Algorithmic anticipation of incoming orders—often called order anticipation or latency arbitrage—changes how the limit order book (LOB) behaves and how resilient it is to shocks. Algorithms that detect a large incoming parent order and submit offsetting or aggressive orders shorten the time between information and execution, which compresses displayed depth and raises the frequency of order cancellations. Empirical research shows that while automation can improve some liquidity measures, it also reorganizes who supplies liquidity and how quickly it can disappear. Terrence Hendershott University of California, Berkeley, Charles M. Jones Columbia Business School, and Albert J. Menkveld Vrije Universiteit Amsterdam document that algorithmic trading overall lowered spreads and increased trading volume, but their work also highlights structural shifts in liquidity provision.
Mechanisms that weaken resilience
Anticipatory strategies increase adverse selection for passive liquidity providers: when brokers or high-frequency systems detect a predictable incoming trade, passive orders face a higher probability of being picked off. That raises the effective inventory and execution risk for traditional market makers, who respond by pulling orders or quoting more conservatively. In the short run this behavior reduces visible depth at the best quotes and concentrates liquidity inside narrower price bands, making the LOB more brittle to order flow imbalances.
Consequences for market stability and participants
The immediate consequence is greater short-term volatility in depth and execution quality. As Eric Budish University of Chicago has argued, latency-sensitive strategies can extract rents and induce an arms race that reallocates resources to speed rather than to standing capital, which may diminish welfare and the capacity of markets to absorb large trades smoothly. For large institutional traders, reduced resilience translates into higher implicit transaction costs and execution risk; for retail markets and smaller venues, the cultural and regulatory environment matters—markets with dispersed execution across many venues or with different tick-size regimes see liquidity migrate and resilience vary territorially.
These dynamics do not remove liquidity entirely; they change its form and durability. Policymakers and exchanges respond with measures such as minimum resting times, batch auctions, or maker-taker tweaks to rebalance incentives. Understanding resilience therefore requires looking beyond spread figures to who is supplying liquidity, why they withdraw it, and how market design and local regulation shape those incentives. Nuanced empirical assessment by venue and asset is essential for credible conclusions.