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
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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
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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 ba ck-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 “doubledip” 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 27 th, 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 h ave 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 frequenc y 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.
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FPGAs can be programmed via National Instruments’ LabVIEW FPGA platform.
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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.
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Stock circuit breakers that halt trading momentarily when certain stock price thresholds are met.
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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.
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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). Courtroom sealed for some testimony in Aleynikov case. THE WALL STREET JOURNAL, Retrieved from http://online.wsj.com/article/SB100014240527487033775045756506 73838921624.html 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-bankruptcystory#comments Meerman, M (Director). (2010). Quants: The Alchemists of Wall Street [Web]. Available from http://www.youtube.com/watch?v=ed2FWNWwE3I Nguyen, A. (2011). JP Morgan supe rcomputer offers risk analysis in near real- time. Unknown Publication , Retrieved from http://www.pcworld.idg.com.au/article/393295/jp_morgan_supercom puter_offers_risk_analysis_near_real-time
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Works Cited Ramel, D. (2011). When milliseconds make millions: why Wall Street p r o g r a m m e r s e a r n t h e b i g b u c k s . A p p l i c a t i o n D e v e l o p m e n t T r e n d s , Retrieved from
http://adtmag.com/articles/2011/07/29/why-hft-
programmers-earn-top- salaries.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
