This paper presents a data-driven complex network approach, to show similarities and differences—in terms of financial risks—between the companies involved in organized crime businesses and those who are not. At this aim, we construct and explore two networks under the assumption that highly connected companies hold similar financial risk profiles of large entity. Companies risk profiles are captured by a statistically consistent overall risk indicator, which is obtained by suitably aggregating four financial risk ratios. The community structures of the networks are analyzed under a statistical perspective, by implementing a rank-size analysis and by investigating the features of their distributions through entropic comparisons. The theoretical model is empirically validated through a high quality dataset of Italian companies. Results highlights remarkable differences between the considered sets of companies, with a higher heterogeneity and a general higher risk profiles in companies traceable back to a crime organization environment.

Evaluating risks-based communities of Mafia companies: a complex networks perspective / Castellano, N. G.; Cerqueti, R.; Franceschetti, B. M.. - In: REVIEW OF QUANTITATIVE FINANCE AND ACCOUNTING. - ISSN 0924-865X. - (2021). [10.1007/s11156-021-00984-3]

Evaluating risks-based communities of Mafia companies: a complex networks perspective

Cerqueti R.;
2021

Abstract

This paper presents a data-driven complex network approach, to show similarities and differences—in terms of financial risks—between the companies involved in organized crime businesses and those who are not. At this aim, we construct and explore two networks under the assumption that highly connected companies hold similar financial risk profiles of large entity. Companies risk profiles are captured by a statistically consistent overall risk indicator, which is obtained by suitably aggregating four financial risk ratios. The community structures of the networks are analyzed under a statistical perspective, by implementing a rank-size analysis and by investigating the features of their distributions through entropic comparisons. The theoretical model is empirically validated through a high quality dataset of Italian companies. Results highlights remarkable differences between the considered sets of companies, with a higher heterogeneity and a general higher risk profiles in companies traceable back to a crime organization environment.
2021
Clustering coefficient; Companies financial risk indicator; Complex networks; Entropy; Organized crime; Rank-size analysis
01 Pubblicazione su rivista::01a Articolo in rivista
Evaluating risks-based communities of Mafia companies: a complex networks perspective / Castellano, N. G.; Cerqueti, R.; Franceschetti, B. M.. - In: REVIEW OF QUANTITATIVE FINANCE AND ACCOUNTING. - ISSN 0924-865X. - (2021). [10.1007/s11156-021-00984-3]
File allegati a questo prodotto
File Dimensione Formato  
RQFA_CastellanoFranceschetti2021.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.01 MB
Formato Adobe PDF
1.01 MB Adobe PDF

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/11573/1542906
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact