We present the first direct evidence of algorithmic imprints during batch auctions. Order anticipation is an integral part of high-frequency traders' strategies. Hence, some participants may have economic incentive to encrypt noise in the data. We use machine learning to identify five types of...

I investigate whether algorithmic trading (AT) affects voluntary disclosure. I predict that AT's advantage over non-algorithmic investors decreases information acquisition. Because investors are less informed, managers increase disclosure to reduce information asymmetry. I find evidence...

As algorithmic trading (AT) has become a dominant component in financial markets, it is important to understand its benefits and costs. We find that AT is positively related to future stock price crash risk and managers’ bad news hoarding behavior. Additional analyses show that the effect of...

The explosion of algorithmic trading has been one of the most prominent recent trends in the financial industry. Algorithmic trading consists of automated trading strategies that attempt to minimize transaction costs by optimally placing orders. The key ingredient of many of these strategies are...

Although previous research has documented a low intraday liquidity commonality across stocks, it has grown over the past decade. Using the introduction of hybrid market in the New York Stock Exchange, I show that at least a part of this growth can be attributed to an increase in algorithmic...

We develop a high frequency (HF) trading strategy where the HF trader uses her superior speed to process information and to post limit sell and buy orders. By introducing a multifactor mutually exciting process we allow for feedback effects in market buy and sell orders and the shape of the...