We study liquidity provision by competitive high-frequency trading firms (HFTs) in a dynamic trading model with private information. Liquidity providers face adverse selection risk from trading with privately informed investors and from trading with other HFTs that engage in latency arbitrage...

We characterise the stochastic interaction of independent learning algorithms as a deterministic system of ordinary differential equations and use it to understand the long-term behaviour of the algorithms in a repeated game. In a symmetric bimatrix repeated game, we prove that the dynamics of...

We use rich regulatory data on intraday transactions and end-of-day positions of traders in nine futures markets over the past ten years to examine how participation of high-frequency traders (HFTs) affects market quality. Absence of market fragmentation and off-exchange trading in the contracts...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

The recent emergence of COVID-19 and the subsequent short-selling restriction (SSR) imposed on some equity markets provide us with a unique framework to analyze the effects of this kind of measure on market quality in the context of increasingly automated equity markets. We contribute to the...