{\alpha}-Indirect Control in Onion-like Networks

TL;DR

This paper introduces an efficient algorithm to identify ultimate controlling entities in large corporate networks, capturing both direct and indirect control, and demonstrates its effectiveness on UK company data.

Contribution

The paper presents the $oldsymbol{ extalpha}$-ICON algorithm, a scalable method for control identification that unifies direct and indirect control and handles circular ownership.

Findings

Identifies over 96% of beneficiary entities in evaluation.

Outperforms existing control identification methods.

Scales linearly with network size.

Abstract

Tens of thousands of parent companies control millions of subsidiaries through long chains of intermediary entities in global corporate networks. Conversely, tens of millions of entities are directly held by sole owners. We propose an algorithm for identification of ultimate controlling entities in such networks that unifies direct and indirect control and allows for continuous interpolation between the two concepts via a factor damping long paths. By exploiting onion-like properties of ownership networks the algorithm scales linearly with the network size and handles circular ownership by design. We apply it to the universe of 4.2 mln UK companies and 4 mln of their holders to understand the distribution of control in the country. Furthermore, we provide the first independent evaluation of the control identification results. We reveal that the proposed $\alpha$-ICON algorithm identifies more than 96% of beneficiary entities from the evaluation set and supersedes the existing approaches reported in the literature. We refer the superiority of $\alpha$-ICON algorithm to its ability to correctly identify the parents long away from their subsidiaries in the network.