Michael Lewis’ best-selling book Flash Boys describes a phenomenon where investors place buy or sell orders for a stock based on an acceptable quoted price only to see that price change unfavorably at the moment the trade is executed. The culprits, according to the book, are high-frequency traders (HFTs), which employ complex computer algorithms and lightning speed wiring to gain a financial edge in markets.

Among the things HFTs have been accused of doing is creating what’s called “phantom liquidity.” Critics say HFTs place—then abruptly cancel—staggering numbers of trade orders to create the illusion of greater supply or demand for a stock in order to move the price to their advantage. As Lewis writes in Flash Boys, “By the summer of 2013, the world’s financial markets were designed to maximize the number of collisions between ordinary investors and high-frequency traders at the expense of ordinary investors and for the benefit of high-frequency traders, exchanges, Wall Street banks, and online brokerage firms.”

The practice of HFTs placing and cancelling large numbers of trade orders has even prompted Democratic presidential nominee Hillary Clinton to propose a tax to curtail such activity.

However, a new paper by Jesse Blocher, assistant professor of finance at Vanderbilt University’s Owen Graduate School of Management, finds little evidence of HFTs creating phantom liquidity. Lead author Blocher and his collaborators based the study on their analysis of a 5.78-terabyte dataset of all message activity (not just completed trades) on S&P 500 stocks for the 2012 calendar year in the NASDAQ limit-order book.

“Phantom liquidity only can really be a problem if those who cannot get good execution find the price moving against them when they try to trade,” Blocher and his co-authors wrote in the study, published in the summer 2016 issue of Journal of Trading. “We do not find that in our data.”

To determine the effects of HFTs cancellations, the researchers identified what they termed “cancel clusters,” brief periods that arise from HFTs cancelling multiple trade orders within a small timeframe. Their investigation yielded three main findings:

Cancel clusters are not a dominant feature of the trading day. The average cancel cluster lasts for just 5.68 seconds and they occupy 6.7 percent of the trading day.

The least common event taking place during a cancel cluster is that trade executions occur, suggesting that HFTs are not systematically luring investors into placing unwanted trades. In fact, the most common behavior after a cancel cluster is that the bid or ask prices revert to their pre-cancellation levels.

Most trading execution takes place outside of cancel clusters, and it is only once trade executions take place that stock prices move.

So why are HFTs engaged in this practice of placing, then cancelling, large batches of orders? Blocher and his colleagues argue that “cancel clusters are a lower-cost means of price discovery.” In other words, HFT computer algorithms run a kind of rapid series of tests to determine the “right” price of an asset, rather than use the method to try to trap (and profit from) unsuspecting investors.

If anything, it appears that HFT “liquidity providers have simply replaced low frequency market makers with lower cost price discovery,” the researchers said. That means ordinary investors are likely experiencing greater market stability and efficiency as a result of these new high-tech trading mechanisms.

Media inquiries:

Ryan Underwood, (615) 322-1003

ryan.underwood@vanderbilt.edu