This study proposes “reflexive crowdedness” as a mechanism through which order flow can become toxic at ultra-high frequencies (UHFs). Crowdedness, a coordination problem arising from the inability of traders to accurately gauge competition, leads to significant unbalanced mispricing in the form of liquidity costs. This mispricing is amplified by (reflexive) feedforward loops between liquidity and price components and can accumulate rapidly when high-speed traders engage. We develop an empirical framework to examine this mechanism in UHF trading. Results on trades of Dow 30 stocks show that reflexive crowdedness triggers speculative algorithmic trading and drives order flow toxicity and market instability at high frequencies. We formulate a UHF measure of reflexive crowdedness and find it predicts various UHF phenomena, including flash crashes and spikes, more reliably than price volatility and the Volume Synchronised Probability of Informed Trading (VPIN). This makes this measure highly relevant to investors, traders, market operators, and regulators.

An asymmetric information microstructural pricing model is proposed in which price responses to information and liquidity vary with every transaction. bid-ask quotes and price components account for learning by incorporating changing expectations of the rate of transacted volume (trading intensity) and the risk level of incoming trades. Analysis of European carbon futures transactions finds expected trading intensity to simultaneously increase the information component and decrease the liquidity component of price changes, but at different rates. This explains some conflicting results in prior literature. Further, the expected persistence in trading intensity explains the majority of the autocorrelations in the level and the conditional variance of price change; helps predict hourly patterns in returns, variance and the bid-ask spread; and differentiates the price impact of buy versus sell and continuing versus reversing trades.