Investigating Competition in Financial Markets: A Sparse Autologistic Model for Dynamic Network Data

TL;DR

This paper introduces a sparse autologistic model with L1 regularization to analyze dynamic financial trading networks, revealing that disintermediation influences market microstructure more than diversification or momentum.

Contribution

The paper presents a novel sparse autologistic model tailored for complex dynamic networks, enabling analysis of trader behavior in financial markets with many parameters.

Findings

Disintermediation drives market microstructure in NYMEX natural gas futures.

Model successfully induces sparsity in high-dimensional network data.

Analysis highlights the role of disintermediation over diversification.

Abstract

We develop a sparse autologistic model for investigating the impact of diversification and disintermediation strategies in the evolution of financial trading networks. In order to induce sparsity in the model estimates and address substantive questions about the underlying processes the model includes an $L^1$ regularization penalty. This makes implementation feasible for complex dynamic networks in which the number of parameters is considerably greater than the number of observations over time. We use the model to characterize trader behavior in the NYMEX natural gas futures market, where we find that disintermediation and not diversification or momentum tend to drive market microstructure.