Event Processing blog

Fixing the Fat Fingered Faux Pas Epidemic

The age of electronic execution has brought with it a niggling problem - fat fingered trading. A fast-paced, stressful trading environment creates an ideal incubator for hatching mistakes. They are often simple mistakes and involve pressing the wrong key on the keyboard, possibly at the wrong time and/or even for the wrong thing.

Anecdotal evidence would have us believe that fat fingered trading is rife. Real-life fingers pushing the wrong buttons include incidents as recent as January when human error caused the Canadian dollar to slump in Asian trading hours. The U.S. dollar shot up from around C$0.99 to over C$1.0030 against the Canadian dollar before immediately dropping back down, Reuters reported. The spike in the rate had little long-term impact on the market as a whole. These sorts of mini-flash crashes happen on a regular basis in many instruments. 
Higher profile fat fingers include:

·         In September 2006 a Bank of America trader’s keyboard was set up to execute an order when a rugby ball landed on it and executed the $50 million trade ahead of schedule. 
·         In June 2005 a Mizuho Securities Trader sold 610,000 shares at 1 yen instead of 1 share at 610,000 yen at a loss of approximately $225 million.
·         In October 2002 a Bear Sterns trader caused a 100 point drop in the Dow Jones Index after entering a 4 billion share sell order rather than 4 million. 
·         In May 2001 a Lehman Brothers dealer in London wiped £30 billion off the FTSE when he inadvertently keyed in £300 million for a trade instead of £3 million, causing a 120-point drop in the FTSE 100.

Human error is part of being human. But what about algorithmic error? Algos, created by humans, can also have fat finger days. Last year the New York Stock Exchange fined Credit Suisse Securities $150,000 for failing to control an algorithm that went haywire in 2007 flooding the exchange trading system with hundreds of thousands of erroneous orders.

Honest mistakes are one thing, but there are also an increasing number of incidents of rogue traders, fraud and greed-gone-wrong. One famous rogue was the 2009 so-called drunken trader - a broker at PVM Futures who clocked up a $10 million loss from trading while intoxicated. A much larger and more grandiose deception occurred in 2008 when Jérôme Kerviel was discovered to have hidden losses valued at approximately €4.9 billion at Société Générale. And a commodities trader at MFG lost $141.5 million in 2007 on a big short position in wheat futures because his management had turned off the trading limit controls. They claimed that the controls “slowed things down” in a classic greed-gone-wrong story. 

Looking at this compendium of fat finger or algorithmic errors and fraudulent or rogue trading, I think we have been lucky so far that their impact has not been more serious. The scary thing is that things could have gone a lot more wrong. The flash crash illustrated how quickly things can move and how inter-related the markets are. In 2010 we have seen incidents in equities, futures, FX and oil markets. A cross-asset “splash crash” that cascades across multiple markets is theoretically possible – whether it’s accidental or even premeditated. 

Fears of algorithmic terrorism, where a well-funded criminal or terrorist organization could find a way to cause a major market crisis, are not unfounded. This type of scenario could cause chaos for civilization and profit for the bad guys and must constitute a matter of national security.

So what can be done? Better real-time monitoring and market surveillance, real-time risk and internal policing – by trading firms, trading venues and regulators. The markets should be free – but protected. Real-time visibility as to what is going on and real-time response to make course corrections when needed is crucial. Discovering or predicting problems and then being able to take immediate corrective action may help to save the world. 
 

The Trouble with Algorithms: Wild Children or Reckless Parents?

Algorithms and high frequency trading have been blamed for everything from the credit crisis to the May 6th flash crash and high speed market abuse, and have attracted unwanted interest from regulators on both sides of the pond. But questions remain whether these tools are really computer models gone wild or whether they are the spoiled children of reckless parents - regulation.

According to Dictionary.com, the definition of reckless is to be utterly unconcerned about the consequences of an action. One could argue that the Regulation National Market System was designed without regard to some of the consequences down the line. Blaming the wild children, algorithms, is to ignore that the parents - RegNMS - were somewhat reckless in designing the system.

In a blog on the TABB Forum on January 24^th,  Steve Wunsch of Wunsch Auction Associates explained that the system was working the way it had been designed.

"What really went wrong in the stock market on May 6? Prices aside, all of the plumbing was working fine. Not only were there no fat fingers, rogue algos, manipulators or terrorists at work, there were no significant breakdowns of order routing systems or data systems or any other elements of the stock trading infrastructure," wrote Wunsch.

Meanwhile, the National Commission on the Causes of the Financial and Economic Crisis in the United States released its report (Jan. 27th) and HFT was not mentioned at all. Nor were algorithms, as such, but 'computer models' were vindicated. The report said: "The crisis was the result of human action and inaction, not of Mother Nature or computer models gone haywire."

And it criticized regulators for not doing their jobs: “Widespread failures in financial regulation and supervision proved devastating to the stability of the nation’s financial markets.”

The result of the credit crisis and market meltdown in Sept. 2008 was the Dodd-Frank Act, which attempts to prevent another Sept. 2008.  But the flash crash insinuated itself into the picture, pointing out that no one had baked that possibility into the market reforms.  And, ironically, the market reforms set the stage for more flash crashes.

At the Tabb Forum Derivatives Reform Event a couple of weeks ago, a lot of people commented that Dodd-Frank puts in place a market structure that injects the equities and futures markets model, along with fragmentation, price transparency, streaming  quotes, into other asset classes. This theoretically invites algorithmic and high frequency trading and the threat of more flash crashes. At the event Peter Fisher of BlackRock said that what keeps him up at night is a flash crash in the interest rate market, citing the market structure argument, but specifically pointed out that this possibility was not envisioned in Dodd-Frank. 

With more and more asset classes becoming tradable electronically, partly thanks to mandated swap execution facilities (SEFs), the possibility of truly wild or rogue algos and market abuse becomes increasingly inevitable. And, as we pointed out last week, the very real possibility of a flash crash splashing across asset classes - we call it a "Splash Crash" - rears its ugly head.

Although the evidence against algos gone wild is thus far anecdotal for the most part, the belief that they can and will go wrong permeates the industry. Market abuse such as insider trading and manipulation are undoubtedly more prevalent. Fat finger errors are easier to prove, and are a fact of life in a high speed, high stress electronic marketplace.

Stay Calm and Remain Vigilant

The antonym of recklessness is vigilance. The regulatory parents must be more vigilant when it comes to their arguably brighter and naughtier children - algorithms and HFT. With algorithms and HFT come the possibility of mistakes and abuse. Many more firms outside of the equities world are embracing HFT and their inexperience can cause market disruptions. A flash crash in oil or other commodities - or even foreign exchange - is not to be scoffed at. In fact, many commodities markets are much less liquid and homogenous than equities, and can be even more vulnerable to mistakes or manipulation.

 There are a number of best practices that can be used to mitigate against algos going wild:

• Diligent backtesting – using historic data and realistic simulation to ensure many possible scenarios have been accounted for. A backtesting process needs to be streamlined of course – as short time to market of new algos is key.  
• Real-time risk monitoring - building a real-time “risk firewall” into your algo environment. Just like a network firewall stops anomalous network packets reaching your computer, so a risk firewall should stop anomalous trades getting to trading venues.
• Real-time market surveillance. Even if trades do not breach risk parameters, they may breach compliance rules, regulations or may be perceived by a regulator as market manipulation.

An algorithm is a tool in a trader's toolkit, not a naughty wild child. If the regulator parents are vigilant, and algos are subject to practical controls and monitored constantly for performance and for errors, market participants can sense and respond to market patterns before the aberrations or errors have a chance to move prices.

-Dan

Beware the Splash Crash

We have had the flash crash, the breathtaking 1000-point drop-then-surge that happened on May 6th, 2010. In the near future we will have a new worry - prepare for the “Splash Crash”, which will cross asset barriers in a single bound.

As asset classes outside equities - energy, commodities, FX, derivatives - become increasingly automated there will be more flash crashes. Increased interdependence of asset classes will lead to cross asset flash crashes – a domino effect where the crashes 'splash' across asset classes, possibly wreaking havoc for market participants and regulators.  

As regulators said following the flash crash: "a complex web of traders and trading strategies" links the fragmented multitude of markets here in the U.S.  And, like dominoes, when one goes the rest follow. The dominos are no longer limited to one asset class. Algorithms are becoming increasingly sophisticated, encompassing all of the elements that may impact a trade in a certain instrument. If a trader wants to take a substantial position in a foreign equity, for example, there are many ingredients that can affect its market price.

Consider news events such as the BP oil spill or the current political crises in Egypt and Tunisia. The impact of these events has illustrated the close relationship between the oil price, equities, foreign exchange, commodity futures and the bond markets. Extreme and possibly unexpected events coinciding can trigger a cascade. We saw with the flash crash how instability in European economies caused nervousness in the market and then an algorithm did something unexpected – causing a cascading effect across futures and equities markets. As the cross-dependencies grow and algorithms become more inter-twined, so the risks for a “splash crash” grow.

It’s not hard to consider a splash crash scenario given the growing inter-linking of markets. For oil companies, such as BP, equity trader’s positions can be affected by the price of the pound and UK interest rates, as well as the dollar and any countries currencies where it is exploring or supplying oil. Oil prices will impact the bottom line, therefore the share price. Political events such as wars in oil-producing countries are important, as are disruptive events such as oil spills, and can impact oil, equity, bond, commodity future and foreign exchange prices. What if China's economy falters and its oil consumption is predicted to fall? Oil prices globally fall, dragging the US dollar higher. Oil derivatives predicting that prices would stay high for the next year or two or three also fall. Share prices for the whole oil sector including BP, Exxon Mobil, Texaco, Chevron, Philips, Sinochem, etc. collapse. Debt markets are stunned and bond rates rally.

All of these factors can be programmed into an algorithm that monitors and makes trading decisions on the BP position. If large banks and hedge funds also have substantial positions in BP, the dollar, the debt and the derivatives and they also have algorithms that will kick in when certain parameters are met. If enough instability and unexpected conditions occur and then one of these algorithms does something strange or unexpected, the cascading impact could be enormous across all asset classes. For example, massive automated sell orders for oil shares, energy futures and derivatives and buy orders for USD and Treasuries. Trading systems could clog up, limited bandwidth could choke orders, exchanges could freeze up - splashing across all of the affected asset classes. Pandemonium.

Splash Crash Prevention Tips

Luckily there exist ever more responsive and intelligent algorithms that can react instantaneously to market anomalies and anticipate interruptions to liquidity. These rapid response algorithms could help to prevent the next flash crash by alerting risk managers of impending issues, or by changing trading strategies to accommodate market glitches.

On top of smarter algos, there are a few other splash crash prevention measures:

• Use real-time pre-trade analytics and risk management. If the mutual fund in question on May 6th or its executing broker had done a thorough back-test of its trading strategy, using some of the dire indicators present, it might have thought twice about selling so aggressively - possibly preventing the crash.
• "Light up" the algorithmic trading process. Visibility during the trading process is crucial. Surveillance technology exists than can monitor the markets for anomalous behaviour and alert the parties involved if it is spotted. Give the regulators the tools, too. 
• Homogenize trading rules across all exchanges and ECNs. When one halts trading they all halt - for the same amount of time.