Thanks to a number of high-performance computing resources in the NSF’s XSEDE network, research by investigators at Cornell University and the University of Illinois at Urbana-Champaign is profoundly changing how regulatory authorities and researchers monitor the major U.S. stock exchanges.
As reported recently in HPCwire, research using Pittsburgh Supercomputing Center’s (PSC’s) Blacklight computer has already led to a scheduled October change in when traders must report their moment-by-moment activities to the New York Stock Exchange and NASDAQ. In addition, new work being pursued on the San Diego Supercomputer Center’s (SDSC’s) Gordon, the Texas Advanced Computing Center’s Stampede and PSC Blacklight will give market authorities the ability to analyze market changes on the order of minutes rather than the hours — or, for heavy trading periods and stocks, days — previously possible.
As Robert Sinkovits, SDSC Scientific Applications Lead, reported at the annual XSEDE13 conference on July 23, automated trading has made possible microsecond movements of stocks that economists generally agree have improved the efficiency and the fairness of the markets. However, further enhancements in the technology have also made nanosecond trading possible — and economists see no real benefit in market efficiency deriving from this additional speedup, but they can see a number of potential problems.
Looming large is the so-called “Flash Crash” of May 6, 2010, in which flaws in automated trading software led to a massive selloff, with the Dow Jones losing nearly 1,000 points in minutes.
“That’s pretty scary,” Sinkovits said. “It appears that the high-frequency traders are really driving these markets to instability.”
The markets gained back most of the Flash Crash’s lost value once traders realized the selloff was a computer glitch. But market authorities and traders alike began to appreciate how much potential for harm super-fast trading could have.
Worse, Sinkovits added, instability might not always be accidental. “In particular, traders start engaging in behaviors that affect the rest of us.” For example, he said, in “quote stuffing” traders cause congestion in the trading system by sending in a huge number of bids on a nanosecond scale, and then cancelling them equally quickly.
In an upcoming paper in The Journal of Finance based on their work with PSC’s Blacklight, Maureen O’Hara, Cornell University, and Mao Ye and Chen Yao of the University of Illinois at Urbana-Champaign report that trades of under 100 shares had become a significant part of the market. Regulators had previously considered such “odd lot” trades to be a “mom and pop” phenomenon because they were too unwieldy for the large purchases of major traders. Therefore, they had not required traders to report them on a moment-to-moment basis.
Today, the new research discovered, “odd lots” have become a favorite of high-volume traders, with about 20 percent of market activity overall not being reported to the moment-by-moment TAQ “ticker tape.” PSC’s Anirban Jana assisted Ye in optimizing the massively memory-dependent calculations to best make use of Blacklight’s shared memory.
More than 50 percent of trades in some major stocks, such as Google, were coming in under the radar in this way, Ye found. While no one knew for sure why the biggest, most sophisticated traders were choosing to do this, the mere fact was alarming enough that market authorities made the decision to require all trades to be reported as of October, based on prepublication copies of the research.
Importantly, this study had to be done retrospectively, long after the trades had taken place. New developments in the code promise near-real-time analysis of market data, potentially allowing market officials to follow, understand, and act on rapid developments such as the Flash Crash.
At XSEDE13, Sinkovits reported improvements he and SDSC’s D. J. Choi made in the code that helped speed the calculations by as much as 126-fold. The new code, with additional I/O optimizations being planned with the help of PSC’s David O’Neal, promises analyses in as fast as 15 minutes instead of hours or days.
“This is a game changer,” Sinkovits said. “It completely changes what the researchers are going to be able to do.”
“We’ve gone from taking about 36 hours to analyze one stock, to just two hours to analyze an entire market” such as the NASDAQ, he added. The results are “a first crack at parallelism across stocks” that could enable meaningful analyses of many stocks at once, leading to a better understanding of a more transparent, fairer market.