KEYWORDS: Algorithmic Trading
Joel Hasbrouck, New York University (NYU) - Department of Finance and Gideon Saar, Cornell University - Samuel Curtis Johnson Graduate School of Managementand
This paper studies market activity in the “millisecond environment,” where computer algorithms respond to each other almost instantaneously. Using order-level NASDAQ data, we find that the millisecond environment consists of activity by some traders who respond to market events (like changes in the limit order book) within roughly 2-3 ms, and others who seem to cycle in wall-clock time. We define low-latency activity as strategies that respond to market events in the millisecond environment, the hallmark of proprietary trading by a new breed of high-frequency trading firms. We construct a measure of low-latency activity by identifying “strategic runs,” which are linked submissions, cancellations, and executions that are likely to be parts of a dynamic strategy. We use this measure to study the impact that low-latency activity has on market quality both during normal market conditions and during a period of declining prices and heightened economic uncertainty. Our conclusion is that in the current environment, increased low-latency activity improves traditional market quality measures such as short-term volatility, spreads, and displayed depth in the limit order book.