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

Author

Listed:
  • Nicola Giuseppe Castellano

    (University of Pisa)

  • Roy Cerqueti

    (Sapienza University of Rome
    London South Bank University)

  • Bruno Maria Franceschetti

    (University of Macerata)

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.

Suggested Citation

  • Nicola Giuseppe Castellano & Roy Cerqueti & Bruno Maria Franceschetti, 2021. "Evaluating risks-based communities of Mafia companies: a complex networks perspective," Review of Quantitative Finance and Accounting, Springer, vol. 57(4), pages 1463-1486, November.
    • Handle: RePEc:kap:rqfnac:v:57:y:2021:i:4:d:10.1007_s11156-021-00984-3
    DOI: 10.1007/s11156-021-00984-3
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    1. Jangho Yang, 2018. "Information Theoretic Approaches In Economics," Journal of Economic Surveys, Wiley Blackwell, vol. 32(3), pages 940-960, July.
    2. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    3. Mastrobuoni Giovanni & Patacchini Eleonora, 2012. "Organized Crime Networks: an Application of Network Analysis Techniques to the American Mafia," Review of Network Economics, De Gruyter, vol. 11(3), pages 1-43, September.
    4. Gianni Pola, 2016. "On entropy and portfolio diversification," Journal of Asset Management, Palgrave Macmillan, vol. 17(4), pages 218-228, July.
    5. Briant, A. & Combes, P.-P. & Lafourcade, M., 2010. "Dots to boxes: Do the size and shape of spatial units jeopardize economic geography estimations?," Journal of Urban Economics, Elsevier, vol. 67(3), pages 287-302, May.
    6. Matthew Elliott & Benjamin Golub & Matthew O. Jackson, 2014. "Financial Networks and Contagion," American Economic Review, American Economic Association, vol. 104(10), pages 3115-3153, October.
    7. Letizia Paoli, 2004. "Italian Organised Crime: Mafia Associations and Criminal Enterprises," Global Crime, Taylor & Francis Journals, vol. 6(1), pages 19-31, February.
    8. Clemente, G.P. & Grassi, R., 2018. "Directed clustering in weighted networks: A new perspective," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 26-38.
    9. Xavier Gabaix, 2009. "Power Laws in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 255-294, May.
    10. Marcel Ausloos & Roy Cerqueti, 2016. "A Universal Rank-Size Law," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-15, November.
    11. M. Ausloos, 2013. "A scientometrics law about co-authors and their ranking: the co-author core," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 895-909, June.
    12. Villani, Salvatore & Mosca, Michele & Castiello, Mauro, 2019. "A virtuous combination of structural and skill analysis to defeat organized crime," Socio-Economic Planning Sciences, Elsevier, vol. 65(C), pages 51-65.
    13. Anil Bera & Sung Park, 2008. "Optimal Portfolio Diversification Using the Maximum Entropy Principle," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 484-512.
    14. Zhiyan Cao & Fei Leng & Ehsan Feroz & Sergio Davalos, 2015. "Corporate governance and default risk of firms cited in the SEC’s Accounting and Auditing Enforcement Releases," Review of Quantitative Finance and Accounting, Springer, vol. 44(1), pages 113-138, January.
    15. Arcagni, Alberto & Grassi, Rosanna & Stefani, Silvana & Torriero, Anna, 2017. "Higher order assortativity in complex networks," European Journal of Operational Research, Elsevier, vol. 262(2), pages 708-719.
    16. Francesco Calderoni, 2011. "Where is the mafia in Italy? Measuring the presence of the mafia across Italian provinces," Global Crime, Taylor & Francis Journals, vol. 12(1), pages 41-69, February.
    17. Bartram, Sohnke M. & Wang, Yaw-Huei, 2005. "Another look at the relationship between cross-market correlation and volatility," Finance Research Letters, Elsevier, vol. 2(2), pages 75-88, June.
    18. Ramchand, Latha & Susmel, Raul, 1998. "Volatility and cross correlation across major stock markets," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 397-416, October.
    19. Dimitrios Koutmos & Konstantinos Bozos & Dionysia Dionysiou & Neophytos Lambertides, 2018. "The timing of new corporate debt issues and the risk-return tradeoff," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 943-978, May.
    20. Firth, Michael & Smith, Andrew, 1995. "Auditor Quality, Corporate Risk, and the Valuation of New Issues," Review of Quantitative Finance and Accounting, Springer, vol. 5(3), pages 241-251, September.
    21. Paolo Pinotti, 2015. "The Economic Costs of Organised Crime: Evidence from Southern Italy," Economic Journal, Royal Economic Society, vol. 125(586), pages 203-232, August.
    22. Karagiannis, Roxani & Karagiannis, Giannis, 2020. "Constructing composite indicators with Shannon entropy: The case of Human Development Index," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    23. Ozgul, Fatih, 2016. "Analysis of topologies and key players in terrorist networks," Socio-Economic Planning Sciences, Elsevier, vol. 56(C), pages 40-54.
    24. Litterio Mirenda & Sauro Mocetti & Lucia Rizzica, 2019. "The real effects of 'ndrangheta: firm-level evidence," Temi di discussione (Economic working papers) 1235, Bank of Italy, Economic Research and International Relations Area.
    25. Fabio La Rosa & Sergio Paternostro & Loredana Picciotto, 2018. "Exploring the determinants of anti-mafia entrepreneurial behaviour: an empirical study on southern Italian SMEs," Entrepreneurship & Regional Development, Taylor & Francis Journals, vol. 30(1-2), pages 81-117, January.
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    More about this item

    Keywords

    Companies financial risk indicator; Organized crime; Complex networks; Clustering coefficient; Entropy; Rank-size analysis;
    All these keywords.

    JEL classification:

    • M19 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Other
    • M49 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Other
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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