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