  Financial networks are typically estimated by applying standard time series
analyses to price-based economic variables collected at low-frequency (e.g.,
daily or monthly stock returns or realized volatility). These networks are used
for risk monitoring and for studying information flows in financial markets.
High-frequency intraday trade data sets may provide additional insights into
network linkages by leveraging high-resolution information. However, such data
sets pose significant modeling challenges due to their asynchronous nature,
nonlinear dynamics, and nonstationarity. To tackle these challenges, we
estimate financial networks using random forests. The edges in our network are
determined by using microstructure measures of one firm to forecast the sign of
the change in a market measure (either realized volatility or returns kurtosis)
of another firm. We first investigate the evolution of network connectivity in
the period leading up to the U.S. financial crisis of 2007-09. We find that the
networks have the highest density in 2007, with high degree connectivity
associated with Lehman Brothers in 2006. A second analysis into the nature of
linkages among firms suggests that larger firms tend to offer better predictive
power than smaller firms, a finding qualitatively consistent with prior works
in the market microstructure literature.
