The current research assesses the risks commonly attributed to the presence of HFT in the context of different market structures deployed by the U.S. exchanges. In particular, we find that, by design, the so-called “normal” exchanges have the lowest market quality, including the highest...

Abstract I demonstrate an important tension between acquiring information and incorporating it into asset prices. As a salient case, I analyze the rise of algorithmic trading (AT), which is typically associated with improved price efficiency. Using a new measure of the information content of...

We develop a model of limit order trading in which some traders have better information on future price volatility. As limit orders have option-like features, this information is valuable for limit order traders. We solve for informed and uninformed limit order traders' bidding strategies in...

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...

Modern Algorithmic Trading ("Algo") allows institutional investors and traders to liquidate or establish big security positions in a fully automated or low-touch manner. Most existing academic or industrial Algos focus on how to "slice" a big parent order into smaller child orders over a given...

Using the adoption of the Arrowhead trading platform in January 2010 as an exogenous event, we investigate the effects of algorithmic trading on stock market liquidity and commonality in liquidity under different market conditions on the Tokyo Stock Exchange. After controlling for endogeneity,...

Using the adoption of the Arrowhead trading platform in January 2010 as an exogenous event, we investigate the effects of algorithmic trading on stock market liquidity and commonality in liquidity under different market conditions on the Tokyo Stock Exchange. After controlling for endogeneity,...

We propose a parsimonious agent-based model of a financial market at the intra-day time scale that is able to jointly reproduce many of the empirically validated stylised facts. These include properties related to returns (leptokurtosis, absence of linear autocorrelation, volatility clustering),...

We propose a state-space modeling approach for decomposing trading volume into its liquidity-driven and information-driven components. Using a set of high-frequency S&P 500 stock data, we show that informed trading is linked with a reduction in volatility, illiquidity, and toxicity/adverse...

We analyze the impact of high frequency (HF) trading in financial markets based on a model with three types of traders: liquidity traders (LTs), professional traders (PTs), and high frequency traders (HFTs). Our four main findings are: i) The price impact of liquidity trades is higher in the...