Order Flows and Limit Order Book Resiliency on the Meso-Scale

TL;DR

This paper analyzes meso-scale limit order book dynamics, revealing nonlinear relationships and asymmetric effects in order flows, and demonstrates that limit order flows and LOB shape are key predictors of price movements.

Contribution

It provides empirical insights into meso-scale LOB behavior, highlighting the predictive power of order flows and LOB shape over traditional imbalance metrics.

Findings

Trade imbalance relates nonlinearly to price change.

Weighted order flows linearize the relationship.

Limit order flows are the most predictive variables.

Abstract

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.