By Adil Rasheed
Article for ECSSR Website published on April 29, 2013
After hacking into the twitter account of an international news agency (Associated Press), criminals send out a message that the White House is under attack and the US President has been injured. Immediately, stock markets press the panic button and high speed algorithmic trading plunges the Dow Jones Industrial Average into virtual free-fall (dropping sharply 143 points), as stocks are dumped in the milliseconds. However, sanity is soon restored as the news is found to be false. Stock markets rebound but only not after Standard and Poor’s has fallen one percent and $136 billion in stock value has been wiped out.
This narration is not a piece of fiction, but a report of the ‘flash crash’ that hit US stock markets on April, 23, 2013. Surprisingly, this event is also not a freak accident or an unfamiliar occurrence anymore. In today’s global financial markets, the trend of a sudden and precipitous drop, known as a ‘flash crash,’ is becoming increasingly frequent and alarmingly commonplace.
The above mentioned ‘flash crash’ was not just the first of its kind it was also not the biggest till date. The first ‘flash crash’ took place on May 6, 2010, in which the Dow Jones Industrial Average fell 1000 points (by 9 percent). There have also been other such crashes involving stocks of major companies, including Google, Apple and Bitcoin. It has also been suggested that commodities like agricultural products, oil and gold remain as vulnerable as the stocks of companies in today’s hi-tech stock exchanges.
Flash crashes have added a new element of uncertainty into today’s financial markets. Although still a matter of debate and speculation, the occurrence of flash crashes has been attributed to the rise of computers and their high-speed algorithmic trades that are fast replacing human traders on the floors of the world’s biggest bourses. Studies suggest that automated trading in major US and British stock exchanges already constitute over 50 percent of the total volume.
The use of sophisticated computer algorithms to trade stocks and securities in seconds, or the fraction of a second, is known as High-Frequency Trading (HFT). In fact, HFT involves trading in and out of investment positions tens of thousands of times a day.
With the introduction of this highly automated form of trading, the human element — particularly human assessment, judgment, initiative and enterprise — seems to be declining in global stock markets. In fact, HFT firms are not only conducting most of the trade in stock exchanges, they are doing so almost entirely with each other, as the human trader is unable to process vast amounts of information or trade as rapidly as them. Thus, even as the conventional human trader continues to look for opportunities on a weekly, monthly or longer-term basis, HFT firms take on short-term trades that involve high risks and rewards, which are often thousands of times higher in returns than those sought by their human counterparts.
The enthusiasts of this new technology contend that HFT improves market liquidity. They cite various studies that show that transaction costs for traders have substantially decreased with the growth of these systems. They also point out that computers make for better and honest traders, have enhanced attention spans, follow instructions properly, do not allow emotions to cloud their judgment, monitor and process information from many sources simultaneously and cost a lot less.
On the other hand, the detractors of HFT bewail a long litany of grievances. To begin with, they claim that HFT often causes instability in financial markets and drains out liquidity, especially when it is most needed. They point out that high-frequency liquidity providers had withdrawn from the market, when the May 6, 2010 flash crash was unfolding.
It has also been claimed that HFT is often used for ‘front-running’, an illegal practice wherein program traders learn about incoming orders before other traders and jump in front to make profits. A study conducted last year by Andrei Kirilenko, the chief economist at the US Commodity Futures Trading Commission, found that high frequency traders often make money at the expense of others as their algorithms are capable of gleaning investing patterns of other traders. Some traders also complain that HFT is even used to manipulate markets for economic and even political ends.
Some market experts have also raised the alarm over the ‘technological arms race’ initiated by the HFT. In order to beat competition, they aver, each HFT firm is increasingly spending large amounts of money on new and expensive technology to outpace rival automated competitors. Many economists suggest that if this ‘technological arms race’ does not stop, average investors will become disillusioned and drop out of the stock markets, which will reduce the real volume of trade and will adversely affect economic welfare.
But the biggest charge against HFT is that it remains vulnerable to several forms of systemic risks. For example, trading systems may at times demand too much liquidity too quickly and may cause prices to fall or rise to unreasonable levels. Again, it has been found that algorithms at times place large number of unanticipated orders or a trader misuses an algorithm by setting parameters that cause it to trade aggressively (as is alleged to have happened in the May 2010 crash). There is also the possibility of HFT trades getting trapped into a negative feedback loop in which they take turns into responding to each other. The other huge concern is that the system remains vulnerable to hacking or infiltration by terrorists or even a rogue trader betting on a meltdown. The April 23 incident offers a grim warning, as just a sentence-long tweet was picked up by the HFT computers to cause a major market sell-off. This also raises questions about the linking of HFT to social media network for information.
Current rules and procedures to prevent a ‘flash crash’ range from using circuit-breakers (or so-called ‘kill-switches’) or effecting a five minute pause if trading is unable to occur within the price band for more than 15 seconds. However, these are not viewed as effective solutions by market experts, as the nature of high-volume, high-speed algorithmic trading, is introducing new and unknown variables in stock market operations at a rapid pace. In addition, a flash crash in times of a negative market sentiment will always have the potential of triggering a major meltdown.
Although the impact of flash crashes has till date been manageable, there is always the possibility that a flash crash caused by automated trading systems might snowball into a major systemic crisis, particularly in times of high volatility and stressed market conditions. There is also the danger that terrorists or criminals design a computer virus that causes major structural damage to global financial markets. In the absence of any serious measure to forestall the problem, it seems an accident is just waiting to happen.