This study examines the impact of corporate earnings announcements on trading activity and speed of price adjustment, analyzing algorithmic and non–algorithmic trades during the immediate period pre– and post– corporate earnings announcements. We confirm that algorithms react faster and...

This study exploits a unique dataset to determine the relative contribution to price discovery of order flow originating from geographically dispersed ASX servers. It is found that transactions of traders on the Sydney, Chicago and London servers have a significant impact on price volatility....

This study exploits a unique dataset to determine the relative contribution to price discovery of order flow originating from geographically dispersed ASX servers. It is found that the transactions of traders on the Sydney, Chicago and London servers have a significant impact on price...

The effectiveness of liquidity provision by HFT firms via the limit order book is an unexplored but central policy issue. Using a unique dataset consisting of limit order placement, execution, and cancellations on Nasdaq, we find that HFT firms do not cancel orders more frequently than non-HFT...

We study the intra-day impact of algorithmic trading on the futures market to increase our understanding of algorithmic trading and its role in the price formation process. First, we find that algorithmic trading provides liquidity when the spread is wide and that algorithms enter the market at...

The Volume-Synchronized Probability of Informed Trading (VPIN) metric is proposed by Easley et al. (2011, 2012) as a real-time measure of order flow toxicity in an electronic trading market. This paper examines the performance of VPIN around inventory announcements and price jumps in crude oil...