A multiple network approach to corporate governance


In this work, we consider corporate governance (CG) ties among companies from a multiple network perspective. Such a structure naturally arises from the close interrelation between the Shareholding network and the Board of Directors network. In order to capture the simultaneous effects of both networks on CG, we propose to model the CG multiple network structure via tensor analysis. In particular, we consider the TOPHITS model, based on the PARAFAC tensor decomposition, to show that tensor techniques can be successfully applied in this context. By providing some empirical results from the Italian financial market in the univariate case, we then show that a tensor–based multiple network approach can reveal important information.

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    Sources: Bloomberg and Borsa Italiana databases. Shareholding structure as of 16 July 2013 and the board composition as of July 2013.

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    The analysis has been performed using the Matlab Tensor Toolbox Version 2.5 (Bader et al. 2012) and the Matlab N–Way Toolbox (Andersson and Bro 2000). Both toolboxes provide very similar results, thus showing a certain degree of numerical robustness of the method proposed.


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We would like to thank the anonymous referees for their valuable comments and suggestions which helped us to improve the manuscript.

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Correspondence to Marco D’Errico.

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Bonacina, F., D’Errico, M., Moretto, E. et al. A multiple network approach to corporate governance. Qual Quant 49, 1585–1595 (2015). https://doi.org/10.1007/s11135-014-0075-y

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  • Multiple networks
  • Tensor analysis
  • Corporate governance