We demonstrate how a machine learning algorithm can be applied to predict and explain modern market microstructure phenomena. We investigate the efficacy of various microstructure measures and show that they continue to provide insights into price dynamics in current complex markets. Some...

This work focuses on two of the more frequent practices in financial (especially capital) markets - the use of hidden orders and High-Frequency Trading (HFT). Although the use of each of them may reach 40% of the market turnover - even 60% for HFT, the actual knowledge on how they affect...

We use the introduction of a fi nancial transaction tax (FTT) in France in 2012 to test competing theories on its impact. We find no support for the idea that an FTT improves market quality by a ffecting the composition of trading volume. Instead, our results are in line with the hypothesis that...

We study performance and competition among high-frequency traders (HFTs). We construct measures of latency and find that differences in relative latency account for large differences in HFTs' trading performance. HFTs that improve their latency rank due to colocation upgrades see improved...

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...

In this presentation, we analyze the explanatory (in-sample) and predictive (out-of-sample) importance of some of the best known market microstructural features. Our conclusions are drawn over the entire universe of the 87 most liquid futures worldwide, covering all asset classes, going back...

We meticulously scrutinize the widely acknowledged measures of the Probability of Informed Trading (PIN) and the Volume-Synchronized Probability of Informed Trading (VPIN), initially posited by David Easley et al., which have achieved considerable eminence within the realm of financial academia....

The ‘flash crash' of May 6th 2010 was the second largest point swing (1,010.14 points) and the biggest one-day point decline (998.5 points) in the history of the Dow Jones Industrial Average. For a few minutes, $1 trillion in market value vanished. In this paper, we argue that the ‘flash...