Trade Co-occurrence, Trade Flow Decomposition, and Conditional Order Imbalance in Equity Markets

TL;DR

This paper introduces a method to classify high-frequency trades based on their proximity, decomposes trade flows into conditional order imbalances, and demonstrates their significant impact on price movements and trading strategies.

Contribution

It proposes a novel classification of trades by proximity, defines conditional order imbalance, and shows their predictive power and profitability in stock trading.

Findings

Strong positive correlation between COIs and contemporaneous returns

Positive association of COIs with future returns for isolated trades

Trading strategies based on COIs yield high returns and Sharpe ratios

Abstract

The time proximity of high-frequency trades can contain a salient signal. In this paper, we propose a method to classify every trade, based on its proximity with other trades in the market within a short period of time, into five types. By means of a suitably defined normalized order imbalance associated to each type of trade, which we denote as conditional order imbalance (COI), we investigate the price impact of the decomposed trade flows. Our empirical findings indicate strong positive correlations between contemporaneous returns and COIs. In terms of predictability, we document that associations with future returns are positive for COIs of trades which are isolated from trades of stocks other than themselves, and negative otherwise. Furthermore, trading strategies which we develop using COIs achieve conspicuous returns and Sharpe ratios, in an extensive experimental setup on a universe of 457 stocks using daily data for a period of four years.