliometric analysis

Michelangelo Misuraca;
2022

Abstract

Clustering is nowadays widely applied in finance, for solving portfolio selection and risk management problems. In this paper, we propose a review related to both state-of-the-art and of the recent developments of this approach. We adopt a bibliometric analysis, mapping the main issues discussed by scholars in the last 30 years with a network-based technique known as thematic analysis.
979-12-80153-31-9
Clustering, Machine learning, Unsupervised learning, Financial time series, Thematic Analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/335722
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