The Price Impact of Order Book Events (1011.6402v3)

Abstract: 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.

• The paper establishes a robust linear relationship between order flow imbalance and price changes with an average R² of about 65%.
• The paper demonstrates that order flow imbalance explains price dynamics more consistently than traditional trade volume metrics, challenging the square-root law.
• The paper uncovers that thinner order books lead to higher price sensitivity, providing insights for optimizing automated trading and execution strategies.

Overview of the Price Impact of Order Book Events

The paper "The price impact of order book events" presents an empirical investigation of the price impact of limit orders, market orders, and cancellations within the order book dynamics of 50 U.S. stocks traded on the NYSE. The research leverages high-frequency trade and quote (TAQ) data to characterize price changes in relation to order flow imbalance (OFI)—a metric quantifying the imbalance between supply and demand at prevailing bid and ask prices.

Core Findings

1. Linear Price Impact Model: The authors establish a robust linear relationship between price changes and the order flow imbalance. They observe that the OFI, accounting for net order flow at the bid and ask prices, is linearly correlated with price fluctuations across varying stocks and time intervals. Notably, this model showcases a high average goodness-of-fit ($R^2$ of about 65%), underscoring OFI as a significant explanatory variable for price changes.
2. Order Flow Imbalance vs Trading Volume: The OFI paradigm surpasses the traditional focus on trade volumes in explaining price dynamics. While price changes and trade volumes exhibit an empirically recognized concave relationship (a "square-root" law), this connection is fraught with noise and appears less consistent than the linear relation derived from OFI. The findings suggest that the squared-root relation with volume emerges as a statistical anomaly rather than a robust, fundamental relationship.
3. Price Impact Coefficient and Market Depth: A critical insight of the paper is the inverse proportionality of the price impact coefficient to the market depth. The paper supports that thinner order books result in higher price sensitivity to order flow imbalances. This connection aligns well with the known intraday patterns—depth and, consequently, price impact fluctuate with predictably varying volumes and liquidity across the trading day.

Theoretical Implications

The paper contributes to an improved theoretical understanding of price impacts by postulating that order flow imbalance, a composite of market and limit orders along with cancellations, provides a more precise explanatory framework than trade size or volume alone. It explains intraday volatility patterns in price impact using observable parameters such as market depth and OFI, as opposed to latent variables like information asymmetry.

Practical Implications and Future Work

The research offers potential to refine automated trading strategies and the calibration of execution algorithms. Exploiting the OFI-dependency of prices can induce more effective strategies by adjusting trading patterns in response to observed liquidity conditions, thus optimizing transaction costs and mitigating price impact.

Future research could expand this model's predictive capacity by considering more complex order book activities beyond the top bid and ask prices. Further exploration could examine the potential implications of hidden liquidity and deeper market impact analytics across varying market structures and different trading venues.

Overall, this paper substantiates the comparative dominance of order flow imbalance as a measure of price movement influence, providing a refined toolset for both academic inquiry and practical order execution strategies.