Limit-order book resiliency after effective market orders: Empirical facts and applications to high-frequency trading

Research paper by Hai-Chuan Xu, Wei Chen, Xiong Xiong, Wei Zhang, Wei-Xing Zhou

Indexed on: 01 Feb '16Published on: 01 Feb '16Published in: arXiv - Quantitative Finance - Trading and Market Microstructure


In order-driven markets, limit-order book (LOB) resiliency is an important microscopic indicator of market quality when the order book is hit by a liquidity shock and plays an essential role in the design of optimal submission strategies of large orders. However, the evolutionary behavior of LOB resilience around liquidity shocks is not well understood empirically. Using order flow data sets of Chinese stocks, we quantify and compare the LOB dynamics characterized by the bid-ask spread, the LOB depth and the order intensity surrounding effective market orders with different aggressiveness. We find that traders are more likely to submit effective market orders when the spreads are relatively low, the same-side depth is high, and the opposite-side depth is low. Such phenomenon is especially significant when the initial spread is 1 tick. Although the resiliency patterns show obvious diversity after different types of market orders, the spread and depth can return to the sample average within 20 best limit updates. The price resiliency behavior is dominant after aggressive market orders, while the price continuation behavior is dominant after less-aggressive market orders. Moreover, the effective market orders produce asymmetrical stimulus to limit orders when the initial spreads equal to 1 tick. Under this case, effective buy market orders attract more buy limit orders and effective sell market orders attract more sell limit orders. The resiliency behavior of spread and depth is linked to limit order intensity. Finally, we present applications for high-frequency arbitrage based on LOB resiliency analysis and find that both long and short arbitrage strategies we design can achieve significantly positive returns.