A tensor-based unified approach for clustering coefficients in financial multiplex networks

TL;DR

This paper introduces a tensor-based method for calculating clustering coefficients in multiplex financial networks, capturing complex inter-asset relationships over time, and unifies existing measures into a comprehensive framework.

Contribution

It proposes new local and global clustering coefficients for multiplex networks, unifies existing formulas, and demonstrates their effectiveness on financial data.

Findings

Effective in describing dependencies between assets over time

Unifies various clustering coefficient measures

Applicable to temporal financial networks

Abstract

Big data and the use of advanced technologies are relevant topics in the financial market. In this context, complex networks became extremely useful in describing the structure of complex financial systems. In particular, the time evolution property of the stock markets have been described by temporal networks. However, these approaches fail to consider the interactions over time between assets. To overcome this drawback, financial markets can be described by multiplex networks where the different relations between nodes can be conveniently expressed structuring the network through different layers. To catch this kind of interconnections we provide new local clustering coefficients for multiplex networks, looking at the network from different perspectives depending on the node position, as well as a global clustering coefficient for the whole network. We also prove that all the well-known expressions for clustering coefficients existing in the literature, suitably extended to the multiplex framework, may be unified into our proposal. By means of an application to the multiplex temporal financial network, based on the returns of the S\&P100 assets, we show that the proposed measures prove to be effective in describing dependencies between assets over time.