  We investigate the behavior of limit order books on the meso-scale motivated
by order execution scheduling algorithms. To do so we carry out empirical
analysis of the order flows from market and limit order submissions, aggregated
from tick-by-tick data via volume-based bucketing, as well as various LOB depth
and shape metrics. We document a nonlinear relationship between trade imbalance
and price change, which however can be converted into a linear link by
considering a weighted average of market and limit order flows. We also
document a hockey-stick dependence between trade imbalance and one-sided limit
order flows, highlighting numerous asymmetric effects between the active and
passive sides of the LOB. To address the phenomenological features of price
formation, book resilience, and scarce liquidity we apply a variety of
statistical models to test for predictive power of different predictors. We
show that on the meso-scale the limit order flows (as well as the relative
addition/cancellation rates) carry the most predictive power. Another finding
is that the deeper LOB shape, rather than just the book imbalance, is more
relevant on this timescale. The empirical results are based on analysis of six
large-tick assets from Nasdaq.
