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;
2022-01-01

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.
2022
979-12-80153-31-9
Clustering, Machine learning, Unsupervised learning, Financial time series, Thematic Analysis
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/335722
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact