  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.
