A Multiple Network Approach to Corporate Governance

TL;DR

This paper introduces a tensor-based multiple network approach to analyze corporate governance by integrating shareholding and board networks, demonstrating its effectiveness with empirical Italian market data.

Contribution

It proposes a novel tensor analysis method, specifically the TOPHITS model, to simultaneously analyze multiple corporate governance networks, which is a new approach in this context.

Findings

Tensor techniques effectively reveal important corporate governance information.

The approach captures the interplay between shareholding and board networks.

Empirical results from the Italian market validate the method's usefulness.

Abstract

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.