This paper examines the relationship between liquidity fragmentation and price jumps. Unexpected changes in intraday liquidity fragmentation predict jumps and jump direction. A shock to ask (bid) side liquidity fragmentation increases the probability of positive (negative) jumps by 36%....

Using machine learning methods we identify the fundamental value and noise components of quarterly stock prices. We show that 28% of stock price variation is attributable to noise, and that 40% of noise is attributable to mutual fund trading. We find spikes in noise around the dot-com bubble,...

We examine the role of high-frequency traders (HFTs) in price discovery and price efficiency. Overall HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors, both on average and on the highest volatility...

We examine empirically the role of high-frequency traders (HFTs) in price discovery and price efficiency. Based on our methodology, we find overall that HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing...

We examine the role of algorithmic traders (AT) in liquidity supply and demand in the 30 DAX stocks on the Deutsche Boerse in January 2008. AT represent 52% of market order volume and 64% of nonmarketable limit order volume. AT more actively monitor market liquidity than human traders. AT...

We analyze the contribution to price discovery of market and limit orders by high frequency traders (HFTs) and non-HFTs. While market orders have a larger individual price impact, limit orders are far more numerous. This results in price discovery occurring predominantly through limit orders....