Algorithms Engulf Wall Street

By Michael Feldman

January 19, 2011

Algorithmic trading is getting another cycle of press scrutiny, thanks mainly to a very well-researched article in Wired by Reuters financial blogger Felix Salmon and Ars Technica writer Jon Stokes. In it, they outline how pervasive these high-tech algorithms have become to the everyday running of financial trading. And the problem is no one knows how this software drives the market behavior — not the investors, not the traders, and not even the people who run Wall Street.

The motivation for all this high-tech trading is, of course, money. And in this case, he who develops the fastest system usually wins. This often comes down to placing the trading servers in the same room as the stock exchange servers to get that millisecond edge on executing transactions. The code too is designed for maximum speed, being constantly tweaked to squeeze the last ounce of performance from the underlying computer chips. Appro recently launched a server based on overclocked Intel “Westmere” CPUs, to give high frequency traders that extra speed boost. But all that digitally enhanced speed means it’s that much harder for humans to control.

That’s partly because that computer-generated bids can be executed so quickly (10,000 bids per second for a single stock) and in such a complex manner that humans cannot comprehend the ramifications. The feedback loops become intertwined, such that the entire trading system exhibits emergent behavior, untraceable to any particular piece of code.

In a recent interview on NPR’s Fresh Air program, Salmon declared. “The man danger about algorithmic trading is that we simply don’t understand it.” He says although the individual algorithms are controlled, and presumably understood, by their masters, the interactions between them are not.

In researching his article, Salmon talked with Michael Kearns, a CS Prof at the University of Pennsylvania, who has developed algorithms for various Wall Street firms. Kearns told him that the financial markets have become what he called an “automated adaptive dynamical system with feedback.” That may sound very cool, but according to Kearns there is no science he’s aware of that is able to understand such a system.

It should come as no surprise that occasionally such a system would run the financial markets into a ditch. That happened last May, with the so-called flash crash, when the Dow Jones Industrial Average plummeted 900 points in a matter of minutes — before regaining most of its value. The cause was traced to a relatively obscure mutual fund company that decided to make a very large trade in a very short amount of time (about 20 minutes). The algorithms monitoring the market interpreted this as a panic and came to the same decision all at once: sell. The reason the mutual fund company decided to dump the shares in the first place was to hedge against the possibility of a future stock market drop. Talk about self-fulfilling prophesies.

In the wake of the flash crash, the Securities and Exchange Commission (SEC) announced some measures intended to prevent a reoccurrence. These include “circuit breakers” procedures, such as automatically halting trading when a stocks share price fluctuates by more than 10 percent in 5 minutes. The SEC is also considering other measures like limiting the size and speed of trades and requiring a complete audit trail of all transactions.

But Salmon considers those rather crude remedies for such a tightly wound system. The flash crash event was actually a rather simple example of what could go wrong. The interactions between all the analytic software inhabiting Wall Street datacenters is much more complex. For example, unlike that mutual fund company that executed the large trade all at once, algorithmic trading software tries to hide a big buy or sell events with a series of smaller transactions so as not to tip their hand.

Meanwhile, other algorithms are simultaneously monitoring the activity to discern the larger patterns that the other codes are trying to hide. In some cases, even more devious codes will purposely initiate transactions with no intention of executing them in order to confuse their rival software. It’s very much algorithmic warfare, with no real thought given to collateral damage.

The quantitative analysts themselves have become somewhat innocent bystanders. The Wired article describes a typical quant shop, in this case Berkeley-based Voleon Capital Management, that specializes in statistical arbitrage. The idea is to process mounds of financial data, looking for patterns that would point to an profitable arbitrage opportunity. But the quants have no knowledge of the underlying fundamentals of the assets; they are simply looking for patterns. To them, it’s just a pile of bits unrelated to any larger reality.

The software is becoming more sophisticated too. Salmon documents a recently launched service, called Dow Jones Lexicon, that mines the text in financial news stories and attempts to map keywords to market conditions, the idea being to help predict market trends based on external events. Although such software is in its nascent stage, this could add a whole other layer of complexity to trading models.

The fact that so much trading — the majority, in fact — is performed algorithmically suggests that the market is no longer balanced between investors and speculators. And since the speculation component is being propelled by superfast computers, the market has become increasingly volatile and unpredictable. Even before and after the May 2010 flash crash, there have been a number of examples of unexplained price fluctuations.

The University of Pennsylvania’s Kearns suggests that we should to build a ginormous stock market simulator in order to provide some much-needed science for our market structures. In a recent Reuters blog by Salmon, Kearns is quoted about this at length. Although, the professor doesn’t see a simulator as a magic bullet, in his estimation it’s certainly the place to start.

Given the importance of the stock market to the economy, its increasing susceptibility to damaging volatility, and the lack of our understanding of current system, a simulator project seems like a no-brainer. Sounds like a nice little science project for the SEC.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

UK to Launch Six Major HPC Centers

March 27, 2017

Six high performance computing centers will be formally launched in the U.K. later this week intended to provide wider access to HPC resources to U.K. Read more…

By John Russell

AI in the News: Rao in at Intel, Ng out at Baidu, Nvidia on at Tencent Cloud

March 26, 2017

Just as AI has become the leitmotif of the advanced scale computing market, infusing much of the conversation about HPC in commercial and industrial spheres, it also is impacting high-level management changes in the industry. Read more…

By Doug Black

Scalable Informatics Ceases Operations

March 23, 2017

On the same day we reported on the uncertain future for HPC compiler company PathScale, we are sad to learn that another HPC vendor, Scalable Informatics, is closing its doors. Read more…

By Tiffany Trader

‘Strategies in Biomedical Data Science’ Advances IT-Research Synergies

March 23, 2017

“Strategies in Biomedical Data Science: Driving Force for Innovation” by Jay A. Etchings is both an introductory text and a field guide for anyone working with biomedical data. Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Quants Achieving Maximum Compute Power without the Learning Curve

The financial services industry is a fast-paced and data-intensive environment, and financial firms are realizing that they must modernize their IT infrastructures and invest in high performance computing (HPC) tools in order to survive. Read more…

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

Google Launches New Machine Learning Journal

March 22, 2017

On Monday, Google announced plans to launch a new peer review journal and “ecosystem” Read more…

By John Russell

Swiss Researchers Peer Inside Chips with Improved X-Ray Imaging

March 22, 2017

Peering inside semiconductor chips using x-ray imaging isn’t new, but the technique hasn’t been especially good or easy to accomplish. Read more…

By John Russell

LANL Simulation Shows Massive Black Holes Break ‘Speed Limit’

March 21, 2017

A new computer simulation based on codes developed at Los Alamos National Laboratory (LANL) is shedding light on how supermassive black holes could have formed in the early universe contrary to most prior models which impose a limit on how fast these massive ‘objects’ can form. Read more…

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

New Japanese Supercomputing Project Targets Exascale

March 14, 2017

Another Japanese supercomputing project was revealed this week, this one from emerging supercomputer maker, ExaScaler Inc., and Keio University. The partners are working on an original supercomputer design with exascale aspirations. Read more…

By Tiffany Trader

Nvidia Debuts HGX-1 for Cloud; Announces Fujitsu AI Deal

March 9, 2017

On Monday Nvidia announced a major deal with Fujitsu to help build an AI supercomputer for RIKEN using 24 DGX-1 servers. Read more…

By John Russell

HPC4Mfg Advances State-of-the-Art for American Manufacturing

March 9, 2017

Last Friday (March 3, 2017), the High Performance Computing for Manufacturing (HPC4Mfg) program held an industry engagement day workshop in San Diego, bringing together members of the US manufacturing community, national laboratories and universities to discuss the role of high-performance computing as an innovation engine for American manufacturing. Read more…

By Tiffany Trader

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

Leading Solution Providers

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This