Order Flow Imbalance and Short-Term Price Impact

Summary

This study examines how limit orders, market orders, and cancellations affect short-term prices in 50 U.S. stocks using NYSE TAQ data. It focuses on order flow imbalance at the best bid and ask, combining changes in supply and demand there into a measure of buying and selling pressure. The analysis finds a linear relation between this imbalance and price changes, with greater market depth corresponding to a smaller impact.

The relation is reported as robust to seasonal patterns and stable across stocks and time scales. The authors connect the linear model and a scaling argument to the commonly observed square-root relation between price changes and trading volume. They caution that the volume-based relation is noisier and less robust than the one based on order flow imbalance. The findings describe empirical price formation and do not by themselves establish a trading strategy or its profitability.

Key ideas

• Best bid and ask order flow imbalance is a key driver of short-interval price changes in the study.
• Price changes vary linearly with order flow imbalance, with impact decreasing as market depth grows.
• The reported imbalance relation is stable across stocks, time scales, and seasonal effects.
• A scaling argument links the linear model to the square-root price-volume pattern.
• Price impact measured against trade volume is noisier than impact measured against order flow imbalance.

Tags

# The Price Impact of Order Book Events


# The Price Impact of Order Book Events









We study the price impact of order book events - limit orders, market orders and cancelations - using the NYSE TAQ data for 50 U.S. stocks. We show that, over short time intervals, price changes are mainly driven by the order flow imbalance, defined as the imbalance between supply and demand at the best bid and ask prices. Our study reveals a linear relation between order flow imbalance and price changes, with a slope inversely proportional to the market depth. These results are shown to be robust to seasonality effects, and stable across time scales and across stocks. We argue that this linear price impact model, together with a scaling argument, implies the empirically observed "square-root" relation between price changes and trading volume. However, the relation between price changes and trade volume is found to be noisy and less robust than the one based on order flow imbalance.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.