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High Frequency Trading
Luz Orlando Ramirez August 7, 2011
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CONTENTS
Problem / Solution, 5
Executive Summary, 6
General Background
Specific Background
Conclusion / Recommendation
Discussion,
May 6, 2010 “Flash Crash”, 8
Could junk debt be connected to the “Flash Crash” of 2010?, 8
Could the Greek debt crisis be connected to the “Flash Crash”?, 8
Waddell & Reed Financial Inc., 9
High Frequency Trading Arms Race, 11
Negative sum games, 11
Harmful effects, 12
Quants, 12
High Frequency Trading Technologies, 13
Programming languages, 13
Why C++?, 13
High Performance Computing (HPC) technology, 14
Extra advantage, 14
Stepping into the light, 14
Unfair advantage, 15
Justification for their activities, 15
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CONTENTS
Stepping into the light (continued)
Size of high frequency trading firms, 15
Influence on government officials, 15
The Move to FPGAs, 16
FPGA based Order Cancel Systems, 16
Advantages of using FPGAs in finance, 17
Limitations of FPGAs, 17
New SEC regulations, 17
Conclusion & Recommendations, 18
Works Cited, 19
4
List of Illustrations
Fig. 1 Greek debt crisis and Dow Jones Industrials, 9
Fig.2 A Flash in The Market, 10
Fig. 3 High-Frequency Lobbying, 16
5
Problem / Solution
On May 6th 2010 over a span of twenty minutes the Dow Jones Industrial Average
experienced nearly a 1000 point drop. The event is known as the “Flash Crash” due to the rapid
decline and recovery of the Dow. Immediately following the “Flash Crash” numerous financial
entities blamed high frequency trading as the primary cause of the “Flash Crash”.
Even though many put off the “Flash Crash” as a fluke, others immediately called for new
regulations dealing with high frequency trading. In addition, new technologies have also been
proposed to prevent the behavior that ultimately led the Dow to drop and recover in such a short
amount of time.
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Executive Summary
General Background
High frequency trading is defined by the ability to make back-to-back trades in a mere
few micro-seconds and is considered a type of algorithmic trading. Even though high frequency
trading was initially used by Wall Street banks and hedge funds, the creation of independent
firms focusing primarily on high frequency trading has changed the stock market. To some,
these relatively new firms have become a problem, blaming them for the “Flash Crash” of 2010.
Specific Background
High frequency trading (HFT) firms claim that lightning fast back-to-back trades make
the environment fair for all investors and help stabilize the markets. The practice of high
frequency trading has begun to spread to other parts of the world such as Europe, Brazil, and
Canada.
Most of the high frequency trading firms are relatively new and account for large part of
the trades that occur in U.S. stock market. Currently, high frequency trading is claimed to be
accountable for “60 percent of the seven billion shares that change hands daily in the United
States stock markets”. In 2009, high frequency trading firms made over $20 billion in profits.
Due to their high activity and large profits high frequency traders have become the center of
attention of SEC regulators.
The attention of SEC regulators has not deterred high frequency trading firms; instead the
shady secretive firms have begun to step into the light. They have begun to justify high
frequency trading and the arms race to have the fastest trading systems.
Conclusions & Recommendations
The threat of another “Flash Crash” stresses the need for the use of new technologies and
regulations in the U.S. stock market. Requiring stock trading firms to have Order Cancel
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Systems with FPGAs as circuit breakers will reduce the possibility of losses due to a “Flash
Crash” type event. Also, the finalization of new regulations by the SEC will further reduce the
chances of another “Flash Crash” type event from occurring.
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Discussion
May 6, 2010 “Flash Crash”
The “Flash Crash” of May 6, 2010, raised numerous warning flags in the U.S. stock
market and world markets. The irregular event is mainly attributed to the algorithms that nearly
all high frequency traders (HFTs) use to make their stock trades. However, primarily blaming
high frequency traders and their complex algorithms would ignore the other conditions that
allowed the “Flash Crash” to occur.
Could junk debt be connected to the “Flash Crash of 2010”?
The behavior of the Standard & Poor’s Depositary Receipts (SPDR) High Yield Junk
Debt exchange trade fund (ETF) could have set the conditions for the May 6, 2010 “Flash Crash”
to occur. A comparison of the price charts for the S&P 500 ETF and the SPDR High Yield Junk
Debt ETF reveals some surprising similarities. Minutes before the S&P 500 ETF took a nose
dive, the SPDR High Yield Junk Debt ETF began to steeply decline and then moments later
recover to levels near those prior to the steep decline. Moments after the SPDR High Yield Junk
Debt ETF steeply fell and recovered the Dow fell virtually 1000 points. Both the High Yield
Junk Debt ETFs’ and Dow crashes exhibited the similar behavior of drastically declining and
then immediately recovering. It is unknown how or if the High Yield Junk Debt ETFs’ and Dow
crashes are related but it is a coincidence in the way that both crashed on May 6, 2010.
Could the Greek debt crisis be connected to the “Flash Crash”?
Had Greece defaulted on its debt, it could have led the world economy into a “double-
dip” recession. Furthermore, months before the “Flash Crash” the newly installed Greek
government revealed that the public debt was greater than previously reported. The revelation
added more panic and fear into the world financial markets. Greece was pushed further into
financial perdition on April 27th, 2010, when the S&P rating agency lowered Greek bonds to
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BB+ or junk status. The warnings and other signals of an evident market crash were somehow
misinterpreted or disregarded by high frequency trading firms. As Gary Dorsch states in the
article “The Forgotten “Flash Crash” – One year later:” “A trend in motion, will stay in motion,
until some major outside force, knocks the market off its upward course.” The following figure
shows the possible relation between the Greek debt crisis and the Dow Jones Industrials.
Figure 1 – Greek debt Crisis and Dow Jones Industrials: The Forgotten “Flash Crash” – One Year later – Gary Dorsch May 2, 2011
The figure above indicates that after the S&P downgraded the Greek debt to BB+ the Dow Jones
Industrials began to decrease and kept decreasing even after the “Flash Crash.”.
Waddell & Reed
The U.S Commodity Futures Trading Commission (CFTC) and Securities & Exchange
Commission (SEC) report “Findings Regarding The Market Events Of May 6, 2010,” explains
that high frequency trading did not initiate the “Flash Crash.” Rather, a fundamental firm made
the conditions ripe for the “Flash Crash” to occur. The fundamental firm, not directly identified
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in the report though later discovered to be Waddell & Reed Financial Inc., cast off 75,000 E-mini
contracts valued at $4.1 billion on the market in a matter of 20 minutes. It is important to
mention that Waddell & Reed only placed the sell order for Barclays to execute. Barclays
executed the sell order in one single trade without any thought as to what the consequences
would be. The E-mini contracts were then picked up by the high frequency trading computers
and sold almost immediately. The mass selling of E-mini contracts by high frequency traders
created a “hot potato” volume effect. The ‘hot potato” volume effect significantly increased
volatility in the market and forced the Chicago Mercantile Exchange (CME) to execute the Stop
Logic Functionality to pause E-mini trading for five seconds. The brief pause was enough to
stabilize prices in the stock market. The following figure from the NEW YORK TIMES
illustrates the events leading to the “Flash Crash”.
Figure 2 – Graph of the “Flash Crash” from the New York Times 2010
The New York Times article, “Lone $4.1 Billion Sale Led to ‘Flash Crash,’” in May made a
surprising revelation: “[Waddell & Reed] said it had sold the contracts because it was worried
about the European crisis spreading to United States.” Waddell & Reed’s statement shows that
the Greek debt crisis influenced the firm to make the large sell order.
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High Frequency Trading Arms Race
For high frequency trading firms having the fastest systems has become of utmost
importance. The need to have the fastest systems translates into larger profits for high frequency
trading firms. For example, a few milliseconds of market data analysis can lead to profits of
millions if not billions of dollars. Furthermore, the stock market has built a 400,000 sq. ft. data
center in Mahwah, New Jersey. The data center would provide HFTs colocation, a service that
will provide almost instantaneous access to raw market data. For high frequency trading firms a
“race to zero” or the ability to execute instantaneous trades has become a kind of arms race.
Negative sum games
This arms race has become a zero sum game for high frequency trading firms. As newer
and faster technologies become available, high frequency trading firms spend millions to
upgrade their systems to ensure that they are staying competitive in the high frequency trading
industry. Richard Bookstaber, a veteran Wall Street risk manager, considers high frequency
trading firms to have no long-term gain because high frequency trading firms are in the same
position they were before they upgraded their trading systems to the newest technologies (Why
high-performance computing needs financial engineering). Upgrading such systems is expensive
and requires many resources to successfully implement. The high frequency trading firms with
many resources have an advantage over smaller firms in that they are able to have newer
technologies implemented sooner and successfully. Another issue that Bookstaber raises is that
eventually HFTs will encounter the speed of light barrier. The speed of light barrier will
eventually limit the speed at which HFTs execute trades. HFTs will then have to find another
means to become more competitive because falling behind in their industry is not an option.
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Harmful Effects
Since all of these high frequency trades are executed by complex algorithms, it is
unknown how these algorithms could react to a piece of news or even a rumor. Even though
most of the algorithms perform as designed, there have been several cases where a news story or
rumor has caused some algorithms to sell stocks and inadvertently cause a harmful chain of
events to occur. For instance, in 2008 a recycled news story about United Airlines filing for
bankruptcy led news reading algorithms to start selling UAL stock. The wild behavior of these
computer algorithms caused trading in UAL stock to stop for nearly an hour after the stock fell
nearly 76%. Without any human intervention there is no means to predict how high frequency
trading algorithms will react to market data, news stories, and speculation. It is also possible that
algorithms could be employed by rival high frequency trading firms to create a bear raid on a
particular stock, such as in the United Airlines case (UAL shares hit by years-old bankruptcy
story).
Quants
Quantitative analysts, or “quants” as they are called, are the individuals who develop the
trading algorithms used by high frequency trading firms. According to Emanuel Derman, known
as the Einstein of Wall Street, “quants primarily use quantitative techniques and computer
science to model the value of financial securities and how to structure them” (Quants: The
Alchemists of Wall Street). The models that quants create are the ones that determine the stock
market prices and guide traders to make buy or sell stock orders. Like engineers, quants know
that sometimes their models can fail and need to be made better than before. Perhaps the current
financial issues are occurring because we are trusting too much in the current models. After the
“Flash Crash” of 2010 the warnings of quants are no longer being ignored, rather their
management is listening and reacting to their warnings.
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High Frequency Trading Technologies
Due to the secrecy of high frequency trading firms we do not know the specific
technologies, algorithms, or computer systems they employ. In fact, during the trial of Sergey
Aleynikov, the C++ developer who illegally downloaded proprietary code from Goldman Sachs,
the judge sealed the courtroom so that testimony of the proprietary code could be discussed
(Courtroom Sealed for Some Testimony in Aleynikov Case). Although the case did not give
many details about the algorithms used by HFTs, it did provide us with one of the programming
language quants use to build their models.
Programming languages
High frequency trading firms use a variety of programming languages to form their
quantitative algorithms. According to Mike O’Hara, High Frequency Trading Review publisher,
the prominent programming languages in the industry are the C languages, Java, Matlab, and
Cuda. However, because the main goal of HFTs is to attain the lowest latency time, the most
used programming language is C++.
Why C++?
It is no surprise why Aleynikov choose to be a C++ developer and why there was so
much secrecy during his trial. High frequency trading expert and CTO at Lab49 Matt Davey
explains that “From a HFT platform perspective, C/C++ is the language of choice due to the
latency requirements, . . . The lower the latency [or time it takes for data to get from one point to
another], the more C/C++ is important” (When Milliseconds Make Millions: Why Wall Street
Programmers Earn the Big Bucks). Furthermore, most high frequency trading programmers
write code in a Linux environment since it is more efficient at using hardware resources. For
high frequency trading low latency is everything.
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High Performance Computing (HPC) technology
The complex models that quants create require a significant amount of computing power
to analyze all the raw data received from the financial markets. This need has pushed high
frequency trading firms to invest in High Performance Computing (HPC) technology. HPC is
the use of supercomputers and computer clusters to analyze and solve complex problems.
Recently, JP Morgan started up its new risk analysis supercomputer developed by Maxeler
Technologies. JP Morgan’s supercomputer is “based on Field-Programmable Gate Array
(FPGA) technology that would allow it to run complex banking algorithms on its credit book
faster.” JP Morgan’s new supercomputer cuts its complete risk run from 8 hours to 12 seconds.
This significant decrease in time has given JP Morgan a serious competitive advantage.
According to Anh Nguyen, “The project took JP Morgan around three years, and the bank is now
looking to push it into other areas of the business, such as high frequency trading” (JP Morgan
supercomputer offers risk analysis in near real-time).
Extra advantage
The JP Morgan case is a good example of how financial firms are moving towards using
HPCs to get that extra competitive advantage. What this means for HFTs is that it allows them
to analyze market data faster than ever before. Since most high frequency trading firms are just
beginning to adopt HPC technology it is unknown just how big a competitive advantage high
frequency trading firms will have in the market.
Stepping into the Light
The “Flash Crash” and the joint CFTC and SEC report “Findings Regarding The Market
Events Of May 6, 2010,” put the high frequency trading firms into the spotlight. Facing
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increasing pressure from regulators and other investors, high frequency trading firms are now
stepping into the spotlight to defend their practice and market activities.
Unfair Advantage
Ordinary investors feel that HFTs have an unfair advantage from the speed at which their
algorithms are able to interpret market data and form buy or sell orders from the data. Critics of
HFTs also feel that colocation will give high frequency trading firms even more of an advantage
in the markets. The increasing opportunities for HFTs in the stock market only worsen the
position of ordinary investors.
Justification for their activities
Traditional investors argue that HFT activities destabilize the market by increasing
liquidity. They point to the “Flash Crash” as an example of how HFTs can negatively affect the
market. However, HFTs argue that their activities triple volume, reduce transaction costs, and
make it easier for everyone to trade stocks, thus creating an even playing field.
Size of the HFT firms
Even though traditional traders argue that HFTs have an unfair advantage, the general
size of high frequency trading firms is still relatively small as compared to financial giants like
JP Morgan. Furthermore, the larger financial firms still have the advantage of having more
resources than high frequency trading firms. JP Morgan’s foray into attaining HPC technology
shows that even the older traditional financial firms are interested in high frequency trading.
Influence on government officials
In the U.S. high frequency trading firms formed a Proprietary Trading Group Lobby to
buffer their image with U.S. lawmakers. In 2010, the group spent $690,000 and gave $550,000
to U.S. lawmakers’ political campaigns. The following chart from The NEW YORK TIMES
highlights various high frequency trading firms and how much they spent from 2006 – 2010.
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Figure 3 – High Frequency Lobbying: High-Frequency Trading 2011
From Figure 3 we can see that from 2006 to 2010 high frequency trading firms have been
increasing spending on lobbying and donations to U.S. lawmakers.
The Move to Field Programmable Gate Arrays (FPGAs)
JP Morgan’s move toward FPGA based technology illustrates the industry push towards
having the speediest and most reliable technologies. High frequency trading firms need new
technologies to react immediately to negative market data. As the “Flash Crash” showed, even
high frequency trading firms were not ready for the consequences from their algorithms’ wild
behavior.
FPGA based order cancel systems
Financial firms need to be able to quickly react to negative market data, such as what
occurred on May 6, 2010, and exit the market before incurring heavy losses. The
implementation of FPGA based order cancel systems may allow financial firms to do just that.
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Advantages of using FPGAs
An FPGA based order cancel system could identify an event such as the “Flash Crash”
and almost instantly cancel all buy/sell orders before incurring heavy loses. FPGAs are
technically faster than a CPU and are favorable for HFTs since an order cancel delay of a few
seconds could cost a high frequency trading firm millions. Also, since the financial industry uses
the Financial Information exchange (FIX) protocol to communicate trade and market data it is
more advantageous to use FPGAs over CPUs for the following reasons:
• FPGAs are more efficient at handling FIX protocol since it is string based.
• FPGAs can be programmed via National Instruments’ LabVIEW FPGA platform.
• Hardware systems based on FPGAs are highly customizable.
Limitations of FPGAs
Although FPGAs have many advantages, they also have some limitations. FPGAs will
eventually be limited by the speed of light. In addition, not all algorithms can be implemented
onto FPGAs. Likewise, the source files from Hardware Descriptive Language (HDL) programs
are often long and tend to accomplish “very little with a lot of effort.”
New SEC Regulations
The May 6, 2010 “Flash Crash” made it clear to government entities around the world to
form and set new regulations to prevent another “Flash Crash” type event from occurring. In the
U.S. the SEC has added the following fixes to the U.S. stock market since the “Flash Crash”:
• Sponsored access rule.
• Stock circuit breakers that halt trading momentarily when certain stock price thresholds
are met.
• New rules on erroneous trades.
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• The prohibition of stub quotes or stock at a price far away from the current market price
for that stock.
• The large trader rule that requires “large traders” to register with the Commission for
recordkeeping, reporting, and limited monitoring on their transactions.
Keep in mind that some of these fixes have not been finalized. The SEC is still taking proposals
from the public to help shape the current regulations and create new ones if needed. If finalized,
the above fixes might eliminate the “unfiltered access” that HFTs are so fond of and quite
possibly, the speed advantage that HFTs currently have.
Conclusions & Recommendations
The threat of another “Flash Crash” stresses the need for the use of new technologies and
regulations in the U.S. stock market. Requiring stock trading firms to have Order Cancel
Systems with FPGAs as circuit breakers will reduce the possibility of losses due to a “Flash
Crash” type event. Also, the finalization of new regulations by the SEC will further reduce the
chances of another “Flash Crash” type event from occurring.
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Works Cited
Bowley, G. (2010). Lone $4.1 billion sale led to 'flash crash' in May. The New York Times,
Retrieved from
http://www.nytimes.com/2010/10/02/business/02flash.html?adxnnl=1&adxnnlx=131230
2923-MfQgm1BFEsw8TqCQ6Odjbw
Bray, C . (2010) . Cour t room sea led fo r some tes t imony i n A leyn i kov
case . THE WALL STREET JOURNAL, Ret r i eved f rom
h t t p : / / on l i ne .ws j . com/a r t i c l e /SB100014240527487033775045756506
73838921624.h tml
Dorsch, G. (2011). The forgotten "flash crash" - one year later. Global Money Trend newsletter,
Retrieved from http://www.sirchartsalot.com/article.php?id=152
High-frequency trading. (2011, July 18). Retrieved from
http://topics.nytimes.com/topics/reference/timestopics/subjects/h/high_frequency_algorit
hmic_trading/index.html
Hinton, C. (2008). UAL hit by years-old bankruptcy story. MarketWatch, Retrieved from
http://www.marketwatch.com/story/ual-shares-hit-by-years-old-bankruptcy-
story#comments
Meerman, M (Director). (2010). Quants: The Alchemists of Wall Street [Web].
Available from http://www.youtube.com/watch?v=ed2FWNWwE3I
Ngu yen , A . (2011) . JP Morgan supercompu te r o f fe rs ri sk ana l ys i s in near
rea l - t ime. Unknown Pub l i ca t ion, Ret r i eved f rom
h t t p : / /www.pcwor ld . i dg .com.au /ar t i c le /393295/ jp_morgan_supercom
pute r_o f fe rs_ r i sk_ana lys i s_near_ rea l - t ime
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Works C i ted
Ramel , D . (2011) . When mi l l i seconds make m i l l i ons : why Wa l l S t ree t
p rogrammers ea rn the b ig bucks . App l i ca t ion Deve lopment T rends,
Ret r i eved f rom h t t p : / / ad tmag.com/ar t i c l es /2011/07/29 /why-h f t -
p rogrammers -earn- top - sa la r ies .aspx
Stokes, J. (2009). Why high-performance computing needs financial engineering, Retrieved from
http://arstechnica.com/business/news/2009/04/why-processors-need-high-finance.ars
Stratoudakis, T. (2011, March). Hardware accelerated fix order cancel system. Retrieved from
http://www.wallstreetfpga.com/index.php?option=com_content&view=article&id=19&It
emid= 12
The Joint Advisory Committee on Emerging Regulatory Issues, CFTC & SEC. (2010). Findings
regarding the market events of May 6, 2010 Retrieved from
http://www.sec.gov/news/studies/2010/marketevents-report.pdf