In this work, we consider Corporate Governance  ties among companies from a multiple network perspective. Such a structurenaturally arises from the close interrelation between the Shareholding Network  and the Board of Directors network . Inorder to capture the simultaneous effects on both networks on Corporate Governance , we propose to model the Corporate Governance 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. After providing some empirical results from the Italian financial market in the univariate case, we will show that a tensor-based multiple network approach can reveal important information.
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 (SH) and the Board of Directors network (BD). 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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Corporate Governance
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tensor-based
tensor--based