High Frequency Trading (HFT) is one of the hottest trends in financial markets since the 2008 recession. Presidential candidates are discussing it, courts are deliberating it, best selling authors are writing about it, and economists at UC Santa Cruz are studying it.
Thanks to seed investment from the Center for Analytical Finance (CAFIN), UC Santa Cruz professors Daniel Friedman, Kristian Lopez-Vargas and Eric Aldrich, joined with CAFIN research affiliate Peter Cramton of the University of Maryland, and other collaborators from Maryland and the University of Cologne, will explore the impact of HFT with a bold experiment: a simulated exchange that will replicate market-like conditions.
HFT is the rapid-fire trading practice that uses powerful computer networks to buy and sell stocks at lightning speed. Under current rules, financial trading rewards whoever reacts fastest to an informational event. In turn, traders and exchanges responded to this incentive by investing billions of dollars to react to markets just a little bit faster.
These new strategies, which include computer algorithms and super charged networks, mean traders can now react in just microseconds (millionths of a second) and even nanoseconds (billionths of a second). Picoseconds (trillionths of a second) will soon be possible. This makes a blink of an eye (300 to 400 milliseconds) seem painfully slow.
High-frequency traders may have an unfair advantage that harms ordinary traders.
“Ordinary people benefit when producers compete to offer the best price. If they compete on speed instead of price, saving a few microseconds wouldn’t seem to help ordinary investors, and it might not even help the high frequency traders any more, because the arms race for speed is so expensive that it gobbles up most of their profits,” says Friedman.
The research team will test a solution to the speed problem: frequent batch auctions. The frequent batch auction format is based on the idea that instead of processing orders in the strict order of when they arrive, they should be grouped by interval - such as every tenth of a second - and processed all at once. This should restore competition on price, say the researchers.
The research team will conduct experiments that will put this format to the test.
They are creating a platform that will simulate the market. Under a simulation, the researchers plan to compare frequent batch auction to how traders currently operate in today’s major financial markets.
The research team also plan to to host competitive, international tournaments that will test between alternative market formats.
The study hopes to shed light on critical questions including:
Do frequent batch auctions reduce the possibility of market instability due to bad algorithms, or other factors? Could frequent batch auctions result in greater market concentration? How would a shift in time allow for more sophisticated exchanges?
CAFIN will share tournament details in the coming months, and study results are anticipated for release in beginning in 2017.
Matching funds from the University of Cologne helped the team conduct an initial set of laboratory experiments this year.
Interested in participating? Friedman’s Learning & Experimental Economics Projects (LEEPS) lab will be conducting experiments. Interested people who want to try out the formats in the lab can sign up for the LEEPS lab subject pool, at: https://econlab.ucsc.edu//public
More information:
Budish, Eric, Peter Cramton, and John Shim (2014) “Implementation Details for Frequent Batch Auctions: Slowing Down Markets to the Blink of an Eye,” American Economic Review P&P, 104:5, 418-424.
Budish, Eric, Peter Cramton, and John Shim (2015) “The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response,” Quarterly Journal of Economics, 130:4, 1547-1621.
